Journal of Retailing and Consumer Services 19 (2012) 67–77 Contents lists available at SciVerse ScienceDirect Journal of Retailing and Consumer Services journal homepage: www.elsevier.com/locate/jretconser Modeling the effect of self-efficacy on game usage and purchase behavior Robert Davis a,n, Bodo Lang b,1 a b Faculty of Creative Industries and Business, Unitec Institute of Technology, Department of Management and Marketing, Private Bag 92025, Auckland, New Zealand 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 abstract Available online 16 December 2011 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 game types; original model (all games) and alternative models, Sports/Simulation/Driving, Role Playing Game/Massively Multiplayer Online RolePlaying 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 Keywords: Self-efficacy Usage Purchase Computer games Confirmatory factors analysis Structural equation modeling 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 $10.5 billion in 2009, (2) sixty-seven percent of American households play computer or video games, (3) the average game player is 34 years old and has been playing games for 12 years Overall, sales of game hardware, software and accessories have eclipsed those of the US box-office, cementing gaming as a dominant force of technological consumption (Khan, 2002; Guth, 2003) Europe has also become a significant industry and market The UK is the third largest market globally with total sales in 2004 of entertainment and leisure software of £1.34b (Boyle and Hibberd, 2005) The interactive entertainment industry in the UK is set to grow by 7.5% between 2009 and 2012 (UKIE, 2011) There are many factors that have fueled this change in the consumers consumption behavior but it is argued in this research that the growth in the importance of games in a consumers entertainment experience has been largely attributable to increased technology related self-efficacy (Allan, 2010) Usage and purchase has grown because the consumer perceives that they have the capability to be interactive with a game and therefore, other stimuli within the game (e.g., Advergames) n Corresponding author Tel.: ỵ649 815 4321 E-mail addresses: rdavis@unitec.ac.nz (R Davis), b.lang@auckland.ac.nz (B Lang) Tel.: ỵ64 923 7162 0969-6989/$ - see front matter & 2011 Elsevier Ltd All rights reserved doi:10.1016/j.jretconser.2011.09.002 As a consequence, marketing practioners and researchers have become more interested in the potential of this medium for marketing 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 cocreation 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 (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 acknowledge 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 advertising 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 68 R Davis, B Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 Recently, other researchers in an attempt to extend our understanding of the consumer response to marketing in the game environment have started to explore avatar-based advertising (Jin and Bolebruch, 2009) In this study, the overall effects of avatarbased 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 understanding 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 stimulus If consumers 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 game types, grouped according to the conceptualization of Myers (1990), namely: (1) all games representing our original model and then the alternative competing 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 RolePlaying Game (MMORPG)/Strategy, which places emphasis on characters that gain experience and power through encounters and (4) Action/Adventure/Fighting, which places emphasis on simulations 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-entertainment 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 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 noncoupon-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 selfefficacy 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 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; Aarseth, 2003) In the context of this research we draw from a 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; Holbrook and Hirschman, 1982) Playing a game involves instantaneous feedback in visual, auditory and kinesthetic forms This feedback 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 (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 selfconcept (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, 1982) and Smith (2002a,b) who defines four sources of self-efficacy: 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 and Vorderer (2003) and Eber (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 R Davis, B Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 operationalized 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 experience 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: Hu1–4 Self-efficacy has an individual effect on game 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 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 game types so the analysis of the path coefficients and SEM model fit will proceed to test hypothesized relationships between self-efficacy and; (1) game purchase and (2) game usage Therefore, models are also compared Table Sample characteristics (n¼ 493) Variable Categories Percent of sample Gender Male Female 82.2 17.8 Age r10 11–15 16–20 21–25 Z26 0.4 4.3 40.2 37.1 18.1 Ethnicity NZ Pakeha Maori Pacific Islander Asian European Others 29.4 7.5 6.5 38.5 9.9 8.1 Marital status Single Widowed Living with partner Married Divorced/separated 77.3 0.2 13.8 7.3 1.4 Education Non-degree Degree 66.1 33.9 Employment Student Full time Self-employed Unemployed Homemaker Part-time Student/part-time 47.7 25.4 4.9 4.3 0.4 6.7 10.8 Annual Income o10,000 10,000–20,000 20,001–30,000 30,001–40,000 40,001–50,000 50,001–60,000 60,001–80,000 Z80,000 47.5 16.6 7.5 11.4 9.5 3.2 2.4 1.8 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 respondents 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 purchased?’’ If the answer was ‘‘Yes’’, they were asked (2) What game did you play most often in the last week? Question established that the respondent was a regular game player of games they had actually purchased and, Question 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 respondents (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 demographics are generally consistent with the recent research by INZ (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 Torkzadeh and Koufteros (1994) computer self-efficacy (CSE) scale based upon Bandura’s (1977, 1982) guidelines on self- 69 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., 2003) Game categories for usage and purchase are derived from Myers (1990) and the retail categories commonly used in consumer purchases (http://store.steampowered.com/) Analysis The analysis tested the proposed conceptual model with confirmatory factor analysis (CFA) and structural equation modeling (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 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 70 Table Questionnaire items 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.) Xbox PS Internet Name of game Very rarely 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 CODE Play usage behavior: thinking about the types of How often you play games on each of the following platforms? games you play please answer the following questions by providing a number between PC/MAC Xbox and where means ‘very rarely’ and means ‘very often’ Playstation Connected to the Internet Wii In a typical week, how many hours you play games? Very Rarely Very Often PB1 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 PB2 PB3 PB4 PB5 PB6 PB7 PB8 1 1 2 2 3 3 4 4 5 5 6 6 7 7 PB9 PB10 PB11 PB12 PB13 game 6–10 games 21–30 games Purchase behavior: thinking about the types of games you buy please answer the following questions by providing a number between and where means ‘very rarely’ and means ‘very often’ How often you buy games? How often you buy the following game types? Action Adventure Driving Fighting Children Educational Massively Multiplayer Online Role Playing Game (MMORPG) Role Playing Game (RPG) Simulation Strategy Sports How many games you own in total? Very Often games 11–15 games 31–40 games 3–5 games 16–20 games More than 40 Very Rarely Very Often 1 1 2 2 Less than h 3–5 h 3 3 4 4 5 5 6 6 7 7 6–10 h PU1 PU2 PU3 PU4 PU5 PU6 R Davis, B Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 PC/Mac 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) 11–20 h 21–30 h More than 30 h How long have you been playing games? PU7 1–2 years 11–15 years Very Often 4 4 4 5 5 5 6 6 6 7 7 7 PU8 PU9 PU10 PU11 PU12 PU13 PU14 4 4 5 5 6 6 7 7 PU15 PU16 PU17 PU18 PU19 PU20 Expert SK1 Thinking about game from Q9, please answer the following questions by providing a number between and where means ‘strongly disagree’ and means ‘strongly agree’ Strongly disagree Self-efficacy 1 1 2 2 3 3 4 4 5 5 6 6 7 7 SE1 SE2 SE3 SE4 SE5 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 SE6 SE7 SE8 SE9 SE10 SE11 SE12 SE13 SE14 SE15 SE16 SE17 11 I expect to become proficient in playing this game 12 I feel comfortable playing this game 13 I am skilled at playing this game 14 I know how to what I want to with this game 15 I know more about the game than most other people who play this game 16 I can play this game if I can call someone for help if I get stuck I have the manual for reference I have a lot of time to practice I have the built-in help assistance I have never played a similar game like it before I have never played it before I have not seen anyone play it before I have played a similar game like this one before I have seen someone else play it before I play Someone else has helped me to get started Someone showed me how to play it first There was no one to help me to show me what to Strongly agree CODE R Davis, B Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 Skill level Less than months 7–11 months 3–5 years 6–10 years 16–20 years More than 20 years How often you play the following game Very Rarely types? Action Adventure Driving Fighting Children Educational Massively Multiplayer Online Role Playing Game (MMORPG) Role Playing Game (RPG) Simulation Strategy Sports If not clear from Q7, ask and circle: which one of these game types you play most? Which one game you play most from that group (Q8)? Write down the name 10 When thinking about insert name of game from Q9 how would you rate your skill level? Beginner 71 72 R Davis, B Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 Subsequently, the data was subjected to multivariate normality 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 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 covariance residuals and modification indices and exclusion of items until the goodness-of-fit was achieved Following Baumgartner and Homburg (1996), conventional measures were used to assess the 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 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 residual (SRMR) as described by Hu and Bentler (1995) computes how much the model explains the 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 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 or preferably less than (Bentler, 1990) The GFI index is above the threshold of 0.90 (Hair et al., 2009), and CFI is close to (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 construct 