Computers in Human Behavior

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1 Computers in Human Behavior 27 (2011) Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China Yong Liu a,,1, Hongxiu Li b,1 a IAMSR, TUCS, Åbo Akademi University, Turku, Finland b Turku Center for Computer Science, Information Systems Science Institute, Turku School of Economics, University of Turku, Turku, Finland article info abstract Article history: Available online 10 December 2010 Keywords: Use context Technology acceptance model usefulness Cognitive concentration Mobile game Mobile services Unlike traditional technologies, the use of mobile technology is exposed to shifting use contexts. Use context has frequently been described as an important factor influencing the adoption of mobile innovations. However, empirical evidence about the impact of use context is limited. This paper investigated the effect of use context on the formation of users perceptions of mobile hedonic services by using mobile gaming as an example. Through the employment of structural equation modelling technology, an adoption model of mobile gaming is proposed and assessed based on results from 267 questionnaires. The results show that use context is the strongest predictor of mobile game adoption. It directly or indirectly affects all different perceptions of mobile gaming in significant ways, including perceived ease of use, perceived usefulness, perceived enjoyment, cognitive concentration, attitude and behavioral intention. Additionally, perceived usefulness, perceived enjoyment and cognitive concentration all have a positive influence on the attitudinal variables of mobile game acceptance. We concluded that the formation of people s perceptions about mobile gaming is conditional and based on the special consideration of certain use contexts. Both theoretical and practical implications are discussed. Ó 2010 Elsevier Ltd. All rights reserved. 1. Introduction The advance of the third generation (3G) network and handheld technologies in recent years has resulted in a number of new mobile innovations being pushed onto the market. Of these innovations, mobile gaming is one of the most promising and profitable services. It is now experiencing a rapid period of worldwide growth. According to the prediction of Garnter, Inc. (2010), the worldwide mobile game end-user revenue will surpass $5.6 billion in 2010 with a 19 percent increase from The market is forecast to grow steadily through to 2014, while the figure is estimated to reach $11.4 billion (Garnter, 2010). Note that Asian users, in particular Chinese users, seem to have an even stronger preference for hedonic mobile services, such as games, music and video (e.g. China Internet Network Information Center (CNNIC), 2010; Mobext Insight, 2009). According to a recent report released by China Internet Network Information Center (CNNIC) (2010), hedonic services, in particular mobile gaming, mobile reading and mobile video, are the favorite services among Chinese mobile Internet users. Consequently, the expected growth rate of the mobile games industry in China is much higher than on average. China s mobile game market arrived at 1.8 billion in 2009 and the figure is predicted to hit 6.5 Corresponding author. Tel.: addresses: Yong.liu@abo.fi (Y. Liu), Hongxiu.li@tse.fi (H. Li). 1 Both the authors have contributed equally to the paper. billion in 2012, according to the prediction of iresearch Consulting Group (2010a). In a recent survey with over 32,000 responses, over 60 percent of Chinese mobile game users said they played mobile games for more than one hour per day (iresearch Consulting Group, 2010b). Note that the speeds of mobile industry development are of great difference in different countries. Researchers have seen a number of mobile services thriving in some countries, but dead in some other countries, such as mobile TV. In many European countries, the mobile industry development appears not to be satisfactory in spite of their upmost GDP and mobile penetration rate, as well as advanced IT infrastructure. Traditional adoption theories failed to give concrete explanation of these phenomenons. In this light, some scholars argued that there is a need to include social and cultural environment into the IS adoption research (e.g. Dahlberg, Mallat, Ondrus, & Zmijewska, 2008). This paper to a degree comes as a research endeavor to bridge this gap. China is estimated to be one of the largest and fastest-growing mobile telephony markets in the world. The current research is trying to explain the prosperousness of mobile industry in China by taking Chinese life style into consideration. This is the first aim of the study. Further, as mobile phones are becoming more and more sophisticated, a number of computer-like services are enabled for mobile users, like music, Internet, chat and games. However, considering their similarities and differences, there are few studies that help /$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi: /j.chb

