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Electronic Theses, Treatises and Dissertations
The Graduate School
2009
The Effects of Relationship Marketing on
Brand Equity
Jeremy S. Wolter
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF COMMUNICATION
THE EFFECTS OF RELATIONSHIP MARKETING ON BRAND EQUITY
By
JEREMY S. WOLTER
A Thesis submitted to the
College of Communication and Information
in partial fulfillment of the
requirements for the degree of
Master of Science
Degree Awarded:
Fall Semester, 2009
The members of the committee approve the thesis of Jeremy Wolter defended on September 18th, 2009.
______________________________
Steven McClung
Professor Directing Thesis
______________________________
Gary Heald
Committee Member
______________________________
Michael Hartline
Committee Member
Approved:
_____________________________________________
Stephen McDowell, Director, School of Communication
_____________________________________________
Lawrence C. Dennis, Dean, College of Communication and Information
The Graduate School has verified and approved the above-named committee members.
ii
ACKNOWLDGEMENTS
I would first like to thank my wife for supporting me through this process. She took the brunt of
any frustration and desperation in the pursuit of this research. Without her love and support, I would not
be anywhere near where I am today.
I would like to thank Dr. Gary Heald for taking time to be a member on my committee. Dr.
Heald’s remarks and criticisms tamed my seemingly wild interpretations of the data. In addition, he laid
the groundwork for my understanding and use of SPSS and the exploration of new ways to tackle data
analysis.
I would also like to thank Dr. Michael Hartline for being on my committee. Dr. Hartline was my
quintessential eye of the hurricane where calm resided despite uncontrollable forces raging just beyond
arm’s reach. His remarks brought clarity where it had been lacking previously.
I owe a special thank you to Dr. Vicki Eveland who helped me begin this process and whose
initial guidance had a significant effect on the final direction. Her patient and timely advice assured me
that the path I had chosen was the correct one. My future research as a doctoral student is indebted to her
initial prodding.
Finally, I would like to thank my chair, Dr. Steven McClung. Dr. McClung was the epitome of
patience and kindness in the long path that was this research. He took over as my chair and helped me
pursue my interest instead of forcing the research to fit his own interest.
By allowing me to follow my desire in research and subsequently pursue a topic that became more
marketing than communication, Dr. McClung had a profound effect on the direction of my future
education. His guidance helped me work through the life altering event that is having children and laid the
framework to undertake future research endeavors. For all of this, I cannot thank him enough.
iii
TABLE OF CONTENTS
List of Tables ……………………………………………………………………………………....……... v
List of Figures ………………………………………………………………………………………......... vi
List of Abbreviations ………………………………………………………………………...………..… vii
Abstract…………………………………………………………………………………//……………... viii
INTRODUCTION ………………………………………………………………………………………... 1
1. LITERATURE REVIEW ………………………………………………….…………………………... 3
Branding and Brand Equity …………………………………………………...………………..... 4
Relationship Marketing ………………………………...……………………………………….. 10
Hypotheses …………….…………...………………………………………………………….... 12
2. METHODOLOGY……………………………………………………………….……………………. 16
3. RESULTS ……….…………………………………………...………………….……………………. 21
Cluster Analysis …………………………………………………...………………..................... 21
Validity Assessment ………………………………...…………………………………………... 30
Common Method Variance Assessment ……………………………………...……………….... 30
Hypothesis Testing ……………………………………………………………………………… 32
CONCLUSION ….…………...…………………………………………………….……………………. 41
Discussion …………………………………………………………………..………………...… 41
Limitations …………………………………………………………………..………………..… 42
Future Research………………………………………………………………..……………...… 43
Conclusions …………………………………………………………………………………...… 44
REFERENCES …………………………………………………………………………………………... 45
BIOGRAPHICAL SKETCH ……………………………………………………………………………. 51
iv
LIST OF TABLES
Table 1. Number of Responses and Average Scores on Brand Equity Component per Brand ….....…… 19
Table 2. Survey Items with Means, Standard Deviations and Cronbach’s Coefficient Alpha ………..… 20
Table 3. Agglomeration Coefficients from Clustering Procedure of Relationship Marketing
Strategy Levels …………………………………………………………………………….…... 22
Table 4. Number of Brands Assigned to Clusters per Relationship Marketing Strategy Level ……........ 27
Table 5. Correlation and Covariance Coefficients Between All Constructs Variables ……………….… 29
Table 6. Correlation Coefficients Adjusted for Common Method Variance .……………….....………... 31
Table 7. Means, Standard Deviations, Skewness, Kurtosis, and Normality Test Among the Variables ... 34
Table 8. Average Brand Equity Component Scores per Relationship Marketing Strategy Level …..…... 36
Table 9. Box’s Test and Levene’s Test Statistics from the Multivariate Test ……………………….….. 37
Table 10. Results of the Multivariate Test Between Relationship Marketing Strategy Usage …..……… 38
Table 11. Results of the Univariate Test Between Relationship Marketing Level Two Strategy Usage .. 39
v
LIST OF FIGURES
Figure 1. Dendogram of Level One Relationship Marketing Clustering using Ward’s Method …........... 23
Figure 2. Dendogram of Level Two Relationship Marketing Clustering using Ward’s Method ..…….... 24
Figure 3. Dendogram of Level Three Relationship Marketing Clustering using Ward’s Method …........ 25
Figure 4. Scatterplots of RM Levels versus Brands and Cluster Solutions ……………………………... 26
Figure 5. Normality Plots of the Residuals of the Dependent Measures …………………………...…… 33
Figure 6. Mean Plots of Perceived Quality and Brand Awareness by Relationship Marketing Level Two
Strategy Clusters ….………………………………………………………………......….…… 39
vi
LIST OF ABBREVIATIONS
CBBE
customer based brand equity
CMV
common method variance
df
degrees of freedom
MANOVA
multivariate analysis of variance
RM
relationship marketing
SD
standard deviation
vii
ABSTRACT
Relationship marketing practices have become ubiquitous in the consumer domain and
their effects on various consumer behaviors are well documented. At the same time, branding
strategies are widely utilized as well and their effect on consumer behavior is also well
documented. What is not known is how relationship marketing strategies impact brand building
activities or brand equity. This study addresses this gap in the literature by examining the brand
equity scores of 46 brands collected from a convenience sample of 356 undergraduates at a
Southeastern university. The effects of relationship marketing strategies on brand equity
components are analyzed using a MANOVA technique. Findings provide mixed support for the
hypothesized effects. Specifically, as a firm’s use of social bonding strategies increases, there is
a significant and positive effect on the brand equity components of brand awareness and
perceived quality. Price incentive strategies and structural bonding strategies had no significant
effect, positive or negative, on any of the brand equity components.
viii
INTRODUCTION
Brand equity and relationship marketing are two areas of research that have generated a
large amount of interest in the marketing literature. Each respective research stream has shown to
predict a multitude of consumer behavior outcomes with some overlap in areas such as loyalty
(Chaudhuri and Holbrook, 2001). Despite this overlap, both areas remain distinct strategies for a
firm to utilize in the quest for market share so that a firm may choose to employ one strategy
singly or both strategies simultaneously. While research has been done on the drivers and
outcomes of both brand equity (Aaker, 1991; Keller and Aaker, 1998) and relationship marketing
(Berry and Parasuraman, 1991; Morgan and Hunt, 1994), in-depth studies into the impact of the
two separate areas on each other have been missing. Instead, research has examined how well
relationship marketing antecedents may crossover onto brand equity and studies have shown that
these antecedents do contribute to the understanding of brand equity, either directly or indirectly
(Chaudhuri and Holbrook, 2001; Delgado-Ballester and Munuera-Alema´, 2005). There exists
then, the potential of a much larger framework as the two strategies are reconciled with each
other. As Chaudhuri and Holbrook (2001) note, “these conceptualizations can be reconciled and
integrated as crucial aspects in an overall process” ( p. 82). Such integration would not only
allow for a fuller understanding of consumer behavior but it would give practitioners a more
parsimonious model for basing their strategies.
The true impact of carrying out relationship marketing strategies on brand equity is still
unknown, despite the aforementioned research. Questions remain as to exactly how a
relationship marketing approach may impact a brand’s brand equity components. It would seem
to reason that if a firm were to offer price incentives for a brand as a starting point to their
relationship marketing endeavors in an attempt to build loyalty (Berry and Parasuraman, 1991)
then the perceived quality component of the brand equity could suffer (Aaker, 1991). However,
some of the top brands in the world as listed by Interbrand such as McDonald’s and IKEA are
known for their pricing incentives but still maintain their high brand value. Thus, the assumed
negative effect of price incentives may be compensated by other relationship marketing
strategies such as social bonding (Berry and Parasuraman, 1991). Since the build up of
associations is another component of brand equity (Aaker, 1991), as the relationship marketing
1
strategy expands to social bonding the positive effect of the increased number of associations
may overtake the negative effects of price incentives. Before a reconciliation of these two
strategies can be completed, the full extent of the impact of each component on each other needs
to be explored. This is the attempt of the present research.
