Download Effectiveness of In-Game Advertisements in Sport Video Games: An

International Journal of Sport Communication, 2008, 1, 195-218
© 2008 Human Kinetics, Inc.
Effectiveness of In-Game Advertisements
in Sport Video Games: An Experimental
Inquiry on Current Gamers
Beth A. Cianfrone
James J. Zhang
Georgia State University, USA
University of Florida, USA
Galen T. Trail
The Ohio State University, USA
Richard J. Lutz
University of Florida, USA
Sport video games (SVGs) are a popular form of sport media and sponsorship,
and advertising in SVGs is increasingly common. This study assessed the effectiveness of SVG in-game advertisements in 3 consumption domains: cognitive,
affective, and conative. An experimental study was designed with 89 gamers
randomly assigned to 1 of 2 conditions: (a) experimental, playing an SVG with
advertisements, or (b) control, playing an SVG without advertisements. Consumption background and identification level were incorporated as covariates to
ensure group equivalence. Participants responded to a questionnaire measuring
brand awareness, brand attitude, and purchase intentions. MANCOVA revealed
that after controlling for the effect of covariate variables, the experimental group
had a significantly (p < .05) greater mean brand-awareness score than the control
group. Mean brand-attitude and purchase-intention scores were not significantly
(p > .05) different between groups. The findings indicated that SVG in-game
advertising was effective in creating awareness.
Keywords: sponsorship and advertising effects, brand awareness, brand attitude,
purchase intention
With the popularity of sports and the fanatical spending nature of sport spectators, advertising and sponsorships in the sport industry have reached record
numbers (Meenaghan, 2001b). Corporations are constantly searching for innovative and effective advertising methods to reach sport audiences. One of the newest
means of reaching sport audiences is the sponsorship of, and advertising within,
sport video games (SVGs). The SVG genre is among the most popular types of
video games, recording sales of $1.2 billion in the United States in 2004 (Adams,
Cianfrone is with the Dept. of Kinesiology and Health, Georgia State University, Atlanta, GA 30302-3975.
Zhang is with the Dept. of Tourism, Recreation and Sport Management, and Lutz, the Dept. of Marketing, University of Florida, Gainesville, FL 32611. Trail is with Sport Management, The Ohio State
University, Columbus, OH 43210-1224.
195
196 Cianfrone et al.
2005). Approximately 33 million SVGs were sold in 2005 (Entertainment Software
Association, 2006). A main reason for the games’ popularity is their authenticity
and similarity to televised sporting events, made possible by licensing contracts
with sport leagues. Sponsorships of SVGs, fulfilled as in-game advertisements,
also enhance the authenticity of the video game because televised sporting events
are generally littered with sponsors’ logos and advertisements.
Similar to traditional sport sponsorships, there are various opportunities to
sponsor SVGs. Sponsorships in the form of brand logos on-screen are common and
known as in-game advertisements (e.g., the Pontiac Player of the Game). Product
placement (e.g., brand of club or equipment selection in golf video games) and
athlete endorsements (e.g., games such as Tiger Woods’ PGA Tour 2007 and Tony
Hawk’s Pro Skater 4) are also common. Other types include on-screen venue signage
(virtual logos similar to the venue signage seen at a live or televised sporting event),
commentary plugs (game announcers’ fulfillment of sponsorships with prerecorded
messages that play in appropriate game segments), and transition advertisements
(the newest form of in-game advertising, in which a logo or advertisement is displayed with a 4- to 5-s pause before reaching the next gaming level). The major
benefit of all the SVG sponsorship types is that they cannot be skipped by the player
(Lefton, 2004). This article focuses on in-game advertisements because they are
very prevalent in SVGs and are similar to sponsorships of televised sports.
Corporations aiming to reach SVG players (gamers) are eager to place promotions in games. For the SVG audience, which includes the highly coveted 18- to
34-year-old male, SVGs often replace televised programs, along with the associated
advertisements, as an entertainment source (Richtel, 2005). Corporations hope to
gain some return on investment (ROI) by advertising in video games to enhance
brand image and drive product sales. Because of this nontraditional approach,
determining the cost of the advertising varies. Electronic Arts (EA) Sports, which
owns 70% of the market for SVGs (Adams, 2005), generally charges 10 cents
per in-game sign, multiplied by the number of games sold (Lefton, 2004). For
EA Sports’ NASCAR 2005: Chase for the Cup, five sponsors (Levi’s, Mr. Clean,
Old Spice, Wal-Mart, and Dodge) purchased a collective $1.5 million of in-game
advertising (Adams, 2004). Parks Associates predicts that from 2005 to 2006 there
will be a 50% increase in advertisement spending in video games, to approximately
$154 million (“Video Game,” 2006).
Corporations invest in SVGs for the potential benefits leading to eventual ROI,
although the response to this form of advertising has rarely been systematically
studied. To date, there is no evidence that SVG in-game advertising enhances consumers’ awareness of the sponsoring brand, which is often seen as a basic goal of
any sponsorship or advertisement. It is expected that awareness would occur, but
because it has never been examined, there is no research evidence to support this
notion. Determining the effectiveness of these advertisements is critical because
SVGs will continue to expand as a medium, especially as the sport leagues and
game publishers become more accustomed to the potential types, opportunities,
and advantages of this form of advertising (Lefton, 2004). In fact, a number of
researchers have recently pointed out the need to assess the impacts of SVGs on
the sport industry and even society at large (Coakley, 2004; Kim & Ross, 2006;
Kim, Walsh, & Ross, 2006). In this initial article in a sequence of papers examining SVG in-game advertisements, we focused on investigating whether awareness
Video-Game In-Game Advertising 197
(cognition) of an advertising brand occurred. The current study intends to provide
an understanding of the cognitive effect of SVG in-game advertisements, which
will allow future examination of the affective and conative domains.
Advertising and Sponsorship Theory
Many theories have been used to explain the potential effects of advertising and
sponsorships. Beginning with the AIDA (attention, interest, desire, and action)
theory, hierarchical models have become prevalent in both the advertising and the
sponsorship literature (Mullin, Hardy, & Sutton, 2000). Lavidge and Steiner (1961)
developed the hierarchy-of-effects model of advertising, which indicated seven
steps starting with the consumer being unaware of the product and ending with
the consumer purchasing the product. The steps correspond with the various goals
of advertising, including creating awareness of the product, influencing consumer
attitudes and feelings about the product, and stimulating desire for the product.
Cognition is the thinking stage and is represented by the first few steps in the model.
