Consumer behavior in online game communities: A motivational factor perspective

Consumer behavior in online game communities: A motivational factor perspective

Computers in Human Behavior Computers in Human Behavior 23 (2007) 1642–1659 Consumer behavior in online game commu...

204KB Sizes 0 Downloads 432 Views

Computers in Human Behavior Computers in Human Behavior 23 (2007) 1642–1659

Consumer behavior in online game communities: A motivational factor perspective Chin-Lung Hsu


, Hsi-Peng Lu




Department of Information Management, Da-Yeh University, 112 Shan-Jiau Road, Da-Tsuen, Changhua, Taiwan, ROC Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC Available online 8 November 2005

Abstract The concept of online communities has been used to improve customersÕ loyalty in recent years. While studies on transaction community such as online auction have received more attention in the literature, entertainment community such as online game has seldom been addressed. This study applies the theory of reasoned action (TRA) and modifies the technology acceptance model (TAM) to propose a research model. An empirical study involving 356 subjects was conducted to test this model. The results indicate that customer loyalty is influenced by perceived enjoyment, social norms and preference. Perceived cohesion has an indirect impact on loyalty. In addition, the findingÕs practical implication suggests that community managers must overcome the problems users encounter, including suffering from an unstable system, malicious players and grief players. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Online games; Community; Loyalty; TRA; TAM

1. Introduction Online communities have been one of the strategies employed to increase customersÕ loyalty recently. Many e-commerce companies launch communities as a business model *

Corresponding author. Tel.: +886 4 8511888x3139; fax: +886 4 851 1500. E-mail address: [email protected] (C.-L. Hsu).

0747-5632/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2005.09.001

C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659


in electronic markets. Specifically, online games have become a killer application of online communities, which are seen as an entertainment community because they allow users to indulge in fantasy and be entertained. In the community of online games, users can perform a special role, interact socially and exchange information. People who interact even create their own virtual worlds. Many researchers have widely explored online communities (Balasubramanian & Mahajan, 2001; Kardaras, Karakostas, & Papathanassiou, 2003; Preece, Nonnecke, & Andrews, 2004; Wachter, Gupta, & Quaddus, 2000; Wasko & Faraj, 2000). While research on transaction community such as online auction has received more attention in the literature (Kwon, Kim, & Lee, 2002; Stafford & Stern, 2002), entertainment community has seldom been addressed. Digital entertainment has been gaining ground and the trend is expected to continue (Loebbecke & Powell, 2002). According to a DFC Intelligence (2004) survey, the global market value of online games will soar from US$ 5.2 billion in 2006 to more than US$ 9.8 billion in 2009. The number of users in the online games increases tremendously as well. It is estimated that 40% of the Internet users have played online games in Taiwan (NetValue, 2002). Moreover, Game sites have also become powerhouses of electronic-commerce and are heavily represented in lists of the 100 most heavily trafficked sites globally (Takahashi, 2000). For these reasons, the online games market has thus become the next hot internet investment. The business value of communities lies in customersÕ loyalty (Hagel & Armstrong, 1997). When a user often plays online games, interaction with other users will increase, which then leads more users to join the games community. In addition, loyal users usually play a key role in expanding the effect of network. Owing to the network effect, when the number of users reaches a point of critical mass, the community revenue increases exponentially. Consequently, exploring what factors affect usersÕ loyalty towards game communities is important. The purpose of this study is to examine what perceived factors contribute to an online game userÕs loyalty. To examine the online games loyalty, this study applies the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975) and modifies the technology acceptance model (TAM) (Davis, 1989) to propose a research model. These theories show that the belief–attitude–intention causal chain can predict user behavior. What kinds of beliefs are affected by participating in online game communities? Combining theories and key success of communities (Preece, 2001), we believe most users play games for entertainment purpose. Interpersonal interaction leads to cohesiveness and norms. Perception of ease of use affects usersÕ motivation for social interaction. Therefore, we proposed beliefs such as perceived enjoyment, perceived cohesion, social norms, and perceived ease of use. These beliefs are hypothesized to predict usersÕ loyalty. Furthermore, this work applies the structure equation modeling (SEM) approach, supported by LISREL software, to assess the empirical strength of the relationships in the proposed model. From a theoretical perspective, this study identifies antecedents of online games user loyalty. From a practical standpoint, the implications of the findings may guide managers in selecting an appropriate strategy to the game users. This study is organized as follows: Section 2 provides a theoretical overview; Section 3 then proposes the research model and develops the hypotheses tested in this study; Section 4 describes the research method; and Section 5 provides the results of empirical tests and presents some discussions. Finally, Section 6 presents the conclusions and some implications for practitioners and researchers.


