Journal of retailing and consumer services volume 19 issue 1 2012 modeling the effect of self efficacy on game usage and purchase behavior

Modeling the effect of self-efficacy on game usage and purchase behavior
Robert Davis
a,
n
, Bodo Lang
b,1
a
Faculty of Creative Industries and Business, Unitec Institute of Technology, Department of Management and Marketing, Private Bag 92025, Auckland, New Zealand
b
Marketing Department, The University of Auckland Business School, Private Bag 92019, Auckland 1142, New Zealand
article info
Available online 16 December 2011
Keywords:
Self-efficacy
Usage
Purchase
Computer games
Confirmatory factors analysis
Structural equation modeling
abstract
This research models the relationship between self-efficacy, game purchase and usage. Four-hundred
and ninety three consumers responded to a questionnaire. We deployed confirmatory factors analysis
(CFA) and structural equation modeling (SEM) across 4 game types; original model (all games) and
alternative models, Sports/Simulation/Driving, Role Playing Game/Massively Multiplayer Online Role-
Playing Game/Strategy and Action/Adventure/Fighting. The impact of self-efficacy on usage and
purchase was modeled both individually and simultaneously. For individual effects; models had
adequate fit with Sports/Simulation/Driving showing an impact between self-efficacy on game usage
and purchase. Our results showed no simultaneous relationship. We conclude that self-efficacy does
impact usage or purchase but game type affects this relationship. Research implications are discussed.
& 2011 Elsevier Ltd. All rights reserved.
1. Introduction

Recent advances in games on PC, MAC, Console, Mobile, iPhone
or iPad have increased the consumers purchase and use of these
entertainment related products and services (Prugsamatz et al.,
2010). According to the Entertainment Software Association in
the U.S.: (1) computer and video game software sales generated
$10.5 billion in 2009, (2) sixty-seven percent of American house-
holds play computer or video games, (3) the average game player
is 34 years old and has been playing games for 12 years. Overall,
sales of game hardware, software and accessories have eclipsed
those of the US box-office, cementing gaming as a dominant force
of technological consumption (Khan, 2002; Guth, 2003). Europe
has also become a significant industry and market. The UK is the
third largest market globally with total sales in 2004 of entertain-
ment and leisure software of £1.34b (Boyle and Hibberd, 2005).
The interactive entertainment industry in the UK is set to grow by
7.5% between 2009 and 2012 (UKIE, 2011).
There are many factors that have fueled this change in the
consumers consumption behavior but it is argued in this research
that the growth in the importance of games in a cons umers enter-
tainment experience has been largely attributable t o i ncreased
technology related s elf-efficacy (Allan, 2010). Usage and purchase
has grown because the consumer perceives that they have the
capability to be interactive with a g ame and therefore, other s timuli
within the game (e.g., Advergames).
As a c onsequence, market ing practioners and researchers have
become more interested in the potential of this medium f or market-
ing. A key focus of this interest is related to three questions, that is,
self-efficacy and the fit between the consumer and: (1) the game,
(2) the marketing stimulus (e.g., advertisement) and (3) the co-
creation of experience with the game and stimulus. All these

questions place emphasis on the consumers belief in their capability
to not only play the game as well as interact with the marketing
stimulus to accomplish specific objectives but also to be an active
player in the c o-creation of experience ( Bandura, 1982).
A review of the existing research shows that much of the work
to date has focused on the effect of advertising within a game on
the consumer (Molesworth, 2006). For example, Prugsamatz et al.
(2010) apply the theory of planned behavior by gamer type,
showing the effects on purchase intentions. Also, Cauberghe and
De Pelsmacker (2010) replicate the effect of in game advertising
on brand recall and attitude. They also take in to account the
mediating effects of product involvement, although, we acknowl-
edge that the games medium is predominately service oriented.
This work is consistent with Nicovich (2005) who have measured
the relationship between consumer involvement on the advertis-
ing effect. Like Cauberghe and De Pelsmacker (2010), many others
have examined the advertising communication effect of product
or brand placement in computer games on the consumer (e.g.,
Schneider and Cornwell, 2005; Mackay et al., 2009; Chaney et al.,
2004; Nelson et al., 2004; Winkler and Buckner, 2006 ; Yang et al.,
2006; Mau, Silberer and Constien, 2008). While this research has
replicated traditional models, they have ignored two important
factors. First, the mediating effect of the service experience and,
second the difference between a product vs. an entertainment
orientation.
Contents lists available at SciVerse ScienceDirect
journal homepage: www.elsevier.com/locate/jretconser
Journal of Retailing and Consumer Services
0969-6989/$ - see front matter & 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jretconser.2011.09.002

n
Corresponding author. Tel.: þ649 815 4321.
E-mail addresses: (R. Davis),
(B. Lang).
1
Tel.: þ64 9 923 7162.
Journal of Retailing and Consumer Services 19 (2012) 67–77
Recently, other researchers in an attempt to extend our u nder-
standing of the consumer response to marketing in the game
environment have s tarted to explore av atar-based advertising (Jin
and Bolebruch, 2009). In this study, the overall effects o f avatar-
based interactive advertising on product involvement a nd attitude
were tested. It was found that consumers ‘‘perceive human-like
spokes-avatars as more attractive, and players who interact with a
human-like spokes-avatar perceive the iPhone advertisement as
more informative t han those who interact with a non-human
spokes-avatar (Jin and Bolebruch, 2009, p57).’ ’
Despite these developments, most of the existing research has
focused on the consumer- advertisement response. Many with
the exception of Prugsamatz et al. (2010) have not compared
different game genres. Little attention has been given to under-
standing the fit between consumer, game and marketing stimulus
from a self-efficacy perspective and the effect of this on use and
purchase. This is concerning because if a consumer does not have
the belief in their capability to be interactive with the game and/
or marketing stimulus concurrently, it is less likely that they will
value the experience. Self-efficacy plays a key mediating role in
the interactivity between consumer, game and marketing stimu-
lus. If consumers do not have a high level of self-efficacy then this
may reduce use and purchase. Also, as some researchers have

argued, there may be negative impacts on the gamers self-and
other aspects of cognition (Boyle and Hibberd, 2005; Anderson
and Bushman, 2002; Dill and Dill, 1998).
While these perspectives are valuable for our understanding, a
fundamental research question has not been addressed, such as
those concerning the consumers’ self-efficacy and its relationship
to game purchase and game usage (Kaltcheva et al., 2011). We
model these relationships across 4 game types, grouped according
to the conceptualization of Myers (1990), namely: (1) all games
representing our original model and then the alternative compet-
ing models, (2) Sports/Simulation/Driving, which places emphasis
on hand/eye co-ordination/reflexes in real world environments,
(3) Role Playing Game (RPG)/Massively Multiplayer Online Role-
Playing Game (MMORPG)/Strategy, which places emphasis on
characters that gain experience and power through encounters and
(4) A ction/Adventure/Fighting, which places emphasis on simula-
tions o f futuristic and historical warfare and/or violent activit y.
This approach is consistent with Apperly (2006, p. 20) and
others (Prugsamatz et al., 2010) who argue that ‘‘strategy and
role-playing genres have their roots in pre-computer forms of p lay,
whereas the si mulation genre can be compared to non-entertain-
ment computer simulations. The action genre is implicitly connected
to cinema through i ts deployment of the t erminology of that
medium to mark key generic distinctions.’’
Usage and purchase are employed as dependent variables and
relate to the frequency of this behavior. Usage and purchase have
often been used in this capacity in marketing research. For
example, Shimp and Kavas (1984) relate the theory of reasoned
action to usage. Usage has also been deployed in an experimental
context. Folkes et al. (1993) relate product supply to usage. Desai