Awareness and knowledge are components of this stage and are usually measured
via brand-recall and -awareness surveys. Affect, or attitude and feeling, is the
second level of the model. Determining advertising effectiveness at this emotional
level usually includes measuring consumers’ preferences for brands. Conation is
related to the level of desire for consumption action, which is a motivational state
that specifically examines intent to purchase. The actual purchase and consumption represent the behavioral stage of the hierarchy, which is usually measured by
the volume, frequency, and longevity of consumption. Because of the time span
required to accurately record behavioral consumption, the behavioral stage is the
least realistic in practice to use to study this domain.
A prominent sport-sponsorship model that follows a hierarchical method is
Madrigal’s (2001) belief-attitude-intentions sponsorship model. This model is
based on Oliver’s loyalty framework, which was designed to explain consumers’
consistent consumption of specific brands or products (Oliver, 1997). Madrigal
claimed support for the belief-attitude-intentions model, noting, “People’s intentions to purchase products from a corporate sponsor are predicted by an attitude
toward that behavior, which is, in turn, influenced by beliefs about consequences
of that behavior” (p. 157).
Although all four domains (cognitive, affective, conative, and behavioral) are
the foundation of other hierarchical models, many researchers also believe that
nonhierarchical models should contain at least one or more of the four domains.
Vakratsas and Ambler (1999) proposed that advertising be studied in a nonhierarchical fashion, with affect, cognition, and behavior as the three dimensions.
They suggested that the domains act independently, although all are indicators of
effectiveness.
In general, the advertising and sponsorship models agree that individuals
receive information from an advertisement and establish or renew interest based
on previous experience, individual attitude, and personal beliefs. These might not
occur in a sequential order, however, and might become salient only when the
need arises. Regardless of which theoretical basis is used, awareness of the brand
is generally the first area to be studied and is an underlying goal of sponsors. It is
apparent that the awareness of SVG in-game advertisements needs to be assessed
198 Cianfrone et al.
to garner a better understanding of such ads and to determine whether a hierarchical
approach to explain their effects should be applied. Without awareness, there is no
support to indicate that SVG in-game advertisements are received by consumers,
similar to other advertising or sponsorship types.
Attitude toward the brand or product is another common measure of sponsorship and advertising effectiveness. Consumers’ attitudes represent the affective range
of Lavidge and Steiner’s (1961) model and the middle stage of Madrigal’s (2001)
belief-attitude-intentions sponsorship model. Brand attitudes are typically paired
with consumers’ purchase intentions in sponsorship studies (e.g., Levin, Joiner, &
Cameron, 2001; Quester & Farrelly, 1998). Similarly, attitudes are useful in predicting consumer behavior (Mitchell & Olson, 1981). A corporation’s sponsorship of
an event or television program or advertising in an SVG might affect consumers’
attitude and feelings toward the sponsoring brand. This can be explained by the
sheer repetition of exposure to the brand (Zajonc, 1968), which has been found to
lead to positive affect toward it. Or perhaps as Gwinner (1997) noted, a favorable
attitude toward an event or product can transfer to the sponsoring brand because of
its affiliation with the event. This is known as the image-transfer effect and might
have a considerable impact on a consumer’s attitude toward a brand (Gwinner,
1997). Favorable brand attitude is often linked with brand loyalty and favorable
purchase intentions. The relationship between consumers’ attitudes and purchase
intentions is critical in assessing sponsorship and advertising effectiveness (Levin
et al.; Quester & Farrelly).
Although recognizing a sponsoring or advertising brand is the first step of
the hierarchy based on the aforementioned theories, acting on the knowledge of
the brand, as manifested through actually purchasing it, is the ultimate objective.
This is the conative stage of the hierarchy-of-effects model (Lavidge & Steiner,
1961). According to Oliver (1999), conative loyalty is the behavioral intention to
purchase a specific brand or product. This is a motivational property and a deeply
held commitment to purchase the brand or product (Oliver, 1997). Purchase intentions for an advertised or sponsoring brand or product are commonly studied and
often found to be related to a spectator’s involvement level. This commitment likely
varies with the consumer’s involvement level with the sport product (Madrigal,
2000; Pitts, 1998, 2004).
In-Game Advertising Depicted as Sport Sponsorship
Sport sponsorship is a common form of advertising and remains an effective
mode of marketing communication (Sandler & Shani, 1993). Sponsorships are
different from traditional advertisements, because a sponsorship has an association between the message and medium (Madrigal, 2001; Meenaghan, 2001a) and
sometimes has a more immediate effect (Rajaretnam, 1993). Meenaghan (2001a)
argued that sponsorships, unlike advertisements, could be viewed by consumers
as being beneficial, as well as indirect, subtle, and disguised. These differences
contribute to effectiveness of sponsorships, and some aspects might apply to SVG
in-game advertisements, as well, because the SVG in-game advertisement seems
to be a hybrid of the two. Advertisements that occur in the SVG often depict a
sport sponsorship that actually exists in the associated sport league. These advertisements add to the realism and authenticity of the game being played; therefore,
Video-Game In-Game Advertising 199
they might be viewed as similar to a sponsorship, with the associated benefits that
a sponsorship has over an advertisement. Because the advertisements are in the
form of brand logos that are flashed on-screen, they are relatively subtle and do
not disseminate a commercial message, other than the assumed affiliation with the
game. The commercial purpose of these advertisements, as presumed sport sponsorships, might be less blatant than other forms of advertisements. In fact, video-game
advertisements appear in forms that do not interrupt the flow of the video game
being played. This low intrusiveness is typically better received by consumers than
television commercials (Pham, 1992). In addition, when these advertisements are
seen by consumers in a relaxed and social environment, it is more likely that the
consumers will develop positive affect toward the advertisements when they appear
as sponsorships (Madrigal, 2001; Meenaghan, 2001a). This is usually the case
for sport video gamers who see the brand logo on their own terms in an interactive environment. Nevertheless, as a relatively new and developing phenomenon,
these advertisements, depicted as sponsorships in SVGs, have not been studied to
determine whether they are similarly effective (Lefton, 2004).
Effectiveness of In-Game Advertising in Sport Video Games
Because of the limited research on SVG in-game advertising and the visual similarities between SVGs and televised sports, it is appropriate to look into studies
that examined sponsorship and advertising effects on viewers of televised sporting
events. Numerous researchers (e.g., Bennett, Henson, & Zhang, 2002; Cianfrone
& Zhang, 2006; Cuneen & Hannan, 1993; Lardinoit & Derbaix, 2001; Levin et al.,
2001; Pitts, 1998; Pope & Voges, 1997; Stotlar, 1993) have examined sponsorship
and advertising effectiveness using one or more of the three consumptive domains:
cognitive via awareness (recall and recognition), affective via consumer attitudes
toward sponsor brands, and conative via consumer purchase intentions of a sponsor
company’s product. Sport-sponsorship-awareness studies typically adopt methods
of surveying event attendees (e.g., Bennett et al.; Cuneen & Hannan; Pitts, 1998;
Pope & Voges; Stotlar; Stotlar & Johnson, 1989), although experimental designs
have been increasingly used in recent years (e.g., Cianfrone & Zhang; Lardinoit &
Derbaix; Levin et al.). Experimental designs allow for a controlled environment to
isolate the varying extraneous variables that influence sponsorship or advertising
effects. This is beneficial when attempting to determine the cause–effect relationship, compare with media sponsorship, and identify differential effects among
various promotional forms (Sadish, Cook, & Campbell, 2002).