C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659

2. Theoretical background 2.1. Online community An online community is defined as groups of people who communicate with each other via electronic media, such as the Internet, share goals and ideas, and no any geographical location nor ethnic origin constraints are imposed. (Kardaras et al., 2003; Romm, Pliskin, & Clarke, 1997). Preece (2000) indicates that an online community consists of (1) people, who interact with each other in the community; (2) purpose, to provide a reason for users to participate in communities; (3) policies, to make rules, protocols, and laws to guide userÕs behavior; and (4) computer systems, to support and mediate social interaction and facilitate a sense of togetherness. According to Armstrong and Hagel III (1996), the usersÕ motive for participating in online community is to satisfy needs such as transaction, interest, fantasy, and relationship. In this study, an online games community is defined as a group of users who interact with each other via Internet, create a fantasy role and develop an online relationship among users, share common interests, and indulge their need for entertainment by playing their own virtual roles. Lineage in Asia, for example, is a hot online game that allows users to assume a virtual role to join adventurous activities in cyberspace. In the Lineage community, users often develop robust cohesion and strive to achieve common goals, including conquering an enemy country. In successful communities, sociability and usability are definitely important influential factors (Preece, 2001). Sociability is primarily concerned with interpersonal relationship while members of a community interact with each other. Usability is concerned with how users interact with technology. From usersÕ perspective, these two factors are important in influencing membersÕ participation in a community. Therefore, we propose social norms and group cohesion as sociable factors and perceived ease of use as a usable factor. In addition, online game communities are mainly concerned with entertainment. Hence, we propose perceived enjoyment as another important influential factor. The following section provides theory supporting these propositions. 2.2. Behavior theories Theory of Reasoned Action (TRA) is a widely studied model in social psychology to explain an individualÕs behavior. According to TRA, a personÕs behavior is predicted by intentions, and intentions are jointly determined by the personÕs attitude and subjective norm concerning the behavior. TRA is a general model, which does not specify beliefs about performing a particular behavior. Therefore, while researchers applied TRA to explain social behavior, the salient beliefs need to be considered for a specific extent. For example, Lu and Lin (2003) proposed that customersÕ beliefs about a particular brandÕs content, context, and infrastructure have an impact on their attitude toward repetitive transaction in the marketspace. TAM is an adaptation of the TRA from psychology specifically tailored to model user acceptance of information technology (IT), as shown in Fig. 1. To provide an explanation and prediction of the determinants of IT usage, Davis (1989) used the cost-benefit paradigm and self-efficacy theory to propose two influential beliefs: perceived usefulness (PU) and perceived ease of use (PE). PU is defined as ‘‘the degree to which a person

C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659


Beliefs Perceived Usefulness







Intention to





Perceived Ease of Use

Fig. 1. Technology Acceptance Model (adapted from Davis et al., 1989).