2 Y. Liu, H. Li / Computers in Human Behavior 27 (2011) to explain users perceptions of the differences between services which are hosted on different technology platforms, such as games on desktop computers or on mobile phones. Specifically, why would a user tolerate a mobile game on a 3-inch screen with relatively limited playability but not accept a similar game on a desktop computer? What is the difference between a user s perceptions of playing a game on a desktop computer with 19-inch screen and on a mobile phone with only a 3-inch screen, in which the user may have the perception of ease of use of the service with regard to both platforms? Hence, the second aim of the paper is to provide evidence to explain these questions alike. Moreover, recent studies have suggested a division between IT innovations regarding system purposes (e.g. Turel, Serenko, & Bontis, 2010), and indicated that system purpose serves as an important boundary for the validity of the technology acceptance model (TAM) (Van der Heijden, 2004). Van der Heijden (2004) stated that perceived usefulness loses its dominant predictive value in favor of enjoyment by testing a Dutch movie website. However, this statement might be problematic when applied to the context of mobile hedonic innovations. For instance, by studying mobile TV adoption, Jung, Perez-Mira, and Wiley-Patton (2009) found a particularly strong and significant relationship between perceived usefulness and behavioral intention. Note that the use of mobile hedonic innovations may relate to various contexts in an individual s everyday activities and, in consequence, are more personal for their users. This may increase the utilitarian value of a mobile innovation. In this light, we sought to assess the validity of that statement in the context of mobile gaming, which is the third aim of this study. Regarding the above-mentioned research aims, the paper integrated use context, cognitive concentration and perceived enjoyment into TAM and built an adoption model of mobile gaming. In particular, the study sought to investigate the influence of use contexts on the formation of different perceptions on mobile gaming. Since TAM is originated from studying computer-based workrelated IT innovations, it doesn t consider the unique nature of mobile technologies which tends to be used personally in a variety of social and cultural contexts. In this light, an inclusion of use context in TAM is necessary in order to better understand individuals acceptance behaviors of mobile innovations. In the paper, mobile gaming is selected to be investigated, considering its increasing popularity and importance in mobile industry and a lack of relevant research. The paper expects to contribute to new insight into the impacts of use context on the adoption of mobile hedonic services. The remainder of the paper is structured as follows. Theoretical backgrounds and research hypotheses will be introduced in Section 2. Then the research methodology is presented in Section 3. In Section 4, the key findings and contributions are discussed, which is followed by a description of the limitations of the research and the potential for future research in Section Theoretical backgrounds and research hypotheses Despite the rapid growth of mobile gaming, there is a lack of research on users adoption of mobile gaming. Even if there is research on the factors influencing users attitude to mobile gaming (see. Ha, Yoon, & Choi, 2007), a study concerning a more influential usage predictor, such as intention to use, is lacking. Hence, this paper develops an adoption model of mobile games built upon an extension of the technology acceptance model (TAM) The technology acceptance model TAM is widely acknowledged as one of the most robust and influential models for explaining user acceptance behavior (Davis, 1989). Rooted in the social psychology theory of reasoned action (TRA) (Ajzen & Fishbein, 1980), TAM postulates that beliefs affect attitude, which influences intention, while intention, in turn, brings about behavior (Davis, 1989). Specifically, TAM posits that users IT acceptance is a function of two cognitive beliefs: perceived ease of use and perceived usefulness (Davis, 1989). On the other hand, perceived usefulness is a function of perceived ease of use, whereas the intention is a function of perceived usefulness. In detail, perceived ease of use refers to the degree to which a person believes that using a particular system would be free from effort (Davis, 1989, p. 320). usefulness is defined as the degree to which a person believes that using a particular system would enhance his or her job performance (Davis, 1989, p. 320). Note that the perceived usefulness structure has been criticized e.g. for being rather broad based (Moore & Benbasat, 1991). Analogous to perceived usefulness, relative advantage, a structure derived from the Innovation Diffusion Theory, has been criticized for being poorly explicated and measured as well (Tornatzky & Klein, 1982). Since its original publication, the perceived usefulness structure, as a key construct of TAM, has been refined and extended in various IT innovations (see. Legris, Ingham, & Collerette, 2003; Li, Qi, & Shu, 2008). In this process, the use of perceived usefulness is slightly changed and extended in consideration of the diversity of IT innovations. Even if perceived usefulness was originally set to relate to job performance, such as task effectiveness and efficiency, it has been extended to measure innovation performance for job/life/study. However, although this extended use of perceived usefulness might be somewhat appropriate for innovations with a multifunctional capability, it may not be a proper use for some innovations, especially for daily life usage or for learning purposes (e.g. Liu, Li, & Carlsson, 2010). Some innovations useful in daily life may have little relation to a user s job, in particular hedonic mobile innovations, such as mobile music, TV and games. For these IT innovations, a strict use of the original definition of the perceived usefulness, as related to job performance is not appropriate. Thus, there have been attempts to revise the original structure of perceived usefulness to fit the context of hedonic services. Jung et al. (2009, p. 124) stated that in research of hedonic IT...if one regards perceived usefulness as a work-related notion as in prior TAM research, one cannot exactly grasp users utility for hedonic IT usage. Hence, they, in general, regarded perceived usefulness as an individual s feeling of the level to which a technology helps a user attain the purpose through a technology s usage (Jung et al., 2009). In