A consumer’s recall and recognition of a company as a sponsor indicate a
level of awareness that is often a basic goal of sport sponsorship and advertising.
Unaided recall, aided recall, and recognition have been studied for their effectiveness in measuring awareness levels. Recognition scores have been found to represent an expression of interest and not merely memory (Sandler & Shani, 1993;
Wells, 2000). Sport-sponsorship and -advertising studies have frequently adopted
unaided-recall, aided-recall, and recognition rates as measures of brand awareness
for event attendees and television viewers. These studies have generally focused
on assessing television commercials, venue signage (by both event attendees and
televised viewers), and athlete endorsements. Sport-sponsorship-recall and -recognition studies have included a few television-viewing-based studies (i.e., Cianfrone
200 Cianfrone et al.
& Zhang, 2006; d’Ydewalle, Vanden Abeele, Van Rensberger, & Couke, 1988;
Lardinoit & Derbaix, 2001; Levin et al., 2001; Pope & Voges, 1997). In these studies, venue signage was usually assessed on television and compared with television
commercials. An early study by d’Ydewalle et al. revealed that soccer television
viewers noticed field signage less than 3% of the time. With the increase in numbers of sponsorships in the form of venue signage and on-screen virtual signage,
more recent studies of television viewers’ awareness of venue signage have shown
more favorable results. For instance, Cianfrone and Zhang found 26% recognition
of venue signage.
Although there are visual similarities between SVGs and televised sports, brand
logos displayed in SVGs are quite different from venue signage and television commercials. Alternatively, these logos can be compared with virtual advertising. A
virtual advertisement, such as a digitally inserted logo of a sponsor into a specific
portion of a game, can lead to positive promotional results (Cianfrone, Bennett,
Siders, & Tsuji, 2006). Television commercials can often be avoided by viewers
who change channels or move away from the television during the breaks, but
sponsorships in SVGs cannot be avoided because they cannot be skipped.
SVG gamers have different experiences than television viewers (Coakley,
2004). Because the gamers’ interaction with the video game is necessary, it is
uncertain whether they notice brands advertised in the games. The gamers’ attention might be much more focused and intense because they are playing a game
and controlling players, rather than watching a game on television. Because of this
high degree of interaction, on-screen brand logos might be noticed less than when
passively watching a televised game. Conversely, because the gamer might be so
engaged in the game, he or she might be more likely to recognize the brands and
develop positive affect toward them. In addition, some gamers spend numerous
hours a week playing, leading to high repetition and exposure to the advertised
brands. Each time a gamer plays an SVG, he or she is exposed to multiple in-game
advertisements. Playing an SVG a few times a week continually promotes the brands
to the gamers. This constant playing with exposure to the same brands also differs
from watching televised sports, which tend to have many different sponsoring and
advertising brands. The high behavioral involvement of the gamer with the sport
game might have a strong influence on sponsorship effectiveness; likewise, those
who play more might have better retention of the sponsorships because of their
greater exposure time. Because of these differences between SVGs and televised
sports, it is uncertain to what extent the findings of previous studies of televised
sports are applicable to SVGs.
The effects of advertising and sponsorship have rarely been assessed in the
medium of SVGs. Nelson (2002) performed an experimental study, the only examination of video games to date, in an effort to determine consumers’ awareness of and
attitudes toward advertisements in computer and video games. This study consisted
of two small, exploratory preexperimental designs: an experiment testing console
gamers’ attitudes toward and awareness of advertisements in a car-racing game
(n = 13) and an experiment testing computer gamers’ attitudes toward and awareness of local- and national-brand advertisements in a different car-racing game (n =
10). The participants were surveyed 5 months after each study to assess the decay
of their recall and recognition of sponsors. The results of the experiments revealed
that advertisements were somewhat recalled and recognized initially but decayed
Video-Game In-Game Advertising 201
drastically by 5 months later. The positive recall scores are interesting to note, but
the design of the study had numerous weaknesses and limitations, including, but
not limited to, the adoption of a one-shot preexperimental design, small sample
size, lack of a control group, lack of controlling for video-game-involvement background, and mixing high-level video gamers and novice players in the studies. For
instance, novice players might be so focused on learning how to play the game that
they miss the advertisements altogether. In brief, findings of this study had limited
generalizability, although the study itself was noteworthy because it has been the
only attempt to study the effectiveness of SVG in-game advertising.
Moderating Factors
Consumer involvement has been shown to influence sponsorship effectiveness.
Involvement is usually composed of three domains: cognitive, affective, and behavioral. Cognitive involvement can be defined in terms of a consumer’s identification
level or psychological commitment. Identification theories, such as the socialidentity theory (Hogg & Abrams, 1988; Stryker, 1980; Tajfel, 1982), indicate that
consumer identification can moderate cognitive, affective, and behavioral responses
to sport products (Trail, Anderson, & Fink, 2000). This suggests that varying levels
of identification result in differing levels of recognition of the sponsor’s product,
feelings toward the product, and consumption of the product (Branscombe & Wann,
1991; Madrigal, 1995; Murrell & Dietz, 1992; Sloan, 1989; Trail et al.; Wann &
Branscombe, 1993). Identification can be composed of different potential points
of attachment that might include team, player, coach, community, sport, university,
and level of play (Robinson & Trail, 2005). In particular, attachment to a team was
found to be a strong indicator of sport consumption (Fink, Trail, & Anderson, 2002).
In an analysis of consumption behaviors associated with SVG playing, Kim et al.
(2006) found that individuals highly identified with a team might act differently
than those less identified, which might in turn influence the effectiveness of SVG
in-game advertisements.
In addition to identification, involvement can be defined by past consumption
of sport (i.e., behavioral involvement; Lardinoit & Derbaix, 2001; Levin et al.,
2001). Previous research suggests that gamers are often sport fans who consume
other forms of sport media (Coakley, 2004; Kim et al., 2006). Thus, they are likely
to respond differently to advertised brands in terms of awareness, attitude, and
purchase intentions as their levels of involvement vary. Because of varying behavioral patterns and levels of repetition of video-game playing, frequent video-game
consumers might have more recall and recognition of the brands than less frequent
game consumers. Gamers who do not play often might be too concerned with
mastering the game and not notice the advertisements. Conversely, if gamers play
too often or are highly involved with the game, they might actually be “immune”
to the advertising and begin to have a lower retention rate than lower level gamers.