believes that using a particular system would enhance his or her job performance’’, and PE as ‘‘the degree to which a person believes that using a particular system would be free of effort’’. According to TAM, the system usage is determined by individualsÕ attitudes toward using the system and PU. Meanwhile, attitude toward using the system is jointly determined by PU and PE. Moreover, both types of beliefs are affected by external variables, such as system features, user characteristics, and situational constraints. Davis, Bagozzi, and Warshaw (1989) recommended these external variables to be tested in future research because by manipulating these factors, system developers can better control usersÕ beliefs about the system, and subsequently, suggest new ways to improve computer usage. Agarwal and Prasad (1999) applied TAM to predict the acceptance of new technologies. The external variables included individual differences. Their results identified several individual difference variables, such as level of education and role with regard to technology, that have significant effects on TAMÕs beliefs. While TAM has been widely applied in acceptance behavior across a broad range of information technology (Bajaj & Nidumolu, 1998; Gefen & Straub, 1997; Hu, Chau, Sheng, & Tam, 1999; Liaw & Huang, 2003; Lin & Lu, 2000; Pin & Lin, 2005; Wu & Wang, 2005), many studies have applied other theoretical theories such as Theory of Planned Behavior (TPB), Task-Technology Fit (TTF), and Perceived Characteristics Innovation (PCI) to challenge TAMÕs validity (Dishaw & Strong, 1999; Plouffe, Hulland, & Vandenbosch, 2001; Riemenschneider, Harrison, & Mykytyn, 2003). Though comparative results are mixed, TAM is still one of the most frequently tested models in IS literature (Legris, Ingham, & Collerette, 2003). In addition, much research extends TAM to enhance the understanding of user acceptance behavior for specific contexts (Kaasinen, 2005; Luo & Strong, 2000; Moon & Kim, 2001; Yu, Ha, Choi, & Rho, 2005). For example, Gefen (2003) proposed a variable (ÔtrustÕ) for studying electronic commerce acceptance. Extending trust factor into the TAM model enabled better explanation of electronic commerce usage behavior. Computer usage can be derived from individualsÕ voluntariness, which suggests that users perceive the adoption decision to be non-mandatory. However, many studies have found that social norms positively influence an individualÕs IT usage (Liker & Sindi, 1997; Lucas & Spitler, 2000; Venkatesh & Morris, 2000), though Davis et al. (1989)


C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659

dropped social norms from the original TAM. In TAM2 (Venkatesh & Davis, 2000), social norm has been reconsidered in improving understanding of user adoption behavior. Social norm, as included in the original TRA, defined as a ‘‘personÕs perception that most people who are important to him think he should or should not perform the behavior in question’’ (Fishbein & Ajzen, 1975). According to TRA, social norm is determined by a multiplicative function of his normative beliefs and motivation to comply. 2.3. Perceived enjoyment From a motivation perspective, people make an effort to use information technology due to both intrinsic and extrinsic motivation (Davis, Bagozzi, & Warshaw, 1992). Intrinsic motivation refers to the pleasure and satisfaction from performing a behavior (Deci & Ryan, 1987), while extrinsic motivation emphasizes performing a behavior to achieve specific goals/rewards (Vellerand, 1997). In the original TAM, IT usage derived from the perceived usefulness, a form of extrinsic motivation. Intrinsic motivation was not explicitly included. In subsequent work, however, Davis et al. (1992) and Teo, Lim, and Lai (1999) empirically verified that intrinsic motivation has a significant effect on computer and Internet usage. Current work considers the main purpose of participating in online games community is for leisure and pleasure, not to achieve specific goals nor improve performances. Therefore, this study replaced perceived usefulness with perceived enjoyment, a form of intrinsic motivation, and proposed the importance of technology use in the entertainment community. 2.4. Perceived cohesion Group cohesion has been described as a sense of membersÕ attraction to the group (Hogg, 1992). Cohesion increases when people in a group perceive that common goals and objectives can be achieved through group action. In many instances, group cohesion has been linked to a number of positive outcomes, such as more positive interpersonal relations, a high degree of commitment to the group task, favorable communication, interactions, and group performance (Goodman, Ravlin, & Schminke, 1987; Klein & Mulvey, 1995; Narayanan & Nath, 1984; Piper, Marrache, LaCroix, Richardsen, & Jones, 1983). Several researchers have identified group cohesion as an important variable to understand the nature of groups more, such as group processes (Carron & Brawley, 2000; Evans & Jarvis, 1980). For instance, group cohesion has a significant effect on task participation and social presence (Yoo & Alavi, 2001). In this study, we have also seen cohesion as an important group characteristic in online game communities. Therefore, we examined the influences of group cohesion on usersÕ participating behavior. 3. Conceptual model and hypotheses development Fig. 2 illustrates the research model, which was built based on modified TAM. It asserts that customer loyalty (intention to reuse online games) is determined by preference (posi-

C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659


Social Perceived Norms Cohesion



Ease of use


Customer Loyalty

Perceived Enjoyment

Fig. 2. Research model.

tive attitude), social norms, and beliefs about cohesion and enjoyment. Further, preference mediated the impact of beliefs about cohesion, ease of use, and enjoyment on customer loyalty. The definition of constructs, network of relationships illustrated in the model, and the rationale for the proposed links are explained in the following section.

