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The Graduate School
2006
The Proposed Model of Attitude Toward
Advertising Through Sport
Do Young Pyun
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF EDUCATION
THE PROPOSED MODEL OF ATTITUDE TOWARD
ADVERTISING THROUGH SPORT
By
DO YOUNG PYUN
A Dissertation submitted to the
Department of Sport Management,
Recreation Management, and Physical Education
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Spring Semester, 2006
Copyright © 2006
Do Young Pyun
All Rights Reserved
The members of the Committee approve the Dissertation of Do Young Pyun
defended on February 27, 2006.
____________________________________
Jeffrey D. James
Professor Directing Dissertation
____________________________________
Charles F. Hofacker
Outside Committee Member
____________________________________
R. Aubrey W. Kent
Committee Member
____________________________________
Michael J. Mondello
Committee Member
Approved:
_________________________________________________________________
Charles H. Imwold, Chair, Department of Sport Management, Recreation
Management, and Physical Education
The Office of Graduate Studies has verified and approved the above named
committee members.
ii
Dedicated to Jesus Christ, my Lord and Savior
iii
ACKNOWLEDGMENTS
I would like to express my sincere gratitude to many people who contributed to
the completion of my dissertation. This dissertation would not exist without the efforts of
committee members: Dr. Jeffrey James, Dr. Charlie Hofacker, Dr. Aubrey Kent, and Dr.
Michael Mondello. I would like to thank, first and foremost, my advisor and mentor, Dr.
James, for his guidance and unflinching support throughout all phases of my doctoral
study at the Florida State University. His insightful knowledge, wisdom, and
encouragement provided me with strength and direction in conducting this research. I am
also grateful to Dr. Hofacker for his invaluable comments and guidance whenever I faced
the daunting task of analyzing the data. I have to deliver special thanks to Dr. Kent for
providing me very detailed reviews for the study as a committee member as well as
guiding my successful graduate studies at FSU as the program director. Dr. Mondello
also deserves commendation for his priceless support and participation to this
achievement.
My special appreciation extends to Dr. In Sung Yeo who was my advisor when I
was a mater student at Yonsei University. Dr. Yeo provided for me the foundation of my
academic endeavors and the power and motivation to study in the U.S. His continued
attention and advice helped me finish this long journey. I also would like to thank all
professors in the Department of Sports and Leisure Studies at Yonsei University.
I can not forget to thank my parents, Young Sik Pyun and Soo Hee Jung, who no
matter what I do support and believe in me. I will never forget their consistent love,
patience, prayers and support. With deep appreciation I would like to thank my parentsin-law, Je Kwan Park and Young Soon Lim for allowing me to marry their lovely
daughter and for their continuous love and support, allowing me to follow my dreams.
Lastly, my endless appreciation goes to my wife, So Youn Park for always
standing on my side and all the sacrifices she has made from her valuable time to
contribute to my successful doctoral study and the completion of this dissertation. Now,
it is my turn to support yours.
iv
TABLE OF CONTENTS
Page
List of Tables ................................................................................................................ xi
List of Figures...............................................................................................................xv
Abstract....................................................................................................................... xvi
I. INTRODUCTION ....................................................................................................1
Conceptual framework of attitude toward advertising through sport ....................3
Attitude toward advertising............................................................................3
Attitude toward advertising in specific mediums ..........................................4
The proposed model of attitude toward advertising through sport ................5
The interpretation of advertising through sport .............................................7
Significance........................................................................................................10
II. LITERATURE REVIEW ....................................................................................12
Overview............................................................................................................12
Distinguishing between beliefs and attitudes.....................................................12
Belief in and belief about...................................................................................17
Measuring beliefs and attitudes .........................................................................19
Indirect measurement of an attitude.............................................................19
Direct measurement of an attitude ...............................................................21
Thurstone scale ......................................................................................22
Likert scale.............................................................................................23
The semantic differential scale ..............................................................24
Attitude toward advertising in general vs. attitude toward the ad (Aad).............26
Attitude toward the ad as a mediator .................................................................27
Attitude toward advertising in general...............................................................31
Attitude toward advertising in specific mediums ..............................................38
Television advertising..................................................................................39
Direct marketing advertising........................................................................41
Outdoor advertising .....................................................................................42
Videocassette advertising.............................................................................42
Online advertising........................................................................................43
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The use of sport in advertising...........................................................................46
In-stadium/sport facility signage..................................................................46
Logos or brand names on uniforms or equipment .......................................47
Beliefs pertaining to advertising through sport..................................................48
Product information .....................................................................................49
Social role and image...................................................................................51
Hedonism/pleasure.......................................................................................52
Annoyance/irritation ....................................................................................53
Good for the economy .................................................................................55
Materialism ..................................................................................................56
Falsity/no-sense............................................................................................57
Measurement theory...........................................................................................59
Reliability test ..............................................................................................60
Test-retest reliability ..............................................................................61
Internal consistency test.........................................................................61
Individual item, composite, and average variance extracted reliability...64
Validity test..................................................................................................65
Content validity......................................................................................66
Predictive validity ..................................................................................67
Construct validity...................................................................................67
III. PILOT TEST .......................................................................................................72
Introduction........................................................................................................72
Method ...............................................................................................................74
Population and sample .................................................................................74
Instrument development...............................................................................75
Data analysis ................................................................................................76
Results................................................................................................................76
Demographic characteristics........................................................................77
Internal consistency .....................................................................................77
Exploratory factor analysis ..........................................................................80
Conclusions........................................................................................................84
vi
IV. METHODOLOGY ..............................................................................................86
Introduction........................................................................................................86
Subjects and procedure of data collection .........................................................89
Instrument development.....................................................................................90
Product information and social role and image ...........................................91
Annoyance/irritation ....................................................................................91
Good for the economy .................................................................................91
Materialism ..................................................................................................92
Falsity/no sense............................................................................................93
Attitude toward advertising through sport ...................................................93
The procedure of data analysis ..........................................................................93
The first data collection ...............................................................................94
The second data collection...........................................................................95
V. RESULTS ..............................................................................................................98
The first data collection ...........................................................................................98
Objectives of the first data collection ................................................................98
Characteristics of sample data ...........................................................................98
Internal consistency tests .................................................................................100
Exploratory factor analysis (EFA) ...................................................................104
Preliminary analysis...................................................................................104
Data transformations..................................................................................104
Maximum likelihood procedure with the oblique rotations.......................107
Model respecification.......................................................................................110
Conclusions......................................................................................................112
The second data collection.....................................................................................114
Objectives of the second data collection..........................................................114
Assessment of measurement model .................................................................115
Characteristics of sample data ...................................................................115
Purification of the measure ........................................................................116
Preliminary analysis...................................................................................119
Data transformations..................................................................................120
Confirmatory factor analysis (CFA) for the initial measurement model.........122
vii
Overview....................................................................................................122
Model specification....................................................................................122
Model identification...................................................................................122
Model estimation .......................................................................................124
Testing model fit ........................................................................................126
Absolute fit.................................................................................................127
Comparative fit ..........................................................................................128
Reliability tests...........................................................................................128
Validity tests ..............................................................................................129
Convergent validity..............................................................................129
Discriminant validity ...........................................................................130
Model respecification.................................................................................130
Confirmatory factor analysis (CFA) for the modified measurement model....133
Model specification....................................................................................133
Model identification...................................................................................133
Model estimation .......................................................................................134
Testing model fit ........................................................................................136
Absolute fit...........................................................................................137
Comparative fit ....................................................................................138
Reliability...................................................................................................138
Convergent validity....................................................................................138
Discriminant validity .................................................................................139
The full structural model with path analysis....................................................140
Preliminary analysis...................................................................................140
Beliefs about advertising through sport .....................................................141
Product information and hedonism/pleasure .......................................142
Social role and image...........................................................................142
Good for the economy .........................................................................142
Annoyance/irritation, materialism, and falsity/no sense......................142
Attitude toward advertising through sport .................................................143
Model identification...................................................................................144
Model estimation .......................................................................................144
Overall model fit ........................................................................................147
The fit of internal structure ........................................................................149
viii
Cross validation test.........................................................................................151
Objective ....................................................................................................151
Characteristics of sample data ...................................................................151
Preliminary analysis...................................................................................153
Data transformations..................................................................................154
The full structural model in the validation sample ..........................................155
Model estimation .......................................................................................155
Overall model fit ........................................................................................159
Summary of results of the study ................................................................160
IV. DISCUSSION AND CONCLUSIONS.............................................................163
Overview..........................................................................................................163
Exploratory factor analysis ..................................................................................163
Overview..........................................................................................................163
Pilot study ........................................................................................................164
The first data collection ...................................................................................167
Confirmatory factory analysis..............................................................................168
Overview..........................................................................................................168
Overall model fit of the model.........................................................................168
Fit of internal structure of the model ...............................................................171
Issues dealing with validity........................................................................173
Annoyance/irritation v. falsity/no sense ....................................................176
Hypothesis testing................................................................................................182
Overview..........................................................................................................182
Attitude toward advertising through sport .......................................................182
Beliefs about advertising through sport ...........................................................184
Product information ...................................................................................184
Social role and image.................................................................................185
Hedonism/pleasure.....................................................................................185
Annoyance/irritation ..................................................................................186
Good for the economy ...............................................................................186
Materialism ................................................................................................186
Falsity/no sense..........................................................................................187
ix
The impact of beliefs on attitude .....................................................................188
Implications of the study......................................................................................193
Academic implications.....................................................................................193
Practical implications.......................................................................................197
Limitations and directions for future research .....................................................200
Conclusions..........................................................................................................203
APPENDICES ............................................................................................................204
A: Approval Letter for Human Subjects in Research ..........................................204
B: Initial Survey Questionnaire for Pilot Test .....................................................206
C: Modified Survey Questionnaire for the First Data Collection ........................210
D: Final Survey Questionnaire for the Second Data Collection ..........................216
E: Data Transformation........................................................................................222
F: Summary Statistics for Standardized Residuals in the Modified Measurement
Model ........................................................................................................235
G: The Full Structure Model Estimation in the Randomly Split
Calibration and Validation Samples ..........................................................237
H: The Assessment of Model fit of the Six-Factor and Seven-Factor Measurement
Models........................................................................................................242
REFERENCES...........................................................................................................244
BIOGRAPHICAL SKETCH ....................................................................................265
x
LIST OF TABLES
Table 2.1: The Strengths and Weaknesses of Measurement Techniques in Direct and
Indirect Procedure....................................................................................................20
Table 2.2: A Summary of Internal Consistency Tests of Attitude and Belief in
Advertising Research ...............................................................................................63
Table 3.1: Demographic Characteristics of the Sample.................................................77
Table 3.2: Reliability Estimates for Global Attitude .....................................................78
Table 3.3: Reliability Estimates of Belief Factors .........................................................79
Table 3.4: Initial Eigenvalues for Factors......................................................................80
Table 3.5: Model Fit Results for Varying Number of Factors.......................................81
Table 3.6: Factor Pattern Matrix....................................................................................82
Table 3.7: Factor Correlations .......................................................................................83
Table 4.1: The Status of the Initial Items Based on the Pilot Test.................................88
Table 4.2: Regenerated Instrument Items for measuring Product Information and Social
Role and Image ........................................................................................................92
Table 5.1: Demographic Characteristics of the Sample.................................................99
Table 5.2: Sports-Related Behaviors of the Sample ....................................................100
Table 5.3: Reliability Estimates for Global Attitude ...................................................101
Table 5.4: Reliability Estimates of Personal Belief Constructs...................................102
Table 5.5: Reliability Estimates of Societal Belief Constructs....................................103
Table 5.6: Descriptive Statistics of the Belief Items....................................................105
Table 5.7: Distribution Values Before and After Transformation...............................107
Table 5.8: Model Fit Results for the Seven-Factor Model ..........................................108
Table 5.9: Factor Pattern Matrix..................................................................................109
xi
Table 5.10: Results of Nested Model Comparisons.....................................................112
Table 5.11: Summaries of Outcomes for Problematic Items.......................................113
Table 5.12: Demographic Characteristics of the Sample.............................................115
Table 5.13: Sports-Related Behaviors of the Sample ..................................................116
Table 5.14: Reliability Estimates for Global Attitude .................................................117
Table 5.15: Reliability Estimates of Personal Belief Constructs.................................118
Table 5.16: Reliability Estimates of Societal Belief Constructs..................................119
Table 5.17: Descriptive Statistics of the Belief Items..................................................120
Table 5.18: Distribution Values Before and After Transformation.............................121
Table 5.19: Factor Loadings, Standard Errors, and R² of Observed Variables in the Initial
Model .....................................................................................................................125
Table 5.20: The Correlations among Seven Latent Variables in the Initial Model .....126
Table 5.21: The Assessment of Model Fit in the Initial Model ...................................127
Table 5.22: Reliability of Seven Latent Variables in the Initial Measurement Model ..129
Table 5.23: The χ² Difference Test of Two Rival Models...........................................133
Table 5.24: Factor Loadings, Error Variance, and R² of Observed Variables in the
Modified Model .....................................................................................................135
Table 5.25: The Correlations among Seven Latent Variables in the Modified Model ..136
Table 5.26: The Assessment of Model Fit in the Modified Model..............................137
Table 5.27: Reliability of Seven Latent Variables in the Modified Model .................139
Table 5.28: Descriptive Statistics of the Attitude Items ..............................................141
Table 5.29: Distribution Values Before and After Transformation.............................141
Table 5.30: Respondents’ Beliefs about Advertising through Sport ...........................143
Table 5.31: Respondents’ Attitude toward Advertising through Sport .......................144
xii
Table 5.32: Factor Loadings, Standard Errors, and R² of Observed Variables in the
Structural Model ....................................................................................................145
Table 5.33: Correlation among Latent Variables in the Structural Model ..................146
Table 5.34: Standardized Parameters Estimated for the Path Analysis .......................147
Table 5.35: The Assessment of Model Fit in the Structural Model.............................148
Table 5.36: Reliability of Constructs in the Structural Model.....................................149
Table 5.37: Correlations between Likert and Semantic Differential Scales................150
Table 5.38: Demographic Characteristics of the Validation Sample...........................152
Table 5.39: Sports-Related Behaviors of the Validation Sample ................................153
Table 5.40: Descriptive Statistics of the Belief and Attitude Items in the Validation
Sample....................................................................................................................154
Table 5.41: Distribution Values Before and After Transformation in the Validation
Sample....................................................................................................................155
Table 5.42: Factor Loadings and R² of Observed Variables in the Structural Models in the
Calibration and Validation Samples ......................................................................156
Table 5.43: Correlation among Latent Variables in the Validation Sample................158
Table 5.44: Standardized Parameters Estimated of the Path Analyses in the Calibration
and Validation Samples .........................................................................................159
Table 5.45: The Assessment of Model Fit of Structural Models in the Calibration and
Validation Samples ................................................................................................160
Table 6.1: Summaries of the Overall Model Fit in the Calibration and Validation
Samples ..................................................................................................................169
Table E.1: Summary of Evaluation Criteria for Significant Skewness and Kurtosis
Statistics Based on Sample Size ............................................................................225
Table E.2: Scores Transformed by the Squares Root and Logarithm..........................227
Table E.3: Statistics of Skewness and Kurtosis in Three Stages .................................228
Table G.1: Factor Loadings and R² of Observed Variables in the Structural Models in the
Randomly Split Calibration and Validation Samples.............................................238
xiii
Table G.2: Correlation among Latent Variables in the Randomly Split Calibration and
Validation Samples.................................................................................................239
Table G.3: Standardized Parameters Estimates of the Structural Models in the Randomly
Split Calibration and Validation Samples ..............................................................240
Table G.4: The Assessment of Model Fit of Structural Models in the Randomly Split
Calibration and Validation Samples.......................................................................241
Table H.1: The Overall Model Fit of the Six-Factor and Seven-Factor Measurement
Models....................................................................................................................243
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LIST OF FIGURES
Figure 1.1: A Proposed Model of Attitude toward Advertising through Sport ...............6
Figure 1.2: Level of Advertising through Sport and Online Advertising Level..............8
Figure 2.1: Schematic Concept Model of Attitudes ......................................................13
Figure 2.2: Modified Structural Model of Aad Formation..............................................30
Figure 3.1: Procedure for Developing a Measurement Scale ........................................73
Figure 3.2: Scree Plot.....................................................................................................80
Figure 4.1: The Structural Model for the Seven Hypothetical Causal Relationships between
Seven Belief Dimensions and Attitude toward Advertising through Sport ...............97
Figure 5.1: A Baseline Model and Its Nested Models .................................................111
Figure 5.2: The Initial Hypothesized Measurement Model for the Latent Variables with
Multiple Indicators.................................................................................................123
Figure 5.3: Constrain and Unconstrained Models for the Chi-square Difference Test..132
Figure 5.4: The Modified Measurement Model for the Latent Variables with Multiple
Indicators................................................................................................................134
Figure E.1: An Example of Three Histograms in the Seven Likert scale: No Transformation,
the Square Root Transformation, and the Logarithm Transformation..................... 231
xv
ABSTRACT
When attending or watching sporting events, amateur or professional, people are
exposed to a variety of advertising. People form attitudes toward advertising that
influence their decision making processes to purchase a particular advertiser’s product.
The current study examines a new construct, attitude toward advertising through sport,
derived from Pollay and Mittal’s (1993) model of attitude toward advertising in general.
Through an exploratory investigation a scale to measure attitude toward advertising
through sport is tested. The preliminary results from several stages provide support for
the conceptualization and measurement of the belief dimensions proposed to influence
attitudes toward advertising through sport. The assessments of the structural equation
model reveal that respondents’ perceived beliefs of product information and
hedonism/pleasure about advertising through sport play significant roles in accounting
for their overall attitude toward advertising through sport.
xvi
CHAPTER I
INTRODUCTION
The growth of the sport industry has resulted in an increase in advertising through
sport over the last several decades, an indication that corporations have acknowledged the
potential of advertising through sport to accomplish a range of goals and objectives
(Lyberger & McCarthy, 2001). Figures reported by Broughton, Lee, and Nethery (1999)
show that companies and organizations paid out approximately $45 billion during 1999
for sport promotions in the United States. The figure accounts for 21% of the estimated
$213 billion spent that year on the sport industry. The majority of sport promotion
dollars comes from advertising. Of the $45 billion spent for sport promotions,
approximately $29.5 billion went to advertising through sport (as cited in Irwin, Sutton,
& McCarthy, 2002).
Advertising through sport has become an important medium for many companies
because of more “flexibility, broader reach, and higher levels of brand or corporate
exposure” that sport platforms afford (Kropp, Lavack, Holden, & Dalakas, 1999, p. 49).
When attending or watching sporting events, amateur or professional, people are exposed
to a variety of advertising. Unlike other advertising mediums such as TV commercials or
online advertising, people involuntarily receive these advertisements because they watch
the game for the sporting action rather than the advertisements (Harshaw & Turner, 1999).
Research has demonstrated that people form attitudes toward products and/or brands
based on advertising (e.g., Brown & Stayman, 1992; MacKenzie & Lutz, 1989;
MacKenzie, Lutz, & Belch, 1986; Shimp, 1981); in the same manner, it is proposed that
people form attitudes based on advertising through sport that influence decisions to
purchase a particular advertiser’s product.
As the spending on advertising through sport as part of corporate marketing
strategies has continued to increase, a growing need for research has emerged (Dodd,
1997). The effectiveness of some formats of advertising through sport such as in-stadium
or outdoor signage, and sponsorship recall and recognition have been examined (e.g.,
Crimmins & Horn, 1996; Cuneen & Hannan, 1993; Harshaw & Turner, 1999; Hume,
1
1990; Nicholls, Roslow, & Dublish, 1999; Pope & Voges, 1997; Pope & Voges, 2000;
Sandler & Shani, 1989, 1993; Shilbury & Berriman, 1996; Stotlar, 1993; Stotlar &
Bennett, 2000; Stotlar & Johnson, 1989; Turco, 1994; Turco, 1996; Turley & Shannon,
2000).
Measures of recall or recognition in previous research provide information about
awareness of companies advertising through sport, but they do not yield crucial
knowledge about consumers’ cognitive structures that may be employed in determining
consumers’ decision making processes (Lyberger & McCarthy, 2001). Meenaghan
(2001b) suggested that “…awareness and association tests are merely first-line
measures…” (p. 97), providing assessment of impact, but they do not offer a more
complete understanding of consumer engagement. The increase in the popularity of
advertising through sport has led to concerns about consumer “identification” and
“differentiation” (Lyberger & McCarthy, 2001, p 431). Are consumers able to identify
organizations that advertise through sport? Are consumers able to differentiate
organizations that advertise through sport, or is there too much clutter?
The perceived lack of a consumer’ “differentiation” and “identification” may be
due to a failure by researchers to understand consumer perceptions of, and information
about attitudes toward advertising through sport (Kim, 2003; Lyberger & McCarthy, 2001,
p. 431). Perceptions of and information about advertising through sport are elements of a
consumer’s cognitive structure, or beliefs, about advertising in general. Consistent with
Meenaghan’s (2001b) statement, the dearth of understanding regarding consumers’
cognitive structures with respect to advertising through sport suggests the need for
researchers to understand consumers’ belief and attitude concepts surrounding advertising
through sport (Lyberger & McCarthy, 2001). However, a review of literature revealed
that no research has examined consumer beliefs underlying attitudes toward advertising
through sport. Consequently, the current study attempts to provide a foundation for the
study of beliefs and attitude toward advertising through sport. The development of the
framework for this study has been initiated from the concept of attitude toward
advertising in general.
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Conceptual framework of attitude toward advertising through sport
Attitude toward advertising. Attitude toward advertising in general is defined as
“a learned predisposition to respond in a consistently favorable or unfavorable manner to
advertising in general” (Lutz, 1985, p. 53). This concept reflects a consumer’s general
attitude toward advertising rather than attitudes toward a specific advertisement or
attitudes toward advertising through a specific medium (Burns, 2003). Researchers have
investigated consumers’ attitudes toward advertising in general for several decades (e.g.,
Andrew, 1989; Bauer & Greyser, 1968; Durvasula, Andrews, Lysonski, & Netemeyer,
1993; Mittal, 1994; Muehling, 1987; Pollay & Mittal, 1993; Reid & Soley, 1982;
Sandage & Leckenby, 1980; Shavitt, Lowrey, & Haefner, 1998).
Historically, the systematic study of attitude toward advertising in general has
been rooted in the work of Bauer and Greyser (1968). Bauer and Greyser’s work
demonstrated that attitudes toward advertising in general consist of two belief dimensions,
economic and social effects, and found that attitudes are normally based on consumers’
beliefs regarding the social effects of advertising and the economic effects of advertising.
Subsequent research has sought to better explain the relationship between beliefs and
attitudes based on Bauer and Greyser’s findings (e.g., Andrews, 1989; Anderson,
Engledow, & Becker, 1978; Greyser & Reece, 1971; Haller, 1974; Larkin, 1977; Reid &
Soley, 1982; Schutz & Casey, 1981; Triff, Benningfield, & Murphy, 1987; Zanot, 1981).
A variety of studies have also attempted to explore advertising as an information source
(e.g., Alwitt & Prabhaker, 1992; Barksdale & Darden, 1972; Durand & Lambert, 1985;
Haller, 1974; Muehling 1987; Russell & Lane, 1987; Sandage & Leckenby, 1980; Soley
& Reid, 1983), materialism (e.g., Larkin, 1977), falsehood and deception (e.g., Muehling,
1987; Ford, Smith, & Swasy, 1990), ethics in advertising (e.g., Triff et al., 1987), poor
taste and sexuality (e.g., Larkin, 1977), enjoyability (Russell & Lane, 1989), social
comparison and self images (e.g., Richins, 1991) and annoyance/irritation (e.g., Ducoffe,
1995; James & Kover, 1992).
Despite the advances in our knowledge, Pollay and Mittal (1993) asserted that
previous work had not yet completely considered “the range of specific beliefs held by
consumers and their relative importance in relation to a global attitude toward advertising
and other consumer behaviors” (p. 100). They concluded that a more comprehensive
3
model should be developed using additional belief dimensions as determinants of
attitudes toward advertising.
According to Pollay and Mittal (1993), “beliefs are descriptive statements about
object attributes (e.g., advertising is truthful) or consequences (e.g., advertising lowers
prices), whereas attitudes are summary evaluations of objects (e.g. advertising is a
good/bad thing)” (p. 101). They proposed that attitudes could be explained by beliefs,
“being the integration of weighted evaluations of perceived attributes and consequences”
(p. 101), based on Fishbein’s (1963) theory of reasoned action. The kernel of Fishbein’s
theory is that beliefs and attitudes are distinct, and beliefs usually function as indicants of
attitudes (e.g., Dillon & Kumar, 1985; Fishbein, 1967a; Fishbein & Ajzen, 1974; Fishbein
& Raven, 1962).
The fundamental concepts derived from Fishbein (1963) led Pollay and Mittal
(1993) to establish seven belief dimensions about advertising in general and selfdeveloped or adapted items for each belief dimension from several prior studies. Pollay
and Mittal (p. 101) divided seven belief dimensions into two categories: one group is
belief dimensions that “explicate personal uses and utilities of advertising” (product
information, social role and image, and hedonic/pleasure), and the other group includes
items that “reflect consumers’ perceptions of advertising’s social and cultural effects”
(good for the economy, materialism, value corruption, and falsity/no sense).
Attitude toward advertising in specific mediums. Based on the concept that
beliefs are antecedents of an individual’s attitude toward advertising in general,
researchers have extended their research interests to attitude toward advertising in a
variety of specific mediums, such as television (e.g., Aaker & Bruzzone, 1981; Alwitt &
Prabhaker, 1992; Biel & Bridgwater, 1990; Mittal, 1994), online (e.g., Burns, 2003; Chen
& Wells, 1999; Cowley, Page, & Handel, 2000; Ducoffe, 1996; Schlosser, Shavitt, &
Kanfer, 1999; Wang, Zhang, Choi, & D’Eredita, 2002), direct marketing (e.g.,
Korgaonkar, Karson, & Akaah, 1997), outdoor signage (e.g., Bhargava, Donthu, & Caron,
1995; Donthu, Cherian, & Bhargava, 1993) or videocassettes (e.g., Lee & Katz, 1993).
There has been a significant increase in the different types of advertising media in recent
decades. As a result, attitude toward advertising in specific mediums has become an
important topic for research along with attitude toward advertising in general. Since
4
Bauer and Greyser (1968) addressed the moderating effects of the advertising medium on
attitude toward advertising in general, a trend with research in recent years has
progressed from a broad study of attitude toward advertising in general to a focus on
attitude toward advertising in specific mediums.
One medium that has grown dramatically, but which has not been a topic of study
relative to attitude toward advertising, is sport. Sport has been considered a natural
platform for advertising “as it carries very strong images, has a mass international
audience, and appeals to all classes” (Gwinner & Swanson, 2003, p. 275). Advertising
with the use of sport may provide substantial “excitement” and “emotional attachment”
among consumers (Copeland, Frisby, & McCarville, 1996, p. 33). Accordingly, while
consumers’ overall attitudes toward advertising in general and other traditional mediums
have become more negative (e.g., Alwitt & Prabhaker, 1992; Andrews, 1989; Mittal,
1994; Zanot, 1981, 1984), it is propose that attitude toward advertising through sport
should be more positive based on the positive feelings consumers are expected to have
toward sport. An important implication of this idea is that the use of sport as an
advertising medium enhances the effectiveness of advertising through sport. The current
study sought to explore attitude toward advertising through sport and to distinguish
between beliefs about and an attitude toward advertising through sport. The following
section presents general information regarding the proposed model.
The proposed model of attitude toward advertising through sport. In an effort to
bridge research on attitudes toward advertising in general and attitude toward advertising
in a specific medium, Korgaonkar et al. (1997) explored consumers’ global beliefs
regarding direct marketing advertising using Pollay and Mittal’s (1993) general model,
and concluded that the general advertising scales were adaptable to assess advertising
through a specific medium. The model of beliefs and attitude toward advertising in
general was adapted for the current study to illustrate the relationship between seven
belief factors and attitude toward advertising through sport (see Figure 1.1).
Drawing from Pollay and Mittal’s (1993) ideas pertaining to beliefs and attitude,
the proposed model illustrates the elements believed to comprise an individual’s attitude
toward advertising through sport. The proposed model of attitude toward advertising
through sport includes four personal utility factors (product information, social role and
5
image, hedonism/pleasure, and annoyance/irritation) and three socioeconomic factors
(good for the economy, materialism, and falsity/no sense) (see Figure 1.1).
Personal (Micro) Factors
Social role
and image
Hedonism/
pleasure
Societal (Macro) Factors
Good
for the
economy
Annoyance/
irritation
Falsity/
no sense
Materialism
Product
information
Attitude toward
advertising
through sport
Figure 1.1. A proposed model of attitude toward advertising through sport (Source:
Adapted and modified from Pollay & Mittal, 1993)
The proposed model includes modifications from Pollay and Mittal’s (1993)
model. Pollay and Mittal’s original model of beliefs and general attitudes toward
advertising included three personal utility factors (product information, social role and
image, and hedonism/pleasure) and four socioeconomic factors (good for the economy,
materialism, value corruption, and falsity/no sense). Previous research (Korgaonkar et
al., 1997; Pollay & Mittal, 1993) has demonstrated that the materialism and value
corruption factors have resisted discrimination in factor analysis. The results reported by
Pollay and Mittal (p. 109) indicated that the impact of value corruption on global attitude
was not significant for either group studied because “its covariance with attitude (which
was significant) was totally absorbed in the materialism-attitude covariance” in their
6
collegian group, and because of “its initial low covariance with attitude” in their
householder group. Accordingly, the materialism factor was retained but the societal
factor, value corruption, was not included in the proposed model of attitude toward
advertising through sport.
One additional factor, annoyance/irritation was included in the proposed model.
A variety of research has dealt with the topic of annoyance/irritation as another belief
dimension about advertising in general; results have shown annoyance/irritation may be
also utilized as a predictor of attitude toward advertising through sport (e.g., Aaker &
Bruzzone, 1981; Alwitt & Prabhaker, 1994; Bauer & Greyser, 1968; Ducoffe 1996; James
& Kover, 1992). According to the results reported by Aaker and Bruzzone (1981),
annoyance with advertising could be one reason for disliking advertising. More
specifically, it is believed that today’s sports fans have become annoyed with too much
advertising during sporting events (Lefton, 1997). Thus, annoyance/irritation was
included as a factor influencing attitude toward advertising through sport. An in-depth
review of literature that provided the background for the model is presented in chapter
two.
The interpretation of advertising through sport. One of the most challenging
tasks of the current study is explaining what is meant by the phrase “advertising through
sport.” The term “sport” as used here includes all types of sporting events, amateur or
professional. Consistent with the definition of attitude toward advertising in general
(Lutz, 1985, p. 46), attitude toward advertising through sport may be defined as a learned
predisposition to respond in a consistently favorable or unfavorable manner toward the
use of sport as an advertising medium. The current study examined attitude toward
advertising through sport with sport functioning as an independent advertising medium.
It is important to note that the focus is not on an individual’s attitude toward any specific
format used in advertising through sport (e.g., in-stadium signage; program
advertisements). The focus is on a general attitude toward advertising through sport. The
phrase “advertising through sport” may include any type of advertising (e.g., TV or radio
commercials; magazine ad) that uses elements of sport such as an athlete or images of a
sporting event. Advertising through sport may also include the presence of advertising at
a sporting event such as in-stadium signage at games.
7
Figure 1.2 illustrates how advertising through sport is different than advertising
through a specific format or vehicle. Figure 1.2 provides a comparison of advertising
through sport with online advertising in terms of four levels: generic, medium, format,
and vehicle. At a generic level, advertising is viewed as an overall phenomenon. The
second level represents the medium through which advertising is presented (e.g., through
sport, online).
Advertising in general
Generic
Medium
Advertising
through sport
• In-stadium signage
• Logos or brand
Format
names on uniform
• TV commercials,
etc.
Vehicle
Online
advertising
• Banner
• Pop-ups
• Floating
• Large rectangle,
etc.
Individual sporting
events (MLB, NBA,
NFL, NHL, etc),
teams, or athletes
Individual web sites
(The Chicago Cubs’
homepage, etc)
Figure 1.2. Level of advertising through sport and online advertising level
The third level represents the format through which advertising is presented. Advertising
through sport may include in-stadium signage, outdoor signage in sporting venues, TV
commercials featuring famous athletes or teams, brand names or logs on uniforms,
magazine advertising with the use of sport, etc. Advertising through an online source
may include screen graphics such as banners, pop-ups, floating ads, etc. (Burns, 2003).
8
The vehicle represents the specific outlet in which a format is presented. Particular
sporting events (e.g., a Major League Baseball game), athletes (e.g., Tiger Woods), or
teams (e.g., The Florida State University Football team), a particular web site (e.g., the
homepage for the Chicago Cubs) are examples of specific vehicles.
It is also important to clarify how attitude toward advertising through sport is
differentiated from previous research. A review of literature found that most prior studies
have concentrated on the investigation of specific advertising formats such as signage at
sporting events (e.g., Cunneen & Hannan, 1993; Harshaw & Turner, 1999; Nicholls et al.,
1999; Nicholls, Roslow, & Laskey, 1994; Pyun & Kim, 2004; Stotlar & Bennett, 2000;
Turco, 1996; Turley & Shannon, 2000). According to Figure 1.2, attitude toward
advertising through sport in the current study may include all types of advertising formats
either promoted on-site where sporting events take place, or utilizing any sport related
elements such as teams, logos, or athletes in advertisements.
It is also important to clarify how attitude toward advertising through sport differs
from measuring attitude toward sponsorship and attitude toward the commercialization of
sport. A variety of research has examined the topic of sport sponsorship, particularly the
effectiveness of sponsorship activities (e.g., Copeland et al., 1996; Kinney & McDaniel,
1996; Lyberger & McCarthy, 2001; Pope & Voges, 2000; Stipp & Schiavone, 1996;
Turco, 1994). Advertising is often included in sponsorship activity (McDonald, 1991);
for example, Kinney and McDaniel (1996) noted that “corporate sponsorship of the
Olympics is frequently supported with advertising touting the sponsorship” (p. 251). As
noted by several researchers, however, (McDonald, 1991; Meenaghan, 1991, 1998,
2001a; Shilbury & Berriman, 1996) sponsorship and advertising are not the same.
The term sponsorship describes a business relationship between a provider (the
sponsor) of funds, resources or services and a property (e.g., a sports team) that offers in
return some rights and an association that may be used for commercial advantage (Sleight,
1989). Advertising involves the activity of attracting public attention to a product or
business, as by paid announcements in the print, broadcast, or electronic media.
Meenaghan (2001a) noted that advertising may be seen as a more direct and less subtle
form of marketing communications than sponsorship. McDonald (1991) defined
sponsorship as “advertising plus,” purporting “more than advertising” (p. 35). Attitude
9
toward sponsorship would reflect an individual’s beliefs about the relationship between
sponsors and (sport) properties; attitude toward advertising through sport examines an
individual’s beliefs about the use of sport as an advertising platform.
In a similar vein, people may have attitudes toward the commercialization of sport.
Some people are upset by the presence of signs, promotional and other advertising
activities, the emphasis on player salaries and the money in sports, and the influence of
media, particularly television, on sporting events. Attitude toward commercialization of
sport is about more than the use of sport as an advertising platform. The focus of the
current paper is an assessment of an individual’s attitude toward advertising through sport,
particularly the beliefs that underlie the attitude. An individual’s attitude toward the
commercialization of sport is likely related to and potentially a consequence of one’s
attitude toward advertising through sport. The current paper will concentrate on the
beliefs underlying an attitude toward advertising through sport in general, not attitude
toward sponsorship or attitude toward the commercialization of sport.
Significance
The proposed model presents attitude toward advertising through sport as an
attitude toward advertising through a specific medium, adapted from Pollay and Mittal’s
(1993) model of attitude toward advertising in general. The belief dimensions proposed
to influence attitude toward advertising through sport were operationalized and measured.
A comprehensive recognition of relationships between beliefs and attitudes in advertising
is expected to have significant implications for advertising researchers and organizations
advertising through sport.
In terms of previous research, despite the growth of advertising through sport,
academic research in this area to date has been limited (Dodd, 1997). Understanding
consumers’ beliefs and attitude concepts surrounding advertising through sport will
provide researchers a comprehensive conceptual framework to identify key belief
dimensions that influence attitudes toward advertising through sport, and subsequently
purchase intentions and behaviors.
This study will also benefit organizations advertising through sport. Advertising
efforts will be more successful if advertisers better understand what beliefs influence
consumer attitudes because images and messages may be crafted that are consistent with
10
positive beliefs, and to avoid activating negative beliefs. A better understanding of belief
dimensions will enable advertisers to establish more effective promotional strategies that
should increase revenues and enhance the image of products or companies.
In order to benefit both the academic community and sport industry, the current
study purports to identify beliefs pertaining to advertising through sport, and to
empirically test the proposed model for the reliability and validity. A valid and reliable
instrument will provide support for the conceptualization and measurement of the belief
dimensions proposed to influence attitude toward advertising through sport. The
implications of the study will identify which belief dimensions enhance or inhibit future
purchase intentions.
The following chapters in this document include a review of literature from which
the conceptual definitions of beliefs and attitudes were derived (Ch. 2), presentation of
the results of a pilot study as an initial step in the scale development process suggested by
Churchill (1999) (Ch. 3), an explanation of the methodology utilized for the main study
(Ch. 4), a reporting of the results from the main study (Ch. 5), and a discussion of the
results along with implications and suggestions for future research (Ch. 6).
11
CHAPTER II
LITERATURE REVIEW
Overview
The current study attempts to measure attitude toward advertising through sport,
emphasizing a distinction between beliefs and attitudes. The conceptual framework of
this exploratory study was derived from the concept of attitude toward advertising in
general. Since the advent of research on attitude toward advertising in general in the
1960s, researchers have developed various belief dimensions about advertising in general
and measured their relationships with attitudes toward advertising in general. A recent
trend in advertising research has been a focus on attitude toward advertising in specific
mediums by applying belief dimensions pertaining to attitude toward advertising in
general, to explore attitudes toward advertising in specific mediums (Burns, 2003). This
study utilized the concepts of beliefs and attitudes toward advertising in general and
specific mediums to construct a proposed model of attitude toward the use of sport as an
advertising platform. Prior to reviewing the literature on attitude toward advertising in
general, the following section provides a discussion of the differences between beliefs
and attitudes in terms of definitions, theories, and measurement techniques.
Distinguishing between beliefs and attitudes
This study will examine the relationship between a belief and an attitude, with
belief functioning as an antecedent of attitude. In prior studies, the terms belief and
attitude have been frequently used as a single concept, meaning that attitudes are often
regarded as including beliefs (e.g., Breckler, 1984; Eagly & Chaiken, 1998; Rosenberg &
Hovland, 1960). Attitude may be defined as, “a hypothetical construct that, being
inaccessible to direct observation, must be inferred from measurable responses” (Ajzen,
1988, p. 4). Using this definition of attitude, Ajzen suggested that the responses should
reflect positive or negative evaluations of the attitude object. However, there is
practically no restriction on the types of responses that can be considered (Ajzen, 1988).
Consequently, attitude-related responses are generally categorized into subgroups (Ajzen,
1988).
12
One of most popular approaches characterizes attitudes as belonging to one of
three classes – cognitive, affective, and behavioral (or conative) (e.g., Breckler, 1984;
Eagly & Chaiken, 1998; Rosenberg & Hovland, 1960). Rosenberg and Hovland (1960)
provided a visual representation of the three-component concept of attitude (see Figure
2.1).
Affect
Stimuli
(Object)
Attitude
Cognition
Behavior
Sympathetic nervous response
Verbal statement of affect
Perceptual responses
Verbal statement of beliefs
Overt action
Verbal statement concerning
behavior
Figure 2.1. Schematic concept model of attitudes
Note: Adapted from “Cognitive, Affective, and Behavior Components of Attitude,” by M. J.
Rosenberg and C. I. Hovland. In C. I. Hovland, & M. J. Rosenberg (Eds.), Attitude organization
and change: An analysis of consistency among attitude components (p. 3), 1960, New Haven, CT:
Yale University Press. Copyright 1960 by the Yale University Press.
The schematic figure represents an attitude as a predisposition to respond to a
stimuli in one of three ways: cognition, which includes “perceptual responses” or “verbal
statements of belief”; affect, which includes “sympathetic nervous responses” or “verbal
statements of affect”; and behavior or conation, which would be “overt action” or “verbal
statement concerning behavior” (Rosenberg & Hovland, 1960, p. 3). Cognition reflects
thoughts which are associated with beliefs; affect includes feelings, emotions, or moods
that people have in response to their encounter with an attitude object; behavior/conation
are a person’s overt actions or intentions to act toward an attitude object (Eagly &
Chaiken, 1998). All three components are believed to provide an understanding of the
phenomenon of attitude (Breckler, 1984; Eagly & Chaiken, 1998; Rosenberg & Hovland,
13
1960). Based on the three-component perspective, changes in the cognitive structure of
an attitude object are not independent of changes in an attitude toward that object.
According to Fishbein (1967a), attitudes may be defined as “learned
predispositions to respond to an object or class of objects in a favorable or unfavorable
way” (p. 257). Beliefs are defined as “hypotheses concerning the nature of objects and
the types of actions that should be taken with respect to them” (Fishbein, 1967a, p. 257).
In contrast to the view of the tripartite of attitude that attitudes always depend on the
direction of beliefs (e.g., Breckler, 1984; Eagly & Chaiken, 1998; Rosenberg & Hovland,
1960), Fishbein’s (1967a) multi-dimensional concept holds that the measure of an attitude
does not necessarily represent all three components; Fishbein maintained that beliefs and
attitudes are distinct and that beliefs are indicants of attitudes. Fishbein’s (1963)
expectancy value theory helps to clarify the relationship between beliefs and attitudes.
According to Fishbein’s theory, attitudes are the functions of beliefs; beliefs are assumed
to have causal effects on attitudes. It is hypothesized that an individual’s strongest belief
has the greatest influence on his/her attitude (Fishbein, 1963).
Consider the following examples relative to Fishbein (1967a)’s position. When
watching a Major League Baseball game at home on television, people are exposed to a
variety of advertising signage in a stadium. One person may have a favorable attitude
toward in-stadium signage because it provides valuable information regarding new
baseball products; another person may also have a positive attitude toward the signage
because s/he thinks it helps to reduce the price of tickets. The two individuals would be
regarded as having similar attitudes toward in-stadium signage, but the belief(s)
influencing the attitudes may be different. People may also have the same beliefs about
an object, but have different attitudes toward the object (Fishbein, 1967a). For instance,
one person may like the New York Yankees baseball team because of a belief that the
team monopolizes star players; another person may dislike the Yankees because of the
exact the same belief.
The preceding examples support Fishbein’s claim that cognition and affect are
not always highly associated (Fishbein, 1967a). Additionally, with respect to measuring
attitudes, researchers have not been able to measure all three components together.
Scales assessing attitude usually depend on measuring people’s evaluation aspects of an
14
attitude object (Fishbein, 1967a). Researchers typically attempt to measure the affective
component as an indicator of a distinct attitude (Fishbein, 1967a). Fishbein proposed that
a more realistic view should include beliefs, attitudes, and behavior, as related but
independent constructs, rather than considering attitude as a multi-component construct
of which researchers primarily focus on one component, affect. Fishbein (1967a) further
proposed, in concert with previous research, that attitudes may be measured by affect, the
strength of a person’s positive or negative feelings toward an object (e.g., Thurstone,
1928; Osgood, Suci, & Tannenbaum, 1957). According to Fishbein’s (1967a) view,
attitude can be conceptualized as “the amount of evaluation associated with the attitude
object, and this need have no necessary relationship with a person’s cognitions or beliefs
about this object, and certainly none with his behavior with regard to it” (as cited in
Lemon, 1973, p.252).
Green (1954) held a view similar to Fishbein (1967a), that the concept of attitude
may be described as a latent variable which is a function of beliefs and actions (as cited
in Kim, 2003). A latent variable labeled attitude may be measured by assessing beliefs
and/or behavioral intentions, because beliefs and/or intentions may be viewed as an
antecedent or consequence of an individual’s attitude (Green, 1954, as cited in Kim,
2003).
Fishbein’s (1967a) hypothesis of a strong relationship between attitudes and
behaviors has been questioned in terms of the validity of an attitude construct by
McGuire (1969), Warner and DeFleur (1969), and Wicker (1969) (as cited in Fishbein &
Ajzen, 1974). Criticisms have been based on the disappointing empirical results from
testing the hypothesis by Berg (1966), Bray (1950), and Kutner, Wilkins, and Yarrow
(1952) (as cited in Fishbein & Ajzen, 1974). Criticisms based on a weak empirical
relationship between attitudes and behaviors have raised doubts regarding the definition
and measurement of a unidimensional attitude construct (Fishbein & Ajzen, 1974).
Bagozzi and Burnkrant (1979) reanalyzed the data previously reported by
Fishbein and Ajzen (1974) on attitude organization and the attitude-behavior relationship.
The validity of a unidimensional model of attitude (Fishbein & Ajzen, 1974) was
reassessed and compared with their two-dimensional (affective and cognitive) model of
attitude through testing of convergent validity. The results found evidence to support
15
their affective-cognitive conceptualization of attitudes. Bagozzi and Burnkrant (1979)
persisted that both cognitive and affective dimensions should be considered for a
complete measurement of attitude and the prediction of behavior.
Dillon and Kumar (1985) reexamined the data reported by Fishbein and Ajzen
(1974) and reanalyzed by Bagozzi and Burnkrant (1979) with convergent and predictive
validity tests between the two-dimensional and single dimensional models of attitude.
Dillon and Kumar questioned Bagozzi and Burnkrant’s empirical support for a twodimensional conceptualization of attitude and noted that “although Bagozzi and
Burnkrant’s two factor model can not be rejected on the basis of the attitude data alone,
there are nevertheless other plausible alternative models that are conceptually consistent
with the single-factor, one-component representation of attitude” (p. 45). However, in
Bagozzi and Burnkrant’s reply to Dillon and Kumar, they reexamined Fishbein and
Ajzen’s data with new methods and supported that their two-component model indicated
good convergent, discriminant, and predictive validity, which was not shown in a single
component model. Bagozzi and Burnkrant also pointed out that Dillon and Kumar
misinterpreted their results of discriminant and predictive validity based on the
insufficiency of theoretical backgrounds. Such debates supporting either a single or two
component attitude construct have continued in psychology and marketing research in
terms of the validity of both attitude models and their utility for predicting behavior.
Sheppard, Hartwick, and Warshaw (1988) conducted two meta-analyses to
investigate the effectiveness of Fishbein and Ajzen (1975)’s model based on 174 studies
published in major marketing and psychology journals that used the model. The authors
were interested in the understanding and prediction of situations that did not seem to fit
within Fishbein and Ajzen’s model; the expectation was that the model would perform
poorly in such situations (Sheppard et al., 1988). The authors concluded after analyzing
their data that “to our surprise, the model performed extremely well in the prediction of
goals and in the prediction of activities…it would seem that the Fishbein and Ajzen
model has strong predictive utility” (Sheppard et al., 1988, p. 338). Fishbein and Ajzen’s
model could be utilized even in situations or activities that do not fall within the
boundary conditions around which the model had been originally specified (Sheppard et
al., 1988). In addition, East (1993) supported Ajzen and Fishbein’s (1980) model through
16
his empirical study predicting and explaining members’ voluntary behavior in three cases
of British companies.
An important controversy to consider is whether a belief should be included as
one component that may provide a measure of attitude, or whether a belief should be
viewed as a separate construct. Belief has often been included in the attitude construct
(Bagozzi & Burnkrant, 1979, 1985; Rosenberg & Hovland, 1960). At the same time,
other studies have supported the position that beliefs and attitudes are distinct constructs
(e.g., Dillon & Kumar, 1985; Fishbein, 1967a; Fishbein & Ajzen, 1974; Fishbein &
Raven, 1962). The current study will follow the latter view, that beliefs and attitudes are
different constructs. An attitude was characterized as a consequence of belief and
described as “a learned, implicit response that mediates evaluative behavior” while the
concept of a belief was defined as “a concept’s position on the probability dimension”
and an indicant of an attitude (Fishbein, 1967a, p. 260).
Based on the definition of a belief thus far as only “a concept’s position on the
probability dimension” (Fishbein, 1967a, p. 260), measurement of beliefs has focused on
“the probability of the existence of an object” (Fishbein & Raven, 1962, p. 40). Two
questions that have emerged with respect to measurement are whether a belief should be
measured as “the perceived probability of existence” or “the precise nature of that
existence” (Fishbein, 1967a, p. 40). The questions pertaining to the measurement of a
belief have led to the necessity of distinguishing between “belief in” an object and “belief
about” an object (Fishbein & Raven, 1962, p. 41).
Belief in and belief about
Fishbein and Raven (1962) recognized the importance of distinguishing between
belief in an object and belief about an object. They defined belief in as “the existence of
an object” and beliefs about as “the various beliefs in the relationships between an object
and other objects or qualities” (p. 41). Relative to the definition of belief in (Fishbein &
Raven, 1962), it is unlikely that anyone would think that advertising through sports does
not exist in our society. Under the definition of belief about (Fishbein & Raven, 1962),
people may have different ideas regarding the various relationships between advertising
through sport and other related objects or concepts. For example, some people may agree
that advertising through sport is important because it helps a local economy; others may
17
disagree and think that advertising through sport only benefits the team or owner.
When Fishbein and Raven (1962) first measured a belief concept, they depended
on the same series of bipolar probabilistic scales used by Osgood et al. (1957). These
scales assessed “the probability of the existence of an object” (Fishbein & Raven, 1962, p.
40). The two questioned whether a belief should be measured relative to “perceived
probability of existence” or “the precise nature of that existence” (Fishbein & Raven,
1962, p. 41). Most psychology and marketing researchers have assessed belief about an
object when measuring beliefs as part of attitude research (Fishbein, 1967a). Questions
raised by their early work (e.g., Fishbein & Raven, 1962) led the researchers to further
develop the notion of belief about objects.
According to Fishbein and Raven (1962), a belief about an object can be
characterized as “a relationship between the object (of belief) and some other object,
value, goal, or concept” (Fishbein, 1967a, p. 259). An example previously mentioned
regarding a belief about advertising through sport was that advertising through sport
helps a local economy. The object of belief would be ‘advertising through sport’, the
other object, value, goal or concept would be ‘local economy’ (a concept here); the
relationship would be ‘helps’.
When faced with a belief statement regarding an object, people may have an
attitude toward that object as well as an attitude toward related objects or concepts. This
attitude toward related objects or concepts could be referred as “the evaluative aspect of a
belief about an object” (Fishbein, 1967a, p. 260). For example, when people encounter a
belief statement like advertising through sport helps the local economy, they may have an
attitude toward advertising through sport and also an attitude toward the local economy.
If people have a positive evaluation of the local economy, and if people believe that there
is an associative relationship between advertising through sport and the local economy,
they may have a positive attitude toward advertising through sport. Likewise, if people
have a negative evaluation of the local economy, and if people believe that there is an
associative relationship between advertising through sport and the local economy, they
may have a negative attitude toward advertising through sport.
Fishbein’s definition of belief about an object is consistent with the description of
beliefs that most researchers have suggested in advertising research (Bauer & Greyser,
18
1968; Korgaonkar et al., 1997; Muehling, 1987; Pollay & Mittal, 1993; Shimp, 1980).
The current study will focus on measuring beliefs about advertising through sport rather
than beliefs in advertising through sport. The study will also differentiate between beliefs
about advertising through sport and attitude toward advertising through sport. It is
important to consider next how beliefs and attitudes may be and have been measured and
manipulated independently (Fishbein & Raven, 1962).
Measuring beliefs and attitudes
This section examines the measurement of attitudes and beliefs. There have been
various measurement techniques developed to measure attitudes. The methods for
measuring attitudes and beliefs generally fall into two categories: indirect and direct
measurements. With an indirect method, a measurement is made to assess a person’s
attitude without the person knowing it; with a direct method, a subject is asked to give a
self-report of his or her attitude; (Petty & Cacioppo, 1981). Petty and Cacioppo (1981)
explain that indirect measurements usually include disguised self-reports, behavioral
indicators of an attitude, physiological indicators of an attitude, etc.; direct measurements
utilize a Thurstone scale, Likert scale, semantic differential scale, or a single item scale
(Petty & Cacioppo, 1981). Beliefs about an object have also been measured with the
same techniques including Thurstone, Likert, and semantic differential scales (Fishbein &
Raven, 1962; Fishbein, 1967a; Hughes, 1974).
Detailed descriptions of indirect and direct measurements follow, particularly
Thurstone, Likert, and the semantic differential scales which are popular techniques in
psychological and marketing research (Aaker, Kumar, & Day, 2000; Churchill &
Iacobucci, 2002; Petty & Cacioppo, 1981). The brief comparisons of the strengths and
weaknesses of each technique in direct and indirect measurements are summarized in
Table 2.1.
Indirect measurement of an attitude. When a respondent recognizes that his/her
attitude is being tested, this awareness reflects in one’s behavior, and negatively
influences the efforts to measure an attitude (Dawes, 1972). This bias is one of most
widely recognized disadvantages of direct measurement, that respondents are not willing
to provide accurate information to researchers about their attitudes (Petty & Cacioppo,
19
Table 2.1
The Strengths and Weaknesses of Measurement Techniques in Direct and Indirect
Procedures
Strength
Thurstone
Scale
• A precise estimate of where a
person stands on the underlying
attitudinal dimension.
Likert scale
• Easier to construct than the
Thurstone scale with the same set
of people.
• Equivalent reliability with and
high correlation with Thurstone.
The semantic
differential
scale
• Equivalent reliability with
Thurstone and Likert scales.
• A good consistency of the mass
of a cross-cultural study (Tanaka,
Oyama, & Osgood, 1963).
• The use of relatively disguised
ways to measure an attitude
(MaGrath & McGrath, 1962).
• The stability of the evaluationpotency-activity framework across
different age groups
(Di Vesta, 1966).
• In overall, a good performance in
reliability and validity tests
• Allow subjects to express the
intensity of feelings toward an
object.
The one-item
Rating scale
• Much easy to construct.
Disguise
Self-reports
• Complementary way to reduce
the obtrusive effects of indirect
measurement.
Direct
Procedure
Weakness
• Difficult to construct and
administer.
• Many judges required (200~300)
Considerable time, effort, and
money required.
• A total score does not reflect a
unique attitude since the total can
be derived in many different ways
(e.g., the total score does not
explain differences of each scores
of items).
• Very restricting in situations
where respondents are not familiar
with an attitude object.
• Adjectives may not exactly
represent an attitude object
(Osgood et al., 1957).
• Not as reliable as the Thurstone,
Likert, and semantic differential
scales.
• Ethical problem: misleading
subjects for the purpose of the
study.
• Difficult to devise a suitable
procedure.
• Particularly useful in assessing
political attitudes in a country
• Somewhat expensive.
Indirect
where people may be afraid to
• Unrepresentative behaviors may
Procedure
give their true opinions to
be observed.
interviewers.