It has yet to be determined how the involvement levels of the individuals influence
the effectiveness of video-game advertisements.
In brief, numerous researchers have found that both identification and behavioral involvement influence consumers’ response to advertising and sponsorships in
the cognitive, affective, and conative domains. For example, Pham (1992) introduced
the concept of fan involvement with respect to recognition of sport sponsorships.
202 Cianfrone et al.
Madrigal (2000) found identification and involvement to be determinants of purchase intention of sponsoring brands in NASCAR, where fans routinely recognized
and purchased from their favorite team’s sponsoring companies. Pitts (1998, 2004)
found supporters of the Gay Games to be very brand aware and willing to purchase
from the event’s sponsoring companies. Thus, to determine the true treatment effect
of exposure to in-game advertisements, identification and behavioral involvement
should be controlled.
This study was designed to assess the effectiveness of SVG in-game advertisements in the cognitive, affective, and conative domains of current gamers. An
experimental study was designed with two conditions: experimental, playing an
SVG with in-game advertisements, and control, playing an SVG without in-game
advertisements. Because of varying backgrounds of sport video gamers, behavioral
involvement and identification level were incorporated as covariate variables to
ensure group equivalence. We hypothesized that after controlling for the effect of
covariates, individuals who were exposed for the first time (acute exposure) to the
advertised brands would display higher cognitive, affective, and conative scores.
Specifically, this study examined the following research questions:
• What is the effect of advertising in SVGs on cognitive consumption?
• What is the effect of advertising in SVGs on affective consumption?
• What is the effect of advertising in SVGs on conative consumption?
Method
Participants
According to a sample-size calculation with a midlevel effect size and an alpha
level of .05, each of the two experimental conditions required about 45 gamers
(Thomas & Nelson, 2005). A total of 89 college undergraduate students from a large
southeastern university with an enrollment of over 50,000 students were recruited
to participate in the study. This university had a strong football tradition in terms
of program achievement and fan support. The institutional culture and tradition
were consistent with the adoption of an NCAA football video game in this study.
To reduce the excessive time needed to learn how to play the college-football video
game that was adopted for the study, and more important, to understand the effect
of in-game advertising on the consumers who would normally play the games,
only individuals who regularly played EA Sports’ NCAA Football games (30 min
or more per week) were recruited for the study. The participants were randomly
assigned into two groups: experimental (n = 45) and control (n = 44).
Descriptive statistics for the demographic-background variables with respect
to the two experimental conditions are presented in Table 1. The participants in
the groups displayed consistency in terms of their gender, age, school year in college, and ethnicity, although there were small variations. Participants were from
35 general physical activity classes available as elective courses to all university
students. In addition, some gamers were obtained via snowball sampling (asking
participants to identify or bring friends who were also gamers). The participants
did not come from classes in which the researchers were teaching, thus eliminating
Video-Game In-Game Advertising 203
Table 1 Descriptive Statistics for the Personal Background
Variables With Respect to Experimental Group
Experimental
Group
(n = 45)
Background
variable
Gender
Age, years
School year
Ethnicity
Control Group
(n = 44)
Total
(N = 89)
Category
n
%
n
%
n
%
male
female
18–19
20–21
22–23
24 or older
freshman
sophomore
junior
senior
African American/
Black
Asian
Caucasian/White
Latin American/
Hispanic
other
42
3
12
17
14
2
4
13
12
16
93.3
6.7
26.6
37.8
31.1
4.4
8.9
28.9
26.7
35.6
40
4
7
21
12
4
3
6
13
22
90.9
9.1
15.9
47.7
27.3
9.1
6.8
13.6
29.5
50.0
82
7
19
38
26
6
7
19
25
38
92.1
7.9
21.3
42.7
29.2
6.7
7.9
21.3
28.1
42.7
6
1
34
13.3
2.2
75.6
4
5
29
9.1
11.4
65.9
10
6
63
11.2
6.7
70.8
2
2
4.4
4.4
5
1
11.4
2.3
7
3
7.9
3.4
any perceived pressure not to withdraw from the study. The sample of the study
represented 21 undergraduate academic majors.
Experimental Design
A randomized-group experimental study was designed that included two conditions: experimental (game with in-game advertisement) and control (game without
in-game advertisement). Before playing a video game as treatment, participants
were first measured on behavioral involvement and identification level as covariate variables. After playing the video game, the participants were measured on
cognitive-, affective-, and conative-consumption levels. The design of the study
is illustrated below, where R represents randomization, O1 represents observation
or measurement of covariate variables, O2 represents observation or measurement
of dependent variables, T1 represents experimental treatment, and T2 represents
control treatment:
R
O1
T1 O2
R
O1
T2
O2
Because of the popularity of college SVGs among college students, this study
adopted EA Sports’ NCAA Football video game as the medium. The NCAA Football
204 Cianfrone et al.
2005 version was used as the treatment medium for the experimental group (T1)
because it has three in-game advertisements that appear numerous times throughout game play. This was also the most recent version of the game at the time of
data collection. The NCAA Football 2003 version was used as the medium for the
control group (T2) because it did not have any in-game advertisements. NCAA
Football 2003 and 2005 are very similar games; NCAA Football 2004 was not used
because it only had one very minor in-game advertisement that was exposed only
once during the entire game.
The experiment lasted approximately 60 min and was conducted in a livingroom-type laboratory setting with a television and gaming console. To make the
playing realistic, the participants used the console they would normally play on,
either Microsoft’s Xbox or Sony’s Playstation2. On arrival, each participant was
presented an informed-consent form explaining that the study was related to videogame playing; however, the exact purpose of the study and experimental conditions were not explained to the participant until after the study. After agreeing to
participate in the study, he or she was randomly assigned to an experimental or
control group and then stationed in one of the four laboratory rooms. To ensure
consistency, a standardized protocol was followed with each participant: (a) filling
out the informed-consent form; (b) being randomly assigned to an experimental
condition; (c) choosing console type; (d) identifying his or her favorite college
football team; (e) responding to a questionnaire measuring covariates; (f) playing the
game; (g) responding to a questionnaire measuring cognitive, affective, and conative
effectiveness of in-game advertising; and (h) receiving poststudy debriefing.
Participants in the experimental group played a single game of NCAA Football
2005. The game consisted of four 3-min quarters, and total game play lasted approximately 30 min. The entire game had three different forms of in-game advertisements: (a) “Old Spice Red Zone” on-screen logo when the player advanced inside
the 20-yd line (i.e., the Red Zone), (b) “Pontiac Drive Summary” on-screen logo
that features the Pontiac logo next to the scoring-drive information (i.e., number of
plays, drive time, and total yards) that follows each touchdown or field goal scored,
and (c) “Pontiac Player of the Game” on-screen logo that features the game’s top
two players, their statistics, and the Pontiac logo at one time near the end of the
fourth quarter. Before playing, the participants selected the teams they wished to
play for and against. They were instructed to score as many points as possible in an
attempt to get a high score, which would enhance the number of times they were
exposed to the Old Spice Red Zone and the Pontiac Drive Scoring Summary.