3.1. Social norm Social norm is defined as ‘‘the degree to which the user perceives that others approve of their participating in the online game community’’. Based on earlier discussion on social norm, the research model proposes a positive relationship between social norm and loyalty. This was confirmed by theoretical model such as TRA, TAM2, TPB, and empirical studies (Lucas & Spitler, 2000; Taylor & Todd, 1995; Venkatesh & Morris, 2000). Accordingly, we hypothesize: Hypothesis 1: Social norm will positively affect customer loyalty.

3.2. Perceived enjoyment Perceived enjoyment represents an intrinsic motivation and is defined as ‘‘the extent to which the activity of participating in the online game community is perceived to be pleasure and satisfaction’’. Past studies have verified that the use of computer technology was influenced by perceived enjoyment (Davis et al., 1992; Igbaria, Schiffman, & Wieckowshi, 1994). In addition, Al-Gahtani and King (1999) present and empirically evaluate a conceptual model of factors contributing to acceptance of information technology. Their findings show that enjoyment has significant effects on attitude. Furthermore, Chin and Gopal (1995) pointed enjoyment as causally affecting the intention to adopt GSS. Accordingly, we hypothesize:


C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659

Hypothesis 2a: Perceived enjoyment will positively affect customer preference. Hypothesis 2b: Perceived enjoyment will positively affect customer loyalty. 3.3. Perceived ease of use TAM suggests that perceived ease of use has a direct effect on preference (positive attitude). This factor refers to ‘‘the degree to which the user believes that participating in an online game community is effortless’’. Empirical studies applying TAM have empirically verified that perceived ease of use has significant effect on preference (Lin & Lu, 2000; Mathieson & Chin, 2001). In addition, systems that are easy to use are likely to be perceived as enjoyable. Venkatesh (2000) indicates that there is a positive relationship between perceived enjoyment and perceived ease of use for the multimedia system. Moreover, in a context of internet, perceived ease of use has also been found to influence perceived enjoyment (Teo et al., 1999). Accordingly, we hypothesize: Hypothesis 3a: Perceived ease of use will positively affect customer preference. Hypothesis 3b: Perceived ease of use will positively affect perceived enjoyment. 3.4. Group cohesion Group cohesion often plays an influential role in the community, which refers to ‘‘the degree to which users are attracted to the group and to each other’’. Take a therapeutic community (TC), for instance; Dermatis, Salke, Galanter, and Bunt (2001) found cohesion was significantly associated with perceived benefit for recovery of TC treatment, and was inversely associated with residentsÕ depression. Recently, some studies have also shown that cohesive group members exhibit significant task performance (Podsakoff, MacKenzie, & Ahearne, 1997), high job satisfaction (Laka, 1996), task participation and membersÕ positive and negative feelings (David, Jonathan, & Letitia, 1986; Evans & Dion, 1991). Accordingly, we hypothesize: Hypothesis 4a: Perceived cohesion will positively affect customer preference. Hypothesis 4b: Perceived cohesion will positively affect customer loyalty. 3.5. Customer preference and loyalty According to TRA, intention is influenced by positive attitude (preference). Lu and Lin (2003) applied TRA, indicated that customer attitude had a significant effect on customer loyalty in the market-space. Here, customer preference is defined as ‘‘the degree of usersÕ positive feelings about participating in online game communities’’. Furthermore, according to LuÕs and LinÕs research, loyalty is an intention to keep on using a service, such as a web site. Loyalty toward the web site will increase while a customer increases repetitive service provided by a web site. Therefore, we define customer loyalty as ‘‘the degree to which a user believes that he/she will re-participate in the online game community’’. Finally, we propose that customer preference will influence customer loyalty in the community of online games. Hypothesis 5: Customer preference will positively affect customer loyalty.