• A “perfect” measure in the
Physiological human body’s natural
• Elaboration preparation and
physiological responses to
expensive equipment.
indicator
attitudinal stimuli (Cacioppo &
• Inconsistent reliable results.
of attitudes
Sandman, 1981).
Source: Summarized and cited from Churchill and Iacobucci (2002); Huges (1974), Lemon (1973); Petty
and Cacioppo, (1981).
Behavioral
indicators
of attitudes
20
1981; Shaw & Wright, 1967). Campbell (1950) asserted the necessity of the developing
attitude measures that will not elicit reactive responses (as cited in Dawes, 1972):
In the problem of assessing social attitudes, there is a very real need for
instruments which do not destroy the natural form of the attitude in the process of
describing it. There are also situations in which one would like to assess
‘prejudice’ without making respondents self-conscious or aware of the intent of
the study. (p. 120, as cited in Dawes, 1972)
With indirect measurement respondents do not deliberately distort their responses
because researchers disguise the purpose of a study or do not reveal the true purpose(s)
immediately (Lemon, 1973). Indirect measurements have been utilized as a useful way
of detecting relatively large differences in affect between respondents (Petty & Cacioppo,
1981).
There is some question as to whether indirect measurements are ethical (Dawes, 1972;
Kidder & Campbell, 1970; Lemon, 1973). Kidder and Campbell (1970) criticized the use
of disguised measurement techniques noting that their use raises important ethical
questions about the rights of respondents to be aware of the true purpose of the
instruments to which they are subject. Therefore, when considering the use of indirect
measurements a researcher should “weigh up these considerations carefully before he
commits himself to measurement of this type, and should exercise this own good sense,
and pay due regard to possible abuses in their use” (Lemon, 1973, p. 121).
Direct measurement of an attitude. In studies of beliefs and attitudes, researchers
have preferred the direct self-report measurement techniques because it is assumed that
the direct scales are superior to the indirect scales in terms of reliability and validity
(Lemon, 1973; Petty & Cacioppo, 1981; Stevens, 1966). Against the view of
respondents’ deliberate transmissions of their incorrect attitudes in direct measurements,
Petty and Cacioppo (1981) argued that this disadvantage is not problematic in attitude
research because “most attitude research does not deal with highly sensitive issues, and in
many studies the subjects’ attitudinal responses are kept anonymous” (p. 22). Thus, they
suggested there is little reason to conclude that indirect measurements misrepresent
21
respondents’ attitudes. Another advantage of using direct measurement is the precision or
sensitivity of scales; direct measurement scales are better at “pinpointing relatively small
differences in attitudes that may exist between subjects” (Petty & Cacioppo, 1981, p. 22).
More detailed descriptions of specific direct measurement techniques such as, Thurstone
scale (Thurstone, 1928), Likert scale (Likert, 1932), and the semantic differential scaling
(Osgood et al., 1957) are presented below.
Thurstone Scale. While working on scaling human perceptions of sensory
stimuli such as light and sound, Thurstone (1928) realized that people could rank opinion
statements in terms of their favorableness toward some objects just as people can rank
noises in term of loudness. The steps in constructing a Thurstone scale to measure an
attitude are summarized below (Petty & Cacioppo, 1981; Thurstone, 1928):
● Development of a Thurstone scale begins with collecting a list of 100 to 150
belief statements from several groups of people who are asked to write out their
opinions on an issue and from the literature.
● Editing collected statements for a list of about one hundred brief statements of
opinion.
● Two or three hundred subjects as judges are asked to arrange the statement in
eleven piles ranging from opinions most strongly affirmative to those most
strongly negative.
● Only those statements that result in a consistent sorting by the judges are
retained; whenever there is much disagreement among the judges’ rating, the
item is eliminated.
● The researcher allocates “a scale value” to statements that are retained
corresponding to the median category to which the judges have assigned it; each
statement is assigned a scale value such that it is rated higher than its value by
half the judges and lower than this value by the other half.
● Based on the criteria of selecting a high degree of agreement among the judges
on their appropriate category, the researcher finally selects a final set of items
(around 20 items) that range all along the favorable-unfavorable continuum.
● In order to measure an attitude, the researcher asks each subject to check all
22
items with which he or she personally agrees in the final statement pool. The
attitude score is the median of the scale values of the statements that the person
endorses.
Through these steps, an attitude can be measured by a Thurstone scale. Although
the Thurstone scale is rather difficult and it is complicated to construct an item pool
compared to other scales, it represents a fairly precise estimate of respondents’ attitude
toward an object a researcher seeks for (Likert, 1932; Petty & Cacioppo, 1981).
As indicated in the steps, the final series of attitude items contains carefully
selected statements of belief about an object with “different evaluate weight” from a
negative evaluation to a positive evaluation, also including a neutral evaluative aspects
(Fishbein, 1967a, p. 264). Respondents simply check their agreement or disagreement
with each statement (Thurstone, 1928). In order to obtain an attitude score, the
researcher computes the mean or median score of the evaluative weights that each agreed
statement contains (Thurstone, 1928). In Thurstone’s scale, an attitude measure, thus,
can be obtained based on very restricted range of belief statements which have strength
(respondents’ agreement) and evaluative aspects (judges’ evaluative range from 1 to 11)
Fishbein, 1967a). Even though a Thurstone scale is highly recognized as a precise
estimate of attitude, it is generally not recommended for use in marketing research
because of it is a time consuming technique, expensive, and disliked by respondents due
to a lot of reading (Huges, 1967; Huges, 1971).
Likert Scale. The Likert scale is to date the most widely used tool to measure
attitudes because it is easy for researchers to construct and to interpret, and simple for
subjects to answer (Schiffman & Kanuk, 2002). As with a Thurston scale, the researcher
starts with a large set of statement items. However, with a Likert scale the researcher
simply decides which items indicate favorableness to the attitude object rather than
giving these items to judges (Fishbein, 1967a). Edward and Kenney (1967) also
indicated that constructing a Likert scale is less time consuming and less laborious than
Thurstone scales. The steps in constructing a Likert scale are summarized as follows
(Likert, 1932):
23
● A large number of opinion statements related to the attitude object are collected
(i.e. this step is similar to Thurstone’s procedure).
● A large sample of subjects rate the extent of their own agreement with each
statement on a five- or seven-point scale.
● Since the scale supposes that each of the items measures the same underlying
attitude, any items that do not correlate highly with the total score (obtained by
summing the responses to the individual items) are removed form the pool.
● The subject’s final attitude score is obtained by summing the response s to the
items that remain.
The principle benefit of the Likert scale has been that it provides the researcher
the option of considering the responses to each statement separately, or of combining the
responses to produce an overall, summated scales (i.e., this scale is often called a
summated scale) (Schiffman & Kanuk, 2002).
Unlike the Thurstone scale, the Likert scale does not contain a neutral evaluative
aspect in the items (Fishbein, 1967a). The key point is that the Likert scale should fall at
one or the other extreme direction of favorableness or unfavorableness (e.g., 1 or 11 on
the Thurstone continuum) in order to measure an attitude (Edwards & Kenney, 1967).
Belief statements that indicate neither favorableness nor unfavorableness should be
deleted from the final item pool (Fishbein, 1967a). Thus, a researcher should carefully
select attitude items which do not have different meanings for different people (Fishbein,
1967a).
The semantic differential scale. The semantic differential scale has been one of
the most ubiquitous techniques in direct measurement of attitudes (Dawes, 1972). The
semantic differential scale was originally designed to investigate the underlying structure
of words (Churchill & Iacobucci, 2002; Osgood et al., 1957). This measurement
technique was later adapted to measure attitude toward a wide variety of people, objects
or issues.
It has been found that the responses to the bipolar scales tended to be correlated
and that three basic uncorrelated dimensions could be found to explain most of the
meaning we assign to different words: “evaluation” (e.g., “good-bad, sweet-sour, or
24
helpful-unhelpful”), “potency” (e.g., “powerful-powerless, strong-weak, or deepshallow”), and “activity” (“fast-slow, alive-dead, or quiet-noisy”) (Churchill & Iacobucci,
2002, p. 382; Osgood et al., 1957). The first dimension, evaluation, corresponds to what
we consider an attitude (Petty & Cacioppo, 1981). Thus, Osgood et al. stated that
attitudes could be measured by having subjects rate an attitude object on bipolar adjective
pairs that represented the evaluative dimension of meaning. However, a validity issue
could be questioned during the development of scales. When a researcher measures an
attitude toward a certain object, s/he should carefully select appropriate adjective pairs in
relation to that object so that different respondents may not have different meanings on
those items, as is the case with the Likert scale (Fishbein, 1967a). For example, when a
researcher attempts to measure attitude toward advertising through sport, s/he uses a
previously reported attitude scale such as “dirty-clean” (Fishbein & Raven, 1962, p. 36).
In this situation, respondents may not connect this adjective pair with their attitude
toward advertising through sport. Respondents may have also difficulty understanding
what “dirty” and “clean” mean in relation to advertising through sport. Thus, one
important matter to consider with the use of semantic differential scales is to focus on
developing samples of adjective pairs instead of using basic adjective pairs so that a score
could be generated for the attitude object (Churchill & Iacobucci, 2002).
Many marketing and social psychology studies have utilized different scales to
directly measure respondents’ belief and attitude toward an object. Fishbein and Raven
(1962, p. 36) also provided a valid and reliable scale to measure belief and attitude which
could be obtained by having the subject judge the concept on a series of bipolar
probabilistic scales (e.g. “impossible-impossible”, “false-true”, “existent-nonexistent”,
“probable-improbable”, and “unlikely-likely” for belief scales; “harmful-beneficial”,
“wise-foolish”, “dirty-clean”, and bad-good” for attitude scales).
The prior discussion described three broadly used indirect attitude measurement
scales. Attitudes can be directly measured by the semantic differential scales in that
respondents are asked to check their overall attitude toward an object (Fishbein & Ajzen,
1974). In contrast, attitudes can be inferred from the evaluating a responses to belief
statements using Thurstone and Likert scales. Comparing the three scales a Likert scale
is easier than a Thurstone scales because unlike the Thurstone scales, the Likert scale can
25
be accomplished with the same set of people (Petty & Cacioppo, 1981). In spite of
simple procedures, the Likert scales have equivalent reliabilities with Thurstone’s scales
(McNemar, 1946; Poppleton & Pilkington, 1964). When the semantic differential scales
has been applied to marketing studies, marketers sometimes failed to development
appropriate items related to the interest of research, especially, when a study explores the
attitude of a new construct. This failure raises questions regarding validity of the
resulting semantic scales (Churchill & Iacobucci, 2002).
As a result, it has been considered that the Likert scale is the most popular form
of attitude scales (Edwards & Kenny, 1946; Petty & Cacioppo, 1981; Schiffman & Kanuk,
2002). In advertising research, many researchers also depend on the Likert Scales to
measure respondents’ beliefs and attitude toward advertising in general or specific
mediums (Andrews, 1989; Ducoffe, 1995, 1996; Korgaonkar et al., 1997; Muehling,
1987; Pollay & Mittal, 1993; Shavitt et al., 1998). In the case of measuring attitude,
almost all studies have used very restricted range of belief statements that reflect the
evaluative dimension of concept. With these rationales, the current study will utilize a
Likert scale to directly measure individuals’ beliefs and attitudes toward advertising
through sport. For measuring beliefs about advertising through sport, a series of belief
statements relevant to describe particular relationships between the use of sport as an
advertising platform and use of other specific mediums as an advertising platform,
general social norms, or cultural values will be applied. For measuring attitude toward
advertising through sport, a series of statements described as “the evaluative dimension
of concept” – “is it good or bad?” (Fishbein & Raven, 1962, p.188) will be utilized in
order to distinguish belief and attitude statements (i.e., Advertising through sport is good,
or do you like advertising through sport?).
Attitude toward advertising in general vs. attitude toward the ad (Aad)
The concepts of attitude and belief have been discussed in the preceding sections.
The current study will utilize both concepts in the proposed model of attitude toward
advertising through sport. At this point, additional examination is needed regarding how
the concepts of belief and attitude have been previously employed in general advertising
research. In particular, the two constructs, attitude toward the ad (Aad) and attitude
toward advertising in general, have been frequent topics of research in the area of
26
advertising. This section attempts to make a distinction between Aad and attitude toward
advertising in general, to review where the two types of attitudes have been placed in
advertising research, and to discuss the relationships between the two attitude constructs.
Attitude toward the ad is generally defined as “a predisposition to respond in a
favorable or unfavorable manner to a particular advertising stimulus during a particular
exposure occasion” (Lutz, 1985, p 46). Lutz (1985, p. 53) also defined attitude toward
advertising in general as “a learned predisposition to respond in a consistently favorable
or unfavorable manner to advertising in general.” The definitions of both attitudes
mentioned by Lutz are consistent with other researchers’ (e.g. Burns, 2003; Dillon &
Kumar, 1985; Fishbein, 1967; Pollay & Mittal, 1993; Wilkie, 1986) definitions, focusing
on measuring an affective or evaluative aspect of an attitude object. Ostensibly the extent
of attitude toward advertising in general seems much broader than that of Aad. It has been
noted that the concept of Aad reflects a particular exposure to a particular advertisement,
not a consumers’ attitude toward advertising in general or even their attitude toward the
ad stimulus of interest at another point in time (Lutz, 1985). On the other hand, the
concept of attitude toward advertising in general indicates consumers’ general attitudes
toward advertising, rather than attitudes toward a specific advertisement or advertising in
specific mediums (Burns, 2003).
MacKenzie and Lutz (1989) stated that earlier researchers focused on the impact
of the content of commercial stimuli on consumers’ cognitive information process as
recall of ad content, aided/unaided recall and recognition. However, the trend of research
has moved to measure consumers’ affective responses to ads (Calder & Sternthal, 1980;
Mitchell & Olson, 1981). Corporations use advertising to influence consumers to buy
their products. In order to predict the effectiveness of advertising, Aad has been widely
used to measure its impact on brand attitude as consumers’ affective responses to
advertisements (e.g., Brown & Stayman, 1992; Haley & Baldinger, 1991; Homer, 1990;
Lutz, 1985; MacKenzie et al., 1986; MacKenzie & Lutz, 1989; Mitchell & Olson, 1981;
Miniard, Bhatla, & Rose, 1990; Shimp, 1981).
Attitude toward the ad as a mediator
Researchers have suggested that Aad, an affective aspect standing for consumers’
internal evaluation, such as favorability/unfavorability, of an object, has been the
27
mediator of brand attitude and purchasing intensions (e.g., Lutz, 1985; MacKenzie &
Lutz, 1989; Mitchell & Olson, 1981; Shimp, 1981). Shimp (1981) questioned the issue
of how advertising influences consumers’ brand choice then introduced the concept of
attitude toward the advertisement approach as a mediator of brand choice. Shimp
proposed a model of “three alternative brand choice mechanisms” based on theoretical
backgrounds and three experiments from previous studies to support his arguments (p.
12). He concluded that consumer brand attitudes, purchasing intentions, and actual
purchasing behaviors were greatly influenced by their Aad.
Mitchell and Olson (1981) applied the basic theoretical proposition of Fishbein’s
attitude theory to advertising research. The initial attempt of their study was to measure
whether beliefs about product attributes were the only mediator of brand attitude. Their
findings revealed that beliefs about product attributes had a major mediating effect on
brand attitudes, while attitudes toward brand considerably mediated behavioral intentions
(Mitchell & Olson, 1981). However, the product attribute beliefs themselves could not
be the sole mediator of brand attitudes. Rather, consistent with Shimp (1981)’s findings,
the results of Mitchell and Olson also showed that Aad, as a construct which is
conceptually distinct form brand attribute beliefs and brand attitude, partially mediated
advertising effects on brand attitudes. These previous studies contributed to explore the
significance of Aad in advertising attitude formations.
Researchers have subsequently been interested in investigating the mediating
role of Aad in depth. Some researchers utilized two components of Aad, cognitive and
affective, which have been derived from Shimp’s (1981) concepts (e.g., Gresham &
Shimp, 1985; Hill & Mazis, 1986; Muehling, 1986, as cited in MacKenzie & Lutz, 1989).
According to MacKenzie and Lutz (1989), there have been several attempts to examine
possible antecedents of Aad. For instance, Aad has been influenced by advertising
repetition (e.g., Calder & Sternthal, 1980; Messmer, 1979), mechanical and executional
aspects of commercials (e.g., Batra & Ray, 1986; Belch & Belch, 1984), advertiser
credibility (e.g., MacKenzie et al., 1986), surrounding advertising and
editorial/programming contexts such as clutter (e.g., Soldow & Principe, 1981), and
attitudes toward advertising in general (e.g., Muehling, 1986).
Lutz (1985) realized the necessity of gathering previous studies’ examination of
28
some antecedents of Aad in “a piecemeal way” (MacKenzie & Lutz, 1989, p. 49) and
generated a systematic conceptual framework. He reviewed and integrated past studies
related to Aad for constructing a comprehensive conceptual model of the cognitive and
affective antecedents of Aad. It has been one of most popular models of Aad called “a
structural model of cognitive and affective antecedents of attitude-toward-the-ad”
proposed by Lutz (p. 48).
Lutz (1985) developed a useful framework for integrating Aad effects and the
more commonly utilized brand attribute ratings based on the Elaboration Likelihood
Model (ELM) hypothesized by Petty and Cacioppo (1981). The ELM is one means for
delineating how advertising works. When a person is exposed to advertising, he/she may
pay to attention to the advertising. If the advertising has succeeded in doing that,
persuasion processing occurs along one of two routes: “central” or “peripheral” (Petty &
Cacioppo, 1981, 1985, p. 94). During central processing, the focus is on message
contents; during peripheral processing, the source of the message or contextual factors is
more dominant than actual message content (Lutz, 1985). The involvement levels are
expected to influence the processing routes. Under high involvement conditions
consumers process information via a central route by elaborating on message content;
under low involvement conditions consumers usually rely on the source of the message
or contextual factors (Petty & Caccioppo, 1981; Mehta, 1994). Based on Petty and
Caccioppo’s ELM model and prior research, Lutz demonstrated a structural model of the
cognitive and affective antecedents of Aad adopted from Lutz, MacKenzie, and Belch
(1983).
The original version of the proposed model (a figure is not included in the texts)
included five first-order antecedents, ad credibility, ad perceptions, attitude toward
advertisers, attitude toward advertising in general, and mood, arrayed along a central to
peripheral processing continuum, with ad credibility and mood anchoring the central and
peripheral processing endpoints, respectively (Lutz, 1985). MacKenzie and Lutz (1989)
refined Lutz’s (1985) original model (see Figure 2.2). The modified model incorporates
second-order determinants, which directly influence the five antecedents of Aad and
indirectly influence Aad through the first-order antecedents (Lutz, 1985).
29
Message
Content
Ad claim
Discrepancy
Execution
Characteristics
Past experience
and Information
Advertising
Credibility
Individual
Differences
Reception
Context
Advertising
Perceptions
Advertiser
Credibility
Advertiser
Perceptions
Attitude
toward
Advertising
Ad
Credibility
Attitude
toward
Advertiser
Ad
Perceptions
Mood
Aad
Brand
Perceptions
Ab
Figure 2.2. Modified structural model of Aad formation.
Note: Adapted from “An Empirical Examination of the Structural Antecedents of Attitude Toward
the Ad in an Advertising Pretesting Context,” by S. B. MacKenzie and R. J. Lutz, 1989, Journal
of Marketing, 53, p.53. Copyright 1989 by Scott B. Mackenzie and Richard J. Lutz.
The current study is concerned with the role of attitude toward advertising in
general, rather than the other four antecedents in the Aad model. According to MacKenzie
and Lutz’s (1989) findings regarding attitude toward advertising in general, a pretest
30
failed to confirm attitude toward advertising in general as an important antecedent of Aad.
However, they insisted that when focusing attention on the evaluation of a specific
construct such as advertiser attitude, subjects were less likely to predict Aad on general
constructs, such as, attitude toward advertising in general (Burns, 2003; MacKenzie &
Lutz, 1989). It might be expected that the relationship between attitude toward
advertising in general and Aad would be significant if a test is performed in a natural
setting, as opposed to a forced exposure situation (MacKenzie & Lutz, 1989). Lutz
(1985) also suggested that the impact of attitude toward advertising in general on Aad
might exist in situations of low involvement conditions.
The theoretical background of Aad model has been discussed in order to explain
the role of attitude toward advertising in general in advertising research. The model of
Aad provides important information regarding the relationships between cognitive and
affective antecedents and Aad, in particular, the role of Aad as a mediator of attitude toward
advertising in general on brand attitude and purchasing intention (Lutz, 1985). An indepth review should examine how attitude toward advertising in general has been
theoretically developed and practically applied through prior literature because the
current proposed model has been derived from the concept of attitude toward advertising
in general.
Attitude toward advertising in general
Attitude toward advertising in general is more comprehensive concept than Aad or
attitude toward advertising through specific mediums, such as television, online, instadium signage, etc. (Lutz, 1985). Researchers have investigated the concept of attitude
toward advertising in general for several decades (e.g., Andrew, 1989; Bauer & Greyser,
1968; Durvasula et al., 1993; Mittal, 1994; Muehling, 1987; Pollay & Mittal, 1993; Reid
& Soley, 1982; Sandage & Leckenby, 1980; Shavitt et al., 1998). The review of literature
relative to the development of attitude toward advertising in general is presented below in
chronological order.
The study of the relationship between consumers’ attitudes toward advertising in
general dates back to Bauer and Greyser’s (1968) classic study. Bauer and Greyser’s first
in-depth study investigated the nature of consumers’ attitude toward advertising in
general. The findings of their study revealed that American consumers were generally
31
favorable toward advertising because of advertising’s positive functions in helping to
increase our living standard, in helping to produce better products to the public, and in
revitalizing market economy; 89% of respondents responded that advertising is essential
to functioning of the economy (as cited in Sandage & Leckenby, 1980). However, the
results also showed that many American consumers respond negatively to individual
advertisements because of negative functions such as annoyance or an ad being offensive
(as cited in Sandage & Leckenby, 1980).
Bauer and Greyser’s (1968) work has demonstrated that attitude toward
advertising in general consists of economic and social dimensions (e.g., Anderson et al.,
1978; Andrews, 1989; Larkin, 1977; Reid & Soley, 1982). Items for the economic
dimension include: “advertising is essential,” “advertising helps raise our standard of
living,” and “advertising results in better products for the public.” Items for the social
dimension include: “in general, advertising presents a true picture of the product
advertised,” “most advertising insults the intelligence of the average consumer,” and
“advertising persuades people to buy things they should not buy” (as cited in Pollay &
Mittal, 1993, p. 100). Both economic and social functions could be allocated to the
institution roles of advertising (Sandage & Leckenby, 1980) like other functions, such as
market information (Carey, 1960), the institutional abundance (Potter, 1954), and the
function to educate consumers (Sandage & Leckenby, 1980).
Sandage and Leckenby (1980) emphasized the institutional facet of advertising
and empirically measured consumers’ perceptions of advertising as an institution. For the
purpose of their study, Sandage and Leckenby insisted that first the distinction between
the institutional and instrumental aspects of advertising should be made. The distinct
between “institution” and “instrument” of advertising is consistent with that between
“advertising” and “advertisement” (Sandage & Leckenby, 1980, p. 29). Institution, in
general, may be viewed as “representing a convention, an arrangement, and a solution to
a problem considered important by the society”; instrument may be observed as
“advertisements, commercials, practitioners, and so forth” (Sandage & Leckenby, 1980, p.
30 & p. 31, respectively). They attempted to differentiate advertising considered as a
social institution from advertising viewed as advertisements in order to grasp the
development of a positive understanding of the role of advertising as institution in our
32
society.
Sandage and Leckenby (1980, p. 30) developed the scales (“good, strong,
valuable, and necessary”) for attitude toward institutions, and utilized previous scales
(“clean, honest, sincere, and safe”) for attitude toward instruments from Kokeach (1973);
these were seven-point semantic differential scales. After conducting a factor analysis,
Sandage and Leckenby differentiated attitude toward the institution from attitude toward
the instrument as two-dimensional factors of attitude toward advertising in general.
Results disclosed that students had significantly more favorable attitudes toward
institutions than attitudes toward the instruments of advertising. Sandage and Leckenby’s
study has been widely recognized for pioneering a significant measurement instrument
that distinguishes attitude toward institutions from attitude toward instruments of
adverting.
Building from the work of Sandage and Leckenby (1980) study, Muehling (1987)
reconstructed dimensions underlying attitude toward advertising in general. He insisted
that given the increased interests in practical importance of the attitude toward the ad,
there has been less consideration to the instrumental aspects of advertising, while
previous study has more focused on the institution functions of advertising (e.g., Bauer &
Greyser, 1968; Carey, 1960; Potter, 1954; Sandage & Leckenby, 1980). They also
questioned the validity issues of the instrument developed by Sandage and Leckenby
(1980) whether only eight-item scales accurately represented both dimensions they
proposed. Muehling (p. 33) added two more dimensions to the previous Sandage and
Leckenby’s instrument: “a thought-elicitation exercise” designed to disclose “the
images/impressions of advertising individuals call from memory” and beliefs statements
about attitude toward advertising in general. With this procedure, Muehling proposed a
multi-dimensional construct of attitude toward advertising in general to effectively
examine the impact of four dimensions on attitude toward advertising in general.
Muehling (1987, p. 34) utilized three items (“good/bad, favorable/unfavorable,
positive/negative”) to measure a global attitude toward advertising in general from
Churchill’s (1979) items; eight items to examine institution and instrument dimensions of
advertising from Sandage and Leckenby (1980); and 20 items to investigate the influence
of beliefs about advertising on attitude toward advertising in general from Bauer and
33
Greyser (1968) and Durand and Lambert (1985). For the measurement of a thoughtelicitation exercise he constructed five categories based on the written thoughts of
respondents: the functions of advertising, the practices of advertising, the advertising
industry, users of advertising, and a miscellaneous category (Muehling, 1987).
Institution and instrument dimensions, two thought categories (i.e., function and
practices), and five belief items were simultaneously regressed on the measure of attitude
toward advertising in general (Muehling, 1987). Overall, the results confirmed the
evidence of previous studies that attitudes toward advertising in general are made up of
institution and instrument dimensions (e.g., Sandage & Leckenby, 1980). In particular,
the use of a thought-elicitation exercise provided a meaningful way of determining
whether attitudes toward advertising were comprised of institution- and instrumentrelated dimensions (Muehling, 1987). In addition, they found some significant belief
items which seemed to deal with the instrument function of advertising. These belief
items could be later absorbed to measuring individual’s perceptions of the creative work
of advertisers as roles forming attitude toward advertising in the further research (e.g.,
Pollay & Mittal, 1993).
From the work of Bauer and Greyser (1968) to the beginning of 1990s, additional
research had been done in order to better explain the relationship between various belief
dimensions and attitudes toward advertising in general. (e.g., Alwitt & Prabhaker, 1992;
Andrews, 1989; Anderson et al., 1978; Barksdale & Darden, 1972; Churchill, 1979;
Dubinsky & Hensel, 1984; Durand & Lambert, 1985; Greyser & Reece, 1971; Haller,
1974; James & Kover; 1992; Larkin, 1977; Petroshius, 1986; Reid & Soley, 1982;
Richins, 1991; Russell & Lane, 1989; Schutz & Casey, 1981; Sandage & Leckenby,
1980; Soley & Reid, 1983; Triff et al., 1987; Zanot, 1981). Research confirmed the
evidence that overall attitudes toward advertising in general have been influenced by a
variety of belief dimensions about advertising in general.
Much of the work has centered and relied on Bauer and Greyser’s (1968) classical
two-dimensional measure of perceived social and economic effects of advertising (e.g.,
Andrews, 1989; Anderson et al., 1978; Greyser & Reece, 1971; Haller, 1974; Larkin,
1977; Reid & Soley, 1982; Schutz & Casey, 1981; Triff et al., 1987; Zanot, 1981); other
studies have examined advertising as an information source (e.g., Alwitt & Prabhaker,
34
1992; Barksdale & Darden, 1972; Durand & Lambert, 1985; Haller, 1974; Muehling
1987; Russell & Lane, 1987; Sandage & Leckenby, 1980; Soley & Reid, 1983),
materialism (e.g., Larkin, 1977), falsehood and deception (e.g., Muehling, 1987; Ford et
al., 1990), ethics in advertising (e.g., Triff et al., 1987), poor taste and sexuality (e.g.,
Larkin, 1977), enjoyability (e.g., Russell & Lane, 1989), social comparison and self
images (e.g., Richins, 1991) and annoyance/irritation (e.g., James & Kover, 1992).
Despite the advances in our knowledge, Pollay and Mittal (1993) proposed that
previous works had not yet fully explored the range of specific beliefs held by consumers
and their relative importance in relation to global attitude toward advertising and other
consumer behaviors. They concluded that a more comprehensive model should be
developed using additional belief dimensions as determinants of attitudes toward
advertising. Following Sandage and Leckenby (1980) and Muehling (1987), Pollay and
Mittal developed a proposed model built on a fundamental distinction between beliefs
and attitudes (e.g., Dillon & Kumar, 1985; Fishbein, 1967; Wilkie, 1986).
According to Pollay and Mittal (1993), beliefs are “descriptive statements about
object attributes (e.g., advertising is truthful) or consequences (e.g., advertising lowers
prices)”, whereas attitudes are “summary evaluations of objects (e.g. advertising is a
good/bad things)” (p. 101). Similar to Fishbein and Ajzen (1975), they proposed that
attitudes have been derived from beliefs, being the integration of weighted evaluations of
perceived attributes and consequences. They developed their thinking about advertisingspecific factors and their inventory items based on previous research. They posited
several antecedents (i.e., “concepts that precede, influence, explain, and/or predict other
concepts”) of attitude toward advertising, created measurement scales for them and
hypothesized interrelationships among them (Polly & Mittal, 1993, p. 101).
Pollay and Mittal (1993) made a primary distinction between belief dimensions
that explicate the personal uses and utilities of advertising from those that reflect
consumers’ perceptions of advertising’s social and cultural effects. Their model of
attitude toward advertising in general includes three factors related to personal uses
(product information, social role and image, and hedonism/pleasure) and four factors
pertaining to societal effects (good for the economy, materialism, value corruption, and
falsity/no sense). The proposed seven factor model was tested on two independent
35
groups: collegians and householders. Each belief dimension has between two and four
items which were self developed or derived from Bauer and Greyser’s (1968) scales.
The results reported by Pollay and Mittal (1993) provide the basis for applying
the general attitude toward advertising model to the proposed model representing attitude
toward advertising through sport. A review of the work by Pollay and Mittal led to the
recognition of some technical problems such as a validity issue. These challenges are
addressed below to provide a rationale for applying Pollay and Mittal’s model in the
current study.
First, results from a principal components procedure with varimax rotation (using
a collegians group) indicated that each of three personal belief categories yielded
individual factors: product information, social role and image, and hedonism/pleasure.
Among the societal factors, only good for the economy was found to be a distinct factor.
Materialism, value corruption and falsity/no sense did not discriminate but rather
conglomerated into a single factor. With the householders group, materialism and value
corruption coalesced into a single factor, good for economy overlapped with information
factor, and falsity/no sense yielded a single factor. Among the householders group, all
three personal belief categories also yielded separate factors.
Two issues bear further discussion based on the results reported by Pollay and
Mittal (1993). First, good for the economy was a distinct factor only in the collegians
sample. It is also important to note that Pollay and Mittal used three items to measure
good for the economy beliefs in the collegians sample, but only one item with the
householder sample. Three items are more reliable than a single item measure;
consequently, the results based on the collegians sample have greater credence (Pollay &
Mittal, 1993). The second inquiry concerns the desirable separation of falsity/no sense
from materialism and value corruption in the householders sample, but not in the
collegians sample. The idea to merge the materialism, value corruption, and falsity/no
sense factors was likely a suggested modification produced by the confirmatory factor
analysis based on the collegians sample (Pollay & Mittal, 1993). With the collegian
sample, a three-factor model was compared with separate sets of two-factor models – the
latter obtained by merging all possible combinations of two factors at a time. A chisquare difference test by Bagozzi and Phillips (1982) of deterioration in model fit was
36
used to compare the results from merging two factors versus the factors being kept
separate (as cited in Pollay & Mittal, 1993). The results indicated that falsity/no sense
was valuable as an individual factor, but the other two were still not discriminated (Pollay
& Mittal, 1993). For figuring out how to deal with two merged factors, the coefficients
of two factors on global attitude in two samples have been scrutinized in the next step.
Second, the LISREL outcomes of causal path analysis showed that of the seven
hypothesized primary antecedents, five were significant in the collegians group and six
were significant in the householders group, explaining 62.4% and 55.9% of the variance
in global attitudes respectively (Pollay & Mittal, 1993). Two personal factors, product
information and hedonism/pleasure were significant in the collegians group and all
personal factors proved significant in the householders group. Three of the four societal
factors were significant for both groups; the exception was value corruption.
Since the impact of value corruption on global attitude was not significant in both
samples, accordingly, a materialism factor was retained but value corruption was not
included in the current proposed model. As a result, a total of six belief dimensions have
been derived from Pollay and Mittal’s (1993) model for the proposed model of attitude
toward advertising through sport.
Since the work of Pollay and Mittal (1993) on beliefs as antecedents of attitude
toward advertising, some researchers have continued to explore attitudes toward
advertising with other belief dimensions (e.g., Donthu et al., 1993; Ducoffe, 1995; Mehta,
1995; Mehta, 2000; Shavitt et al., 1998). One belief dimension which has been recently
introduced to empirically measure its important role with attitude toward advertising in
general is annoyance/irritation (e.g., Ducoffe, 1995; James & Kover, 1992; Mehta, 2000).
James and Kover (1992) factor-analyzed five questions regarding attitude toward
advertising in general and concluded that irritation with advertising has been considered
as one factor. Based on James and Kover’s results, Ducoffe’s (1995) mall-intercept study
revealed a significant and negative correlation between irritation and advertising value (r
= -.52). He also found that informativeness and entertainment dimensions have
positively influenced advertising value (r = .65 and r = .48, respectively). An expectation
that there is a negative relationship between annoyance/irritation and attitude toward
advertising in general could be derived from these results (Ducoffe, 1995; James & Kover,
37
1992).
For the relationship between other belief dimensions and attitude toward
advertising, Shavitt et al. (1998) examined the general public’s current perceptions and
their attitude toward advertising in general using a large and nationally representative
sample. They measured four belief dimensions: enjoyment and indignity, trustworthiness
or usefulness of ad content, ad effects on product prices and product value, and regulation
of advertising. Respondents’ perceptions of enjoyment and indignity with advertising
played the strongest role in predicting their global attitude toward advertising, followed
by trustworthiness or usefulness of advertising (Shavitt et al., 1998). Respondents’
perceptions of the effects of advertising on product prices and advertising regulations did
not influence attitude toward advertising in general (Shavitt et al., 1998).
Other studies have been interested to focus on the relationship between attitude
toward advertising in general and advertising effectiveness (Mehta, 1995; Mehta, 2000).
Their results revealed that respondents’ attitude toward advertising in general positively
influenced advertisement recall, recognition, or purchasing intentions. The more positive
the respondents’ feelings about attitude toward advertising in general, the more attention
they pay to advertisements, and the more they are persuaded to purchase advertised
products (Mehta, 1995; Mehta, 2000). However, there has been no significant
relationship between an annoyance/irritation belief statement and either recall or buying
intention (Mehta, 2000).
Through the literature review, it seems that the number of researchers
investigating attitude toward advertising in general decreased during the 1990s. One
reason for this diminution has been a focus on measuring attitude toward advertising in
specific medium (e.g., television, web, outdoor signage, magazine, etc.). The belief
dimensions pertaining to attitude toward advertising in general have been used to explore
attitude toward advertising in specific mediums (Burns, 2003). The following section
examines how the concept of attitude toward advertising in general has been applied to
attitude toward advertising in specific mediums.
Attitude toward advertising in specific mediums
Advertising researchers have been interested in specific mediums as important
adverting platforms because new technologies have been developed and absorbed in
38
people’s life since 1990s. These specific mediums include television (e.g., Aaker &
Bruzzone, 1981; Alwitt & Prabhaker, 1992; Biel & Bridgwater, 1990; Mittal, 1994),
online (e.g., Burns, 2003; Chen & Wells, 1999; Cowley et al., 2000; Ducoffe, 1996;
Schlosser et al., 1999; Wang et al., 2002), direct marketing (e.g., Korgaonkar et al., 1997),
outdoor signage (e.g., Bhargava et al., 1995; Donthu et al., 1993), and videocassettes (e.g.,
Lee & Katz, 1993). Advertising managers or researchers should be aware of how
consumers perceive advertising in various mediums (Pollay, 1986).
In the initial phase of these applied studies, researchers focused on attitudes
toward television advertising. More recently, researchers have moved their interests to
online advertising as the online industry has developed and provided a new source of
advertising mediums. Brackett and Carr (2001) proposed that online would become the
most important advertising medium in the future. The current section will discuss
various studies, specifically concerning attitude toward advertising in a variety of
mediums, from television to online. Detailed information regarding how people’s
attitudes toward advertising in specific mediums have been measured and what belief
dimensions have been considered as critical roles in predicting overall attitudes provide a
useful background to the current study.
Television advertising. Aaker and Bruzzone (1981) investigated people’s
perceptions and likeability of TV commercials and their relationships using a nation wide
sample. Aaker and Bruzzone categorized belief dimensions as being entertaining,
personal relevance, dislike, and warm, which were explained by 75.4 % of the variance.
They compared their four belief dimensions with those of other previous studies and
found notable consistency (e.g., Leavitt 1970; Schlinger, 1979; Wells, Leavitt, &
McConville, 1971). Aaker and Bruzzone’s factor analysis showed that entertaining and
personal relevance were first two dominant factors among four so did previous studies
(e.g., Leavitt 1970; Schlinger, 1979; Wells et al., 1971). Based on the results, they
offered three suggestions to elicit positive attitudes toward TV advertising: “make it
entertaining”, emphasizing the amusing aspect of advertisements; “make it warm”,
underlying the relationships with other, such as, family, kids, or friends; and “make it
personally relevant,” providing useful information (Aaker & Bruzzone, 1981, p. 23).
Alwitt and Prabhaker (1992) indicated that the impact of consumers’ negative
39
attitudes toward advertising in general (Andrews, 1989; Bartos, 1981; Zanot, 1981, as
cited in Alwitt & Prabhaker, 1992) have influenced their negative attitudes toward TV
advertising (Bartos, 1981, Sepstrup, 1985, as cited in Alwitt & Prabhaker, 1992). Alwitt
and Prabhaker found the degree that people dislike TV advertising has been more serious
than people dislike advertising in general. They also evaluated why consumers tend to
have unfavorable attitudes toward TV advertising with four functional and six belief
dimensions: knowledge (α = .66), hedonic (α = .69), social learning (α = .72), and
affirmation of value (α = .75) for functional dimensions, and benefits/costs (α = .86),
execution (α = .43), deceptive (α = .71), offensive (α = .57), amount shown (α = .58), and
no information (α = .61) for belief dimensions (Alwitt & Prabhaker, 1992). Specifically,
they differentiated beliefs from functions that “what people know about advertising”
refers to consumers’ beliefs about TV advertising, while its association refers to functions
that “television advertising serves for a viewer and how it fits into his or her life” (Alwitt
& Prabhaker, 1992, p. 30).
The results revealed that two belief dimensions (benefits/costs and executions)
and all four functional dimensions were significantly related with overall attitude toward
TV advertising (Alwitt & Prabhaker, 1992). They concluded that consumer attitudes
toward individual advertisements or specific brands could be influenced by their overall
attitudes toward TV advertising based on five factors: social costs of TV advertising,
characteristics of the target audience, attitudes toward TV programs, personal benefit
from TV advertising, and general functions of TV advertising (Alwitt & Prabhaker, 1992).
Mittal (1994) also considered that viewers’ attitude toward TV advertising may
become more negative because it reduces entertaining aspects of TV programs. He found
that almost half of those participating tended to dislike TV advertising. In order to better
understand why public attitudes are negative, he categorized ten belief dimensions to
assess consumers’ perceptions of TV advertising based on previous literature:
marketplace information, buying confidence, social-image information, entertainment
value, materialism value congruence, effects on children, economic effects, free TV, and
manipulation (Mittal, 1994).
In the relationships between overall attitudes and beliefs about TV advertising,
the product information dimension contributed the most to one’s overall attitude,
40
followed by effect on children, social information, free TV, economic benefits, and
materialism (Mittal, 1994). Mittal suggested that more information and entertaining
values could be included during the construction of TV commercials so that these
dimensions influence more positive attitudes toward TV commercials.
In summary, studies reported that consumers have more negative attitudes toward
TV advertising than advertising in general. These studies used similar belief dimensions
which were already deemed as prime belief dimensions in prior studies of attitude toward
advertising in general and showed consistent results with them. Specific belief
dimensions such as information, economic values, irritation, and entertainment have been
considered as dominant dimensions for determining attitudes toward TV advertising and
attitudes toward advertising in general (Aaker & Bruzzone, 1981; Alwitt & Prabhaker,
1992; Andrews, 1989; Bauer & Greyser, 1968; Mittal, 1994; Pollay & Mittal, 1993;
Russell & Lane, 1989; Sandage & Leckenby, 1980).
Direct marketing advertising. In an effort to bridge research on attitudes toward
advertising in general and attitude toward advertising in a specific medium, Korgaonkar
et al. (1997) explored consumer global beliefs regarding direct marketing advertising
(DMA) using Pollay and Mittal’s (1993) advertising in general model. They replicated
the same belief dimensions Pollay and Mittal proposed in order to explain DMA: product
information, social role and image, hedonic/pleasure, good for the economy, materialism,
and value corruption.
Korgaonkar et al. (1997) found that respondents had generally positive attitudes
toward DMA in contrast to TV advertising (Alwitt & Prabhaker, 1992; Mittal, 1994);
they insisted that DMA would be a helpful way for advertisers who seek a substitute for
TV advertising. In reliability tests of the belief dimensions, the results showed all
coefficient alphas approached or exceeded the 0.60 figure recommended for early phases
of scale development (Nunnally & Bernstein, 1994). Additionally, the coefficient alphas
for each of the belief categories in this study were approximate to those tested in Pollay
and Mittal’s (1993) study (Korgaonkar et al., 1997). Like Pollay and Mittal’s study,
Korgaonkar et al. (1997) also demonstrated that materialism and values corruption have
failed to discriminate in factor analysis. The results confirmed the researchers’ model
that the general advertising scales could be adapted to DMA (Korgaonkar et al., 1997).
41
Even though only one study have been found to date in terms of assessing people’s
beliefs and attitudes toward DMA, Korgaonkar et al.’s study (1997) verified the
categorization of advertising beliefs into personal uses and societal effects and illustrated
the utility of the model proposed by Pollay and Mittal (1993) for measuring attitude
toward advertising through specific mediums.
Outdoor advertising. As the gravitation of TV advertising has been scattered,
outdoor advertising has became a significant advertising medium (Donthu et al., 1993).
The size of outdoor advertising has increased in various product classes during last
decades because it’s of high visibility and low amount of clutter (Bhargava et al., 1994;
Donthu et al., 1993; Woodside, 1990). While most studies have focused on just
measuring the effectiveness of outdoor advertising on awareness or behavior (Eastlack &
Rao, 1989; Fitts & Hewett, 1977; King & Tinkham, 1989; Woodside, 1990, as cited in
Donthu et al., 1993), a few studies sought to determine independent factors that improve
the effectiveness of outdoor advertising (Bhargava et al., 1994; Donthu et al., 1993).
In particular, Donthu et al. (1993) included an attitude toward advertising in
general factor into a group of factors which were hypothesized to influence the
effectiveness of outdoor advertising. The results showed that respondents had somewhat
negative attitudes toward advertising in general (59.2%) and revealed that respondents
who had positive attitudes toward advertising in general showed a higher aided/unaided
recall ability of outdoor advertisements than respondents who had negative attitudes
(Donthu et al., 1993).
When scrutinizing Donthu et al. (1993) items measuring attitude toward
advertising in general, however, it has been found that items were organized by general
belief statements about TV, radio, or outdoor advertising in terms of information,
annoyance, entertainment, and amount shown. Under the previously defined concepts of
both belief and attitude, it seems that they measured consumers’ belief about advertising
in general, rather than attitude toward advertising in general. It is concluded that there
has been no study to date measuring beliefs and attitudes toward outdoor advertising as
conceptually distinct constructs.
Videocassette advertising. Videotapes have also become an important
advertising medium since 1990, because of viewers’ increasingly negative attitudes
42
toward TV commercials (Lee & Katz, 1993). As the amount of the videotape rentals and
sales has increased (Wallach, 1990, as cited in Lee & Katz, 1993), many companies have
moved their interests to videotapes to advertise their products (Magiera, 1988, as cited in
Lee & Katz, 1993).
Lee and Katz (1993) investigated this new advertising medium with a sample of
video store patrons using a telephone survey. They measured attitude toward advertising
on videocassettes and compared respondents’ degree of attitudes and their recall ability.
The results disclosed that respondents had generally negative attitudes toward advertising
on videocassettes (Lee & Katz, 1993). Regarding a comparison of attitudes between
respondents who recalled advertisements on a tape and respondents who did not
remember, the overall findings showed that there was no statistical difference on the
recall ability between the two groups (Lee & Katz, 1993). Though, like outdoor
advertising, there has been little research in this area, this preliminary study has been
meaningful as a exploratory step to measure attitude toward the new advertising medium
and suggested a guide for future research on the effectiveness of videocassettes
advertising (Lee & Katz, 1993).
Online advertising. As use of the Internet has grown, advertising online has
increased and become more cluttered and annoying to people (Burns, 2003). These
problems have required researchers to understand how people have perceived online
advertising (Ducoffe, 1996). Most studies attempting to measure attitude toward online
advertising derived their theoretical frameworks from previous studies of attitude toward
advertising in general (Burns, 2003; Ducoffe, 1996; Cowley et al., 2000; Schlosser et al.,
1999; Wang et al., 2002).
Ducoffe (1996) measured causal relationships among three perceptual
antecedents (informativeness, irritation, and entertainment) and Web advertising values,
and attitudes toward Web advertising based on his previous work (Ducoffe, 1995). In
particular, Ducoffe (1996, p. 24) distinguished advertising value from advertising attitude
as “a narrower construct than advertising attitudes, a cognitive assessment of the extent to
which advertising gives consumers what they want.” The reliability tests showed that all
dimensions were generally reliable: informativeness (.82), irritation (.78), entertainment
(.85), and advertising value (.84) (Ducoffe, 1996). The validity test also provided a good
43
fit to the data (GFI = .949, AGFI = .914, RMSR = .032) (Ducoffe, 1996). The path
analyses showed that all perceptual antecedents influenced advertising value; advertising
value influenced attitude toward Web advertising; and entertainment was also an
important predictor of attitude toward Web advertising (Ducoffe, 1996). Ducoffe (1996)
confirmed that his previous model (Ducoffe, 1995) in the traditional media could be also
supported through online media.
Schlosser et al. (1999) were interested in assessing the relationships between
attitudes toward Internet advertising (IA) and several belief dimensions using a national
sample. Principle component factor analysis with varimax rotation produced five belief
factors: advertising utility (informative, entertaining, and useful for making decision),
indignity, trust, price perceptions, and regulation. The regression procedures revealed
that the advertising utility factor was the most predictive factor of attitudes toward IA
(Schlosser et al., 1999). When they examined the contributions of individual items on
attitudes toward IA, an entertaining item was the most important factor to explain
attitudes toward IA: this finding was also consistent with Ducoffe’s (1996)’s outcome.
Cowley et al. (2000) provided a proposed model of attitudes toward Web
advertising. They categorized three belief dimensions based on the conceptual
frameworks of three previous studies: Alwitt and Prabhaker (1992), Mittal (1994), and
Pollay and Mittal (1993). The belief dimensions include institutions (economic and
social benefits/costs), instrument (sex in advertising, advertising frequency, and
deceptive/offensive), and function (hedonic, social role and image, and product
information) (Cowley et al., 2000). They expected that all belief dimensions would
influence respondents’ attitudes toward Web advertising based on the results of previous
studies in other adverting mediums (Alwitt & Prabhaker, 1992; Johnson, Slack, & Keane,
1999; Mittal, 1994; Muehling, 1987; Pollay & Mittal, 1993, as cited in Cowley et al.,
2000). Though they did not measure further empirical tests for relationships between
beliefs and attitudes, they attempted to synthesize and utilize various belief dimensions in
their proposed model (Cowley et al., 2000).
Brackett and Carr (2001) extended Ducoffe’s (1996) model of attitude toward
Web advertising by adding two more antecedents, credibility (MacKenzie & Lutz, 1989)
and relevant demographic variables. They found that four antecedents (informativeness,
44
entertainment, irritation, and credibility) had significant relationships with advertising
value; relevant demographic variables (gender, major, class, and age) were not predictors
of attitude toward Web advertising. In the direct relationships to attitude toward Web
advertising, informativeness, entertainment, credibility, advertising value, and gender
influenced attitude toward Web advertising (Brackett & Carr, 2001). One interesting
finding was that the student sample expected the Web would be the most important
advertising medium in the future (Brackett & Carr, 2001).
Wang et al. (2002) also introduced a proposed model of attitudes toward Web
advertising to better explain the distinction between consumers’ perceptions of
advertising values and advertising attitudes. They also adapted the model proposed by
Ducoffe (1996) and added two more factors into their model. The difference between
their proposed model and both Ducoffe (1996) and Brackett and Carr (2001) was that
Wang et al. did not distinguish advertising value from advertising attitude. They
considered antecedents of advertising value as important factors to explain a consumer’s
attitude toward Web advertising. The factors that contributed to consumers’ perceptions
of Web advertising consisted of entertainment, informative, irritation, credibility, and
interactivity (Wang et al., 2002).
Burns (2003) proposed a new domain, attitude toward online formats, and
assessed its relationship with attitude toward the ad. The proposed structural model
consists of two antecedents of attitude toward the ad: attitude toward online ad format
and attitude toward online advertising, and four antecedents of attitude toward online ad
formats: attitude toward Internet, online ad format perceptions, attitude toward web site,
and attitude toward online advertising (Burns, 2003). The online ad format perceptions
included entertainment, annoyance, and information; ad formats contained six types:
banner, pop-up, skyscraper, large rectangle, floating, and interstitial (Burns, 2003). The
results revealed that two perceptual factors (entertainment and annoyance) influenced
attitude toward all formats; an information factor influenced only some formats (Burns,
2003). Attitude toward web site and attitude toward online advertising also influenced
attitude toward some formats; lastly, attitudes toward all of formats influenced attitudes
toward the ad in each format (Burns, 2003).
The current section has reviewed previous research examining attitudes toward
45
advertising in various mediums. The review of literature identified particular dimensions
that were found to be important predictors of attitude toward advertising in particular
mediums. Specifically, people generally had more negative attitudes toward TV
advertising than advertising in general because of their negative beliefs about irritation or
manipulation. Positive attitudes toward TV advertising were based on the beliefs that ads
were entertaining, informative, and components of social learning. Regarding online
advertising, people’s attitudes tended to be somewhat more favorable than attitude toward
advertising in general or TV advertising. Beliefs that ads were entertaining, annoying,
and informative played important roles in explaining attitudes toward online advertising.
Before applying these ideas to the current model, it is important to review prior literature
which examined attitudes or beliefs about the use of sport as an advertising platform.
The current study should verify, first, whether there have been previous studies on the use
of sport in advertising, second, if yes, which formats of advertising through sport have
been investigated, and lastly, how previous information regarding the use of advertising
through different formats can be applied to the current topic, advertising through sport.
The use of sport in advertising
As mentioned in the previous section, online advertising may include various
formats including banners, pop-ups, floating ads, skyscrapers, large rectangles, and
interstitials (Burns, 2003). Based on Burns’ ideas, the formats of advertising through
sport may be classified as in-stadium/sport facility signage, TV commercials featuring the
use of sport, print advertising with the use of sport, logos or brand names on uniforms
and equipment, and virtual advertising during televised sporting events. This section will
focus on discussing previous studies that measured attitudes toward the use of specific
formats of advertising through sport.
In-stadium/sport facility signage. Many researchers have focused on
investigating the use of sport in one advertising format, in-stadium signage or signage in
other sport facilities (e.g., Boa, 1995; Cunneen & Hannan, 1993; Harshaw & Turner,
1999; Nicholls et al., 1999; Nicolls et al., 1994; Porkrywczynski, 1992; Pyun & Kim,
2004; Stotlar & Bennett, 2000; Stotlar & Johnson, 1989; Turco, 1996; Turley & Shannon,
2000). Most studies have assessed the effectiveness of advertising signage in terms of
respondents’ recall or recognition based on attending or watching a sporting event
46
broadcast: National Association for Stock Car Auto Racing (e.g., Harshaw & Turner,
1999), collegiate basketball games (e.g., Stotlar & Bennett, 2000; Stotlar & Johnson,
1989; Turley & Shannon, 2000), Major League Baseball games (e.g., Pyun, Han, & Ha,
2004), and golf courses (e.g., Cunneen & Hannan, 1993; Nicholls et al., 1999; Nicholls et
al., 1994). Some researchers analyzed the exposure frequency of in-stadium signage (e.g.,
Boa, 1995; Porkrywczynski, 1992; Pyun & Kim, 2004; Stotlar & Bennett, 2000) and
virtual advertising (Pyun & Kim, 2004).
There have been few studies to date, however, measuring attitudes toward stadium
signage (Pyun et al., 2004; Turco, 1996). Turco (1996) investigated spectators’ attitude
changes toward courtside advertisers at home games of a men’s NCAA Division I
basketball teams from preseason to postseason. The findings revealed that more frequent
spectators had marginally more positive attitudes toward advertisers for seven of eight
businesses. Unfortunately, Turco did not directly measure a person’s attitude toward
courtside advertising. Pyun et al. (2004) assessed attitudes toward advertising in general,
attitudes toward in-stadium signage, attitudes toward virtual advertising, and the
effectiveness of virtual advertising on Major League Baseball. Results showed that
attitudes toward advertising in general influenced attitudes toward in-stadium advertising;
attitudes toward in-stadium advertising influenced attitudes toward virtual advertising;
attitudes toward virtual advertising influenced respondents’ recognition of virtual
advertisements (Pyun et al., 2004). The results have been consistent with those of
previous studies that have found a positive relationship between attitude toward
advertising in general and attitude toward advertising in specific mediums (Donthu et al.,
1993; Mehta, 2000). The results also confirmed previous results that there are positive
relationships between attitudes toward advertising in specific mediums and attitudes
toward their formats (Burns, 2003) as well as positive relationships between attitudes and
effectiveness of advertising formats, such as virtual advertising, banners, or pup-ups
(Burns, 2003; Cho, 1999).