Similarly, participants in the control group played a single game of NCAA Football 2003 and received identical instructions. This version, however, did not have
any in-game advertising. We included a control group as part of the experimental
study to obtain baseline information without the purposeful treatment. It is likely
that all participants in both experimental and control conditions had been somewhat
exposed to varying advertisements through different types of media outlets before
the study. Those in the control group and not exposed to the treatment could have
been exposed to advertisements in their other sport-consumption behaviors. In
addition, there could be a guessing factor to the cognitive, affective, and conative
measures. Having the control group provided a parameter for comparison so that
we could determine whether there was a treatment effect.
Video-Game In-Game Advertising 205
Measurement
Two questionnaires were formulated, one for the pretreatment test and one for the
posttreatment test. The first questionnaire included three sections: demographics,
behavioral involvement, and identification level. The demographic section had five
variables: age, gender, ethnicity, school year, and academic major. Formulation of
behavioral-involvement items was based on the work of previous researchers (Lardinoit & Derbaix, 2001; Levin et al., 2001; Madrigal, 2000; Meenaghan, 2001b). The
gamers were asked to identify versions of games they had played in the past year,
versions of games they currently owned, and amount of time they spent per week
playing each game. To account for the effect of past in-game-advertising exposure,
the latest three versions were only assessed because they all had advertisements.
Because NCAA football sponsors are also NCAA Football video-game sponsors,
overall NCAA football consumption levels in terms of live, televised, radio, and
Internet games were also measured (Kim, Scott, & Crompton, 1997). Three factors
(team, university, and sport) from the Points of Attachment Index (PAI; Robinson
& Trail, 2005) were used to represent identification level. Each factor had three
items on a Likert-type, 7-point scale. The PAI has good measurement properties in
terms of validity and reliability. Two recent studies (James & Ross, 2002; Kwon,
Trail, & Anderson, 2006) found that team, university, and sport were significant
factors affecting sport consumption; thus they were included in the current study.
To accurately assess identification levels, we asked each participant to identify his
or her favorite college football team and then fill out the three PAI factors for the
appropriate team and university.
Based on a review of literature (Cianfrone & Zhang, 2006; Cuneen & Hannan,
1993; Stotlar, 1993; Stotlar & Johnson, 1989), the second questionnaire included five
sections that measured cognitive-, affective-, and conative-consumption domains
(i.e., unaided recall, aided recall, recognition, brand attitude, and purchase intention). For unaided recall, participants were asked to provide a list of sponsors in the
video game. For aided recall, hints about the spots of the advertisers were given (e.g.,
what brands or logos, if any, did you see in the Red Zone Banner? in the Scoring
Drive Summary Banner? in the Player of the Game Banner?). For recognition, eight
different brands were listed in the questionnaires for the different types of advertising
spots. Two of the brands were the correct sponsors (Pontiac and Old Spice), and the
other six brands acted as dummy brands. The dummy brands included two brands
in the same product categories as Pontiac and Old Spice (Chevy and Axe). The
other four brands (Gatorade, PowerAde, State Farm, and Allstate) were included
because they were current advertisers in college sports. The brands were phrased
into a Likert-type 3-point scale (1 = definitely did not see it to 3 = definitely saw
it). The brand-attitude scale contained the same eight potential advertising brands
(i.e., two actual brands and six dummy brands) on a Likert 7-point scale (1 = dislike
very much to 7 = like very much). This scale was consistent with previous brandattitude measures (Machleit & Wilson, 1998; Munch, Boller, & Swasy, 1993). The
purchase-intention scale listed the same eight potential brands on a Likert 7-point
scale (1 = definitely would not buy to 7 = definitely would buy). Participants were
instructed to answer the questions with respect to the video game they just played
to ensure the testing of an acute measure of their exposure (experimental group)
or nonexposure (control group) to the in-game advertisements.
206 Cianfrone et al.
Data Analysis
SPSS for Windows version 14.0 (Norusis, 2006) was used to summarize each of
the three behavioral-involvement variables of NCAA Football video games (versions of games played in the past year, versions of games currently owned, and
amount of time spent per week on each game). For the purpose of data reduction
(Nunnally & Bernstein, 1994), a principal-components analysis was conducted for
these NCAA Football video-game-consumption variables. Likewise, a principalcomponents analysis was conducted for the NCAA football-games-consumption
variables in terms of live event, as well as televised, radio, and Internet broadcasts.
Although some researchers have suggested a minimum of 250 respondents for an
exploratory-factor analysis (Comrey, 1988; Guadagnoli & Velicer, 1988), others
have indicated that a factor analysis is appropriate when there are at least 10 times
as many observations as there are variables to be analyzed, which is particularly
suitable when the purpose is merely data reduction (Disch, 1989; Nunnally &
Bernstein; Tabachnick & Fidell, 1996). Thus, the sample size of 89 in the current
study was adequate for factor analyses with three and four variables. The KaiserMeyer-Olkin (KMO) measure of sampling adequacy was also used to assist with this
judgment (Kaiser, 1974). Descriptive statistics were calculated for the behavioralinvolvement variables and the three PAI factors.
For cognitive-consumption variables, data were first transformed to a percentage to represent unaided, aided, and recognition rates. For the affective consumption variables, data were summarized into two categories: attitude toward
sponsoring brands and attitude toward nonsponsoring brands. Similarly, for the
conative-consumption variables, data were also summarized into two categories:
intent to purchase advertised brands and intent to purchase nonadvertised brands.
A multivariate analysis of covariance (MANCOVA) was conducted for the consumption variables in the cognitive, affective, and conative domains to compare
mean vector scores between the two experimental conditions after adjusting for the
effects of behavioral consumption- and identification-level variables. According
to Pedhazur and Schmelkin (1991) and Stevens (1996), there should be at least
20 times as many participants as there are dependent variables to be analyzed in
a multivariate analysis of variance. Because there were three dependent variables
for the cognitive domain, two dependent variables for the affective domain, and
two dependent variables for the conative domain, group sample sizes were rather
small for a total of seven dependent variables. Thus, a decision was made to conduct
separate MANCOVA for the three domains while using Bonferroni’s adjustment
of α level (Norusis, 2006). To reduce Type I error in hypothesis testing, the alpha
level was set at .0167 for statistical significance for each analysis as indicated
by the Bonferroni procedure, which requires the alpha level to be divided by the
number of tests. Because there were three MANCOVA analyses conducted, an
alpha level of .05 was divided by 3, resulting in a level of .0167 (Hair, Anderson,
Tatham, & Black, 1998).