C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659


4. Methodology 4.1. Sample The questionnaire was designed to be placed on the home page of the web site. Javascript programming was developed to handle the data collection process. In order to increase the response rate of online game users, we placed messages on over 100 heavily trafficked online message boards on popular game-related web sites and game-related BBS for three weeks. The message outlined the aim of this study, provided a hyperlink to the survey form, and as an incentive, offered respondents an opportunity to join a draw for a prize. The online survey yielded 356 usable responses. Seventy-six percent of the respondents were male and 24% were female. The majority of respondents (82%) were under 25 years old. About 51% of the respondents had 1–3 years experience in playing online games, 24% above 3 years. Seventry-three percent had used ADSL as their main means of access to online games. Thirty-one percent played online games for 1–3 h each time, 40% for 3–6 h, 28% for more than 6 h. Finally, 83% percent played online games for more than 10 h per week. Playing time data show that users spend a lot of time participating in the online games community. These demographic findings on respondents confirmed previous findings (MIC, 2003). 4.2. Measurement development The questionnaires were developed from the literature, and the list of items is displayed in Appendix A. In recent years, many studies developed and validated instruments for measuring TAM and TRA constructs, such as social norm, perceived ease of use, preference, and intention (Doll, Hendrickson, & Deng, 1998; Liker & Sindi, 1997; Luo & Strong, 2000). Hence, the items in the instrument were derived from the existing literature and slightly modified to suit the context of online game community. Furthermore, to develop a scale to measure perceived enjoyment and perceived cohesion, we utilized measures of Davis et al. (1992) and Hunton, Arnold, and Gibson (2001), with modifications to suit the setting of online game community. Each item was measured on a five-point Likert scale, ranging from ‘‘disagree strongly’’ (1) to ‘‘agree strongly’’ (5). Before conducting the main survey, we performed both a pre-test and a pilot to validate the instrument. The pre-test involved 10 respondents who were selected experts in the community of online games. Respondents were asked to comment on list items that corresponded to the constructs, including scales wording, instrument length, and questionnaire format. Finally, to reduce questionsÕ possible ambiguity, a pilot test that involved 52 respondents, self-selected from the population of online game community, was performed. 5. Results 5.1. Descriptive statistics Table 1 presents the means and standard deviations of the constructs. It can be found that, on average, users responded positively to participating in game communities (the averages are all greater than 3 out of 5, except for social norm). Rationally, users


C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659

Table 1 Descriptive statistics (means and SD)

Social norms Perceived ease of use Perceived cohesion Perceived enjoyment Preference Loyalty



2.9 4.1 3.4 4.1 3.8 3.8

0.8 0.6 0.7 0.6 0.7 0.7

would feel normative effects from interacting with other users if they were involved in community activities. However, the means of social norm was slightly lower than average. This may explain that the reasons of participating in an entertainment-oriented community would satisfy an individualÕs enjoyment and leisure. Social norm is likely to be an important perception of another type of communities, such as relationshiporiented communities. 5.2. Analytic strategy for assessing the model The proposed model was tested using the SEM. SEM is a powerful second-generation multivariate technique for analyzing causal models involving an estimation of the two components of a causal model: the measurement and the structural models. While the measurement model is to specify how the latent variables are measured in terms of the observed variable, the structural model is to investigate the strength and direction of the relationship among theoretical constructs. Such analyzed technique has been widely used by IS researchers in recent years (Chau & Hu, 2002; Lee & Pai, 2003). In our study, the software Lisrel 8.3 was used in order to assess the measurement and the structure model (Joreskog & Sorbom, 1996). 5.3. The measurement model The test results of the measurement model are presented in Table 2. Data show that item reliability ranges from 0.58 to 0.94, which exceeds the acceptable value of 0.50. The internal consistency of the measurement model was assessed by computing the composite reliability. Consistent with the recommendations of Fornell (1982), all composite reliabilities were above the benchmark of 0.60. The average variance extracted for all constructs exceeded the threshold value of 0.5 recommended by Fornell and Larcker (1981). Since the three values of reliability were above the recommended thresholds, the scales for evaluating these constructs were deemed to exhibit adequate convergence reliability. Table 3 shows that the variances extracted by constructs are greater than any squared correlation among constructs; this implies that constructs are empirically distinct. In summary, the measurement mode test, including convergent and discriminant validity measures, is satisfactory. The fitness measures for the measurement models are shown in Table 4. All the fitness measures are acceptable. Consequently, all the measures taken in this work show that the model provides a good fit to the data.