and Hoyer (2000) examine the composition of memory-sets to
different usage. Purchase behavior has also played a key role in
marketing research as a dependent variable (Sriram et al., 2010;
Hui et al., 2009; Liu, 2007). For example, Bawa and Shoemaker
(1987) develop a model of coupon usage across product classes to
explain the purchasing behavior between coupon-prone and non-
coupon-prone households. Also, Sismeiro and Bucklin (2004) use
binary probit models of navigational behavior to predict actual
purchase online.
Our work has implications for current research focusing on the
fit between consumer, game and marketing stimulus from a self-
efficacy perspective and the effect of this on use and purchase.
Through this understanding it provides an important direction for
the advertising of games and for game designers. Through a better
understanding of what consumers’ value and whether it drives
usage and purchase, advertising within games may well become
more effective in terms of reaching communication goals such as
brand recall and awareness. Product and game involvement may
also increase.
This paper is organized as follows. F irst, w e present the conceptual
model which begins with a definition of the concept of t he game
leading to our hypotheses. T he paper follows with the methodology
and results. The paper concludes with the discussion, managerial and
research implications.
2. Conceptual model
A wide variety of concepts have been applied to conceptualize
the consumers interaction with games such as; narratives and
interactive texts (Juul, 2001, Ryan, 2001), cultural art ifacts (Prensky,
2001) and technological drivers ( Woods, 2003; Bushnell, 1996 ;
Aarseth, 2003). In the context of this research we draw from a

conceptual model t hat d efines t he game from the consumers’
experience (Newman, 2002a, 2004; Ma nninen, 2003; Aarseth,
2003) of t he consumption o r p lay of t he game ( Ch en, 2 008;
Holbrook and Hirschman, 1982 ). Playing a game involves instanta-
neous feedback in visual, auditory and kinesthetic forms. This feed-
back helps to create interactivity and shape the consumers
experience in cognition and within the medium, create rich virtual
worlds that blur the boundaries between imagination a nd reality
(Jessen, 1999).
The process of consumption is not singular, but rather an
experience that varies with the consumer and their level of
interaction, both within the game and with other game players.
A game has an explicit structure that defines how it is to be
played (Choi and Kim, 2004), yet it is open to interpretation and
experimentation. It is also a representation of the functional and
recreational desires of the immediate consumer (Newman,
2002a). Eber (2001) demonstrates that the choice to interact with
the game may be driven by a hedonic need. This enforces the
concept brought forward by Mortensen (2002) and Fromme
(2003) that the attraction of the game depends on the subjective
interpretation and desire of the consumers and by their self-
concept (Walther, 2003; Gottschalk, 1995).
We propose that when a consumer plays a game they experience
interactivity. The effect of this feedback is t o transform their
perceptions of s elf-efficacy; the belief they hold in their capability
to accomplish a task, which, in this respect refers to their ability t o
play the g ame (Agarwal et a l., 2000; Bandura, 1982). In essence it
changes their fundamental belief that they are capable through
game play to achieve the d esired goals and outcomes.
This argument is supported by Allan (2010), Bandura (1977,

1982) and Smith (2002a,b)
who defines four sources of self-efficacy:
mastery e xperiences (pe rformance accompli shments), vicarious
learning a nd experience, social persuasion and affective states
(emotional arousal). Allan (2010, p. 36) posits; ‘‘video games can
produce both positive and negative emotional a rousal in those who
play them. Watching another person play a video game provides the
observer with vicarious experience to make efficacy comparisons.
Verbal persuasion influences video game self-efficacy when a player
receives feedback from others. Finally, v ideo games are generally
performance accomplishment tasks. They p rovide a player with a
constant stream of input. This i nput supplies the player with
ongoing m astery experience to build video game self-efficacy.’’
These findings are consistent with Newman’s (2002a,b, 2004)
continuum of engagement and Vorderer (2003) and Eber (2001),
who define a game as a ‘form of mastery’ (i.e. t he acquisition and
perfection of a skill). Consequently, self-efficacy has primarily been
R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–7768
operationalized in the form of prior experience to represent both
mastery and vicarious learning experiences (Igbaria and Iivari, 1995)
and is considered t o b e dynamic in nature since the consumer is
expected to become more capable in performing a t ask as their
exposure to the task increases.
We argue that through the games consumption and experi-
ence of interactivity; consumers will have positive self-efficacy.
Thus, the belief in their capability to be interactive with the game
will drive the value of the experience, positively impacting usage
(Allan, 2010) and purchase. Therefore, it is hypothesized that:
H
u1–4

. Self-efficacy has an individual effect on game purchase mea-
sured across four game model types; (1) original model, (2) Sp orts,
Simulation and Driving ; (3) RPG, MMORPG and Strategy and (4) A ctio n,
Adventure and Fighting.
H
p1–4
. Self-efficacy has an i ndivid ual effect on g ame usage measured
across four game model types; (1) original model , (2) Sports, Simula tion
and Driving; (3) RPG, MMORPG and Strategy and (4) Action, Adventure
and Fighting.
H
5–8
. Self-efficacy has a simultaneous effect on game usage and
purchase measured across four game model types; (1) original model,
(2) Sports, Simulation and Driving; (3) RPG, MMORPG and Strategy
and (4) Action, Adventure and Fighting.
As we have noted in our hypotheses; these hypotheses are
extended over the 4 game types so the analysis of the path
coefficients and SEM model fit will proceed to test 8 hypothesized
relationships between self-efficacy and; (1) game purchase and
(2) game usage. Therefore, 8 models are also compared.
3. Method
Data was gathered through face-to-face interviews with 493
consumers in Auckland, New Zealand. All consumers who walked
past the interviewers were considered to be potential respon-
dents. The interviewers were rotated around four locations in
Auckland; east, west, south and north. Every potential respondent
was asked to participate so they had an equal chance to complete
the survey. Those that agreed to participate were asked t o respond
to a structured questionnaire. Respondents were screened with two