Logos or brand names on uniforms or equipment. A few researchers have
investigated advertising on players’ uniforms and equipments (Boa, 1995; Stotlar &
Bennett, 2000). Stotlar and Bennett (2000) examined the recognition accuracy of instadium signage as well as brand names on players and bench seats with respect to
47
various demographic factors during a televised NCAA basketball game. Boa (1995)
content analyzed the exposure that logos and brand names on players’ uniform or
equipment received during 16 live sports telecasts. However, there has been no research
measuring attitudes toward advertising on uniforms or equipment.
The review of literature found that attitude toward advertising research has
evolved through a variety of advertising mediums, from advertising in general to
advertising through sport. Even though there has been little information regarding
advertising through sport, the current study can employ conceptual backgrounds from
other mediums to propose the current model of advertising through sport. Especially, the
results from Pollay and Mittal (1993) and the work by Korgaonkar et al. (1997) which
provides support for modifying Pollay and Mittal’s model from an assessment of general
attitude toward advertising, to a model representing attitude toward advertising through
sport.
The fundamental idea of the current study is to understand the development of a
consumer’s attitude toward advertising through sport. This study will attempt to assess
how consumers build their attitudes toward advertising through sport based on an
understanding of the belief dimensions thought to comprise an attitude toward advertising
through sport (Alwitt & Prabhaker, 1992). The literature review further found that
previous studies have attempted to understand the reasons shaping a consumer’s attitude
toward advertising in general or different mediums. These reasons could be derived from
beliefs about various types of advertising (Alwitt & Prabhaker, 1992). The following
section will scrutinize various belief dimensions employed from previous research,
particularly focusing on seven belief dimensions included in the proposed model.
Beliefs pertaining to advertising through sport
To better utilize information regarding attitude toward advertising through sport,
researchers and advertisers should identify the determinants of advertising attitude
formation (Mitchell & Olson, 1981). The basic theoretical foundation of Fishbein’s
attitude theory is that an attitude is influenced by beliefs (Fishbein, 1967; Mitchell &
Olson, 1981). Many empirical studies have supported the relationship between beliefs
and attitudes in advertising research (e.g., Bauer & Greyser, 1968; Ducoffe, 1996;
Muehling, 1987; Pollay & Mittal, 1993; Sandage &Leckenby, 1980).
48
The current study purports to measure four personal (product information, social
role and image, hedonism/pleasure, and annoyance/irritation) and three societal factors
(good for the economy, materialism, falsity/no sense) and their relationship to a global
attitude toward advertising through sport. Followings are descriptions of the seven
factors comprising the two dimensions of personal uses and societal effects believed to
account for attitude toward advertising through sport, in terms of definitions, conceptual
backgrounds, empirical evidences from previous studies, and applications to advertising
through sport.
Product information. Advertising through sport may contain a large amount of
information to provide consumers clear and relevant explanations about products. The
fact that advertising includes product information is important because it results in better
decision-making by consumers (Alwitt & Prabhaker, 1992). Bauer and Greyser (1968)
also emphasized the importance of advertising as a provider of product information and
described informative ads as follows:
These are ads that you learn something from that you are glad to know or know
about. They may tell you about a new product or service or they may tell you
something new about a product or service you were already familiar with. The
main thing is that they help you in one way or another because of the
information they provide. (p. 182)
Bauer and Greyser (1968) reported that a majority of respondents responded that
information is the important reason for liking advertising, and found that informationrelated reasons positively influenced attitude toward advertising. Since Bauer and
Greyser’s study, additional research has demonstrated that advertising is perceived as a
valuable source of product information (e.g., Albion & Farris, 1981; Korgaonkar et al.,
1997; Mittal, 1994; Norris, 1984; Pollay & Mittal, 1993; Schlosser et al., 1999; Stigler,
1961). Albion and Farris (1981, as cited in Korgaonkar et al., 1997) supported that the
positive roles of advertising as a source of product information generates many benefits
as producing “competition”, promoting “new product and brand entry”, and aiding
“consumer shopping” (p. 42). This role as product information directs greater
49
marketplace efficiencies, as consumers are better able to match their needs and wants
against producers’ offerings (Norris, 1984, as cited in Pollay & Mittal, 1993).
The fact that advertising provides product information can be easily found in the
context of advertising through sport. The Super Bowl is one example. It has been
illustrated that the Super Bowl event is an effective vehicle for advertisers to generate
brand awareness and to increase market share for their products (Bloom, 1998). The
Super Bowl is also attractive because it has helped companies successfully launch new
products (Yelkur, Tomkovick, & Traczyk, 2004). A commercial in the Super Bowl today
airs in 40 percent of all U.S. households (Tomkovick, Yelkur, & Traczyk, 2001). The
most remarkable Super Bowl product launch in history was Apple’s impressive
introduction of its Macintosh computer in 1984 (Horton, 1990; Twitchell, 2000). Apple
computer noted that this advertisement resulted in sales of 72,000 Macintosh computers
during their first 100 days of availability following the Super Bowl telecast (Horton,
1990; Twitchell, 2000). More recently, Chrysler, Gillette, and Victoria’s Secret had
enormous success in premiering their new products during the Super Bowl (Yelkur, et al.,
2004).
Empirically, many studies have shown that product information strongly
influences attitudes toward advertising in general or other mediums (Alwitt & Prabhaker,
1992; Docoffe, 1995, 1996; Korgaonkar et al., 1997; Mittal, 1994; Pollay & Mittal, 1993;
Schlosser et al, 1999). Alwitt and Prabhaker (1992) found that information functions of
TV advertising are significantly correlated with attitudes toward TV advertising, but did
not significantly influence attitudes in their regression model.
Mittal (1994) concluded that of 10 perceptions considered, perceptions of the
informational value of advertising was the most important contributor to consumers’
overall attitude toward television advertising. Pollay and Mittal (1993) found product
information was a significant predictor of attitude toward advertising. Information has
also been found to be positively related with attitude toward Internet advertising (Ducoffe,
1996; Schlosser et al., 1999) and attitude toward direct marketing advertising
(Korgaonkar et al., 1997). The expectation for a product information dimension about
advertising through sport as a direct predictor of attitude toward advertising through sport
is hypothesized as follows:
50
H1: A respondent’s belief(s) about advertising through sport representing
“product information” will influence his/her attitude toward advertising
through sport.
Social role and image. The meaning of advertising as social role and image
represents the idea that advertising often attempts to sell the consumers an image or
lifestyle as well as a product or service (Burns, 2003). Many studies have measured the
role of advertising functions in creating product meaning (Friedmann & Zimmer, 1988;
Tharp & Scott, 1990) and self-image (Richins, 1991). National advertising, for example,
often includes “life style imagery, and its communication goals often specify a brand
image or personality, the portrayal of typical or idealized users, associated status or
prestige, or social reactions to purchase, ownership, and use” (Pollay & Mittal, 1993, p.
102). Thus, many consumers are willing to purchase luxury accessories and put on
clothing featuring favorite logos and designs to show off themselves to others (Pollay &
Mittal, 1993). Korgaonkar et al. (1997) also showed a good example in direct marketing
advertising: some direct marketing companies often attempt to sell the prestige and
reputation associated with possession of their products (e.g., Eddie Bauer or Sharper
Image). Consumers also expect advertisers to promote their products in a positive or
idealized image and may even prefer advertisements with attractive models (Richins,
1991).
In the relation to advertising through sport, advertisers often use attractive athlete
endorsers to more closely approach the ideal image by product use. Although this
attractiveness is usually related with physical beauty, it appears to have another, nonphysical dimension based on personality, lifestyle, and intellect (Shank, 1999).
Attractiveness works using the process of identification (Shank, 1999). Gatorade’s
classic, “I wanna be like Mike” campaign, featuring Michael Jordan in a good example of
the identification development (Shank, 1999).
This primary antecedent has been demonstrated to have a significant impact on
attitude toward advertising in general (Pollay & Mittal, 1993), attitude toward direct
marketing (Korgaonkar et al., 1997), and attitude toward television advertising (Alwitt &
51
Prabhaker, 1992; Mittal, 1994). When advertising through sport helps consumers
develop their own identity or style, attitude toward advertising through sport is expected
in the current study as follows:
H2: A respondent’s belief(s) about advertising through sport representing
“social role and image” will influence his/her attitude toward advertising
through sport.
Hedonism/pleasure. An important indicator of positive and negative attitudes is
likeability, which represents how people react to a product or a message, and one
likeability technique has been considered as the use of hedonism/pleasure in advertising
(Wells, Burnett, & Moriarty, 2000). Advertisers have often designed the advertisements
that look like shows and believed that such entertainment values in the advertisements
easily get consumers’ attentions (Wells et al., 2000). Bauer and Greyser (1968) also
defined enjoyable ads as follow:
These are ads that give you a pleasant feeling for any reason whatsoever. They
may be entertaining, amusing, especially attractive or well done. You might
enjoy them whether or not you are interested in what is advertised. The main
thing is that you like them and are pleased you saw or heard them. (p. 182)
Advertisements have been sometimes directed to be “beautiful to look at, touching in
their sentiment, funny in their portrayed events, or uplifting in their music, pace, and
attitude” (Pollay & Mittal, 1993, p. 102)
Hedonism or pleasure can be used for those target audiences that participate in
sports or watch sports games for fun, social interaction, or enjoyment. These advertising
characteristics would motivate the positive relationships that can be built up among
family members, friends, or business associates by participating in sport or attending
games (Shank, 1999). For instance, a recent advertisement by a major credit card
company captured pleasure of a father taking his son to a baseball game. The spirit of the
appeal was that though you might not be able to pay for it at this time, you will never be
52
able to replace the priceless moment of taking your child to his or her first baseball game
(Shank, 1999).
Entertainment-related advertising has been investigated in previous research (e.g.,
Alwitt & Prabhaker, 1992; Bauer & Greyser, 1968; Ducoffe, 1996; Mehta, 2000; Mittal,
1994; Pollay & Mittal, 1993; Schlosser et al., 1999; Shavitt et al., 1998). The enjoyment
dimension has been a significant contributor to global attitude toward advertising (Shavitt
et al., 1998; Pollay & Mittal, 1993), attitude toward online advertising (Ducoffe, 1996;
Schlosser et al., 1999), and attitude toward television advertising (Alwitt & Prabhaker,
1992; Mittal, 1993). Many prior studies have found hedonism/pleasure to be a strong
predictor of attitude. Thus, the current study supposes the following hypothetical causal
relationship:
H3: A respondent’s belief(s) about advertising through sport representing
“hedonism/pleasure” will influence his/her attitude toward advertising
through sport.
Annoyance/irritation. Bauer and Greyser (1968) defined annoying ads as follow:
These are ads that irritate you. They may be annoying because of what they say
or how they say it. They may annoy you because they are around so much, or
because of when and where they appear. They may be other reasons for ads to be
annoying –the main thing is that they bother or irritate you. (p. 182)
Bauer and Greyser (1968) found that the main reason people criticize advertising
is related to annoyance or irritation. Bogart (1985) also proposed that advertising had a
negative function as an annoying or irritating medium. He pointed out that the
tremendous number of advertisements that consumers are exposed to on a daily basis
makes it impossible to give significant attention to most of them. The frequency of
exposure to advertising sometimes fosters consumers to be annoyed or irritated (Bauer &
Greyser, 1968). Aaker and Bruzzone (1981) also pointed out that TV commercials during
prime-time have become much intrusive and made viewers annoyed so that TV viewers’
53
attitudes could be adversely influenced.
Annoyance/irritation with advertising is also an important issue for attitude
toward advertising through sport. More specifically, it is believed that today’s sports fans
are fed up with too much advertising during sporting events (Lefton, 1997). Some team
owners respect the opinion of fans and refuse to use advertising in their stadium (e.g.,
Chicago Cubs’ Wrigley Field). When considering the impact of annoyance/irritation on
attitude toward advertising, it might be included as one factor influencing attitude toward
advertising through sport. Thus, the annoyance/irritation has been also included in the
current proposed model of attitude toward advertising through sport.
Recent studies have investigated the impact of an annoyance or irritation
dimension on attitude toward advertising in general or other mediums (e.g., Aaker &
Bruzzone, 1981; Alwitt & Prabhaker, 1994; Ducoffe, 1996; James & Kover, 1992; Rettie,
Robinson, & Jenner, 2001; Wang et al., 2002; Zhang, 2000). When advertisers utilize the
intrusive tactics that annoy, offend, or are overly manipulated, consumers are likely to
perceive advertising as an unwanted and irritating influence (Alwitt & Prabhaker, 1994;
Rettie et al., 2001; Zhang, 2000).
James and Kover (1992) factor analyzed five questions about attitude toward
advertising in general and concluded that irritation with advertising has been considered
as one factor. Alwitt and Prabhaker (1992) revealed that respondents were unfavorable
toward advertising when they perceived that the same advertisements were shown too
frequently. Results from Ducoffe’s (1995) mall-intercept study revealed a significant and
negative correlation between irritation and advertising value (r = -.52). His applied study
to web advertising also disclosed that the there has been significant and negative
correlations between irritation and both advertising value (r = -.72), and attitude toward
advertising in general (r = -.57) (Ducoffe, 1996).
Based on the results of previous studies, the hypothesis of causal relationship
between annoyance/irritation and attitudes can be suggested as follows:
H4: A respondent’s belief(s) about advertising through sport representing
“annoyance/irritation” will influence his/her attitude toward advertising
through sport.
54
Good for the economy. Advertising also contains many good aspects for society.
The American Advertising Federation (1992) described the economic benefits of
advertising:
It speeds acceptance of new goods and technologies, fosters full employment,
lowers the average cost of production, promotes a healthy competition between
producers to all consumers’ benefit, and generally is a prudent use of national
resources that raises the average standard of living. (as cited in Pollay & Mittal,
1993, p. 102)
Bauer and Greyser (1968) revealed that attitudes toward advertising are made up
of our beliefs toward the economic effects of advertising. Galbraith (1967) also
mentioned that “advertising and its related arts thus help develop the kind of man
(person) the goals of the industrial system require – one that reliably spends his income
and works reliably because he is always in need of more” (as cited in Pollay & Mittal,
1993, p. 102). When the economic benefits from advertising are closely related with an
individual’s interests, s/he is more likely to respond positively to a particular
advertisement (Alwitt & Prabhaker, 1992). Korgaonkar et al. (1997) showed that
consumers can benefit from DMA as they can save money and time when they are unable
to leave their home if they are in situations such as shortage of time or money.
The function of advertising as good for the economy can be easily found through
in-stadium signage and commercials during televised sporting events. Consumers who
have been exposed to company or product names by signage during their favorite
sporting events were more likely to have actually patronized a company or product
(Turley & Shannon, 2000). Such behavior may subsequently lead to product loyalty
among spectators because those companies or products were advertised at their favorite
sporting events (Turco, 1996). Another example is the effectiveness of advertising
through the Super Bowl (Bloom, 1998); Chrysler, Gillette, and Victoria’s Secret each
increased their revenues as a result of TV commercials aired during the Super Bowl
(Yelkur et al., 2004). Active consumption resulting from advertising through sport helps
revitalize the local and global market economies.
55
There is some empirical evidence that there is a positive relationship between
good for the economy and attitude (e.g., Alwitt & Prabhaker, 1992; Mittal, 1994; Pollay
& Mittal, 1993). The regression model of beliefs about TV advertising by Alwitt and
Prabhaker (1992) disclosed that respondents’ perception of cost and benefits of TV
advertising significantly influenced their overall attitudes toward TV advertising. In
Pollay and Mittal’s (1993)’s study, good for the economy beliefs best explained their
global attitude for collegians and was also a significant predictor of global attitude for
older householders. The public perceptions that TV advertising improves people’s
standard of living were also found to be a positive and significant contributor to attitudes
toward television advertising (Mittal, 1994). As a result, it is expected that a respondent’s
attitudes toward advertising through sport can be influenced by his/her beliefs regarding
the economic benefits of advertising through sport.
H5: A respondent’s belief(s) about advertising through sport representing
“good for the economy” will influence his/her attitude toward advertising
through sport.
Materialism. One of the harshest criticisms of advertising concerns the negative
social effects some attribute to advertising (Mittal, 1994). Illustratively, Pollay (1986)
argued that the aim of advertising, especially in the aggregate, is to preoccupy society
with material concerns, seeing commercially available goods or services as the path to
happiness. Pollay and Mittal (1993) defined materialism as “a set of belief structures that
sees consumption as the route to most, if not all, satisfaction” (p. 102). In the area of
direct marketing advertising, direct markers are often charged with boosting materialism
as “violating consumers’ privacy or creating junk mail” because of the ardor that direct
marketers’ willing to sell their products (Korgaonkar et al., 1997, p. 43).
The current study also assumes some negative functions of advertising through
sport that raise materialism in our society. For example, sports celebrity endorsers have
long been used as a tool to advertise goods and services (Boyd & Shank, 2004). Sports
celebrities who are endorsing products used in their competition communicate a more
powerful “match-up” effect (Boyd & Shank, 2004). Such an effect may cause consumers
56
to have a more positive brand attitude toward products (Petty, Cacioppo, & Schuman,
1983), and influence them to choose those products (Agrawal & Kamakura, 1995). Good
examples are Michael Jordan’s basketball shoe and Tiger Wood’s golf equipment.
Teenagers may buy expensive Nike shoes just to show off, or golfers may purchase a new
Nike driver because it is the one Tiger uses.
Many studies have been interested in the relationship between materialism and
advertising (e.g., Larkin, 1977; Lee & Lumpkin, 1992; Mittal, 1994; Pollay & Mittal,
1993). There has been a significant and negative relationship between perceptions of
materialism and attitude toward television advertising (Mittal, 1994) and advertising in
general (Larkin, 1977; Pollay & Mittal, 1993). The current study also expects that there
would be a negative relationship between materialism and attitude toward advertising
through sport.
H6: A respondent’s belief(s) about advertising through sport representing
“materialism” will influence his/her attitude toward advertising through
sport.
Falsity/no-sense. Pollay and Mittal (1993) demonstrated a falsity/no sense belief
that “some of these characteristic impacts on the personal usefulness of advertising as an
information source, but it also has potential societal consequences by making
commonplace the telling of half-truths and other self-serving deceptiveness, and
justifying cynicism” (p. 103). Bauer and Greyser (1968) pointed out that advertising
might be considered offensive as a result of its use of deception. Bauer and Greyser
defined offensive ads as follow:
These are ads that are vulgar or morally bad in your opinion. They may be
dishonest, or untrue. They may be ads for something you don’t think should be
sold or used. They may be offensive because of the way in which they were done,
and you may think that such ads should not be allowed. The main thing is that
you feel strongly that such ads are wrong. (p. 182)
57
A relevant instance of advertising misleading consumers in advertising through
sport is presented in the form of ambush marketing. An ambush marketing tactic is “a
planned effort (campaign) by an organization to associate themselves indirectly with an
event in order to gain at least some of the recognition and benefits that are associated
with being an official sponsor” (Sandler & Shani, 1989, p. 11). Through advertising, the
ambushing corporations try to confuse consumers and to misrepresent the official
sponsorship of the event (Deutsch, 2000). Schlossberg (1999) showed that most
consumers can not correctly identify the true Olympic sponsors. This confusing and
deceptive nature of ambush marketing lends itself to claims of false advertising under the
Lanham Act (Deutsch, 2000).
The deceptive beliefs of advertising have been studied as one of the primary
belief dimensions of attitude toward advertising in some media vehicles and in general
(e.g., Alwitt & Prabhaker, 1992; Bauer & Greyser, 1968; Larkin, 1977; Mehta, 2000;
Muehling, 1987, Pollay & Mittal, 1993; Schlosser et al., 1999; Shavitt et al., 1998).
Shavitt et al. (1998) found that perceptions of the indignity of advertising were one of the
strongest predictors of attitude toward advertising in general. Pollay and Mittal (1993)
also found a strong significant and negative relationship between the falsity belief and
attitude toward advertising in general. This study assumes that falsity/no-sense as a
primary antecedent negatively influences attitude toward advertising through sport. Even
though the study of Alwitt and Prabhaker (1992) revealed that the deceptive aspect of
advertising did not contribute overall attitudes toward TV commercials, based on the
evidences from the results of other studies, the hypothesis regarding the relationship
between falsity/no sense and attitudes toward advertising through sport in the current
study would be proposed as follows:
H7: A respondent’s belief(s) about advertising through sport representing
“falsity/no sense” will influence his/her attitude toward advertising through
sport.
The current study has discussed the conceptual backgrounds of the proposed
model of attitude toward advertising through sport. The theoretical domains of
58
measurement of the current study including attitudes and beliefs about advertising
through sport have been derived from an investigation of prior studies. The proposed
relationships between attitude and several belief constructs have been also determined.
The current study now faces a task with regard to how the proposed research questions
could be efficiently answered. Prior to assessing empirical tests, the development of
correct measurement of these constructs should be understood in terms of reliability and
validity. The validation issues of the measures of unobserved constructs are usually
derived from criteria of measurement theory (Peter & Churchill, 1986). The following
section provides identifies key issues pertaining to validity and reliability with respect to
development an instrument to measure attitude toward advertising through sport.
Measurement theory
Researchers are often faced with the challenge of how to assess unobserved
constructs with raw data using precise measurement. Measurement used here is defined
as “rules for assigning numbers to objects to represent quantities of attributes” (Churchill
& Iacobucci, 2002, p. 401). Churchill and Iacobucci (2002) pointed out that a
researcher’s challenge could be figured out when s/he has “the rigor” that rules can be
determined and “the skill” to execute the rules (p. 401). The characteristics of good rules
are generally described as reliability and validity (Nunnally & Bernstein, 1994). The
current study contains seven belief constructs about and one attitude construct toward
advertising through sport and examines the relationships between beliefs and attitudes.
The inferential research eventually purports to imply generalization from statistics
obtained by the current sample to parameters in the population (Nunnally & Bernstein,
1994). The precise measurement of the current sample values is essential step to forward
the scientific generalization of the study. Therefore, the study concerns the development
of measurement for the observed data that underlie their respective belief and attitude
constructs.
Measurement theory purports to provide a precise measurement of variables
(Kline, 2000, as cited in Ross, 2003). In order to increase precision of measurement, the
measurement error should decrease (Kline, 1998). There have usually been two types of
measurement error: random and systematic error (Churchill & Iacobucci, 2002; Kline,
1998; Peter, 1981). Random error is not always constant but generally obtained by other
59
personal or measurement situations. However, systematic error is constant (also known
as constant error) and is obtained by the measurement instrument itself, such as a poorly
developed scale (Churchill & Iacobucci, 2002). It has been deemed that reliability tests
are concerned with the issues of random measurement errors; validity tests measure both
random and systematic measurement errors (Kline, 1998; Peter, 1981). The following
sections will discuss the general information regarding types of reliability and validity
tests and show some examples of previously reviewed literature relevant to each
technique.
Reliability test. A researcher usually can obtain observed scores, not true scores
from his/her respondents in a single administration of an instrument (Leong & Austin,
1996). Thus, it is plausible that a researcher obtains different test scores whenever they
replicate the test, even using the same samples (Shavelson & Towne, 2002). This
variability is called measurement error, specifically, random error (Leong & Austin,
1996). The important issues of reliability represent the accuracy or consistency of
measurement (Grove & Savich, 1979). Thus, reliability can be defined as “the similarity
of results provided by independent but comparable measures of the same object, trait, or
construct” (Churchill & Iacobucci, 2002, p.413). It is assumed that when scales are
perfectly reliable, there is no measurement error: a respondent’s observed score is equal
to his/her true score. However, it should be noted that a reliable measurement does not
indicate that measures are valid: reliability is “a necessary”, but not “a sufficient
condition” for validity (Churchill & Iacobucci, 2002, 410).
Popular methods to assess the reliability of a measurement scale include testretest, internal consistency, individual item reliability, the composite reliability, and
average variance extracted (AVE). Test-retest and internal consistency reliability are
basic and simple methods: they are easily computed through the statistical packages such
as SPSS; individual item reliability, the composite reliability, and AVE can be obtained
from the structural equation modeling (SEM) through advanced statistics programs such
as LISREL (Jöreskog & Sörbom, 2005), AMOS (Arbuckle, 1997), or EQS (Bentler & Wu,
1995). The following sections will briefly review a variety of reliability techniques,
including definitions, characteristics, and empirical examples (if applicable) of each
reliability test. In addition, Table 2.2 summarizes reliability test scores of attitude or
60
belief measurements in advertising research obtained from the reviewed literature.
Test-retest reliability. Test-retest reliability is defined as “a technique of
measuring scale reliability by administering the same scale to the same respondents at
two different times or to two different samples of respondents under similar conditions”
(Hair, Bush, & Ortinau, 2000, p. 390). This test determines the degree of reliability with
correlations of mean scores of items between two collected scales. During the procedure
of data collections, sometimes, internal or external validity of this technique can be easily
threatened by some factors, such as the absence of the respondents at the second time,
insincere responses, or other environmental or personal factors (Aaker, Kumar, & Day,
2001; Hair et al., 2000). Thus, Segal (1984) performed an alternative way to avoid these
biases, called a technique of equivalent form (cited in Hair et al., 2000).
Test-retest reliability has not been utilized much in advertising attitude research
(Burns, 2003). During the review of literature, only one study was found using the
technique. Burns (2003) utilized the test-retest method to assess reliability (α = .79) for
respondents’ attitudes toward online advertising; a paired samples t test showed that there
was no significant difference between two means. Kline (1986) and Guilford (1956)
suggested a .70 cut-off value for accepting standard error of an obtained score regarding
this test.
Internal consistency test. Internal consistency has been one of the most popular
reliability techniques and has been utilized in a variety of areas of research. Internal
consistency refers to “estimates of reliability based on the average correlation among
items within a test” (Nunnally & Bernstein, 1994, p. 251). Internal consistency usually
can be assessed by two popular techniques: split-half test and coefficient alpha (also
known as Cronbach’s alpha) (Cronbach, 1951; Hair et al., 2000; Lemon, 1973).
In a split-half test, all items in the construct are split by two equal halves (e.g.,
odd v. even numbered items), and then the correlation can be computed between two sets
(Hair et al., 2000). However, this method has been not much recommended in marketing
research because of its limitation that the correlation estimates could be slightly variable
depending on how two groups of items are obtained, even though they are randomly split
(Churchill, 1979; Lemon, 1973; Leong & Austin, 1996). The literature review found no
previous work pertaining to attitude toward advertising and attitude toward advertising
61
through various mediums that assessed reliability using this technique.
More researchers prefer to measure internal consistency (Lemon, 1973). Among
many indices of consistency, coefficient alpha is the most widely used technique (Hair et
al., 2000; Lemon, 1973; Leong & Austin, 1996). Coefficient alpha is defined as “a
technique of taking the average of all possible split-half coefficients to measure the
internal consistency of the scale items” (Hair et al., 2000, p. 391).
Coefficient alpha has been usually applied as the first step when researchers
determine the quality of an instrument, especially in the item reduction procedure
(Churchill, 1979). In order to ordain individual items on a construct, the item-to-total
correlation could be employed as a criterion of item reduction procedure. When a certain
item has a low correlation coefficient with total items in a construct, this item should be
deleted. The cut-off value of less than .50 of item-to-total correlations is considered as a
low correlation (Bearden, Netemeyer, Teel, 1989; Zaichkowsky, 1985).
In the previous sections, the review of literature indicated that most researchers
have measured their interest domains with alpha coefficient (e.g., Alwitt & Prabhaker,
1992; Chen & Wells, 1999; Deshpande, Hoyer, & Donthu, 1986; Ducoffe, 1996;
Durvasula et al., 1993; Korgaonkar et al., 1997; Pollay & Mittal, 1993) (see Table 2.2).
Among these studies, Korgaonkar et al. (1997) and Pollay and Mittal (1993)’s studies are
closely connected with the current study so that will be briefly mentioned below.
Pollay and Mittal (1993) assessed reliabilities of their seven belief dimensions and
an overall attitude construct using alpha coefficients for two samples. The results
indicated that coefficients of belief dimensions ranged from .47 to .78 for the collegians
sample, and from .54 to .71 for the householders group (Pollay & Mittal, 1993, refer to
Table 2.2). The coefficients of an attitude construct were .88 and .79 for the collegians
and householders sample, respectively. Korgaonkar et al. (1997) also evaluated
reliability tests of seven belief dimensions about direct marketing advertising. The
results showed that coefficient alphas ranged from .504 to .698 (refer to Table 2.2). They
indicated outcomes to be acceptable based on a .60 figure recommended for early phases
of scale development by Nunnally and Bernstein (1994) (as cited in Korgaonkar et al.,
1997). The coefficient alphas for each of the belief categories in their study were
approximated to those obtained in Pollay and Mittal’s (1993) study.
62
Table 2.2
A Summary of Internal Consistency Tests of Attitude and Belief in Advertising Researchª
Reliability (Cronbach’s alpha)
Author(s)
Advertising
medium
Attitude
Product
information
Social role
and image
Hedonic/
pleasure
Annoyance/
irritation
Good for
the
economy
Materialism
Falsity/
no sense
Deshpande et al.
(1986)
In general
.88
-
-
-
-
-
-
-
Meuhling (1986)
In general
.88
-
-
-
-
-
-
-
Muehling (1987)
In general
.97
-
-
-
-
-
-
-
Alwitt & Prabhaker
(1992)
TV
-
.66
.72
.69
.58
.86
-
.71
Durvasula, et.al.,
(1993)
In general
.88 ~ .94¹
-
-
-
-
-
-
-
Pollay & Mittal
(1993)
In general
.83 (.79)²
.68 (.59)
.47 (.71)
.57 (.54)
-
.65 (-)
.78 (.64)
.60 (.69)
Ducoffe (1996)
Online
-
.82
-
.85
.78
-
-
-
Korgaonkar et al.
(1997)
DMA
-
.650
.655
.608
-
.641
.697
.504
Chen & Wells (1999)
The website
-
.94
-
.92
-
-
-
-
Burns (2003)
Online
.82 ~ .86³
.83
-
.88
.92
-
-
-
Note:
ª Only seven belief dimensions applied to the current study were considered.
¹ Coefficients measured by samples of five different countries (New Zealand, Denmark, Greece, United States, and India).
² Collegians sample (householders sample).
³ Three versions of sample were used.
63
Even though the cut-off value, .60 can be utilized as a marginally acceptable value
of coefficient alpha (Hair et al., 2000), the value of .70 is usually recommend as a modest
criterion (Kline, 1998; Nunnally & Bernstein, 1994; Leong & Austin, 1996). Nunnally
and Bernstein (1994) even suggested that much higher values, such as, .90 or .95 should
be needed when the study requires a certain important decisions based on test score.
According to the .70 cut off value applied in the current study, it could be noted that both
studies (Korgaonkar et al., 1997; Pollay & Mittal, 1993) have not obtained satisfactory
internal reliability scores, except some dimensions (refer to Table 2.2, the dimension of
value corruption of both studies was not shown in the Table 2.2). Thus, the current study
should pay careful attention to the development of belief dimensions. The current study
will utilize the item to total correlation and coefficient alpha reliability tests for the initial
instrument in terms of the procedure of data reduction in a pilot study.
Individual item, composite, and average variance extracted reliability. The testretest and internal consistency tests sometimes underestimate the reliability score of the
congeneric test which indicates that the indicators of a construct have a good fit on a onedimensional construct (Bollen, 1989; Lucke, 2005). In order to figure out the problems
of traditional methods and evaluate more accurate estimations, the individual item
reliability, composite reliability, and average variance extracted (AVE) tests can be
utilized from the results of the structural equation modeling (SEM) (Bagozzi & Yi, 1988;
Fornell & Larcker, 1981). The SEM provides measurement theory tests with unobserved
constructs and measurement errors as well as parameter estimations (Fornell & Larcker,
1981).
The individual item reliability is described as “the reliability of a measure is
equal to its true score variance divided by the total variance” (Bagozzi & Yi, 1988, p. 80).
The true score variance for each indicator with respect to its underlying latent construct is
the square of a loading in the measurement model (λ²), and the total variance is the
summation of the true score variance (λ²) and the error variance (θ). When the true score
variance and the error variance are standardized, the summation of both variances
becomes one. Thus, the individual item reliability values are shown in the LISREL
outcomes as R², the squared multiple correlations coefficients of x and y variables
(Bagozzi & Yi, 1988). Unfortunately, there has been no rules-of-thumb suggested for the
64
acceptability of the individual item reliability (Bagozzi & Yi, 1988).
Like the individual item reliability, the composite reliability is operated by a
similar formula, just summating all observed indicators underlying a construct (Bagozzi
& Yi, 1988). The general acceptable value of the composite reliability needs to be greater
than .50 (Fornell & Larcker, 1981) or .60 (Bagozzi, & Yi, 1988). Fornell and Larcker
(1981) pointed out one drawback of the individual item and composite reliability tests is
that they can not estimate “the amount of variance that is captured by the construct in
relation to the amount of variance due to measurement error” (p. 45). It could be
accepted as a reliable measure whenever the values of individual item and composite
reliability exceed .50 (or .60) regardless of considerations of measurement error (Fornell
& Larcker, 1981).
Fornell and Larcker (1981) introduced average variance extracted (AVE) to
overcome this weakness of the previous two tests, comparing variance explained by the
construct with variance due to measurement due to measurement error. AVE values
larger than .50 are suggested as a desirable value for indicators and unobserved variables:
variance explained by the construct is larger than variance of measurement error (Fornell
& Larcker, 1981). Unfortunately, the review of literature found no study relative to
advertising attitude research that applied these three techniques. The current study will
assess the individual item, composite, and average variance extracted reliability tests
using a confirmatory factor analysis for the final instrument in the main study.
Validity test. As mentioned, though the instrument is reliable, researchers should
assess validity because reliable measurement is not necessarily valid (Kline, 1998).
Validity can be defined as “the extent to which differences in scores on it reflect true
differences among individuals on the characteristic we seek to measure, rather than
constant or random errors” (Churchill & Iacobucci, 2002). Briefly, it can be
propositioned: “a test is valid if it measures what it purports to measure” (Angoff, 1988,
as cited in McDonald, 1999, p. 197). It is often noted that validity tests are the most
important parts because there will be no reason to keep processing the test if the measures
are not valid (Nunnally & Bernstein, 1994).
According to McDonald (1999), there are three important issues when researchers
are concerned with validity assessment: 1) the appropriate technical methods of
65
measurement test; 2) some of ways the technical methods of measurement test is applied
to validation; and 3) the interpretation of test scores. There have been at large three types
of direct measurement of validity: content validity, predictive validity and construct
validity (Nunnally & Bernstein, 1994; Churchill & Iacobucci, 2002; Lemon, 1989). The
following sections will discuss the three validity tests in turn, based on McDonald’s
(1999) three considerations.
Content validity. Content validity is generally considered as a first step for the
measurement test (Lemon, 1973). Content validity is assessed by determining the degree
to which the domain of the characteristic is identified by the items of the scale (Shaw &
Wright, 1967; Churchill & Iacobucci, 2002). Content validity is sometimes referred to as
face validity because it is measured by determining the items with “an eye toward
ascertaining the main domain being sampled (Churchill & Iacobucci, 2002, p. 409; Hair
et al., 2000). However, a distinction should be drawn by a clear line between two
concepts. Shaw and Wright (1967) described face validity as, “the superficial appearance
of the items”; content validity is evaluated by “a subjective, judgmental procedure” (p.
10).
The current study purports to measure a new construct, attitude and belief
constructs about advertising through sport. As mentioned, content validity is an essential
process when researchers develop an instrument. This validity test should be performed
to define representative items for each attitude and belief construct. Based on the general
process suggested by Churchill and Iacobucci (2002), as a first step of the procedure, the
current study should specify what measures are needed for assessing belief and attitude
domains, and how they have been determined and utilized in prior studies. The current
study identified seven belief constructs and one attitude construct. After the domains of
the study are conceptually defined, a large set of items relevant to these constructs should
be obtained from reliable and validated previous instruments (Churchill & Iacobucci,
2002). Churchill (1979) also introduced some techniques to generate in case of the
exploratory research. A researcher can obtain a set of items for the specified domain
through a literature search, experience surveys, insight-stimulating examples, critical
incidents, and focus groups (Churchill, 1979). As a next step, the evaluation of content
validity is performed by the relevance of items to a construct with information obtained
66
from a panel of experts in the field, rather than statistical techniques (Kline, 1998).
Most of the studies examined in the review of literature did not report content
validity test for the respective measures. Muehling (1987) measured the impact of
various factors of an attitude toward advertising in general. As one of these factors, he
collected respondents’ thoughts about advertising and defined five self-developed
dimensions. In order to categorize these items to five dimensions, he assessed content
validity of these items with two judges based on the level of agreement.
Predictive validity. Predictive validity supplements the weakness of content or
face validity as providing more objective information on the meaning of scales (Heeler &
Ray, 1972). Briefly, predictive validity computes the correlation between a predictor
variable and its criterion variable (Nunnally & Bernstein, 1994). This type of validity is
concerned with how a measure precisely estimates an external criterion behavior from the
information of predictor variables (Shaw & Wright, 1967).
Predictive validity is very important in attitude toward advertising research (Shaw
& Wright, 1967). Attitude research usually measures consumers’ attitude as an
antecedent of purchasing behaviors. The strong relationship between attitude and future
behavior will provide the evidence of predictive validity of the attitude scales (Heeler &
Ray, 1972). For example, the current study could predict respondents’ purchasing
behaviors of a certain brand with their attitudes toward the ad. The strong evidence of
relationship between attitudes toward the ad and future purchasing behaviors will provide
a validity of the current attitude scales.
Shaw and Wright (1967) distinguished predictive validity from concurrent
validity according to the time of collecting data of predictor and criterion variables.
When both variables are collected at the same time, it is called concurrent validity; when
a predictor variable is obtained first, and then a criterion variable is collected later, it is
deemed predictive validity (Shaw & Wright, 1967). They also demonstrated that a
predictive validity test is more precise test because the criterion could be unintentionally
influenced by a predictor or vice a versa when subjects respond the items. The reviews of
literature related to attitudes toward advertising have found no study utilizing predictive
or concurrent validity test.
Construct validity. Construct validity is concerned with psychological attributes
67
(Nunnally & Bernstein, 1994). Construct validity can be defined as “the degree to which
researchers measure what they intended to measure, and to which the proper
identification of the independent variables were included in the investigation” (Hair et al.,
2000, p. 651).
Two of most popular techniques to evaluate construct validity include convergent
and discriminant validity techniques. Nunnally and Bernstein (1994) demonstrated the
assessment of construct validity from the view of Campbell and Fiske (1959)’s multitraitmultimethod matrix method that,
Generally, construct validation demands that trait correlations be high to reflect
convergent validity, and that method correlations be relatively low to reflect
discriminant validity. (p. 94)
The results of a confirmatory factor analysis of measurement model provide crucial
information for the assessment of both validity tests.
Convergent validity is generally defined as “confirmation of the existence of a
construct determined by the correlations exhibited by independent measures of the
construct” (Churchill & Iacobucci, 2002, p. 973). Convergent validity can be assessed by
the degree of correlation between two dissimilar measures of the same construct. A good
convergent validity is generally determined by a high correlation between existing
measures and the researchers’ measures that both purport to measure the same construct
(Hair et al., 2000; Lemon, 1973). Thus it could be considered that the basic concept of
convergent construct validity is similar with predictive and concurrent validity (Heeler &
Ray, 1972; Lemon, 1973). However, convergent validity tests have a critical drawback.
The high correlation between two measures could be often affected by the use of similar
measures (e.g. “common method variance”) of even different constructs (Kline, 1998;
Lemon, 1973, p. 53). For instance, assume the situation that the current study seeks the
evidence of convergent validity for one measure of consumers’ belief of materialism
about advertising through sport as comparing with another measure which is also similar
type with the first measure (e.g., the seven-Likert scales), but conceptually designed to
measure a different construct (e.g., value corruption). Even if they measure different
68
concept of construct, both measures could be still highly correlated. This result could be
derived from “methodological characteristics in common” due to similar measures
(Lemon, 1973, p. 53). In order to avoid this systematic variance, Peter (1981) suggested
that researchers need to use independent measures (e.g., Likert scale v. Thermometer
scale) rather than just different types of self-report scale when they assess convergent
validity (as cited in Peter & Churchill, 1986).
Convergent validity can be also measured by indicators’ estimated pattern
coefficients (loading values) on a common respective construct (Anderson & Gerbing,
1988). When the pattern coefficients on their underlying construct are significant
(“greater than twice its standard error”), the convergent validity is supported (Anderson
& Gerbing, 1988, p. 416). Therefore, a good convergent validity of measure of a
construct should have not only a strong relationship with another measure of the
construct but also significant loadings on that factor in a factor analysis (Nunnally &
Bernstein, 1994).
There was no study that assessed convergent validity with the correlations
between two independent methods of the same concepts in the review of prior studies.
Most studies depended on the evaluation of indicators’ loadings on their respective
factors as a test of convergent validity. Muehling (1987) determined eight items to
represent two independent belief dimensions about advertising in general: institution and
instrument. In order to assess convergent validity of measures, he used a confirmatory
factor analysis (CFA). Each of eight items significantly loaded on two proposed factors
and showed the evidence of convergent validity of belief scales. Andrews (1989) also
established convergent validity using factor loadings. He proposed a two-dimensional
model of beliefs about advertising in terms of economic and social domains. A CFA
revealed all items significantly loaded on each of two domains.
Peter (1981) noted regarding a correlation comparison technique of convergent
validity that most marketing researchers still depend on just different types of self-report
scale as a term of different measure, rather than more dissimilar two methods, without the
consideration of systematic variance (as cited in Peter & Churchill, 1986). Given the
characteristics of convergent validity, it can be posited that the measure of a certain
construct should be unrelated with similar measures which are initially designed to
69
measure different constructs (Peter & Churchill, 1986). With this respect, Campel and
Fiske (1959) also introduced an alternative technique, discriminant validity.
Discriminant validity is generally defined as “criterion imposed on a measure of a
construct requiring that it not correlate too highly with measures from which it is
supposed to differ” (Churchill & Iacobucci, 2002, p. 974). One popular assessment of
discriminant validity is comparing the estimated correlations between two different
constructs that are supposed to measure different concepts. The cut-off value of the
correlation is often considered as .85 (Kline, 1998) or .10 (Anderson & Gerbing, 1988).
If the correlation exceeds the criterion value, the measure is hardly considered to measure
two distinct factors.
The previous section described average variance extracted (AVE) as a reliability
technique. AVE can also be utilized to assess discriminant validity (Fornell & Larcker,
1981). In order to provide the evidence of discriminant validity, the AVE of each
construct should be larger than the squared parameter coefficients between exogenous
and endogenous variables constructs: “ρvc(η) > γ² and ρvc(ξ) > γ²” (Fornell & Larcker, 1981,
p. 46). The γ² can be replaced with the squared correlation coefficients between each two
constructs, when exogenous and endogenous variables are standardized (Fornell &
Larcker, 1981).
Bagozzi and Phillip (1982) introduced a chi-square difference test to assess
discriminant validity using a series of CFA comparing different factor-models. They
described the evidence of discriminant validity as following:
A significantly lower χ² value for the model in which the trait correlations are not
constrained to unity would indicate that the traits are not perfectly correlated and
that discriminant validity is achieved. (p. 476)
If an overall chi-square goodness-of-fit of a certain factor model is better than the fit of
any other factor models, the discriminant of that factor model is supported.
In Pollay and Mittal’s study (1993), three belief dimensions, materialism, value
corruption, and falsity/no sense did not discriminate but converged into a single factor in
one sample. They compared three-factor model with separate sets of two-factor models
70
that obtained by merging all possible combinations of two factors at a time using a chisquare difference test (Pollay & Mittal, 1993). The results indicated that falsity/no sense
was best maintained as a separate factor; the other factors were still not discriminated
(Pollay & Mittal, 1993). Durvasula et al. (1993) also utilized a chi-square difference test
to test validity of the proposed model of attitude toward advertising in general. They
compared a chi-square fit of the three-factor model with the fit of the two-or one-factor
model, and found that a three-factor model showed evidence of discriminant validity.
The current section has presented principal psychometric criteria of measurement
theory. Reliability and validity can be utilized as good rules to help to develop better
measures of constructs of interest and evaluate whether the developed scales of constructs
could be properly performed for the purposes of the study. The fundamental premises of
the current section are categorized by two purposes: the precise measurement of scales of
belief and attitude constructs concerning advertising through sport and the assessment of
their relationship.
The current chapter of literature review has specified domains of constructs based
on their theories and concepts, the hypothesized relationships among constructs, and
basic backgrounds for the development of scales for constructs. The following chapter
will present a particular research procedure of the development of a reliable and valid
instrument and further analyses for empirical tests of the proposed model.
71
CHAPTER III
PILOT TEST
Introduction
The pilot study is the stage in which to perform an initial test of a proposed model,
prior to a main test with final scales in a dissertation (Kim, 2003; McMillan &
Schumacher, 1989). The questionnaire should be tested for reliability and dimensionality
underlying the constructs because a pilot test generates important information concerning
instrument deficiency as well as suggestions for improvement (Gay, 1996). The feedback
from the pilot study provides a chance to remove ambiguities and to ensure that items are
producing the desire information (Keeves, 1988). Based on evidence obtained from a
pilot test, the initial instrument could be revised or finalized for the main study.
Churchill (1979, p. 66) suggested eight procedures of developing better measures
of constructs: “1) specify domain of construct, 2) generate sample of items, 3) collect
data, 4) purify measure, 5) collect data, 6) assess reliability, 7) assess validity, and 8)
develop norms” (see Figure 3.1). The first two steps are closely related with the
preceding literature review. Based on the review of literature, a model of attitude toward
advertising through sport was proposed (see Figure 1.1, Pollay & Mittal, 1993). The
proposed model included four personal belief domains (product information, social role
and image, hedonism/pleasure, and annoyance/irritation) and three socioeconomic
domains (good for the economy, materialism, and falsity/no sense). Prospective scale
items for each domain were developed based on the literature review, and an initial
questionnaire was prepared. For the third and forth stages of Churchill’s process, a pilot
study was conducted to test the questionnaire (see Figure 3.1).
72
Procedures
Techniques utilized by
the current study
1. Specify domain of construct
· Literature review
The review of
literature
2. Generate sample of items
· Literature review
3. Collect data
· Convenience sampling
The pilot study
4. Purify measure
· Content validity
· Internal consistency
· EFA
5. Collect new data
· Convenience sampling
6. Assess reliability
· Internal consistency
· Confirmatory factor analysis
- Individual item
reliability
- Composite reliability
- Average variance
extracted
The main study
· Confirmatory factor analysis
- Convergent validity
- Discriminate validity
7. Assess validity
8. Develop norms
Note: Adapted from Churchill (1979, p. 66).
Figure 3.1. Procedure for developing a measurement scale for the proposed model of
attitude toward advertising through sport
73
The proposed model was adapted and modified from the work of Pollay and
Mittal (1993), and the work of Korgaonkar et al. (1997) who confirmed Pollay and Mittal
(1993)’s seven-factor model in the context of direct marketing advertising. The results
from the work by Korgaonkar et al., and Pollay and Mittal, demonstrated that materialism
and values corruption failed to discriminate in factor analysis. Pollay and Mittal (1993)
found the impact of value corruption on attitude toward advertising was not significant.
Accordingly, a materialism factor was retained but value corruption was not included in
the proposed. In addition, annoyance/irritation was included as a personal factor in the
proposed model based on the evidence of its impact on attitude toward advertising in
general and other mediums (e.g., Aaker & Bruzzone, 1981; Alwitt & Prabhaker, 1994;
Ducoffe, 1996; James & Kover, 1992; Rettie et al., 2001; Wang et al., 2002; Zhang, 2000).
The proposed model of attitude toward advertising through sport includes four
personal utility factors (product information, social role and image, hedonic amusement,
and annoyance/irritation) and three socioeconomic factors (good for the economy,
fostering materialism, and falsity/non-sense). The current study proposes to explore
attitude toward advertising through sport, and develop a distinction between attitudes and
beliefs about advertising through sport based on the critical literature.
Methods
The following section explains the methods used in the pilot study. The
population and sample, instrument development, and data analysis are presented in order.
Population and sample. The population of the current study consists of students
attending a large university in the southeastern United States. A convenience sampling
technique from the same population was used for data collection in the pilot test. The
sample of subjects was selected from available classes during the fall semester in 2004.
The researcher contacted each instructor before the beginning of classes. After receiving
the instructor’s approval, the questionnaire was distributed to the students just before
each class. Questionnaires were administered to a total of 127 participants; two
questionnaires were excluded from the data pool because they were not filled out
completely. According to McMillan and Schumacher (1989), the size of the pretest
should be greater than 20. Accordingly, the sample size was deemed sufficient for the
pilot test.
74
Instrument development. The initial version of questionnaire included three
sections: three items for attitude toward advertising through sport, 27 items for belief
factors about advertising through sport: product information (3 items), social role/image
(3 items), hedonic/pleasure (3 items), annoyance/irritation (8 items), good for the
economy (3 items), falsity/no sense (3 items), and materialism (4 items), and three items
capturing demographic information. Items for each section were modified from the
Pollay and Mittal (1993) scale and a review of relevant literature (Alwitt & Prabhaker,
1992; Ducoffe, 1995; James & Kover, 1992; Pollay & Mittal, 1993).
All items for attitude toward advertising through sport and the six beliefs factors
about advertising through sport, except annoyance/irritation, were modified from Pollay
and Mittal (1993)’s original items. Items for an annoyance/irritation belief dimension
were derived from related literature (Alwitt & Prabhaker, 1992; Ducoffe, 1995; James &
Kover, 1992). Since the items in existing scales pertained to advertising in general or
advertising in specific mediums, changes were made in the wording and/or phrasing of
each item to assess attitude toward advertising through sport. For example, the item,
“sometimes I take pleasure in thinking about what I saw or heard or read in
advertisements” was changed to “advertising through sport gives me pleasure when I
think about what I saw or heard or read”
The initial draft of the items was examined for content validity. In establishing
content-related evidence, expert judges typically examine the test items and indicate
whether the items measure predetermined criteria, objectives, or content (McMillan &
Schumacher, 1989). The assessment of validity for the initial survey questionnaire was
performed by the researcher and a professor in the sport management program.
Comments and suggestions on the content validity pertained to clarifying sentences,
using appropriate word, relocating an item to an appropriate factor, and developing new
items.
Based upon the discussion of items, modifications were made to the initial
questionnaire. First, one belief item (“advertising through sport makes people buy
unaffordable products just to show off”) in the materialism dimension was moved to the
“social role and image” dimension. It was considered that this item might be more
appropriately related to a social role and image dimension. Second, one item
75
(“advertising through sport is confusing”) selected from a previous study (Ducoffe, 1995)
was added to a falsity/no sense dimension. Third, two items (“advertising through sport
is enjoyable” and “advertising through sport generally helps the local economy”) were
newly developed for hedonism/pleasure and good for the economy dimensions,
respectively. Lastly, for an annoyance/irritation dimension, four items were adapted and
modified from previous studies (Alwitt & Prabhaker, 1992; Ducoffe, 1995; James &
Kover, 1992). Based on these revisions, an initial questionnaire including three attitude
and 26 belief items was prepared for the pilot test (see Appendix B). The items in the
questionnaire were measured using a seven-point Likert scale, anchored with strongly
disagree (1) and strongly agree (7). One exception was a question in the global attitude
section which used the anchors strongly dislike (1) and strongly like (7).
Data analysis. The data were analyzed using the Statistical Package for the
Social Science (SPSS 13.0). Descriptive statistics, internal consistency reliability, and
exploratory factor analysis were used in the data analysis procedure. Descriptive
statistics were utilized to the measure sample’s demographic information. Cronbach’s
alpha coefficients and item-to-total correlation coefficients were examined for each
dimension of the proposed model as a test of internal consistency. Exploratory Factor
Analysis (EFA) was conducted to ascertain whether the items would load on the proposed
dimensions, and to describe the adequacy of fit of the resulting factor model to the data.
First, Principle Components Analysis (PCA) was used to determine the number of
possible dimensions for the factor model, based on eigenvalues and the scree plot of
eigenvalue versus factor number. Second, a Maximum Likelihood procedure was used to
test the adequacy of model fit for a series of retained models in a PCA, based on
comparing a specified percent of the variance, goodness-of-fit test, communality, and
residual values. Third, once the dimensionality decision was made, a maximum
likelihood procedure with oblique (OBLIMIN) rotation was implemented to identify
alternative interpretations of model fit, based on factor pattern matrix and factor
correlations. Lastly, other alternative rotated solutions were also considered to confirm
the adequacy of final model fit to the data.
Results
The sections below report the results of the statistical tests examined in the pilot
76
study. Descriptive statistics assessing the demographic characteristics of the sample are
presented, followed by the results of the internal consistency reliability measure. Finally,
the results of the exploratory factor analysis are reported.
Demographic characteristics. Three demographic characteristics - gender, school
year, and ethnic background - were measured in the questionnaire for the pilot study. As
recorded in Table 3.1, out of 125 respondents, nearly two-thirds of the respondents were
male. In terms of school year, a large majority of the respondents (87.2%) were juniors
and seniors. Racially, White/Caucasian (72.0 %) was a dominant group (see Table 3.1).
Table 3.1
Demographic Characteristics of the Sample
Demographic Variables
Gender
Total
School year
Total
Ethnic
background
Frequency
Male
Female
Percent
82
43
125
4
7
46
63
4
1
125
65.6
34.4
100.0%
3.2
5.6
36.8
50.4
3.2
.8
100.0%
Black/African American
18
14.4
Native American
Latino/Latina
White/Caucasian
Asian or Pacific Islander
Other
2
7
90
4
4
125
1.6
5.6
72.0
3.2
3.2
100.0%
Freshman
Sophomore
Junior
Senior
Graduate
Other
Total
Internal consistency. Internal consistency among the respective items served as a
measure of reliability for each of the dimensions in the model. Cronbach’s alpha and
item-to-total correlation were utilized to eliminate the poor performing items from the
initial pool. A .70 alpha value was used as a cut-off value to determine which items to
retain (Leong & Austin, 1996; Nunnally & Bernstein, 1994; Robinson, Shaver, &
77
Wrightsman, 1991); a value of .50 was considered as an accepted level of corrected itemto-total correlation to maintain an item (Bearden et al., 1989; Zaichkowsky, 1985).
The three items for global attitude toward advertising through sport had a
Cronbach’s alpha coefficient of .8280. The corrected item-to-total correlation ranged
from .6699 to .7235. Based on suggested criteria (Bearden et al., 1989; Nunnally &
Bernstein, 1994; Robinson et al., 1991), all items measuring attitude toward advertising
through sport were considered adequate (see Table 3.2)
Table 3.2
Reliability Estimates for Global Attitude
Corrected itemtotal correlation
Global attitude items
My general opinion of advertising through sport is
.6699
unfavorable (att1).
Overall, I consider advertising through sport a good
.7235
thing (att2).
Overall, do you like or dislike advertising through
.6725
sport (att3).
Cronbach’s α coefficient for attitude toward advertising through sport = .8280
Alpha if item
deleted
.7852
.7580
.7757
For the personal belief dimensions, the Cronbach’s alphas ranged from .6811
to .8427; the corrected item-to-total correlation coefficients ranged from .2184 to .7249.