Video-Game In-Game Advertising 207
Results
Descriptive statistics for the behavioral consumption variables of the groups are
presented in Table 2. On average, participants in the groups had played two versions of NCAA Football video game, owned one version of the game, and played
almost 3 hr of games weekly. There were some differences in actual NCAA football
consumption in the forms of game attendance and watching broadcasted programs
via television, radio, and Internet. Although group membership was randomly
assigned, this apparently did not ensure a complete equivalence between groups
with respect to all behavioral consumption background variables. These differences
provided supporting evidence to conduct MANCOVAs.
A principal-components analysis was conducted for the behavioral consumption
variables of the NCAA Football video games. The KMO was .593, indicating that
the sample size was acceptable for the factor analysis. Bartlett’s test of sphericity
Table 2 Descriptive Statistics for the Behavioral Involvement Variables
(NCAA Football Video Game and NCAA Football Consumption) With
Respect to Experimental Group
Consumption variable
NCAA football video games
1. Which version(s) of NCAA Football have you played in the last year? (3 maximum)
2. Which version(s) of NCAA Football do you own? (3 maximum)
3. Amount of time spent each week playing NCAA Foot-
ball games (hr)
NCAA football games
1. NCAA football games watched on TV per week
2. NCAA football games attended per season
3. NCAA football games lis-
tened to on the radio per week
4. NCAA football games fol-
lowed on the Internet per week
Experimental
group,
M (SD)
Control
group, M (SD)
1.73 (0.83)
2.00 (0.78)
1.87 (0.81)
.618
0.80 (0.89)
1.34 (1.08)
1.07 (1.02)
.765
2.72 (2.78)
2.81 (2.24)
2.76 (2.51)
.787
4.07 (2.40)
5.42 (3.97)
4.74 (3.33)
.739
3.70 (2.59)
3.84 (2.75)
3.77 (2.66)
.775
0.35 (0.49)
0.74 (1.90)
0.54 (1.39)
.291
2.09 (2.90)
4.13 (6.79)
3.12 (5.31)
.565
Total,
M (SD)
Factor
loadings
208 Cianfrone et al.
yielded 22.131 (p = .000), indicating that the hypothesis of the variance and
covariance matrix of the variables as an identity matrix was rejected; therefore, the
factor analysis was appropriate. One factor had an eigenvalue ≥1.0, with a total of
52.88% variance explained. Adopting a criterion that an item had a factor loading
≥.40 without double loading (Disch, 1989; Nunnally & Bernstein, 1994; Tabachnick
& Fidell, 1996), all three items were retained under the new factor labeled NCAA
football-video-game consumption, and factor scores were calculated.
Similarly, a principal-components analysis was conducted for the behavioral
consumption variables of the NCAA football games. Values for both the KMO (.591)
and Bartlett’s test of sphericity (18.406, p = .005) on the behavioral consumption
variables of the NCAA football games were good. One factor had an eigenvalue
≥1.0, with a total of 38.77% of the variance explained. Three of the four items had
a factor loading >.40 and were retained under the factor labeled NCAA football
consumption. One item, “listening to NCAA football games on radio,” was eliminated because of a low factor loading. Factor scores are shown in Table 2.
Descriptive statistics for the three PAI factors are presented in Table 3. Considering that PAI factors were evaluated on a Likert-type 7-point scale, participants in
the three groups consistently displayed high levels (i.e., 5.0 or higher) of identification with the team, sport, and university. Among them, identification with team
had the highest mean scores for the groups. In addition, the mean scores of the
factors were quite similar across groups. Alpha coefficients for the three factors
were all above .70 across groups, indicating that the factors exhibited adequate
internal consistency (Hair et al., 1998). Descriptive statistics for the cognitive-,
affective-, and conative-consumption variables are presented in Table 4. The means
and standard deviations did not take into consideration the effects of the covariate
variables. The overall mean scores for unaided recall, aided recall, and recognition
were 11.24%, 15.73%, and 32.58%, respectively.
Results from the MANCOVA and adjusted descriptive statistics are presented
in Tables 5 and 6, respectively. For the first MANCOVA, two covariates (NCAA
football-video-game consumption and NCAA football consumption) were significant (p < .05) factors related to the three cognitive variables, explaining 35.4% of
variance. After adjusting for the effects of behavioral involvement and identification
levels, the mean vector scores of the cognitive variables for the experimental group
were significantly (p < .017) higher than for the control group and indicated that the
treatment explained 19.5% of variance in cognitive consumption of SVG in-game
advertisements. To further illustrate the comparisons of the cognitive variables
among the groups, the adjusted mean scores are presented in Figure 1.
Table 3 Descriptive Statistics for the Points of Attachment Index’s
Involvement Factors
Point of attachment
1. Attachment to team
2. Attachment to sport
(football)
3. Attachment to university
Experimental
Group
M
SD
α
M
6.10
0.93
.791
5.95
1.31
5.08
5.99
1.55
0.71
.894
.726
5.11
5.64
1.91
1.31
Control Group
SD
α
M
Total
SD
α
.832
6.03
1.13
.817
.897
.829
5.82
5.09
1.06
1.73
.895
.809
Table 4 Descriptive Statistics for Cognitive-, Affective-, and ConativeConsumption Variables
Experimental
Group
M
SD
Dependent variable
Cognitive variables
unaided-recall percentage
aided-recall percentage
recognition percentage
Affective variables
attitudes toward advertised brands (1–7 scale)
attitudes toward nonadvertised brands (1–7 scale)
Conative variables
intent to purchase advertised brands (1–7 scale)
intent to purchase nonadvertised brands (1–7 scale)
Control
Group
M
SD
Total
M
SD
19.05
20.63
40.47
31.14
24.36
27.58
4.55
9.85
24.24
21.07
21.05
28.18
11.24
15.73
32.58
26.92
23.62
28.75
4.31
0.89
4.45
0.99
4.40
0.94
4.61
0.93
4.51
1.02
4.54
0.97
3.85
1.13
4.22
1.50
4.07
1.34
4.58
1.17
4.67
1.25
4.62
1.19
Table 5 Multivariate Analysis of Covariance on the Effect of In-Game
Advertising After Adjusting for the Effects of Behavioral Involvement and
Identification Levels
Variable
Cognitive variables
NCAA video-game consumption
NCAA football consumption
team identification
university identification
sport identification
treatment
Affective variables
NCAA video-game consumption
NCAA football consumption
team identification
university identification
sport identification
treatment
Conative variables
NCAA video-game consumption
NCAA football consumption
team identification
university identification
sport identification
treatment
Wilks’s
Λ
F
Hypo.