questions: (1) In the last week, did you play games on your
computer (PC or MAC), or on a games console (perhaps through
the Internet), s uch as an Xbox, Playstation or Wii that you pur-
chased?’’ I f the answer was ‘‘Yes’’, they were asked (2) What game
did you play most often in the last week?
Question 1 established that the respondent was a regular game
player of games they had actually purchased and, Question
2 checked that the game was not a game preloaded on a computer
such as Solitaire. Four-hundred and ninety three respondents
provided usable data. Eighty-two percent of the respondents were
male and 18% were female (Table 1). The majority of t he respon-
dents (77%) were 25 years and under. About 66% of the respondents
had not received a degree and 77% were single. Thirty-nine percent
of respondents were Asians and 48% of the respondents were
students. Forty-eight percent of the respondents had an annual
income of less than $10,000. The samples d emographics are gen-
erally consistent with the recent research by INZ (2010) on the
New Zealand gaming consumer (N¼ 1958).
The questionnaire (see Table 2) was designed to measure
multi-item constructs. Throughout the whole questionnaire a
seven point scale was used to measure the constructs of interest
(1¼‘‘strongly disagree’’, 7¼‘‘strongly agree’’). To operationalize
self-efficacy we use Smith (2002a,b) with an adapted form of
Torkzadeh and Koufteros (1994) computer self-efficacy (CSE)
scale based upon Bandura’s (1977, 1982) guidelines on self-
efficacy and social cognitive theory. Purchase behavior is based
on an adaption of the scale of Bristol and Mangleburg (2005).
Usage behavior is based on the Technology Acceptance Model
(Venkatesh et al., 2 003). Game categories for usage and purchase
are derived from Myers (1990) and the retail categories commonly

used in consumer purchases ( />4. Analysis
The analysis tested the proposed conceptual model with
confirmatory factor analysis (CFA) and structural equation mod-
eling (SEM). The commonly used approach was employed as we
wanted to use an analysis method that not only supported model
refinement but could rigorously assess model fit across four gaming
types. It also helped us m easure the individual and s imultaneous
effects in the relationship between self-efficacy, usage and purchase.
5. Confirmatory factor analysis
This study adopted a two-stage process (Kline, 1998). The first
stage of the process was to construct separate measurement models
for each latent variable. The struc tural model is constru cted as the
second stage of the process. Initial data screening was done for
missing values, outliers and the normality of the dataset was tested.
We examined all scale items and reverse-coded w hen applicable to
reflect the hypothesized directions.
Table 1
Sample characteristics (n¼493).
Variable Categories Percent of sample
Gender Male 82.2
Female 17.8
Age r 10 0.4
11–15 4.3
16–20 40.2
21–25 37.1
Z 26 18.1
Ethnicity NZ Pakeha 29.4
Maori 7.5
Pacific Islander 6.5
Asian 38.5

European 9.9
Others 8.1
Marital status Single 77.3
Widowed 0.2
Living with partner 13.8
Married 7.3
Divorced/separated 1.4
Education Non-degree 66.1
Degree 33.9
Employment Student 47.7
Full time 25.4
Self-employed 4.9
Unemployed 4.3
Homemaker 0.4
Part-time 6.7
Student/part-time 10.8
Annual Income o 10,000 47.5
10,000–20,000 16.6
20,001–30,000 7.5
30,001–40,000 11.4
40,001–50,000 9.5
50,001–60,000 3.2
60,001–80,000 2.4
Z 80,000 1.8
R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 69
Table 2
Questionnaire items.
Screen question: in the last week, did you play
games on your computer (PC or MAC), or on a
games console (perhaps through the Internet),

such as an Xbox, Playstation or Wii that you
purchased? (check the game was purchased)
PC/Mac Xbox PS Internet
SCREEN question: if yes—what game did you
play most often in the last week? [(check the
game is not a game preloaded on a computer
such as solitaire, etc.)
Name of game
This questionnaire is about games you can play
on your computer (PC or MAC) or on a games
console, such as an Xbox, Playstation or Wii.
We will call these console games, simply
‘‘games’’ in this questionnaire
Very rarely Very Often CODE
Purchase behavior: thinking about the types of
games you buy please answer the following
questions by providing a number between
1 and 7 where 1 means ‘very rarely’ and
7 means ‘very often’.
1. How often do you buy games? 1 2 3 4 5 6 7 PB1
2. How often do you buy the following game
types?
Very Rarely Very Often
Action 1 2 3 4 5 6 7 PB2
Adventure 1 2 3 4 5 6 7 PB3
Driving 1 2 3 4 5 6 7 PB4
Fighting 1 2 3 4 5 6 7 PB5
Children 1 2 3 4 5 6 7 PB6
Educational 1 2 3 4 5 6 7 PB7
Massively Multiplayer Online Role Playing

Game (MMORPG)
1 234567 PB8
Role Playing Game (RPG) 1 2 3 4 5 6 7 PB9
Simulation 1 2 3 4 5 6 7 PB10
Strategy 1 2 3 4 5 6 7 PB11
Sports 1 2 3 4 5 6 7 PB12
3. How many games do you own in total? PB13
1 game 2 games 3–5 games
6–10 games 11–15 games 16–20 games
21–30 games 31–40 games More than 40
Play usage behavior: thinking about the types of
games you play please answer the following
questions by providing a number between
1 and 7 where 1 means ‘very rarely’ and
7 means ‘very often’.
4. How often do you play games on each of
the following platforms?
Very Rarely Very Often
PC/MAC 1 2 3 4 5 6 7 PU1
Xbox 1 2 3 4 5 6 7 PU2
Playstation 1 2 3 4 5 6 7 PU3
Connected to the Internet 1 2 3 4 5 6 7 PU4
Wii 1 234567 PU5
5. In a typical week, how many hours do you
play games?
PU6
Less than 2 h 3–5 h 6–10 h
R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–7770
11–20 h 21–30 h More than 30 h
6. How long have you been playing games? PU7

Less than 6 months 7–11 months 1–2 years
3–5 years 6–10 years 11–15 years
16–20 years More than 20 years
7. How often do you play the following game
types?
Very Rarely Very Often
Action 1 2 3 4 5 6 7 PU8
Adventure 1 2 3 4 5 6 7 PU9
Driving 1 2 3 4 5 6 7 PU10
Fighting 1 2 3 4 5 6 7 PU11
Children 1 2 3 4 5 6 7 PU12
Educational 1 2 3 4 5 6 7 PU13
Massively Multiplayer Online Role Playing
Game (MMORPG)
1 2 3 4 5 6 7 PU14
Role Playing Game (RPG) 1 2 3 4 5 6 7 PU15
Simulation 1 2 3 4 5 6 7 PU16
Strategy 1 2 3 4 5 6 7 PU17
Sports 1 2 3 4 5 6 7 PU18
8. If not clear from Q7, ask and circle: which
one of these game types do you play most?
PU19
9. Which one game do you play most from
that group (Q8)? Write down the name
PU20
Skill level 10. When thinking about insert name of game from Q9 how would you rate your skill level? Beginner 1234567Expert SK1
Thinking about game from Q9, please answer the following questions by providing a number between 1 and 7 where 1 means ‘strongly disagree’ and
7 means ‘strongly agree’.
Strongly disagree Strongly agree CODE
Self-efficacy 11. I expect to become proficient in playing this game. 1 234567 SE1