This study looks at how relationship marketing strategies affect the brand equity of
existing brands. Specifically, the present study examines the effects of different relationship
marketing strategies as demarcated by Berry and Parasuraman (1991) on the brand equity
components of brand awareness, brand associations, perceived quality, and brand loyalty as
outlined by Aaker (1991). These strategies of relationship marketing are conceptualized along
three levels with the first level representing pricing incentives, the second level representing
social bonding, and the third level representing structural bonding (Berry and Parasuraman,
1991).
2
CHAPTER 1
LITERATURE REVIEW
Branding and Brand Equity
A brand has been identified as “a name, term, sign, symbol, or design, or combination of
them which is intended to identify the goods and services of one seller or groups of sellers and to
differentiate them from those of competitors” (Kotler, 1991, p. 442). This definition was
representative of early conceptualizations of branding in that the components of the brand were
tangible aspects that could easily be changed. With the introduction of brand equity in the early
1990s, the focus of branding started moving away from the branded product and into the mind of
the consumer. It was this consumer-based perspective that Aaker (1991) and Keller (1993)
based much of their research and developed what Keller termed as consumer based brand equity
(CBBE).
The advent of a consumer-based approach to brand equity established two distinct ways
of conceptualizing brand equity, namely a financial, market-based method and a consumer-based
method. The financially based concept was derived from the desire to bring brand equity into a
financial perspective so it could be accounted for on a company’s balance sheet and subsequently
leveraged for market capitalization. This financial driven method has resulted in the proposal of
multiple options for measurement (Lassar, Mittal, and Sharma, 1995) and has been termed as
brand value (Wood, 2000). The consumer-based approach is based on the consumer’s perception
of the brand. Both conceptualizations take a long term approach to the return on brand building
investments in the form of brand loyalty (Wood, 2000). Since this study is focused on the dual
effects of brand equity and relationship marketing on the consumer, consumer-based measures as
well as consumer-based conceptualizations will be utilized whenever the opportunity arises.
The value of brand equity is in the behavior outcomes of consumers; brands with high
equity obtain reduced marketing costs as well as greater market share, trade leverage, price
premiums, and loyalty (Aaker, 1991; Keller, 1993; Park and Srinivasan, 1994). Consumer-based
brand equity has also been linked to the overall financial performance of the firm (Kim, Kim,
and An, 2003). In Managing Brand Equity, David Aaker’s (1991) seminal work on brand equity,
3
he defines brand equity as, “A set of brand assets and liabilities linked to a brand, its name and
symbol, that add to or subtract from the value provided by a product or service to a firm and/or
that firm’s customers.” ( p. 15). He then broke brand equity into five components: brand loyalty,
brand awareness, perceived quality, brand associations, and other proprietary brand assets. Of
these five components, most research has focused on the first four and deemed them to be more
important for research purposes than the final component of ‘other proprietary brand assets’.
Following the lead of such research, this study utilizes these four components at the exclusion of
the final component (Atilgan, Aksoy, and Akinci, 2005; Pappu, Quester, and Cooksey, 2005).
Keller (1993) built off of Aaker’s work and conceptualized CBBE as “the differential
effect of brand knowledge on consumer response to the marketing of the brand” ( p. 2). This
definition placed the concept of brand equity fully in the mind of the consumer and has led to the
idea that if the brand has no meaning to consumers, it has no meaning at all (Cobb-Walgren,
Ruble, and Donthu, 1995; Keller, 1993; Rio, Vazquez, and Iglesias, 2001). Brand knowledge is
then broken down by Keller into two components, brand awareness and brand image, with brand
image representing the set of consumer’s associations tied to the brand.
The conceptualizations of Keller and Aaker are very similar but remain distinct. Both
have been used in brand equity research extensively and have shown to be valid across multiple
product categories (Cobb-Walgren, Ruble, and Donthu, 1995; Krishnan, and Hartline, 2001).
This study will follow the work of Aaker for a couple of reasons. While Keller’s model would
seem to be more parsimonious and therefore have greater extendibility, the antecedents in
Aaker’s brand equity model have all shown to be distinct and valid. While some of the
antecedents have equitable definitions across the two models, others do not. Keller’s
conceptualization of brand equity seems to be an attempt to create a simpler and more extendable
model. However, Aaker’s model contributes more to the study of the separate antecedents that
create brand equity. Since this study’s purpose is to examine how the varying levels of
relationship marketing will affect the brand equity components, Aaker’s (1991) model and
conceptualization of brand equity will be used as a set of brand assets and liabilities linked to a
brand, its name and symbol, that add to or subtract from the value provided by a product or
service to a firm and/or that firm’s customers (p. 15).
Brand Equity: Brand Loyalty
4
Aaker (1991) defined brand loyalty as the “measure of the attachment that a customer has
to a brand” (p. 32). Brand loyalty is unique when compared to the other components in that it is
both a driver of brand equity as well as a behavioral outcome. As Aaker (1991) states, “Note that
brand loyalty is both one of the dimensions of brand equity and is affected by brand equity. The
potential influence on loyalty from the other dimensions is significant enough that it is explicitly
listed as one of the ways that brand equity provides value to the firm.” (p. 18)
Because of this dualism, the practice of loyalty measurement has been split into primarily
two areas as well, one that measures the intention of the consumer and one that measures the
actual behavior. Aaker (1991) advocated both measures but researchers who have measured
brand loyalty based on behavior claim that measuring intentions misrepresent loyalty. This
argument is based on studies that have questioned the intention-behavior link of consumer
behavior, especially concerning the multifaceted process of real-world decision making (Miniard
and Cohen, 1979; Morrison, 1979). In his foundational study into developing brand loyalty,
Tucker (1963) noted, “No consideration should be given to what the subject thinks or what goes
on in his central nervous system; his behavior is the full statement of what brand loyalty is.” ( p.
32). A counter claim by those who study the consumer’s intention is that repurchase behavior
does not necessarily mean the customer was loyal; measuring the resulting behavior does not
conclude the reason behind the behavior. Repeat purchase behavior can be a result of simple
habit and as such, this type of behavior has been termed ‘spurious loyalty’ (Knox and Walker,
2001). Also, the behavioral research practice has been plagued by defining the best method of
operationalizing and measuring brand loyalty. In comparison, the measures used to determine the
attitudinal loyalty construct are far more homogenous and therefore offer more external validity.
In addition, new research has shown that attitudinal measures of loyalty can be reliably used to
explain or predict purchase behavior (Bennett and Rundle-Thiele, 2002).
To dispel the confusion behind the brand loyalty construct, there has now been a recent
push to break the intentional aspect away from a single concept of brand loyalty and into its own
measurement. Unfortunately the newly separated construct has already been identified with
several different names, such as brand commitment (Warrington and Shim, 2000), true brand
loyalty (Amine, 1998), attitudinal loyalty, and brand support (Bennett and Rundle-Thiele, 2002;
Bloemer and Kasper, 1995; Chaudhuri and Holbrook, 2001; Knox and Walker, 2001). In an
attempt to develop a perceptual measure of brand equity, Lassar, Mittal, and Sharma (1995)
5
determined that it was better to focus on commitment as a feeling since behavior was “to be a
consequence of brand equity rather than brand equity itself” (p. 13). This break away of the
attitudinal dimension has been further dissected into measures of a consumer’s propensity to be
loyal and the attitude towards the brand or repurchase (Bennett and Rundle-Thiele, 2002).
Interestingly, this thinking may give a reason why some researchers have deemed that brand
loyalty cannot be measured from a universal set of research questions, but instead the questions
must be product specific. If part of brand loyalty lies in a consumer’s propensity to be loyal, then
obviously this variable could change between market segments and product categories. This
study follows this line of reasoning by focusing on a consumer’s attitudinal loyalty towards a
brand and leaving the behavioral loyalty as a consequence, similar to that of relationship
marketing frameworks. Given that Aaker had allowed for such a possibility in his original
conceptualization, we utilize his original conceptualization of brand loyalty as the “measure of
the attachment that a customer has to a brand” (Aaker, 1991).
Brand Equity: Brand Awareness
Aaker (1991) defined brand awareness as “the ability of a potential buyer to recognize or
recall that a brand is a member of a certain product category” ( p. 61). Brand awareness is an
obvious necessity to building brand equity since a consumer would need to be aware of a brand
before that brand could have any equity, but brand awareness can also be used solely by
consumers for purchase decisions often in low involvement based purchasing situations
(Broniarczyk and Alba, 1994; Hoyer and Brown, 1990; Macdonald and Sharp, 2000). From these
findings, research has shown the worth of using advertising to build awareness since it can be
used as a simple choice heuristic leading a consumer to choose a remembered brand over even a
higher quality brand (Hoyer and Brown, 1990; Macdonald and Sharp, 2000).
Aaker broke brand awareness into four levels representing a hierarchy of awareness
within the consumer with each level using a different recall test. These levels ranged from the
lowest level where the consumer is “unaware of the brand” to the highest level where the brand
is “top of mind” (Aaker, 1991). This “top of mind” area has also been coined as brand recall as
well as brand salience (Alba and Chattopadhyay, 1986). Keller (1993) broke brand awareness
into two components, brand recognition and brand recall, that mimicked Aaker’s components but
leaves off the two extremes. Brand recognition is correctly identifying the product category when
6
given the brand name and brand recall is recalling the brand name when given the product
category. Keller then recognizes that the relative importance of each item depends on how the
brand is purchased. For example, if the brand is purchased in a store, then brand recognition is
more important (Keller, 1993). Most studies concerning brand awareness have centered on the
process and measurement of recalling the brand. Recent studies show that the measures change
in usefulness over a brand’s lifetime, with unaided tests showing more receptiveness to change in
an older brand and aided recall tests showing more receptiveness in a newly launched brand
(Laurent, Kapferer, & Roussel, 1995).