For the societal belief dimensions, the Cronbach’s alpha coefficients ranged from .7234
to .8236; the corrected item-to-total correlation coefficient ranged from .2442 to .7522
(see Table 3.3). The results of internal consistency tests revealed that the Cronbach’s
alpha value for social role and image and item-to-total correlations for several items (I1,
S2, S4, A4, F3, & F4) did not meet the cut-off value. .50.
78
Table 3.3
Reliability Estimates of Belief Factors
Factors
Item-total
correlation
Alpha if
item deleted
.4084
.6114
.5582
.7545
.4838
.5745
.5010
.4120
.5907
.6477
.5884
.5357
.3774
.6762
.6866
.7249
.7165
.6013
.7973
.7821
.7834
.8413
.6462
.6182
.5792
.2184
.5569
.5731
.5826
.8212
.7522
.6738
.6372
.5345
.7285
.7655
.7825
.8276
.5657
.6868
.5305
.6461
.7187
.5884
.6817
.6949
.2442
.4897
.5490
.5398
.8097
.6799
Personal Factor
Product information
is a valuable source of information about local sales (I1).
tells me which brands have the features I am looking for (I2).
helps me keep up-to-date about products available in the marketplace (I3).
Cronbach’s α coefficient for product information = .7015
Social role and image
helps me learn about fashions and about what to buy to impress others (S1).
tells me what people with life styles similar to mine are buying and using (S2).
helps me know which products will or will not reflect the sort of person I am
(S3).
makes people buy unaffordable products just to show off (S4).
Cronbach’s α coefficient for social role and image = .6811
Hedonism/pleasure
is often amusing and entertaining (H1).
is enjoyable (H2).
gives me pleasure when I think about what I saw or heard or read (H3).
is sometimes even more enjoyable than other media contents (H4).
Cronbach’s α coefficient for hedonism/pleasure = .8427
Annoyance/irritation
is annoying (A1).
is irritating (A2).
often interrupts programs just when I am getting interested (A3).
seems to occur more often now than in the past (A4).
Cronbach’s α coefficient for annoyance/irritation = .7051
Societal Factor
Good for the economy
in general helps our nation’s economy (G1).
generally helps the local economy (G2)
is usually a waste of economic resources (G3).
generally promotes competition, which benefits the consumer (G4).
Cronbach’s α coefficient for good for the economy = .8236
Materialism
is making us a materialistic society, overly interested in buying and owning
things (M1).
influences people to buy things they do not really need (M2)
makes people live in a world of fantasy (M3).
Cronbach’s α coefficient for materialism = .7500
Falsity/no sense
in general is misleading (F1).
often insults the intelligence of the average consumer (F2).
generally presents a true picture of the product advertised (F3).
is confusing (F4).
Cronbach’s α coefficient for falsity/no sense = .7234
79
Exploratory factor analysis. Principle Components Analysis (PCA) was used to
determine the number of possible factors, based on eigenvalues and the scree plot of
eigenvalue versus factor number (Fabrigar, Wegener, MacCallum, & Strahan, 1999).
Table 3.4
Initial Eigenvalues for Factors
Scree Plot
10
Initial eigenvalues
8.772
3.185
1.613
1.218
1.150
1.101
.900
.877
8
6
4
Eigenvalue
Component
1
2
3
4
5
6
7
8
2
0
1
3
5
7
9
11
13
15
17
19
21
23
25
Figure 3.2. Scree plot
Six eigenvalues exceeded 1.00. According to Kaiser’s rule, it can be suggested
that factors with eigenvalues larger than one be retained as a proper number of factors in
the initial model. However, in some cases the criterion may discriminate between factors
that have little difference in eigenvalue (Rummel, 1970). The results showed that factors
5 and 7 have an eigenvalue of .1.150 and .900, respectively (see Table 3.4). For a study
in which the eigenvalues range from .133 to 8.772, this small variance difference between
factors appears hardly meaningful to retain one factor or drop the other (Rummel, 1970).
Thus, the eigenvalue greater than or equal to one criterion should not be employed
exclusively (Rummel, 1970).
The scree plot criterion may be used by conjunction with the eigenvalue greaterthan-or-equal-to-one cutoff (Rummel, 1970; Tate, 1998). The location of the knee in the
scree plot of eigenvalues versus component number suggested the possibility of five to
seven factors (see Figure 3.2). Considering the eigenvalues and the scree plot, it was
reasonable to suggest that five, six and seven factor models be retained for the next step.
80
The adequacy of model fit for the three retained models was assessed through a
Maximum Likelihood procedure, which also helped in determining the dimensionality of
the factor model based on a specified percent of the variance, goodness-of-fit test,
communalities, and residual values.
Based on the results of Maximum Likelihood extraction (see Table 3.5), it was
concluded that a seven-factor provided a reasonable compromise between model
parsimony and adequacy of fit. Summarizing the model fit: (a) the seven-factor model
accounted for 58.4% of the total variance; (b) the individual variables in a seven factor
model were explained reasonably well (having communalities ranging from 0.30 to 0.98;
(c) the difference between observed and implied correlations (i.e., residual values) were
small, with only two values exceeding 0.10 (the largest was 0.115); (d) for the chi square
test of goodness-of-fit, only the seven factor model failed to reject a hypothesis that a
seven factor model is the correct model, assuming α = 0.01 (see Table 3.5).
Table 3.5
Model Fit Results for Varying Number of Factors
Number of
Factors
5
Explained
Variance º
52.9 %
Range of
Communalities¹
.224 ~ 1.00
Number of
Residuals >.10²
16 (5.0 %)
p-value for
goodness-fit-test³
.000
6
55.2 %
.280 ~ .951
7 (2.0 %)
.001
7
58.4%
.300 ~ .982
2 (0.6 %)
.025
º “Explained variance” is the percent of the total variance of standardized variables explained by model.
¹ The communality for variable is the proportion of the variance of that variable explained by the model. The range of
the communalities for the 26 variables is shown.
² “Residuals” represent the differences between the observed correlations and the corresponding correlations implied
by the model. There are a total of 26(26-1)/2=325 residuals.
³ The p-value is for the chi-square test of the null hypothesis that the specified model is the correct model.
After selecting a seven-factor model, a maximum likelihood procedure with
oblique (OBLIMIN) rotation was computed to identify alternative interpretations of
model fit based on the factor pattern matrix and factor correlations. The reason oblique
rotation was chosen is that the resulting factors (to be described) were believed to be
81
correlated. Orthogonal rotation would be used when there is no correlation among
factors. The resulting pattern matrix is shown in Table 3.6.
Table 3.6
Factor Pattern Matrixª
Factor loadingsª
I1
I2
I3
S1
S2
S3
S4
H1
H2
H3
H4
A1
A2
A3
A4
G1
G2
G3
G4
M1
M2
M3
F1
F2
F3
F4
Annoyance/
irritation
Good for
the economy
Falsity/
no sense
Product
info./social
role and image
Hedonism/
pleasure
Materialism
Other
.267
.126
.141
.054
.097
-.044
-.056
.044
.341
-.002
.106
-.455
-.836
-.349
-.055
-.001
-.016
.251
-.057
-.116
.144
-.194
.040
.037
-.017
-.251
.283
.063
.044
.037
.245
.044
-.163
.102
.046
.084
.049
-.080
.024
-.101
-.138
.655
.937
.349
.287
-.009
.093
-.053
-.113
-.014
-.045
.098
.103
-.198
-.098
.198
.057
.140
.378
-.080
-.025
-.013
-.129
.142
-.012
.221
.008
-.077
-.004
-.088
.029
-.029
.049
.143
.757
.796
.147
.513
.202
.767
.627
.478
.502
.437
.293
-.095
.129
.064
.022
-.130
-.130
-.022
.161
.182
-.098
.179
.405
.087
-.063
-.033
-.093
-.137
-.237
.118
.021
.005
-.012
.200
.091
.145
.173
.743
.590
.711
.561
-.219
-.118
-.139
.035
.070
.123
.058
.251
.020
-.163
.100
-.020
-.111
.088
-.137
.049
-.103
.055
.044
.067
-.135
-.182
.074
.005
.098
-.074
-.148
-.095
-.044
-.145
.085
-.161
.202
.135
-.684
-.691
-.640
-.129
-.042
-.051
-.009
-.184
.006
.075
-.142
.079
-.306
-.298
.167
.203
.013
-.227
.061
.005
.222
.477
.081
-.200
.031
.158
-.030
.176
-.025
.164
-.008
.411
.031
ª Pattern coefficient larger than 0.4 are shown in bold face.
Factor loadings in Table 3.6 range from 0.411 to 0.937. For the pilot study, items
having a factor loading less than .40 were not considered to be acceptable (Hair,
Anderson, Tatham, & Black, 1998). The following factors were identified:
82
annoyance/irritation (factor 1), good for the economy (factor 2), falsity/no sense (factor
3), product information/social role and image (factor 4), hedonic/pleasure (factor 5),
materialism (factor 6), and other (factor 7). The product information items and the social
role and image items loaded together, along with item (G4) which was purported to
assess good for the economy. Among 26 items, four items (I1, S4, A3, and G3) did not
load on any factor. One item (A 4) from the annoyance/irritation factor and one item
(F3) from the falsity/no sense factor loaded on factor 7, which was named "other" (see
Table 3.6).
Table 3.7 shows the correlation estimates among seven factors. If two factors are
distinct, the measures of one construct should not be too highly correlated with those of
the other (Kline, 1998). That is to say, if the correlation between two different constructs
exceeds the general 0.85 threshold (Kline, 1998), it indicates a warning of
multicollinearity between these two factors so that there is serious deterioration of the
power and precision. Table 3.7 shows the correlation estimates between factors ranged
from .012 to -.441. The correlations among the seven factors were low, suggesting
discrimination among the factors.
Table 3.7
Factor Correlations
Factor
Annoyance/
irritation
Good for
the economy
Falsity/
no sense
Product
info./Social
role and
image
Hedonism/
pleasure
Annoyance/
irritation
Good
for the
economy
Falsity/
no sense
Product
info/
Social role
and image
Hedonism/
pleasure
Materialism
Other
1.000
.373
1.000
-.393
-.213
1.000
.270
.364
.012
1.000
.384
.399
-.146
.432
1.000
Materialism
.344
.271
-.441
.024
.176
1.000
Other
-.068
-.183
.015
-.215
-.097
-.091
83
1.000
Other alternative rotated solutions were considered to confirm the adequacy of
the final model fit to the data. For example, an orthogonal varimax rotation also showed
similar interpretations to results of an oblique rotation. However, a solution in which the
factors were uncorrelated was not considered to be reasonable given the proposed model.
Also, another oblique rotation with different delta parameter (-1) was attempted but did
not provide more competitive results (the results of both are not included in the current
study).
Conclusions
Based on the results of the pilot study, Cronbach’s alpha, item-to-total correlation,
and factor loading will be used as the criteria for item reduction. For internal consistency
reliability test, .70 alpha and .50 item-to-total correlation value were used as a cut-off value
to determine which items to retain (Bearden et al., 1989; Leong & Austin, 1996; Nunnally
& Bernstein, 1994; Zaichkowsky, 1985). In addition, Hair et al. (1998) suggested factor
loadings greater than ±.30 are considered adequate to meet the minimal level, factor
loadings of ±.40 are considered more important, and factor loadings of ±.50 or greater are
considered practically significant. In the main study, items having a factor loading less
than .40, high loadings on more than one factor, and misclassification, will be deleted or
modified from the item pool.
The results of the reliability test and EFA indicated that while the data fit the model
reasonably well, there is room for improvement. First, the item-to-total correlations
indicated that the item, "advertising through sport seems to occur more often now than in
the past (A4)" and the item, "advertising through sport generally presents a true picture of
the product advertised (F3)" operated to decrease reliability values for
annoyance/irritation and falsity/no-sense factors, respectively. Both items, especially the
A4 item, do not seem to be representative of the proposed factors. It is suggested the
item (A4) should be deleted. The item (F3) also needs to be deleted or reworded.
Several items (I1, S2, S4, & F4) which did not meet the .50 criterion will be also
reconsidered.
Second, an exploratory factor analysis using Maximum Likelihood with oblique
rotation was performed as an initial step to identify the underlying factors. Some
problems were identified through the EFA. The results failed to discriminate product
84
information and social role and image; the two merged together. One item (G4) in good
for the economy also loaded on the merged factor. Before using the confirmatory factor
analysis for discrimination, additional steps should be taken to develop items that are
more representative of the proposed factors.
Third, four items (I1, S4, A3, and G3) failed to load on any factor based on a
loading criterion of .40. Two items (I1 and S4) did not load well on any one factor. The
item (I1) did not properly fit with the remaining three items in a product information
factor. The Item (I1) deals with “information about sales.” The other product
information items are related to a “product itself”. Item (S4) originally came from the
materialism factor during the scale development procedure. This item had a low loading
value on materialism, falsity/no sense, and other. The item (S4) is not deemed suitable
for any of the factors. The item (A3) and item (G3) loaded on annoyance/irritation and
good for the economy factors with loading figures, -.349 and .349, respectively. Hair et al.
(1998) suggested factor loadings greater than ± .30 are considered to meet the minimal
level. Thus, these two items could be retained in the current item pool.
Lastly, one item (A4) from the annoyance/irritation factor and one item (F3)
from the falsity/no sense factors loaded together on the other. They were also
problematic based on the reliability test. This problem will disappear because both items
will be deleted after all.
85
CHAPTER IV
METHODOLOGY
Introduction
The purposes of the current study were to develop a proposed model of attitude
toward advertising through sport, to provide a valid and reliable instrument to support the
conceptualization and measurement of the belief dimensions, and to test the relationships
between belief dimensions and attitude toward advertising through sport. For the
development of a reliable and valid instrument, Churchill’s (1999) procedures were
utilized. The review of literature, presented in Chapter 2, specified seven domains of
beliefs about advertising through sport and generated appropriate items for each domain
following Steps 1 and 2 of Churchill’s framework. The pilot test reported in Chapter 3
represented completion of Steps 3 and 4 in the scale development procedure (see Figure
3.1). Before collecting new data for Step 5, the results of the pilot study were reviewed
in order to identify problems with the proposed scale. It was important to decide whether
to proceed to Step 5 or return to an early step in the scale development process.
Churchill (1999) suggested three directions that might be followed based on the
results of a pilot test. First, the ideal scenario would be for the alpha coefficients for each
dimension to exceed the satisfactory value (.70), and for each dimension to load as
conceptualized (Churchill, 1999). New data would then be collected for further tests
without modifications of the initial instrument. Second, Churchill indicated EFA
performed in the early stage of scale development results in the situation where two or
three dimensions which were originally conceptualized as distinct merge on one
dimension. This outcome could result from the selection of inappropriate items for sampling
each dimension or distorted items in the wording/phrasing (Churchill, 1979). In this
condition, the researcher may consider the items that load on a single dimension by
assessing the internal consistency of the “new” dimension, and proceed to the next step if
the coefficient alpha value of the new dimension is acceptable (Churchill, 1999). Third,
it could be that all outcomes from the pilot test are not acceptable in terms of coefficient
alpha values and dimensionality of scales (Churchill, 1999). At this juncture the
86
researcher is advised to go back to the initial steps and verify where the troubles with the
dimensions may lie (Churchill, 1999).
According to the results of the pilot test, a factor analysis revealed that items for
conceptualizing two dimensions, product information and social role and image, were not
productive so that they were merged. The values of coefficient alpha of social role and
image were lower than the suggested value. The results of the pilot test also found that some
items (A4, F3, F4, & G4) of annoyance/irritation, falsity/no sense and good for the economy
were problematic. The results of the pilot study were similar to the second scenario
described by Churchill (1999). The measures of the proposed model have been generally
deemed reliable and valid. However, two dimensions, product information and social
role and image overlapped, and several items loaded on a new dimension (I2, I3, S1, S2,
S3, & G4). According to Churchill’ suggestions, those items (I2, I3, S1, S2, S3, & G4) in
a new dimension could be retained and the internal consistency of the new dimension
should be assessed. Evaluation of the new dimension’s internal consistency resulted in a
satisfactory value (α = .82), indicating it would be appropriate to move to the next step in
the scale development process. However, it may be possible that the empirically derived
modifications from one sample can capitalize on chance variations in the data (Kelloway,
1998). Thus, the modifications should be based on theoretical justifications.
The review of literature indicated that both dimensions have played an important
role in explaining consumers’ attitudes toward traditional advertising mediums, and are
also expected to function as significant predictors of attitudes toward advertising through
sport in the current study. Thus, the current study followed Churchill’s (1979) third
suggestion. The item pools of two dimensions were supplemented through a variety of
item-generation techniques, and then content analyzed by a panel of judges. Other
problematic items were also deleted or reconsidered when the initial instrument was
modified. The following table shows the guideline for the future development of scales,
indicating which items have been retained and deleted based on the results of the pilot
study (see Table 4.1).
87
Table 4.1
The Status of the Initial items Base on the Pilot Test
Factors
Status
Product information
is a valuable source of information about local sales (I1).
tells me which brands have the features I am looking for (I2).
helps me keep up-to-date about products available in the marketplace (I3).
Deleted*
Retained
Retained
Social role and image
helps me learn about fashions and about what to buy to impress others (S1).
tells me what people with life styles similar to mine are buying and using (S2).
helps me know which products will or will not reflect the sort of person I am (S3).
makes people buy unaffordable products just to show off (S4).
Retained
Retained
Retained
Deleted*
Hedonism/pleasure
is often amusing and entertaining (H1).
is enjoyable (H2).
gives me pleasure when I think about what I saw or heard or read (H3).
is sometimes even more enjoyable than other media contents (H4).
Retained
Retained
Retained
Retained
Annoyance/irritation
is annoying (A1).
is irritating (A2).
often interrupts programs just when I am getting interested (A3).
seems to occur more often now than in the past (A4).
Retained
Retained
Retained
Deleted*
Good for the economy
In general helps our nation’s economy (G1).
generally helps the local economy (G2).
is usually a waste of economic resources (G3).
generally promotes competition, which benefits the consumer (G4).
Retained
Retained
Retained
Deleted*
Materialism
is making us a materialistic society, overly interested in buying and owning things
(M1).
influences people to buy things they do not really need (M2)
makes people live in a world of fantasy (M3).
Retained
Retained
Retained
Falsity/no sense
In general is misleading (F1).
often insults the intelligence of the average consumer (F2).
generally presents a true picture of the product advertised (F3).
is confusing (F4).
Retained
Retained
Deleted*
Retained**
* Items which did not load on the conceptualized factor and/or meet the .50 cut-off value of item-to-total correlation.
** The item-to-total correlation value for F4 was .49.
The current chapter will explain continued development of the scale based on
Churchill’s framework. The methodology for the main study of the dissertation included
the collection of new data (i.e., Churchill’s fifth step), followed by instrument
88
development and data analysis procedures for further analyses.
Subjects and procedure of data collection
The population for the main study consisted of students attending a university in
the southeastern United States. The main study included two data collections which both
occurred in the 2005 fall semester using a convenient sampling technique. The number
of participants in the first data collection was 230; 443 subjects participated in the second
data collection. Participants included students enrolled in Lifetime Activity Program
(LAP) classes and several lecture courses such as Introduction to Sociology and Basic
Marketing Concepts.
Ferber (1977) identified some prerequisites in order to utilize the students sample
by a convenience sampling: first, subjects should be relevant to the topic of the study;
second, student subjects can be utilized when a study is an exploratory research; and
lastly, the sample size should be large enough for analytical purposes. It is believed that
the data collections in the current study have been satisfied with these three conditions.
The study was designed to analyze exploratory factor analysis (EFA) and
structural equation modeling (SEM) through two data collections. First, for the sample
size in EFA, Comfrey and Lee (1992) recommended that “the adequacy of sample size
might be evaluated very roughly on the following scale: 50 - very poor; 100 - poor; 200 fair; 300 - good; 500 - very good; 1000 or more - excellent” (p. 217). Some researchers
suggested a minimum observation to item ratio of at least 5:1 in EFA (Gorsuch, 1983;
Hair et al., 1998; Hatcher, 1994). However, such rules-of-thumb based on the subject to
variable ratio (e.g., 1:5, 1:10, or 1:20) have been recently considered “not sufficiently
sensitive to a variety of important characteristics of the data (e.g., Barrett & Klein, 1981;
Fabrigar et al., 1999, p. 274; MacCallum, Widaman, Zhang, & Hong, 1999; Velicer &
Fava, 1998). Particularly, Fabrigar et al. (1999) insisted the following:
The primary limitation of such guidelines is that adequate sample size is not a
function of the number of measured variables per se but is instead influenced by
the extent to which factors are overdetermined (i.e., at least three or four
measured variables represent each common factor) and the communalities are
high (i.e., an average of .70 or higher), accurate estimates of population
89
parameters can be obtained with samples as small as 100 (MacCallum et al.,
1999). However, under more moderate conditions a sample size of at least 200
might be needed; when these conditions are poor it is possible that sample as
large as 400 to 800 might not be sufficient. (p. 274)
Accordingly, it was conclude that the sample size of 200 would be acceptable for EFA
when each common factor has at least 3 observed variables, and communalities of each
observed variable are moderately high. Thus, the current sample size for EFA in the first
data collection seemed adequate.
Meanwhile, for the sample size in SEM, Anderson and Gerbing (1984, 1988)
suggested a sample size should be at least 150 for the SEM in order to obtain practically
useful parameters in the model with three or more indicators per factor. Other
researchers recommended at least 200 subjects for the SEM using a maximum likelihood
estimation (e.g., Hair et al., 1998; Kelloway, 1998). Accordingly, it was deemed the
sample size was acceptable for an analysis of SEM in the second data collection.
Instrument development
Based on the suggestions derived from the pilot study, the questionnaire was
modified. Two dimensions, product information and social role and image resulted in
poor dimensionality and reliability outcomes. Accordingly, the study attempted to
generate as many items as possible for both dimensions using two techniques: a review of
literature and experience surveys. The review of literature was scrutinized to identify
what related belief dimensions with product information and social role and image have
been specified, what instruments have been employed to measure those belief dimensions,
and how these instruments has been performed in terms of reliability tests and factor
analyses in the previous studies. The procedures of experience survey include the
discussions with practitioners and academic researchers in the area of advertising through
sport as well as consumers (Churchill, 1979).
The pilot study also revealed that several items (I1, S4, A4, G4, & F3) in product
information, social role and image, annoyance/irritation, good for the economy, and
falsity/no sense were problematic, those items were deleted. The same item generation
techniques mentioned above were recruited to supplement more proper items which
90
represent product information, social role and image, annoyance/irritation, good for the
economy, and falsity/no sense dimensions.
Product information and social role and image. First, two items were eliminated
from each. The item (I1), “advertising through sport is a valuable source of information
about local sales,” was deleted from the product information dimension. The item
showed not only poor results from the pilot study but also dealt with “information about
sales” rather than “information about product itself.”
The item (S4), “advertising through sport makes people buy unaffordable
products just to show off,” was also removed. The item S4 was originally transferred
from the materialism dimension in Pollay and Mittal’s (1993) instrument during the
initial scale development procedure for the pilot test. However, the item was not
relocated to the original dimension, materialism, because the pilot study showed that it
did not load on any dimension.
Additional items were modified from past research to increase the number of
items in both dimensions. Six items were modified and added to the item pool for
production information based on previous studies (Ducoffe, 1995, 1996; Durand &
Lambert, 1985) (see Table 4.2). For social role and image, an additional eight items were
modified and added from previous studies (Alwitt & Prabhaker, 1992; Haller, 1874;
Mittal, 1994) (see Table 4.2).
Annoyance/irritation. The initial instrument contained four items for the
annoyance/irritation dimension. The pilot study indicated that the item (A4), “advertising
through sport seems to occur more often now than in the past,” contributed to poor results
in terms of reliability. The item (A4) was removed from the item pool. Second, due to
the reduced number of items in this dimension, the researcher, through the experience
survey, self-developed one new item, “advertising through sport is intrusive” and added it
to the item pool.
Good for the economy. The item (G4), “advertising through sport generally
promotes competition, which benefits the consumer,” loaded on the merged factor that
included product information and social role and image in the pilot study. As a result,
this item was removed for the modified instrument; an additional three items were
developed based on Bauer and Greyser’s (1968) instrument: “advertising through sport
91
helps raise our standard of living,” “advertising through sport results in better products
for the public,” and “advertising through sport, in general, results in lower prices.”
Table 4.2
Regenerated Instrument Items for measuring Product Information and Social Role and
Image
Items
Dimension
Sources
Advertising through sport
…gives people enough information about the product being
advertised
…is a good source of product information.
Product
information …supplies relevant product information.
(6 items)
Durand & Lambert,
(1985)
Ducoffe (1995)
…provides timely information.
…makes product information immediately accessible.
…supplies complete product information.
…helps me learn what is in fashion and what I should buy for
keeping a good social image.
Ducoffe (1996)
Mittal (1994)
…lets me see what brands other people use.
…helps me be more confident in the brands/products I actually
use.
Social role
and image
(8 items)
…gives me ideas about fashion.
Alwitt & Prabhaker
(1992)
…gives me ideas about ways to act.
…helps me find products that match my personality and
interests.
…gives me a good idea about products by showing the kinds of
people who use them.
Alwitt & Prabhaker,
(1992, originally
derived from Ogilvy
& Mother, 1985)
…portrays people the way they really are.
Haller (1974)
Materialism. The results of the pilot test for materialism were satisfactory in
terms of reliability and factor analysis. However, the results in the EFA do not guarantee
the three items would be reliable and valid in a different sample because it is possible the
results capitalized on chance variations in the data (Kelloway, 1998). Accordingly, two
items relevant to materialism were borrowed from Durand and Lambert’s (1985)
92
instrument for the advertising in general domain. The items were properly modified and
added to the current materialism dimension: “advertising through sport results in a larger
volume of goods being produced,” and “advertising through sport leads to a waste of
natural resources by creating desires for unnecessary goods.”
Falsity/no sense. The item (F3) was problematic according to the factor analysis
and the reliability tests. This item was eliminated from the initial item pool. Second, the
item (F4) had a low item-to-total correlation score which did not meet the .50 criterion.
However, the item was retained for the modified instrument because there was a very
minute gap between the observed item-to-total correlation score (.49) and the suggested
criterion (.50), and the factor analysis showed an acceptable outcome for this item (F4).
Due to the elimination of the item, two additional items were added: “advertising through
sport is truthful”, and “advertising through sport is deceptive” (Ducoffe, 1996; Durand &
Lambert, 1985).
Attitude toward advertising through sport. All items measuring attitude toward
advertising through sport were retained for the modified instrument because the pilot
study revealed a very good internal consistency among the current items. Furthermore,
the modified instrument include three semantic differential scales for an attitude construct
for the purpose of assessing of convergent validity of the current Likert scales. These
attitude scales were adapted from Mackenzie and Lutz’s (1989) three semantic
differential scales for measuring attitude toward advertising in general: “bad/good,”
“unpleasant/pleasant,” and “unfavorable/favorable”.
After the new items were added, a modified questionnaire was reviewed by a
panel of three experts to finalize the development of instrument. The panel of experts
included two professors, one from marketing and one from sport marketing, and one
advertising expert. The panel members were asked to evaluate the items in the modified
scale for content validity. Based on information provided, the modified questionnaire
was prepared for further analyses including six attitude items for two measurement scales
and 43 belief items (see Appendix C).
The procedure of data analysis
The main study included two data collections. The first data collection was used
to perform the internal consistency tests, exploratory factor analysis (EFA), and chi-
93
square difference tests. The data in the second collection was split into two samples.
With split 1, the measurement model was purified and the structural model was tested.
With split 2, a cross-validation test confirmed the results through structural equation
modeling. All analyses employed in the second data collection were performed using
LISREL 8.72 (Jöreskog & Sörbom, 2005).
The first collection. Since the initial instrument was revised from results of the
pilot test, it was necessary to re-examine the internal consistency and structure of the
measures to ensure the quality of the instrument (Churchill, 1979). First, the internal
consistency of each dimension was assessed by coefficient alphas and item-to-total
correlations. The coefficient alphas for all dimensions were guided by the cut off value
of .70 (Leong & Austin, 1996; Nunnally & Bernstein, 1994; Robinson et al., 1991); the
item-to-total correlations utilized the .50 cut-off value for purifications of data in the
current modified questionnaire (Bearden et al., 1989; Zaichkowsky, 1985).
As a next step, an EFA using maximum likelihood with oblique rotation was
employed to confirm whether the number of conceptualized belief dimensions could be
empirically verified (Churchill, 1999). The findings from two analyses in the pilot study,
a principle components analysis and a maximum likelihood procedure, determined that
the seven-factor model of belief dimensions was reasonable. Thus, the data for the first
collection was directly analyzed by a maximum likelihood procedure with oblique
rotation. A loading value of .40 was considered acceptable (Hair et al., 1998). Hair et al.
(1998) suggested guidelines for identifying significant factor loadings: when a loading is
higher than .40 in the current sample size (n=215) and in a power level of 80 percent, it is
considered statistical significant at a .05 alpha level.
Sequentially, the chi-square difference tests were implemented to evaluate the
problem of three dimensions loading on a single factor based on the results from the EFA.
Chi-square values and other fit indexes were employed to determine whether there were
significant differences between the baseline model (a three-factor model) and its several
rival models. Based on the results from the first data collection, several items from
dimensions were deleted, and the current questionnaire was re-modified for the second
data collection (see Appendix D).
94
The second data collection. The second data collection employed structural
equation modeling (SEM) which consisted of two sub-models: a measurement model and
a structural model. While a measurement model determines how well indicators capture
their proper constructs, a structural model represents the hypothetical relationships
among the latent variables (Tate, 1998). For the second data collection, a larger set of
data from the same population was collected for the final questionnaire and split in half.
The first split data set (i.e., calibration sample) was utilized to purify the proposed scale.
The first CFA for the initial measurement model was performed to confirm
overall goodness-of-fit, reliability, and validity, of belief measures which were purified
from the first data collection. First, for the goodness-of-fit tests for the proposed model,
absolute fit and comparative (relative) fit assessment indexes were utilized to assess the
fit of the proposed model (Kelloway, 1998; Maruyama, 1998). For the absolute fit, χ²
statistic, which is the most fundamental fit index, was first assessed. However, due to
some limitations of χ² statistic (e.g., the sensitivity to sample size and its basis on the
central χ² distribution) (Byrne, 2001; Kline 1998), alternative goodness-of fit indices
were examined to reduce sensitivity of χ² statistic to a sample size such as χ²/df ratio,
standardized root mean squared residual (SRMR), root mean squared error of
approximation (RMSEA), and goodness-of-fit index (GFI). For the comparative
(relative) fit, several indexes, such as, normed fit index (NFI), incremental fit index (IFI),
relative fit index (RFI), and comparative index (CFI) were employed to determine
whether the proposed model is better than other possible models with the current data
(Kelloway, 1998; Maruyama, 1998).
Some techniques reviewed in Chapter 2 were selected to examine the reliability
and validity of the final instrument. The current study employed the internal consistency,
individual item reliability, composite reliability, and average variance extracted (AVE)
techniques as reliability tests.
Convergent and discriminate validity tests were to determine whether the
measures of the specified domains were valid. Convergent validity was assessed by
examining the indicators’ coefficients on their conceptualized construct, AVEs, and the
standardized residual matrix. The factor loadings should be high (e.g., .707) and
significant in order to satisfy convergent validity of the proposed dimensions. In addition,
95
convergent validity of an attitude construct was evaluated by the degree of correlations
between Likert scales and semantic differential scales of attitude toward advertising
through sport.
Discriminant validity was examined by evaluating the correlation coefficients
among dimensions. The evidence of discriminate validity requires that a dimension
should be not highly correlated with other dimensions which are supposed to measure
different concepts. A cut-off value of a .85 correlation coefficient was considered in this
study (Kline, 1998). In addition, AVEs score were also utilized to evaluate discriminant
validity. In order to satisfy the necessities of discriminant validity, the AVE score for each
construct should exceed the square of a correlation between constructs (Fornell & Larcker,
1981).
After deleting several problematic items based on the results the first CFA, a
second CFA was employed to test the modified measurement model with the reduced
item pool. The same procedures and techniques used for the first CFA were reemployed
for the second CFA. After evaluating the proposed measurement model using a series of
CFAs, a structural model was employed to assess the hypothetical relationships between
seven belief dimensions and attitude toward advertising through sport. The overall model
fit as well as path estimates for the seven hypothetical relationships among latent
variables were measured in the structural model (see Figure 4.1).
As a last step in the study, a cross validation of the results from the hypothesis
tests as well as of other estimated parameters was performed. For the cross validation
test, the second half of the split sample (i.e., validation sample) from the second data
collection was used to confirm the predictive ability of the model.
96
Product
information
Social role
and image
H1
H2
Hedonism/
pleasure
Annoyance/
irritation
H3
Attitude
toward
advertising
through sport
H4
H5
Good
for the
economy
H6
H7
Falsity/
no sense
Materialism
Figure 4.1. The structural model for the seven hypothetical causal relationships between
belief dimensions and attitude toward advertising through sport
97
CHAPTER V
RESULTS
The current chapter illustrates the findings of the statistical analyses for the
proposed model of attitude toward advertising through sport. The first section describes
the results from the analysis of internal consistency and exploratory factor analysis of the
modified questionnaire based on the first data collection. The second section includes the
results from the assessment of a structural equation model. In the final section, crossvalidation was performed using a new sample to ascertain whether the findings from the
previous data collection may be validated within the same population.
The first data collection
Objectives of the first data collection
The primary purposes of the first data collection were to examine the structure of
a set of belief items believed to influence attitude toward advertising through sport and to
reduce the number of items in the proposed scale using internal consistency reliability
tests and exploratory factor analysis (EFA). Since new items were generated through the
instrument development process after the pilot study, additional reliability tests and an
EFA were performed to assess the quality of items in the underlying dimensions. This
step was deemed important in order to ascertain whether the belief items were grouped
into their proposed factors, and to delete inappropriate items which were not merged into
the proposed factor or/and had low item-to-total correlations.
Characteristics of sample data
A total of 230 questionnaires were conveniently collected from the population.
Among them, 15 incomplete questionnaires were excluded from the data pool. The
remaining 215 responses were utilized in the data analyses. This sample size for factor
analysis was deemed to meet the minimum cases per variable ratio of 5 to 1 (Gorsuch,
1983; Hair et al., 1998) and also satisfied guidelines suggested by recent literature: when
the expected communalities are moderate (e.g., .40 to .70) and each underlying
dimension has at least three or four variables, a sample size of 200 is sufficient (e.g.,
98
Barrett & Kline, 1981; Fabrigar et al., 1999; MacCallum et al., 1999). In the current
factor analysis, each of conceptualized factors had four to 11 items, and the initial
communalities of all items used in the EFA ranged from .409 to .762.
Seven demographic characteristics, gender, school year, ethnic background, time
watching sports games in a day, frequency participating in sports activity in a month, and
frequency purchasing sporting goods in a month were measured. The results are
presented in Table 5.1. There were more male than female respondents, and the highest
percentage of responses came from students classified as seniors. The majority of
respondents were white/Caucasian.
Table 5.1
Demographic Characteristics of the Sample
Gender
Demographic Variables
Male
Female
Total
School year
Ethnic background
Freshman
Sophomore
Junior
Senior
Graduate
Other
Total
Black/African
American
Native American
Latino/Latina
White/Caucasian
Asian or Pacific
Islander
Other
Total
Frequency
123
92
215
30
36
52
79
16
2
215
Percent
57.2
42.8
100.0%
14.0
16.7
24.2
36.7
7.4
.9
100.0%
14
6.5
0
17
160
0
7.9
74.5
14
6.5
10
215
4.7
100.0%
Table 5.2 summarizes the respondents’ behaviors relating to watching sports
games, participating in sports activity, and purchasing sport merchandise. Approximately
half of the respondents (49.8%) watched sports games at least one hour in a typical day.
Most students participated in many kinds of sports activities at least once a month
99
(87.9%) and purchased sports merchandise three times or less (93.9%) in a month.
Table 5.2
Sports-Related Behaviors of the Sample
Behavioral Variables
0 hour
Less than 1 hour
Time watching sports games in
1 hour
a typical day
2 hours
3 hours
More than 3 hours
Total
Never
Less than once
Frequency participating in
1-4 times
sports activity in a month
5-10 times
11-20 times
More than 20 times
Total
Never
Less than once
Frequency purchasing sporting
1-3 times
goods in a month
4-6 times
7-9 times
10 times or more
Total
Frequency
45
63
42
47
18
0
215
12
14
48
52
53
36
215
23
103
76
7
3
3
215
Percent
20.9
29.3
19.5
21.9
8.4
0
100.0%
5.6
6.5
22.3
24.2
24.7
16.7
100.0%
10.7
47.9
35.3
3.3
1.4
1.4
100.0%
Internal consistency tests
Internal consistency among the respective items served as a measure of reliability
for each dimension in the current model as in the pilot study. The reliability of an attitude
dimension and seven belief dimensions was assessed with Cronbach’s alpha and item-tototal correlation. A value of .70 alpha was used as the cut-off to determine the amount of
variance due to the random errors of content heterogeneity (Leong & Austin, 1996;
Nunnally & Bernstein, 1994; Robinson, Shaver, & Wrightman, 1991); .50 was considered
a satisfactory level of corrected item-to-total correlation to maintain an item (Bearden,
Netemeyer, & Teel, 1989; Zaichkowsky, 1985).
The three items for global attitude toward advertising through sport had a
100
Cronbach’s alpha coefficient of .8821. The corrected item-to-total correlation ranged
from .6345 to .8705. Based on suggested criteria (Bearden, Netemeyer, & Teel, 1989;
Nunnally & Bernstein, 1994; Robinson, Shaver, & Wrightman, 1991), all items
measuring attitude toward advertising through sport were considered adequate (see Table
5.3).
Table 5.3
Reliability Estimates for Global Attitude
Global attitude item
My general opinion of advertising through sport is favorable
(Att1).
Overall, I consider advertising through sport a good thing (Att2).
Overall, do you like or dislike advertising through sport (Att3).
Corrected itemtotal correlation
Alpha if
item deleted
.8241
.7842
.8705
.7403
.6345
.9451
Cronbach’s α coefficient for product information = .8821
For the personal belief dimensions, a total of 27 items (eight items for product
information, 11 items for social role and image, four items for hedonism/pleasure, and
four items for annoyance/irritation) were tested. The Cronbach’s alpha coefficients for
the four dimensions ranged from .7858 to .9090; the corrected item-to-total correlation
coefficients ranged from .4326 to .7481 (see Table 5.4). The results of internal
consistency tests revealed that the Cronbach’s alpha value for personal belief constructs
were acceptable but the item-to-total correlations for two items (S9 in social role and
image and H3 in hedonism/pleasure) did not meet the suggested guideline.
101
Table 5.4
Reliability Estimates of Personal Belief Constructs
Corrected
item-total
correlation
Alpha if
item deleted
tells me which brands have the features I am looking for (I1).
helps me keep up-to-date about products available in the marketplace (I2).
gives people enough information about the product being advertised (I3).
is a good source of product information (I4).
supplies relevant product information (I5).
provides timely information (I6).
makes product information immediately accessible (I7).
supplies complete product information (I8).
.7048
.6579
.6835
.7401
.6271
.5900
.6590
.6913
.8751
.8797
.8772
.8715
.8825
.8857
.8795
.8766
helps me learn about fashions and about what to buy to impress others (S1).
tells me what people with life styles similar to mine are buying and using (S2).
helps me know which products will or will not reflect the sort of person I am
(S3).
helps me learn what is in fashion and what I should buy for keeping a good
social image (S4).
lets me see what brands other people use (S5).
helps me to be more confident in the brands/products I actually use (S6).
gives me ideas about fashion (S7).
gives me ways to act (S8).
portrays people the way they really are (S9).
helps me find products that match my personality and interests (S10).
gives me a good idea about products by showing the kinds of people who use
them (S11).
.7071
.7246
.8980
.8970
.6602
.9007
.7444
.8958
.5735
.6244
.6687
.6486
.4840
.6473
.9049
.9026
.9001
.9013
.9090
.9013
.7192
.8794
is often amusing and entertaining (H1).
is enjoyable (H2).
gives me pleasure when I think about what I saw or heard or read (H3).
is sometimes even more enjoyable than other media contents (H4).
.6155
.7383
.4326
.6052
.7214
.6574
.8142
.7270
is annoying (A1).
is irritating (A2).
often interrupts programs just when I am getting interested (A3).
is intrusive (A4).
.6223
.7481
.5688
.5757
.7573
.6949
.7893
.7816
Personal belief construct
Product information
Cronbach’s α coefficient for product information = .8922
Social role and image
Cronbach’s α coefficient for social role and image = .9090
Hedonism/pleasure
Cronbach’s α coefficient for hedonism/pleasure = .7858
Annoyance/irritation
Cronbach’s α coefficient for annoyance/irritation = .8060
For the societal belief dimensions, a total of 16 items were assessed, six items for
good for the economy, five items for materialism, and five items for the falsity/no sense.
Cronbach’s alpha coefficients for three dimensions ranged from .6901 to .7728; the
102
corrected item-to-total correlation coefficients ranged from .0206 to .6993 (see Table 5.5).
The results of reliability tests for three societal belief dimensions showed that six items
were problematic (G3 & G6 in good for the economy, M4 & M5 in materialism, and F3
& F4 in falsity/no sense).
Table 5.5
Reliability Estimates of Societal Belief Constructs
Societal belief construct
Corrected
item-total
correlation
Alpha if
item deleted
.6197
.5828
.4529
.5410
.5305
.3908
.7127
.7222
.7548
.7331
.7358
.7724
.5841
.5755
.6243
.5915
.0206
.5544
.5710
.7734
.4137
.6546
.6993
.5348
.3842
.3563
.6871
.6519
.7175
.7618
.7705
.6521
Good for the economy
in general helps our nation’s economy (G1).
generally helps the local economy (G2).
is usually a waste of economic resources (G3).
helps raise our standard of living (G4).
results in better products for the public (G5).
,in general, results in lower prices (G6).
Cronbach’s α coefficient for good for the economy = .7728
Materialism
is making us a materialistic society, overly interested in buying and owning
things (M1).
influences people to buy things they do not really need (M2).
makes people live in a world of fantasy (M3).
results in a larger volume of goods being produced (M4).
leads to a waste of natural resources by creating desires for unnecessary goods
(M5).
Cronbach’s α coefficient for materialism = .6901
Falsity/no sense
in general is misleading (F1).
often insults the intelligence of the average consumer (F2).
is confusing (F3).
is truthful (F4).
is deceptive (F5).
Cronbach’s α coefficient for falsity/no sense = .7595
Before proceeding with an EFA, the researcher carefully considered the
psychometric properties of each variable based on the findings of the reliability tests. The
results of the reliability tests in Table 5.4 and 5.5 showed that the item (M4) had
extremely low item to total correlation (r = .0206); the item was removed from the item
103
pool because the construct’s alpha value would improve from .6901 to 7734 if the item
was deleted. Though seven items (i.e., S9, H3, G3, G6, M5, F3, & F4) still did not meet
the suggested criterion in the reliability tests, they were retained for the EFA since the
problematic items did not significantly influence the magnitude of a factor’s overall alpha
values if those items were deleted (see Table 5.4 & 5.5).
Exploratory Factor Analysis (EFA)
Preliminary analysis. An exploratory factor analysis was employed to identify a
set of factors among 42 items to explain how respondents characterize their beliefs about
advertising through sport. As a first step, preliminary analyses were conducted to inspect
individual problematic observations or violations of assumptions. Descriptive statistics
including mean, standard deviation, range, and frequency showed that there were no
outliers and invalid data resulting from invalid responses or input error (see Table 5.6).
In terms of testing the assumptions of normality of the distribution of observed
variables, each observed variable was tested based on z-scores of both skewness and
kurtosis. Hair et al. (1998) indicated that if a z-score of a certain variable exceeds a
critical value of ±2.58, the assumption that the variable is normally distributed can be
rejected at the .01 probability level. According to the criterion of ±2.58, 13 items (I1, I2,
I4, I7, S2, S4, S6, S7, H1, H2, H4, G1, and F3) violated the normality of the distribution
(see Table 5.6). EFA with maximum likelihood extraction could create false results when
assumptions of normality are violated. The researcher determined that procedures for
remedying non-normality should be performed before conducting EFA.
Data transformations. There are various ways to deal with non-normal data
using transformations. Some possible approaches to transform non-normal data are the
utilizations of certain mathematical operations such as square root (
(log 10 ), odd root ( 3
), logarithm
), or odd power polynomial (X³) (Kline, 1998). The square root or
logarithm is usually preferred when data is skewed while the odd root and odd power
polynomial properly work for kurtosis (Kline, 1998). Considering that most of nonnormal data found in the current study were skewed, the violated data was repaired by
taking the square root or logarithm as pulling their outlying scores closer to the center of
the distribution (e.g., Hair et al., 1998; Kline, 1998; West, Finch, & Curran, 1995).
104
Table 5.6
Descriptive Statistics of the Belief Items
Items
Product information 1
Product information 2
Product information 3
Product information 4
Product information 5
Product information 6
Product information 7
Product information 8
Social role and image 1
Social role and image 2
Social role and image 3
Social role and image 4
Social role and image 5
Social role and image 6
Social role and image 7
Social role and image 8
Social role and image 9
Social role and image 10
Social role and image 11
Hedonic/pleasure 1
Hedonic/pleasure 2
Hedonic/pleasure 3
Hedonic/pleasure 4
Annoyance/irritation 1
Annoyance/irritation 2
Annoyance/irritation 3
Annoyance/irritation 4
Good for the economy 1
Good for the economy 2
Good for the economy 3
Good for the economy 4
Good for the economy 5
Good for the economy 6
Materialism 1
Materialism 2
Materialism 3
Materialism 5
Falsity/no sense 1
Falsity/no sense 2
Falsity/no sense 3
Falsity/no sense 4
Falsity/no sense 5
Mean
4.69
5.14
4.01
4.87
4.54
4.30
4.47
3.66
3.75
4.14
3.73
4.03
4.49
4.47
4.28
3.27
3.07
4.44
4.06
5.46
5.10
4.00
5.00
3.14
3.13
3.84
3.27
4.73
4.46
5.02
3.61
4.30
3.44
4.36
4.26
3.65
3.34
3.51
3.47
2.55
4.43
3.51
Skewness
S.D.
1.43
1.24
1.36
1.33
1.18
1.14
1.28
1.43
1.47
1.44
1.32
1.60
1.25
1.48
1.41
1.53
1.27
1.31
1.41
1.31
1.30
1.37
1.30
1.43
1.37
1.54
1.16
1.24
1.25
1.21
1.25
1.23
1.32
1.49
1.51
1.51
1.50
1.36
1.44
1.24
1.25
1.48
Kurtosis
Statistic
z-scoreª
Statistic
z-scoreª
-.468
-.631
-.172
-.626
-.337
-.125
-.460
.054
-.155
-.464
-.153
-.437
-.216
-.472
-.496
.185
.253
-.337
-.321
-1.014
-.609
-.271
-.540
.361
.172
-.016
-.195
-.493
-.191
-.330
-.117
-.106
.076
-.190
-.246
-.140
.283
.086
.147
.648
-.104
.007
-2.82*
-3.80*
-1.04
-3.77*
-2.03
-.75
-2.77*
.33
-.93
-2.80*
-.92
-2.63*
-1.30
-2.84*
-2.99*
1.11
1.52
-2.03
-1.93
-6.11*
-3.67*
-1.63
-3.25*
2.17
1.04
-.10
-1.17
-2.97*
-1.15
-1.99
-.70
-.64
.46
-1.14
-1.48
-.84
1.70
.52
.89
3.90*
-.63
.04
.106
.285
-.261
.401
.160
.784
.300
-.525
-.458
-.259
-.064
-.537
.022
-.062
.008
-.449
.067
.118
-.140
1.206
.256
-.216
.145
-.685
-.584
-.597
-.163
.289
.163
.131
.111
-.137
-.311
-.444
-.573
-.704
-.431
-.319
-.434
-.081
.277
-.667
.32
.86
-.79
1.22
.48
2.38
.91
-1.59
-1.39
-.78
-.19
-1.63
.07
-.19
.02
-1.36
.20
.36
-.42
3.65*
.78
-.65
.44
-2.08
-1.77
-1.81
-.49
.88
.49
.40
.34
-.42
-.94
-1.35
-1.74
-2.13
-1.31
-.97
-1.32
-.25
.84
-2.02
ª The z-scores are calculated by diving the statistics by the standard errors of .166 (skewness) and .330 (kurtosis).
* Significant at the .01 level.
105
It should be noted that data transformations with the square root or logarithm are
useful under the situation that the distribution is positively skewed (West, Finch, &
Curran, 1995). Table 5.6 showed most problematic variables were negatively skewed
except F3. Accordingly, the negative skewness among original variables should be
changed to positive skewness by subtracting every score from a constant that is the
highest score plus one. The modified variables still had the exactly same amount of
skewness as did the original variables but in a positive direction rather than in a negative
direction (Wuensch, 2005). After the subtractions, the researcher employed the square
root on every score in the modified variables. The reason the square root rather than the
logarithm was utilized for the non-normal data in EFA was that a selection of either
technique is based on the severity of skewness of the data, and all non-normal data in the
analysis was not extremely skewed. More detail information regarding the normality test
of data, the procedures of data transformations performed during the entire of the study,
potential problems derived from data transformations, and possible solutions to figure out
the problems is introduced in Appendix E. It is believed the Appendix will provide
important information to researchers when dealing with non-normal data in future
research.
Table 5.7 includes the effects of the transformations on the 13 variables
comparing their original values with the transformed values. After the transformations,
the study failed to reject the assumption of the normal distribution of 13 variables at a
0.01 significant level; all variables were determined to be normally distributed.
106
Table 5.7
Distribution Values Before and After Transformation
Original values
Item
I1
I2
I4
I7
S2
S4
S6
S7
H1
H2
H4
G1
F3
Skewness
Transformed values
Kurtosis
Skewness
statistic
z-score²
statistic
z-score²
statistic¹
-.468
-.631
-.626
-.460
-.464
-.437
-.472
-.496
-1.014
-.609
-.540
-.493
.648
-2.82*
-3.80*
-3.77*
-2.77*
-2.80*
-2.63*
-2.84*
-2.99*
-6.11*
-3.67*
-3.25*
-2.97*
3.90*
.106
.285
.401
.300
-.259
-.537
-.062
.008
1.206
.256
.145
.289
-.081
.32
.86
1.22
.91
-.78
-1.63
-.19
.02
3.65*
.78
.44
.88
-.25
-.139
.059
.009
-.129
-.058
-.028
-.113
-.103
.365
.032
-.029
-.073
.174
Kurtosis
z-score²
-.84
.36
.05
-.78
-.35
-.17
-.68
-.62
2.20
.19
-.17
-.44
1.05
statistic¹
z-score²
-.220
-.284
-.104
.153
-.039
-.411
-.176
.170
-.202
-.354
-.328
-.065
-.711
-.67
-.86
-.32
.46
-.12
-1.25
-.53
.52
-.61
-1.07
-.99
-.20
-2.15
¹ The statistics of skewness and kurtosis for all items were computed by taking the square root transformation after
subtracting every score except F3 from a constant that is one greater than the higher score (i.e., 8).
² The z-scores are calculated by diving the statistics by the standard errors of .166 (skewness) and .330 (kurtosis).
* Significant at the .01 level.
Note: ‘I’ (product information); ‘S’ (social role and image); ‘H’ (hedonism/pleasure); ‘G’ (good for the
economy); ‘F’ (falsity/no sense).
Maximum likelihood procedure with the oblique rotations. The results from the
pilot study indicated that a seven factor model provided a reasonable compromise
between model parsimony and adequacy of fit. Thus, the current stage employed a
maximum likelihood procedure with oblique rotation with a seven-factor model.
Table 5.8 summarizes the overall model fit: (a) The factor matrix of the sevenfactor model provided information that accounted for 56.9% of the total variance; (b) the
individual variables in a seven factor model were explained reasonably well, having
communalities ranging from 0.27 to 0.78 (only one item, G6, was less than .40); (c) the
difference between observed and implied correlations (i.e., residual values) were small,
with only three values exceeding ±.10 (the largest was -.139); (d) for the chi square test
of goodness-of-fit, the model rejected a hypothesis that a seven factor model is the
correct model, assuming α = 0.01.
107
Table 5.8
Model Fit Results for the Seven-Factor Model
Number of
Factors
7
Explained
Varianceº
56.9%
Range of
Communalities¹
.27 ~ .78
Number of
Residuals >.10²
3 (0.3 %)
p-value for
goodness-fit-test³
.00
º "Explained variance” is the percent of the total variance of standardized variables explained by model.
¹ The communality for variable is the proportion of the variance of that variable accounted for by the common factors.
The range of the communalities for the 42 variables is shown.
² "Residuals" are the differences between the observed correlations and the corresponding correlations implied by the
model. There were a total of 42(42-1)/2=861 residuals.
³ The p-value is for the chi-square test of the null hypothesis that specified model is the correct model
Table 5.9 shows the model fit based on the factor pattern matrix. Using a .40
factor loading criterion, the following results were revealed: a) items for product
information (I1, 2, 4, 6, and 7) loaded on factor one, ranging from -.440 to -.756; b) items
for annoyance/irritation (A1, 2, 3, & 4), materialism (M1, 2, 3, & 5), and falsity/no sense
(F1, 2, 3, & 5), ranging from .414 to .738, were all merged on factor two; c) items for
social role and image (S1, 2, 3, 4, 5, 7, 8, & 11) were merged on factor three, ranging
from -.436 to -.838; d) items for hedonism/pleasure (H1, 2, & 4) were merged on factor
four, ranging from -.557 to -.734; e) items for good for the economy (G1 & 2) were
merged on factor five, ranging from .612 to .657 (see Table 5.9).
The remaining items either had a factor loading on a proposed factor slightly
below .40 (i.e., G4), loaded on other factors (i.e., I3, I5, I8, S6, S10, G3, G5, and F4), or
did not load on any factor (S9, H3, & G6) (see Table 5.9). Five items (S9, H3, G4, G6, &
F4) had low item-to-total correlation scores. Ten items (I3, I5, I8, S6, S9, S10, G5, G6,
M4, & F4) were deleted based on the results of the reliability tests and/or the EFA.