their study, a strong and significant impact from perceived usefulness in relation to the intention to use mobile TV is found (Jung et al., 2009). Similarly, Liao, Tsou, and Shu (2008) studied factors affecting the adoption of multimedia-on-demand services and related perceived usefulness to an improvement in life quality in their survey questionnaire. Furthermore, perceived usefulness was found to positively influence both attitude and behavioral intention (Liao et al., 2008). Hence, we widely define perceived usefulness as the degree to which an individual believes that playing mobile game would enhance his/her life quality. We believe that a hedonic innovation can be useful for a user to the extent that, for instance, it helps to kill time, alleviates stress, etc. Based on above studies on TAM, we made following hypotheses (see Fig. 1): H1. ease of use positively influences perceived usefulness of H2. ease of use positively influences attitude to play H3. usefulness positively influences attitude to play H4. usefulness positively influences intention to play

3 892 Y. Liu, H. Li / Computers in Human Behavior 27 (2011) ease of use usefulness Attitude Use context enjoyment Behavioral intention Cognitive concentration Fig. 1. Research model. H5. Attitude positively influences intention to play 2.2. enjoyment enjoyment can be defined as the extent to which an activity is perceived to be enjoyable in its own right, and this property is separate from any beneficial performance consequences that may be anticipated (Davis, Bagozzi, & Warshaw, 1992). As a sort of intrinsic motivation, perceived enjoyment has been found to be a significant predictor of various IT innovations. Thong, Hong, and Tam (2006) found that perceived enjoyment significantly impacts the intention to continue certain IT services usage. In a study on instant messaging service adoption, Lu, Zhou, and Wang (2009) indicated that perceived enjoyment has a significant and positive influence on users attitude. Heijden (2003) incorporated perceived enjoyment into TAM to explore the factors influencing the usage of websites and found that perceived enjoyment has positive effects on both users attitudes and usage intention. In a study on the adoption of mobile games in a mobile broadband wireless access environment, Ha et al. (2007) argued that perceived enjoyment should be a part of the basic nature of games, and TAM should add perceived enjoyment into it when used for game systems. In their study, perceived enjoyment was found to influence users attitudes in a positive way. Liaw and Huang (2003) made an investigation of user attitudes toward search engines as an information retrieval tool, and found that perceived enjoyment (intrinsic motivation) has a positive impact on perceived usefulness (extrinsic motivation). Further, perceived enjoyment is found to significantly relate to attitude toward 3G mobile services (Liao, Tsou, & Huang, 2007), and intentions to use both mobile data services (Kim, Choi, & Han, 2009) and 3G mobile services (Liao et al., 2007). Van der Heijden (2004) suggested that perceived enjoyment is a key driver of the use of hedonic systems. Since mobile gaming is also a kind of hedonic systems, we made following hypotheses: H6. enjoyment positively influences perceived usefulness of H7. enjoyment positively influences attitude on mobile game. H8. enjoyment positively influences intention to play 2.3. Cognitive concentration Flow, developed by Csikszentmihalyi and LeFevre (1989), describes people s sensation when they are so involved in an activity they become immersed in it. According to Csikszentmihalyi and LeFevre (1989), flow is the holistic experience that people feel when they act with total involvement. The definition of flow suggests that the state of flow is characterized by the narrowing of the awareness of the activity itself, intrinsic enjoyment, a loss of self-consciousness and a sense of control (Hoffman & Novak, 1996). Recently, flow experience has been the subject of studies in the IS and web context, and has been suggested as having an influence on users adoption behavior (Hoffman & Novak, 1997; Novak, Hoffman, & Yung, 2000). Ghani and Deshpande (1994) examine the impact of flow experience on users adoption of computers in the working context, and argued that perceived enjoyment and concentration are the two dimensions of flow experience which encourage users to use computers in their work. Koufaris (2002) examines the influence of flow experience on online consumers intention to return to a site in the online shopping context. In his model, the flow experience of online consumers is measured by perceived enjoyment, perceived control and attention focus. Chung and Tan (2004) examine flow in the web context and consider perceived playfulness to be one dimension of flow experience. Jung et al. (2009) investigated the influence of flow experience on users adoption of mobile TV. In their study, the flow experience of mobile TV users is measured by cognitive concentration, which is defined as the extent to which an individual s attention is absorbed by the activity (Hoffman & Novak, 1996). Prior studies have frequently used perceived enjoyment as a measure of flow experience (Csikszentmihalyi & LeFevre, 1989; Ghani & Deshpande, 1994; Koufaris, 2002). In the current study, perceived enjoyment is defined as one of the purposes of using mobile games (Davis et al., 1992; Heijden, 2003; Thong et al., 2006; Van der Heijden, 2004). Therefore, perceived enjoyment is not included as a measure of flow experience. In the current study, cognitive concentration is expected to be relevant in the context of mobile games. Cognitive concentration reflects the effort required by users during their mobile game involvement and mobile users can normally perform multiple tasks at the same time. According to Koufaris (2002), for an individual to be in flow, they must concentrate on their activity, and concentration will also play a role in online consumer behavior. Webster, Trevino, and Ryan (1993) found that concentration as a measure of flow has a positive influence on users intention to use a system repeatedly. Jung et al. (2009) also empirically verified the positive association between concentration and the intention to use mobile TV. They also found that cognitive concentration has a critical influence on users beliefs, which drive behavioral intentions. Their finding is consistent with the prior study results of Yi and Hwang (2004) and Agarwal and Karahanna (2000) in the web context. When users are intensely absorbed in playing mobile