The tricky nature of using aided recall tests has also been an interesting area. Specifically,
aided recall can actually inhibit the recall of competing brands that are not shown (Alba and
Chattopadhyay, 1986). The size of the effect was related to both the number of brands given as
an aid and the time of exposure (Alba and Chattopadhyay, 1986). Other potential inhibitors of
recall are the product and category cues that are used (Nedungadi, Chattopadhyay, and
Muthukrishnan, 2001) as well as the presence of “market leaders” (Laurent, et al., 1995).
Significantly, recall can correlate with brand attitude depending on what attributes are actually
recalled (Chattopadhyay and Alba, 1988).
The simplicity of the aforementioned awareness definitions has recently been contested;
with researchers noting a memory network in play with the brand name and product category as
it has been noted with brand associations. This line of research questions the usefulness of
employing a one cue test (product category) when the actual process used by consumers utilizes
far more cues (Romaniuk and Sharp, 2004). In some cases it has been shown that a brand can
have a strong awareness but be in a weak or relatively unknown product category (Nedungadi,
Chattopadhyay, and Muthukrishnan, 2001). To give credence to the complexity of the recall
function, Romaniuk and Sharp (2004) proposed to separate brand salience away from brand
awareness and its conception as a “top of mind” ranking to an operationalization as “the
propensity of the brand to be thought of in buying situations”. These findings give more credence
to the depth of brand awareness, but for the sake of simplicity and parsimony, awareness is
operationalized using Aaker’s (1991) original definition, as “the ability of a buyer to recognize
or recall that a brand is a member of a certain product category” ( p. 61).
Brand Equity: Brand Associations
7
While researchers have postulated that brand associations are the driving force behind
brand equity (Chen, 2001), Aaker (1991) himself stated “The underlying value of a brand name
often is its set of associations” ( p. 110). There are various ways that brand associations
contribute value to both the consumer and the business and these include helping to process and
retrieve information, differentiation, reason-to-buy, the creation of positive attitudes, and a basis
for extensions. Aaker (1991) defined brand associations simply as “anything linked in memory to
a brand” but then went on to also state that “the association not only exists but has a level of
strength (p. 109). Keller (1993) similarly defines associations as the other informational nodes
linked to the brand node in memory and containing the meaning of the brand for consumers. He
states that associations can vary in strength, favorability and uniqueness and these variations
thereby hold the various associations’ strength and “play an important role in determining the
differential response that makes up brand equity” (p. 3). Aaker (1991) classified brand
associations into an expansive eleven categories. Keller’s (1993) classification has fewer
categories but is actually wider in scope with just attributes, benefits, and attitudes. This
classification increases in abstraction from attributes and attitudes with benefits representing the
personal value consumers attach to the product and attitudes representing their overall evaluation
of the product.
The nature and further classification of brand associations has been the focus of much of
the research on brand associations. The first categorization was between attributes and nonattributes, effectively distinguishing between informational and emotional associations (Park and
Srinivasan, 1994). Recent research has identified another set of associations as organizational
associations, which has also been linked to a new area of branding research, that of
organizational branding (Aaker, 2004). This new category of associations has been broken down
into corporate ability associations and corporate social responsibility associations (Keller and
Aaker, 1998) with additional research attesting to the insignificance of the corporate social
responsibility associations for predicting consumer behavior (Chen, 2001). The abstractness of
an association has been shown to be a predictor of the strength of the association as well as an
indicator of how long the memory or association will stay in the consumer’s mind (Alba and
Chattopadhyay, 1986; Keller, 1993).
A standard measurement of associations has been elusive. One line of research combines
brand associations with brand awareness, even though they are conceptually different (Egan,
8
2003; Yoo and Donthu, 2001; Yoo, Donthu, and Lee, 2000). Other research, though, has shown
that these two areas are distinctive dimensions (Sinha, Leszeczyc, and Pappu, 2000; Sinha and
Pappu, 1998). A separate line of research has shown that different goods and services will
require different measures (Low and Lamb Jr., 2000) and those different associations can have
varying effects on the accepted consumer behavior outcomes of brand equity (Broniarczyk and
Alba, 1994; Rio, et al., 2001).
Rather than determining a product specific scale, the pattern of Cobb-Walgren, Ruble,
and Donthu (1995) was followed. Thus, the present study utilizes a more universal scale for
brand associations that will allow for the comparison of brand associations across different
product categories. As such, the concept of brand associations is operationalized simply as the
summation of anything linked in memory to the brand. This operationalization allowed for the
measurement of the favorability as well as the strength of the associations by considering the
number of associations since Aaker (1991) deemed that associations “not only exist but have a
level of strength” and that an association will be stronger “when it is supported by a network of
other links” (p. 109).
Brand Equity: Perceived Quality
Perceived quality (PQ) can almost be viewed as another brand association since it is “a
perception of customers” rather than the actual product quality (Aaker, 1991). The importance of
perceived quality has been attested to by research that has linked it to brand equity responses
such as willingness to pay a price premium, brand purchase intent, and brand choice as well as
accept brand extensions (Aaker, 1996; Keller, 1993). The exact nature of the perceived quality
component antecedent has been debated. Perceived quality has been changed into a perceived
performance variable (Lassar, et al., 1995) as well as incorporated into a grouping with
“perceived value for the cost” (Netemeyer, et al., 2004).
An interesting note about perceived quality is the relationship marketing literature’s use
of the construct as service quality. The long use of customer satisfaction in relationship
marketing caused a debate regarding the difference between these two constructs once service
quality was introduced. This debate gave further clarity to service quality, and thereby perceived
quality, namely that service quality can be from an outsider’s perspective where information is
learned primarily second-hand (Storbacka, Strandvik, and Grönroos, 1994). The second-hand
9
nature of service quality relates to research borne out on perceived quality, specifically that
advertising spending has been linked to perceived quality as well as the perception of a
marketing campaign’s expense (Moorthy and Zhao, 2000). The fact that perceived quality has
shown to be significant in both relationship marketing as well as brand equity gives credence not
only to the similarity of these two paradigms but also to the possibility that more antecedents are
extendable between the paradigms.
To operationalize PQ, we look to Netemeyer et al.’s 2004 study, which operationalizes
PQ as “the customer’s judgment of the overall excellence, esteem, or superiority of a brand (with
respect to its intended purposes) relative to an alternative brand” (p. 210).
Relationship Marketing
The advent of relationship marketing pushed marketers to look beyond discrete
transactions or the transactional view of marketing, which focuses on single transactions, to a
broader scope of relational exchanges made up of multiple transactions (Dwyer, Schurr, and Oh,
1987; Grönroos, 1994). The long term view that is implicit in such a paradigm shift is similar to
that of brand equity since the CBBE components are built up over time. Relationship marketing
was conceptualized as marketing to existing customers and therein was the value to firms since it
was deemed that marketing to existing customers would be cheaper than attempting to obtain
new customers (Berry and Parasuraman, 1991). From the onset, customer loyalty was the
primary behavioral goal when relationship marketing strategies were considered. The focus on
loyalty is one of the main areas of overlap between the relationship marketing and brand equity
strategies. This is not a new discovery since even Jacoby and Chestnut realized that, “brand
loyalty is essentially a relational phenomenon” in their original brand loyalty research (1973).
The relationship marketing research and application initially lay in the realm of businessto-business marketing (Morgan and Hunt, 1994) and the service industry (Egan, 2003) but
received additional interest when researchers started extending it into the consumer goods
market. This widened view of exchange led to the definition of relationship marketing by
Morgan and Hunt as “all those market activities directed toward establishing, developing, and
maintaining successful relational exchanges” (1994). It was here, in the study of consumer-
10
organization and consumer-brand relationships, that one of the key features of relationship
marketing became evident: exchange between organizations and consumers are not just limited
to economic boundaries but can encompass emotions as well (Bolton and Bhattacharya, 2000).
Hence, part of the increased value generated by relationship marketing to the consumer is an
emotionally anchored one, similar to some of the benefits of brand equity as discussed before.
Increased value is also possibly generated by the reduction of choices from ongoing loyalty. This
seemingly counter intuitive method of value creation has several theoretical foundations such as
task simplification, expertise development, risk reduction, and cognitive consistency (Sheth and
Parvatiyar, 2000). Researchers have shown that this particular value component is not as
straightforward as some have stated since engaging in relationships with companies can provide
consumers more choices in their particular consideration set through the reduction of unwanted
choices (Peterson, 1995).