108
Table 5.9
Factor Pattern Matrix
Factor*
1
2
3
4
5
6
7
I1
-.756ª
-.053
-.029
.243
.029
-.059
-.171
I2
-.646ª
.098
-.062
.197
-.100
-.012
-.041
I3
-.094
-.046
.019
-.127
.064
.711
-.033
I4
-.548ª
.113
-.147
.163
-.002
-.316
.001
I5
-.125
-.063
-.013
-.129
.167
.512
-.057
I6
-.440
-.073
-.043
-.087
-.007
.219
-.165
I7
-.449ª
.080
.121
-.022
-.134
-.248
.106
I8
-.187
.068
-.040
-.123
.012
.657
-.074
S1
-.019
.052
-.838
-.118
-.152
.017
-.107
S2
.149
.170
-.544ª
-.168
-.182
-.215
.072
S3
-.089
.052
-.472
-.034
.067
.257
.204
S4
.180
-.020
-.804ª
.111
.067
.095
.108
S5
-.136
-.158
-.436
.032
.221
.040
-.239
S6
.638
.036
-.235ª
-.048
-.114
-.059
.085
S7
-.035
.106
-.770ª
.067
-.117
.103
.032
S8
.073
.105
-.727
-.010
.005
.108
.263
S9
-.264
.056
-.273
.086
-.128
.282
.190
S10
-.496
-.037
-.303
.010
.062
.085
.069
S11
-.268
-.014
-.507
.191
.040
.215
-.069
H1
.241
-.043
-.098
-.734ª
.023
-.121
.002
H2
.019
.123
.170
-.657ª
-.054
-.196
-.043
H3
-.090
-.072
-.528
-.130
.037
.195
.088
H4
.025
.084
.118
-.557ª
-.256
.045
.001
A1
.028
.519
.103
.365
.193
-.105
.027
A2
.133
.733
.093
.142
.065
-.007
.018
A3
.116
.516
.207
.050
.235
.036
-.243
A4
.080
.680
.096
.064
.065
.067
.056
G1
.041
-.037
.076
.224
.612ª
-.063
-.004
G2
-.179
.004
.125
-.021
.657
.137
-.016
G3
.126
-.572
-.162
-.209
.340
.026
.015
G4
.061
.010
-.329
.040
.374
.208
.164
G5
-.433
-.141
-.207
.025
.217
.070
.119
G6
-.111
.142
-.091
-.047
.291
-.029
.335
M1
-.193
.423
-.159
-.019
-.031
-.211
-.396
M2
.158
.414
-.107
-.112
-.160
.051
-.347
M3
.155
.500
-.223
-.108
-.060
.186
-.379
M5
-.038
.522
.081
.097
-.247
.082
-.149
F1
.029
.697
-.052
-.073
-.096
-.241
.045
F2
-.001
.738
-.028
-.006
.070
-.036
.034
F3
.077
.557
-.180
.143
-.120
.062
.199
F4
.096
.108
.115
-.007
-.101
-.446
-.254
F5
.024
.619
.046
-.125
-.026
-.268
-.117
ª Items which were conveniently transformed to a positive skewness (+) were replaced by their original
directions.
Item
Note: ‘I’ (product information); ‘S’ (social role and image); ‘H’ (hedonism/pleasure); ‘A’
(annoyance/irritation); ‘G’ (good for the economy); ‘M’ (materialism); ‘F’ (falsity/no sense).
109
Model respecification
In terms of the convergence of items for annoyance/irritation (A1, A2, A3, &
A4), materialism (M1, M2, M3, & M5), and falsity/no sense (F1, F2, F3, & F5) on one
factor, nested model comparisons were made to determine if the model should be respecified. First, the proposed three-factor model was identified as a baseline model based
on the theoretical background (e.g., Bauer & Greyser, 1968; Korgaonkar, Karson, &
Akaah, .1997; Pollay & Mittal, 1993) (see Figure 5.1, Model A). Next, four plausible
rival models relating to a nested sequence with the baseline model were considered (See
Figure 5.1, Model B ~ E). Finally, direct comparisons between the baseline model and its
competing models were investigated by a χ² difference test (Bagozzi & Phillips, 1982)
and a comparative fit index using CFA.
The results in Table 5.10 show that the χ² differences between the baseline model
and nested models ranged 23.46 to 100.34; the differences in degrees of freedom ranged
from two to three. Considering the critical values for χ² with two and three degrees of
freedom (i.e., 5.99 and 7.81), there were significant differences between the baseline
model and all nested models. The comparative fit index (CFI) also suggested that the
baseline model had a better fit to the data than any other nested models. Accordingly, the
researcher concluded that three factors, annoyance/irritation, materialism, and falsity/no
sense should be retained as independent factors in subsequent analyses.
110
Model A: Three-factor model
A
M
Model B: Two-factor model (A+M)
A+M
F
A1 A2 A3 A4 M1 M2 M3 M5
F1 F2 F3 F5
A1 A2 A3 A4 M1 M2 M3 M5
Model C: Two-factor model (M+F)
A
F1 F2 F3 F5
F1 F2
F3
A+F
A1 A2 A3 A4 F1 F2
M
F3 F5
M1 M2 M3 M5
Model E: One-factor model (A+M+F)
A+M+F
A1 A2 A3 A4 M1 M2 M3 M5
F5
Model D: Two-factor model (A+F)
M+F
A1 A2 A3 A4 M1 M2 M3 M5
F
F1 F2 F3 F5
Figure 5.1. A baseline model and its nested models
Note: “A”, “M”, and “F” stand for annoyance/irritation, materialism, and falsity/no sense,
respectively.
111
Table 5.10
Results of Nested Model Comparisons
Goodness of fit
Model
Test of invariance
χ² (df)
p - value
χ²/df
CFI
χ² difference with
the baseline model
p - value
Three factor model
100.92
(51)
.00
1.98
.95
-
-
Two factor model
(A+M)
197.97
(53)
.00
3.74
.86
χ² (2) = 97.05
<.05*
Two factor model
(M+F)
161.98
(53)
.00
3.06
.90
χ² (2) = 61.06
<.05*
Two factor model
(A+F)
124.38
(53)
.00
2.35
.93
χ² (2) = 23.46
<.05*
One factor model
(A+M+F)
201.26
(54)
.00
3.73
.86
χ² (3) = 100.34
<.05*
* The critical value of χ² with two df is 5.99 at an alpha of .05; 7.81 with three df.
Conclusions
The results from the reliability tests and EFA indicated there were 15 problematic
items which violated the previously suggested guidelines for either the reliability tests
and/or the EFA. Table 5.11 shows a summary from both analyses of the15 items and the
decision to delete or retain an item for the second data collection.
First, Ten items (three item from product information, three items from social
role and image, two items form good for the economy, and one item from each of
materialism and falsity/no sense) which were added to the questionnaire after the pilot
study were deleted because of unsatisfactory results from reliability tests and/or the EFA.
Second, in terms of the item (H3), the results showed that the item had a low
item-to-total correlation and did not load on its conceptualized factor. This item seemed
to be a candidate for deletion. It should be recognized, however, that in the pilot study
the same item had a .72 item-to-total correlation and high loading (.71) value on the
proposed dimension. Such inconsistencies from replication efforts often occur because
the results from one sample may contain considerable capitalization on chance variations
112
in the data (Bagozzi & Yi, 1988; Kelloway, 1998). In addition, the item (H3) had an
inter-item correlation that exceeded the .30 of a rule of thumb (Robinson, Shaver, &
Wrightsman, 1991, as cited in Hair et al., 1998) and a high communality score (.57) from
the current results. The communality indicates how well the selected factors explain the
variance of the item. Fabrigar et al. (1999) mentioned that “…in some ways, the
communalities are more informative than the reliabilities regarding the soundness of the
EFA results” (p. 292). Accordingly, the decision was made to retain the item at this point
and to closely observe the replication of results in the next analysis with another sample.
Table 5.11
Summaries of outcomes for problematic items
Reliability test
Item
Item-to-total
correlation
Inter-item
correlation
EFA
Loading on the
proposed factor
Communality
Decision
-.09
-.13
-.19
-.24
-.27
-.30
-.13
.34
.37
.22
.29
.52
.56
.11
.67
.51
.65
.67
.40
.57
.57
.65
.44
.56
.27
.47
.43
.51
Deleted
Deleted
Deleted
Deleted
Deleted
Deleted
Retained
Retained
Retained
Deleted
Deleted
Deleted
Retained
Retained
Deleted
I3
I5
I8
S6
S9
S10
H3
G3
G4
G5
G6
M4¹
M5
F3
F4
>.50
>.30
>.50
>.30
>.50
>.30
>.50
>.30
<.50
<.30
>.50
>.30
<.50
>.30
<.50
>.30
>.50
>.30
>.50
>.30
<.50
>.30
<.50
<.30
<.50
>.30
<.50
>.30
<.50
<.30
¹ The item (M4) was not included in EFA.
Third, the two items (G3 & G4), were retained even though their factor loadings
(.34 and .37) did not exceed the criterion of .40, and one item (G3) had a .45 item-to-total
correlation. For each item, the inter-item correlations were larger than the suggested
criterion. Hair et al. (1998) also considered a factor loading of .30 as a minimum level.
Considering that the current study is an exploratory research, these two items were
113
retained.
The items M5 and F3 in materialism and falsity/no sense respectively were
considered problematic because they had low item-to-total correlations and loaded on the
same factor. The inter-item correlations were used as a guide regarding the two items in
spite of their weak item-to-total correlations. Regarding the convergence issue, the
results of the testing discussed in a previous section indicated that retaining materialism
and falsity/no sense as separate dimensions was reasonable when considering the model
fit. Hence, both items were included in the item pool for replication. The final decision
was to delete 10 of the problematic items (I3, I5, I8, S6, S9, S10, G5, G6, M4, & F4) and
retain the other five (H3, G3, G4, M5, & F3).
The second data collection
Objectives of the second data collection
Based on the results of the EFA, a seven-factor model of attitude toward
advertising through sport seemed reasonable and could be utilized for further analyses in
the second data collection. For more powerful estimations of structural equation
modeling, Anderson and Gerbing’s (1988) two-step approach was employed in the
second data collection. A sequence of measurement models through a series of CFAs
(e.g., the overall goodness-of-fit, reliability, and validity) was first assessed to select a
model providing the best fit to the data. The full hypothesized model incorporating both
the structural model of latent variables and the measurement model was then assessed to
estimate parameters of interest.
In addition, the results obtained from the full latent path analysis model were
validated through a cross validation test in order to confirm the predictive ability of the
proposed model of attitude toward advertising through sport. LISREL 8.72 (Jöreskog &
Sörbom, 2005) was utilized to compute the CFAs, the full latent variable path analysis,
and the cross validation analysis for the second data collection.
A larger sample (n = 443) was conveniently obtained from the same population in
the second data collection. After excluding 19 incomplete questionnaires, 424
questionnaires were equally split into two groups; the first half (n=212) was used as a
calibration sample, and the second half (n=212) was used as a validation sample in order
to develop and validate the hypothesized model.
114
Assessment of measurement model
Characteristics of sample data. The first split sample (a calibration sample,
n=212) was utilized to develop the best fitting model to data. The sample size was
deemed appropriate for obtaining proper results from SEM (Anderson & Gerbing, 1984;
1988; Hair et al., 1998; Kelloway, 1998).
Six demographic characteristics, gender, school year, ethnic background, time
watching sports games in a day, frequency participating in sports activity in a month, and
frequency purchasing sporting goods in a month are reported in Table 5.12. There were
more female than male respondents, and the highest percentage of students responding
were juniors. The majority of respondents were white/Caucasian.
Table 5.12
Demographic Characteristics of the Sample
Demographic Variables
Male
Female
Gender
Total
School year
Total
Ethnic background
Freshman
Sophomore
Junior
Senior
Graduate
Other
Black/African American
Native American
Latino/Latina
White/Caucasian
Asian or Pacific Islander
Other
Total
Frequency
102
110
212
1
21
136
53
0
1
212
23
0
13
153
16
7
212
Percent
48.1
51.9
100.0%
.5
9.9
64.2
25.0
.0
.5
100.0%
10.8
.0
6.1
72.2
7.5
3.3
100.0%
Table 5.13 summarizes the respondents’ behaviors relative to watching sports
games, participating in sports activity, and purchasing sport merchandise. Approximately
half (51.4%) watched sports games more than 30 minutes in a typical day. The majority
of the respondents participated in various sports activities at least once a month (75.9%)
115
and purchased sports merchandise six times or less (93.8%) in a month.
Table 5.13
Sports-Related Behaviors of the Sample
Behavioral Variables
0 minute
1-30 minutes
Time watching
31-60 minutes
sports games in a
61-90 minutes
typical day
91-120 minutes
More than 120 minutes
Total
Never
Frequency
Less than once
participating in
1-4 times
sports activity in a
5-10 times
month
11-20 times
More than 20 times
Total
Never
Less than once
Frequency
1-3 times
purchasing sporting
4-6 times
goods in a month
7-9 times
10 times or more
Total
Frequency
31
72
58
18
17
16
212
15
36
56
29
42
34
212
36
86
62
15
12
1
212
Percent
14.6
34.0
27.4
8.5
8.0
7.5
100.0%
7.1
17.0
26.4
13.7
19.8
16.0
100.0%
17.0
40.6
29.2
7.1
5.7
.5
100.0%
Purification of the measure. A total of 36 attitude and belief items were included
in the second data collection. In this stage, the internal consistency of seven belief
dimensions was assessed for the purpose of data purification. A Cronbach’s alpha score
of .70 and a corrected item-to-total correlation value of .50 were used as cut-offs for data
reduction. The attitude toward advertising through sport construct with three items still
had a good Cronbach’s alpha coefficient of .91. The corrected item-to-total correlations
ranged from .72 to .87. Thus, all items were finalized for measures of attitude toward
advertising through sport (see Table 5.14).
116
Table 5.14
Reliability Estimates for Global Attitude
Global attitude item
Corrected itemtotal correlation
Alpha if
item deleted
.86
.83
.87
.82
.72
.94
My general opinion of advertising through sport is favorable
(Att1).
Overall, I consider advertising through sport a good thing (Att2).
Overall, do you like or dislike advertising through sport (Att3).
Cronbach’s α coefficient for product information = .91
For the personal belief dimensions, a total of 21 items were tested, five items for
product information, eight items for social role and image, four items for
hedonism/pleasure, and four items for annoyance/irritation. The Cronbach’s alpha
coefficients for the four personal belief dimensions ranged from .82 to .85; the corrected
item-to-total correlation coefficients ranged from .49 to .77 (see Table 5.15). The results
of internal consistency tests revealed that the Cronbach’s alpha values for personal belief
constructs were acceptable, but the item-to-total correlation of one item (S8 in social role
and image) fell slightly short of the .50 suggested cut-off value.
For the societal belief dimensions, a total of 12 items were tested, four items for
good for the economy, four items for materialism, and four items for the falsity/no sense
were assessed for reliability. Cronbach’s alpha coefficients for the three societal
dimensions ranged from .72 to .83; the corrected item-to-total correlation coefficients
ranged from .31 to .73 (see Table 5.16). The results of reliability tests for three societal
belief dimensions indicated that alpha values of three dimension were greater than .70;
but item-to-total correlations of two items did not meet the cut-off value, .50 (G3 in good
for the economy and M4 in materialism).
Three items (S8, G3, and M4) in Table 5.15 and 5.16 which did not meet the .50
cut off value in the seven belief dimensions were deleted from the item pool.
Accordingly, three items assessing attitude toward advertising through sport and 30 items
measures the seven belief dimensions were included in the measurement and structural
models.
117
Table 5.15
Reliability Estimates of Personal Belief Constructs
Personal belief construct
Corrected
item-total
correlation
Alpha if
item
deleted
.69
.71
.69
.63
.64
.82
.81
.82
.84
.83
.59
.54
.81
.81
.50
.82
.63
.80
.53
.65
.54
.81
.80
.81
.49
.82
.64
.76
.65
.74
.84
.79
.84
.80
.70
.77
.61
.54
.76
.73
.81
.82
Product information
tells me which brands have the features I am looking for (I1).
helps me keep up-to-date about products available in the marketplace (I2).
is a good source of product information (I3).
Provides timely information (I4).
makes product information immediately accessible (I5).
Cronbach’s α coefficient for product information = .85
Social role and image
helps me learn about fashions and about what to buy to impress others (S1).
tells me what people with life styles similar to mine are buying and using (S2).
helps me know which products will or will not reflect the sort of person I am
(S3).
helps me learn what is in fashion and what I should buy for keeping a good social
image (S4).
lets me see what brands other people use (S5).
gives me ideas about fashion (S6).
gives me ways to act (S7).
gives me a good idea about products by showing the kinds of people who use
them (S8).
Cronbach’s α coefficient for social role and image = .83
Hedonism/pleasure
is often amusing and entertaining (H1).
is enjoyable (H2).
gives me pleasure when I think about what I saw or heard or read (H3).
is sometimes even more enjoyable than other media contents (H4).
Cronbach’s α coefficient for hedonism/pleasure = .85
Annoyance/irritation
is annoying (A1).
is irritating (A2).
often interrupts programs just when I am getting interested (A3).
is intrusive (A4).
Cronbach’s α coefficient for annoyance/irritation = .82
118
Table 5.16
Reliability Estimates of Societal Belief Constructs
Societal belief construct
Corrected
item-total
correlation
Alpha if
item deleted
.61
.56
.31
.56
.59
.62
.76
.62
.63
.63
.53
.63
.69
.63
.38
.77
.73
.67
.57
.68
.76
.78
.82
.78
Good for the economy
in general helps our nation’s economy (G1).
Generally helps the local economy (G2).
is usually a waste of economic resources (G3).
helps raise our standard of living (G4).
Cronbach’s α coefficient for good for the economy = .72
Materialism
is making us a materialistic society, overly interested in buying and owning
things (M1).
influences people to buy things they do not really need (M2).
makes people live in a world of fantasy (M3).
leads to a waste of natural resources by creating desires for unnecessary goods
(M4).
Cronbach’s α coefficient for materialism = .75
Falsity/no sense
in general is misleading (F1).
often insults the intelligence of the average consumer (F2).
is confusing (F3).
is deceptive (F4).
Cronbach’s α coefficient for falsity/no sense = .83
Preliminary analysis. Before considering the measurement models, the current
data was screened for the purposes of inspections of problematic observations and
normality of measures. Descriptive statistics including mean, standard deviation, range,
and frequency showed that there were no outliers and invalid data resulting from invalid
responses or input error.
For the assessments of normality in observed variables, each observed variable
was tested using skewness and kurtosis with the same procedures as in the first data
collection. First, each variables statistic value and standard error of both skewness and
kurtosis was calculated. Then, each statistical value of skewness and kurtosis was
divided by the standard errors of skewness (.167) and kurtosis (.333); computed z-scores
of skewness and kurtosis for each observed variable are reported in Table 5.17.
119
Table 5.17
Descriptive Statistics of the Belief Items
Items
Mean
S.D.
Skewness
Statistic
Z-scoreª
Kurtosis
Statistic
Z-scoreª
Product information 1 (I1)
Product information 2 (I2)
Product information 3 (I3)
Product information 4 (I4)
Product information 5 (I5)
Social role and image 1 (S1)
Social role and image 2 (S2)
Social role and image 3 (S3)
Social role and image 4 (S4)
Social role and image 5 (S5)
Social role and image 6 (S6)
Social role and image 7 (S7)
Hedonic/pleasure 1 (H1)
Hedonic/pleasure 2 (H2)
Hedonic/pleasure 3 (H3)
Hedonic/pleasure 4 (H4)
Annoyance/irritation 1 (A1)
Annoyance/irritation 2 (A2)
Annoyance/irritation 3 (A3)
Annoyance/irritation 4 (A4)
Good for the economy 1 (G1)
Good for the economy 2 (G2)
Good for the economy 4 (G4)
Materialism 1 (M1)
Materialism 2 (M2)
Materialism 3 (M3)
Falsity/no sense 1 (F1)
Falsity/no sense 2 (F2)
Falsity/no sense 3 (F3)
Falsity/no sense 4 (F4)
4.71
1.26
-.629
-3.77*
.332
1.00
5.18
1.13
-.782
-4.68*
.658
1.98
4.83
1.24
-.526
-3.15*
-.148
-.44
4.35
1.03
-.360
-2.16
.748
2.25
4.52
1.25
-.560
-3.35*
.183
.55
3.75
1.37
-.195
-1.17
-.376
-1.13
4.25
1.23
-.588
-3.52*
-.056
-.17
3.65
1.37
-.175
-1.05
-.623
-1.87
4.30
1.25
-.328
-1.96
-.324
-.97
4.70
1.04
-.561
-3.36*
.883
2.65*
4.22
1.24
-.745
-4.46*
.341
1.02
3.13
1.39
-.185
-1.11
-1.084
-3.26*
5.54
1.22
-1.166
-6.98*
2.107
6.33*
5.19
1.16
-.661
-3.96*
1.046
3.14*
4.52
1.28
-.131
-.78
.348
1.05
5.05
1.26
-.610
-3.65*
.269
.81
3.05
1.42
.486
2.91*
-.387
-1.16
2.94
1.32
.541
3.24*
.049
.15
3.62
1.61
.086
.51
-.800
-2.40
3.20
1.28
.160
.96
.035
.11
4.50
1.13
-.120
-.72
.454
1.36
4.18
1.12
-.226
-1.35
.667
2.00
4.16
1.05
-.119
-.71
.176
.53
4.31
1.43
.004
.02
-.821
-2.47
5.06
1.29
-.484
-2.90*
.003
.01
4.20
1.36
.017
.10
-.409
-1.23
3.27
1.35
.558
3.34*
-.097
-.29
3.18
1.34
.452
2.71*
-.188
-.56
2.76
1.23
.698
4.18*
.232
.70
3.69
1.47
.316
1.89
-.523
-1.57
ª The z-scores are calculated by diving the statistics by the standard errors of .167 (skewness) and .333 (kurtosis).
* Significant at the .01 probability level.
Several items rejected the hypothesis of the normality of the distribution at
the .01 probability level: I1, I2, I3, and I5 in product information, S2, S5, S6, and S7 in
social role and image, H1, H2, and H4 in hedonism/pleasure, A1 and A2 in
annoyance/irritation, M2 in materialism, and F1, F2, F3 in falsity/no sense (see Table
5.17). The non-normality of problematic items was treated with data transformations.
Data transformations. The non-normally distributed data in each variable was
120
repaired by taking the square root on scores in order to change the shape of the
distribution (Hair et al., 1998; Kline, 1998; West, Finch, & Curran, 1995). The
negatively skewed distributions were, first, modified to positive skewness by subtracting
every score from one greater than their highest score (i.e., 8); however, one greater than
the highest score in S7 was seven because its scores ranged from one to six. The scores
in all problematic items were transformed by taking the square root (e.g., Gorsuch, 1983).
Table 5.18 shows the comparisons between original values and transformed
values of 17 previously problematic items. The previously significant items in terms of
skewness and kurtosis on the null hypothesis that scores were normally distributed were
no longer significant at the probability level .01 after transformations.
Table 5.18
Distribution Values Before and After Transformation
Original values
Item
Skewness
statistic
z-score³
Transformed values
Kurtosis
statistic
Skewness
z-score³
statistic¹
z-score³
Kurtosis
statistic¹
z-score³
-3.77*
1.00
-.629
.332
.078
.47
-.029
-.09
-4.68*
1.98
-.782
.658
.193
1.16
.071
.21
-3.15*
-.44
-.526
-.148
.048
.29
-.345
-1.04
-3.35*
.55
-.560
.183
.029
.17
.035
.11
-.588
-3.52*
-.056
-.17
.189
1.13
-.257
-.77
-.561
-3.36*
.883
2.65*
-.084
-.50
.809
2.43
-.745
-4.46*
.341
1.02
.275
1.65
.173
.52
-.185
-1.11
-1.084
-3.26*
-.119²
-.71
-.817²
-2.45
-1.166
-6.98*
2.107
6.33*
.427
2.56
.173
.52
-.661
-3.96*
1.046
3.14*
-.020
-.12
.056
.17
-.610
-3.65*
.269
.81
.025
.15
-.209
-.63
.486
2.91*
-.387
-1.16
.016
.10
-.698
-2.10
.541
3.24*
.049
.15
-.011
-.07
-.442
-1.33
-.484
-2.90*
.003
.01
-.064
-.38
-.424
-1.27
.558
3.34*
-.097
-.29
.065
.39
-.393
-1.18
.452
2.71*
-.188
-.56
-.071
-.43
-.396
-1.19
.698
4.18*
.232
.70
.171
1.02
-.401
-1.20
¹ The statistics of skewness and kurtosis for all items were computed by taking the square root transformation after
subtracting every score, except A1, A2, F1, F2, and F3, from a constant that is one greater than the higher score (i.e., 8).
² The statistics of skewness and kurtosis for S7 were computed by taking the square root transformation after
subtracting its scores from 7 because scores of the item ranged from 1 to 6.
³ The z-scores are calculated by diving the statistics by the standard errors of .167 (skewness) and .333 (kurtosis).
* Significant at the .01 level.
I1
I2
I3
I5
S2
S5
S6
S7
H1
H2
H4
A1
A2
M2
F1
F2
F3
121
Confirmatory factor analysis (CFA) for the initial measurement model
Overview. In the current section, the study deals with the operations of the
measurement model with seven belief dimensions about advertising through sport. Two
CFAs were conducted to estimate and test the initial and alternative measurement models
in terms of the overall model fit, reliability, and validity. The study followed Bollen and
Long’s (1993) procedures of CFA with five steps (as cited in Kelloway, 1998) for results
of each measurement model: model specification, identification, estimation, testing
model fit, and respecification.
Model specification. The researcher first specified the initial hypothesized
measurement model with seven latent variables. Based on results from the first data
collection and the data purification for the current sample, appropriate indicators to each
latent variable were identified. Figure 5.2 shows the relationships between seven latent
variables and 30 indicators in the proposed initial measurement model: production
information with five indicators, social role and image with seven indicators,
hedonism/pleasure with four indicators, annoyance/irritation with four indicators, good
for the economy with three indicators, materialism with three indicators, and falsity/no
sense with four indicators. Seven latent variables are assumed to be standardized (i.e.,
each latent variable has one variance) and related to each other; thus, their covariances
represent correlations. Each indicator is also caused by a unique factor (i.e.,
measurement error), and each unique factor is assumed to be uncorrelated with other
unique factors. The unique factors are not presented in Figure 5.2.
Model identification. One of most important rules to identify the measurement is
“t-rule”. When the number of variances and covariances of the observed variables are
equal or greater than the number of model parameters to be estimated, the “t-rule” is
accepted. In the initial measurement model with 30 observed variables, the number of
variances and covariances of observed variables are 465 (p[p+1]/2, where p is the number
of observed variables); the number of parameters to be estimated is 81 (30 factor loadings,
21 covariances among latent variables, and 30 measurement error variances). Thus, the
current model is deemed over identified; the degrees of freedom were 384.
122
I1
I2
I3
Product
information
I4
I5
S1
S2
S3
Social role
and image
S4
S5
S6
S7
H1
H2
H3
Hedonism/
pleasure
H4
A1
A2
Annoyance/
irritation
A3
A4
G1
G2
Good for the
economy
G4
M1
M2
Materialism
M3
F1
F2
F3
Falsity/
no sense
F4
Figure 5.2. The initial hypothesized measurement model for the latent variables with
multiple indicators
123
In addition, Bollen (1989) suggested two rules for model identification: (a) the
model is identified when each latent variable has at least three indicators; (b) the model is
identified both when each latent variable has two indicators and is assumed to be
uncorrelated with other latent variables. However, measurement errors are still
uncorrelated with each other under the two rules. The study concluded that the initial
measurement model met all requirements for the model identification and was ready for
estimating parameters of the proposed model.
Model estimation. The maximum likelihood technique which has been the most
popular technique for estimation in SEM was utilized to estimate parameters by
minimizing the fitting function in the initial model. The standardized estimates from the
output were interpreted and reported in Table 5.19. First, the R² values represent the
amount of variance of each observed variable explained by the latent variable. The R²
values for each observed variable ranged from .48 to .59 in product information; .23
to .56 in social role and image; .52 to .74 in hedonism/pleasure; .40 to .83 in
annoyance/irritation; .44 to .59 in good for the economy; .49 to .63 in materialism; .40
to .71 in falsity/no sense.
The factor loadings ranged from .69 to .77 in product information; .48 to .75 in
social role and image; .72 to .86 in hedonism/pleasure; .64 to .81 in
annoyance/irritation; .66 to .77 in good for the economy; .70 to .79 in materialism; .63
to .84 in falsity/no sense. Table 5.20 shows the correlation values among the seven latent
variables. The absolute correlation values among the seven belief dimensions ranged
from .07 to 91. The correlation between annoyance/irritation and falsity/no sense was
very high (γ = .91). One interesting finding is that the matrix revealed a positive
correlation between social role and image and materialism (i.e., .24) which was
hypothesized as a negative relationship from literature review.
124
Table 5.19
Factor Loadings, Standard Errors, and R² of Observed Variables in the Initial Model
Latent variable
Product
information
Social role and
image
Hedonism/
pleasure
Annoyance/
irritation
Good for the
economy
Materialism
Falsity/no sense
Observed
variable
Completely
standardized loading
Completely standardized
error variance
R²
I1
I2
I3
I4
I5
S1
S2
S3
S4
S5
S6
S7
H1
H2
H3
H4
A1
A2
A3
A4
G1
G2
G4
M1
M2
M3
F1
F2
F3
F4
.77*
.76*
.74*
.69*
.69*
.69*
.58*
.48*
.75*
.54*
.74*
.57*
.72*
.86*
.76*
.81*
.80*
.81*
.66*
.64*
.77*
.73*
.66*
.70*
.70*
.79*
.84*
.73*
.63*
.77*
.41
.42
.45
.52
.53
.53
.67
.77
.44
.71
.45
.68
.48
.26
.42
.35
.36
.17
.57
.60
.41
.46
.56
.51
.51
.37
.29
.46
.60
.41
.59
.58
.55
.48
.48
.48
.33
.23
.56
.29
.55
.32
.52
.74
.58
.65
.64
.83
.44
.40
.59
.54
.44
.49
.49
.63
.71
.54
.40
.59
* Significant at the .05 probability level.
125
Table 5.20
The Correlations among Seven Latent Variables in the Initial Model
Product
Social role
information and image
Hedonism/ Annoyance/ Good for the
Materialism
pleasure
irritation
economy
Product
information
1
Social role
and image
.49*
(.24)¹
1
Hedonism/
pleasure
.69*
(.48)
.46*
(.21)
1
Annoyance/
irritation
-.49*º
(.24)
-.30*º
(.09)
-.62*º
(.38)
1
Good for the
economy
.54*º
(.29)
.33*º
(.11)
.38*º
(.14)
-.15
(.02)
1
Materialism
-.10
(.01)
.24*
(.06)
-.12
(.01)
.47*º
(.22)
.07º
(.01)
1
Falsity/no
sense
-.42*º
(.18)
-.09º
(.01)
-.44*º
(.19)
.91*
(.83)
-.16*
(.03)
.60*º
(.36)
Falsity/
no sense
1
* Significant at the .05 probability level.
º The directions shown in the output were replaced by their original directions.
¹ The parenthesis represents the squared correlation.
Testing Model Fit. The most important reason for testing a measurement model
is to assess how the proposed model fits to the data. LISREL provides a variety of fit
indexes; various indexes can be sorted by three categories: absolute fit, comparative fit,
and parsimonious fit (Kelloway, 1998). Unlike absolute and comparative fit,
parsimonious fit indexes have no standard to determine whether a model fits well to the
data (Kelloway, 1998). Accordingly, the results of goodness-of-fit of the model were
reported by both absolute and comparative fit indexes (see Table 5.21).
The goodness-of-fit tests represent the overall adequacy of the model but do not
guarantee that each construct in the model is valid and reliable (Bagozzi & Yi, 1988).
Thus, the internal structures of the model were also evaluated in terms of reliability and
validity along with the global measures of fit.
126
Table 5.21
The Assessment of Model Fit in the Initial Model
Test of model fit
Absolute fit
Fit index
Value
χ²
df
942.59
384
p-value
.00
>. 05 or .01
χ²/df
2.45
1.0 ~ 2.0 (Hair et al., 1998)
< 2.0 (Byrne, 1989)
< 3.0 (Kline, 1998)*
RMSEA
.083ª
< .06 (Hu & Bentler, 1999)
< .10 (Steiger, 1990)
SRMR
.078
< .05 (Kelloway, 1998)
< .08 (Hu & Bentler, 1999)
< .10 (Kline, 1998)
GFI
.77
> .90 (Kelloway, 1998; Kline,
1998)
NFI
.90
> .90 (Bentler & Bonett, 1980;
Kelloway, 1998; Kline,
1998; Hair et al., 1998)
IFI
.94
> .95 (Hu & Bentler, 1999)
CFI
.94
> .95 (Hu & Bentler, 1999)
> .90 (Kelloway, 1998; Kline,
1998)
RFI
.88
> .90 (Kelloway, 1998)
Comparative fit
The common rule of thumb
-
ª The p-value of the null that RMSEA < .05 was .00.
* Klein (1998) indicated that a ratio value less than 3.0 may be applied when a large sample set was
analyzed; “In small samples, a χ²/df ratio of, say, 2.5, may arise even if the overall fit of the model is poor”
(p. 131).
Absolute fit. Table 5.21 shows that the chi-square statistic (χ² = 942.59, df = 384)
resulted in a conclusion to reject the null hypothesis that the proposed model is correct at
the .05 probability level. However, the χ² statistic is very sensitive to a sample size; the χ²
statistic is typically significant when a sample size is large. Thus, it is usually
recommended that the ratio of χ² statistic to the degrees of freedom (χ²/df) is also used as
an indicator of global fit of the model in order to reduce the sensitivity of the χ² statistic
127
to a sample size (Kelloway, 1998). The initial model had the χ²/df, 2.45, which only met
the Kline’s (1998) suggested criterion (see Table 5.21).
The reported root mean square residual of approximation (RMSEA) value
(i.e., .083) met the criterion suggested by Steiger (1990) but did exceed the recommended
value by Hu and Bentler (1999). In addition, the p-value for the test of close fit that the
RMSEA is less than .05 was .00: the null hypothesis was rejected. The standardized root
mean square residual (SRMR) was deemed acceptable to the criteria established by Kline
(1998) and Hu and Bentler (1999). Lastly, goodness of fit index (GFI) did not meet the
suggested criterion, .90 (see Table 5.21).
Comparative fit. Three reported comparative fit indexes, the normed fit index
(NFI), incremental fit index (IFI), and comparative fit index (CFI) met the .90 criterion
recommended by several researchers (Bentler & Bonett, 1980; Hair et al., 1998;
Kelloway, 1998; Kline, 1998); the values of IFI and CFI were less than Hu and Bentler’s
(1999) .95 cut-off. (see Table 5.21). The relative fit index (RFI) also did not surpass
the .90 cut-off (Kelloway, 1998).
Reliability tests. Table 5.22 shows the results of four independent reliability tests
employed to measure the adequacy of internal structural of the initial model: Cronbach’s
alpha, individual item reliability, composite reliability, and the average variance extracted
(AVE) scores. First, the Cronbach’s alphas scores for the seven belief constructs ranged
from .76 to .86; all alphas exceeded the suggested criterion, .70 (Leong & Austin, 1996;
Nunnally & Bernstein, 1994). Second, the individual item reliabilities (R²) were
measured to assess whether indicators were representative of their constructs; the R²
values ranged from .23 to .83. Event though there have been no rules-of-thumb as to the
cut-off criteria (Bagozzi & Yi, 1988), several items (S2, S3, S5, S7, A4, and F3, ranging
from .23 to .40) seemed problematic (see Table 5.19 and 5.22). Third, the composite
reliability of measures of the seven belief constructs (i.e., construct reliability named by
Fornell and Larcker, 1981) satisfied all suggested values of .60 (Bagozzi, & Yi, 1988)
and .70 (Hair et al., 1998). Lastly, the average variance extracted (AVE) scores were
computed for each construct, based on the equation defined by Fornell and Larcker
(1981). The AVE scores ranged from .39 to .62; the AVE value for social role and image
(.39) failed to exceed the .50 suggested value (Fornell & Larcker, 1981).
128
Table 5.22
Reliability of Seven Latent Variables in the Initial Measurement Model
Latent Variables
α*
Composite
reliability
Average variance
extracted (AVE)
Individual item
reliability (R²)ª
Product information
.86
.85
.53
.48 ~ .59
Social role and image
.82
.82
.39
.23 ~ .56
Hedonism/pleasure
.86
.87
.62
.52 ~ .74
Annoyance/irritation
.83
.83
.58
.40 ~ .83
Good for the economy
.76
.77
.52
.44 ~ .59
Materialism
.77
.78
.54
.49 ~ .63
Falsity/no sense
.83
.83
.56
.40 ~ .71
* The alpha values were calculated by the original scores (i.e. untransformed scores).
ª For more detail values, see Table 5.19.
Validity tests. Two validity tests were performed to evaluate the meaning of
measures toward what they are expected to measure, convergent validity and discriminant
validity.
Convergent validity. The tests for convergent validity included the evaluation of
significance of factor loadings, AVE scores, and the standardized residual matrix. First,
all loading were significant at the .05 probability level indicating that each loading is
greater than twice its standard error (Anderson & Gerbing, 1988). However, the
loading, .707 has been often utilized as a more conservative criterion for convergent
validity (e.g., James, Kolbe, & Trail, 2002): Table 5.19 shows that 13 items (43%) were
less than the .707 recommended loading (i.e. R² = .50), indicating that those items had
more unique variance than common variance (Klein, 1998). Second, the AVE of each
construct was also used for a determination of convergent validity because the AVE value
represents “the amount of common variance captured by the construct in relation to the
amount of variance due to measurement error” (Fornell & Larcker, 1981, p.45). The
range of AVE values for the seven constructs was .39 to .62: the AVE value of social role
and image was less than the suggested value of .50 (see Table 5.22), indicating more than
a half of its total variance was derived from measurement errors (Fornell & Larcker,
129
1981).
Lastly, Bagozzi and Yi (1988) suggested the standardized residual are a powerful
indicator of the internal structure of the model along with the overall fit of model.
Among a total of 465 standardized residuals, 50 residuals (10.8%) were greater than the
±2.58 cut-off criterion (i.e. the .01 probability level). Considering the guideline that only
five percent (i.e., 23.3 residuals) should be allowed to exceed ±2.58 (Hair et al., 1998),
the current number of problematic items was more than twice the guideline. In particular,
ten residuals exceeding ±2.58 were related with the S5 item in social role and image.
This information should be considered when the current model is respecified. The
standardized residual matrix for the initial measurement model was not reported in the
document.
Discriminant validity. Discriminant validity is usually assessed by the
correlation coefficients among latent variables and the AVE scores to determine the
extent to which the conceptually different constructs are distinct. Kline (1998) suggested
that when a correlation between two constructs is excessively high (e.g., > .85), their
indicators may not correctly measure what they are supposed to measure. Table 5.20
presents that all correlation coefficients seemed moderate in magnitude except the
correlation coefficient between annoyance/irritation and falsity/no sense (i.e., .91).
Accordingly, the study employed a more accurate method to assess discriminant validity
that AVE score for each construct could be applied to the correlations among constructs.
Fornell and Larcker (1981) indicated that when the AVE value for a construct is greater
than the squared correlations between the construct and other constructs, the construct is
discriminate. The range of the squared correlations among constructs was from .01 to .83
(see Table 5.20); the range of the AVE scores for the constructs was .39 to .62 (see Table
5.22). The tests failed to discriminate annoyance/irritation from falsity/no sense; the other
relations between AVE scores and correlations among constructs indicated discrimination.
Model respecification. The current seven factor model with 30 indicators needed
to be modified to provide the best fit to the data based on suggestions from the tests of
model estimations and fit of the internal structure. Two basic criteria for the
respecification were established: the elimination of indicators which resulted in poor fit to
the data and the retention of at least three indicators per construct (Bollen, 1989).
130
First, two items (I4 and I5) in product information were not presumed to
correctly measure the construct: factor loadings of two items did not surpass the
suggested .707 value, resulting in low individual reliability. Subsequently, the items were
removed for further analyses. Second, several items in social role and image failed to
provide evidences for convergent validity in terms of the AVE, factor loadings, and
standardized residual. Five items (S1, S2, S3, S5, and S7) had factor loadings which
were less than .707, resulting in the low AVE score for social role and image (i.e., .39).
Additionally, the tests of standardized residuals indicated that the elimination of the item,
S5, would improve the overall fit and internal fit of the model. Hence, the four poorest
performing items (S2, S3, S5, and S7) among five problematic items were deleted from
the construct as keeping the “three indicators per construct” rule; the S1 item (λ = .69),
was retained. Third, factor loadings of two items (A3 and A4) in annoyance/irritation
and one item (F3) in falsity/no sense did not meet the .707 criterion; the worse item, A4
(λ = .64) in annoyance/irritation and F3 (λ = .63) in falsity/no sense were eliminated from
both constructs. The item A3 (λ = .66) was retained in annoyance/irritation with the
existing two items. Lastly, all items in hedonism/pleasure showed good evidences for
reliability and validity; as a result, all items were retained in the construct. Meanwhile,
good for the economy and materialism had only three items in each construct. All items
in each construct were kept for further examinations even though three items, G4 (λ
= .66) in good for the economy, M1 (λ = .70), and M2 (λ = .70) in materialism did not
meet the .707 cut-off.
In addition, the initial measurement model failed to distinguish two constructs,
annoyance/ irritation and falsity/no sense due to their high correlation (γ = .91). Three
conditions were considered to solve this problem: (a) the integration of two constructs as
one construct; (b) the elimination of either construct; (c) the retention of two constructs.
First, the study considered the suggested conditions (a) and (c). There were two
plausible models: a two-factor model that annoyance/irritation and falsity/no sense are
distinct and a unidimensional model integrating two constructs (see Figure 5.3). In other
words, one model does not constrain the estimated correlation parameter between two
dimensions (i.e., a free parameter), and the other model constrains the estimated
correlation parameter between two dimensions to one.
131
Model B: One-factor model (γ AF = 1)
Model A: Two-factor model (γ AF = free)
Free
1
A
A1
A2
A
F
A3
F1
F2
A1
F4
A2
F
A3
F1
F2
F4
Figure 5.3. Constrained and unconstrained models for the chi-square difference test
Note: “A” and “F” represent annoyance/irritation and falsity/no sense, respectively.
Figure 5.3 shows that two models are in a nested sequence. All relationships
embodied in Model B are present in Model A. Model A has one more parameter
indicating the freely estimated correlation between two dimensions. When one model
(i.e., Model B) is nested in the other model (i.e., Model A), the direct comparison
between two models can be tested by a χ² difference test (Kelloway, 1998).
The results of the χ² difference test of two rival models are reported in Table 5.23.
When the chi-square of the unconstrained model (i.e., Model A) is significantly lower
than the chi-square of the constrained model (i.e., Model B), two dimensions are deemed
discriminate (Bagozzi & Phillip, 1982) The results showed that there was a significant
difference between two models at the .05 probability level: the difference of χ² statistics
was 20.42 with one degree of freedom. It was concluded that the two-factor model
provided the better model fit to data than the one-factor model, consequently, both factors
were retained as independent constructs.
The decision was made to retain both factors rather then eliminating one of the
two because the review of literature revealed that the measures in both constructs play
important roles in explaining attitude toward advertising so that the elimination of either
construct may causes the loss of significant information (e.g., Bauer & Greyser, 1968).
132
Therefore, the current seven factor model with the reduced number of indicators was
utilized for further analyses of the modified measurement model and the structural model.
Table 5.23
The χ² Difference Test of Two Rival Models
Goodness of fit
Model
Test of invariance
χ²
df
p-value
CFI
χ² difference with the
baseline model
p-value
Model A
15.64
8
.0478
.98
-
-
Model B
36.06
9
.0000
.98
Model B – Model A
χ² difference (1) = 20.42
<.05*
* The critical value of χ² with one degree of freedom is 3.84 at the .05 probability level.
Confirmatory factor analysis (CFA) for the modified measurement model
Model specification. After testing the initial measurement model, the model was
modified so that the total number of indicators was reduced from 30 to 22. Figure 5.4
shows the relationships between seven latent variables and 22 indicators in the modified
measurement model: production information with three indicators, social role and image
with three indicators, hedonism/pleasure with four indicators, annoyance/irritation with
three indicators, good for the economy with three indicators, materialism with three
indicators, and falsity/no sense with three indicators.
Model identification. The “t-rule” indicated that the number of variances and
covariances of the observed variables (i.e., 253) was greater than the number of model
parameters to be estimated (e.g., 65: 22 loadings, 22 measurement error covariances, and
21 covariances among constructs). Each latent variable also contained three indicators
(Bollen, 1989). Accordingly, the modified measurement model was identified with the
188 degrees of freedom
133
I1
I2
Product
information
I3
S1
S4
Social role
and image
S6
H1
H2
H3
Hedonism/
pleasure
H4
A1
A2
Annoyance/
irritation
A3
G1
G2
Good for the
economy
G4
M1
M2
Materialism
M3
F1
F2
Falsity/
no sense
F4
Figure 5.4. The modified measurement model for the latent variables with multiple
indicators
Model estimation. Maximum likelihood estimation was employed to estimate
parameters by minimizing the fitting function in the modified model. The standardized
estimates from the output were interpreted and are reported in Table 5.24. First, the R²
values for observed variables ranged from .58 to .63 in product information; .50 to .60 in
134
social role and image; .53 to .73 in hedonism/pleasure; .44 to .85 in annoyance/
irritation; .45 to .62 in good for the economy; .50 to .60 in materialism; .50 to .72 in
falsity/no sense. In terms of factor loadings of observed variables, the loading values
ranged from .76 to .80 in product information; .71 to .77 in social role and image; .73
to .86 in hedonism/pleasure; .66 to .92 in annoyance/irritation; .67 to .79 in good for the
economy; .71 to .78 in materialism; .71 to .85 in falsity/no sense.
Table 5. 24
Factor Loadings, Error Variance, and R² of Observed Variables in the Modified Model
Latent variable
Product
information
Social role and
image
Hedonism/
pleasure
Annoyance/
irritation
Good for the
economy
Materialism
Falsity/no sense
Observed
variable
Completely
standardized loading
Completely standardized
error variance
R²
I1
I2
I3
S1
S4
S6
H1
H2
H3
H4
A1
A2
A3
G1
G2
G4
M1
M2
M3
F1
F2
F4
.78*
.80*
.76*
.71*
.77*
.73*
.73*
.86*
.76*
.81*
.81*
.92*
.66*
.79*
.71*
.67*
.71*
.71*
.78*
.85*
.71*
.78*
.39
.37
.42
.50
.40
.44
.47
.27
.43
.35
.35
.15
.56
.38
.50
.55
.50
.49
.40
.28
.50
.39
.61
.63
.58
.50
.60
.54
.53
.73
.57
.65
.65
.85
.44
.62
.50
.45
.50
.51
.60
.72
.50
.61
* Significant at the .05 probability level.
Table 5.25 illustrates the correlation values among seven constructs. The
absolute correlations values among seven belief constructs ranged from .03 to 88. The
135
correlation between annoyance/irritation and falsity/no sense was still high (γ = .88).
The correlation between social role and image and materialism was still significant and
positive.
Table 5.25
The Correlations among Seven Latent Variables in the Modified Model
Product
Social role
information and image
Hedonism/ Annoyance/ Good for the
Materialism
pleasure
irritation
economy
Product
information
1
Social role
and image
.43*
(.18)ª
1
Hedonism/
pleasure
.65*
(.42)
.41*
(.17)
1
Annoyance/
irritation
-.47*º
(.22)
-.25*º
(.06)
-.60*º
(.36)
1
Good for the
economy
.47*º
(.22)
.28*º
(.08)
.38*º
(.14)
-.15
(.02)
1
Materialism
-.03
(.0009)
.31*
(.10)
-.12
(.01)
.45*º
(.20)
.09º
(.008)
1
Falsity/no
sense
-.38*º
(.14)
-.04º
(.002)
-.43*º
(.18)
.88*
(.77)
-.19*
(.04)
.64*º
(.41)
Falsity/
no sense
1
* Significant at the .05 probability level.
º The directions shown in the output were replaced by their original directions.
ª The parenthesis represents the squared correlation.
Testing Model Fit. In order to assess the overall fit of the modified model to the
data, first, various fit indexes were inspected from the aspects of absolute fit and
comparative fit (see Table 5.26). The internal structures of the current model were also
scrutinized by scientific criteria for reliability and validity of measures.
136
Table 5.26
The Assessment of Model Fit in the Modified Model
Test of model fit
Absolute fit
Fit index
Value
χ²
df
360.69
188
p - value
.00
>. 05 or .01
χ²/df
1.92
1.0 ~ 2.0 (Hair et al., 1998)
< 2.0 (Byrne, 1989)
< 3.0 (Kline, 1998)*
RMSEA
.066ª
< .06 (Hu & Bentler, 1999)
< .10 (Steiger, 1990)
SRMR
.064
< .05 (Kelloway, 1998)
< .08 (Hu & Bentler, 1999)
< .10 (Kline, 1998)
GFI
.87
> .90 (Kelloway, 1998; Kline,
1998)
NFI
.93
> .90 (Bentler & Bonett, 1980;
Kelloway, 1998; Kline,
1998; Hair et al., 1998)
IFI
.96
> .95 (Hu & Bentler, 1999)
CFI
.96
> .95 (Hu & Bentler, 1999)
> .90 (Kelloway, 1998; Kline,
1998)
RFI
.91
> .90 (Kelloway, 1998)
Comparative fit
The common rule of thumb
-
ª The p - value of the null that RMSEA < .05 was .0064.
* Klein (1998) indicated that a ratio value less than 3.0 may be applied when a large sample set was
analyzed; “In small samples, a χ²/df ratio of, say, 2.5, may arise even if the overall fit of the model is poor”
(p. 131).
Absolute fit. Table 5.26 illustrates that the test failed to reject the null hypothesis
that the population covariances matrix (Σ) is equal to the population reproduced
covariance matrix (Σθ) with the 360.69 χ² statistic (df = 188) at the .05 probability level.
However, due to the sensitivity of the χ² statistic to sample size, the ratio of χ² statistic to
the degrees of freedom is used as another indicator of global fit of the model in order to
137
reduce the sensitivity of the χ² statistic to sample size (Kelloway, 1998). The value of
χ²/df (i.e., 1.92) was considered acceptable (see Table 5.26).
The value of RMSEA (i.e., .066) was below the .10 criterion suggested by
Steiger (1990) but slightly exceeded the Hu and Bentler’s (1999) criterion (i.e., .06). In
addition, the test of close fit (p = .0064) rejected the null that the RMSEA is less at
the .05 probability level. The SRMR (i.e., .064) was adequate to meet the cut-off lines
recommended by Kline (1998) and Hu and Bentler (1999). Lastly, goodness of fit index
(GFI) did not satisfy the generally suggested criterion, .90; the .87 of GFI was at a
marginal acceptance level according to Hair at al. (1998) (see Table 5.26).
Comparative fit. Table 5.26 presents that values of both NFI and RFI surpassed
the .90 cut-off line. The IFI and CFI which did not exceed the .95 cut-off score suggested
by Hu and Bentler (1999) in the previous initial model were adequate to this criterion in
the modified model (see Table 5. 26).
Reliability. The same reliability tests used in the initial measurement model were
employed to assess the acceptance of internal structural of the modified measurement
model (e.g., Cronbach’s alpha, individual item reliability, composite reliability, and the
AVE). Table 5.27 summarizes the results of each reliability test.
First, the Cronbach’s alphas for seven latent variables ranged from .76 to .86; all
alphas seemed acceptable to the .70 criterion. Second, the individual item reliability (R²)
for each indicator ranged from .44 to .85; all indicators were deemed representative of
their constructs except A3 in annoyance/irritation and G4 in good for the economy (see
Table 5.24). Third, the composite reliability of measures of seven belief constructs (i.e.,
construct reliability, Fornell & Larcker, 1981), ranging from .77 to .87, exceeded all
suggested criteria, .60 (Bagozzi, & Yi, 1988), and .70 (Hair et al., 1998). Lastly, the
reported AVEs ranged from .52 to .65; all values were above the .50 suggested cut-off
(Fornell & Larcker, 1981).
Convergent validity. The tests for convergent validity included evaluating the
significance of factor loadings, AVE scores, and the standardized residual matrix. First,
all loadings were significant at the .05 probability level indicating that each loading was
greater than twice its standard error (Anderson & Gerbing, 1988). However, two
indicators, A3 (λ = .66) and G4 (λ = .67) had the loadings which were less than the .707
138
value, indicating that two indicators contained more unique variance than common
variance (see Table 5.24). Second, the AVE scores of the seven constructs were used for
the determination of convergent validity; they ranged from .52 to .65. Table 5.27 shows
that all AVE scores met the suggested value of .50 (Fornell & Larcker, 1981), and it was
concluded that all latent variables had more variance explained by the construct than the
variance derived from the measurement errors.
Table 5.27
Reliability of Seven Latent Variables in the Modified Model
Latent Variables
α*
Composite
reliability
Average variance
extracted (AVE)
Individual item
reliability (R²)ª
Product information
.83
.83
.61
.58 ~ .63
Social role and image
.79
.78
.55
.50 ~ .60
Hedonism/pleasure
.86
.87
.62
.53 ~ .73
Annoyance/irritation
.82
.84
.65
.44 ~ .85
Good for the economy
.76
.77
.52
.45 ~ .62
Materialism
.77
.78
.54
.50 ~ .60
Falsity/no sense
.83
.82
.61
.50 ~ .72
* The alpha values were calculated by the original scores (i.e. untransformed scores).
ª For more detail information, see Table 5.24.
Lastly, the standardized residuals matrix was utilized as an indicator of covergent
validity (Bagozzi & Yi, 1988). Among a total of 253 standardized residuals, 28 residual
(11%) were greater than the cut-off criterion, ±2.58 (Hair et al., 1998). The current
percentage of problematic residuals was greater than twice the 5% tolerance (Hair et al.,
1998). The most problematic item was (F2) in falsity/no sense which was related with
five problematic standardized residuals. The proportion of largest positive and negative
standardized residuals in the modified model (11%) was approximately same with those
in the initial model (i.e., 10.8%) (see Appendix F for more information).
Discriminant validity. Discriminant validity is usually assessed by the
139
correlation coefficients among constructs and AVE values. Table 5.25 illustrates that the
correlation between annoyance/irritation and falsity/no sense was less than the
correlation shown in the initial measurement model (i.e., .91), but still high (i.e., .88);
other correlations among constructs were acceptable (Kline, 1998).
Another technique which compares the AVE of a construct with the squared
correlations related with the construct (Fornell & Larcker, 1981) indicated that all
correlations were less than their respective AVE scores except the correlation between
annoyance/irritation and falsity/no sense (see Table 5.25 and 5.27). Two constructs,
annoyance/irritation and falsity/no sense were still not discriminate in the modified
measurement model.
In conclusion, the model providing the best fit to the data was developed through
two consecutive confirmatory factor analyses. The scales for the modified measurement
model were more reliable and valid than those of the initial measurement model.
However, the study failed to present evidence for discriminate validity of
annoyance/irritation and falsity/no sense from two measurement models.
The full structural model with path analysis
A review of the results from the assessment of the modified measurement model
through the overall fit as well as the fit of the internal structure supported a consideration
of the full structural model (Anderson & Gerbing, 1988). The current section precedes
the structural relationships among latent variables and the fit of the full latent variable
model, followed by a preliminary analysis.
Preliminary analysis. The study screened normality in only the data for a new
construct, attitude toward advertising through sport in the structural model because the
other data for seven belief constructs was already inspected. No invalid or outliers were
found through the descriptive statistics. In normality tests for three attitude items,
however, the descriptive statistics indicated that two attitude items (Att2 and Att3)
violated the assumption of normal distribution in terms of skewness at the .05 probability
level (see Table 5.28).