df
Error
df
p
η2
1–β
.816
5.947
3
79
.001
.184
.948
.830
.989
.973
.995
.805
5.384
0.303
0.735
0.134
6.368
3
3
3
3
3
79
79
79
79
79
.002
.823
.534
.939
.001
.170
.011
.027
.005
.195
.942
.106
.200
.074
.961
.972
1.142
2
80
.324
.028
.245
.987
.937
.923
.979
.992
0.538
2.669
3.343
0.868
0.335
2
2
2
2
2
80
80
80
80
80
.586
.075
.040
.424
.716
.013
.063
.077
.021
.008
.136
.516
.617
.195
.102
.945
2.322
2
80
.105
.055
.458
.982
.879
.974
.981
.987
0.749
5.501
1.067
0.795
0.537
2
2
2
2
2
80
80
80
80
80
.476
.006
.349
.455
.587
.018
.121
.026
.019
.013
.173
.838
.231
.181
.136
209
210 Cianfrone et al.
Table 6 Adjusted Descriptive Statistics for Cognitive-, Affective-,
and Conative-Consumption Variables
Dependent variable
Cognitive variables
unaided-recall percentage
aided-recall percentage
recognition percentage
Affective variables
attitudes toward advertised brands (1–7 scale)
attitudes toward nonadvertised brands (1–7 scale)
Conative variables
intent to purchase advertised brands (1–7 scale)
intent to purchase nonadver-
tised brands (1–7 scale)
Experimental
Group
M
SE
Control
Group
M
SE
Total
M
SE
18.79
23.97
44.07
4.05
3.30
3.75
3.94
7.84
21.46
4.05
3.30
3.75
11.36
15.91
32.76
2.77
2.25
2.56
4.31
0.15
4.48
0.15
4.40
0.10
4.53
0.15
4.56
0.15
4.55
0.10
3.93
0.21
4.18
0.21
4.06
0.14
4.49
0.18
4.73
0.18
4.61
0.12
Figure 1 — Adjusted mean percentage of unaided recall, aided recall, and recognition for
experimental and control groups.
Video-Game In-Game Advertising 211
For the second MANCOVA, only one covariate (identification with university)
was identified as a significant (p < .05) factor related to the two affective variables,
explaining 7.7% of the variance. After we adjusted for the effects of covariates, the
mean vector scores of affective variables were not significantly (p > .017) different
between the groups and explained merely 0.8% of variance in affective consumption
of SVG in-game advertisements. Adjusted mean scores for the affective variables
were similar for advertised brands and nonadvertised brands, with most being
slightly above the midpoint (4.0).
For the third MANCOVA, only one covariate (identification with team) was
identified as a significant (p < .05) factor related to the two conative variables,
explaining 12.1% of variance. After adjusting for the effects of covariates, the
mean vector scores of conative variables were not significantly (p > .017) different
between the groups and explained merely 1.3% of variance in conative consumption of SVG advertisements. Adjusted mean scores for the conative variables were
similar for advertised brands and nonadvertised brands, with most being slightly
above 4.0.
Discussion
With $1.2 billion in sales in 2004, SVGs have grown from a popular hobby to a
serious business (Adams, 2005). Because of this popularity, corporate sponsorship
and in-game advertising in SVGs is an emerging trend in the sport industry. In many
SVGs, virtually inserting a brand in the game has become commonplace. Today,
the sport industry is intertwined with the video-game culture, as many professional
leagues and amateur-sport organizations have endorsed SVGs.
Corporations have spent an increasing amount of money on SVG advertisements in an effort to reach the SVG consumers (Lefton, 2004), despite little academic research on the effectiveness of such sponsorship. Similar to other forms of
sponsorships and advertisements, SVG in-game advertising is aimed at generating
and increasing brand awareness, communicating information about the brand, developing or changing an image or personality, associating a brand with feelings and
emotions, creating norm groups, and enhancing behavioral consumption. Corporations are eager to know whether they receive adequate ROI from investing in SVGs
(Batra, Myers, & Aaker, 1996). Recently, a number of researchers (e.g., Coakley,
2004; Kim et al., 2006) called for studying the impact of SVGs as a new segment
of the sport industry and as a marketing tool. Thus, examining the effectiveness
of SVG in-game advertising provides valuable information for game publishers,
corporate sponsors, sport leagues and organizations, and gamers. Vakratsas and
Ambler (1999) pointed out that affect, cognition, and experience should be studied
as three separate dimensions when one is examining advertising effectiveness. This
study was designed to assess the effectiveness of SVGs in terms of consumer cognition, affect, and conation of the advertised brands while controlling for behavioral
involvement and identification levels of gamers.
Although advertising in SVGs is a relatively new business venture, the findings of this exploratory study provide some encouraging information about the
value of SVG in-game advertising. The college football gamers usually owned
the most recent versions of the football video games with in-game advertising and
212 Cianfrone et al.
normally played for about 3 hr/week. The evidence of consumers purchasing the
most recent versions of the game is valuable for the game publishers, advertisers,
and sport organizations. Likewise, the high level of SVG consumption indicates
that the gamers had repetitive exposure to the advertised brands. The effect of the
repeated exposure was revealed by the high levels of unaided recall, aided recall,
and recognition of in-game advertisements. With 44% of the experimental group
recognizing the in-game advertisements of the SVG, awareness level was as high
as other forms of sport advertising and sponsorship that have been examined, such
as spectators’ recognition rates of venue signage at live or televised events, which
are typically less than 40% (Cianfrone & Zhang, 2006; d’Ydewalle et al., 1988;
Lardinoit & Derbaix, 2001; Levin et al., 2001; Pope & Voges, 1997).
The first of the three research questions of this study was, what is the effect
of advertising in SVGs on cognitive consumption? Based on Lavidge and Steiner
(1961), a consumer’s awareness of an advertiser is the first step toward consumption.
The rate of unaided recall of sponsors was lower than aided recall and, in turn, lower
than recognition for both groups. Aided recall and recognition provide additional
cues to enhance cognitive recollection. This is consistent with the difficulty level
of memory recollection and also with previous research findings (e.g., Cianfrone
& Zhang, 2006; d’Ydewalle et al., 1988; Lardinoit & Derbaix, 2001; Levin et al.,
2001; Pope & Voges, 1997). The experimental group reported much greater brand
awareness in terms of unaided recall, aided recall, and recognition than the control
group, indicating that gamers were aware of the in-game advertising, and they were
able to distinguish the advertised brands from nonadvertised brands. It is interesting
to note, however, that even though the gamers in the control group did not play
a game with the in-game advertisements, they still reported moderate levels of
awareness (e.g., approximately 21% in adjusted recognition). This indicates that
the gamers were so accustomed to playing the games with in-game advertisements
that they did not even realize the advertisements were not in the game they played
in the experiment and assumed that they had seen them.