The renewed research interest into research marketing was spearheaded by attempting to
prove the validity of not only consumer-business relationships but also consumer-brand
relationships. The conceptualization of a consumer-brand relationship has taken the form of an
imagined relationship with the actual brand or real relationships within a brand sub-culture or
brand community (Ambler, 1997; Fournier, 1998; Muniz and O'Guinn, 2001). Reducing
technology costs and rising communication abilities has not only made it possible for
manufacturers to engage in exchange directly with consumers, much like Dell, it has also
allowed for the customization through technology, such as just-in-time inventory control,
whereby the customer has now become a co-creator (Bolton and Bhattacharya, 2000; Sheth and
Parvatiyar, 1995). When the customer is involved in such depth within the creation process, it is
easier to imagine how a brand-consumer relationship would develop.
The benefit of a brand-consumer relationship is anchored in the continued relationship or
ongoing series of transactions with the consumer that can be quantified in a probable lifetime
value or increased customer retention rate and thereby reduced marketing costs (Reichheld and
Teal, 1996). Whether a lifetime value can actually ever be counted on has been questioned with
researchers noting that the expectation of an exclusive relationship might be a fallacy (O'Malley
and Tynan, 2000). This has led to considerable debate as to the applicability and feasibility of a
relationship marketing framework to the consumer level. Research has borne out that businessto-consumer exchange can be either relational or transactional, depending on the consumer or the
11
product category (Garbarino and Johnson, 1999; Szmigin and Bourne, 1998). Even the lifetime
probable value of a customer has been called into question (Sheth and Kellstadt, 2002) with
research revealing that some customers are not profitable in a long term setting (Reinartz &
Kumar, 2000) and that relationship marketing can have diminishing returns (Hibbard, Brunel,
Dant, & Iacobucci, 2001).
The reassessment of relationship marketing’s value has also led to a reassessment of the
conceptualization. It was initially conceptualized as a marriage, which researchers noted was
flawed due to the expectation of fidelity by the consumer (O'Malley and Tynan, 2000). A more
recent conceptualization of the relationship is that of a ‘good friend’, where the consumer has
many relationships but a few are considered privileged (Szmigin and Bourne, 1998). This less
exclusive social metaphor has gained ground as the number of studies specifically focused on the
benefits of relationship marketing to the consumer has increased (Fournier, Dobscha, and Mick,
1998; O'Malley and Tynan, 2000).
Hypotheses
Despite the extensive amount of research that has been done on relationship marketing
and branding, there is no research that attempts to explain how these two areas interact. The
question of how relationship marketing strategies impact brand equity has gone unaddressed.
This is particularly perplexing considering that both strategies are long-term focused and
therefore should have considerable overlap if a company pursues or attempts both strategies at
the same time. As mentioned previously, the focus of the present study is to attempt to
understand how the two strategies of relationship marketing and brand equity interact. Since
branding and brand equity has been established longer and therefore used by practitioners for a
longer length of time, the present study studies the effects of relationship marketing strategy on
brand equity rather than vice-versa.
One possible reason for the aforementioned inattention by researchers into the interaction
of relationship marketing and brand equity is that relationship marketing research has typically
focused on services (Berry and Parasuraman, 1991) while branding research has typically
focused on goods (Krishnan and Hartline, 2001). As mentioned previously, some of the criticism
of relationship marketing has been leveled at the application of it at the business-consumer
12
relationship, especially considering mass-produced non-durable goods (O'Malley and Tynan,
2000). In the same vein, branding has been shown to be a less sufficient strategy for services
(Krishnan and Hartline, 2001). However, both strategies have been used across product types
(Wentzel, 2009; Sheth and Parvatiyar, 2000). In addition, the new marketing strategy of servicedominant logic conceptualizes all products as services (Vargo and Lusch, 2004). Thus, attention
at how branding and relationship marketing might interact is warranted. Due to the above history
of the two strategies, product type is controlled for in the hypothesis testing. Before hypotheses
can be constructed, though, the various strategies need to be conceptualized.
Many definitions of relationship marketing can still be considered too vague to allow
proper testing or demarcation from marketing theory in general (Peterson, 1995). Berry and
Parasuraman (1991) did create a demarcation of relationship marketing when they
conceptualized three successive relationship marketing strategies. It is through these levels that
the incremental effect of utilizing relationship marketing on the brand equity components can
become apparent. A level one strategy is characterized by the primary use of financial incentives
to capture “retention marketing”. The incentives can represent rewards programs, discounted
services, and additional coupons to name a few. This level is the most prevalent of the three
levels in the market place and the least effective in establishing a competitive advantage since it
is easily copied (Berry and Parasuraman, 1991). More recent research has shown that a
customer’s shopping pattern before the loyalty program influences their long term behavior
(Yuping, 2007). Since it is known that price can be used as an extrinsic cue for perceived quality
(Aaker, 1991), it is expected that a brand employing level one strategies will suffer a reduction in
the perceived quality component of brand equity. The ease of copying and ubiquitous presence
of the strategies from level one means that there should not be any unique associations created by
the strategies alone. Thus, it is also expected that such strategies will have no effect on brand
associations or brand awareness. However, since these strategies are aimed explicitly at
increasing loyalty, it is expected that brand loyalty will increase as a firm employs level one
relationship marketing. It is then we hypothesize:
H1)
Brands that engage in more relationship marketing level one strategies
will have (H1a) lower perceived quality, and (H1b) higher brand
13
loyalty when compared to brands that engage in less relationship
marketing level one strategies.
Level two relationship marketing strategies are characterized by social bonding and the
building of relationships (Berry and Parasuraman, 1991). It is at this level that much of the
criticism of relationship marketing to consumers has been aimed (O'Malley and Tynan, 2000),
especially in the consideration of goods. As mentioned before however, research has shown that
consumers can establish relationships with brands (Fournier, 1998) and the increased
communication that is a hallmark of this level is also possible in a mass audience context through
database technology (Schultz and Schultz, 2003) and Internet communities (Andersen, 2005).
Thus, the benefits from this level, that of increasing customer retention and allowing a company
time to respond to mistakes before customer defections (Berry and Parasuraman, 1991), can be
realized at the brand level. The effects of a level two strategy on brand equity can be postulated
based on the increased communication and social bonding components. Increased contact
between the brand and consumer should allow for increased brand associations and brand
awareness since both of these components are memory and learning based. Similar to the last
level, brand loyalty should also be increased. In consideration of these relationships, we
hypothesize:
H2)
Brands that engage in more relationship marketing level two strategies
will have (H2a) increased brand awareness, (H2b) increased brand
associations, and (H2c) higher brand loyalty when compared to brands
that engage in less relationship marketing level two strategies.
Level three relationship marketing is the solidification of relationships through structural
bonds (Berry and Parasuraman, 1991). These bonds are unique and are usually built into the
‘system’ of the product thereby allowing the relationship to be based on the offering of the brand
rather than depending on marketing communications or front line employees (Berry and
Parasuraman, 1991). This strategy was conceptualized within a service context but an example of
this strategy from a goods perspective is that of high customization such as utilized by Dell and
Nike, both of whom allow their customers to customize products within the purchasing process
14
on their respective websites. Since some brand-relationship research has specifically touted the
increased possibility that a brand relationship can form through such means (Bolton and
Bhattacharya, 2000), it is reasonable to expect this level of relationship marketing to have an
increased benefit on the brand equity components. However, it still remains that building
structural bonds can be problematic for brands of nondurable goods such as Coca-Cola. The
difficulty of creating structural bonds gives increased benefits to the firms who can employ such
strategies. These benefits include increased switching costs for customers as well as increased
value that is not anchored in price which in turn results in insulating customers from competitors
pricing incentives (Berry and Parasuraman, 1991). The uniqueness of an offering by a brand that
employs structural bonding should increase brand associations and brand awareness. Such a
strategy should also increased perceived quality since additional features can be seen as a signal
that a brand understands the needs of their customers (Aaker, 1991). Similar to the last two levels
and indicative of relationship marketing in general, brand loyalty can be expected to increase as a
brand practices level three relationship marketing. Thus,
H3)
Brands that engage in more relationship marketing level three
strategies will have (H3a) increased brand awareness, (H3b) increased
brand associations, (H3c) higher perceived quality, and (H3d) higher
brand loyalty when compared to brands that engage in less
relationship marketing level three strategies.
The relative strength of each relationship marketing strategy compared to each other is not
as straightforward as the effects of each strategy alone. Even more difficult is the consideration
of comparing firms that are practicing multiple strategies. For example, what would be the
difference in brand equity components between a firm practicing level one and level two
strategies together compared to a firm practicing level one and level three strategies. We leave
these differences to be deductively found through the analysis with the hopes of informing future
research.
15
CHAPTER 2
METHODOLOGY
The options for testing brand equity theories involve either using real brands or creating
fictitious brands. Fictitious brands would be good for measuring how the antecedents of brand
equity are built but not for the measurement and comparison of these antecedents as has been
proposed. It would be difficult at best to attempt to create rich brand associations that mimic the
depth of associations some real world brands have created. As stated before, the purpose of this
study is to study the impact of relationship marketing on brand equity. To study this
phenomenon, 50 of the top 100 brands were used as delineated by Interbrand and reported by
BusinessWeek (BusinessWeek, 2008). Since the nature of this study is a further attempt to
reconcile two marketing areas whose distinct origins lay in either the service or packaged goods
industry into one workable framework, a large range of existing brands that span across goods
and services were used. This will allow for post hoc tests regarding the difference between goods
and service brands. The literature states that generally, brand equity is more important where
goods are concerned (Cobb-Walgren, et al., 1995; Krishnan and Hartline, 2001) but that brand
equity can contribute to a richer understanding of the services market.