4 Y. Liu, H. Li / Computers in Human Behavior 27 (2011) games, they may have the feeling that they are experiencing the highest level of enjoyment of playing mobile games. Thus, we hypothesize: H9. Cognitive concentration positively influences perceived enjoyment of H10. Cognitive concentration positively influences attitude on H11. Cognitive concentration positively influences intention to play 2.4. Use context Use context (UC) can be defined as the very concrete environment in which a technology is going to be used (Van de Wijngaert & Bouwman, 2009, p. 86). Apparently, the use of a mobile phone, which is always being carried by users everywhere, is exposed to various social and use contexts. This new capability, in turn, brings new features to users adoption behavior. Van der Heijden, Ogertschnig, Gast, and van der Gaast (2005) argued that it is important to have a fit between context, or social setting and the mobile information service. If a technology does not fit with the context of use, a user may not evaluate the service positively (Van der Heijden et al., 2005). In other words, it indicated that a user would be more likely to use a mobile service when situated in a right context. Van der Heijden (2005, p. 5) defined context relevance as the users perception regarding the degree to which a mobile information service is applicable to a particular social setting and found a significant impact of the context relevance on utilitarian value of mobile services. Mallat (2007) stated that the adoption of mobile payment is dynamic; relying on certain situational factors, such as a lack of other payment methods or urgency. Van de Wijngaert and Bouwman (2009) stated that the adoption and use of technology is context-dependent. They found that context-related characteristics are important predictors for explaining people s willingness to use wireless grids (Van de Wijngaert & Bouwman, 2009). Verkasalo (2009) suggested the existence of contextual value of mobile services when integrated with environment. Hence, a consumer may value a service when situated in certain use contexts. Note that influential contexts leading to the adoption of a mobile service are related to users lifestyles and tend to vary regarding the unique nature of the mobile service in question. Use context is more than simply location. It also relates to situational and social contexts. Studying the police s adoption of mobile services, Bouwman, Van de Wijngaert, and Vos (2008) pointed out that contextual characteristics play an important role in the use of specific communication technologies. In an urgent context, policeman will select mobile technologies for communication purposes. In contrast, for non-urgent situations traditional technologies will be favored (Bouwman et al., 2008). Studying GPS-based taxi-dispatching acceptance, Xu, Zhang, and Ling (2008) provided empirical evidence that location, weather, time, mobility and urgency influence decisions on whether to take a taxi service. Concerning mobile ticketing adoption, Mallat, Rossi, Tuunainen, and Öörni (2008, 2009) found that use context has a significant relationship with perceived usefulness and that use context is a significant determinant of service adoption. Specifically, these use contexts include budget constraints, the availability of other alternatives and time pressure in the service usage situation (Mallat, Rossi, Tuunainen, & Öörni, 2008). In this light, they suggested that contextual factors are important variables that should be added into traditional adoption models. Similarly, the research of Liu and Li (2010) found that users have a high preference for using the mobile Internet, for instance, when they felt bored or urgently needed to access the Internet when outdoors. These contextual factors were found to have significant impacts on early adopters usage of mobile Internet technology (Liu & Li, 2010). We regard the use contexts influencing the intention to use mobile games as situations in which (i) a user feels bored or (ii) has nothing else to do, or (iii) wants to kill time. We tend to utilize these contexts to represent some common feature of situations in which people would like to play a According to the report by the iresearch Consulting Group (2010b), 82 percent of users tend to play a mobile game on days off and during commuting time. Note that China is among the countries having the longest commuting time (Worldmapper, 2006), in which the use of a laptop in public transportation becomes difficult due to the overcrowd environment, but there is always space for a mobile phone. A long commute time has become an important feature of Chinese daily life, particularly for people living in big cities. In a report by Nokia, based on research from 17 countries (Nokia, 2007, p. 6), it stated that it s common today for people to use their devices to fill commuting time watching mobile video, TV or playing games. In China, where a 4-h commute is standard (for the urban population), they enjoy sophisticated entertainment in cabs and on public transport. Hence, we purposely amplified use context to