C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659


Table 2 Reliability Construct


Item reliability

Composite reliability

Average variance extracted

Social norm (SN)


0.62 0.85 0.93



Perceived ease of use (PE)


0.86 0.86 0.58



Perceived cohesion (PC)


0.77 0.90 0.74



Perceived enjoyment (PN)


0.80 0.81 0.88



Preference (P)

P1 P2

0.89 0.83



Loyalty (Intention to reuse) (L)

L1 L2

0.88 0.94



Table 3 Discriminant validity of users








0.654 0.057 0.150 0.110 0.135 0.149

0.606 0.052 0.135 0.161 0.132

0.647 0.191 0.240 0.168

0.690 0.278 0.262

0.740 0.484


Table 4 Fit indices for the measurement

Results Recommended criteria Suggested by authors

X2/ df







2.10 <3.0 Hayduck (1987)

0.94 >0.9 Scott (1994)

0.91 >0.8 Scott (1994)

0.97 >0.9 Bagozzi and Yi (1988)

0.94 >0.9 Bentler and Bonett (1980)

0.96 >0.9 Bentler and Bonett (1980)

0.067 <0.1 Bagozzi and Yi (1988)

5.4. Tests of the structural model We examined the structural equation model by testing the hypothesized relationships among various constructs, as shown in Fig. 3. The results support the influence of social norm on loyalty (b = 0.19, p < 0.001), supporting H1. The hypothesized paths from perceived enjoyment are significant in the prediction of online game community preference and loyalty (b = 0.42, p < 0.001; b = 0.13, p < 0.05), supporting H2a and


C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659





0.32*** Perceived Ease of use 0.43***





Customer Preference


Customer Loyalty

0.13* *** significant at .01


** significant at .05



significant at .1

Fig. 3. Results of structural modeling analysis.

H2b. The effect of preference on loyalty was significant, as shown by the path coefficient of 0.75 (p < 0.001), supporting H5. More specifically, social norms, perceived enjoyment, and preference explain 63% of the variance on usersÕ loyalty toward the online game community. Perceived ease of use influences both preference and perceived enjoyment significantly (b = 0.23, p < 0.001; b = 0.43, p < 0.001), supporting H3a and H3b. There is partial support for H4: perceived cohesion on preference (b = 0.32, p < 0.001), supporting H4a. Contrary to expectations, perceived cohesion has no direct influence on loyalty ( 0.02, p > 0.05). Notably, however, perceived cohesion has indirect effects, mainly through preference, on loyalty toward the online game community, as shown in Table 5. Together, the three paths, perceived cohesion, perceived ease of use, and perceived enjoyment, account for 47% of the observed variance in preference toward participating in online game community. 5.5. Purpose and problems facing online game community To gain further insight into the information about online game community, the questionnaire was also designed to obtain the following information:

Table 5 Effects on loyalty toward the online game community (n = 356) Construct Social norms Perceived cohesion Perceived ease of use Perceived enjoyment Preference * ** ***

p < 0.05. p < 0.01. p < 0.001.

Direct effects ***

0.19 0.04 0 0.13* 0.75***

Indirect effects

Total effects

0 0.24*** 0.36*** 0.32*** 0

0.19*** 0.19*** 0.36*** 0.45*** 0.75***

C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659


Table 6 Purpose of participating in online game community Items

No. of respondents


Entertainment Kill time Release life pressure Interest Relationship

277 259 174 167 147

77 72 48 46 41

Table 7 Problems faced by participating in online game community Items

No. of respondents


A sudden disconnection from system Encountering the malicious players Too many grief players Network congestion Inefficiently connect to system