The brand equity of these products was calculated similar to (Chaudhuri and Holbrook,
2001) in that the brand equity score for each brand was elicited from a sample that was then
summed to create the score for each brand equity component. A convenience sample of students
was utilized for the brand equity scores which is a common practice in branding research
(Washburn and Plank, 2002; Yoo and Donthu, 2001). The 50 brands were chosen from the list of
100 based on their relevance to the sample. Thus, financial investment brands (ex.
GoldmanSachs), luxury brands (ex. Rolex), and gender specific brands (ex. L’oreal) were not
chosen in favor of automobile brands (ex. Ford, BMW), retail brands (ex. GAP), as well as food
and fast food brands (ex. Coca-Cola, McDonalds). Most of the financial investment brands were
eliminated due to inapplicability to the sample so two local bank brands were added (Wachovia
and Bank of America) since relationship marketing has often been characterized as being very
useful for financial clients (Berry and Parasuraman, 1991).A list of brands used in the study can
16
be seen in Table 1 (Four more brands are removed from the analysis after a cluster analysis as
explained later bringing the total number of brands used to 46).
Data collection consisted of the distribution of an anonymous online-based survey
through the website Qualtrics.com. A basic marketing class of 507 undergraduates was given the
URL of the survey and offered extra credit for taking the survey. A total of 356 unique responses
were collected giving a response rate of 70%. Each participant was shown a brand at random
with three iterations giving a total of 1,068 observations across the 50 brands. Since the brands
were shown randomly, the number of responses for each brand varied from 13 (Nike) to 32
(Ford). The complete listing of the number of responses is shown in Table 1. The responses were
reviewed for any brands that elicited an abnormally low awareness score and two brands were
removed (HandM and Nescafe).
The brand equity measures were either taken from pre-existing methods and scales or
were developed based on the work of Aaker (1991). All items from the survey showed
acceptable reliability and are included in Table 2 along with the reliability scores. CobbWalgren, Ruble, and Donthu’s 1995 study into brand equity provided measures of awareness and
associations. The awareness measure is from an unaided recall question, from which are counted
two separate scores: 1) the number of mentions and 2) the number of top-of-mind mentions. Two
Likert scale items were also included for awareness to increase the total number of items above
three to assess their reliability. All scores for the awareness construct were standardized and then
summed together to create a brand awareness index.
The brand association measure was also measured with an unaided recall type question.
The participants were asked to write down any associations they had for the chosen brand. An
association was explained as “anything that you think of when you think of the brand (ex. lumpy,
foreign, nice). An association can be positive, negative or neutral.”Five different items were
obtained from the listing: 1) the total positive associations, 2) the total negative associations, 3)
the total number of times the first association mentioned was positive, 4) the total number of
times the first association mentioned was negative, and 5) the total number of associations. The
associations were assessed as being positive, negative or neutral by two separate judges. The
reliability of the judgments was then assessed with Krippendorf’s alpha (Krippendorf, 1980) as
outlined by Hayes (2007) utilizing syntax to be run in SPSS. The smallest computed alpha was
.85 showing moderate agreement. Each judges’ count was averaged together to create the five
17
separate counts as outlined above. Each of the positive and negative counts were divided by the
total number of associations since the brands had a different number of responses, thus each
count became a percentage of that brand’s total associations. The negative association counts
were then subtracted from one to make sure all of the counts were scaled in the same direction.
Thus, the two negative counts actually became the percentage of the total associations that were
not negative. All four of the counts were then combined together to create a final brand
association scale.
The only item that had to be deleted to increase the reliability of any scale was the count
of the total number of associations. In fact, this item made the Cronbach’s alpha score for the
brand associations construct .17 before the deletion. This is most likely because the positive and
negative counts would have shown similar variance since they can be thought of as opposites of
each other whereas the total number of associations would have been high with either negative or
positive and therefore would not have varied at the same rate.
A four measure scale for brand loyalty was adapted from Aaker’s description of the
construct (Aaker, 1991). The measures for perceived quality were taken from Netemeyer et al.
(2004). All of the relationship marketing scales were developed based on the description of each
level from Berry and Parasuraman (1991). Each level contained three questions. All scales were
created by averaging the related measures together. Only the brand awareness scores were
standardized before being summed due to using scales and counts together.
18
Table 1
Number of Responses and Average Scores on Brand Equity Component per Brand (standardized)
BRAND
Adidas
American Express
Amazon
Apple
Bank of America
BMW
Budweiser
Canon
Coca-Cola
Colgate
Dell
Duracell
Disney
Ebay
FedEx
Ford
GAP
Gillette
Google
Gucci
Honda
Hewlett Packard
Hyundai
IKEA
Intel
Kellogg’s
Kentucky Fried Chick.
McDonald’s
Microsoft
Nike
Nokia
Nintendo
Pepsi
Panasonic
Pizza Hut
Samsung
Shell
Sony
Starbuck’s
Toyota
UPS
Visa
Volkswagen
Wachovia
Xerox
Yahoo
# of
responses
22
17
24
14
17
19
27
23
13
20
23
22
23
19
17
32
27
21
20
26
19
22
28
28
19
22
13
17
15
13
20
24
21
21
18
28
16
25
15
20
19
23
17
17
15
23
Brand
Awareness
.23
05
-.56
-.14
.41
-.20
.80
.46
1.18
.47
-.17
.71
.79
.93
1.04
-.16
-.61
.84
.77
-.58
-.14
-.10
.71
-.85
-.24
.03
-.69
.82
.79
.71
-1.08
.06
.32
-.05
.53
-.37
-.40
.45
1.06
.26
.62
.85
-.78
-.64
-.58
-.06
Brand
Associations
-.33
.11
-.31
.09
.44
.68
-.21
1.16
.74
.41
-.98
1.15
.39
.89
.97
-1.10
-1.34
1.19
.88
-.92
.57
-.38
-.79
.89
.61
-.25
-.90
-2.67
-.86
.78
-2.10
.32
-.34
-.06
-.70
.32
-.54
.27
-.46
.50
.96
.21
.05
.03
.31
.31
19
Perceived
Quality
-.37
-.19
.30
.78
.61
.32
.13
.64
1.6
.22
-.48
.60
1.38
1.00
.97
-1.67
-1.34
.72
1.48
.45
-.66
-1.03
.49
-.40
.56
.90
-1.80
-2.10
.81
.92
-2.24
-.24
.04
-.29
-1.70
.05
-.40
.94
.94
-.26
.63
.53
-1.55
-.80
.05
-.53
Brand
Loyalty
.06
-.79
.06
.34
.96
-.13
.22
.64
1.37
-.05
-.44
.93
1.01
.75
.98
-1.77
-1.47
.69
1.84
-.14
-.50
-.82
-.34
.06
.49
.84
-1.50
-1.81
1.34
1.07
-2.52
.17
-.46
-.45
-1.29
.18
-.50
1.06
.91
-.07
.54
.78
-1.08
-.83
-.10
-.23
Table 2
Survey Items with Means, Standard Deviations and Cronbach’s Coefficient Alpha
Construct
(α)
Measures
Mean
SD
Count of number of brand mentions
.67
.27
Count of number of times brand is mentioned first
.33
.29
“I am aware of [brand X].”
4.75
.18
“I am familiar with [brand X].”
4.54
.31
α if deleted
“List all brands that come to mind for the [product category X].”
Brand
Awareness
“List any associations that come to your mind for [brand X].” [ PA=positive associations | NA = negative associations | TA = total assocations ]
Brand
Associations
Perceived
Quality
(.98)
Brand
Loyalty
(.96)
PA/TA
.44
.15
1– (NA/TA)
.86
.11
PAtom/TA
.38
.18
1- (NAtom/TA)
.89
.15
Compared to other brands of [product category X], [brand X] is of very high quality.
3.90
.56
.98
[Brand X] is the best brand in its product class.
3.40
.62
.97
[Brand X] consistently performs better than all other brands of [product category X].
3.40
.59
.97
I can always count on [brand X] for consistent high quality.
3.70
.56
.98
Overall, I have been satisfied with my usage of [brand X].
3.90
.47
.96
In general, I have a positive feeling towards [brand X].
3.90
.45
.95
I am committed towards [brand X]
3.20
.57
.93
I would choose to use [brand X] over other brands of [product category X].
3.20
.63
.95
3.28
.35
.94
3.23
.34
.87
There are financial incentives for continuing to purchase [brand X]
3.20
.32
.95
I feel like I have a social bond with [brand X].
2.95
.47
.88
[Brand X] attempts to establish a relationship with their customers.
3.48
.40
.78
3.20
.38
.85
3.56
.37
.90
3.27
.35
.93
3.46
.35
.82
[Brand X] has a rewards program that gives some kind of benefit for frequent or
RM Level 1:
Price
Incentives
(.95)
RM Level 2:
Social
Bonding
(.88)
multiple purchases.
[Brand X] offers price incentives to encourage me to continue to purchase their
product if I have purchased it before.