represent this typical part of the daily lives of people living in China in our design for the measurement of use context. Based on prior studies on use context, we argue that users tend to play mobile games in a number of typical situations. Therefore, users perceptions in relation to a mobile game should be contextrelated. For instance, a user is likely to reduce his/her expectations on the usability of mobile gaming, when a mobile phone results in the only device available for entertainment at certain situation. Accordingly, this makes it more possible for the user to have a promoted feeling of ease of use. Also, a user would be possible to subjectively engage himself/herself in playing a mobile game in order to get ride of the boredom of a long train trip, and to forget the overcrowded environment around. In this way, we purposely established a connection between social lifestyles and the adoption of mobile services. We therefore made the following hypothesis: H12. Use context positively influences perceived ease of use of H13. Use context positively influences perceived usefulness of H14. Use context positively influences perceived enjoyment of H15. Use context positively influences cognitive concentration of H16. Use context positively influences attitude on H17. Use context positively influences intention to play mobile game. 3. Research methodology 3.1. Data collection tools To evaluate the research model, empirical data was collected via survey questionnaires and then assessed using structural equation

5 894 Y. Liu, H. Li / Computers in Human Behavior 27 (2011) modelling technology. Based on the research model proposed, seven key constructs was measured with multiple items. These items were either adopted or adapted from the extant literature, except for the use context which was self-developed. The items measuring perceived usefulness were adopted from the study by Jung et al. (2009), Agarwal and Karahanna (2000). The scales for intention to use and perceived ease of use were adopted from the instrument developed by Davis (1989). The items for cognitive concentration were adapted from the work of Jung et al. (2009). Each item was measured with a seven-point Likert scale, ranging from strongly disagree (1) to strongly agree (7). The questionnaire was first developed in English and then translated to Chinese by one of the manuscript s authors. A back translation was conducted by another manuscript author to ensure the accuracy of the translation. The specific items are presented in Table 1. Table 2 Demographic information on the respondents. Demographic profile Items Frequency Percent (%) Gender Female Male Total Phone usage experience (years) Under Over Total Computer game experience Play frequently Play sometimes Never play Total Sample In April 2010, a total of 350 questionnaires were distributed to students studying at Zhejiang Normal University in China face to face. In total, 305 questionnaires were returned of which 38 questionnaires were discarded as they were only partly completed. Therefore, 267 questionnaires were retained for analysis, giving an effective response rate of 76 percent. Note that students, as a group, are the largest users of mobile games in China (37.7%), according to a recent report (iresearch Consulting Group, 2010b). The sample demographics are depicted in Table 2. Of the respondents, 143 are female and 124 are male. Most respondents are experienced mobile phone users, as 69.7 percent of them had used a mobile phone for more than 2 years. Most respondents have had a similar experience of playing computer games, as 83.5 percent of the respondents said they play computer games sometimes or frequently Research results Scale reliability and validity Confirmative factor analysis was conducted using AMOS One item measuring cognitive concentration was deleted due to a low factor loading. Thereafter, all the standardized factor loadings (FLs) satisfy the threshold of 0.7. As in this paper the definition of perceived usefulness has been slightly changed to fit the context of mobile hedonic services, exploratory factor analyses were further conducted to ensure high loads on perceived usefulness. In this regard, SPSS 17.0 was used to extract seven factors, in which all items were found to well fit their respective factors, as shown in Appendix A. There is no cross loading above 0.4. Additionally, composite reliabilities (CR) and average variance extracted (AVE) were assessed to ensure the convergent validity of our measurements. Convergent validity indicates the degree to which the measure of a construct that is theoretically related is also related in reality. As suggested by Fornell and Larcker (1981), it can be Table 1 Measurement items. Constructs and items a CR AVE FL usefulness (PU) Mobile gaming improves my quality of life Mobile gaming makes my life better Mobile gaming is useful for my life ease of use (PEOU) : I think learning to play mobile games is very simple : It would be easy for me to become skilful at playing mobile games : I think playing mobile games is easy enjoyment (PE) : I think it is fun to play mobile games : I think the process of playing mobile game would be pleasant : I think playing mobile game would bring me pleasure : I enjoy playing mobile games Cognitive concentration (CC) : During the playing of a mobile game, I am usually intensely absorbed in the activity. Deleted 2: During the playing of a mobile game, I concentrate fully on the activity : During the playing of a mobile game, I am deeply engrossed in the activity Use context (UC) When bored, I will consider playing a For me, playing mobile games is a good way to kill time When I have nothing serious to do, I will consider playing a Attitude (ATT) I think playing mobile games is a good idea I have positive perception about playing mobile games I am in favour of the idea of playing mobile games Behavioral intention (BI) I intend to play mobile games in the future I believe I will play mobile games in the future

