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 Pl
Trang 1Modeling the effect of self-efficacy on game usage and purchase behavior
Robert Davisa,n
, Bodo Langb,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
a r t i c l e i n f o
Available online 16 December 2011
Keywords:
Self-efficacy
Usage
Purchase
Computer games
Confirmatory factors analysis
Structural equation modeling
a b s t r a c t
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 consumers
enter-tainment experience has been largely attributable to increased
technology related self-efficacy (Allan, 2010) Usage and purchase
has grown because the consumer perceives that they have the
capability to be interactive with a game and therefore, other stimuli
within the game (e.g., Advergames)
As a consequence, marketing practioners and researchers have become more interested in the potential of this medium for 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 co-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
showing the effects on purchase intentions Also,Cauberghe and
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 withNicovich (2005)who have measured the relationship between consumer involvement on the advertis-ing effect LikeCauberghe 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.,
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 atSciVerse 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.
n
Corresponding author Tel.: þ649 815 4321.
E-mail addresses: rdavis@unitec.ac.nz (R Davis),
b.lang@auckland.ac.nz (B Lang).
1 Tel.: þ64 9 923 7162.
Trang 2Recently, other researchers in an attempt to extend our
under-standing of the consumer response to marketing in the game
environment have started to explore avatar-based advertising (Jin
avatar-based interactive advertising on product involvement and 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 than 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
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 ofMyers (1990), namely: (1) all games
representing our original model and then the alternative
compet-ing models, (2) Sports/Simulation/Drivcompet-ing, 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) Action/Adventure/Fighting, which places emphasis on
simula-tions of futuristic and historical warfare and/or violent activity
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 play,
whereas the simulation genre can be compared to
non-entertain-ment computer simulations The action genre is implicitly connected
to cinema through its deployment of the terminology 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
different usage Purchase behavior has also played a key role in
marketing research as a dependent variable (Sriram et al., 2010;
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 First, we present the conceptual model which begins with a definition of the concept of the game leading to our hypotheses The 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 artifacts (Prensky,
2001) and technological drivers (Woods, 2003; Bushnell, 1996;
conceptual model that defines the game from the consumers’ experience (Newman, 2002a, 2004; Manninen, 2003; Aarseth,
2003) of the consumption or play of the game (Chen, 2008;
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 and reality
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,
the game may be driven by a hedonic need This enforces the concept brought forward by Mortensen (2002) and Fromme
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 to transform their perceptions of self-efficacy; the belief they hold in their capability
to accomplish a task, which, in this respect refers to their ability to play the game (Agarwal et al., 2000;Bandura, 1982) In essence it changes their fundamental belief that they are capable through game play to achieve the desired goals and outcomes
This argument is supported by Allan (2010), Bandura (1977,
mastery experiences (performance accomplishments), vicarious learning and experience, social persuasion and affective states (emotional arousal).Allan (2010, p 36) posits; ‘‘video games can produce both positive and negative emotional arousal 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, video games are generally performance accomplishment tasks They provide a player with a constant stream of input This input supplies the player with ongoing mastery experience to build video game self-efficacy.’’ These findings are consistent with Newman’s (2002a,b, 2004)
continuum of engagement andVorderer (2003) andEber (2001), who define a game as a ‘form of mastery’ (i.e the acquisition and perfection of a skill) Consequently, self-efficacy has primarily been
Trang 3operationalized in the form of prior experience to represent both
mastery and vicarious learning experiences (Igbaria and Iivari, 1995)
and is considered to be dynamic in nature since the consumer is
expected to become more capable in performing a task 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
Hu1–4 Self-efficacy has an individual effect on game purchase
mea-sured across four game model types; (1) original model, (2) Sports,
Simulation and Driving; (3) RPG, MMORPG and Strategy and (4) Action,
Adventure and Fighting
Hp1–4 Self-efficacy has an individual effect on game usage 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
H5–8 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 to 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), such as an Xbox, Playstation or Wii that you
pur-chased?’’ If 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 the
respon-dents (77%) were 25 years and under About 66% of the responrespon-dents
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 demographics are
gen-erally consistent with the recent research byINZ (2010) on the
New Zealand gaming consumer (N¼1958)
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
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
are derived fromMyers (1990)and the retail categories commonly used in consumer purchases (http://store.steampowered.com/)
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 measure the individual and simultaneous 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 structural model is constructed 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 when applicable to reflect the hypothesized directions
Table 1 Sample characteristics (n¼ 493).
Variable Categories Percent of sample
Pacific Islander 6.5
Marital status Single 77.3
Living with partner 13.8
Divorced/separated 1.4
Self-employed 4.9
Student/part-time 10.8 Annual Income o10,000 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
Trang 4Table 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)
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
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’.
2 How often do you buy the following game types?
Massively Multiplayer Online Role Playing Game (MMORPG)
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?