246 239 212 195 169

69 67 59 54 47

1. Purposes: To find out usersÕ purposes in participating in online game community, users were asked why they joined the online game community. 2. Problems: To investigate the problems of online game community, users were asked what they perceived to be the main hurdles to online game communities. Tables 6 and 7 present investigation results, including purpose and problems of participating online game community. Table 6 shows that 77% of respondents participated in online game community for entertainment, about 72% of the respondents to Ôkill timeÕ, 48% to release pressure, 46% to interest, and 41% for relationships. Beside Ôkilling timeÕ, online game community seems to reflect consumer needs (Armstrong & Hagel, 1996). From Table 7, many respondents believe that the most important problems facing the online game community are unstable systems, such as sudden disconnection from the system (69%). While users enjoy participating in online game community, this problem makes users uncomfortable. Encountering malicious players is viewed as the next important problem, with a slightly lower percentage than the system problem (67%). Although malicious players cause many problems and break online communities playing policies, online crime and violence are being managed by online game developers. The issue of grief player is also considered as an important problem (59%). In a game community, a grief player is seen as an impolite and unethical person. Too many grief players seriously affect the regularity of the community. Network congestion (54%) and inefficient connection are also rated by respondents to be important. In general, many respondents feel that it is necessary for convenience and more efficiency to access the online game system. 6. Discussion This study reveals that participantsÕ loyalty to online game community can be predicted by the proposed model (R2 = 63%). Perceived enjoyment and social norm significantly and directly influence loyalty. Perceived enjoyment, as intrinsic motivation, plays a key role in


C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659

explaining the customerÕs behavior of participating in an entertainment-oriented community. This finding stresses the point that if the users do not perceive the participation as enjoyment, they are unlikely to join it. Consistent with Moon and Kim (2001) study, which indicates that entertainment-purpose users are motivated by intrinsic motivation, perceived usefulness has a significant effect only for the work-purpose users. Furthermore, in the workplace, many empirical studies had evidence about the importance of the role of intrinsic motivation on technology use (Davis et al., 1992; Igbaria, Parasuraman, & Baroudi, 1996; Venkatesh, 2000). Consequently, we recommend that the theoretical model contributing to explain why users have an intrinsic motivation to use technology, should not be ignored, and it is definitely an influential factor, especially in the entertainment technology context. Another plausible finding is that users are loyal participants in online game communities because of the compliance-based effect of social norm. This is confirmed in other theories including TRA, TPB, Triandis model, and TAM2. While users want to belong to a community, they want to comply with mandates. Although the community is an open environment, individualsÕ participation is typically affected by other usersÕ opinion. In order to maintain individualsÕ identification or relationship in the community, usersÕ participation is mandatory rather than voluntary. This resembles mandatory system usage settings in workplace. Social norm significantly influences technology use (Venkatesh & Davis, 2000). Perceived cohesion appears to be the important determinant of a userÕs preference for participating in the community. This highlights the critical role of perceived cohesion in community growth. In the process of satisfying individualsÕ needs, such as achieving a common interest and building relationships, users are likely to perceive membersÕ attraction to the community or each other. The collective sense would develop cohesion and consequently form a positive attitude toward the community. Perceived ease of use appears to have significant effects on both perceived enjoyment and preference. Easy-to-use interface enhance enjoyment and encourage people to re-participate. On the contrary, difficulties of use make people resist and thus lose the loyalty toward the community. Overall, findings from the study suggest modified TAM to be an appropriate model to explain individualsÕ behavior of participating in an entertainment community. The model provides a conceptual depiction of what motivates people to participate in a community with reasonably strong empirical support. Social norms, customer preference, and perceived enjoyment play a critical role in determining loyalty toward a community. 7. Implication 7.1. Implication for IS researchers For IS researchers, this study provides a theoretical understanding of the factors contributing to loyalty toward entertainment community. First, while studies on extrinsic motivation about IT use have received more attention in TAM-related literature, our model implies that intrinsic motivation, such as perceived enjoyment, can be an influential factor in entertainment-purpose use. Second, social norm and cohesion are also the determinants of consumer preference and loyalty. In a community context, these two factors should be considered. Finally, like TAM and other empirical studies, ease of use still plays an important role in shaping both customer preference and perceived enjoyment.