6 Y. Liu, H. Li / Computers in Human Behavior 27 (2011) evaluated using three criteria: (1) all indicator factor loadings should be significant and exceed 0.7, (2) the value of CR should exceed 0.80, and (3) the value of AVE by each construct should exceed the variance due to measurement errors for that construct. Thus, the AVE should exceed 0.5 (Fornell & Larcker, 1981). As described in Table 1, the Cronbach s alpha (a) values range from to 0.923, which are all over the 0.7 level. The CRs and AVEs of all the constructs exceed the recommended threshold of 0.8 and 0.5 respectively, thereby indicating good internal consistency (Fornell & Larcker, 1981). Discriminant validity can be demonstrated as the square root of the AVE for each construct is higher than any correlation between this and any other construct (Fornell & Larcker, 1981). As shown in Table 3, the square roots of the AVE of all constructs are greater than the correlation estimate with the other constructs. This indicates that each construct is more closely related to its own measures than to those of other constructs. Therefore, discriminant validity is supported (Fornell & Larcker, 1981). The actual and recommended values of the model fit indices are listed in Table 4. The actual values of all the model fit indices were better than the recommended values, except for GIF which is slightly lower. This demonstrated a good fit between the model and the data. Table 3 Correlation matrix and discriminant assessment. Variables PU PEOU PE CC UC ATT BI PU PEOU 0.269** PE 0.559** 0.349** CC 0.422** 0.249** 0.542** UC 0.345** 0.410** 0.566** 0.412** ATT 0.345** 0.410** 0.566** 0.412** 0.345** BI 0.468** 0.248** 0.525** 0.479** 0.495** 0.637** Correlation is significant at the 0.01 level (2-tailed). The bold items on the diagonal represent the square roots of the AVE; the off-diagonal elements are the correlation estimates The structural model assessment and hypothesis testing Of a total of 17 hypotheses, 12 were supported, as shown in Fig. 2. Inconsistent with previous research, there are no significant relationships between perceived ease of use and perceived usefulness and neither between perceived ease of use and attitude. Also there are no significant influences from both perceived enjoyment and perceived usefulness on behavioral intention. However, perceived usefulness (b = 0.49, p < 0.001) and perceived enjoyment (b = 0.166, p < 0.05) were found to positively and significantly relate to attitude. In addition, perceived enjoyment, as a significant antecedent of perceived usefulness (b = 0.554, p < 0.001), can be predicted by cognitive concentration (b = 0.358, p < 0.001). On the other hand, cognitive concentration is a significant predictor of both attitude (b = 0.18, p < 0.01) and behavioral intention (b = 0.145, p < 0.05). Consistent with previous studies, behavioral intention was found to be a function of attitude (b = 0.524, p < 0.001). No significant and direct relationship was found between use context and perceived usefulness. The results show that use context is a very important factor in influencing the adoption of mobile games. Expect for perceived usefulness, it significantly and directly influences all other variables, including perceived ease of use (b = 0.464, p < 0.001), perceived enjoyment (b = 0.458, p < 0.001), cognitive concentration (b = 0.463, p < 0.001), attitude (b = 0.161, p < 0.05) and intention to use (b = 0.205, p < 0.01). All the path coefficients are summarized in Table Total effects The total effects of all constructs were evaluated in order to offer a more complete view of their predictive power. Except for perceived usefulness, use context is the strongest predictor of all other variables. It has a high predictive power for perceived enjoyment (b = 0.624, p < 0.01) when compared to cognitive concentration. In addition, it is the most influential antecedent of attitude (b = 0.542, p < 0.01) and behavioral intention (b = 0.547, p < 0.01). Note that, for behavioral intention, it is also a more influential predictor than attitude. Without a direct influence, the use context Table 4 The recommended and actual values of fit indices. Fit index { 2 /df GFI AGFI CFI NFI TLI RMSEA Recommended value <3 >0.90 >0.80 >0.90 >0.90 >0.90 <0.08 Actual value Note: { 2 /df is the ratio between the Chi-square and the degrees of freedom, GFI is the Goodness of Fit Index, AGFI is the Adjusted Goodness of Fit Index, CFI is the Comparative Fit Index, NFI is the Normed Fit Index, TLI is the Tucker Lewis coefficient, RMSEA is the Root Mean Square Error of Approximation. Use context ease of use R 2 =21.5% usefulness R 2 =34.4% R 2 =49.0% enjoyment R 2 =21.5% Cognitive concentration Fig. 2. The structural model. R 2 =63.9% Attitude Behavioral intention R 2 =53.6% Significant path Insignificant path