Trang 56 How long have you been playing games? PU7
16–20 years More than 20 years
7 How often do you play the following game types?
Massively Multiplayer Online Role Playing Game (MMORPG)
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 1 2 3 4 5 6 7 Expert 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
15 I know more about the game than most other people who play this game 1 2 3 4 5 6 7 SE5
16 I can play this game if
Trang 6Subsequently, 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 byWothke
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 estimates in a way that minimizes the 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 exclusion of items until
the goodness-of-fit was achieved Following Baumgartner and
model fit: goodness-of-fit indices, chi-squared (X2), the comparative
fit index (CFI) and normalized fit index (NFI) For CFI and NFI values
close to 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 toBentler (1990), values below 0.05
indicate close fit and values up to 0.08 are reasonable Finally, the
standardized root mean squared residual (SRMR) as described byHu
correlations to within an average error.Bentler (1990)argues that a
model is regarded as having an acceptable fit if the SRMR is less than
0.10, while a SRMR of 0 indicates a perfect fit (Browne and Cudeck,
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
(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
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 (X2)/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 the alternative models focus on each game genre, namely (2) Sports, 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 inTables 3 and 4 The final model met suggestions from the literature regarding the minimum number of items attached to a construct (Hair et al.,
For the original model: the game usage results indicate inade-quate model fit (GFI¼0.88, CFI¼ 0.75, TLI ¼0.69, RMSEA¼ 0.12, SRMR¼0.09, X2/DF¼7.58 and B–S p¼ 0.00) Similarly, the self-efficacy results for game purchase were inadequate (GFI¼ 0.86, CFI¼0.81, TLI ¼0.76, RMSEA ¼0.12, SRMR¼0.08, X2/DF¼8.31 and B–S p¼0.00) 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 (po0.001) The average variance extracted (AVE) value was 0.63 For the Sports, Simulation and Driving Model: The game usage results suggest adequate model fit (GFI¼0.99, CFI¼0.99, TLI¼0.98, RMSEA¼0.04, SRMR¼0.03, X2/DF¼1.83 and B–S p¼0.45) Similarly the self-efficacy results for game purchase were adequate (GFI¼ 0.99, CFI¼0.99, TLI¼0.99, RMSEA¼0.03, SRMR¼ 0.02, X2/DF¼1.36 and B–S p¼0.79) 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 (po0.001) The average variance extracted (AVE) value was 0.63 With these results, both models (game usage and game purchase) in the Sports, Simulation and Driving genre are accepted The results for Sports, Simulation and Driving game classification reveal that a significant positive relationship for the path between self-efficacy and game
Table 3
SEM model fit (step 1): individual effect.
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, 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;
Trang 7SRMR—standar-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 (GFI¼0.97, CFI¼0.95, TLI¼0.91, RMSEA¼0.09, SRMR¼0.06, X2/DF¼5.44 and B–S p¼0.00) Similarly the self-efficacy results for game purchase were adequate (GFI¼0.99, CFI¼0.98, TLI¼0.97, RMSEA¼0.06, SRMR¼0.04, X2/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 highly 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 was 0.63 Considering the Bollen– Stine (B–S) p values of these models, the game usage and purchase models are rejected We note that there is a significant relationship between self-efficacy and game purchase
For the Action, Adventure and Fighting Model: The game usage results an adequate model fit (GFI¼0.99, CFI¼0.98, TLI¼0.97, RMSEA¼0.06, SRMR¼0.04, X2/DF¼2.89 and B–S p¼0.06) Similarly the self-efficacy results for game purchase were adequate (GFI¼ 0.98, CFI¼0.98, TLI¼0.96, RMSEA¼0.07, SRMR¼0.04, X2/DF¼3.50 and B–S p¼0.02) 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 (po0.001) 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 (seeTables 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
‘‘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 toAllan’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
Standardized loading
Un-standardized loading
H1u
H1p
H2u
H2p
H3u
H3p
H4u
H4p
Trang 8Table 6
SEM path coefficients (step 2): simultaneous effect.
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 po0.5
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 po0.5
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 po0.5
Original model Game usage (GU) Self-Efficacy GP 0.15 0.16 0.06 2.69 0.01 H 8u Model rejected, B–S po0.5
SE—standard error; the above models are rejected with, B–S po0.5.
Table 5
SEM model fit (step 2): simultaneous effect.
Dependent variables Game group X 2
Game, purchase
Game, purchase
Game, Purchase
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 po0.5.
Trang 9self-efficacy has an impact because this game type places
empha-sis on hand/eye co-ordination/reflexes in real world environments
accomplish tasks, play the game and achieve defined objectives
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 gamFight-ing 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 exampleAllan (2010,
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 developmarket-ers 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 capabilities to play the
game, their purchase and usage behavior will be affected Practioners
also should consider the impact of game type While our findings are
only related to self-efficacy, we suggest that different game types
will reveal different results when measuring the consumers’
cogni-tive response to game consumption and experience
8 Limitations
Future research may wish to ascertain the applicability of the
results to other geographical areas Also, it could be argued that
grouping the games together in terms of genre types is a limitation
of the data analysis We believe that grouping the games is
appropriate as they exhibit similar 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 towards
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 for computer games US market statistics from the Entertainment Software Association showed that in 2008 sixty percent of all game players are men
We acknowledge that a balance will evolve between the numbers of male and female gamers over time as 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 on 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 dimensions of the game (play, game-structure and game-world) Such research would require these dimensions to be expanded further 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, exploratory world/restrictive world and game-play could be expanded using elements of 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, different from the New Zealand context
Acknowledgments Manukau Institute of Technology, Chi Main Ong and Josephino San Diego
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