The same procedures used in the measurement model test were employed for
data transformations. The distributions of the items (Att2 and Att3) were all negatively
distributed. Hence, two items was transformed by taking the square root after subtracting
140
every score from a constant (i.e., 8). Table 5.29 revealed that the previously significant
items failed to reject the null at the .01 probability level after transformations.
Table 5.28
Descriptive Statistics of the Attitude Items
Items
Mean
S.D.
Skewness
Statistic
Kurtosis
z-scoreª
Statistic
z-scoreª
Attitude 1 (Att1)
5.25
1.28
-.392
-2.35
.623
1.87
Attitude 2 (Att2)
5.36
1.26
-.785
-4.70*
.405
1.22
Attitude 3 (Att3)
5.00
1.23
-.676
-4.05*
.485
1.46
ª All z-scores are calculated by diving the statistics by the standard errors of .167 (skewness) and .333 (kurtosis).
* Significant at the .01 probability level.
Table 5.29
Distribution Values Before and After Transformation
Original values
Item
Skewness
Transformed values
Kurtosis
Statistic
z-scoreª
Statistic
Att2
-.785
-4.70*
.405
Att3
-.676
-4.05*
.485
Skewness
z-scoreª
Kurtosis
Statistic
z-scoreª
Statistic
z-scoreª
1.22
.242
1.45
.439
1.32
1.46
.073
.44
-.035
-.11
ª All z-scores are calculated by diving the statistics by the standard errors of .167 (skewness) and .333
(kurtosis).
* Significant at the .01 probability level.
Beliefs about advertising through sport. For better interpretations of the findings
derived from the structural model for the relationships between belief dimensions and
attitude, it is important to report the results of descriptive statistics for belief dimensions
as well as attitude. The current section shows respondents’ perceived values relative to
four positive beliefs and three negative beliefs (about advertising through sport. All
belief items were measured by a seven Likert scale (e.g., 1 = strongly disagree; 7 =
141
strongly agree) (see Table 5.30), followed by a section including the results of descriptive
statistics of the attitude construct.
Product information and hedonism/pleasure. Mean scores of product
information and hedonism/pleasure were relatively high, ranging from 4.71 to 5.18 for
product information, and from 4.52 to 5.54 for hedonism/pleasure. Approximately, two
thirds of the respondents believed that advertising through sport is informative and
hedonic. However, only 47% of the respondents believed that advertising through sport
gives them pleasure when they think about what they saw or heard or read (i.e., H3)
Social role and image. Half of respondents believed that they learn what is in
fashion, what they should buy for keeping a good social image, or obtain ideas about
fashion through advertising through sport (S4 & S6). At the same time, more participants
disagreed (37 %) than agreed (31 %) that advertising through sport helps them learn
about fashions and about what to buy to impress others (S1).
Good for the economy. Respondents generally felt that advertising through sport
is good for the economy, ranging mean scores from 4.16 to 4.50. In fact, the significant
proportions of the respondents, ranging from 42.5 % to 53.3 %, were neutral.
Annoyance/irritation, materialism, and falsity/no sense. Respondents perceived
that advertising through sport promotes materialism (i.e., mean = 4.52). More
participants agreed that advertising through sport makes them a materialistic society,
overly interested in buying and owning things (M1) and makes people live in a world of
fantasy (M3) than respondents who disagreed. Particularly, a majority of the respondents
(72 %) believed that advertising through sport influences people to buy things they do not
really need (M2).
In contrast, one interesting finding is that respondents did not consider
advertising through sport to be particularly annoying or irritating and false or incorrect.
Approximately, two third of the respondents disagreed that advertising is not annoying or
an irritation; half of the respondents reported that advertising through sport does not
interrupt programs just when they are getting interested. In terms of falsity/no sense, a
majority of respondents perceived that advertising is not misleading (60.4 %) and does
not often insult the intelligence of the average consumer (63.1 %); only 27.8 % believed
that advertising through sport is deceptive.
142
Table 5.30
Respondents’ Beliefs about Advertising through Sport
Belief item
Product information
tells me which brands have the features I am
looking for (I1).
helps me keep up-to-date about products available
in the marketplace (I2).
is a good source of product information (I3).
Social role and image
helps me learn about fashions and about what to
buy to impress others (S1).
helps me learn what is in fashion and what I should
buy for keeping a good social image (S4).
gives me ideas about fashion (S6).
Hedonism/pleasure
is often amusing and entertaining (H1).
is enjoyable (H2).
gives me pleasure when I think about what I saw or
heard or read (H3).
is sometimes even more enjoyable than other media
contents (H4).
Annoyance/irritation
is annoying (A1).
is irritating (A2).
often interrupts programs just when I am getting
interested (A3).
Good for the economy
in general helps our nation’s economy (G1).
Generally helps the local economy (G2).
helps raise our standard of living (G4).
Materialism
is making us a materialistic society, overly
interested in buying and owning things (M1).
influences people to buy things they do not really
need (M2).
makes people live in a world of fantasy (M3).
Falsity/no sense
in general is misleading (F1).
often insults the intelligence of the average
consumer (F2).
is deceptive (F4).
Mean
S.D.
Percentª
Disagree
Neutral
Agree
4.91*
4.71
1.26
16.5
20.8
62.7
5.18
1.13
8.0
13.7
78.3
4.83
4.09*
1.24
15.1
20.3
64.6
3.75
1.37
37.3
32.1
30.7
4.30
1.25
25.9
23.6
50.5
4.22
5.08*
5.54
5.19
1.24
25.0
24.5
50.4
1.22
1.16
5.7
6.6
8.5
16.0
85.8
77.4
4.52
1.28
14.6
38.2
47.2
5.05
1.26
10.8
17.0
72.1
3.20*
3.05
2.94
1.42
1.32
63.7
70.8
20.8
16.5
14.6
12.7
3.62
1.61
48.1
18.9
33.0
4.28*
4.50
4.18
4.16
4.52*
1.13
1.12
1.05
12.3
17.9
17.9
42.5
48.1
53.3
45.3
34.0
28.7
4.31
1.43
33.0
17.9
49.1
5.06
1.29
13.7
14.6
71.7
4.20
3.38*
3.27
1.36
31.1
27.8
41.0
1.35
60.4
21.7
17.9
3.18
1.34
63.1
20.9
16.1
3.69
1.47
44.8
27.4
27.8
ª A seven-Likert scale items were collapsed into “disagree”, “neutral”, and “agree”, based statements
coding: 1, 2, and 3 represent “disagree”; 4 represents “neutral”; 5, 6, and 7 represent “agree”.
* A single mean score averaged by mean scores of responses to the three or four items.
Attitude toward advertising through sport. Respondents’ overall attitudes toward
advertising through sport were associated with three items. The results of descriptive
143
statistics in Table 5.31 revealed that, overall, the respondents’ general attitudes toward
advertising were positive: 67% indicated that their general opinions of advertising
through sport were favorable (e.g., mean = 5.25); 76% considered advertising through
sport a good thing (e.g., mean = 5.36); 71% liked advertising through sport (e.g., mean =
5.00). Less than 10 % of the respondents indicated that their overall attitude toward
advertising through sport was generally negative.
Table 5.31
Respondents’ Attitude toward Advertising through Sport
Percentª
Attitude item
Mean
S.D.
Disagree/
dislike
Neutral
Agree/
like
My general opinion of advertising through
5.25
1.28
7.1
25.9
67.0
sport is favorable (Att1).
Overall, I consider advertising through sport a
5.36
1.26
7.1
17.0
76.0
good thing (Att2).
Overall, do you like or dislike advertising
5.00
1.23
9.5
19.8
70.7
through sport (Att3).
ª A seven-Likert scale items were collapsed into “disagree/dislike”, “neutral”, and “agree/like”, based
statements coding: 1, 2, and 3 represent “disagree/dislike”; 4 represents “neutral”; 5, 6, and 7 represent
“agree/like/”.
Model identification. The structural model was identified by criteria of the t-rule
(e.g., degrees of freedom = 247) and Bollen’s (1989) “three indicators per construct”.
The model is also recursive, that is, there is no reciprocal causation or causal feedback,
and the equation errors are uncorrelated (Tate, 1998). A primary purpose of the current
study was to measure the proposed relationships between belief dimensions and an
attitude. As previously hypothesized, the current structural model includes the one-way
causal flow from seven exogenous variables (i.e. beliefs) to one endogenous variable (i.e.,
an attitude).
Model estimation. Maximum likelihood was used to estimate parameter of
interest. Table 5.32 summarized standardized factor loadings, error variances, and R² of
indicators for all latent variables. The loadings of observed variables in belief constructs
144
ranged from .66 to .92 (e.g., R² ranged from .44 to .84); the loadings of observed
variables in the attitude construct ranged from .76 to .94 (e.g., R² ranged from .58 to .89).
In terms of correlations among constructs, the absolute correlations among exogenous
variables ranged from .04 to .84; the absolute correlations between exogenous and
endogenous variables ranged from .13 to .77 (see Table 5.33).
Table 5.32
Factor Loadings, Standard Errors, and R² of Observed Variables in the Structural Model
Latent variable
Product
information
Social role and
image
Hedonism/
pleasure
Annoyance/
irritation
Good for the
economy
Materialism
Falsity/no sense
Attitude
Observed
variable
Completely
standardized loading
Completely standardized
error variance
R²
I1
I2
I3
S1
S4
S6
H1
H2
H3
H4
A1
A2
A3
G1
G2
G4
M1
M2
M3
F1
F2
F4
Att1
Att2
Att3
.81*
.79*
.75*
.71*
.78*
.74*
.77*
.88*
.75*
.84*
.83*
.92*
.69*
.80*
.71*
.66*
.71*
.70*
.78*
.85*
.71*
.78*
.93*
.94*
.76*
.34
.37
.44
.49
.40
.46
.41
.23
.43
.29
.31
.16
.53
.37
.50
.56
.49
.51
.40
.27
.50
.39
.13
.11
.42
.66
.63
.56
.51
.60
.54
.59
.77
.57
.71
.69
.84
.47
.63
.50
.44
.51
.49
.60
.73
.50
.61
.87
.89
.58
* Significant at the .05 probability level.
145
Table 5.33
Correlation among Latent Variables in the Structural Model
Social
Product
Hedonism/ Annoyance/ Good for the Material- Falsity/
Attitude
role and
information
pleasure
irritation
economy
ism
No sense
image
Product
information
1
Social role
and image
.45*
1
Hedonism/
pleasure
.66*
.42*
1
Annoyance/
irritation
-.53*ª
-.26*ª
-.66*ª
1
Good for the
economy
.48*ª
.29*ª
.41*ª
-.20*
1
Materialism
-.07
.29*
-.13
.42*ª
.09ª
1
Falsity/no
sense
-.40*ª
-.04ª
-.43*ª
.84*
-.18*
.66*ª
1
Attitude¹
.77
.45
.73
-.63ª
.45ª
-.13
-.45ª
1
* Significant at the .05 probability level.
ª The directions shown in the output were replaced by their original directions
¹ Significance levels of correlations between ETA and KSI were not shown in the output.
Table 5.34 presents the standardized parameter estimates implied by the model
for the proposed causal relationships between belief constructs and an attitude construct.
The effects of two belief constructs (i.e., product information and hedonism/pleasure) on
attitude were significant at the .05 probability level. The dominant determinant of
attitude toward advertising through sport was product information with the estimated
effect of .42, followed by hedonism/pleasure with the estimated effect of .22 (see Table
5.34). These effects of product information and hedonism/pleasure on attitude can be
interpreted as there are expected .42 and .22 increases in attitude associated with a one
unit increase in product information and hedonism/pleasure, respectively, controlling for
146
other exogenous variables. The .71 of structural equation fit indicated that approximately
71% of the variance of attitude toward advertising through sport was explained by the
seven determinants.
Table 5.34
Standardized Parameters Estimates for the Structural Model
Endogenous
construct
Attitude toward
advertising through
sport
Standardized
regression
coefficient
Standard
error
t-value
Product
information
.42
.09
4.99*
Social role and
image
.08
.07
1.16
Hedonism/
pleasure
.22
.10
2.22*
Annoyance/
irritation
-.32
.19
-1.70
Good for the
economy
.11
.07
1.49
Materialism
-.07
.10
-.65
Falsity/no sense
.15
.20
.74
Exogenous
construct
R²
.71
* Significant at the .05 probability level
Overall model fit. The goodness-of-fit tests in a structural model represent an
overall adequacy of the entire causal relationships among constructs (Hair et al., 1998).
First, in terms of absolute fit measures shown in Table 5.35, the chi-square statistic (χ² =
479.40, df = 247) was statistically significant at the .05 probability level. The alternative
measure of absolute fit, the ratio of χ² statistic to the degrees of freedom (χ²/df), resulted
in a satisfactory score of 1.94 (Byrne, 1989; Hair et al., 1998; Kline, 1989).
The reported RMSEA score (i.e., .067) indicating the .0014 probability value for
test of close fit (RMSEA < .05) met the .10 criterion recommended by Steiger (1990), but
was slightly greater than the .06 criterion by Hu and Bentler (1999). The standardized
RMR value (i.e. .069) satisfied the criteria, .08 and .10 (Hu & Bentler, 1999; Kline, 1998),
147
but did not meet the .05 criterion (Kelloway, 1998). Lastly, The GFI with .85 was not an
adequate value for goodness-of-fit of the model (Kelloway, 1998; Kline, 1998).
Table 5.35
The Assessment of Model Fit in the Structural Model
Test of model fit
Absolute fit
Fit index
Value
Χ²
df
479.40
247
p–value
.00
>. 05 or .01
χ²/df
1.94
1.0 ~ 2.0 (Hair et al., 1998)
< 2.0 (Byrne, 1989)
< 3.0 (Kline, 1998)*
RMSEA
.067ª
< .06 (Hu & Bentler, 1999)
< .10 (Steiger, 1990)
SRMR
.069
< .05 (Kelloway, 1998)
< .08 (Hu & Bentler, 1999)
< .10 (Kline, 1998)
GFI
.85
> .90 (Kelloway, 1998; Kline,
1998)
NFI
.94
> .90 (Bentler & Bonett, 1980;
Kelloway, 1998; Kline,
1998; Hair et al., 1998)
IFI
.97
> .95 (Hu & Bentler, 1999)
CFI
.97
> .95 (Hu & Bentler, 1999)
> .90 (Kelloway, 1998; Kline,
1998)
RFI
.93
> .90 (Kelloway, 1998)
Comparative fit
The common rule of thumb
-
ª The p-value of the null that RMSEA < .05 was .0014.
* Klein (1998) indicated that a ratio value less than 3.0 may be applied when a large sample set was
analyzed; “In small samples, a χ²/df ratio of, say, 2.5, may arise even if the overall fit of the model is poor”
(p. 131).
Table 5.35 also includes the results of comparative fit indexes. All reported
measures of comparative fit (i.e., NFI, IFI, CFI, and RFI) surpassed the suggested criteria
148
of .95 or .90.
The fit of internal structure. The fit of internal structure in the latent variable
model was assessed by reliability and validity tests for measures of exogenous constructs
as well as an endogenous variable. Table 5.36 summarizes the results of each reliability
test in the structural model. In particular, the results of tests for the new construct,
attitude toward advertising through sport were initially reported in the current section.
First, for the reliability of measures, the tests of the structural model revealed that
all exogenous constructs were reliable based on composite reliability and AVE scores;
three items’ (A3, G4, and M2) individual reliability (R²) ranging from .44 to .49 seemed
low (also see Table 5.32). In terms of an attitude construct, all reliability tests resulted in
a conclusion that measures were reliable, and more than a half of the variance for all
measures of attitude were explained by the construct (Fornell & Larcker, 1981) (see Table
5.36).
Table 5.36
Reliability of Constructs in the Structural Model
α*
Composite
reliability
AVE
Individual item
reliability (R²)ª
Exogenous
construct
Product information
Social role and image
Hedonism/pleasure
Annoyance/irritation
Good for the economy
Materialism
Falsity/no sense
.83
.79
.86
.82
.76
.77
.83
.83
.79
.89
.86
.77
.77
.83
.62
.55
.66
.67
.52
.53
.61
.56 ~ .66
.51 ~ .60
.57 ~ .77
.47 ~ .84
.44 ~ .63
.49 ~ .60
.50 ~ .73
Endogenous
construct
Attitude
.91
.91
.78
.58 ~ .89
Construct
* The alpha values were calculated by the original scores (i.e. untransformed scores).
ª For more detail information, see Table 5.32.
Next, for the convergent validity of measures, first, the AVEs of all exogenous
and endogenous constructs in Table 5.36 were greater than the .50 cut off and indicated
that the measures in belief and attitude constructs were well converged into their
149
proposed constructs. In addition, Table 5.32 shows partial outcomes of convergent
validity tests in the structural model in terms of factor loadings. Factor loadings of three
indicators (A3, G4, & M2) in exogenous constructs (i.e. belief constructs) fell short
of .707; the other indicators met the cut off. All indicators’ loadings in the endogenous
construct (i.e. attitude construct) were higher than .707, indicating that these indicators’
common variance was greater than their unique variance.
One more technique to assess convergent validity was employed for an attitude
construct. Convergent validity has been also evaluated by the degree of correlation
between two different measures of the same construct. The current study purported to
measure the attitude construct with the seven-Likert scales. For the purpose of assessing
convergent validity of attitude items, the study also measured an attitude construct with a
different method (i.e., the semantic differential scale with three items).
Table 5.37 shows that significant correlations among items in both methods
ranged from .37 to .59. The significant overall correlation of .52 between two different
methods at the .05 probability level provided good evidence of convergent validity of
attitude measures.
Table 5.37
Correlations between Likert and Semantic Differential Scales
Semantic differential scale
Likert scale
Bad v. good
Unpleasant v.
pleasant
Unfavorable v.
favorable
My general opinion of advertising
through sport is favorable.
.47*
.42*
.39*
Overall, I consider advertising
.47*
.39*
through sport a good thing.
Overall, do like or dislike
.59*
.43*
advertising through sport.
Overall correlation between two scales: .52*ª
* Significant at the .05 probability level
ª The overall correlation was computed by the average scores from both scales
.37*
.46*
In terms of discriminant validity, the correlation coefficient (i.e., .84) between
150
two exogenous constructs, annoyance/irritation and falsity/no sense was barely
acceptable based on the Kline’s (1998) .85 cut-off (also see Table 5.33). However, the
squared correlation between both constructs (i.e., .71) was greater than their AVE values
(i.e., .67 for annoyance/irritation and .61 for falsity/no sense), thus failed to discriminate
annoyance/irritation from falsity/no sense (see Table 5.33 and 5.36). The tests of
discriminant validity were not applied for the attitude construct because the study
considered only one endogenous variable in the structural model.
Cross validation test
Objective. To date the current study has finalized the seven-factor model of
attitude toward advertising through sport through tests of a series of measurement models.
The structural model then tested the hypothetical relationships between seven belief and
attitude constructs. The assessments of measurement models and structural model were
performed within the same sample (i.e., calibration sample). However, the results
observed from previous tests might capitalize on the characteristics inherent within the
same sample. As a result, the findings obtained from the calibration sample would not
have the robustness for further replications. Accordingly, the study employed a cross
validation test to determine the accuracy of the current structural equation model.
For the cross validation test, the study utilized the second split sample (i.e.
validation sample, n=212). The primary purpose of a cross validation is to test “the
ability of the model to be invariant across two or more random samples from the same
population” (Mel, 2004, p. 27). Therefore, the current section focuses on the
comparisons between parameter estimates derived from the calibration samples and reestimated parameters from the validation sample. The section starts with the descriptive
and preliminary analyses of the validation sample, followed by the primary tests.
Characteristics of sample data. The second split sample (a validation sample,
n=212) was used to cross-validate the results from the selected model. Six demographic
characteristics, gender, school year, ethnic background, time watching sports games in a
typical day, frequency participating in sports activity in a month, and frequency
purchasing sporting goods in a month are summarized in Table 5. 38 and 5.39. There
were slightly more male than female respondents, and the highest percentage of
respondents were juniors. In addition, the majority of respondents was white/Caucasian.
151
Respondents who answered more than one choice in an ethnic background item were
considered “other”.
Table 5.38
Demographic Characteristics of the Validation Sample
Demographic Variables
Male
Female
Gender
Total
School year
Total
Ethnic background
Freshman
Sophomore
Junior
Senior
Graduate
Other
Black/African American
Native American
Latino/Latina
White/Caucasian
Asian or Pacific Islander
Other
Total
Frequency
111
101
212
42
42
65
39
3
1
212
25
0
16
162
8
1
212
Percent
52.4
47.6
100.0%
19.8
19.8
30.7
27.8
1.4
.5
100.0%
11.8
.0
7.5
76.4
3.8
.5
100.0%
Table 5.39 shows respondents’ behaviors relative to watching sports games,
participating in sports activities, and purchasing sport merchandise. Approximately half
of the respondents (50.5%) watched sports games more than 30 minutes in a typical day.
Most students participated in various kinds of sports activities at least once a month
(87.3%) and purchased sports merchandise six times or less (96.2%) in a month. The
samples collected for the cross validation test had similar demographic characteristics
with the samples used to develop the model.
152
Table 5.39
Sports-Related Behaviors of the Validation Sample
Behavioral Variables
0 minute
1-30 minutes
Time watching
31-60 minutes
sports games in a
61-90 minutes
typical day
91-120 minutes
More than 120 minutes
Total
Never
Frequency
Less than once
participating in
1-4 times
sports activity in a
5-10 times
month
11-20 times
More than 20 times
Total
Never
Less than once
Frequency
1-3 times
purchasing sporting
4-6 times
goods in a month
7-9 times
10 times or more
Total
Frequency
25
80
35
25
26
21
212
14
13
50
41
54
40
212
25
76
78
25
8
0
212
Percent
11.8
37.7
16.5
11.8
12.3
9.9
100.0%
6.6
6.1
23.6
19.3
25.5
18.9
100.0%
11.8
35.8
36.8
11.8
3.8
0
100.0%
Preliminary analysis. The data in the validation sample was screened for the
purposes of inspections of problematic observations and normality of measures.
Descriptive statistics including mean, standard deviation, range, and frequency showed that
there were no outliers and invalid data resulting from invalid responses or input error.
For the assessments of normality in observed variables, each observed variable
was also tested by skewness and kurtosis with the same procedures as did in the
calibration sample. The computed z-scores of skewness and kurtosis for each observed
variable are reported in Table 5.40. Several items rejected the hypothesis of the
normality of the distribution at the .01 probability level in terms of either skewness or
kurtosis: I1, I2, and I3 in product information, S6 in social role and image, H1, H2, and
H4 in hedonism/pleasure, A1 in annoyance/irritation, G1 in good for the economy, and
all items in attitude toward advertising through sport (see Table 5.40). The problematic
items which violated the assumption of normality were transformed to repair their non-
153
normality.
Table 5.40
Descriptive Statistics of the Belief and Attitude Items in the Validation Sample
Items
Product information 1 (I1)
Product information 2 (I2)
Product information 3 (I3)
Social role and image 1 (S1)
Social role and image 4 (S4)
Social role and image 6 (S6)
Hedonic/pleasure 1 (H1)
Hedonic/pleasure 2 (H2)
Hedonic/pleasure 3 (H3)
Hedonic/pleasure 4 (H4)
Annoyance/irritation 1 (A1)
Annoyance/irritation 2 (A2)
Annoyance/irritation 3 (A3)
Good for the economy 1 (G1)
Good for the economy 2 (G2)
Good for the economy 4 (G4)
Materialism 1 (M1)
Materialism 2 (M2)
Materialism 3 (M3)
Falsity/no sense 1 (F1)
Falsity/no sense 2 (F2)
Falsity/no sense 4 (F4)
Attitude 1 (Att1)
Attitude 2 (Att2)
Attitude 3 (Att3)
Mean
4.58
5.16
4.68
3.83
4.15
4.22
5.47
4.97
4.33
4.80
3.11
3.24
3.89
4.44
4.24
3.90
4.38
4.86
4.21
3.58
3.51
3.87
5.16
5.16
4.83
S.D.
1.39
1.22
1.41
1.44
1.41
1.33
1.27
1.32
1.31
1.42
1.62
1.55
1.71
1.08
1.17
1.02
1.39
1.27
1.37
1.45
1.49
1.49
1.37
1.36
1.36
Skewness
Kurtosis
Statistic
z-scoreª
Statistic
z-scoreª
-.72
-.73
-.50
-.30
-.32
-.51
-1.04
-.77
.00
-.68
.57
.29
-.04
-.54
-.37
-.41
-.06
-.38
.03
.160
.31
.05
-.58
-.64
-.66
-4.24*
-4.29*
-2.94*
-1.76
-1.88
-3.00*
-6.12*
-4.53*
.00
-4.00*
3.35*
1.71
-.24
-3.18*
-2.18
-2.41
-.35
-2.24
.18
.94
1.82
.29
-3.41*
-3.76*
-3.88*
-.22
.40
-.17
-.72
-.26
-.19
1.04
.79
-.20
.06
-.45
-.50
-.81
1.00
.58
.58
-.59
-.22
-.68
-.73
-.35
-.83
.09
.02
.49
-.67
1.21
-.52
-2.18
-.79
-.58
3.15*
2.39
-.61
.18
-1.36
-1.52
-2.45
3.03*
1.76
1.76
-1.79
-.67
-2.06
-2.21
-1.06
-2.52
.27
.06
1.48
ª The z-scores are calculated by diving the statistics by the standard errors of .17 (skewness) and .33 (kurtosis).
* Significant at the .01 probability level.
Data transformations. The non-normally distributed data in each variable was
remedied by taking the square root or logarithm on scores in order to change the shape of
the distribution to be closer to the means score (Hair et al., 1998; Kline, 1998; West,
Finch, & Curran, 1995). The same procedures employed in the calibration sample were
applied for the data transformations. Particularly, when the H1 item was transformed by
taking the square root, the distribution was still non-normal. Gorsuch (1983) indicated
that the logarithm is a more effective mathematical operation when a distribution is
154
severely skewed. Thus, the H1 was transformed by taking the logarithm and failed to
reject the normal assumption at the .01 significance level. Table 5.41 compares the
distributions of original scores with those of transformed scores and shows that the
previously significant items on the assumption of normality were no longer significant at
the probability level .01 after transformations.
Table 5.41
Distribution Values Before and After Transformation in the Validation Sample
Original values
Item
I1
I2
I3
S6
H1
H2
H4
A1
G1
Att1
Att2
Att3
Skewness
Transformed values
Kurtosis
Skewness
Kurtosis
Statistic
z-score³
Statistic
z-score³
Statistic¹
z-score³
Statistic¹
z-score³
-.72
-.73
-.50
-.51
-1.04
-.77
-.68
.57
-.54
-.58
-.64
-.66
-4.24*
-4.29*
-2.94*
-3.00*
-6.12*
-4.53*
-4.00*
3.35*
-3.18*
-3.41*
-3.76*
-3.88*
-.22
.40
-.17
-.19
1.04
.79
.06
-.45
1.00
.09
.02
.49
-.67
1.21
-.52
-.58
3.15*
2.39
.18
-1.36
3.03*
.27
.06
1.48
.35
.16
-.03
.07
-.13²
.11
.13
.10
-.08
.05
.13
.03
2.10
.96
-.18
.42
-.78
.66
.78
.60
-.48
.30
.78
.18
-.55
-.19
-.36
-.24
-.58²
.01
-.31
-.84
.72
-.64
-.60
-.09
-1.65
-.57
-1.08
-.72
-1.74
.03
-.93
-2.52
2.16
-1.92
-1.80
-.27
¹ The statistics of skewness and kurtosis of all items were computed by taking the square root transformation after
subtracting every score from a constant that is one greater than the higher score (i.e., 8); A1 did not have the step for
subtractions because it had the positive statistic.
² The statistics of skewness and kurtosis of H1 were computed by taking the logarithm transformation after subtracting
every score from a constant that is one greater than the higher score (i.e., 8).
³ The z-scores are calculated by diving the statistics by the standard errors of .167 (skewness) and .333 (kurtosis).
* Significant at the .01 level.
The full structural model in the validation sample
Model estimation. The parameters of interest in the structural model were reestimated using the validation sample. Table 5.42 summarizes the standardized factor
loadings and R² of indicators of all latent variables in the cross validation sample and
compares re-estimated parameters with those estimated from the calibration sample.
155
Table 5.42
Factor Loadings and R² of Observed Variables in the Structural Models in the
Calibration and Validation Samples
Latent variable
Product
information
Social role and
image
Hedonism/
pleasure
Annoyance/
irritation
Good for the
economy
Materialism
Falsity/no sense
Attitude
Observed
variable
I1
I2
I3
S1
S4
S6
H1
H2
H3
H4
A1
A2
A3
G1
G2
G4
M1
M2
M3
F1
F2
F4
Att1
Att2
Att3
Completely standardized
loading
Calibration
Validation
sample
sample
.81*
.79*
.79*
.78*
.75*
.86*
.71*
.73*
.78*
.88*
.74*
.78*
.77*
.77*
.88*
.89*
.75*
.78*
.84*
.70*
.83*
.81*
.92*
.86*
.69*
.72*
.80*
.74*
.71*
.80*
.66*
.60*
.71*
.61*
.70*
.78*
.78*
.76*
.85*
.80*
.71*
.75*
.78*
.81*
.93*
.91*
.94*
.94*
.76*
.73*
R²
Calibration
sample
.66
.63
.56
.51
.60
.54
.59
.77
.57
.71
.69
.84
.47
.63
.50
.44
.51
.49
.60
.73
.50
.61
.87
.89
.58
Validation
sample
.62
.62
.75
.53
.77
.60
.59
.79
.60
.49
.65
.73
.51
.55
.63
.36
.38
.61
.58
.65
.56
.65
.84
.88
.53
* Significant at the .05 probability level.
All loadings of observed variables in belief constructs ranged from .60 to .89
(e.g., R² ranged from .36 to .79); the loadings of observed variables in an attitude
construct ranged from .73 to .94 (e.g., R² ranged from .53 to .88) in the validation sample.
Two items’ (A3 & M2) loadings which did not meet the .707 cut-off in the calibration
sample exceeded the cut-off value in the validation sample. However, another two items’
(H4 & M1) loadings which were greater than the .707 in the calibration sample did not
meet the criterion in the validation sample. One item (G4) did not exceed the criterion in
156
either the validation sample or the calibration sample (see Table 5.42).
In terms of correlations among constructs shown in Table 5.43, the previously
significant and positive correlation between social role and image and materialism was
not significant; the validation study confirmed that the previously positive relationship
capitalized on chance variations in the calibration sample. The validation test also
showed that three previously insignificant correlations (i.e., social role and image and
materialism, good for the economy and falsity/no sense, and annoyance/irritation and
good for the economy) were significant in the validation sample. The correlation between
annoyance/irritation and falsity/no sense was still high enough to be undistinguishable
(Klein, 1998). The other correlations seemed nearly consistent with the previous
relationships observed in the calibration sample.
Table 5.44 presents standardized parameter estimates for the proposed causal
relationships between belief and attitude constructs derived from validation sample. The
impacts of two belief constructs (i.e., product information and hedonism/pleasure) on
attitude toward advertising through sport were significant at the .05 probability level.
The significant belief constructs obtained by the calibration sample model were
comparable to the results of the current sample. However, unlike product information in
the calibration sample, hedonism/pleasure was the most dominant determinant of attitude
in the validation sample.
The equation in the structural model indicated that approximately 64% of the
variance of attitude toward advertising through sport was represented by seven
exogenous variables. The proportions of variance in the attitude construct in both
samples seemed to be well explained by the relationships modeled (i.e., R² = .71 in the
calibration sample; R² = .64 in the validation sample). According to the results from two
samples, the study supports two proposed relationships that product information and
hedonism/pleasure significantly influence attitude toward advertising through sport.
157
Table 5.43
Correlation among Latent Variables in the Validation Sample
Product Social role Hedonism/ Annoyance/ Good for the Material- Falsity/
Attitude
information and image pleasure
irritation
economy
ism
no sense
Product
information
1
Social role
and image
.59*
(.45*)ª
1
Hedonism/
pleasure
.68*
(.66*)
.50*
(.42*)
1
Annoyance/
irritation
-.61*
(-.53*)
-.41*
(-.26*)
-.70*
(-.66*)
1
Good for the
economy
.31*
(.48*)
.36*
(.29*)
.22*
(.41*)
-.11
(-.20*)
1
Materialism
-.09
(-.07)
.09
(.29*)
-.10
(-.13)
.45*
(.42*)
.03
(.09)
1
Falsity/no
sense
-.48*
(-.40*)
-.27*
(-.04)
-.48*
(-.43*)
.87*
(.84*)
-.08
(-.18*)
.62*
(.66*)
1
Attitude¹
.69
(.77)
.47
(.45)
.74
(.73)
-.66
(-.63)
.28
(.45)
-.16
(-.13)
-.50
(-.45)
ª The correlations among latent variables in the calibration sample were shown in parentheses.
¹ Significance levels of correlations between ETA and KSI were not shown in the output.
* Significant at the .05 probability level.
158
1
Table 5.44
Standardized Parameters Estimates of the Structural Models in the Calibration and
Validation Samples
Endogenous
construct
Attitude
Exogenous
Construct
Calibration sample
(R² = .71)
Validation sample
(R² = .64)
Standardized
coefficient
SE
t-value
Standardized
coefficient
SE
t-value
Product
information
(H1)
.42
.09
4.99*
.27
.09
2.99*
Social role and
image (H2)
.08
.07
1.16
.003
.08
.04
Hedonism/
pleasure (H3)
.22
.10
2.22*
.40
.11
3.55*
Annoyance/
irritation (H4)
-.32
.19
-1.70
-.20
.23
-.87
Good for the
economy (H5)
.11
.07
1.40
.08
.06
1.35
Materialism
(H6)
-.07
.10
-.65
-.01
.09
-.12
Falsity/no
sense (H7)
.15
.20
.74
.01
.20
.03
* Significant at the .05 probability level
Overall model fit. As a last step, the goodness-of-fit of the structural model in
the validation sample was assessed and is reported in Table 5.45. In overall, the
structural model well fits to data in the validation sample. In terms of absolute fit
measures, the model yielded better fit to the validation sample data than to the calibration
sample data (see Table 5.45). The model provided the approximately same fit to the both
sample data in terms of comparative fit indexes (see Table 5.45). Accordingly, the cross
validation test supports the calibration sample results that the current structural model
provides a good model fit to the data.
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Table 5.45
The Assessment of Model Fit of Structural Models in the Calibration and Validation
Samples
Test of model fit
Absolute fit
Fit index
Calibration
sample
Validation
sample
Χ²
df
479.40
247
441.35
247
p – value
.00
.00
>. 05 or .01
χ²/df
1.94
1.79
1.0 ~ 2.0 (Hair et al., 1998)
< 2.0 (Byrne, 1989)
< 3.0 (Kline, 1998)*
RMSEA
.067ª
.061ª
< .06 (Hu & Bentler, 1999)
< .10 (Steiger, 1990)
SRMR
.069
.061
< .05 (Kelloway, 1998)
< .08 (Hu & Bentler, 1999)
< .10 (Kline, 1998)
GFI
.85
.86
> .90 (Kelloway, 1998; Kline,
1998)
NFI
.94
.94
> .90 (Bentler & Bonett, 1980;
Kelloway, 1998; Kline,
1998; Hair et al., 1998)
IFI
.97
.97
> .95 (Hu & Bentler, 1999)
CFI
.97
.97
> .95 (Hu & Bentler, 1999)
> .90 (Kelloway, 1998; Kline,
1998)
RFI
.93
.93
> .90 (Kelloway, 1998)
Comparative fit
The common rule of thumb
-
ª The p - value of the null that RMSEA < .05 was .0014 in the calibration sample; .026 in the validation
sample.
* Klein (1998) indicated that a ratio value less than 3.0 may be applied when a large sample set was
analyzed; “in small samples, a χ²/df ratio of, say, 2.5, may arise even if the overall fit of the model is poor”
(p. 131).
Summary of results of the study
The chapter in which the results are reported consists of two parts: the first data
collection (n = 215) and the second data collection (n = 424). The purposes of the first
data collection were to determine the structure of a set of belief items about advertising
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through sport and to select the most appropriate items representing their conceptualized
belief dimensions. Based on the suggestions from the pilot study, three attitude items and
43 belief items were prepared, and internal consistency tests (one item was deleted after
the interval consistency tests), EFA, and chi-square difference tests were performed. The
results determined that the seven-factor model of attitude toward advertising through
sport was reasonable and also found 15 problematic items which either had low-item-tototal correlations or did not load on the proposed factor. Considering that the study is
exploratory and willing to purify data again in the further analyses, the study retained five
items which violated the suggested criteria during either reliability tests or factor analysis
and deleted ten items which were more problematic at the first data collection stage.
The second data collection utilized a data set (n = 424). The primary purposes of
the second data collection were to develop the model and confirm the results obtained
from the analyses. The data set was split into a calibration sample (n = 212) and a
validation sample (n = 212). First, a series of measurement models and the structural
model were assessed using the calibration sample with three attitude items and 33 belief
items. The first CFA finalized the 22 belief and three attitude items and reconfirmed
justification for retaining the seven-factor model employing chi-square difference tests.
The sequent CFA provided evidences of a good model fit to the data based on various
overall and internal model fit indexes. Lastly, the structural model supported two
hypotheses that product information and hedonism/pleasure significantly influence
attitude toward advertising through sport.
In the second part of the second data collection the cross validation of observed
results from the calibration sample was assessed using the validation sample (n = 212).
The cross validation test confirmed the predictive ability of the current model that the
structural equation would accurately predict the expected outcomes when replicated in
other samples in the same population. In addition, the cross validation test supported the
significant impacts of product information and hedonism/pleasure on attitude toward
advertising through sport.
Before concluding the results chapter, the researcher should consider the
possibility of errors derived from the analyses due to the current sample split method.
The researcher conveniently rather than randomly split the data from the second
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collection in order to cluster two evenly split sample sets. Among the 424 usable
questionnaires, the researcher grouped the first 212 questionnaires for the calibration
sample and the remaining questionnaires for the validation sample. A risk of sampling
errors was not recognized until the analyses had been completed. To ascertain whether
there were findings that may have been unduly impacted by the mistake, the data from
the second data collection was randomly split (i.e., 220 for the calibration sample; 204 for
the validation sample), and the analyses of the structural models for both samples were
computed again. The results from the analyses with the randomly spilt samples were
included in the Appendix G. The results overall were consistent with the information
reported in the current chapter: two belief dimensions, product information and
hedonism/pleasure were confirmed as significant determinants of attitude from both
samples. The last chapter will present a discussion of the results that have been reported,
along with interpretations and implications of the results.
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CHAPTER VI
DISCUSSION AND CONCLUSIONS
Overview
People are exposed to variety of advertising when attending or watching sporting
events. As consumers, people form attitudes toward advertising that influence their
decisions to purchase a particular advertiser’s product. Considering that one goal of
advertising is to persuade consumers to purchase a product, advertisers should recognize
what, how and why target consumers believe and feel about their advertisements. Then,
advertisers may select a specific advertising medium and design an advertisement that
effectively appeals to the target market.
The current study examined attitude toward the use of sport as an advertising
medium derived from Pollay and Mittal’s (1993) model of attitude toward advertising in
general. Through an exploratory investigation a scale to measure beliefs believed to
influence attitude toward advertising through sport was tested. The results provided
support for the conceptualization and measurement of the belief dimensions proposed to
influence attitude toward advertising through sport.
The current chapter presents an evaluation and interpretation of the results,
implications in terms of academic and practical perspectives, and guidelines for future
research. First, the current chapter begins with a discussion about the key findings and
then presents interpretations and inferences with respect to a variety of issues that
emerged during the scale development procedures. The flow of the discussion is based
on the order of the major analyses employed in the study (e.g., EFA, CFA, and structural
model for hypothesis testing).
Exploratory factor analysis
Overview
The primary functions of EFA are to identify the defined dimensions of a domain
and to reduce data (Floyd & Widaman, 1995). Two EFAs were computed, one in the
pilot test and the other in the first data collection in the main study. The relationships
among a set of observed variables underlying each belief dimension conceptually defined
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in Chapter 2 were tested, and the items in the instrument were purified using EFA.
During the pilot test, more weight was placed on whether the seven conceptualized belief
dimensions were reasonable and independent, rather than focusing on data reduction
considering there was a lack of strong a priori information about the proposed
measurement model. The analysis of the pilot study included principle component
analysis to extract the sequential dimensions of the model, a series of maximum
likelihood procedures to test the adequacy of model fit for three plausible models retained
through a principle component analysis, and maximum likelihood estimation with
oblique rotation to make the final factor model more interpretable.
Based on the results from the pilot study, the instrument was modified. Then, a
second EFA was performed to identify the structure of the relationship among the
variables in the modified instrument by confirming the seven-factor model using
maximum likelihood estimation with oblique rotation. Information from the first data
collection was used at this stage. Some data purification was employed following the
second EFA. Several problematic variables which did not well represent their proposed
factors were deleted. Following is a discussion about the factor structure of the belief
dimensions and issues that emerged through the EFA procedures employed in the pilot
study and the analysis based on the first data collection.
Pilot study
Conducting a pilot test is an important step because it gives “advance warning
about where the main research project could fail, where research protocols may not be
followed, or whether proposed methods or instruments are inappropriate or too
complicated” (Teijlingen & Hundley, 2001, p. 1). When a researcher attempts to develop
a new instrument, s/he may finally obtain the best instrument through a process of trial
and error. Completing a pilot study is one means by which to reduce the potential errors
that may be associated with the research process. Several ideas provoked from the pilot
study are discussed below in an attempt to aid other researchers to design a better
research protocol.
The first issue to consider based on the results of the pilot study was the loading
of items on a single factor that were conceptualized as distinct constructs. The results
from the EFA revealed that two belief dimensions, production information and social role
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and image, which were conceptualized as distinct dimensions merged as one. Several
reasons may be explain this result. First, the sample size utilized in the pilot study may
have been a problem. It often seems that researchers do not collect enough data for their
pilot study; as a result, findings based on small sample sizes do not provide accurate
predictions (Teijlingen & Vanora, 2001). The sample size of the pilot test was 125. The
appropriate sample size for EFA is often determined by the number of observed variables
required in the EFA (e.g., Gorsuch, 1983; Hair et al., 1998). Researchers have suggested
subjects-to-variable ratios of 1:5 (e.g., Gorsuch, 1983) and 1:10 (e.g., Everitt, 1975) to
determine the sample size. Considering that 26 variables were included in the analysis, a
sample of 125 is marginally acceptable at the minimum ratio of 1:5. However, several
researchers indicated that the determination of sample size should not be solely based on
the number of measured variables because such traditional criteria may not accurately
represent a variety of critical characteristics the data contain (MacCallum et al., 1999;
Velicer & Fava, 1998, as cited in Fabrigar et al., 1999).
Researchers should consider the number of measured variables representing each
latent variable and communalities of each measured variable (Fabrigar et al., 1999).
MacCallum et al. (1999) suggested that when each factor has at least three indicators and
each indicator has a high communality (i.e., .70 or higher), even a sample size of 100
could produce accurate estimates of parameters (as cited in Fabrigar et al., 1999).
However, a sample of more than 200 may be required when researchers have fewer
indicators per each factor and lower communalities than the suggested conditions
(Fabrigar et al., 1999). Each proposed factor in the pilot study had at least three
measured variables (i.e., from three to four); however, their communalities ranged
from .30 to .98 when a seven factor model was selected (see Table 3.5). Overall, the
communalities of measured variables in the pilot study seemed inadequate.
Communalities of 20 variables (out of 26) were less than the .70 criterion; 6 variables had
communalities that were .50 or less. As a result, the sample size for the EFA in the pilot
study was not sufficient under the condition of communality suggested by MacCallum et
al.
Other researchers have insisted that factor loading values of indicators may be
used to determine sample size (Guadagnoli & Velicer, 1988; Hair et al., 1998).
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Guadagnoli and Velicer (1988) noted that sample size could be determined by the degree
of indicators’ saturation with the factors. When most indicators in a model are saturated
with their proposed factors (e.g., high factor loadings), a small sample size might be
acceptable (Guadagnoli & Velicer, 1988). Hair et al. (1998) suggested more concrete
guidelines for identifying significant factor loadings based on sample size by statistical
tests. With a sample of 125, factor loadings higher than .50 seem significant at the .05
probability level with a power level of 80% (BMDP Statistical Software, Inc., 1992, as
cited in Hair et al., 1998). The researcher used a 0.40 cut-off and retained the items
whose loadings were .40 or higher. Several items which loaded on their proposed factors
did not exceed the 0.50 criterion (see Table 3.6). In order to be satisfied with a 0.40 cutoff in terms of both practical and statistical significance, a sample size of at least 200 is
recommended for EFA.
A second possibility as to why the items merged on one dimension may be
derived from the low communalities of some observed variables. When interpreting a
factor matrix, researchers should assess communalities as well as factor loadings for
observed variables. A communality of an observed variable represents the proportion of
the variance that the variable shares with the latent variables underlying the set of
observed measures (Floyd & Widaman, 1995). Accordingly, when a communality value
of a variable is less than .50, it is deemed that the variable is not reasonably explained in
the factor solution (Hair et al., 1988).
The communalities for items purported to assess product information and social
role and image were relatively low. The communalities of three items associated with
product information ranged from .40 to .74 (e.g., the average score was .55), and the
communalities of four items in social role and image ranged from .47 to .52 (e.g., the
average score was .49). The items used to assess product information and social role and
image were not fully saturated with these two dimensions.
A third reason why the items may have merged on one factor is problems with the
wording of respective items. The words in the items can be scrutinized for their
appropriateness given the constructs of interest. In terms of product information, the item
I1 (“advertising through sport is a valuable source of information about the local sales”)
does not seem to be not properly related to product information. This item deals with
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“sales information” rather than “product information”. In the social role and image
dimension, the item S4 (“advertising through sport makes people buy unaffordable
products just to show off”) does not seem to assess social role and image. One major
reason why the item performed poorly is that this item was originally used as part of the
materialism dimension in Polly and Mittal’s study (1993). The item was moved to the
social role and image dimension during the item generation stage.
The poor wording of items likely contributed to the problems with the pilot study
results. Therefore, after the pilot test, more items were developed to capture the domains
as specified, particularly, for those two dimensions because “having highly over
determined factors might be especially helpful when communalities are low (MacCallum
et al., 1999, p. 90).
The first data collection
The second EFA with the data from the first data collection discriminated
production information and social role and image with high loadings on each
conceptualized factor; the analysis, however, failed to discriminate three dimensions:
annoyance/irritation, materialism, and falsity/no sense (see Table 5.9). Most items in the
three dimensions loaded on a single factor. Previous studies have reported similar results
in that they failed to discriminate three dimensions, value corruption, materialism, and
falsity/no sense (e.g., Korgaonkar, et al., 1997; Pollay & Mittal, 1993). Pollay and Mittal
(1993) utilized a series of chi-square different tests and showed that falsity/no sense could
be independent from materialism and value corruption; however, the other two
dimensions were not separated. The differences between the previous studies and the
current study were that the current study included the annoyance/irritation dimension and
excluded the value corruption dimension.
The three dimensions loading as one factor was an unexpected result.
Annoyance/irritation and falsity/no sense merging together would have been less of a
surprise; that result may be somewhat plausible because people can be annoyed or
irritated when they believe that advertising is false. Bauer and Greyser (1963) also
indicated the most significant reason people dislike advertising was that people perceived
that advertising is often offensive. A more detailed discussion about the structures of
annoyance/irritation and falsity/no sense is presented in a later section. The results were
167
surprising considering the sample size was adequate and no problems with the wording of
the items in materialism was detected. The materialism dimension also seemed to be
conceptually different. The researcher did replicate the factor analysis using the other
two samples in second data collection (i.e., calibration and validation samples) but did
not report the results derived from the replications. The results from two samples were
consistent in that materialism was discriminated from annoyance/irritation and falsity/no
sense. However, the remaining two dimensions still merged as one. It is possible that
random error influenced the results; therefore, the researcher utilized the chi-square
difference tests before making decisions about model respecification. The conclusion,
reported in a later section, was that retaining the three dimensions as separate constructs
was reasonable.
Confirmatory factor analysis
Overview
The sample from the second data collection (n = 424) was split in half: one for the
development of the study (i.e., the calibration sample) and the other for the validation of
the study (i.e., the validation sample). The first CFA was computed to evaluate the initial
measurement model which was specified by two EFAs. The initial pool of 43 belief
items was reduced to 30 items based on the results from the EFA in the first data
collection and the reliability tests in the calibration sample.
The primary purpose of the assessment of the measurement model was to develop
reliable and valid measures for the proposed model with a better fit to the data. While the
initial measurement model provide an acceptable overall model fit via several fit indexes
(e.g., RMSEA, SRMR, IFI, & CFI), there was still room for improvement in terms of the
other fit indexes (e.g., χ²/df, GFI, and RFI) and the fit of internal structure (e.g., the AVE
of social role and image) (see Table 5.21 & Table 5.22). Accordingly, the researcher
deleted several poor indicators from the initial model, proposed the modified model with
22 indicators, and evaluated the modified measurement model through a second CFA.
Lastly, the researcher tested a cross validation of the modified measurement model with a
new sample set.
Overall model fit of the model
The overall fit of the final model to the data was evaluated by a variety of fit
168
indexes with respect to the measurement model and the full structural model. Although
the model had an acceptable fit to the calibration sample the results might be only limited
to this sample. Accordingly, the cross validation of the model fit was evaluated using the
validation sample set. The results are summarized in Table 6.1. The overall fit of the
models generally seems acceptable in both sample sets; the model fit from the validation
sample is slightly better than the fit from the calibration sample. However, Table 6.1
shows that the values of goodness-of-fit index (GFI) in both samples do not surpass
the .90 rule-of-thumb.
Table 6.1
Summaries of the Overall Model Fit in the Calibration and Validation Samples
Calibration sample (n = 212)
Fit index
Validation sample
(n = 212)
Measurement model
Full structural model
Full structural mode
χ²
360.69
479.40
441.35
df
188
247
247
p – value
.00
.00
.00
χ²/df
1.92
1.94
1.79
RMSEA
.066
.067
.061
SRMR
.064
.069
.061
GFI
.87
.85
.86
NFI
.93
.94
.94
IFI
.96
.97
.97
CFI
.96
.97
.97
RFI
.91
.93
.93
The GFI is generally deemed as an alternative to the χ² index because the χ²
statistic is very sensitive to sample size so that a model with a good fit can be rejected for
a large sample size and a model with a poor fit can be accepted for a small sample size
(Marsh, Balla, & McDonald, 1988). Researchers, however, have also contended that the
GFI is often influenced by sample size (e.g., Bollen, 1989; Gerbing & Anderson, 1993;
169
Shevlin & Miles, 1998). Shevlin and Miles (1998) indicated that the GFI could be
affected by three conditions, sample size, model specification, and magnitude of factor
loadings. They found the relationships among three factors as following:
Although the mean GFI for the correct and approximate models increase with
sample size irrespective of the magnitude of the factor loadings, the mean of GFI
for misspecified models was reasonably stable for sample sizes 100 or greater
when the factor loadings were medium and high” (p. 88).
In other words, the GFI could be always high regardless of model specifications when
factor loadings are low, and a sample size is 100 or greater. Applying Shevlin and
Miles’s findings to the current study, it is inferred that the low GFI value may be due to
the misspecification of the current model. The reason is that the current study has a
sample size greater than 200 and relatively high factor loadings; as result, there is little
possibility of having a distorted GFI derived from the magnitude of the factor loadings
and the sample size. Accordingly, the high correlation between annoyance/irritation and
falsity/no sense in the current model might be one plausible reason to explain the low GFI.
A second possibility accounting for the low GFI score may be deduced from the
algebraic function of the GFI, GFI ML = 1 - [tr (∑ −1 S − 1) 2 / tr (∑ −1 S ) 2 ] (Jöreskog &
Sörbom, 1984). Jöreskog and Sörbom (1984) defined GFI as “a measure of the relative
amount of variances and covariances in S that are accounted for by the implied model Σ”
(as cited in Hu & Bentler, 1995, p. 86). When there is no difference between the matrix
of observed variances and covariances (S) and the corresponding reproduced matrix
observed and reproduced matrix (Σ), the GFI indicates a perfect fit of the model to the
data (GFI = 1). Accordingly, the lack of the GFI can be detected by a more detailed
assessment of the covariance residual matrix in the current measurement model. A
summary of statistics for standardized residuals in the modified measurement model is
reported in Appendix F. The standardized covariance residuals matrix showed that there
were 28 problematic covariance residuals, indicating a fit problem. Considering a total
253 residuals in the matrix, the current number of the problematic covariance residuals is
relatively high. Accordingly, such discrepancies between the observed and the
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corresponding reproduced covariances in the current matrix function to decrease the
value of GFI and represent the lack of the amount of observed variance explained by the
a priori specified model (Jöreskog & Sörbom, 1981, as cited in Marsh, Balla, &
McDonald, 1988). The detailed information regarding what problematic items are and
how the researcher deals with such items will be discussed in the next section.
Fit of internal structure of the model
After assessing the overall fit of the final measurement model, the researcher
evaluated the internal structure in terms of the reliability and validity computed from the
results of the CFA. First, the reliability of the belief measures were evaluated based on
Cronbach’s alpha, individual item reliability, composite reliability, and AVE; the attitude
measures were tested by Cronbach’s alpha. The results showed excellent internal
consistency among attitude indicators (see Table 5.14), and the high reliabilities of the
belief measures shows that seven belief dimensions could be appropriately measured by
the current scale (see Table 5.27).
The researcher found, however, that for two indicators (A3 & G4), the individual
item reliabilities (i.e., R²) were lower than the remaining items (.44 and .45 respectively).
Even though there have been no suggested rules-of-thumb for individual item reliability
as to proper criteria, these values seem inadequate. According to Fornell and Larcker
(1981), AVE scores greater than .50 are considered acceptable for reliabilities of each
construct. Considering that the AVE is represented by the average score of all items’ R²s
in a construct, it is believed that individual item reliabilities greater than .50 may be
considered adequate. The modified measurement model had only two problematic
indicators, A3 and G4.
The A3 item (“advertising through sport often interrupts programs just when I am
getting interest”) seems different than the other two annoyance/irritation items (A1:
“advertising through sport is annoying” and A2: “advertising through sport is irritating”).
The wording in the A3 item deals with TV advertising through sport while the other two
items pertain to overall beliefs about advertising through sport representing
annoyance/irritation. Having worded the item around a particular format, TV, could have
influenced the respondents to only consider TV commercials rather than advertising
through sport in general when they responded to the item. Considering the previous
171
findings that consumers generally believed that TV advertising was annoying or irritating
(e.g., Alwitt & Prabhaker, 1992; Mittal, 1994), the current respondents’ perceptions on
the A3 item would be expected to be more negative than those on the A1 and A2 items.