7 896 Y. Liu, H. Li / Computers in Human Behavior 27 (2011) Table 5 Path coefficients. * Hypothesis Path Path coefficient (T-Values) Supported or not H1 PEOU? PU 0.087(1.312) No H2 PEOU? ATT 0.008(0.147) No H3 PU? ATT 0.490(8.086) *** Yes H4 PU? BI 0.30( 0.379) No H5 ATT? BI 0.524(5.713) *** Yes H6 PE? PU 0.554(6.969) *** Yes H7 PE? ATT 0.166(2.235) * Yes H8 PE? BI 0.004(0.045) No H9 CC? PE 0.358(5.921) *** Yes H10 CC? ATT 0.180(3.143) ** Yes H11 CC? BI 0.145(2.227) * Yes H12 UC? PEOU 0.464(7.037) *** Yes H13 UC? PU 0.002(0.019) No H14 UC? PE 0.458(7.296) *** Yes H15 UC? CC 0.463(6.991) *** Yes H16 UC? ATT 0.161(2.370) * Yes H17 UC? BI 0.205(2.954) ** Yes p < ** p < *** p < Table 6 Standardized total effects. * UC CC PE EOU PU ATT CC ** PE ** ** EOU ** PU ** ** ** ATT ** ** ** ** BI ** ** * * ** p < ** p < impacts perceived usefulness indirectly (b = 0.388, p < 0.01). The total effects of the different variables are shown in Table Discussion 4.1. Key findings Compared to previous research on mobile gaming adoption, the present research found that perceived ease of use has no significant impact on attitude. It may result from a constant advance of handheld technologies in recent years. In addition, the paper added use context, cognitive concentration and perceived enjoyment to TAM to study mobile gaming adoption for Chinese users. A number of new relationships are found for the first time, in particular related to use context. This paper contributes to identifying a number of predictors of the intention to use mobile gaming. It indicated that the factors influencing attitude and the behavioral intention to play a mobile game are different. Both perceived usefulness and perceived enjoyment are direct predictors of attitude, although not for behavioral intention. Their indirect impacts on intention are mediated via attitude. This indicates that the capability of mobile games to improve life quality and to entertain users can only induce a positive attitude, but fails to directly promote the intention to play. On the other hand, the results show that people s feeling about a mobile game s capability to improve his/her life quality is not related to whether a mobile game is easy to use or not, which is similar to findings on mobile TV (see. Jung et al., 2009). Similarly, this feeling does not come from the possibility to play mobile games in certain contexts. According to the results, people only experience that feeling when the innovation is capable of bringing happiness, e.g. by entertaining them. Here it is worth noting that the direct impacts of cognitive concentration and the indirect impacts of the use context on perceived enjoyment are mediated via cognitive concentration. This suggests that the enjoyment brought about by playing a mobile game largely comes from its capability to absorb users intensely in a possibly boring environment, such as on a bus. Also a direct influence of use context on perceived enjoyment indicates, to some extent, why a user thinks playing a mobile game brings happiness and that this is due to the possibility to play the game in certain, normally dull, contexts. In other words, being able to play a game in certain environments, such as during a commute, makes users happy, apart from the playability of the game itself. Note that use context strongly and significantly influences the formation of peoples perceptions of all aspects of mobile games, including perceived ease of use, perceived usefulness (indirect), perceived enjoyment and cognitive concentration. It is safe to state that people s feelings about the different aspects of mobile games are not established alone; instead their formation is conditional and based on the special consideration of mobile game usage in certain use contexts. Also this special consideration will further, directly and indirectly, affect the formation of attitude and intention to play mobile games. In particular, use context is found to be the strongest predictor of intention to use, even more so than attitude. It indicates that use context is the key factor that triggers the use of mobile game. To some extent, it suggests that people intend to play mobile games since it is the only way available for them to kill time in certain contexts. Under these circumstances, people become more tolerant of the mobile technology. Therefore, a feeling of ease of use and enjoyment can be easily raised, even if the service is only available on a relatively small mobile phone. This suggests that people s decisions on the use of mobile innovations are context-related. When situated in the right context a user will be more willing to use or actually use a mobile innovation, such as a As our purposely defined use context is based on a key feature of Chinese daily life, long commute time, this factor also helps to explain the success and rapid growth rate of the mobile gaming industry in China and similar Asian counties, such as Japan and South Korea. Note that these countries are all famous for their long commute time, which provides a typical context for using mobile services, in particular hedonic services. For example, mobile TV and mobile novel reading are services that are typically used on public transportation and widely adopted by consumers in those three countries (Chiu, Chipchase, & Jung, 2007; McNeill, 2008). Based on the results of the mobile game, we propose that the success of a mobile innovation depends on the existence of contexts which make the innovation necessary. The frequency and importance of such contexts in people s lives will thus decide the popularity and success level of such mobile innovations. Based on the above research findings, we attempt to depict a typical scenario for using a mobile game: A typical user is someone who feels bored on a bus/train/etc. or is waiting for somebody or something. To alleviate the boredom s/he takes out his/her mobile phone to play a game. The user enjoys this possibility to play a game (kill time). On the other hand, if the game itself can cognitively make the user become absorbed in its world, to some extent, s/he will feel happier. Furthermore, due to a possibly crowded environment or the fact that the mobile phone is the only entertainment method available, s/he lowers their requirement regarding the playability of the game. In that situation, the happiness initiated further generates in users an emotion that their life quality is improved. Overall, all these positive feelings result in the user having a positive attitude towards mobile gaming. This attitude, together with a possibly boring environment and a game that has an acceptable level of attractiveness raises the user s intention to play mobile games in the future.