[Brand X] creates the sense of a relationship through direct mailings, email or other
forms of direct and indirect contact.
[Brand X] can be customized in some form to my personal tastes and/or choices.
RM Level 3:
Structural
Bonding
(.92)
There are offerings available from [brand X] that are not readily available from
somewhere else.
There are offerings from [brand X] that are tailored to my tastes.
The numbers in parentheses represent the Cronbach’s alpha score for that construct’s items.
Items that were not based on counts were based on a 5 point scale that was anchored by strongly agree, neither agree or disagree, and
strongly disagree.
20
CHAPTER 3
RESULTS
Cluster Analysis
In order to determine the differential effect of utilizing relationship marketing strategies
on brand equity components, brands were grouped according to each level of relationship
marketing strategy separately through cluster analysis. This method is preferable to utilizing a
median split for two reasons. The first reason is that cluster analysis can be used as an
exploratory measure to determine underlying similarities rather than making arbitrary cut off
points as would be done through a split. The second reason is that cluster analysis can also be
used to minimize the variance within the clusters and thereby increases the variance between the
groups. Before the cluster analysis was computed, the brands were analyzed for outliers on the
relationship marketing levels since outliers can have an overly powerful influence on clustering
processes (Punj and Stewart, 1983). Four brands were shown to be outliers (3 or more standard
deviations from the mean) on one or more of the levels and were removed (Heinz, Marlboro,
Motorola, and MTV) leaving a total of 46 brands for the analysis.
Following the work of Punj and Stewart (1983), a two-step methodology was employed.
First, an agglomerative hierarchical cluster procedure was run with Ward’s method as an
inductive technique to determine the number of clusters to be used for each level. This type of
procedure was chosen to assess the natural underlying groupings rather than determine an
artificial structure from the onset as would be required with the use of a nonhierarchical
clustering method alone (Bailey, 1975). The method used for choosing the appropriate cluster
number was the change in the agglomeration coefficient. An appropriate cluster is chosen based
on the largest percentage change in the agglomeration coefficient as the number of clusters
decreases. Interestingly, two of the three cluster procedures produced a strong case for a two
cluster solution. Although a two cluster solution often produces a larger change in the
agglomeration coefficient compared to a solution utilizing a larger number of clusters, the cluster
analysis for the relationship marketing levels two and three all showed an abnormally large
difference between three clusters as compared to two as shown in Table 3. Relationship
21
Table 3
Agglomeration Coefficients from Clustering Procedure of Relationship
Marketing Strategy Levels
RM Level 1:
Price Incentives
RM Level 2:
Social Bonding
RM Level 3:
Structural Bonding
2 clusters
3 clusters
4 clusters
15.401
7.894
3.807
(195.10%)
(207.35%)*
(142.00%)
23.466
10.523
8.138
(223.00%)*
(129.31%)
(134.00%)
17.527
7.226
4.282
(242.55%)*
(168.50%)
(125.24%)
The agglomeration coefficient is shown in the corresponding box for each RM level and cluster.
Percentage change from the previous cluster solution (additional cluster) is shown in parenthesis.
A star (*) represents the cluster solution used.
22
Figure 1. Dendogram of Level One Relationship Marketing Clustering using Ward’s Method
23
Figure 2. Dendogram of Level Two Relationship Marketing Clsutering using Ward’s Method
24
Figure 3. Dendogram of Level Three Relationship Marketing Clustienrrg using Ward’s Method
25
Figure 4. Scatterpl
rplots of RM Levels versus Brands and Cluster
ter Solutions
26
Table 4
Number of Brands Assigned to Clusters per Relationship Marketing
Strategy Level
Low
Medium
High
TOTAL
RM Level 1
Strategies
15
(2.9)
20
(3.2)
11
(3.47)
46
RM Level 2
Strategies
23
(2.9)
-
23
(3.5)
46
RM Level 3
Strategies
27
(3.2)
-
19
(3.8)
46
Note:
Numbers in parentheses represent the mean of the corresponding averaged scale for the corresponding level of
strategy (i.e. when referring to the top-left corner, there are 15 brands that are categorized as low in RM Level 1
strategies and their mean on the averaged scale for RM Level 1 Strategies is 2.9).
27
marketing level one showed the largest coefficient change at the three cluster solution. These
differences are also apparent in the dendograms as shown in Figures 1, 2, and 3. Starting from
the right, each horizontal line represents a cluster and the distance of the line represents the
amount of change in cluster membership from breaking that cluster apart into two clusters. To
use a dendogram for choosing a cluster solution, a vertical line is drawn through the point in the
dendogram in which it intersects the largest horizontal lines possible. Each horizontal line it
intersects represents an additional cluster. The longest horizontal lines that are apparent in the
dendograms is at the far right for relationship marketing level two strategies (Figure 2) and level
three strategies (Figure 3) and would therefore intersect only two lines. The vertical line for level
one strategies (Figure 1) would be drawn more towards the middle and would therefore intersect
three lines. Thus, a two cluster solution was chosen for the brands based on their relationship
marketing level two and three strategies while a three cluster solution was chosen for the brands
based on their relationship marketing level one strategies. The number of brands in each cluster
for each relationship marketing strategy level is shown in Table 4.
Once the correct number of clusters was determined, the second step in the clustering
procedure was undertaken. This step consisted of utilizing K-means clustering as an iterative
partitioning procedure to fully refine the clusters since an iterative procedure implicitly attempts
to minimize the variance within each cluster (Punj and Stewart, 1983). The cluster centers from
the hierarchical procedure were used as the initial centers for the K-means procedure. The final
clusters were analyzed utilizing an adjusted Rand index to assess their similarities. The adjusted
Rand index computes a score ranging from zero to one with a zero representing no similarity and
a one representing complete similarity. The index can be calculated even if there are a different
number of clusters as in this research (Rand, 1971). The clustering on relationship marketing
level one showed a strong difference from the clustering on level two (AR=.08) and level three
(AR=.16). The clustering on levels two and three however showed a marked similarity (AR=.47)
compared to the other index scores. This would suggest that levels two and three have a higher
than normal level of covariance. The scatterplots of the clustering solution on each independent
variable are shown in Figure 4. Once the independent variables were demarcated, the construct
correlations and covariances were assessed as shown in Table 5. While a clustering solution was
used for the creation of the independent variables, each of the relationship marketing level items
28
Table 5
Correlation and Covariance Coefficients Between All Constructs Variables
1
2
3
4
5
6
7
-
.15
.35
.36
.10
.25
.19
(2) Brand Associations
.31*
-
.54
.56
.09
.35
.31
(3) Perceived Quality
.59**
.66**
-
.87
.31
.71
.61
(4) Brand Loyalty
.63**
.70**
.95**
-
.28
.67
.60
(5) RM Level 1
.17
.10
.32*
.30*
-
.63
.53
(6) RM Level 2
.41**
.41**
.72**
.71**
.63**
-
.88
(7) RM Level 3
.33*
.37*
.63**
.68**
.53**
.88**
-
(1) Brand Awareness
Note: Construct correlations are shown in the lower left triangle. Construct covariation based on standardized scores are shown in the upper right
triangle.
* p <.05
** p < .01
29
were summed together to assess any potential problems with the data. The high degree of
covariance between relationship marketing levels two and three that was suggested by the Rand
index score is confirmed by the high correlation (r2 = .88). A large amount of multicollinearity
among the dependent variables, particularly brand loyalty and perceived quality, is also apparent
(r2 = .95).
Validity Assessment
Since the measures for all of the relationship marketing levels and brand loyalty were
created based on previous conceptualizations rather than adapted from previous questions, the
convergent and discriminant validity of these measures was assessed. The tool used for the
assessment of convergent and discriminant validity was Pearson’s correlation coefficient since a
confirmatory factor analysis was not possible due to sample size (Hair, Black, Babin, Anderson,
and Tatham, 2006). For convergent validity the correlations of each of the individual items of a
single construct with each other are expected to be high. For discriminant validity, the
correlations of the summated scales with the summated scales of conceptually distinct constructs
are expected to be small. There is no specific cutoff for the correlation coefficient for either
validity assessment (Hair, et al., 2006). The within-construct correlations (between items of each
construct) all were relatively high (r2 range of .39 to .94). The discriminant validity can be
gauged by the correlation matrix shown in Table 5. It can be seen that two sets of constructs have
discriminant validity issues. These sets are RM Level 2 / RM Level 3 (r2 = .88) and perceived
quality / brand loyalty (r2 = .95). While quality and loyalty are known to be linked, a correlation
of .95 would suggest that these items are not distinct. Subsequently, brand loyalty and
relationship marketing level three was removed from the analysis. Due to the removal of
relationship marketing level three from the analysis, all hypotheses regarding relationship
marketing level three (H3a, H3b, H3c, and H3d) were not tested.