12. I feel comfortable playing this game. 1 234567 SE2
13. I am skilled at playing this game. 1 234567 SE3
14. I know how to do what I want to do with this game. 1 234567 SE4
15. I know more about the game than most other people who play this game. 1 234567 SE5
16. I can play this game if
I can call someone for help if I get stuck. 1 234567 SE6
I have the manual for reference. 1 234567 SE7
I have a lot of time to practice. 1 234567 SE8
I have the built-in help assistance. 1 234567 SE9
I have never played a similar game like it before. 1 234567 SE10
I have never played it before. 1 234567 SE11
I have not seen anyone play it before. 1 234567 SE12
I have played a similar game like this one before. 1 234567 SE13
I have seen someone else play it before I play. 1 234567 SE14
Someone else has helped me to get started. 1 234567 SE15
Someone showed me how to play it first. 1 234567 SE16
There was no one to help me to show me what to do. 1 234567 SE17
R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 71
Subsequently, the data was subjected to multivariate normal-
ity testing. Results show that the Mardia coefficient was greater
than 15, very much higher than the 3.0 cutoff advised by Wothke
(1993). Thereby, the Bollen–Stine bootstrap method was used
(Bollen and Stine, 1992). Cunningham (2008) stresses that if the
Bollen–Stine (B–S) p value is less than 0.05, the model should be
rejected.
Convergent and discriminant validity of the constructs were
tested using the confirmatory factor analysis (CFA) that combined
all constructs concurrently. Maximum likelihood estimation (MLE)
was used to fit the models. MLE is a procedure that improves
parameter e stimates in a w ay that minimizes t he differences

between the observed and estimated covariance matrices ( Pampel,
2000). Construct refinement was enabled by an analysis of covar-
iance residuals and modification indices and e xclusion of items until
the goodness-of-fit was achieved. Following Bau mgartner and
Homburg (1996), conventional measures were used to assess the
model fit: goodness-of-fit indices, chi-squared (X
2
), the comparative
fit index (CFI) and normalized fit index (NFI). For CFI and NFI values
close t o 1 are indicative of good model fit (Bentler, 1990). The root
mean square error of approximation (RMSEA) was calculated for the
overall model and according to Bentler (1990), values below 0.05
indicate close fit and values up to 0 .08 are reasonable. Finally, the
standardized root mean squared r esidual ( SRMR) a s d escribed by Hu
and B entler (1995) computes how much the m odel explains the
correlations to within an average error. Bentler (1990) argues tha t a
model is regarded as having an acceptable fit if the SRMR is less than
0.10, while a SRMR o f 0 indicates a perfect fit (Browne and Cudeck,
1993).
The final measurement models show a reasonably good fit and
most of the fit indices are above or close to the required minimum
threshold level. The ratio of minimum discrepancy to degree of
freedom (chi-square/DF ratio) should be less than 5 or preferably
less than 2 (Bentler, 1990). The GFI index is above the threshold of
0.90 (Hair et al., 2009), and CFI is close to 1 (Bentler, 1990) for
every construct.
Composite reliability is an indicator of the shared variance
among the set of observed variables used as indicators of a latent
construct (Bacon et al., 1995; Kandemir et al., 2006). The three
items included in self-efficacy are: (1) respondents have a manual

for reference; (2) respondents have the built-in help assistance
and (3) respondents have never played this game before. The con-
struct reliability for these self-efficacy items was 0.83, above the
recommended value of 0.70 or higher. In addition, the coefficient
alpha value was 0.83, above the threshold value of 0.70 that
Nunnally (1978) recommends. The average variance extracted
(AVE) value was 0.63. It reflects the average communality for each
latent factor and is used to establish convergent validity. The AVE
value is above the threshold value of 0.50 (Chin, 1998; H
¨
ock and
Ringle, 2006; Fornell and Larcker, 1981).
6. Structural equation modeling
The structural equation modeling process had two competing
steps. The first step assessed the conceptual model measuring the
individual effects of self-efficacy on purchase and usage sepa-
rately. The second step measured the simultaneous effect of self-
efficacy on purchase and usage together.
6.1. Individual effects
In the first step SEM, the same conventional measures were
used to assess the model fit as in the CFA, that is, the goodness-of-
fit indices (GFI), the chi-squared (X
2
)/degrees of freedom (DF)
ratio, the comparative fit index (CFI), the normalized fit index
(NFI), the root mean squared error of approximation (RMSEA), the
standardized RMR (SRMR) and the Bollen–Stine (B–S) p value.
The SEM focused on the analysis of the hypotheses of the four
competing forms of this model; (1) the original model includes all
the game types while t he alternat ive models focus on each game

genre, namely ( 2) S ports, Simulation and Driving; (3) RPG, MMORPG
and Strategy and (4) Action, Adventure and Fighting. The results of
the SEM analysis for both models are displayed in Tables 3 and 4.
The final model met suggestions from the literature regarding the
minimum number of items attached to a construct (Hair et al.,
2009).
For the original model: the game usage results indicate inade-
quate model fit (GFI¼ 0.88, CFI¼0.75, TLI¼0.69, RMSEA¼0.12,
SRMR¼ 0.09, X
2
/DF¼ 7.58 and B–S p¼ 0.00). Similarly, the self-
efficacy results for game purchase were inadequate (GFI¼ 0.86,
CFI¼ 0.81, TLI¼0.76, RMSEA¼0.12, SRMR¼0.08, X
2
/DF¼ 8.31 and
B–S p¼ 0.00). With poor fit indices results and unacceptable B–S p
values, the models should be rejected. The standardized factor
loadings for self-efficacy (game usage) ranged from 0.69 to 0.87
and were highly significant (po0.001). The standard factor loading
for self-efficacy (game purchase) were similar and highly significant
(po 0.001). The average variance extracted (AVE) value was 0.63.
For the Sports, Simulation and Driving Model: The game usage
results suggest adequate model fit (GFI¼ 0.99, CFI¼0.99, TLI¼ 0.98,
RMSEA¼0.04, SRMR¼0.03, X
2
/DF¼1.83 and B–S p¼0.45). Similarly
the s elf-efficacy results for game purchase w ere adequate (GFI¼
0.99, C FI¼ 0.99, TLI¼0.99, R MSEA¼0.0 3, SRMR¼ 0.02, X
2
/DF¼1.36