8 Y. Liu, H. Li / Computers in Human Behavior 27 (2011) Contributions The paper makes several contributions to theory on the subject. First, the paper indentified the fact that a hedonic system can also have utilitarian function, together with findings of June et al. (2009). Taking the unique feature of mobile games into consideration, it was found that perceived enjoyment is not necessarily a stronger predictor than perceived usefulness in a hedonic system. usefulness can be still a good predictor of the acceptance of mobile hedonic innovations. In particular, perceived enjoyment is not necessarily a direct predictor of the intention to use a hedonic system as the enjoyment is conditional upon the use context. Secondly, the paper slightly changes the definition of perceived usefulness to fit the context of mobile hedonic services and found that this construct had a significant influence on other variables. We propose that the so-called usefulness of an innovation is different and depends on whether we are considering the contexts of job, life or study. A consequence of that for theory building is that IS research communities may need a new theoretical approach for defining the perceived usefulness of innovations for daily life usage, in particular for hedonic systems. Thirdly, the paper investigated the factors driving mobile game adoption by identifying the fact that use context, perceived usefulness, perceived enjoyment, cognitive concentration and attitude are key drivers of an individual s intention to play a Fourthly, the paper identified the key use contexts influencing the adoption of mobile games and provided concrete evidence showing that users perceptions of a mobile service are conditional. This helps to explain the difference in perceptions towards a service hosted on different platforms. On the other hand, this finding indicated that the use contexts influencing information innovation adoptions are not necessarily unified. Users may be more willing to use different mobile innovations in different typical use contexts. Further, a number of new relationships between use context and other variables were found, which may be useful for researchers trying to identify the impact of use context on other mobile innovations. Finally, as prior adoption theories are mostly initiated in an organizational environment by studying work-related innovation and employees, they, to a large degree, fail to capture the distinguishing features of mobile innovations. We call for an adoption model that pertains to mobile innovations, in which the important role of use context should not be neglected. Even if, in the present paper, we discussed the impacts of use context in the contexts of mobile hedonic services, but it is worth noting that use context has also been found to exert significant influence on the adoption of utilitarian services, such as mobile ticket (Mallat et al., 2008), mobile Internet (Liu & Li, 2010), and taxi services (Xu et al., 2008). In this sense, we would suggest that, given the use of mobile services is exposed to various social and environmental contexts, it should always be possible to find certain use context, that is capable of promoting the use of particular mobile services. Regarding those mobile services which use context research has not been touched yet, we can not conclude that use context has no significant influence on the service adoption. In this regards, we tends to believe the reason is that the possible promoting use context has not yet been found by researchers or formed, such as long commute in Asia countries for mobile gaming, but not exist in some other countries. The research findings of the paper may offer a number of implications for practitioners. To a large degree, the insignificant influence of perceived ease of use on other factors suggests that usability issues are not necessary a problem for users who currently play mobile games. Furthermore, it might be an effective strategy to market mobile game services via the typical context of usage, such as via bus and train video advertising. Also mobile game designers should take the typical context of mobile game usage into consideration, such as on a bus and train. Additionally, as use context in the present study partly represented the commute time, the length of commuting time should be seen as an important predictor when studying the market potential of mobile hedonic services in some countries, for example, Japan and South Korea. Additionally, as cognitive concentration is a strong predictor of attitude and intention to use, mobile game designers should enhance the interactivity of games and challenge players by designing games that fully involve users cognitive abilities. This would, most likely, raise the adoption of the service. On the other hand, it might be an effective strategy for a practitioner to advertise mobile games as an enjoyable service which promotes people s quality of life. This will help to induce a positive attitude and the further adoption of mobile games. 5. Limitations and future research As with all research, we acknowledge some limitations of this study that should be considered. First, the sample was collected from students living in China. Hence, the study has some limitations when generalizing our findings to other age groups and cultural environments. The research model proposed may need to be validated in different user groups in other countries in future studies. Secondly, even if the results indicate that perceived ease of use has no significant impact on perceived usefulness, it may have significant influences on such as either perceived enjoyment or cognitive concentration, which is not examined in the present study. This also gives a possible avenue for future research. Thirdly, the use contexts analyzed in the study only reflect a part of people s daily lives. There might be other significant contexts that impact on the use of mobile games and other mobile innovations. Hence there is a need to investigate the impact of other use contexts. Also the present study only measured the intention to play mobile games. Therefore, a further evaluation of actual use may deepen our understanding of users behaviors. Lastly an inclusion of mediating factors, such as gender and age, would possibly offer some fresh insights and provide new directions for future research. Appendix A Cross-loading matrix EOU EOU EOU PE PE PE PE CC CC PU PU PU UC UC UC ATT ATT ATT BI BI

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