The descriptive statistics in Table 5.17 confirm this expectation that the respondents’
mean score on the item, A3 (i.e., 3.62), is relatively higher than their mean scores on the
items, A1 (i.e., 3.05) and A2 (i.e., 2.94). Accordingly, the relatively low factor loading on
this item could be derived from the wording of the item.
Second, wording problems were also detected in one good for the economy item
(G4). The item G4 (“advertising through sport helps raise our standard of living”) did not
fit with the other two items (G1: “advertising through sport in general helps our nation’s
economy” and G2: “advertising through sport generally helps the local economy”). The
G1 and G2 items concern the matter of advertising’s “economic impact on our society.”
The G4 item pertains to advertising’s impact on “living quality,” and may not be directly
related the respondents’ perceptions about advertising roles in the overall economy.
In summary, the two poorly worded items do not seem representative of the
constructs. The wording of items in a scale designed to measure a domain of interest
need to capture the general concepts of the domain while a measure should not be
identical with other measures within the domain. For example, good for the economy
may be measured by several different dimensions, such as a product-related dimension or
a price-related dimension. If a researcher generates items based on each of the different
dimensions in order to measure people’s overall perception toward the good for the
economy construct, s/he may have difficulty providing evidence of reliability or validity
of the scale. Accordingly, a researcher should carefully generate items providing better
measures of the domain of interest.
Another important issue to be discussed regarding the model fit is the
modification index. The modification index from the CFA included suggestions to
correlate many error terms in order to decrease the overall chi-square score. When
researchers experience poor model fit to the data near the last stage of a research project,
they might be tempted to depend on empirical evidence such as a modification index to
improve the model fit, particularly, by re-specifying correlations among measurement
errors. Bentler (1980) noted that parameters representing correlations among error terms
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may be added for the purpose of making a better fit. However the considerations of any
new parameter suggested from modification indexes should be based on theoretical
backgrounds as well as empirical results (Anderson & Gerbing, 1988; Jöreskog, 1993,
Kelloway, 1995). Particularly, Kelloway (1995) questioned the construct validity of the
latent variables with the following reason:
When one allows correlated uniqueness terms among the observed variables the
implication is that there is some ‘factor’ other than the specified latent variables
that is affecting the observed variables. Incorporating correlated uniqueness
terms changes the definition of the latent variables and, by extension, impugns
the validity of subsequent analyses (p. 221).
Additionally, several researchers (e.g., Bagozzi, 1983; Fornell, 1983; Gerbing &
Anderson, 1984) strongly opposed to the use of correlated error terms “unless (1) it is
warranted on theoretical or methodological grounds, or (2) it does not significantly alter
the structural parameter estimates” (Fornell, 1983, p. 447), or “(3) unless it does not also
significantly alter the measurement parameter estimates (Bagozzi, 1983, p. 450). In the
current study, the decision was made not to correlate error terms in order to increase the
model fit of the current measurement model since there was no theoretical rationale for
such action.
Issues dealing with validity. Next, the researcher will discuss several issues that
emerged from the validity tests. First, in terms of validity tests of the attitude construct,
the researcher employed a semantic differential scale method to verify convergent
validity of the current attitude construct measured by the Likert scale method. When
there is a high correlation between two independent methods purporting to measure the
same construct, a good convergent validity of the construct is achieved (Hair, Bush, &
Ortinau, 2000; Lemon, 1973). However, a convergent validity test using a correlation
between two different methods may produce inaccurate information. Although the two
different scales employed in the study have been popularly utilized to measure attitudes
toward advertising in general in advertising research and have been considered valid and
reliable measures (e.g., MacKenzie & Lutz, 1989; Polly & Mittal, 1993), the observed
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high correlation could be misled by the use of similar measures in both methods, derived
from common method variance (Lemon, 1973) (see Table 5.37).
One possible solution to reduce such errors yielded by using the correlation
between independent scales measured at the same time would be to evaluate a
measurement model of attitudes using CFA. The researcher tested the convergent
validity of attitude toward advertising through sport with the factor loadings of three
indicators and the AVE during the assessments of the full structural model. However, it
would be more desirable to proceed to a consideration of the full structural model after
researchers obtain a reliable and valid measurement model (Anderson & Gerbing, 1988).
In the current study, unfortunately, the attitude construct was unidimensional with three
indicators. That means that the measurement model of attitudes is a just-identified model
which fits the data perfectly merely as a mathematical necessity. As a result, researchers
obtain no information regarding the internal structure fit as well as the overall fit of the
just-identified model. Accordingly, it is suggested that a study may have an overidentified attitude model by developing more items. It would be more reasonable that
when two independent measurement models of beliefs and attitudes provide acceptable
overall and internal structure fits to the data, they could be incorporated to the full
structural equation model for further analyses.
In terms of the validity tests for the belief dimensions, convergent and
discriminant validity were assessed. First, convergent validity was examined based on
factor loadings, AVE scores, and a review of the standardized residuals matrix. Based on
the criterion of AVEs (i.e., .50), all sets of indicators for each dimension represent the
same construct. However, CFA revealed that two factor loadings, A3 (λ = .66) and G4 (λ
= .67) were less than the suggested cut-off of .707 although they were greater than twice
its standard error (Anderson & Gerbing, 1988). The loadings less than .707 indicate that
these two indicators include more than unique variance than common variance. Although
factor loadings and AVE scores are often utilized to evaluate convergent validity, AVE
scores do not evaluate the convergence of individual indicators on their proposed
dimension because the AVE computation produces a single value for a dimension,
computed by employing an indicator’s loadings and error variances within a dimension.
Therefore, it is deemed that using factor loadings is a more accurate method for
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convergent validity of indicators.
The researcher also gained information regarding convergent validity by
scrutinizing the standardized covariance residuals matrix. The residual covariance matrix
includes the differences between the observed covariances and the reproduced
covariances. Accordingly, sometimes individual covariance residuals could provide
researchers inaccurate information because individual indicators’ variances and
covariances could be significantly different (Bagozzi & Yi, 1988). Thus, using
standardized residuals matrix is preferred as an alternative indicator for measurements of
internal quality of indicators (Anderson & Gerbing, 1988; Bagozzi & Yi, 1988; Hair et al.,
1998; Tate, 1998). The results indicated that among a total of 253 standardized residuals
in the final measurement model, 28 (11%) were greater than the ± 2.58 criterion,
approximately twice the 5% tolerance excused (Hair et al., 1998) (refer to Appendix F).
The matrix usually contains a typical pattern among large standardized residuals (e.g., >
2.58) that a few indicators (e.g., A3, M1, F2, or F4) repeatedly produce large residuals
with other indicators. For a better understanding of a future model respecification, the
structures of the standardized residual matrix should be investigated in terms of the
identifications of problematic items or dimensions and ideas for dealing with such items
or dimensions.
A review of the residual matrix led to the conclusion that four items (A3, M1, F2,
& F4) were related with 17 out of 28 residuals exceeding ‫׀‬± 2.58‫׀‬. One way to deal with
the problematic residuals was to eliminate them. It was assumed that lowering the
number of high residuals would lead to significant improvement in model fit. The
decision to eliminate the four items was also supported by other information. The item
A3 was problematic in the reliability test in the calibration sample; M1 was problematic
in the reliability test in the validation sample. In addition, the A3, F2, and F4 items were
included in the annoyance/irritation and falsity no sense dimensions which showed the
possibility of multicollinearity. When the current model is respecified for future testing,
researchers should carefully decide whether they will delete the items based the
considerations of the results from other tests of internal structure fit of the model.
The discriminant validity of the seven belief dimensions was evaluated using the
correlations among constructs and AVEs. The results showed the correlation between
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annoyance/irritation and falsity/no sense was very high in the final measurement model
(i.e., .88), and the AVEs for both dimensions were less than the squared correlation values
of the dimensions. Although the study failed to discriminate annoyance/irritation and
falsity/no sense from the validity tests, the chi-square differential tests supported retaining
annoyance/irritation and falsity/no sense as independent dimensions and the CFAs
provided an acceptable scale for both attitude and belief constructs with strong empirical
evidence. A review of previous studies (e.g., Alwitt & Prabhaker, 1992; Bauer & Greyser,
1968) also found that there have been no theoretical justifications for the integration of
the two dimensions or the elimination of either dimension. Thus, the researcher
determined that the measurement model with seven belief dimensions should be
incorporated into a full structural equation model in order to assess seven hypothesized
relationships between belief and attitude constructs. Future research should consider the
problem of annoyance/irritation and falsity/no sense loading as one factor because the
cross validation test for the structural model also confirmed the convergence of these two
dimensions. More discussion regarding this matter is provided in the next section.
Annoyance/irritation v. falsity/no sense
The possibility that annoyance/irritation and falsity/no sense actually represent
one factor was detected in the first EFA with the sample from the first data collection.
The subsequent discriminant validity tests from two different samples produced the same
results; the estimated correlation coefficients between two dimensions were higher than
the suggested cut-off values (e.g., .85), and the squared correlations were also higher than
each AVE score. As a result, the study failed to provide evidence of discriminant validity
for the annoyance/irritation and falsity/no sense dimensions.
When the first CFA failed to discriminate annoyance/irritation and falsity/no
sense in the initial model, as a subsequent test, Bagozzi and Phillip’s (1982) chi-square
difference test was performed to compare the overall model fits between the constrained
and unconstrained models with these two belief dimensions. Bagozzi and Phillips
mentioned that “a significantly lower χ² value for the model in which the trait correlations
are not constrained to unity would indicate that the traits are not perfectly correlated and
that discriminant validity is achieved” (p. 476). Furthermore, Anderson and Gerbing
(1988) noted the following concerning the Bagozzi and Phillips’ simultaneous test of all
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pairs of correlations at a time:
This test should be performed for one pair of factors at a time, rather than as a
simultaneous test of all pairs of interest. The reason for this is that a
nonsignificant value for one pair of factors can be obfuscated being tested with
several pairs that have significant values. (p. 416)
Consequently, Anderson and Gerbing (1988) recommended the use of the
adjusted significance level for assessments of discriminant validity for each pair of
factors when there are many pairs of factors being assessed. The adjusted significance
level for each test can be computed by the formula, “α 0 = 1 - (1 - α i ) t , where α 0 is the
overall significance level, typically set at .05; α i is the significance level that should be
used for each individual hypothesis test of discriminant validity; and t is the number of
tests performed” (p. 416).
The chi-square difference test for discriminant validity of annoyance/irritation
and falsity/no sense from this study met both requirements (e.g., Anderson & Gerbing,
1988; Bagozzi & Phillips, 1982). The results showed that the chi-square value of the
unconstrained model (i.e., 15.64) was significantly lower than the value of the
constrained model (i.e., 36.06) at the .05 significance level (see Table 5.23). The
researcher employed only one pair of factors at a time so that the true overall significance
level (α 0) was still equal to the adjusted significance level (α i ) for assessment of
discriminant validity of this pair (i.e., α 0 = α i = .05). Thus, there was no possibility that
this significant value for the pair of annoyance/irritation and falsity/no sense was
capitalized by being tested with other significant pairs (Anderson & Gerbing, 1988). To
summarize, the study provided conflicting empirical results regarding discriminant
validity of annoyance/irritation and falsity/no sense: the tests using the squared
correlations and AVEs did not support, but the chi-square difference test supported
annoyance/irritation and falsity/no sense as independent constructs.
The decision as to whether the two dimensions should be separated or combined
may not be solely dependent on such empirical results without consideration of
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theoretical justifications when the model is respecified. Researchers have examined
people’s perceptions of annoyance/irritation or falsity/no sense toward advertising in
general or other mediums (e.g., Alwitt & Prabhaker, 1992; Andrews, 1989; Ducoffe,
1996; Haller, 1974; Muehling, 1987, Pollay & Mittal, 1992). The review of literature
revealed that there have been only a few studies which utilized both dimensions to
explain consumers’ attitudes toward TV advertising (e.g., Alwitt & Prabhaker, 1992;
Haller, 1974). Unfortunately, neither study provides an evaluation of the discriminant
validity between the belief dimensions. Even though no empirical evidence of
discriminant validity was found between the two dimensions, the review of literature
revealed that they have been frequently utilized as important beliefs accounting for
attitude toward advertising and dealt as independent domains. The researcher should
consider why the two dimensions were highly correlated each other.
There may be two possible reasons to explain this convergence. One reason that
may explain the merging of the two dimensions on one factor is poor wording of items.
The generated items for annoyance/irritation and falsity/no sense may not capture each of
the domains as specified. Most of the items were derived from the literature review,
while one annoyance/irritation item (“advertising through sport is intrusive”) was
developed by the researcher. The self developed item was later removed from the item
pool during the scale development process. Accordingly, all items which were actually
employed to measure the two dimensions were based on traditional advertising research.
It is important to note that annoyance/irritation and falsity/no sense have been
regarded as different concepts. Bauer and Greyser (1963) insisted that consumers’
primary reason for considering advertising as an annoyance/irritation is closely related
with the “stimulus qualities” (p. 211) of advertising such as intrusiveness (e.g., people
heard or saw advertising too often or advertising is too long or loud) or an insult to
intelligence (advertising is silly or ridiculous). Bauer and Greyser explained why ads
may be considered annoying with the following statement:
When Americans consider ads as annoying, it is more because of these ads’ direct
irritation as unpleasant events in one’s daily life, and less because they fail to give
accurate or interesting marketing information to customers or engender moral
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concern. (p. 219)
Bauer and Greyser also proposed that a prime reason why consumers perceived
advertising as falsity/no sense was the dimension’s association with consumers’ “moral
concern” (p. 215) toward advertising (e.g., type of product should not be advertised, or
advertising is bad for children). These ideas clearly indicate that the two dimensions may
be conceptually different.
The study primarily purported to utilize the items in annoyance/irritation and
falsity/no sense items to obtain the respondents’ overall perceptions of negative roles of
advertising through sport as annoyance/irritation and falsity/no sense, respectively. After
encountering the problems with discrimination, the researcher went back and examined
the constructs more closely. As a result, it has been determined that there were problems
with the items used to represent the two dimensions. Considering reasons why people
believe advertising as annoyance/irritation and falsity/no sense from Bauer and Greyser’s
(1963) study, the researcher found some flaws in contents of the items in the falsity/no
sense. The item, F2 (“advertising through sport often insults the intelligence of the
average consumer”) is directly related with “stimulus qualities” (p. 211) of advertising
which was the most significant reason why advertising was considered annoying or
irritating in Bauer and Greyser’s study. In addition, the remaining two items in falsity/no
sense, F1 (“advertising through sport in general is misleading) and F4 (“advertising
through sport is deceptive”) are associated with “informational failure” (p. 214) which
was the second important reason why people believed that advertising as
annoyance/irritation in their study.
It is important to clarify the current problem that all items in the falsity/no sense
are related with the first and second substantive reasons explaining people perceptions of
annoyance/irritation while no item in falsity/no sense is related with “moral concern” (p.
215), which was the most important reason for people’s perception of falsity/no sense in
Bauer and Greyser’s study. Accordingly, a lack of appropriate items capturing the current
domain of falsity/no sense is a likely reason why the items in the two dimensions were
highly correlated. It is recommended that more items concerning people’s moral
concerns over either inappropriate products being advertised or the effects of advertising
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on children be included to better represent falsity/no sense.
The modification indices obtained during the estimation of the measurement
model also provided evidence to support this idea. The modification indices suggested
adding a path from annoyance/irritation factor to the F2 item in falsity/no sense. This
modification aids the greatest decrease in a chi-square score (i.e., 21.0) among all paths
suggested in the indices. In addition, the modification indices recommend adding the
path from annoyance/irritation to the F4 in falsity/no sense which would also
significantly decrease the chi-square score (i.e., 17.7). Based on the theoretical and
empirical considerations, therefore, it is concluded that the contaminated items in
falsity/no sense limits the validity of the results.
A second possibility that should be considered is that annoyance/irritation and
falsity/no sense are not truly distinct. The two beliefs may be not conceptually different
constructs. Churchill (1979) indicated that when researchers specify the domain of a
construct, researchers “must be exacting in delineating what is included in the definition
and what is excluded” (p. 67). Regarding the definitions of annoyance/irritation, Bauer
and Greyser (1968) defined annoying ads as following:
Theses are ads that irritate you. They may be annoying because of what they say
or how they say it. They may annoy you because they are around so much, or
because of when and where they appear. They may be other reasons for ads to be
annoying – the main thing is that they bother or irritate you. (p. 182)
In terms of falsity/no sense, Bauer and Greyser defined offensive ads as following:
These are ads that are vulgar or morally bad in your opinion. They may be
dishonest, or untrue. They may be ads for something you don’t think should be
sold or used. They may be offensive because of the way in which they are done,
and you may think such ads should not be allowed. The main thing is that you
feel strongly that such ads are wrong. (p. 182)
Based on the definitions, people may be annoyed or irritated when they perceive
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that advertisements are dishonest or untrue. Bauer and Greyser (1963) tried to
differentiated between the two dimensions by limiting annoyance as “intrinsic quality of
the advertisements” (p. 219) and offensiveness as “the degree of moral concern” of the
advertisements (p. 220). While it may be successfully argued that annoyance/irritation
and falsity/no sense are conceptually different, it is also possible that people react in a
similar manner to the two belief dimensions. Bauer and Greyser’ study also revealed that
untruthful or exaggerated advertising was one of important reasons advertising is
considered annoying. People may believe that annoyance/irritation is a consequence of
falsity/no sense. This may explain why the two dimensions were not distinct.
Following the presumption that annoyance/irritation and falsity/no sense items
measure the same dimension, the researcher did examine a model with
annoyance/irritation and falsity/no sense as one construct. If a researcher experiences a
situation where items in two dimensions which were originally conceptualized as distinct
merge on one dimension, Churchill (1979) suggested that s/he may create a new
dimension with the items which highly loaded on the dimension. The items in the two
dimension (A1, A2, A3, F1, F2, & F4) were included in a new dimension (e.g.,
annoyance/falsity); they had very good internal consistency, the Cronbach’s alpha value
was .89 and item-to-total correlations ranged from .64 to .80. Accordingly, the researcher
tested a six factor measurement model to compare with the seven factor model.
Two subsequent confirmatory factor analyses using the calibration sample were
performed to compare model fit indices of the six- and seven-factor models. The results
of model fit comparisons are included in Appendix H. The results revealed that there was
no substantial improvement in fit between the six and seven factor models. In terms of
absolute fit and comparative fit, the seven-factor model had a slightly better fit to the data
than the six factor model. In terms of parsimonious fit, the six-factor model showed
better parsimony; however, the difference was minimal (see Appendix H). The
researcher concluded that there was no difference in fit between two models. Therefore,
another attempt to figure out the convergence of annoyance/irritation and falsity/no sense
should be considered in future research. Detailed information regarding the issue of what
to do with the two dimensions will be discussed in the limitation and future research
section. The researcher, to date, has discussed several issues raised during the
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development of a scale measuring beliefs and attitude toward advertising through sport.
The next section includes a discussion of the results obtained from the evaluation of the
structural equation model.
Hypothesis testing
Overview
After confirming that the proposed instrument was reliable and valid, the final
measurement model with seven belief dimensions was integrated into a structural
equation model for examination of the hypothesized relationships between the belief and
attitude constructs. Before scrutinizing the findings it is important to consider whether
consumers’ attitudes and beliefs about advertising through sport are different than their
attitudes toward advertising in general or other mediums. Understanding consumers’
overall attitudes and beliefs about advertising through sport relative to their attitudes and
beliefs about advertising in general or other advertising mediums would help to inform
the researcher as to why the current findings are important and useful to academic
researchers and practitioners.
Attitude toward advertising through sport
Earlier studies of attitudes toward adverting in general in the 1940s and 1950s
indicated that consumers had generally favorable attitudes toward advertising in general
(e.g., Association of National Advertisers, 1942; McFadden Publications, 1951; Redbook
Special Report, 1959, as cited in Bauer & Greyser, 1968). Since Bauer and Greyser’s
(1968) mixed results regarding consumers’ overall attitudes toward advertising, consumer
attitudes have become more negative (e.g., Andrews, 1989; Haller, 1974; Mehta, 2000;
Muehling, 1987; Zanot, 1981, 1984). Shavitt et al. (1998) indicated that their
respondents had more favorable attitudes toward adverting in general than those in
previous studies. They revealed that 44 percent of respondents had favorable attitudes
toward adverting; however the proportion was still less than a majority of the respondents.
Researchers in recent years have extended their research interests to attitude
toward advertising in a variety of specific mediums, such as television or online. First,
consumers’ attitudes toward TV advertising have been more unfavorable than attitudes
toward advertising in general (e.g., Alwitt & Prabhaker, 1992; Bartos, 1981; Mittal, 1994).
Mittal (1994) showed that only 23 percent of respondents liked TV advertising; 48
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percent of the respondents disliked TV advertising due to its negative effects on children
or its negative attributes of deception, boredom, annoyance, and triviality. Moreover,
such consumers’ increasing antipathies to TV commercials have motivated several
companies (e.g., TiVo, DirectTV, or LG) to invent new technologies such as personal
video recorder (PVR) or digital video recorder (DVR) which helps TV viewers skip
unwanted TV advertisements. Recent research by Accenture (2005) revealed that eight
percent of U.S. houses have PVR or DVR devices today, and they have already skipped
approximately 70 percent of the TV commercials aired. The study also forecast that 40
percent of U.S. houses will posses such devices by 2009; as a result, 22 percent of all TV
commercials would be skipped by then. The new inventions and consumers’ unfavorable
attitudes toward TV commercials highlight the difficulty of reaching a mass consumer
audience using TV ads. However, the study noted that consumers’ ad skipping behaviors
using the devices were not effective during live TV programs such as sporting events or
news.
In terms of attitudes toward online advertising, previous research produced
mixed results (Burns, 2003; Previte, 1998; Schlosser et al., 1999). Previte (1998) showed
that 38 percent of respondents liked online advertising; 54% agreed that online
advertising was a good thing; 47% disagreed that online advertising was unfavorable (as
cited in Burns, 2003). Schlosser et al. (1999) revealed that 38 percent liked online
advertising, 28 percent was neutral, and 35 percent disliked. Burns (2003) did not
describe respondents’ proportions on attitudes toward online advertising; she showed an
average mean score of 3.13 in a five-point Likert from two versions of the survey. Based
on previous research, consumers’ attitudes toward online advertising seem neutral or
somewhat positive. In terms of specific formats in online advertising, Grimes, Hough,
and Signorella (2003) revealed that consumers’ attitudes toward spam mail were
extremely negative. Burns (2003) also indicated that people usually had unfavorable
attitudes toward pop-ups. Recently, computer software companies (e.g., Microsoft) and
email sites (e.g., hotmail.com) developed new technologies such as a pop-up blocker or
email filter and distributed them to protect Internet users from advertisements. This reflects
today’s consumers’ growing aversion toward online advertising.
The results from the current study revealed that the respondents’ mean score on
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attitude toward advertising through sport was high: two thirds of the respondents had
generally positive attitudes toward advertising through sport. The observed negative
skewness statistics in the attitude items in the results also indicated that most of the
respondents rated their scores above the mean scores.
Given the previous findings that young and highly educated samples are usually
negative in their attitudes toward advertising in traditional mediums (e.g., Mittal, 1994;
Schlosser et al., 1999), the current findings in the demographically similar samples
appear surprising. One possible explanation for the finding is that college students
actively participate in sport activities and watch sporting events. Familiarity and
attachment to a sport or with a sports team could overcome an individual’s initial
negative reaction to advertising in general and foster a positive attitude with advertising
that features elements such as a favorite team or athlete. Unlike other advertising
mediums, a consumer’s positive attitude toward advertising through sport would explain
why many companies have preferred the use of sport as an advertising medium, and
support the current endeavor to develop a scale measuring attitudes toward advertising
through sport.
Beliefs about advertising through sport
For better understanding of the current belief dimensions predicting the
respondents’ favorable attitudes, it is also valuable to examine how the respondents’
perceived beliefs about advertising through sport are different than consumers’ beliefs
about advertising in general or other advertising mediums in past research. This
comparison may help answer the question of why people have different attitudes toward
advertising through sport compared to their attitude toward traditional advertising.
Product information. A review of literature revealed consistent findings that
people perceive that advertising in general (e.g., Ducoffe, 1995; Pollay & Mittal, 1993;
Schlosser et al., 1999; Shavitt et al., 1998) or in other mediums (e.g., Alwitt & Prabhaker,
1992; Ducoffe, 1996; Korgaonkar et al., 1997; Mittal, 1994; Schlosser et al., 1999) was
generally informative. The current study produced similar findings studies. Two-thirds of
respondents believed that advertising through sport provided them with a variety of
product information. Like other advertising mediums, people may believe that
advertising through sport is a good way to obtain information regarding what goods or
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services are available.
Social role and image. Social role and image has been frequently employed as a
belief dimension in studies examining belief in advertising in general or other mediums
(e.g., Alwitt & Prabhaker, 1992; Korgaonkar et al., 1997; Mittal, 1994; Pollay & Mittal,
1993). In advertising in general research, people were unlikely to believe advertising’s
functions of social role and image (Pollay & Mittal, 1993). In research for other
advertising mediums, Alwitt and Prabhaker’s (1992) study noted that 14 % of their
respondents believed that TV advertising gave them a good idea about products by
showing the kinds of people who use the product; Korgaonkar et al. (1997) also indicated
that mean scores of three items relative to social role and image in direct marketing
advertising were low, ranging from 2.49 to 3.04 in a five-Likert scale.
The current study found that respondents’ perceptions of social role and image in
advertising through sport were somewhat positive; however, the overall mean score of
this dimension was the lowest among the four positive belief dimensions utilized in the
study. Unlike previous studies on advertising in general and in other mediums, the
current participants usually agreed that advertising through sport helped them learn what
was in fashion, what they should buy for keeping a good social image, and gave them
ideas about fashion.
Hedonism/pleasure. Past research has generally indicated that people’s
perceptions of hedonism/pleasure in advertising were quite high (e.g., Pollay & Mittal,
1993; Schlosser et al., 1999). Hedonism/pleasure has been reported to be one of the most
important reasons why consumers like TV advertising (e.g., Aaker & Bruzzone, 1981;
Alwitt & Prabhaker, 1992). Mittal (1994) noted that more respondents agreed than
disagreed that sometimes TV advertising were even more enjoyable than TV programs.
In terms of online advertising, Ducoffe (1996) showed that respondents agreed that online
advertising was entertaining; however, Schlosser et al. (1999) demonstrated that
respondents did not hold a positive view of online advertising’s hedonism or pleasure.
This study revealed that a mean score of respondents’ perceptions of
hedonism/pleasure in advertising through sport was the highest value among the seven
belief dimensions. A majority of the respondents believed that advertising through sport
was entertaining or enjoyable. It seems that advertisers often attempt to utilize
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hedonism/pleasure themes in advertising through sport when they design advertisements.
The respondents’ perception that advertising through sport was hedonic might be one
important reason they had positive attitudes toward advertising through sport.
Annoyance/irritation. In contrast to hedonism/pleasure, annoyance/irritation has
been considered one important reason people dislike advertising (e.g., Haller, 1974;
Alwitt & Prabhaker, 1992). Alwitt and Prabhaker (1992) indicated that more than 80 %
of their participants believed that most commercials on TV had too many commercials in
a row and the same TV commercials were constantly shown again and again. However,
unlike past research, the current study showed that approximately two thirds of the
respondents disagreed that advertising was annoying or irritating; more respondents
disagreed (48%) than agreed (33%) that advertising through sport often interrupted
programs just when they were getting interested in a program. These findings showed
that respondents usually did not consider advertising through sport to be annoying or
irritating.
Good for the economy. Previous studies revealed inconsistent results regarding
people’s perceptions about good for the economy (e.g., Alwitt & Prabhaker, 1992;
Andrews, 1989; Mittal, 1994; Muehling, 1987). People generally believe that advertising
in general is good for the economy, resulting in better products or raising our standard of
living (e.g., Andrews, 1989; Muehling, 1987). However, in terms of TV advertising,
Alwitt and Prabhaker (1992) showed that 18 % of respondents perceived that TV
advertising resulted in better products for the public; Mittal (1994) indicated 59 % of
respondents did not agree that TV advertising raised people’s standard of living.
The current study showed similar results with past research. More respondents
agreed than disagreed that advertising through sport helped our nation’s economy and the
local economy, and raised our standard of living. However, one interesting result was
that a sizable proportion of respondents indicated “neutral”. It is possible that the high
proportion of neutral responses for good for the economy may explain why this
dimension did not influence their overall attitude toward advertising through sport (e.g.,
Alwitt & Prabhaker, 1992).
Materialism. Andrews (1989) found that student respondents agreed that
advertising often persuades people to buy things they should not buy. Mittal (1994)
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reported that a majority of respondents agreed with TV advertising’s materialism function.
Korgaonkar et al. (1997) also indicated that materialism was generally respondents’
negative reactions toward direct marketing advertising. There has been a consensus
between past research and the current study in that the results revealed that respondents
perceived materialism to be a negative social effect of advertising through sport.
Falsity/no sense. Since Bauer and Greyser (1968)’s work, subsequent studies
have indicated that advertising has been considered false due to its use of deception.
Many studies demonstrated that most of their respondents believed that advertising in
general was misleading and insulted people’s intelligence (Andrews, 1989; Haller, 1974;
Muehling, 1987). Unlike previous studies, the results from the current study revealed
that a significant proportion of respondents did not agree that advertising through sport
was generally misleading or insulted the intelligence of the average consumer. There was
also a larger number of respondents who did not believe that advertising through sport is
deceptive.
Overall, the study contributes to the knowledge of beliefs about advertising
through sport. If the results revealed that the current respondents’ perceptions of
advertising through sport were identical with those of traditional advertising, the
respondents’ positive attitudes toward advertising through sport were not due to the
unique characteristics of sport as an advertising medium. Some findings were similar
with past research in that respondents’ beliefs regarding product information,
hedonism/pleasure, good for the economy, and materialism in relation to advertising
through sport were consistent with beliefs regarding advertising through other mediums
(e.g., Andrews, 1989; Alwitt & Prabhaker, 1992; Ducoffe, 1996; Korgaonkar et al., 1997;
Mittal, 1994; Muehling, 1987; Schlosser et al., 1999; Shavitt et al., 1998). There were,
however, differences in beliefs with respect to social role and image. Beliefs about social
role and image in relation to advertising through sport were more positive (higher)
compared to research regarding advertising in general or in other mediums. One
interesting finding is that unlike previous studies in advertising in general or other
mediums (e.g., Alwitt & Prabhaker, 1992; Haller, 1974; Pollay & Mittal, 1993), the
results from the current study indicated that most respondents did not believe that
advertising through sport was associated with the dimensions of annoyance/irritation and
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falsity/no sense. This finding may be very important for understanding why consumers’
overall attitudes toward adverting through sport were different than their attitudes toward
advertising in general or other mediums. The next section presents information regarding
the structure of attitude toward advertising through sport, particularly why certain belief
dimensions influenced attitude toward advertising through sport?
The impact of beliefs on attitude
A valid and reliable scale was used to measure beliefs and attitude toward
advertising through sport. With a reliable and valid measure, the next task was to assess a
structural model to ascertain the extent to which beliefs about advertising through sport
influenced an individual’s attitude toward advertising through sport. One structural
model using the calibration data supported two proposed relationships, and the other
structural model using the validation data confirmed the findings derived from the
calibration data.
The results from two data collections produced similar findings that two belief
dimensions, product information and hedonism/pleasure, were important determinants in
shaping the respondents’ overall attitude toward advertising through sport. The current
findings were generally consistent with the findings of previous research (e.g., Alwitt &
Prabhaker, 1992; Bauer & Greyser, 1968; Burns, 2003; Ducoffe, 1995, 1996; Mittal,
1994; Pollay & Mittal, 1993; Schlosser et al., 1999; Shavitt et al., 1998).
The findings indicated that respondents’ one important reason for liking
advertising was if advertising included more product information (e.g., Bauer & Greyser,
1968; Burns, 2003; Mittal, 1994; Pollay & Mittal, 1993; Schlosser et al., 1999; Shavitt et
al., 1998). In terms advertising in general, Bauer and Greyser (1968) showed that the
most important reason people liked advertising in general was that advertising played a
role as “a conveyor of information about products, services, prices, and so on” (p. 183).
Bauer and Greyser indicated that people considered advertising as informative because
people generally believed that they learned something from advertising. The current
respondents also liked advertising through sport relative to a belief that advertising
through sport provides information in which they are interested. The respondents
generally believed that advertising through sport offers a variety of information such as
brand’ features they are interested in or new products in the market place.
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The fact that advertisers benefit from advertisements including product
information is easily illustrated with advertising through sport. One example is the Super
Bowl. Many companies have had success in increasing sales of their products by airing
advertisements during the Super Bowl that emphasize product information (e.g., Apple
computer, Chrysler, and Victoria’s Secret) (Twitchell, 2000; Yelkur et al., 2004).
The results also indicated the significant impact of hedonism/pleasure on college
students’ attitude toward advertising through sport. Past studies have indicated
hedonism/pleasure was a dominant belief influencing attitude toward advertising in
general and in other mediums (e.g., Bauer & Greyser, 1968; Burns, 2003; Ducoffe, 1996;
Mittal, 1994; Pollay & Mittal, 1993; Schlosser et al., 1999; Shavitt et al., 1998). The
respondents indicated they were more likely to be favorable toward advertising through
sport when they believed advertising was more entertaining or enjoyable. It seems that
consumers generally prefer hedonic content when they hear or watch advertising
regardless of the advertising medium. Accordingly, hedonism/pleasure content should
increase the acceptance and persuasiveness of advertising through sport communications
(Schiffman & Kanuk, 2002).
The current study also generated some unique findings. The negative belief
dimensions (i.e., annoyance/irritation, materialism, and falsity/no sense) did not
negatively influence the respondents’ overall attitude toward advertising through sport.
These findings stand in contrast to past research which have found that consumers’
overall attitudes toward advertising in general or in other mediums were influenced by
negative belief dimensions such as annoyance/irritation (e.g., Bauer & Greyser, 1968;
Burns, 2003; Mittal, 1994), materialism (e.g., Mittal, 1994; Pollay & Mittal, 1993), or
falsity/no sense (e.g., Alwitt & Prabhaker, 1992; Bauer & Greyser, 1968; Muehling, 1987;
Pollay & Mittal, 1993; Schlosser et al., 1999; Shavitt et al., 1998). The researcher should
consider why there was no impact of such negative belief dimensions on attitude toward
advertising through sport, unlike findings in the previous studies.
Past research has considered that people perceive annoyance/irritation and
falsity/no sense as important negative beliefs about traditional advertising and has
showed that those dimensions play significant roles in explaining people’ overall attitudes
toward advertising (e.g., Bauer & Greyser, 1968; Alwitt & Prabhaker, 1992; Pollay &
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Mittal, 1993; Schlosser et al., 1999). However, the current results indicated that
annoyance/irritation and falsity/no sense did not contribute to overall attitudes toward
advertising through sport. Particularly, such contrary results were somewhat anticipated
because the initial results from descriptive statistics of seven belief dimensions indicated
that the respondents’ annoyance/irritation and falsity/no sense beliefs were not negative.
It was unusual to find that respondents did not perceive advertising through sport to be
annoying/irritating or false compared to previous research in traditional advertising
mediums; as a result, the findings help explain why those belief dimensions had little
influence on overall attitudes toward advertising through sport. There might be two
possible reasons to explain why the respondents did not negatively react to advertising
through sport.
First, it is possible that sport advertisers have been successful in creating
advertising which does not elicit annoyance/irritation and falsity/no sense reactions from
consumers. For instance, a company features a famous golf player to advertise their new
driver because consumers may be more willing to trust the advertisement messages
conveyed by the expert. In addition, if the company airs the advertisement during a
televised golf tournament, the advertisement may not be regarded as an interruption to the
program. Instead, such an ad may capture the TV viewers’ interest. However, it is also
true that such reactions, particularly falsity/no sense may be not totally in the control of
the advertisers because “it is difficult to envisage a world in which there would not be
some products or services to which some people would not take objection” (Bauer &
Greyser, 1968, p. 234). For example, an advertiser using in-stadium signage may
promote that their product can help a consumer lose 30 pounds in 30 days. In this case,
people would likely view this message skeptically based on their previous experiences
with the product or other personal reasons. Accordingly, the researcher may consider
another possibility for these findings.
Second, it is believed that college students may have stronger levels of attachment
or identification toward sport than other demographic groups. Such strong attachment or
identification toward sport may influence the current respondents to have disparate
responses to advertising through sport. This possibility may also account for the findings
that the respondents’ perceptions of advertising through sport promoting materialism did
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not influence their overall attitudes. Unlike annoyance/irritation and falsity/no sense, the
respondents perceived that advertising through sport is fostering a materialistic society by
influencing people to buy things they do not really need. However, such negative
perceptions about advertising through sport did not contribute their attitudes. It is
supposed that even though advertising through sport contains some negative functions,
college students may not negatively react to the use of sport as an advertising medium, or
that any negative reactions to advertising through sport do little influence the overall
attitudes due to their unilateral favorability toward sport.
Lastly, one concern was that the study revealed somewhat different results
between the calibration and validation samples. Although both tests indicated the same
determinants of the attitude, product information was the most dominant belief dimension
in the calibration sample, but hedonism/pleasure was the most dominant belief dimension
in the validation sample. Consistent with the current findings from the conveniently split
samples, the results from the tests with the randomly split samples also revealed that the
product information dimension had an influence in both samples, and the
hedonism/pleasure dimension had a stronger influence in the validation sample (see
Appendix G). Past research has also showed the conflicting findings regarding which
dimension plays a more significant role in explaining consumers’ attitudes toward
traditional advertising (e.g., Alwitt & Prabhaker, 1992; Burns, 2003; Ducoffe, 1996;
Mittal, 1994; Pollay & Mittal, 1993; Schlosser, et al., 1999; Shavitt et al., 1998).
In terms of advertising in general, hedonism/pleasure was usually more
significant than product information in accounting for consumers’ overall attitudes (e.g.,
Pollay & Mittal, 1993; Schlosser et al., 1999; Shavitt, et al., 1998). However, in Pollay
and Mittal’s (1993) collegian sample, a coefficient of product information was somewhat
greater than that of hedonism/pleasure. In terms of TV advertising, product informationrelated beliefs contributed the most to overall attitudes; a hedonism/pleasure-related
belief was second (e.g., Alwitt & Prabhaker, 1992; Mittal, 1994). In online advertising,
hedonism/pleasure was usually the most dominant belief accounting for consumers’
attitudes, followed by product information (e.g., Burns, 2003; Ducoffe, 1996; Schlosser
et al., 1999).
In sum, hedonism/pleasure generally contributes most to consumers’ attitudes
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toward advertising in general and online advertising; product information generally
influence most consumers’ attitudes toward TV advertising. The current study employs
the cross validation stage for confirming the significant tests of the seven hypothetical
relationships between beliefs and attitude. The study cross-validates two hypothetical
causal relationships that both product information and hedonism/pleasure influence the
respondents’ overall attitudes. However, the study fails to cross-validate the most
significant antecedent of attitude from the analysis of the convenient and randomly split
sample.
In addition, the researcher recognized that the good for the economy dimension
was a new significant determinant of attitude in the randomly split validation sample.
Even though a t-score of good for the economy barely exceeded a cut-off of 1.96, and its
impact on attitude was not cross validated through the randomly split samples, it should
be considered why good for the economy was significant in the randomly split validation
sample along with an explanation of why hedonism/pleasure had a higher standardized
coefficient in the both randomly and conveniently split validation samples. Considering
that the respondents’ demographic characteristics were not deemed significantly different
in the calibration and validation samples, such conflicting results might be mainly due to
random errors in the measured data. Such random errors could be derived from variation
in the researcher or the respondents. The researcher could not supervise all participants
when the data was collected. In some LAP classes, instructors rather than the researcher
charged the data collection. Even though the instructors were fully trained by the
researcher before distributing questionnaires, there might be variation in the instructors
when they introduced the purpose of the study and the specific procedures of the survey
to their students. The errors could be also resulted from the respondents. The data was
collected in classrooms and out of classrooms or before classes and after classes. The
different environments or time may cause changes in the respondents. It is also possible
that not all respondents had same interpretations toward items in the questionnaire. One
solution to minimize such random errors may be to increase the sample size.
In addition, considering that the study is exploratory, further replications of the
study should be completed to further explore this point. Future research may employ
cross validation tests for examining the differences in the strengths of each relationship
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using a chi-square difference test (e.g., MacKenzie & Lutz, 1989). This technique may
help researchers determine whether each parameter estimate is consistent across samples.
Implications of the Study
As people’ interests in sport have increased, sport has rapidly grown as an
important advertising medium. Many companies have utilized sports content to advertise
their products or brands. Today, it is not difficult to see advertisements including sport
themes via a variety of mediums such as television, magazine, or newspaper. When
people attend sporting events, they are also exposed to various types of advertisements
around a stadium, such as signage or logos on athletes’ uniforms. Whenever people
encounter an advertisement, they may have a certain perception or belief about the
advertisement. People then may have certain feelings or emotions toward the
advertisement. Lastly, people make a decision at some point as to whether they will
purchase the advertised product or not.
These advertising communication processes generally describe how advertising
works. Researchers may be more interested in consumers’ cognitive structures that
influence their decision making; practitioners may be more interested in planning and
designing an advertising campaign that positively affects consumers’ cognitive structures
toward their advertisement. An effective advertisement can be produced when both
researchers and practitioners successfully fulfill required roles in their positions. Among
the advertising communications, the researcher was interested in evaluating consumers’
cognitive structures of advertising through sport which would benefit practitioners as
they strive to develop advertising strategies to foster positive attitudes. Accordingly, the
findings from the current study will provide valuable information to advertisers. The
study also benefits academic researchers by offering a conceptual framework of
consumers’ cognitive structures in a new domain of advertising research. The current
section will present the implications of the study for both academic and practical
perspectives.
Academic implications
One of the most significant aspects of the study is to provide a valid and reliable
scale for evaluating consumers’ cognitive structures underlying attitude toward
advertising through sport. The reason for beginning this study was that there has been
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little research undertaken to explore consumers’ complex structure of attitude toward
advertising through sport. The study provides a reliable and valid scale to measure
consumers’ beliefs and attitude toward advertising through sport and uncover consumers’
attitude structures as examining the relationship between a variety of beliefs and attitudes.
The researcher believes that the findings have important implications for academic
researchers.
First, the study will help researchers build a comprehensive conceptual
framework in attitude toward advertising through sport. In general advertising attitude
research, a conceptual framework generally begins with attitude toward advertising in
general as an overall phenomenon. Since Bauer and Greyser’s (1963) study, many
researchers have investigated consumers’ attitude structures toward advertising in general
for several decades (e.g., Muehling, 1987; Pollay & Mittal, 1993; Sandage & Leckenby,
1980). Based on the developed concepts of attitude toward advertising in general,
researchers have extended their research interests to attitude toward advertising in
specific mediums such as television, online, or magazine. Recently, researchers have
focused on measuring consumers’ attitudes toward various formats within advertising in a
specific medium (Brill, 1999; Burns, 2003). For example, Burns (2003) measured
consumers’ attitudes toward a variety of formats in online advertising (e.g., banner, popup, skyscraper, large rectangle, floating ad, and interstitial).
Unlike the systematic conceptual framework in traditional advertising research,
there has been no specific framework for advertising through sport. As a first attempt,
the study defines and conceptualizes the development of overall attitude structure toward
advertising through sport at the stage of an advertising medium. Consistent with the
situation in online advertising (Burns, 2003), advertising through sport has continuously
evolved to incorporate a variety of advertising formats. Therefore, the study provides a
framework to guide how advertising through sport is different than advertising through a
specific format or vehicle (see Figure 1.2). Figure 1.2 shows the level of advertising
through sport. From the generic concept of advertising in general, the study developed a
new construct, advertising through sport, in the second level. The current study focused
on this second stage and examined the structural antecedents of attitude toward
advertising through sport. The third stage in Figure 1.2 introduced a variety of
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advertising formats which can be potential research topics for further exploration.
However, little research has been interested in measuring consumers’ beliefs and
attitude toward advertising through sport in individual formats such as in-stadium signage,
TV commercials featuring athletes or teams, or magazine advertising with the use of
sport contents. The instrument developed in this study will aid researchers who are
willing to examine consumers’ beliefs and attitudes toward advertising through sport in
various formats. Researchers may apply various advertising vehicle provided in the last
stage to advertising through sport in various formats. For example, researchers can
investigate the relationship between consumers’ beliefs and attitude toward in-stadium
signage in Major League Baseball or TV commercials during the Super Bowl game. In
addition, researchers may compare consumers’ cognitive structures toward advertising
signage in Wimbledon games with those in National Association for Stock Car Auto
Racing (NASCAR) events. Tennis and NASCAR fans may have different beliefs which
explain their unique attitudes toward advertising signage in each event. Such
comparisons will provide interesting research topics to understand how different target
consumers form their attitudes.
In the same manner with the current model of attitude toward advertising through
sport as a subdivision of the model, researchers may propose a model of attitude toward
in-stadium signage or a model of attitude toward TV commercial with the use of athletes.
Examining the impact of attitude toward advertising through sport in specific
formats/vehicles will provide a foundation for future development of the current model
which will be discussed below.
Second, the previous implications may limit the range of a future model within
an overall attitude construct and several belief constructs as antecedents of the attitude. A
better understanding of attitude toward advertising through sport allows researchers to
extend the current model with various constructs which more effectively explain full
advertising through sport communications. MacKenzie and Lutz (1989) proposed a
comprehensive structural model of Aad formation. The model includes five cognitive and
affective constructs as antecedents of Aad (i.e., Ad credibility, ad perception, attitude
toward advertising, attitude toward advertising, and mood) and two consequences of Aad
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(i.e., brand perceptions and attitude toward the brand). Each antecedent of Aad is also
influenced by several constructs which are immediate precursors of the antecedent.
The current model in the study is related with only small parts within MacKenzie
and Lutz’s (1989) model, the relationships between advertising perceptions and attitude
toward advertising. Researchers may apply the current model to the full structural model
of attitude toward the ad formation by considering other antecedents and consequences of
attitude toward the advertisement through sport (e.g., MacKenzie & Lutz, 1989). A
proposed model of attitude toward the advertisement through sport will provide a more
systematic conceptual framework for the advertising through sport communication. In
addition, as previously mentioned, researchers may develop a structural model of Aad
formation in certain formats in advertising through sport, such as in-stadium signage or
the Super Bowl commercials by utilizing Mackenzie and Lutz’s model. By examining
various cognitive and affective constructs explaining Aad as well as a significant role of
Aad in determining brand perceptions and attitude toward the brand in a specific format in
advertising through sport, researchers may provide more detailed information to
practitioners who attempt to plan or develop an advertising campaign using a particular
advertising format. The current reliable and valid scale will be still able to measure
consumers’ attitude toward the advertisement and attitude toward the brand (e.g.,
MacKenzie & Lutz, 1989).
Researchers may also assess relationships between attitude and behavior
components. Lutz (1985) indicated the role of Aad as a mediator of attitude toward
advertising in general on brand attitude and purchasing behaviors. The understanding of
causal mechanisms of consumers’ affective and behavioral outcomes surrounding advertising
through sport will allow researchers to identify key factors that eventually contribute to the
effectiveness of advertising through sport. The ultimate goal of advertisers is to increase
brand images and sales. A better understanding of the relationships between consumers’
attitudes and purchasing behaviors will also benefits advertisers to enhance their brand
images or increase product sales.
Lastly, the study conceptualizes and specifies seven belief constructs including four
positive and three negative beliefs based on past research. Among seven beliefs, the study
reveals that only two positive beliefs influence consumers’ overall attitude toward advertising
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through sport. Considering the current findings that the negative belief constructs (i.e.,
annoyance/irritation, materialism, and falsity/no sense) and two positive belief constructs
(i.e., social role and image and good for the economy) were not significantly related with
attitude, future researchers need to apply to different ways to determine whether these
dimensions are really not related with attitude. A researcher could consider different types of
populations to see whether the current findings are only applied to the collegians samples. It
is also important to consider whether other belief dimensions may better explain attitude
toward advertising through sport. A closer look into the nuances of advertising through sport
will provide more impacts to understand consumers’ cognitive structure about attitudes.
Accordingly, researchers could discover additional belief constructs as determinants of
attitude toward advertising through sport. One example may be that future researchers may
reconsider value corruption as a negative function of advertising through sport given the
recent uproar about sexual issues in sport (e.g., Larkin, 1977; Pollay & Mittal, 1993). In
addition, future researchers could develop other positive belief dimensions (e.g., drama,
confidence, or beauty) that fit to sport and may significantly influence consumers’ attitude
toward advertising through sport. A better understanding of the impact of a variety of belief
constructs on attitude will make a better representative model of attitude toward advertising
through sport.
Practical implications
An understanding of consumers’ beliefs and attitude toward advertising through
sport will be important to advertisers. The success of advertising is generally determined
by how advertisers effectively create advertising which fosters a positive attitude among
consumers toward advertising, enhances the brand preference, and increases sales.
Specifically, consumers’ attitudes are important because they are closely related with
brand preference and purchasing behaviors which eventually promote sales (MacKenzie
& Lutz, 1989). Consumers’ attitude toward advertising could be built by their
perceptions toward advertising itself as well as an advertising medium. Accordingly, for
the development of effective advertising, advertisers should understand how their target
consumers perceive adverting and which advertising mediums the target consumers
prefer. In addition, the findings from the study will help advertisers develop more
effective advertising to increase their target consumers’ attitude by recognizing a
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percentage of the respondents who have favorable attitudes toward the use of sport as an
advertising medium.
First, it is very important for advertisers to create campaigns to build a positive
attitude toward advertising. The study reveals the internal structures of consumers’
attitude toward advertising through sport. Two beliefs, product information and
hedonism/pleasure significantly influenced attitude toward advertising through sport.
When advertisers consider college students as the target market, they can focus on
designing advertisements with more product information to generate positive attitudes
toward the advertisements. As a result, advertising through sport with product
information yields greater marketplace efficiencies, as the target consumers are better
able to match their needs and wants against a producer’ offerings (Pollay & Mittal, 1993).
Advertising with relevant product or brand information to the target consumers will help
the consumers to make better decisions. However, not all types of advertising through
sport are suitable to provide consumers a variety of information about products, services
or prices.
Unlike TV or print advertising through sport, companies usually have difficulty
including product information as part of in-stadium signage because of its limited space
for the message. Most companies have promoted only their brand names or logos
through the signage. However, considering the advantage of in-stadium signage
providing high levels of impact on consumers (Harshaw & Turner, 1999), companies
need to better utilize in-stadium signage as their promotional tool. It would be predicted
that consumers are more wiling to form positive attitude toward in-stadium signage if a
company included a brief message for a brand explanation. For example, the Coca Cola
company may design a message like “Coca Cola Zero - 0% calories” on the signage
instead just placing its’ traditional Coca Cola logo on signage. Such attempts would
capture consumers’ attentions by introducing Coca Cola’s new brand as well as providing
precise product information consumers may be interested in.
Advertisers should also consider hedonism/pleasure as an important technique to
increase consumers’ advertising likeability. Characteristics such as humor, enjoyable
messages, or exciting sport scenes can easily get consumers’ attention. Bauer and
Greyser (1968) indicated that people might enjoy watching and hearing advertisements
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with hedonism/pleasure regardless of their interest in what is advertised. A better
understanding of the impact of hedonism/pleasure on attitude would benefit sport
advertisers in that they could design ad campaigns that emphasize hedonism/pleasure to
generate positive feelings from a market segment such as college students.
Recently, many companies have included humor or comic characteristics into
their advertisements to foster consumers’ positive attitudes. Good examples are TV
commercials during the 2006 Torino Olympic Games. Sport advertisers often attempted
to create enjoyable ads by connecting ad contents to real games. For example, just after
watching a pairs figure-skating game, TV audiences could see a commercial featuring
one old couple who was figure skating on a lake with a comic theme. Such advertisers’
endeavors may influence the audiences to positively react to the advertisements.
Therefore, consumers’ significant beliefs about advertising through sport representing
product information and hedonism/pleasure could yield most useful for advertisers when
developing advertising executions.
Second, advertisers should establish a medium strategy when they plan a new
advertising campaign. Whether advertising messages could effectively reach a target
audience sometime depends on selection of a relevant medium (West et al., 2000). When
an advertiser selects old people as one’s target audience, it may be not a good idea to
utilize online as an advertising medium. The study reveals that the respondents’ attitudes
toward advertising through sport were markedly positive unlike traditional advertising.
Consumers’ positive attitudes toward advertising through sport represents that they are
more willing to receive what messages advertisers want to deliver. Accordingly, such
consumers’ different attitude toward advertising through sport than other mediums would
be an important element to determine a target consumer. Advertising could be more
effective if advertisers use sport as an advertising medium, particularly for college
students as their target consumers.
Overall, for the establishment of effective advertising, advertisers should first
select target consumers, decide on a proper medium through which to reach them, and
then design advertising contents in a manner that is appropriate to each medium and their
target consumers (Schiffman & Kanuk, 2002). The study reveals that college students
have positive attitudes toward the use of sport as advertising mediums, and product
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information and hedonism/pleasure functions in advertising through sport are significant
determinants of their overall attitudes. The findings from the study provide valuable
information to advertisers in establishing successful advertising planning and strategies.
Limitations and directions for future research
The current section provides several limitations the researcher has encountered
during the entire process of the study. The current section first will address the
limitations of the study then suggest some guidelines relative to each limitation for future
research. Three particular limitations of the current study have been identified.
First, one limitation of the study is related with the sample the study utilized.
The finding that consumers’ overall attitude toward advertising through sport is more
positive than a general attitude toward advertising or other mediums might be due to the
demographic characteristics of the current samples rather than the unique characteristics
of advertising through sport. Not all previous research has utilized collegians samples to
measure attitude toward advertising in general or other mediums. Rather, more recent
research has tended to use large and nationwide samples for more precise measurements
of advertising attitude (e.g., Schlosser et al., 1999; Shavitt et al., 1998). It is supposed
that attitude toward advertising through sport may be inconsistent across different
demographic samples (Shavitt et al., 1998). Schlosser et al. (1998) showed that young,
highly educated, and affluent males had a tendency to dislike online advertising and
traditional advertising. Accordingly, the direct comparisons of attitudes between
advertising through sport and advertising in general or advertising in other mediums
across different demographic samples may cause a critical flaw in the study.
In addition, there have been wide time differences between the current study and
previous research. As the advertising industry has been increased, simultaneously,
consumers’ attitudes toward advertising have been changed. For example, consumers’
attitudes toward advertising in general were mixed in 1960s (e.g., Bauer & Greyser,
1968). However, consumers’ attitudes toward advertising in general have been more
negative in 1980s and 1990s (e.g., Andrews, 1989; Bartos, 1981; Mehta, 2000; Muehling,
1987; Zanot, 1981). Accordingly, results derived from comparisons of people’ attitudes
or perceptions of advertising between the current study and past studies could be
inaccurate although similar demographic groups were utilized in these studies.