and B–S p¼ 0.79). The standardized factor loadings for self-efficacy
(game usage) ranged from 0.69 to 0.87 and were highly significant
(po 0.001). The standard factor loading for self-e fficacy (game
purchase) were similar and highly si gnificant (po 0 .001). The
average variance extracted (AVE) value was 0.63. With these results,
both models (game u sage and g ame purchase) in the Sports,
Simulation a nd Driving genre are accepted. The results for Sports,
Simulation and Driving game classification reveal that a significant
positive relationship fo r the path between self-efficacy and g ame
Table 3
SEM model fit (step 1): individual effect.
Dependent variable Game group X
2
(DF) X
2
/DF ratio p CFI TLI GFI RMSEA SRMR B-S p
Game, usage Original 401.77 (53) 7.58 0.00 0.75 0.69 0.88 0.12 0.09 0.00
Game, purchase Original 440.44 (53) 8.31 0.00 0.81 0.76 0.86 0.12 0.08 0.00
Game, usage Sports, Simulation, Driving 194.67 (8) 1.83 0.07 0.99 0.98 0.99 0.04 0.03 0.45
Game, purchase Sports, Simulation, Driving 10.90 (8) 1.36 0.21 0.99 0.99 0.99 0.03 0.02 0.79
Game, usage RPG, MMORPG, Strategy 43.51 (8) 5.44 0.00 0.95 0.91 0.97 0.09 0.06 0.00
Game, purchase RPG, MMORPG, Strategy 21.85 (8) 2.73 0.01 0.98 0.97 0.99 0.06 0.04 0.09
Game, usage Action, Adventure, Fighting 23.12 (8) 2.89 0.00 0.98 0.97 0.99 0.06 0.04 0.06
Game, purchase Action, Adventure, Fighting 27.97 (8) 3.50 0.00 0.98 0.96 0.98 0.07 0.04 0.02
X
2
—chi-square; CFI—comparative fit index; TLI—Tucker Lewis index; GFI—goodness-of-fit-index; RMSEA—root-mean-square error of approximation; SRMR—standar-
dized root-mean-squared residual; B—S p—Bollen–Stine bootstrap p;DF—degrees of freedom.
R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–7772
usage. Likewise a significant positive relationship exists for the path

between self-efficacy and game purchase.
For the RPG, MMORPG and Strategy Model: The game usage results
in an adequate model fit (GFI¼ 0.97, CFI¼ 0.95, TLI¼ 0.91,
RMSEA¼0.09, SRMR¼0.06, X
2
/DF¼ 5.44 and B–S p¼0.00). Similarly
the self-efficacy results for game purchase were adequate (GFI¼0.99,
CFI¼0.98, TLI¼ 0.97, RMSEA¼0.06, SRMR¼ 0.04, X
2
/DF¼2.73 and
B–S p¼0.09). The standardized factor loadings for self-efficacy (game
usage) ranged from 0. 69 to 0.87 and were h ighly significant
(po0.001). The standard factor loading for self-efficacy (game
purchase) were similar and highly significant (po0.001). The average
variance extracted ( AVE) value w as 0.63. Considering the Bollen–
Stine (B–S) p values of these models, the game usage and p urchase
models are rejected. We note that there is a significant relationship
between self-efficacy and game p urchase.
For the Action, Ad venture and Fighting M odel:Thegameusage
results an adequate m odel fit ( GFI¼ 0.99, CFI¼ 0.98, TLI¼0.97,
RMSEA¼ 0.06, SRMR¼ 0.04, X
2
/DF¼2.89 and B–S p¼ 0.06). Similarly
the s elf-efficacy results for game purchase were adequate (GFI¼
0.98, CFI¼0.98, TLI¼0.96, RMSEA¼ 0.07, SRMR¼0.04, X
2
/DF¼3.50
and B–S p¼ 0.02). The standardized factor loadings for self-efficacy
(game usage) ranged from 0.6 9 to 0.87 and were highly significant
(po 0.001). The standard factor loading for self-efficacy (game

purchase) were similar and highly s ignificant (po 0 .001). The
average variance extracted (AVE) value was 0.63. Considering the
Bollen Stine (B–S) p values of both models, they are rejected.
6.2. Simultaneous effect
We have also investigated the impact of self-efficacy on game
usage and purchase behavior simultaneously across the game types
and the original model. Given the Bollen–Stine (B–S) p values are
less than 0.5 all models should be rejected (see Tables 5 and 6).
7. Discussion
We investigated the impact of self-efficacy on game usage and
purchase behavior, both individually and simultaneously. It was
concluded in the individual effects analysis that self-efficacy
impacts game usage and purchase for only the Sports/Simula-
tion/Driving genre. Our results showed no simultaneous relation-
ship across all games types. Overall, we conclude that consumers
self-efficacy does impact usage and/or purchase behavior but
game type has a significant impact on this relationship. The game
types that showed no relationship between self-efficacy and
usage or purchase were:
1. All game genres combined.
2. Action/Adventure/Fighting.
3. Role Playing Game/Massively Multiplayer Online Role-Playing
Game/Strategy.
The positive relationship between self-efficacy and consumer
value evaluations and usage intentions is supported by Van
Beuningen et al. (2009) and Dash and Saji (2007). More recently,
Allan (2010, p. 36) concurred with our findings, stating that ‘‘self-
efficacy may not be the only determinant of one’s motivation to
play a video game, but it appears to be an important one.’’ It was
also argued that; (1) males had higher video game self-efficacy

and (2) usage frequency was related to video game self-efficacy.
In our study, Eighty-two percent of the respondents were male so
we suggest a similar effect to Allan’s (2010) gender correlations.
Given the game type, that is, Sports/Simulation/Driving showed a
significant model fit, we further contend subjectively that our
results may be influenced by gender. Also, it is not surprising that
Table 4
SEM path coefficients (step 1): individual effect.
Game group Indicator Direction Construct Standardized
loading
Un-standardized
loading
S.E. t-Value p Hypothesis Conclusion
Sports, Simulation, Driving Game usage (GU) ’ Self-efficacy GU .24 .13 .04 3.55 .00 H
1u
Supported
Game purchase (GP) Self-efficacy GP .20 .15 .04 3.43 .00 H
1p
Supported
RPG, MMORPG, Strategy Game usage (GU) Self-efficacy GU .24 .25 .06 4.58 .00 H
2u
Model rejected, B–S po 0.5
Game purchase (GP) Self-efficacy GP .15 .14 .05 2.70 .01 H
2p
Model rejected, B–S po 0.5
Action, Adventure, Fighting Game usage (GU) Self-efficacy GU À.01 À.01 .04 À.13 .90 H
3u
Model rejected, B–S po 0.5
Game purchase (GP) Self-efficacy GP À.03 À.02 .04 À .49 .63 H
3p

Model rejected, B–S po 0.5
Original model Game usage (GU) Self-efficacy GU .14 .08 .03 2.44 .02 H
4u
Model rejected, B–S po 0.5
Game purchase (GP) Self-efficacy GP .11 .07 .03 2.00 .05 H
4p
Model rejected, B–S po 0.5
SE—standard error.
R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 73
Table 6
SEM path coefficients (step 2): simultaneous effect.
Game group Indicator Direction Construct Standardized
loading
Un-standardized
loading
S.E. t-Value p Hypothesis Conclusion
Sports Simulation Driving Game usage (GU) ’ Self-Efficacy GU 0.24 0.25 0.07 3.70 0.00 H
5u
Model rejected, B–S po 0.5
Game purchase (GP) 0.20 $0.20 0.06 3.33 0.00 H
5p
Model rejected, B–S po 0.5
RPG MMORPG Strategy Game usage (GU) Self-Efficacy GP 0.24 0.24 0.05 4.79 0.00 H
6u
Model rejected, B–S po 0.5
Game Purchase (GP) 0.16 0.16 0.05 3.31 0.00 H
6p
Model rejected, B–S po 0.5
Action Adventure Fighting Game usage (GU) Self-Efficacy GP 0.003 0.003 0.06 0.05 0.96 H
7u