Common Method Variance Assessment
Common method variance was assessed since the data were collected at one time using a single
instrument (Podsakoff, MacKenzie, Jeong-Yeon, and Podsakoff, 2003). The Lindell and
30
Table 6
Correlation Coefficients Adjusted for Common Method Variance (Lindell and Whitney,2001)
1
(1) Brand Awareness
2
3
4
5
6
7
-
(2) Brand Associations
.22
-
(3) Perceived Quality
.48**
.62**
-
(4) Brand Loyalty
.51**
.67**
.94**
-
(5) RM Level 1
.09
MV
.24
.22
-
(6) RM Level 2
.31*
.34*
.69**
.68**
.59**
-
(7) RM Level 3
.22
.30*
.59**
.64**
.48**
.87**
Adjusted construct correlations are shown in the lower left triangle
MV represents the correlation used as the marker variable for the adjustment of the other correlations
* signifies significance at the .05 alpha level
** signifies significance at the .01 alpha level
Double underlined correlations loss significance at the .05 alpha level after being adjusted.
Single underlined correlations loss significance at the .01 alpha level but retained significance at the .05 alpha level after being adjusted.
31
-
Whitney (2001) partial marker variable was used to ensure that common method variance did not
overly influence the observed relationships between the variables. The partial marker variable
method consists of using the smallest correlation between a marker variable and a primary
variable to partial out the effects of common method variance. In effect, the smallest correlation
is subtracted from the remaining correlations which are then assessed to see if the correlations
lose significance. Since there was no marker variable used in the present study, the smallest
correlation between two constructs based was used. This correlation is between relationship
marketing level one and brand associations (r = .1). After partialling out the chosen correlation,
four relationships were impacted. The brand awareness / brand associations, relationship
marketing level one / perceived quality, relationship marketing level one / brand loyalty, and
relationship marketing level three / brand awareness relationships all lost significance at the .05
alpha level after being adjusted as shown in Table 6. Of these relationships, the ones of most
concern are those that might impact hypothesis testing. Since relationship marketing level three
and brand loyalty were chosen for removal from the analysis due to lack of discriminant validity,
the only remaining relationship of concern is between relationship marketing level one and
perceived quality. The possible impact of a common method bias on the results will need to be
taken into account if the data supports hypothesis one.
Hypothesis Testing
A MANOVA was used to test the hypotheses with the clusters as the independent
variables and the brand equity components as the dependent variables. First the assumptions of
MANOVA were assessed. The assumption of normality for the dependent variables was assessed
by plotting the standardized residuals in a histogram as well as evaluating kurtosis and skewness
statistics. As shown in Figure 5, the histograms suggest that the variables are normal with a slight
negative skew. This departure from normality is expected since the brands that were used were
from a list of the top 100 brands worldwide and would therefore group around the upper end of
the scale.
The skewness and kurtosis statistics were used to probe further into the normality of the
variables as shown in Table 7. Brand associations and perceived quality both had a departure
32
Figure 5. Normality Plots of the Residuals of the Dependent Measures
from normality since the skewness statistic of brand associations (-.85) was more than double the
standard error of skewness (.35) and perceived quality (-.65) was close to this threshold as well.
Finally, a Shapiro-Wilks test was assessed which showed that the distributions of
perceived quality (p = .031) and brand associations (p = .046) were not drawn from a normal
population (also shown in Table 7). In order to alleviate and problems from this divergence
from normality, both brand associations and perceived quality were transformed by cubing the
data (Hoyle, 1973). The statistics for the transformed data are also shown in Table 7. Hypothesis
testing was performed twice, once with the transformed data and once with the original data. The
33
Table 7
Means, Standard Deviations, Skewness, Kurtosis, and Normality Test
Among the Variables
Mean
SD
Kurtosis
(.69)
Skewness
(.35)
S-W
Brand Awareness
.57
1.66
-.85
-.36
.078
Brand Associations
2.58
.50
1.00
-.85
.046
Perceived Quality
14.39
2.27
-.29
-.65
.031
Brand Loyalty
14.16
2.02
-.05
-.49
.41
Brand Associations
Cubed
3,190
1,321
-.81
.15
.41
19
9
-.64
-.04
.44
Perceived Quality Cubed
Standard error of the skewness and kurtosis is in parentheses.
The S-W column represents the p value for the Shapiro-Wilk’s test
34
results of both tests were compared. Since there were no differences in the results as related to
the testing of the hypotheses, the original data is used to shown the mean differences of the brand
equity components across the relationship marketing strategies (Table 8) and plots of the mean
differences (Figure 6) whereas the transformed data is used to present the test statistics used for
hypothesis testing (Table 9 and Table 10).
The assumption of multicollinearity is also not met since all of the dependent measures
are highly correlated. However, this was assumed at the beginning of the research since the
collinear nature of the brand equity components have already been established (Pappu, et al.,
2005). The two highest correlated components, perceived quality and brand loyalty, have also
been shown to be related in multiple studies and lines of research (Cronin, Brady, and Hult,
2000; Han, Kwortnik, and Wang, 2008; Lassar, et al., 1995). Thus, while the power of the
overall test will be reduced, this is a necessity for the chosen area of study. The assumption of
the equality of the covariance matrices was evaluated using Box’s test. As shown in Table 9, the
F statistic was not significant at the .05 level; therefore the assumption was met. Levene’s
univariate test of variance equality was then evaluated. All of the dependent variables met the
univariate assumption as the smallest p-value was .54.
Since relationship marketing has been said to be more beneficial to services while
branding is considered better suited for goods, the type of product for each brand was intended to
be used as a covariate in the analysis. However, since the product type did not correlate with any
of the independent or dependent measures, it was not included. The results of the overall model
are shown in Table 10. Hypothesis one stated that there would be a difference in perceived
quality (H1a) and brand loyalty (H1b) for brands that used more relationship marketing level one
strategies compared to those that used less. The data did not support hypothesis one at the
multivariate level (Wilk’s λ = .12, p value = .99). Hypothesis two stated that there would be
higher brand awareness (H2a), brand associations (H2b), and brand loyalty (H2c) for brands that
used more relationship marketing level two strategies compared to those brands who used less.
The data supported hypothesis two at the multivariate level (Wilk’s λ = 8.0, p value < .01). There
were no specific hypotheses formulated for any interactions between the relationship marketing
strategy levels. The interaction effect between relationship marketing level one usage and
relationship marketing level two usage was not significant (p = .61).
35
Table 8
Average Brand Equity Component Scores per Relationship Marketing Strategy Level
RM Level 1
Strategies
RM Level 2
Strategies
Brand
Awareness
Brand
Associations
Perceived
Quality
High
2.7 (.20)
.67 (.10)
3.9 (.34)
23
Low
2.5 (.20)
.62 (.14)
3.2 (.54)
23
2.6 (.24)
.67 (.08)
4.0 (.35)
11
2.6 (.24)
.67 (.08)
4.0 (.35)
11
-
-
-
2.6 (.20)
.64 (.14)
3.5 (.52)
20
High
2.7 (.15)
.66 (.13)
3.8 (.34)
8
Low
2.5 (.20)
.63 (.15)
3.3 (.58)
12
2.6 (.22)
.63 (.14)
3.4 (.65)
15
High
2.7 (.24)
.67 (.13)
4.0 (.35)
4
Low
2.5 (.21)
.62 (.14)
3.2 (.58)
11
High
High
Low
Medium
Low
-
N
Note:
Numbers represent the average of each brand equity component for that specific relationship marketing strategy (except for the far
right column which represents the number of brands assigned to each category by the cluster analysis). Numbers in parentheses
represent the standard deviation (i.e. when referring to the bottom-left number, there are 11 brands that are categorized as low in
RM Level 1 strategies and low in RM Level 2 strategies and these brands have a mean of 2.5 and a standard deviation of .21 on
the averaged scale for Brand Awareness).
36
Table 9
Box’s Test and Levene’s Test Statistics from the Multivariate Test
Levene’s Test
Box’s Test
Brand
Awareness
Brand
Associations
Perceived
Quality
1.30
.53
.79
.21
df1:
24
4
4
4
df2:
1005
41
41
41
.15
.71
.54
.93
F:
p:
37
Table 10
Results of the Multivariate Test Between Relationship Marketing Strategy Usage
Wilk’s λ
P value
Partial Eta2
Hypothesis Tested
RM Level 1 Strategies
.12
.99
.01
H1 – not supported
RM Level 2 Strategies
7.97
<.01
.38
H2 - supported
RM Level 1 * RM Level 2
.61
.61
.05
No hypothesized
effect
Each Relationship Marketing Level Strategy represents a categorical variable of brands that are ranked as either high or low (RM Level
2 Strategies) or ranked as high, medium, or low (RM Level 1 Strategies)
38
Figure 6. Mean Plots of Perceived Quality and Brand Awareness by Relationship Marketing Level
Two Strategy Clusters
Next, the impact of relationship marketing level two strategies was assessed for each
brand equity component at the individual level as shown in Table 11. As postulated in hypothesis
H2a, it was expected that brands that utilized more relationship marketing level two strategies
would have higher brand awareness scores. The univariate test revealed that the data did support
this specific hypothesis (F = 5.2, p value < .05).As stated in hypothesis H2b, it was expected that
brands that utilized more relationship marketing level two strategies would have higher brand
association scores. The data did not support this particular hypothesis (F = .3, p value = .40). The
final hypothesis regarding relationship marketing level two strategies (H2c) could not be tested
since brand loyalty was removed from the analysis. Though no effect was postulated, there was a
significant difference in perceived quality (F = 16.6, p value < .01) between brands that utilized
more relationship marketing level two strategies. To further assess the differences, all of the
significant effects were graphed on mean plots as shown in Figure 6. The mean plots illustrate
that as brands increase their level two relationship marketing strategies, their perceived quality
and brand loyalty increase as well. The data thus supports hypothesis 2a regarding the positive
effects of relationship marketing strategies on brand awareness but no effect was hypothesized
for the effect of the level two strategies on perceived quality.