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In order to support the current idea that advertising through sport fosters a more
positive attitude toward advertising, the researcher suggests that further investigations are
needed to measure whether attitude toward advertising through sport is really different
than attitude toward advertising in general or in other mediums across demographically
similar samples (cf. Schlosser et al., 1999). One example is that several types of
advertising (e.g., advertising through sport, online advertising, or TV advertising) can be
individually measured by different collegians samples collected from the same population.
A researcher then can compare collegians’ attitude toward advertising through sport with
other collegians’ attitudes toward advertising in different mediums. Findings from such
research will provide more substantial evidence to support the uniqueness of sport as an
advertising medium if there are significant differences between attitude toward
advertising through sport and attitudes toward advertising in different mediums.
Second, another limitation of the study is about the validity of the scale. The
primary purpose of the study was to provide a reliable and valid scale measuring seven
beliefs and attitude toward advertising through sport. It was determined that the current
instrument is generally reliable and valid. The results provided support for five of the
proposed dimensions; there is, however, a lack of discrimination among two dimensions,
annoyance/irritation and falsity/no sense. The results of the discrimination test is
technically attributed to a reason that two dimensions share a substantial amount of
variance, at least for the current data, indicating that the variance shared among two
dimensions is greater than the variance in the indicators explained by each of both
dimensions (Fornell & Larcker, 1981). The researcher also addressed several
fundamental possibilities why two dimensions merged in the previous section and
considered several options to figure out this problem. In order to provide a more valid
scale for these two dimensions, the best way will be to follow Churchill’s (1979)
suggestion to go back to the initial step of a scale development and verify where the
troubles with dimensions may lie.
The study suggests that, first, future research should carefully define the domains
of annoyance/irritation and falsity/no sense and specify sound guidelines for the selection
of measured variables (Fabrigar et al., 1999). Future researchers may need to develop
more items which well represent the two dimensions through various item generation
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techniques such as in-depth literature review, experience surveys, or free thoughtelicitations in future research. The study already confirmed the effectiveness of the
suggested technique relative to product information and social role and image
considering that the two dimensions were not distinct in the results from the pilot study.
Then, future researchers need to test the discriminant validity of both dimensions using
various techniques employed in the study.
If a convergence of annoyance/irritation and falsity/no sense still exists even
after generating more items, one suggestion is to employ a more powerful statistical
technique for the assessment of discriminant validity. In the previous section, the study
introduced the model comparisons (i.e., six factor model v. seven factor mode) with
overall model fit indices. A cross validation test has been generally considered as a better
indicator of model comparisons. Among various cross validation techniques, particularly,
Cudeck and Browne’s (1983) double cross validation using two independent samples in
the same population has been considered the most stringent method for model selection
(Bagozzi & Yi, 1988). The application of double cross validation tests would provide a
researcher the most predictive model for underlying population structures (Homburg,
1991).
Lastly, the collegian sample employed in the study limits the generalizability of
the results. Even though college students project a very large market segment (Haller,
1974), they are not representative of all consumers. A primary concern of the
generalizability is replication of the observations under different circumstances (Elmes,
Kantowitz, & Roediger, 1992). The current study is an exploratory study and a first
attempt to measure consumers’ beliefs and attitudes toward advertising through sport.
There is more to learn regarding the current findings. Can the findings be generalized to
all college students? Will the scale be reliable and valid when measuring householders’
beliefs and attitudes toward advertising through sport? These questions can be answered
by replications of the study with the same measures and variables. Future research
should include systematic replications of the current study with other populations (e.g.,
householders or professionals) to establish the external validity of the results.
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Conclusions
People can develop different attitudes toward in a certain type of advertising
medium, on the basis of different beliefs. For example, one person may have a favorable
attitude toward TV advertising because it provides useful information for new brands;
another person may have a negative attitude toward TV advertising because there is too
much advertising on television. The study begins with a basic theory that an individual’s
strongest belief has the greatest influence on his/her attitude which is proposed to
influence one’s behaviors (Fishbein, 1963) and investigates consumers’ attitude toward
the use of sport as an advertising medium. The study utilized four positive and three
negative belief dimensions and examined to what extent those beliefs contributed to
people’s attitude toward advertising through sport. For the precise measurement of the
domains of interest, the study utilized a valid and reliable scale measuring people’s
beliefs and attitude toward advertising through sport.
Unlike advertising in general (e.g., Pollay & Mittal, 1993) and advertising in
traditional mediums (e.g., Alwitt & Prabhaker, 1992; Mittal, 1994; Schlosser et al., 1999),
the findings of the study indicate that respondents generally have positive attitudes
toward advertising through sport. The reasons respondents tend to like advertising
through sport are consistent with the results of past research on advertising in general or
traditional mediums (e.g., Alwitt & Prabhaker, 1992; Pollay & Mittal, 1993; Shavitt et al.,
1998). In contrast to previous studies (e.g., Alwitt & Prabhaker, 1992; Pollay & Mittal,
1993), the respondents’ negative beliefs about advertising through sport are not related
with their overall attitudes regarding the subject at hand. It is believed that the unique
characteristics of sport as an advertising medium contribute the respondents’ relatively
positive attitudes toward advertising through sport even through they perceive
advertising’s negative functions. The findings of the study provide a foundation for
understanding the development of consumers’ attitude toward advertising through sport
and guide implications for marketing and advertising practice.
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APPENDIX A
APPROVAL LETTER FOR HUMAN SUBJECTS IN RESEARCH
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APPENDIX B
INITIAL SURVEY QUESTIONNAIRE FOR PILOT TEST
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Do Young Pyun
Department of Sport Management, Recreation Management, and Physical Education
College of Education
Florida State University
Tel: 850-942-2776
Email: [email protected]
Dear Survey Participants:
I am a doctoral candidate under the direction of Dr. Jeffrey James in the Department of
Sport Management, Recreation Management, and Physical Education at the Florida State
University. I am currently conducting my doctoral dissertation regarding “The Proposed
Model of Attitude toward Advertising through Sport”. This study involves research to
assess your attitude toward advertising through sport, particularly the beliefs that underlie
the attitude.
I am requesting your participation in this study, which is composed of three sections: 1)
attitude items, 2) belief items, and 3) demographic items. The survey will take
approximately 10 minutes. Your participation in this survey is voluntary. If you do not
want to complete the questionnaire at any time, please return it to your instructor. You
have the right to withdraw from the survey at any time without consequence. There is no
penalty for not participating. The questionnaire is anonymous, and all the responses will
be kept strictly confidential. Your response will be used for research purposes only. You
will not be compensated for the participation, but you may request a copy of results from
this phase of my research.
If you have any questions concerning this study, please do not hesitate to contact me at
(850) 942-2776 or to email me call me at [email protected]. Also, contact to
my advisor, Dr. Jeffrey James at (850) 644-9214 or to Human Subjects Committee at
(850) 644-5260.
Return of the questionnaire will be considered your consent to participate. Thank you for
taking time and making the effort to complete the questionnaire.
Sincerely,
Do Young Pyun
207
The purpose of this survey is to measure your beliefs about and general attitude toward
advertising through sport. The phrase “advertising through sport” may include any type of
advertising (e.g., TV or radio commercials; magazine ad) that uses elements of sport such as an
athlete or images of a sporting event. Advertising through sport may also include the presence of
advertising at a sporting event such as in-stadium signage at games.
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
1. My general opinion of advertising through sport is unfavorable.
2. Overall, I consider advertising through sport a good thing.
Please indicate the extent to which you dislike or like the following
by circling the appropriate number in the scale next to each statement.
3. Overall, do you like or dislike advertising through sport.
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
Strongly
Disagree
1
2
1
2
Neutral
3
3
Strongly
Dislike
1
4
4
5
5
Strongly
Agree
6
7
6
7
Strongly
Like
Neutral
2
3
Strongly
Disagree
4
5
6
7
Strongly
Agree
Neutral
Advertising through sport…
1.
is a valuable source of information about local sales.
1
2
3
4
5
6
7
2.
helps me learn about fashions and about what to buy to impress
others.
1
2
3
4
5
6
7
3.
is often amusing and entertaining.
1
2
3
4
5
6
7
4.
in general helps our nation’s economy.
1
2
3
4
5
6
7
5.
tells me which brands have the features I am looking for.
1
2
3
4
5
6
7
6.
is enjoyable.
1
2
3
4
5
6
7
7.
is annoying.
1
2
3
4
5
6
7
8.
generally helps the local economy.
1
2
3
4
5
6
7
9.
gives me pleasure when I think about what I saw or heard or read.
1
2
3
4
5
6
7
10. helps me keep up-to-date about products available in the
marketplace.
1
2
3
4
5
6
7
11. is usually a waste of economic resources.
1
2
3
4
5
6
7
12. tells me what people with life styles similar to mine are buying and
using.
1
2
3
4
5
6
7
208
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
Strongly
Disagree
Strongly
Agree
Neutral
Advertising through sport…
13. is making us a materialistic society, overly interested in buying and
owning things.
1
2
3
4
5
6
7
14. generally promotes competition, which benefits the consumer.
1
2
3
4
5
6
7
15. is irritating
1
2
3
4
5
6
7
16. generally present a true picture of the product advertised.
1
2
3
4
5
6
7
17. often interrupts programs just when I am getting interested.
1
2
3
4
5
6
7
18. is sometimes even more enjoyable than other media contents.
1
2
3
4
5
6
7
19. influences people to buy things they do not really need.
1
2
3
4
5
6
7
20. is confusing.
1
2
3
4
5
6
7
21. makes people live in a world of fantasy.
1
2
3
4
5
6
7
22. helps me know which products will or will not reflect the sort of
person I am.
1
2
3
4
5
6
7
23. seems to occur more often now than in the past.
1
2
3
4
5
6
7
24. makes people buy unaffordable products just to show off
1
2
3
4
5
6
7
25. in general is misleading.
1
2
3
4
5
6
7
26. often insults the intelligence of the average consumer.
1
2
3
4
5
6
7
Please check the appropriate response to each item below.
Your gender is
1) Male_____
2) Female_____
You are a
1) Freshman_____
2) Sophomore_____
3) Junior_____
4) Senior_____
5) Graduate student_____
6) other_____
1) Black/African American_____
2) Native American_____
3) Latino/Latina_____
4) White/Caucasian_____
5) Asian or Pacific Islander_____
6) Other_____
Race:
Thank you for your participation.
209
APPENDIX C
MODIFIED SURVEY QUESTIONNAIRE FOR
THE FIRST DATA COLLECTION
210
Do Young Pyun
Department of Sport Management, Recreation Management, and Physical Education
College of Education
Florida State University
Tel: 850-942-2776
Email: [email protected]
Dear Survey Participants:
I am a doctoral candidate under the direction of Dr. Jeffrey James in the Department of
Sport Management, Recreation Management, and Physical Education at the Florida State
University. I am currently conducting my doctoral dissertation regarding “The Proposed
Model of Attitude toward Advertising through Sport”. This study involves research to
assess your attitude toward advertising through sport, particularly the beliefs that underlie
the attitude.
I am requesting your participation in this study, which is composed of three sections: 1)
attitude items, 2) belief items, and 3) demographic items. The survey will take
approximately 10 minutes. Your participation in this survey is voluntary. If you do not
want to complete the questionnaire at any time, please return it to your instructor. You
have the right to withdraw from the survey at any time without consequence. There is no
penalty for not participating. The questionnaire is anonymous, and all the responses will
be kept strictly confidential. Your response will be used for research purposes only. You
will not be compensated for the participation, but you may request a copy of results from
this phase of my research.
If you have any questions concerning this study, please do not hesitate to contact me at
(850) 942-2776 or to email me call me at [email protected]. Also, contact to
my advisor, Dr. Jeffrey James at (850) 644-9214 or to Human Subjects Committee at
(850) 644-5260.
Return of the questionnaire will be considered your consent to participate. Thank you for
taking time and making the effort to complete the questionnaire.
Sincerely,
Do Young Pyun
211
The purpose of this survey is to measure your beliefs about and general attitude toward
advertising through sport. The phrase “advertising through sport” may include any type of
advertising (e.g., TV or radio commercials; magazine ad) that uses elements of sport such as an
athlete or images of a sporting event. Advertising through sport may also include the presence of
advertising at a sporting event such as in-stadium signage at games.
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
1. My general opinion of advertising through sport is favorable.
2. Overall, I consider advertising through sport a good thing.
Strongly
Disagree
1
2
1
2
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
Strongly
Disagree
Neutral
3
3
4
4
5
5
Strongly
Agree
6
7
6
7
Strongly
Agree
Neutral
Advertising through sport…
3.
is often amusing and entertaining.
1
2
3
4
5
6
7
4.
tells me which brands have the features I am looking for.
1
2
3
4
5
6
7
5. helps me keep up-to-date about products available in the
marketplace.
1
2
3
4
5
6
7
6.
is a good source of product information.
1
2
3
4
5
6
7
7.
is annoying.
1
2
3
4
5
6
7
8.
provides timely information.
1
2
3
4
5
6
7
9.
supplies complete product information.
1
2
3
4
5
6
7
10. generally helps the local economy.
1
2
3
4
5
6
7
11. helps me to be more confident in the brands/products I actually use.
1
2
3
4
5
6
7
12. makes product information immediately accessible.
1
2
3
4
5
6
7
13. results in a larger volume of goods being produced.
1
2
3
4
5
6
7
14. is confusing.
1
2
3
4
5
6
7
212
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
Strongly
Disagree
Strongly
Agree
Neutral
Advertising through sport…
15. gives people enough information about the product being
advertised.
1
2
3
4
5
6
7
16. supplies relevant product information.
1
2
3
4
5
6
7
17. is enjoyable.
1
2
3
4
5
6
7
18. ,in general, helps our nation’s economy.
1
2
3
4
5
6
7
19. leads to a waste of natural resources by creating desires for
unnecessary goods.
1
2
3
4
5
6
7
20. is sometimes even more enjoyable than other media contents.
1
2
3
4
5
6
7
21. is usually a waste of economic resources.
1
2
3
4
5
6
7
22. influences people to buy things they do not really need.
1
2
3
4
5
6
7
23. makes people live in a world of fantasy.
1
2
3
4
5
6
7
24. helps me find products that match my personality and interests.
1
2
3
4
5
6
7
25. results in better products for the public.
1
2
3
4
5
6
7
26. in general, results in lower prices.
1
2
3
4
5
6
7
27. tells me what people with life styles similar to mine are buying and
using.
1
2
3
4
5
6
7
28. is truthful.
1
2
3
4
5
6
7
29. helps me learn what is in fashion and what I should buy for keeping
a good social image.
1
2
3
4
5
6
7
30. gives me pleasure when I think about what I saw or heard or read.
1
2
3
4
5
6
7
31. ,in general, is misleading.
1
2
3
4
5
6
7
32. gives me a good idea about products by showing the kinds of people
who use them.
1
2
3
4
5
6
7
33. is deceptive.
1
2
3
4
5
6
7
34. helps me learn about fashions and about what to buy to impress
others.
1
2
3
4
5
6
7
35. is irritating.
1
2
3
4
5
6
7
36. often interrupts programs just when I am getting interested.
1
2
3
4
5
6
7
37. often insults the intelligence of the average consumer.
1
2
3
4
5
6
7
38. portrays people the way they really are.
1
2
3
4
5
6
7
39. lets me see what brands other people use.
1
2
3
4
5
6
7
40. is intrusive.
1
2
3
4
5
6
7
213
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
Strongly
Disagree
Strongly
Agree
Neutral
Advertising through sport…
41. helps me know which products will or will not reflect the sort of
person I am.
1
2
3
4
5
6
7
42. helps raise our standard of living.
1
2
3
4
5
6
7
43. is making us a materialistic society, overly interested in buying and
owning things.
1
2
3
4
5
6
7
44. gives me ideas about fashion.
1
2
3
4
5
6
7
45. gives me ways to act.
1
2
3
4
5
6
7
Strongly
Dislike
Please indicate the extent to which you dislike or like the following
by circling the appropriate number in the scale next to each statement.
1
46. Overall, do you like or dislike advertising through sport?
Strongly
Like
Neutral
2
3
4
5
6
7
I think advertising through sport is…
47. Bad
1
2
3
4
5
6
7
Good
48. Unpleasant
1
2
3
4
5
6
7
Pleasant
49. Unfavorable
1
2
3
4
5
6
7
Favorable
I think advertising through sport is…
50. Unconvincing
1
2
3
4
5
6
7
Convincing
51. Unbelievable
1
2
3
4
5
6
7
Believable
52. Biased
1
2
3
4
5
6
7
Unbiased
The items below measure your general attitude about advertising you encounter everyday. The
phrase “advertising” may include, but is not limited to, TV and radio commercials, ads in
newspapers and magazines, billboards, ads received in the mail, and online advertising.
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
53. My general opinion of advertising is favorable.
54. Overall, I consider advertising a good thing.
Please indicate the extent to which you dislike or like the following
by circling the appropriate number in the scale next to each statement.
55. Overall, do you like or dislike advertising?
Strongly
Disagree
1
1
214
2
2
3
3
Strongly
Dislike
1
Strongly
Agree
Neutral
4
4
5
5
6
6
Strongly
Like
Neutral
2
3
4
7
7
5
6
7
Please check the appropriate response to each item below.
1. Your gender is
1) Male_____
2) Female_____
2. You are a
1) Freshman_____
2) Sophomore_____
3) Junior_____
4) Senior_____
5) Graduate student_____
6) other_____
1) Black/African American_____
2) Native American_____
3) Latino/Latina_____
4) White/Caucasian_____
5) Asian or Pacific Islander_____
6) Other_____
3. Race:
4. How many hours do you spend
watching sports games in a typical day?
5. How often do you participate in sports
activities in a month?
6. How often do you purchase sporting
goods in a month?
1) 0 hour____
2) Less than 1 hour_____
3) 1 hour_____
4) 2 hours____
5) 3 hours _____
4) More than 3 hours_____
1) Never_____
2) Less than once a
month_____
3) 1-4 times a month_____
4) 5-10 times a
month_____
5) 11-20 times a
month_____
6) More than 20 times a
month_____
1) Never_____
2) Less than once a
month_____
3) 1-3 times a month_____
4) 4-6 times a
month_____
5) 7-9 times a
month_____
6) 10 times or more_____
Thank you for your participation.
215
APPENDIX D
FINAL SURVEY QUESTIONNAIRE FOR THE SECOND DATA COLLECTION
216
Do Young Pyun
Department of Sport Management, Recreation Management, and Physical Education
College of Education
Florida State University
Tel: 850-942-2776
Email: [email protected]
Dear Survey Participants:
I am a doctoral candidate under the direction of Dr. Jeffrey James in the Department of
Sport Management, Recreation Management, and Physical Education at the Florida State
University. I am currently conducting my doctoral dissertation regarding “The Proposed
Model of Attitude toward Advertising through Sport”. This study involves research to
assess your attitude toward advertising through sport, particularly the beliefs that underlie
the attitude.
I am requesting your participation in this study, which is composed of three sections: 1)
attitude items, 2) belief items, and 3) demographic items. The survey will take
approximately 10 minutes. Your participation in this survey is voluntary. If you do not
want to complete the questionnaire at any time, please return it to your instructor. You
have the right to withdraw from the survey at any time without consequence. There is no
penalty for not participating. The questionnaire is anonymous, and all the responses will
be kept strictly confidential. Your response will be used for research purposes only. You
will not be compensated for the participation, but you may request a copy of results from
this phase of my research.
If you have any questions concerning this study, please do not hesitate to contact me at
(850) 942-2776 or to email me call me at [email protected]. Also, contact to
my advisor, Dr. Jeffrey James at (850) 644-9214 or to Human Subjects Committee at
(850) 644-5260.
Return of the questionnaire will be considered your consent to participate. Thank you for
taking time and making the effort to complete the questionnaire.
Sincerely,
Do Young Pyun
217
The purpose of this survey is to measure your beliefs about and general attitude toward
advertising through sport. The phrase “advertising through sport” may include any type of
advertising (e.g., TV or radio commercials; magazine ad) that uses elements of sport such as an
athlete or images of a sporting event. Advertising through sport may also include the presence of
advertising at a sporting event such as in-stadium signage at games.
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
Strongly
Disagree
Strongly
Agree
Neutral
1. My general opinion of advertising through sport is favorable.
1
2
3
4
5
6
7
2. Overall, I consider advertising through sport a good thing.
1
2
3
4
5
6
7
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
Strongly
Disagree
Strongly
Agree
Neutral
Advertising through sport…
3.
tells me which brands have the features I am looking for.
1
2
3
4
5
6
7
4.
is often amusing and entertaining.
1
2
3
4
5
6
7
5. helps me keep up-to-date about products available in the
marketplace.
1
2
3
4
5
6
7
6.
is a good source of product information.
1
2
3
4
5
6
7
7.
is annoying.
1
2
3
4
5
6
7
8.
provides timely information.
1
2
3
4
5
6
7
9.
generally helps the local economy.
1
2
3
4
5
6
7
10. makes product information immediately accessible.
1
2
3
4
5
6
7
11. is confusing.
1
2
3
4
5
6
7
12. is enjoyable.
1
2
3
4
5
6
7
13. ,in general, helps our nation’s economy.
1
2
3
4
5
6
7
14. helps raise our standard of living.
1
2
3
4
5
6
7
15. is sometimes even more enjoyable than other media contents.
1
2
3
4
5
6
7
218
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
Strongly
Disagree
Strongly
Agree
Neutral
Advertising through sport…
16. gives me pleasure when I think about what I saw or heard or read.
1
2
3
4
5
6
7
17. influences people to buy things they do not really need.
1
2
3
4
5
6
7
18. makes people live in a world of fantasy.
1
2
3
4
5
6
7
19. tells me what people with life styles similar to mine are buying and
using.
1
2
3
4
5
6
7
20. helps me learn what is in fashion and what I should buy for keeping
a good social image.
1
2
3
4
5
6
7
21. is usually a waste of economic resources.
1
2
3
4
5
6
7
22. leads to a waste of natural resources by creating desires for
unnecessary goods.
1
2
3
4
5
6
7
23. gives me a good idea about products by showing the kinds of people
who use them.
1
2
3
4
5
6
7
24. is deceptive.
1
2
3
4
5
6
7
25. helps me learn about fashions and about what to buy to impress
others.
1
2
3
4
5
6
7
26. is irritating.
1
2
3
4
5
6
7
27. often interrupts programs just when I am getting interested.
1
2
3
4
5
6
7
28. often insults the intelligence of the average consumer.
1
2
3
4
5
6
7
29. lets me see what brands other people use.
1
2
3
4
5
6
7
30. is intrusive.
1
2
3
4
5
6
7
31. helps me know which products will or will not reflect the sort of
person I am.
1
2
3
4
5
6
7
32. ,in general, is misleading.
1
2
3
4
5
6
7
33. is making us a materialistic society, overly interested in buying and
owning things.
1
2
3
4
5
6
7
34. gives me ideas about fashion.
1
2
3
4
5
6
7
35. gives me ways to act.
1
2
3
4
5
6
7
Please indicate the extent to which you dislike or like the following
by circling the appropriate number in the scale next to each statement.
36. Overall, do you like or dislike advertising through sport?
219
Strongly
Dislike
1
Strongly
Like
Neutral
2
3
4
5
6
7
I think advertising through sport is…
37. Bad
1
2
3
4
5
6
7
Good
38. Unpleasant
1
2
3
4
5
6
7
Pleasant
39. Unfavorable
1
2
3
4
5
6
7
Favorable
I think advertising through sport is…
40. Unconvincing
1
2
3
4
5
6
7
Convincing
41. Unbelievable
1
2
3
4
5
6
7
Believable
42. Biased
1
2
3
4
5
6
7
Unbiased
The items below measure your general attitude about advertising you encounter everyday. The
phrase “advertising” may include, but is not limited to, TV and radio commercials, ads in
newspapers and magazines, billboards, ads received in the mail, and online advertising.
Please indicate the extent to which you disagree or agree with the
following by circling the appropriate number in the scale next to
each statement.
Strongly
Disagree
Strongly
Agree
Neutral
43. My general opinion of advertising is favorable.
1
2
3
4
5
6
7
44. Overall, I consider advertising a good thing.
1
2
3
4
5
6
7
Please indicate the extent to which you dislike or like the following
by circling the appropriate number in the scale next to each statement.
Strongly
Dislike
45. Overall, do you like or dislike advertising?
1
Strongly
Like
Neutral
2
3
4
5
6
7
Please check the appropriate response to each item below.
1. Your gender is
1) Male_____
2) Female_____
2. You are a
1) Freshman_____
2) Sophomore_____
3) Junior_____
4) Senior_____
5) Graduate student_____
6) other_____
1) Black/African American_____
2) Native American_____
3) Latino/Latina_____
4) White/Caucasian_____
5) Asian or Pacific Islander_____
6) Other_____
3. Race:
Continued to a next page…
220
4. How much do you spend watching
sports games in a typical day?
5. How often do you participate in sports
activities in a month?
6. How often do you purchase sporting
goods in a month?
1) Never ____
2) 1-30 minutes _____
3) 31-60 minutes _____
4) 61-90 minutes
____
5) 91-120 minutes _____
6) More120 minutes
_____
1) Never _____
2) Less than once a month
_____
3) 1-4 times a month
_____
4) 5-10 times a
month _____
5) 11-20 times a month
_____
6) More than 20 times a
month _____
1) Never _____
2) Less than once a month
_____
3) 1-3 times a month
_____
4) 4-6 times a
month _____
5) 7-9 times a month
_____
6) 10 times or more
_____
Thank you for your participation.
221
APPENDIX E
DATA TRANSFORMATION
222
Normality test of data
The factor analyses and SEM computed in the study utilized the maximum
likelihood estimation procedures. Maximum likelihood estimation of multivariate
analysis assumes normal distribution of individual variables. When observed variables
have the problem of non-normality, a maximum likelihood estimation inflates the chisquare value and underestimates several fit indices and standard errors of parameter
estimates (West, Finch, & Curran, 1995). Accordingly, the normality of data has been
considered the most important assumption when researchers deal with factor analysis or
SEM (Hair et al., 1998).
The normality of all individual variables was evaluated in terms of skewness and
kurtosis during factor analyses and SEM. For the normality test, the researcher employed
statistical tests to assess normality based on standardized skewness and kurtosis scores.
The standardized scores were calculated by dividing observed skewness and kurtosis
values by their standard errors via SPSS. The results indicated that all items whose
absolute standardized scores of skewness and kurtosis were greater than 2.58
significantly violated the assumption of normality at the .01 probability level. However,
researchers may select the ± 1.96 criterion at the .05 probability level if they want to
apply more stringent critical value to the test the normality of their data. The study
revealed that when the critical value of 2.58 goes backward, the non-normality of data
started from the .43 statistic of skewness and .86 statistic of kurtosis considering the
current sample size of 212 in each of the calibration and validation samples. In other
words, if an observed variable in a certain research has a .43 or more skewness statistic
given the current sample size, the item seems non-normal at either .05 or .01 significance
level.
Recent studies in sport marketing have utilized the ± 1.00 criteria for skewness
and kurtosis statistics rather than statistical tests to test the normality of data (e.g., Kwon
& Armstrong, 2004; Kwon & Trail, 2003). The test of normality for kurtosis using the ±
1.00 criterion might be acceptable because the recommended criterion for kurtosis is
much looser than that for skewness (e.g., Klein, 1998; West, Finch, & Curran, 1995).
However, the application of the ± 1.00 values on skewness may still retain the items
which could violate this assumption and, such non-normal data may produce invalid
223
results from the hypothesis testing (Byrne, 1995). In addition, another extreme example
(e.g., n = 40; skewness statistic = 1.00; standard error of skewness = .39; z-score = 2.56)
shows the possibility of the opposite situation that the ± 1.00 criterion may not accept the
items which still fail to reject the assumption of normality at the .01 level in the sample
size of 40 or less. Even though the exampled sample size is unrealistic and may not be
utilized by researchers, it shows the importance of standard errors to determine the
normality of data. Without considerations of standard errors of skewness and kurtosis,
the fixed criteria of skewness and kurtosis (e.g., ± 1.00) could not be applied to every
sample size. The use of a more rigorous value of skewness for the normality test may be
considered based on the sample size.
Therefore, the researcher suggests the evaluation criteria for skewness and
kurtosis statistics given different sample sizes (see Table E.1). Unfortunately, SPSS does
not compute z-statistics of skewness and kurtosis for single variables. Researchers need
to calculate each standardized score of skewness and kurtosis with using reported
statistics by themselves. Table E.1 will provide researchers a priori knowledge regarding
approximate statistical significance levels on the assumption of normality of their data
before proceeding on actual statistical tests. Sometimes, researchers even may not need
the procedure of statistical tests; when they recognize ranges of skewness and kurtosis
statistics of their data from the results of SPSS, they could determine the statistical
significance of their variables based on guidelines suggested in the Table. For example, a
researcher has the maximum values of .30 (skewness) and .60 (kurtosis) statistics given
the sample size of 240, s/he fails to reject the assumption of the normality at the either .01
or .05 probability level. S/he can assume that all variables are normally distributed
because the obtained skewness (i.e., .30) and kurtosis (i.e., .60) values in the sample size
of 240 are less the recommend values of skewness (i.e., .304) and (i.e., .608) in the even
bigger sample size of 250 (see Table E.1). The researcher also used SPSS to get each
statistic value of skewness and kurtosis and took one more step to obtain observed
variables’ standardized scores for statistical tests. However, if a researcher utilizes
LISREL for data analysis, s/he easily gets results of statistical tests of univariate
normality through PRELIS.
224
Table E.1
Summary of Evaluation Criteria for Significant Skewness and Kurtosis Statistics Based
on Sample Size
Sample
size (n)
Standard error
for skewnessº
Standard error
for kurtosis¹
Absolute value of skewness
statistic at the .05²/.01³
significance level
Absolute value of kurtosis
statistic at the .05²/.01³
significance level
150
.200
.400
.392/.516
.784/1.032
200
.173
.346
.339/.446
.678/.893
250
.155
.310
.304/.400
.608/.800
300
.141
.283
.276/.364
.555/.730
350
.131
.262
.257/.338
.514/.676
400
.122
.245
.239/.315
.480/.632
450
.115
.231
.225/.297
.453/.596
500
.110
.219
.216/.284
.429/.565
º The standard error for skewness was calculated by the formula, 6 / n
¹ The standard error for kurtosis was calculated by the formula, 24 / n
² Skewness statistic at the .05 probability level was calculated by the formula, standard error of
skewness × 1.96
³ Kurtosis statistic at the .01 probability level was calculated by the formula, standard error of
kurtosis × 2.58
It should be also noted that statistical tests of univariate normality is sensitive to
the size of sample. When a sample size increases, the acceptable statistic values of
skewness and kurtosis decease (e.g., the statistic values of skewness and kurtosis in the
sample size of 500 are only .216 and .426 at the .05 probability level). It indicates that
only a small degree of departure from univariate normality could be significant in a big
sample size (Kline, 1998).
Lastly, the assumption of normality also concerns with multivariate normality.
Among various multivariate test techniques, particularly, Mardia (1970)’s skewness and
kurtosis measures have been popularly utilized (Kline, 1998; West, Finch, & Curran,
1995). However, such multivariate indices are very sensitive to the sample size, thus,
usually significant on the assumption of multivariate normality with just small departures
from multivariate normality (Kline, 1998). Therefore, researchers normally assume that
their variables are multivariate normal when all observed variables do not violate the
225
assumption of the univariate normality even though univariate normality is a necessary,
but not a sufficient condition for multivariate normality (Hair et al., 1998). Detection of
multivariate outliers or data transformations to repair non-normal distribution may also
benefit multivariate normality (Klein, 1998). The next section will discuss about issues
raised during the procedures of data transformations.
Data transformation
When researchers notice the items with non-normal distributions, they may often
delete the items themselves or ignore and keep continuing with analyses if the nonnormality seems not serious. Indeed, a minimal violation of the normal distribution
assumption could create errors in estimates, and the deletion of problematic items may
cause the loss of significant measures representing latent variables researchers are
interested in. The best way is to repair non-normality of the problematic items using
certain mathematical operations and to bring them to measurement tests to determine
whether they are reliable and valid.
Data transformations were performed on all data sets used for multivariate
analyses through the study: the data for EFA in the first data administrations; both
calibration and validation data sets for CFA or SEM in the second data administration.
The researcher transformed scores in the items which were considered non-normal from
the results of statistical tests, and several problematic items were found in all data sets.
Consequently, some important issues and problems occurred during data transformations
and when results were interpreted and reported. The current section will discuss about
those issues questioned and present possible solutions for further data transformations.
First, the negatively skewed items were transformed by taking the square root or
logarithm after subtracting every score in the items by a constant, one greater than the
highest score in the entire study. When the range of scores in the items was one to seven,
the constant was eight; when the range of scores in the items was one to six, the constant
was seven (e.g., S7 in the calibration sample). After subtractions, a negative skewed
distribution changes to a positive distribution because highest scores (e.g., seven) become
lowest scores (e.g., one), and lowest scores become highest scores. A reason the
researcher followed the procedure of subtractions for negative skewed items was that the
techniques used in transformations such as the square root and logarithms properly work
226
in positive skew (Kline, 1998; West, Finch, & Curran, 1995). The negatively skewed
distribution represents that more scores are located above a mean score, and a tail is long
to the left. In others words, an item with a positively skewed distribution has more scores
below a mean and a long tail to the right.
The primary function of the square root and logarithm in data transformations is
to pull outlying scores to a mean of the distribution in an item. As taking the square root
or logarithm on scores in a positively skewed distribution, the scores below a mean (e.g.,
one, two, or three) spread their intervals apart and are pulled closer to the mean. A reason
is that the intervals between adjacent scores transformed by taking square root or
logarithm get smaller when the interval between
6 and
between
1 and
2 progresses to the interval
7 (see Table E.2). Thus, the square root or logarithm changes the
shape of a positively skewed distribution to be closer to a normal distribution.
Table E.2
Scores Transformed by the Squares Root and Logarithm
Original score
1
2
3
4
5
6
7
Square root
Score taken by
Interval
1.000
.414
1.414
.318
1.732
.268
2.000
.236
2.236
.213
2.449
.197
2.646
Logarithm
Score taken by log
.000
Interval
.301
.301
.176
.477
.125
.602
.097
.699
.079
.778
.067
.845
In addition, Table E.3 presents an empirical example to better understand how
the subtraction process efficiently performs on non-normal items. Table E.3 includes
three attitude items used to test a cross validation in the second data administration and
227
compares various skewness and kurtosis statistics computed in three possible situations:
no transformation, transformation without data subtractions, and transformation with data
subtractions. The critical value for statistical tests was based on the .05 significance level
and all transformed scores were computed by taking the square root.
Table E.3
Statistics of Skewness and Kurtosis in Three Stages
Item
Original score
Transformed score without
data subtractionsª
Transformed score with
data subtractionsª
Skewness
Kurtosis
Skewness
Kurtosis
Skewness
Kurtosis
Att1
-.582*
.090
-1.141*
1.947*
.050
-.638
Att2
-.640*
.018
-1.119*
1.510*
.126
-.595
Att3
-.655*
.487
-1.295*
2.318*
.030
-.091
* Significant at the .05 probability level
ª Scores were transformed by taking the square root
Table E.3 provides evidence that the square root does not work well in a negative
distribution. Particularly, the skewness of all items became more serious when scores in
the items were transformed without data subtractions than when scores in the items were
not transformed. It indicates that when scores were transformed without subtractions, the
square root functions to merge more scores over the mean score and to lengthen the tail
longer to the left because intervals between
between 1 ,
2 , and
5,
6 , and
7 are smaller than intervals
3 (see Table E.2). The kurtosis of each distribution also
becomes more serious because of the same reason. However, when scores in the items
are transformed with data subtractions, the square root makes the items close to the
normal distribution and fails to reject the assumption as previously mentioned.
Second, when researchers find non-normal univariate distributions in their
studies, they usually depend on some mathematical techniques (e.g., square root or
logarithm) to repair non-normal items (Klein, 1998). The current study also utilized the
square root transformations or logarithm to deal with several non-normal distributions.
228
The selection of either of two techniques for data transformation is usually based on the
severity of skewness. However, there has been no rule-of-thumb to determine the
severity of skewness of distributions for the selection of the proper transformation
technique. Researchers are usually recommended to utilize both transformation
techniques to non-normal data and then may choose the more appropriate technique
based on results (Hair et al., 1998).
During the preliminary analyses for the validation sample, the researcher
recognized that the item, H1, had the highest skewness statistic among all observed
variables (i.e., -1.04). When scores in H1 were transformed by taking the square root,
they were still slightly non-normal (i.e., skewness statistic = .44; SE of skewness = .167)
while the other problematic items were well remedied by taking the square root.
Accordingly, the researcher applied the logarithm to H1 and then fail to reject the normal
assumption at the .01 probability level (i.e., skewness statistic = -.13; SE of skewness
= .167). It is confirmed that the application of the logarithm may be more effective than
the square root when a distribution is severely skewed (e.g., Gorsuch, 1983).
In addition, the research selected a .01 critical level for statistical tests and
showed that several items barely failed to reject the normal assumption at the .01
probability level. That means that if the researcher had selected the .05 probability level,
there might have been more potential items which may reject the assumption after
utilizing the square root transformations. However, the logarithm would be more
frequently utilized when researchers deal with the variables that are expected to have
severely skewed distributions (e.g., students’ commitment toward their school team or
online users’ attitude toward spam mail) or select more stringent critical level (e.g., .10
or .05). Therefore, understanding logarithm’s functions on a non-normal distribution
would be valuable, particularly along with functions of the square root.
The researcher borrowed the item, H1, from the calibration data and compared its
distribution in terms of original scores, the square root transformation, and the logarithm
transformation in Figure E.1. The Figure presents an example of the procedures of data
transformation with three histograms when researchers encounter a severely skewed
distribution and shows how the shapes of distribution are changed when different
transformation techniques are applied.
229
First, the Figure shows the histogram of original scores in H1. It is easily found
that there are many scores over the mean score of 5.47, the distribution is severely
skewed to the right (see Histogram A in Figure E.1). Next, the negatively skewed
distribution is changed to the positively skewed distribution by subtractions, and then the
square root was applied to the modified scores. The shape of distribution is changed to
be close to the normal distribution, but still slightly violates the assumption being
positively skewed (see Histogram B in Figure E.1). Lastly, the logarithm rather than the
square root was employed to the scores; the distribution now seems to meet the
assumption with the very small skewness statistic (see Histogram C in Figure E.1). One
plausible reason the logarithm works on a severely skewed distribution could be derived
from mathematical characteristics of the logarithm: the range of scores under the
logarithms (i.e., .00 to .85) is narrower than under the square root (i.e., 1 to 2.65), and the
ratios of intervals between smallest scores under the logarithm (i.e., log1, log2, log3) are
relatively larger than those under the square root (i.e., 1 , 2, and 3). For example, the
location of the second bar in Histogram C is approximately similar with that of the third
bar in Histogram B (see Figure E.1 or Table E.2). The logarithm alters the shape of
distribution to be more normal than the square root does by pulling more extreme
outlying scores close to the center of the distribution and lengthening a left tail. Thus,
researchers may effectively utilize the logarithm transformation to repair seriously
violated items without considerations of just deleting them.
Third, researchers should fully recognize which items have been transformed
with a subtraction step (i.e., the items with negatively skewed distributions) when
interpreting the derived results. During the procedures of transformation, the scores were
subtracted by one greater than the highest score in several items to alter their negatively
skewed distributions to positively skewed distributions; the items whose scores were
positively skewed did not need the step for subtractions. As a result, several factors (i.e.,
product information, social role and image, hedonism/pleasure, and materialism) in the
calibration sample had mixed items in their factors; there were some items with
subtractions and the other items without subtractions within the same factor.
230
h1
80
(A) Distribution of original scores by
7-likert scale
Frequency
60
- Range: 1 ~ 7
- Mean: 5.47 (n = 212)
- SD: 1.54
- Skewness: -1.04 (SE: .167)
- Kurtosis: 1.04 (SE: .333)
40
20
Mean = 5.467
Std. Dev. = 1.27083
N = 212
0
0.00
2.00
4.00
6.00
8.00
h1
sqrh1
80
(B) Distribution of transformed
scores by the square root
Frequency
60
- Range: 1 ~ 2.65
- Mean: 1.54 (n = 212)
- SD: .39
- Skewness: .44 (SE: .167)
- Kurtosis: -.12 (SE: .333)
40
20
Mean = 1.5443
Std. Dev. = 0.38597
N = 212
0
1.00
1.50
2.00
2.50
3.00
sqrh1
logh1
80
(C) Transformed scores by the
logarithm
Frequency
60
- Range: 0 ~ .85
- Mean: .35 (n = 212)
- SD: .22
- Skewness: -.13 (SE: .167)
- Kurtosis: -.58 (SE: .333)
40
20
Mean = 0.3502
Std. Dev. = 0.21947
N = 212
0
0.00
0.20
0.40
0.60
0.80
1.00
logh1
Figure E.1 An example of three histograms in the seven Likert scale: no transformation,
the square root transformation, and the logarithm transformation
231
Accordingly, the directions of items within the factor may be inconsistent; during
CFA, it is possible that negative loading values appear to certain items based on the
number of items whose scores were subtracted during transformations. That means that
when the subtracted items are dominant in a factor, the unsubtracted items usually have
negative loadings; when the unsubtracted items are dominant in a factor, the subtracted
items usually have negative loadings. It is not a major problem to researchers when
interpreting and reporting items’ factor loadings because they may assume that all
loadings would be positive regardless of items’ transformations under the assumption that
items in factors are conceptually generated.
However, when interpreting and reporting results of a correlation matrix among
factors, it may be difficult to detect their directions of relationships from the output
analyzed. For instance, there are three factors: annoyance/irritation with no subtracted
items, product information with four subtracted item, and social role and image with four
subtracted items in the calibration sample. Original directions of relationships between
annoyance/irritation and both production information and social role and image are all
negative. However, after data transformations, the output indicated that the relationship
between annoyance/irritation and product information was positive, but the relationship
between annoyance/irritation and social role and image was negative. In particular, it
would be more undetectable when a correlation coefficient is very close to zero (e.g., the
-.09 correlation between social role and image and falsity/no sense in the calibration
sample) or the analyzed direction of relationship between two construct is unexpectedly
different than the conceptually defined relationship (e.g., the .24 correlation between
social role and image and materialism).
Therefore, one possible solution is that, like the previously subtracted items due
to their negative distribution, all scores in the other items, which even did not violate the
assumption of the normality, in the same factor could be subtracted by a constant. For
example, among a total of seven items in social role and image in the calibration sample,
four items had negative distributions which violated the assumption so that every score in
four items was subtracted by a constant. Even though scores in the other three items
were normally distributed, every score in the other three items were subtracted by a
constant; however, scores in those items did not need to be transformed by taking either
232
the square root or logarithm because the researcher just wanted all items’ direction to be
same. As a result, all items may have positive factor loadings in their factors, and the
original directions of correlations among factor could be easily detected.
However, the directions of certain factor correlations in the output are still
different from those of original correlations due to the combinations of two factors, one
factor with subtracted items and the other factor with unsubtracted items (e.g., the
positively expected relation between social role and image and good for the economy was
negative). It could be, however, easily solved by replacing the original directions from
the correlation matrix in the output if researchers recall which factors’ items were
subtracted by a constant (e.g., the negative correlation between social role and image and
good for the economy in the output can be interpreted and replaced by the positive
correlation because researchers recall that all items’ scores in social role and image were
subtracted, but there were no item subtractions in good for the economy). In other words,
the positively expected correlation between product information and social role and
image is still positive after transformations because all items’ scores in both factors were
subtracted by a constant. The study followed this method to correctly report results from
confirmatory factor analyses and structural models. However, there were no changes in
terms of the amount of all coefficients shown in the output whether partial or full items’
scores within a factor are subtracted by a constant.
It may be also recommended that researchers may pre-analyze the original data
before data transformations and keep the results for future reference. When researchers
get real results devoted to the study after data transformations, the results from the
original data prevent researchers from misinterpreting real results in terms of the
directions of relations (e.g., factor loading, correlations among factors, or causal
relationship among latent variable). In addition, researchers may have a chance to test
robustness of maximum likelihood estimations to moderate violations of normality by
comparing both results (e.g., Gorsuch, 1983; Tate, 1998). The current study also
compared both results to determine whether CFA is somewhat robust against violation of
normality and revealed that the results derived after data transformations were consistent
with the result from the original scores in terms of the overall test and standard errors of
estimates. No significant differences that may affect the study have been found between
233
both results.
234
APPENDIX F
SUMMARY STATISTICS FOR STANDARDIZED RESIDUALS
IN THE MODIFIED MEASUREMENT MODEL
235
Largest Negative Standardized Residuals in the Modified Measurement Model (16)
Residual for hedonism/pleasure3 and hedonism/pleasure1: -2.63
Residual for annoyance/irritation2 and social role and image1: -2.85
Residual for annoyance/irritation3 and product information2: -3.65
Residual for annoyance/irritation3 and social role and image1: -3.26
Residual for annoyance/irritation3 and hedonism/pleasure1: -2.72
Residual for good for the economy1 and social role and image4: -3.08
Residual for good for the economy2 and product information1: -3.19
Residual for materialism1 and social role and image4: -2.60
Residual for materialism1 and hedonism/pleasure3: -2.69
Residual for materialism1 and annoyance/irritation3: -2.72
Residual for materialism1 and good for the economy1: -3.86
Residual for materialism2 and materialism1: -2.67
Residual for falsity/no sense1 and materialism1: -3.18
Residual for falsity/no sense4 and social role and image1: -2.70
Residual for falsity/no sense4 and annoyance/irritation2: -4.21
Residual for falsity/no sense4 and materialism1: -4.42
Largest Positive Standardized Residuals in the Modified Measurement Model (12)
Residual for hedonism/pleasure2 and hedonism/pleasure1: 2.81
Residual for hedonism/pleasure3 and product information1: 2.81
Residual for annoyance/irritation2 and social role and image6: 2.69
Residual for good for the economy4 and social role and image6: 3.49
Residual for materialism2 and product information2: 2.83
Residual for materialism3 and materialism2: 3.50
Residual for falsity/no sense1 and materialism3: 2.66
Residual for falsity/no sense2 and social role and image6: 3.16
Residual for falsity/no sense2 and hedonism/pleasure2: 2.79
Residual for falsity/no sense2 and annoyance/irritation2: 2.98
Residual for falsity/no sense2 and annoyance/irritation3: 3.36
Residual for falsity/no sense2 and materialism3: 4.57
Smallest Standardized Residual = -4.42
Median Standardized Residual = 0.00
Largest Standardized Residual = 4.57
A total number of covariance residuals: 253
A total number of problematic residuals (> ‫׀‬± 2.58‫)׀‬: 28
236
APPENDIX G
THE FULL STRUCTURE MODEL ESTIMATION IN THE RANDOMLY SPLIT
CALIBRATION AND VALIDATION SAMPLES
237
Table G.1
Factor Loadings and R² of Observed Variables in the Structural Models in the Randomly
Split Calibration and Validation Samples
Latent variable
Product
information
Social role and
image
Hedonism/
pleasure
Annoyance/
irritation
Good for the
economy
Materialism
Falsity/no sense
Attitude
Observed
variable
I1
I2
I3
S1
S4
S6
H1
H2
H3
H4
A1
A2
A3
G1
G2
G4
M1
M2
M3
F1
F2
F4
Att1
Att2
Att3
Completely standardized
loading
Calibration
Validation
sample
sample
.81*
.76*
.82*
.75*
.83*
.79*
.75*
.68*
.82*
.85*
.74*
.77*
.73*
.77*
.86*
.87*
.73*
.78*
.73*
.74*
.82*
.76*
.90*
.86*
.75*
.62*
.72*
.84*
.75*
.73*
.57*
.69*
.65*
.69*
.76*
.70*
.82*
.72*
.84*
.81*
.76*
.72*
.73*
.87*
.91*
.93*
.93*
.96*
.72*
.77*
* Significant at the .05 probability level.
238
R²
Calibration
sample
.65
.68
.69
.56
.68
.54
.54
.73
.53
.53
.67
.81
.57
.53
.56
.32
.42
.57
.67
.71
.57
.54
.83
.86
.52
Validation
sample
.58
.56
.63
.46
.72
.59
.60
.75
.60
.55
.58
.75
.38
.71
.54
.48
.47
.49
.52
.66
.51
.75
.86
.93
.60
Table G.2
Correlation among Latent Variables in the Randomly Split Calibration and Validation
Samples
Product Social role Hedonism/ Annoyance/ Good for the Material- Falsity/
Attitude
information and image pleasure
irritation
economy
ism
no sense
Product
information
1
Social role
and image
.59*
(.47*)ª
1
Hedonism/
pleasure
.70*
(.68*)
.54*
(.35*)
1
Annoyance/
irritation
-.57*
(-.53*)
-.42*
(-.22*)
-.66*
(-.61*)
1
Good for the
economy
.38*
(.46*)
.34*
(.34*)
.44*
(.24*)
-.21*
(-.10)
1
Materialism
-.11
(-.02)
.01
(.38*)
-.17*
(-.02)
.47*
(.44*)
-.03
(-.13)
1
Falsity/no
sense
-.44*
(-.43*)
-.29*
(-.05)
-.50*
(-.41*)
.88*
(.85*)
-.20*
(-.10)
.61*
(.66*)
1
Attitude¹
.72
(.76)
.51
(.39)
.72
(.75)
-.68
(-.57)
.38
(.40)
-.20
(-.07)
-.55
(-.40)
ª The correlations among latent variables in the validation sample were shown in parentheses.
¹ Significance levels of correlations between ETA and KSI were not shown in the output.
* Significant at the .05 probability level.
239
1
Table G.3
Standardized Parameters Estimates of the Structural Models in the Randomly Split
Calibration and Validation Samples
Endogenous
construct
Attitude
Exogenous
Construct
Calibration sample
(R² = .71)
Validation sample
(R² = .64)
Standardized
coefficient
SE
t-value
Standardized
coefficient
SE
t-value
Product
information
(H1)
.35
.09
3.95*
.37
.10
3.64*
Social role and
image (H2)
.02
.07
.34
.04
.08
.48
Hedonism/
pleasure (H3)
.21
.10
2.08*
.39
.10
4.05*
Annoyance/
irritation (H4)
-.37
.20
-1.82
-.26
.18
Good for the
economy (H5)
.08
.07
1.16
.14
.07
Materialism
(H6)
.02
.08
.20
-.13
.12
-1.07
Falsity/no
sense (H7)
.05
.20
.27
.23
.20
1.16
* Significant at the .05 probability level
240
-1.41
2.07*
Table G.4
The Assessment of Model Fit of Structural Models in the Randomly Split Calibration and
Validation Samples
Test of model fit
Absolute fit
Fit index
Calibration
sample
Validation
sample
Χ²
df
452.08
247
494.44
247
p – value
.00
.00
>. 05 or .01
χ²/df
1.83
2.00
1.0 ~ 2.0 (Hair et al., 1998)
< 2.0 (Byrne, 1989)
< 3.0 (Kline, 1998)*
RMSEA
.062ª
.070ª
< .06 (Hu & Bentler, 1999)
< .10 (Steiger, 1990)
SRMR
.065
.064
< .05 (Kelloway, 1998)
< .08 (Hu & Bentler, 1999)
< .10 (Kline, 1998)
GFI
.86
.84
> .90 (Kelloway, 1998; Kline,
1998)
NFI
.95
.93
> .90 (Bentler & Bonett, 1980;
Kelloway, 1998; Kline,
1998; Hair et al., 1998)
IFI
.97
.96
> .95 (Hu & Bentler, 1999)
CFI
.97
.96
> .95 (Hu & Bentler, 1999)
> .90 (Kelloway, 1998; Kline,
1998)
RFI
.93
.91
> .90 (Kelloway, 1998)
Comparative fit
The common rule of thumb
-
ª The p - value of the null that RMSEA < .05 was .019 in the calibration sample; .00019 in the validation
sample.
* Klein (1998) indicated that a ratio value less than 3.0 may be applied when a large sample set was
analyzed; “in small samples, a χ²/df ratio of, say, 2.5, may arise even if the overall fit of the model is poor”
(p. 131).
241
APPENDIX H
THE ASSESSMENT OF MODEL FIT OF THE SIX-FACTOR
AND SEVEN-FACTOR MEASUREMENT MODELS
242
Table H.1
The Overall Model Fit of the Six-Factor and Seven-Factor Measurement Models
Test of model fit
Absolute fit
Fit index
Six-factor
model
Seven-factor
model
Χ²
df
419.20
194
360.69
188
p – value
.00
.00
>. 05 or .01
χ²/df
2.16
1.92
1.0 ~ 2.0 (Hair et al., 1998)
< 2.0 (Byrne, 1989)
< 3.0 (Kline, 1998)*
RMSEA
.074ª
.066ª
< .06 (Hu & Bentler, 1999)
< .10 (Steiger, 1990)
SRMR
.069
.064
< .05 (Kelloway, 1998)
< .08 (Hu & Bentler, 1999)
< .10 (Kline, 1998)
GFI
.85
.87
> .90 (Kelloway, 1998; Kline,
1998)
NFI
.92
.93
> .90 (Bentler & Bonett, 1980;
Kelloway, 1998; Kline,
1998; Hair et al., 1998)
IFI
.95
.96
> .95 (Hu & Bentler, 1999)
CFI
.95
.96
> .95 (Hu & Bentler, 1999)
> .90 (Kelloway, 1998; Kline,
1998)
RFI
.90
.91
> .90 (Kelloway, 1998)
NFI
.77
.75
-
IFI
.65
.64
-
Comparative fit
The common rule of thumb
-
Parsimonious fit
ª The p - value of the null that RMSEA < .05 was .00 in the six-factor model; .0064 in the seven-factor
model.
* Klein (1998) indicated that a ratio value less than 3.0 may be applied when a large sample set was
analyzed; “in small samples, a χ²/df ratio of, say, 2.5, may arise even if the overall fit of the model is poor”
(p. 131).
243
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BIOGRAPHICAL SKETCH
Do Young Pyun was born in Daegu, the Republic of Korea, on April 3, 1973.
After completing his work at Hyup-Sung High School, he attended Yonsei University,
majoring Sports and Leisure Studies in 1992 and received a Bachelor of Science from
Yonsei University in 1997. Do Young then entered the Graduate School of Yonsei
University, majoring in Physical Education in March, 1997 and received a Master of
Science also from Yonsei University in February, 1999. After serving as an instruct at
Yonsei University for two and a half years, Do Young was admitted to the Graduate
School of the Florida State University with a Fellowship, majoring in Sport Management
in August, 2001. One of his primary research interests is related to an area of advertising
through sport that has received little attention from sport management scholars,
particularly focusing on consumers’ cognitive structures that influence their attitudes
toward advertising through sport. During his graduate studies at the FSU, Do Young has
presented 14 papers at major conferences in the realm of Sport Management and
published one refereed journal article. He received a certificate in Educational
Measurement and Statistics from the Department of Educational Psychology and
Learning Systems in August, 2005. Do Young is now expecting to receive the degree of
Doctor of Philosophy in Sport Management from the Florida State University in April of
2006.
265