Model rejected, B–S po 0.5
Game purchase (GP) À0.01 À 0.01 0.05 À0.11 0.92 H
7p
Model rejected, B–S po 0.5
Original model Game usage (GU) Self-Efficacy GP 0.15 0.16 0.06 2.69 0.01 H
8u
Model rejected, B–S po 0.5
Game purchase (GP) 0.11 0.11 0.05 2.01 0.04 H
8p
Model rejected, B–S po 0.5
SE—standard error; the above models are rejected with, B–S po 0.5.
Table 5
SEM model fit (step 2): simultaneous effect.
Dependent variables Game group X
2
(DF) X
2
/DF ratio p CFI TLI GFI RMSEA SRMR B–S p
Game, usage Sports, Simulation, Driving 509.63 (24) 21.24 0.00 0.74 0.62 0.85 0.20 0.08 0.00
Game, purchase
Game, usage RPG, MMORPG, Strategy 65.89 (24) 2.75 0.00 0.97 0.96 0.97 0.06 0.05 0.01
Game, purchase
Game, usage Action, Adventure, Fighting 584.40 (24) 24.35 0.00 0.74 0.61 0.82 0.22 0.09 0.00
Game, Purchase
Game, usage Original 2961.16 (186) 15.92 0.00 0.47 0.40 0.63 0.17 0.13 0.00
Game, purchase
X
2
—chi-square; CFI—Comparative fit index; TLI—Tucker Lewis index; GFI-goodness-of-fit-index; RMSEA—root-mean-square error of approximation; SRMR—standardized root-mean-squared residual; B—S p—Bollen–Stine
bootstrap p;DF—degrees of freedom; the above models are rejected with, B–S po 0.5.

R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–7774
self-efficacy has an impact because this game type places empha-
sis on hand/eye co-ordination/reflexes in real world environments
(Myers, 1990). Consumers must have a belief in their capability to
accomplish tasks, play the game and achieve defined objectives
(Agarwal et al., 2000; Bandura, 1982). Such games also have a
high level of interactivity between consumer and game during the
consumption process.
What is interesting to explore is why the consumer’s process
of self-efficacy with Action/Adventure/Fighting games, which
place emphasis on simulations of futuristic and historical warfare
and/or violent activity did not affect purchase or usage. It would
appear that there is no match between the actual self and the
ideal self when they experience these games. This finding may
also indicate that players of this genre differ from players of other
genres. For example, gamers in the Action, Adventure and Fight-
ing genre may engage in gaming to a greater extent and thus,
have a smaller gap between their actual and ideal self in the
game. That is, they are highly proficient already and self-efficacy
is not a key driver of purchase. It may also suggest that such
games do not impact self-concept and their maybe a low level of
interactivity. This finding may conflict with the view that, for
example, violent computer games create violent consumers. This
view maybe tempered by other findings. For example Allan (2010,
p. 4) and others (Carnagey et al., 2007; Anderson et al., 2003)
argues that ‘‘violent video games have been shown to increase
aggression and physiological arousal of those who play themy
attributed to the desensitization effect.’’
A similar non-significant result was found for Role Playing
Game/Massively Multiplayer Online Role-Playing Game/Strategy

games, where self-efficacy was not related to usage or purchase.
As with Action/Adventure/Fighting games, this may be related to
the effect of multiple self’s. It is proposed that with the consumer
there may be some confusion about which character is supposed
to have game self-efficacy. Is it the consumer or the game player
(character within the game)? These types of games do not have
well defined goals. A lot more emphasis is placed on exploration
and experimentation. It may be more difficult for a consumer to
assess self-efficacy with this level of ambiguity.
One of the key managerial findings of the study relates to
marketing stimuli within a game. Our findings suggest that market-
ers and gamer developers must first consider the mediating effect of
self-efficacy on the effectiveness of their advertisement or product/
service placement within the gaming environment. Simply put, if the
consumer does not perceive they have the ca pabili ties to play the
game, their purchase and usage behavior will be affected. Practioners
also should c onsider t he impac t of g ame type. W hile o ur findi ngs are
only related to self-efficacy, we suggest t hat different game types
will reveal different resul ts when measuring the c onsumer s’ cogni-
tive response to game consumption and experience.
8. Limitations
Future research may w ish to ascertain the applicability of t he
results to other geographical areas. Also, it could be argued that
grouping the g ames together in terms of g enre types is a limitation
ofthedataanalysis.Webelievethatgroupingthegamesis
appropriate as they exhibit simila r characteristics and thus repre-
sent similar acts of consumption. Our study also differentiated game
types but did not examine the differences of online vs. offline
gaming. Would we expect a difference in the results? Further
studies may uncover differences but we are yet to uncover any

convincing evidence. We note that the sample is biased t owards
males. We could have controlled for this during data collection, but
this would have manipulated the randomly generated sample.
It could be argued that having a male biased sample may be more
representative of the market population f or computer games. US
market statistics from the Entertainment Software Association
showed that in 2008 sixty percent of all g ame players are men.
We acknowledge that a balance will evolve between the numbers of
male and female gamers over time a s more games are developed
with a specific gender orientation. Future research should also take
account of this change.
9. Future research
Future studies should now introduce specific marketing stimuli
within different types of games and measure the mediating effect of
self-efficacy on involvement, brand recall and awareness. There is
also the need to clarify the relationship between self-efficacy and
multiple self-concepts. Given that the act of playing a game is a
learning experience that is often concerned with the mastery of a
skill, Prensky’s (2001) research on consumer learning styles may be
integrated to classify gamers using alternative criteria. The focus
could be o n defining the consumer’s personality and learning style
to support the self-concept as key antecedents of game selection and
gaming behavior. Another extension to the research model would be
to focus on the three d imensions o f t he game (game-play, game-
structure and game-world). Such research would require these
dimensions to be expanded fur ther to identify the key elements
that constitute each of these dimensions. For example, game-world
could be expanded into elements such as the use of 3D graphics,
based on real-life/fantasy, explora tory world/restrictive world and
game-play could be expanded using e lements o f interactivity,