39
Table 11
Results of the Univariate Test Between Relationship Marketing Level Two Strategy Usage
RM Level 2
Strategies
F
P value
Partial Eta2
Hypothesis Tested
Brand
Awareness
5.2
.03
.11
H2a – Supported
Brand
Associations
.73
.40
.02
H2b – Not Supported
Perceived
Quality
16.6
<.01
.29
No hypothesized effect
Note:
The statistics shown are based on the test of the difference between the brands ranked high in RM Level 2 Strategies versus brands that were ranked
low in RM Level 2 Strategies on the brand equity components.
40
CONCLUSION
Discussion
The purpose of this research was to make an attempt at the reconciliation of two distinct
marketing strategies, that of relationship marketing and brand equity. The brand equity scores
and perceived relationship marketing strategy usage for forty six different brands was captured
by surveying college students regarding top performing brands. Using the forty six different
brands, this study was able to show that certain relationship marketing strategies can impact the
brand equity of a brand.
However, of the nine hypothesized relationships, only one was supported by the data. The
supported hypothesis pertained to the positive relationship between relationship marketing level
two strategies and brand awareness. The fact that only level two strategies had a positive effect
on the brand equity components suggests that only social bonding can add an additional
contribution to brand equity. This finding is represented in the literature as well since other
researchers have shown that brand personality (Pappu, et al., 2005) and brand trust (DelgadoBallester and Munuera-Alema´, 2005) can contribute to brand equity. Thus, it would seem that a
brand’s social bonding strategy makes the brand more salient in the mind of the consumer. It
could be that the additional increase in trust from personality factors and increased contact may
be the drivers behind the increase in salience.
The particular components of brand equity that were significantly different are
interesting. While it was postulated that level two would result in increased brand loyalty, it was
not expected for perceived quality. This result could be based on the apparent high collinearity
between the brand equity components of perceived quality and brand loyalty. It would be wrong
to assume that such high collinearity invalidates the findings since the components of brand
equity are expected to be correlated (Yoo and Donthu, 2001). In fact, given that quality has been
shown to lead to loyalty (Cronin, Brady, and Hult, 2000; Han, Jr., and Wang, 2008), the present
collinearity is expected and possibly lends more credence to the findings.
The insignificance of the main effects of level one relationship marketing is of particular
interest. Given that much of the original research on brand equity (Aaker, 1991) as well as the
41
conventional wisdom as spouted by the popular press (Ries and Ries, 2002) states that pricing
incentives will erode brand equity through perceived quality, the lack of findings in this area
suggests a deeper look is warranted. It is already known that a customer’s previous shopping
pattern can moderate the effect of level one strategies (Yuping, 2007). Perhaps there are other
moderators that can increase the long term benefit of what is often decried as only short term
gain. One such moderator may be age. College students may not have a negative perception of
price incentives since their economic status would seemingly put them in a place to be more
open to incentives. If age were a moderator, it would explain why the level one strategies had no
effect in the present study since the sample was based on college students. Price based constructs
such as price sensitivity may also moderate the negative effect of level one strategies on
perceived quality.
In the final analysis, social bonding strategies hold the most promise for marketing
practitioners who are attempting to communicate the benefits of their brand. The difference in
brand awareness and perceived quality between brands that were perceived to use more social
bonding strategies compared to those that used less was substantial. This substantial difference is
supported by two points of the study. The first point is regarding common method variance.
Considering that an adjustment by a marker variable did not impact the correlation between
relationship marketing level two strategies and the brand equity components, the impact of
common method variance on the results can be ruled out. The second factor is how the
relationship marketing level two variable was created. Once the cluster analysis was used to
create the categorical variable, the difference between brands that were categorized as low in
usage of relationship marketing strategies and those as high was rather small (on a 5 point scale,
high usage was 3.5 and low usage was 2.9 as shown in Table 4). Thus, the difference in brand
awareness and perceived quality between these brands is interesting. If brands were sought out
that used no social bonding strategies then the difference in brand awareness and perceived
quality should be even more pronounced.
The lack of findings for relationship marketing strategies level one and three should not
be used as a basis to spurn these strategies. There are several reasons why significant findings for
level one strategies or discriminant validity for level three strategies may not have been found.
These possibilities are further discussed in the subsequent limitation and future research sections.
42
Limitations
Any conclusions that are drawn need to be tempered by the limitations of the study. One
possible reason for the insignificance of many of the findings probably lies in the number of
brands used. The final number of brands used after assumption testing and data cleaning were
forty six. When these brands were clustered into groups, some of the groups had extremely small
cell sizes, especially when considering interactions. The positive application of such a small
sample size is that it gives testament to the significant findings of the study. Another possible
reason for the insignificant findings may be based on the chosen brands. Since these brands were
rated as some of the top brands worldwide, they had a negative skew in the distribution along the
dependent variables. This skew was highest in the brand awareness component which also
happened to contain some of the least significant main effects of the all the dependent variables.
Another limitation is that of generalizability. Since the present study was based on a
convenience sample of college students, the effects found may not translate to working adults or
any other sample that excludes college students. A result of using college students was that some
brands such as luxury brands and financial investment brands were excluded from the present
study. It could be that the hypothesized negative effect of relationship marketing one strategies
on perceived quality may be present for only certain brands such as luxury brands. Thus, the
results of the present study are only generalizable to the types of brands that were used.
A last limitation of the present research is the use of the participants’ perceptions for
demarcating brands into relationship marketing strategies. While there are strengths to such a
method over using external devices such as the assurance that the participants were witness to
the strategies, it is possible that using a more objective approach could give different results. One
way to do so in a “real world” setting would be to measure the brand equity components for
brands during marketing campaigns that specifically utilized one of the relationship marketing
strategies. Such a method would lend itself well to the use of longitudinal data that could be used
to measure the incremental changes in brand equity as relationship marketing strategies are
utilized.
Future Research
43
Some future research opportunities present themselves based on the limitations of the
present study as discussed above. Thus, using a larger sample of brands, a different sample of
respondents, or measuring the effect of a relationship marketing campaign as it is being
implemented may offer different results than those presently found. Other opportunities can be
extrapolated from the findings or lack of findings of the present research. One such opportunity
is to replicate the apparent relationship between relationship marketing level two strategies and
perceived quality. Does this effect exist outside of the brand equity domain? Current research
would tend to support the idea that affective responses can be used in place of cognitive
evaluations (Homburg, Koschate, and Hoyer, 2006). However, the direct link between a social
bond and higher quality perceptions needs to be established.
An issue that still needs to be addressed is the impact of relationship marketing level
three strategies (structural bonding) on brand equity. Though the variable was removed from the
analysis, prior conceptualizations and research suggest that it is a distinct strategy and should
therefore affect brand equity beyond social bonding strategies. One possible reason for the lack
of discriminant validity between relationship marketing level two and three strategies is due to
the sample. College students may not have been adequately aware of brands that used structural
bonding strategies and therefore confused the strategies with social bonding strategies.
Considering that structural bonding strategies should cost more and therefore require a higher
price, a sample that is more likely to have the discretionary income to use such customization
may be necessary to find any true effects.
Conclusion
In conclusion, it can be assessed that a firm is safe in employing relationship marketing
strategies for brands when considering the potential impact on the brands’ brand equity since
there were no negative effects found. In fact, utilizing social bonding strategies can create a
synergy with branding strategies since the brand equity components of perceived quality and
brand awareness can be increased. Based on the fact that there were no interactions found
between relationship marketing strategies of level one or three on the brand equity components,
it is probably best to keep the conceptualization of branding and relationship marketing distinct.
44
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BIOGRAPHICAL SKETCH
Jeremy S. Wolter
Jeremy S. Wolter was born in Crescent City, FL on July 27, 1976 and was raised in St.
Augustine, FL. Jeremy received his Bachelors Degree from the University of Central Florida in
Management of Information Systems and subsequently decided to not go into the IT industry as
previously planned.
After graduation, Jeremy married Katie Clifford and both moved to Tallahassee FL so
that they could further pursue their educational goals. While Katie began her studies at the
College of Medicine, Jeremy began a Master’s of Science in Integrated Marketing
Communication under the College of Information and Communication. During Jeremy’s and
Katie’s educational pursuits, Jude Wolter was born. Subsequently, Jeremy took a nine month
break to stay at home with the new baby. Using this time to think and research thesis topics,
Jeremy fully realized his interest in marketing. To pursue this interest, Jeremy began a Marketing
PhD under the College of Business while finishing his Master’s degree.
Jeremy is currently a second year PhD student in the marketing program at Florida State
University. He currently has two children, Jude and Liam Wolter, who demand a lot of time but
are worth every moment (no matter how many of those moments are spent awake rather than
sleeping). He plans to continue his research into service marketing strategy with an emphasis on
customer and marketing metrics.
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