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 ă value is above the threshold value of 0.50 (Chin, 1998; Hock and Ringle, 2006; Fornell and Larcker, 1981) 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 separately The second step measured the simultaneous effect of selfefficacy 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-offit 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 in Tables and 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 inadequate 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 selfefficacy 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 SEM model fit (step 1): individual effect Dependent variable Game group X2 (DF) Game, Game, Game, Game, Game, Game, Game, Game, Original Original Sports, Simulation, Driving Sports, Simulation, Driving RPG, MMORPG, Strategy RPG, MMORPG, Strategy Action, Adventure, Fighting Action, Adventure, Fighting 401.77 440.44 194.67 10.90 43.51 21.85 23.12 27.97 usage purchase usage purchase usage purchase usage purchase X2/DF ratio (53) (53) (8) (8) (8) (8) (8) (8) p CFI TLI GFI RMSEA SRMR B-S p 7.58 8.31 1.83 1.36 5.44 2.73 2.89 3.50 0.00 0.00 0.07 0.21 0.00 0.01 0.00 0.00 0.75 0.81 0.99 0.99 0.95 0.98 0.98 0.98 0.69 0.76 0.98 0.99 0.91 0.97 0.97 0.96 0.88 0.86 0.99 0.99 0.97 0.99 0.99 0.98 0.12 0.12 0.04 0.03 0.09 0.06 0.06 0.07 0.09 0.08 0.03 0.02 0.06 0.04 0.04 0.04 0.00 0.00 0.45 0.79 0.00 0.09 0.06 0.02 X2—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 R Davis, B Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 00 00 00 01 90 63 02 05 H1u H1p H2u H2p H3u H3p H4u H4p Supported Supported Model rejected, Model rejected, Model rejected, Model rejected, Model rejected, Model rejected, B–S B–S B–S B–S B–S B–S po 0.5 po 0.5 po 0.5 po 0.5 po 0.5 po 0.5 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 .04 04 06 05 04 04 03 03 13 15 25 14 À 01 À 02 08 07 24 20 24 15 À 01 À 03 14 11 3.55 3.43 4.58 2.70 À 13 À 49 2.44 2.00 Conclusion Hypothesis p t-Value Un-standardized S.E loading Standardized loading 73 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 and 6) Discussion Self-efficacy Self-efficacy Self-efficacy Self-efficacy Self-efficacy Self-efficacy Self-efficacy Self-efficacy ’ All game genres combined Action/Adventure/Fighting Role Playing Game/Massively Multiplayer Online Role-Playing Game/Strategy SE—standard error Sports, Simulation, Driving Game Game RPG, MMORPG, Strategy Game Game Action, Adventure, Fighting Game Game Original model Game Game usage (GU) purchase (GP) usage (GU) purchase (GP) usage (GU) purchase (GP) usage (GU) purchase (GP) Construct Indicator Game group Table SEM path coefficients (step 1): individual effect Direction GU GP GU GP GU GP GU GP 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/Simulation/Driving genre Our results showed no simultaneous relationship 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: 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 ‘‘selfefficacy 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 74 Table SEM model fit (step 2): simultaneous effect X2 (DF) Sports, Simulation, Driving usage purchase usage purchase usage Purchase usage purchase RPG, MMORPG, Strategy Action, Adventure, Fighting Original CFI TLI GFI RMSEA SRMR B–S p 509.63 (24) 21.24 0.00 0.74 0.62 0.85 0.20 0.08 0.00 2.75 0.00 0.97 0.96 0.97 0.06 0.05 0.01 584.40 (24) 24.35 0.00 0.74 0.61 0.82 0.22 0.09 0.00 15.92 0.00 0.47 0.40 0.63 0.17 0.13 0.00 X2—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 p o0.5 Table SEM path coefficients (step 2): simultaneous effect Game group Indicator Direction Construct Standardized loading Un-standardized S.E loading t-Value p Hypothesis Conclusion Sports Simulation Driving Game Game Game Game Game Game Game Game ’ Self-Efficacy GU 0.24 0.20 0.24 0.16 0.003 À 0.01 0.15 0.11 0.25 $0.20 0.24 0.16 0.003 À 0.01 0.16 0.11 3.70 3.33 4.79 3.31 0.05 À 0.11 2.69 2.01 0.00 0.00 0.00 0.00 0.96 0.92 0.01 0.04 H5u H5p H6u H6p H7u H7p H8u H8p Model Model Model Model Model Model Model Model RPG MMORPG Strategy Action Adventure Fighting Original model usage (GU) purchase (GP) usage (GU) Purchase (GP) usage (GU) purchase (GP) usage (GU) purchase (GP) SE—standard error; the above models are rejected with, B–S po 0.5 Self-Efficacy GP Self-Efficacy GP Self-Efficacy GP 0.07 0.06 0.05 0.05 0.06 0.05 0.06 0.05 rejected, rejected, rejected, rejected, rejected, rejected, rejected, rejected, B–S B–S B–S B–S B–S B–S B–S B–S po 0.5 po 0.5 po 0.5 po 0.5 po 0.5 po 0.5 po 0.5 po 0.5 R Davis, B Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 Game, Game, Game, Game, Game, Game, Game, Game, p 2961.16 (186) Game group X2/DF ratio 65.89 (24) Dependent variables R Davis, B Lang / Journal of Retailing and Consumer Services 19 (2012) 67–77 self-efficacy has an impact because this game type places emphasis 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 Fighting 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 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 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 marketers 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 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’ cognitive response to game consumption and experience 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 represent 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 75 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 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 (game-play, gamestructure 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 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Lang / Journal of Retailing and Consumer Services 19 (2 012 ) 67–77 Recently, other researchers in an attempt to extend our understanding of the consumer response to marketing in the game environment