competition and teamwork. Given this conceptual model is new
within this research context it may be argued that there is a lack of
qualitative data to support its development and use. This would
consist of a phenomenological design utilizing grounded theory as
the primary research methodology of both new and experienced
players. Future research should extend the model into other
samples, differ ent from the New Z ealand context.
Acknowledgments
Manukau Institute of Technology, Chi Main Ong and Josephino
San Diego.
References
Aarseth, E., 2003. Playing Research: Methodological Approaches to Game Analysis.
In: Proceedings of Conference of Digital Arts and Cultures (DAC). Melbourne,
VIC.
Agarwal, R., Sambamurthy, V., Stair, R.M., 2000. Research report: the evolving
relationship between general and specific computer self-efficacy—an empiri-
cal assessment. Information Systems Research 11 (4), 418–430.
Allan, J.D., 2010. An Introduction to Video Game Self-Efficacy. Masters Thesis.
Faculty of California State University, Chico.
Anderson, C., Berkowitz, L., Donnerstein, E., Huesmann, L., Johnson, J., Linz, D.,
Wartella, E., 2003. The influence of media violence on youth. Psychological
Science in the Public Interest 4 (3), 81–110.
Anderson, C.A., Bushman, B.J., 2002. The effects of media violence on society.
Science 295, 2377–2379.
Apperley, T.H., 2006. Genre and game studies: toward a critical approach to video
game genres. Simulation and Gaming 37 (1), 6–23.
Bacon, D.R., Sauer, P.L., Young, M., 1995. Composite reliability in structural
equation modeling. Educational and Psychological Measurement 55, 394–406.
Bandura, A., 1977. Self-efficacy: toward a unifying theory of behavioural change.
Psychological Review 84, 191–215.

Bandura, A., 1982. Self-efficacy mechanism in human agency. American Psychol-
ogist 37 (2), 122–147.
Baumgartner, H., Homburg, C., 1996. Applications of structural equation modeling
in marketing and consumer research: a review. International Journal of
Research in Marketing 13, 139–161.
Bawa, K., Shoemaker, R.W., 1987. The coupon-prone consumer: some findings
based on purchase behavior across product classes. Journal of Marketing 51
(4), 99–110.
R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 75
Bentler, P.M., 1990. Comparative fit indexes in structural models. Psychological
Bulletin 107, 238–246.
Bollen, Kevin A., Stine, Robert A., 1992. Bootstrapping goodness-of-fit measures
in structural equation models. Sociological Methods and Research 21,
205–229.
Boyle, Raymond, Hibberd, Mathew, 2005. Review of research on the impact of
violent computer games on young people. Her Majesty’s Department of Trade
and Industry/Department of Culture, Media and Sport, London.
Browne, M.W., Cudeck, R., 1993. Alternative ways of assessing model fit. In: Bollen,
K.A., Long, J.S. (Eds.), Testing structural Equation Models, SAGE, Newbury Park,
CA, pp. 136–162.
Bristol, Terry, Mangleburg, Tamara F., 2005. Not telling the whole story: teen
deception in purchasing. Journal of the Academy of Marketing Science 33 (1),
79–95.
Bushnell, N., 1996. Relationships between fun and the computer business.
Communications of the ACM 39 (8), 31–37.
Carnagey, N., Anderson, C., Bushman, B., 2007. The effect of video game violence on
physiological desensitization to real-life violence. Journal of Experimental
Social Psychology 43 (3), 489–496.
Cauberghe, V., De Pelsmacker, P., 2010. Advergames: the impact of brand
prominence and game repetition on brand responses. Journal of Advertising

31 (1), 5–18.
Chaney, I.M., Lin, K., Chaney, J., 2004. The effect of billboards within the gaming
environment. The Journal of Interactive Advertising 5 (1), 37–45.
Chen, M.K., 2008. Rationalization and Cognitive Dissonance: Do Choices Affect or
Reflect Preferences? Cowles Foundation Discussion Paper No. 1669. Yale
University, New Haven, CT.
Chin, W.W., 1998. The partial least squares approach for structural equation
modeling. In: Macoulides, G.A. (Ed.), Modern Methods for Business Research,
Lawrence Erlbaum Associates, Mahwah, NJ, pp. 295–336.
Choi, D., Kim, J., 2004. Why people continue to play online games: in search of
critical design factors to increase customer loyalty to online contents.
Cyberpsychology and Behaviour 7 (1), 11–24.
Cunningham, E., 2008. A Practical Guide to Structural Equation Modeling Using
AmosStatsline, Melbourne, VIC.
Dash, S., Saji, K.B., 2007. The role of consumer self-efficacy and website social-
presence in customers’ adoption of B2C online shopping: an empirical study in
the Indian context. Journal of International Consumer Marketing 20 (2), 33–48.
Desai, K.K., Hoyer, W.D., 2000. Descriptive characteristics of memory-based
consideration sets: influence of usage occasion frequency and usage location
familiarity. Journal of Consumer Research 27 (3), 309–323.
Dill, Karen E., Dill, Jody C., 1998. Video game violence: a review of the empirical
literature. Aggression and Violent Behavior: A Review Journal 3, 407–428.
Eber, D.E., 2001. Towards computer game aesthetics—editorial. Digital Creativity
12 (3), 129–132.
Folkes, V.S., Martin, I.M., Gupta, K., 1993. When to say when: effects of supply on
usage. Journal of Consumer Research 20 (3), 467–477.
Fornell, C., Larcker, D., 1981. Structural equation models with unobservable variables
and measurement error. Journal of Marketing Research 18 (1), 39–50.
Fromme, J., 2003. Computer games as a part of children’s culture. International
Journal of Computer Game Research 3 (1) (available at: accessed May 2010)

/ />Gottschalk, S., 1995. Videology: video-games as postmodern sites/sights of
ideological reproduction. Symbolic Interaction 18 (1), 1–18.
Guth, R.A., 2003. Choosing sides: game giant links with Sony, snubbing
Microsoft—electronic arts show its clout and wariness of allowing Xbox
to dominate market—John Madden on play-by-play. Wall Street Journal
(New York, 12 May).
Hair, Joseph F., Black, William, Babin, Barry, Anderson, Rolph E., 2009. Multivariate
Data Analysis, seventh edition. Prentice Hall, Upper Saddle River, NJ.
H
¨
ock, Michael, Ringle, Christian M., 2006. Strategic Networks in the Software
Industry: An Empirical Analysis of the Value Continuum. Paper Presented at
the IFSAM Viiith World Congress. 2006, Berlin. Available at: /http://www.
Ibl-Unihh.De/IFSAM06.PdfS (accessed 11.06.10.).
Holbrook, M.B., Hirschman, E.C., 1982. The experiential aspects of consumption:
consumer fantasies, feelings, and fun. Journal of Consumer Research 9 (2),
132–140.
Hu, L T., Bentler, P.M., 1995. Evaluating model fit. In: Hoyle, R.H. (Ed.), Structural
Equation Modeling: Concepts, Issues and Applications, SAGE, Thousand Oaks,
CA, pp. 76–99.
Hui, S.K., Bradlow, E.T., Fader, P.S., 2009. Testing behavioral hypotheses using an
integrated model of grocery store shopping path and purchase behavior.
Journal of Consumer Research 36 (3), 478–493.
INZ, 2010. National Research Prepared by Bond University for the Interactive
Games and Entertainment Association.
Igbaria, M., Iivari, J., 1995. The effects of self-efficacy on computer usage. Omega.
International Journal of Management Science 23 (6), 587–605.
Jessen, Carsten, 1999. Computer Games and Play Culture—An Outline of an
Interpretative Framework. Available at: / />games.htmlS.
Jin, S.A., Bolebruch, J., 2009. Avatar-based advertising in second life: the role of