The second and third research questions of the study were, what is the effect of
advertising in SVGs on affective consumption, and, what is the effect of advertising in SVGs on conative consumption? Although playing an SVG for 30 min did
influence awareness levels, it did not enhance the affective and conative levels of
gamers. Possibly, affective and conative reactions to in-game advertisements are
more personal and psychological than pure awareness, thus making them more
resistant to change. Previous studies on advertising and sponsorships found that
consumers’ cognitive awareness of a sponsoring or advertising brand did not necessarily lead to positive brand attitudes or purchase intentions (Boddewyn, 1994;
McDaniel & Heald, 2000; Mizerski, 1995). The gamers had high recognition rates,
but the inability of the advertisements to influence their conative levels was evident.
McDaniel and Heald also found that sport-event sponsorship did not influence
purchase intentions; perhaps SVGs follow a similar pattern. A consumer’s past
exposure to other promotional methods (e.g., television commercials during sporting
events, print advertisements, and other sport or venue sponsorships) might create
schematic memory used to interpret promotional stimuli (Braun, 1999; Goodstein,
1993; McDaniel & Heald). The gamers’ previous knowledge and their schematic
memory might have influenced their attitudinal responses and purchase intentions
for the advertised brands; thus, the gamers might have had preconceived affective
Video-Game In-Game Advertising 213
and conative responses for the brands used in this study that could not be controlled
for and, therefore, do not necessarily indicate whether they were influenced by the
SVG in-game advertising. It is possible that more playing time is needed to alter
affective and conative states; with repeated exposure, an individual’s views might
change through image-transfer effects.
In addition, possibly because of their newness in the sport industry, SVG ingame advertising has yet to reach full effectiveness in transforming consumers’
attitudes and purchase intentions. Stewart and Rice (1995) suggest that behavioral
intentions might not be influenced by nontraditional promotions. When SVG ingame advertising becomes more common, it might be better received by gamers.
Nonetheless, the experimental group’s low attitudes toward and low intent to
purchase advertised brands might have been a result of their immediate exposure
to the advertised brands and attitude toward in-game advertising in general. The
gamers who only played the game with advertisements (experimental group) were
exposed to the brand logos repeatedly throughout the game (on average 11 times
per 30-min game). This repeated exposure might have annoyed them and perhaps
was reflected in this group’s poor attitudes toward the brands and low intent to
purchase. This is an area that should be further assessed, because in-game advertising’s effectiveness via the affective and conative domain might be influenced
by gamers’ annoyance with or tolerance of the sponsorship of the SVGs and their
overall attitudes toward SVG in-game advertising.
The brands used in the study (Pontiac and Old Spice) might have also influenced
the affective and conative responses of the gamers. The two advertised brands in the
video game might not be popular products among college students in general. When
studying sport-sponsorship effectiveness of highly familiar brands, Carrillat, Lafferty, and Harris (2005) found that consumers’ affective- and conative-consumption
levels did not change when a brand was a sponsor of a sporting event compared
with being a nonsponsor. They suggest that with the brand’s high familiarity and
the consumers’ associative memory (Anderson & Bower, 1973), the brand association might not be affected by the sponsorship stimuli (Carrillat et al.). This might
have been the case in this study, because Pontiac and Old Spice are highly familiar
brands, especially in the realm of sport advertising and sponsorship. Gamers’ associative memory of sport advertising might have negated the influence of the SVG
in-game-advertising exposure in the experiment. This might simply be a result of
the preferences of the age group studied.
Concluding Comments
The descriptive statistics and covariance analyses indicated that SVG players had
high levels of behavioral involvement with college-football video games, as well
as with live and broadcast college football. They also had high identification levels
with the team, university, and sport (football). Essentially, the SVG players were avid
consumers of college football, watching many games in person and on television.
The high level of behavioral involvement and identification is positive to note for
companies that use in-game advertising, sport organizations, and game publishers.
Corporations who advertise both in an SVG and the corresponding league or
organization profit because the gamers are following both the actual and the virtual
games. These league-sponsorship packages (such as the NCAA’s agreements
214 Cianfrone et al.
with Pontiac and Old Spice) that include various media outlets (television and
video game) appear beneficial because they are providing repeated exposure to
the same target market. SVG publishers such as EA Sports can be confident that
in-game advertising can be effective in creating awareness. For companies with
the goal of brand awareness, SVGs were found to be effective based on the high
recall and recognition rates. The differences noted between the experimental and
control groups are indicative of the fact that gamers can recognize and distinguish
between advertisers and nonadvertisers. The high brand-awareness rates indicate
that future studies on SVGs can examine the hierarchy of effects and other elements
of in-game-advertising response.
This study was limited to the experimental medium used. The use of actual
SVGs and advertised brands was somewhat problematic because of participants’
previous knowledge, thoughts, feelings, experience, and consumption of the game or
advertising brand. It would be interesting if a new SVG could be created to alleviate
any previous history and thereby permit a clear focus on the effects of the in-game
advertising. If a new SVG is used, however, there might be a steep learning curve
to play the new game, which would affect attention paid to sponsorships.
Although this study might serve as a foundation for future SVG in-gameadvertising studies, future studies should examine the effectiveness of in-game
advertising in other types of SVGs, other types of sponsorships (e.g., athlete
endorsement or product placement in the games), and different target markets.
In this study, college football was pertinent to and popular with the participant
groups—college students. Data were collected during the 2005–06 academic year,
so the 2005 version of the game was the most up-to-date SVG product at the time
of data collection. Because of rapidly changing technology and new games, the
procedures of advertising in SVGs are constantly being advanced. Future researchers
should adopt the newest versions of SVGs when studying the effects of in-game
advertisements. It is common knowledge that last year’s video games are already
in the discount section of stores; therefore, researchers need to keep up with technology. Future studies are also needed to examine differential effects of various
types of SVG advertising and types of games in the context of diverse consumer
demographics. Considering that a major market segment of the SVG audience is
highly coveted 18- to 34-year-old males (Richtel, 2005), future research needs to
expand into other segments of this age group. Meanwhile, female consumers of
video games, although a small minority at present, are on the rise (Entertainment
Software Association, 2006); thus, future studies should consider potential gender
differences, as well. In addition, this study examined the effect of advertising in
SVGs on cognitive, affective, and conative consumption, respectively. Future
studies need to take into consideration the hierarchical nature and linkage among
the cognitive, affective, and conative domains (e.g., Madrigal, 2001; Mullin et al.,
2000). Although consumer-involvement variables in terms of sport identification
and past consumption were included in the current study as covariate variables,
gamers’ attitudes toward the in-game advertisements could be incorporated into
the design of future studies.
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