presence and attractiveness of virtual spokespersons. The Journal of Inter-
active Advertising 10 (1), 51–60.
Juul, J., 2001. Games Telling stories?—a brief note on games and narratives.
International Journal of Computer Game Research. Available at /http://www.
gamestudies.org/0101/Juul-Gts/SS (last accessed July 2010).
Kaltcheva, Velitchka D., Patino, Anthony D., Chebat, Jean-Charles, 2011. The impact
of retail environment extraordinariness on customer self-concept. Journal of
Business Research 64 (6), 551–557.
Kandemir, Destan, Attila, Yaprak, Tamer Cavusgil, S., 2006. Alliance orientation:
conceptualization, measurement, and impact on market performance. Journal
of the Academy of Marketing Science 34 (3), 324–341.
Khan, T.L.Tran, 2002. U.S. game industry posts record sales. Wall Street Journal 7
(February).
Kline, R.B., 1998. Principles and Practice of Structural Equation ModelingGuildford
Press, New York.
Liu, Y., 2007. The long-term impact of loyalty programs on consumer purchase
behavior and loyalty. Journal of Marketing 71 (4), 19–35.
Mackay, T., Ewing, M., Newton, F., Windisch, L., 2009. The effect of product
placement in computer games on brand attitude and recall. International
Journal of Advertising 28 (3), 423–438.
Manninen, T., 2003. Conceptual, communicative and pragmatic aspects of inter-
action forms—rich interaction model for collaborative virtual environments.
In: Proceedings of Computer Animation and Social Agents (CASA) Conference.
IEEE Computer Society Press, pp. 168–174.
Mau, G., Silberer, G., Constien, C., 2008. Communicating brands playfully: effects of
in-game advertising for familiar and unfamiliar brands. International Journal
of Advertising 27 (5), 827–851.
Molesworth, Mike, 2006. Real brands inimaginary worlds: investigating players’
experiences of brand placement indigital games. Journal of Consumer Beha-
viour 5 (4), 355–366.

Mortensen, T., 2002. Playing with players. Potential methodologies for muds.
International Journal of Computer Game Research 2 (1) (available at: accessed
July 2010)/ />Myers, D., 1990. Computer game genres. Play and Culture 3, 286–301.
Nelson, M.R., Keum, H., Yaros, R.A., 2004. Advertainment or adcreep? Game
players’ attitudes toward advertising and product placements in computer
games. Journal of Interactive Advertising 5 (1), 3–21.
Newman, J., 2002a. In search of the game player—the lives of Mario. New Media
and Society 4 (3), 405–422.
Newman, J., 2002b. The myth of the ergodic Game. International Journal of
Computer Game Research 2 (1) (available at: accessed July 2010)/http://
www.gamestudies.org/0102/Newman/S.
Newman, J., 2004. GamesTaylor Francis Group, Routledge.
Nicovich, S.G., 2005. The effect of involvement on ad judgment in a video game
environment—the mediating role of presence. Journal of Interactive Advertis-
ing 6 (1), 29–39.
Nunnally, J.C., 1978. Psychometric Theory, second ed. McGraw-Hill, New York.
Pampel, Fred C., 2000. Logistic Regression: A Primer. Sage Quantitative Applica-
tions in the Social Sciences Series #132Sage Publications, Thousand Oaks, CA.
Prensky, M., 2001. Fun, Play and Games: What Makes Games Engaging. Digital
Game-Based LearningMcgraw-Hill, NY.
Prugsamatz, S., Lowe, B., Alpert, F., 2010. Modelling consumer entertainment
software choice: an exploratory examination of key attributes, and differences
by gamer segment. Journal of Consumer Behaviour 9 (5), 381–392.
Ryan, M., 2001. Beyond myth and metaphor*—the case of narrative in digital
media. International Journal of Computer Game Research 1 (1) (available at:
accessed July 2010)/ />Schneider, L.P., Cornwell, T.B., 2005. Cashing in on crashes via brand place-
ment in computer games. International Journal of Advertising 24 (3),
321–343.
Shimp, T.A., Kavas, A., 1984. The theory of reasoned action applied to coupon
usage. Journal of Consumer Research 11 (3), 795–809.

Sismeiro, C., Bucklin, R.E., 2004. Modeling purchase behavior at an E-commerce
web site: a task-completion approach. Journal of Marketing Research 41 (3),
306–323.
Smith, S.M., 2002a. The role of social cognitive career theory in information
technology based academic performance. Information Technology, Learning,
and Performance Journal 20 (2), 1–10.
Smith, S.M., 2002b. Using the social cognitive model to explain vocational interest
in information technology. Information Technology, Learning, and Perfor-
mance Journal 20 (1), 1–9.
Sriram, S., Chintagunta, P.K., Agarwal, M.K., 2010. Investigating consumer purchase
behavior in related technology product categories. Marketing Science 29 (2),
291–314.
Torkzadeh, G., Koufteros, X., 1994. Factorial validation of a computer self-efficacy
scale and the impact of computer training. Education and Psychological
Measurement 54 (3), 813–821.
UKIE, 2011. The Association for UK Interactive Entertainment, he.
infoi.
Van Beuningen, J., de Ruyter, K., Wetzelfs, M., Streukens., S., 2009. Customer self-
efficacy in technology- based self-service. Journal of Service Research,
407–428.
Venkatesh, Viswanath, Morris, Michael G., Davis, Gordon B., Davis, Fred D., 2003.
User acceptance of information technology: toward a unified view. MIS
Quarterly 27 (3), 425–478.
Vorderer, P., 2003. Explaining the enjoyment of playing video games: the role of
competition. In: Proceedings of the Second International Conference on
Entertainment Computing (May).
Walther, B.K., 2003. Playing and gaming. Reflections and classifications. Interna-
tional Journal of Computer Game Research 3 (1) (available at: accessed May
2010)/ />R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–7776
Winkler, T., Buckner, K., 2006. Receptiveness of gamers to embedded brand

messages in advergames: attitudes towards product placement. Journal of
Interactive Advertising 7 (1), 24–32.
Woods, S., 2003. An Investigation into the Cultural Significance of Computer
Mediated Games. Candidacy Proposal. Doctor of Philosophy (Media and
Information). Curtin University of Technology, Perth, WA.
Wothke, Werner, 1993. Non positive definite matrices in structural modeling. In:
Bollen, K.A., Long, J.S. (Eds.), Testing Structural Equation Models, Sage Pub-
lication, Newbury Park, CA.
Yang, M., Roskos-Ewoldsen, D.R., Dinu, L., Arpan, L.M., 2006. The effectiveness of
‘‘in-game’’ advertising: comparing college students’ explicit and implicit
memory for brand names. Journal of Advertising 35 (4), 143–152.
R. Davis, B. Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 77