C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659


7.2. Implication for online game community practitioners Our study generates the ensuing insights for managers and developers of online game community. 1. The community of online games is seen as an entertainment product, which is used primarily for leisure and enjoyment. Therefore, managers should strive to motivate usersÕ intrinsic motivation such as enjoyment, fun, curiosity, exploratory behaviors, and flow experience (Choi & Kim, 2004; Hsu & Lu, 2004). These elements should be reflected in the online gameÕs design. 2. Because loyalty can be predicted reasonably well from social norm, we suggest managers should build solid relationships with opinion leaders, who have a normative power to affect other usersÕ participation. 3. In order to increase usersÕ perceived cohesion, we recommend that community practitioners should organize a periodic party or contest to increase usersÕ cohesive perceptions. Moreover, a community-based design in online games such as guild is needed to maintain relationships among users. 4. Designers should improve user friendliness of the game system, making it easy to use and more accessible. 5. Online game community must overcome the problems users are concerned, with including suffering from an unstable system, malicious players and grief players. 8. Conclusion and limitations This study investigated factors essential to loyalty of online game communities. Although we modified TAM by examining intrinsic motivation in the context of the online game community instead of extrinsic motivation, and social norm and perceived cohesion as extended factors, the findings suggest that these factors have a significant effect in shaping consumersÕ behavior. The proposed model is validly verified in explaining and predicting usersÕ behaviors in the online game community context. From a practical perspective, insights into loyalty provided by the study also suggest better strategies to implement online game communities. The results of this study should be interpreted and accepted with caution for several reasons. First, it should be noted that a bias exists because the sample was self selected. Second, loyalty is measured here by intention to re-participate in the online game community, that is, stay with the service provider without finding a better alternative – or what Dick and Basu (1994) called behavioral loyalty. Although another loyalty, called attitudinal loyalty, as deep commitment to a service provider, is not easily swayed by a slightly more attractive alternative, past research has indicated a strong tie between ‘‘behavioral loyalty’’ and ‘‘attitudinal loyalty’’ (Methlie & Nysveen, 1999). Third, the other difficulty is the limitation on generalization. Since intrinsic motivation, perceived cohesion and social norm are additional antecedents of loyalty, it is impossible to generalize the findings to other entertainment communities. Finally, this study suggests that researchers should investigate how beliefs (including social norm, perceived enjoyment, perceived ease of use and perceived cohesion) are influenced by external factors, such as community support and policy, individualsÕ personalities, and system characteristics to better understand the usage of entertainment communities.


C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659

Appendix A. List of items by construct



Social norm

(SN1): My colleagues think that I should participate in the online game community (SN2): My classmates think that I should participate in the online game community (SN3): My friends think that I should participate in the online game community

Perceived ease of use

(PE1): Learning to participate in the online game community is easy for me (PE2): It is easy for me to become skillful at participating in online game community (PE3): I think it is easy to participate in the online game community

Perceived cohesion

(PC1): I fit in well with the online game community (PC2): I like the members of the online game community (PC3): In general, online game communities act as a cohesive unit

Perceived enjoyment

(PN1) The process of participating in online game community is enjoyable (PN2): While participating in online game community, I experienced pleasure (PN3): Overall, I believe that online game community is playful


(P1): I like participating in online game community (P2): I feel good about participating in online game community

Loyalty (Intention to reuse)

(L1): I will frequently re-participate in online game community in the future (L2): I intend to revisit the online game community

References Al-Gahtani, S. S., & King, M. (1999). Attitudes, satisfaction and usage: factors contributing to each in the acceptance of information technology. Behaviour and Information Technology, 18(4), 277–297. Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391. Armstrong, A., & Hagel, J. III, (1996). The real value of online communities. Harvard Business Review, MayJune, 134–141. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94.

C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659


Bajaj, A., & Nidumolu, S. R. (1998). A feedback model to understand information system usage. Information and Management, 33(4), 213–224. Balasubramanian, S., & Mahajan, V. (2001). The economic leverage of the virtual community. International Journal of Electronic Commerce, 5(3), 103–137. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. Carron, A. V., & Brawley, L. R. (2000). Cohesion. Small Group Research, 31, 89–106. Chau, P. Y. K., & Hu, H. P. (2002). Investigating healthcare professionalsÕ decisions to accept telemedicine technology: an empirical test of competing theories. Information and Management, 39, 297–311. Choi, D., & Kim, J. (2004). Why people continue to play online games: in search of critical design factors to increase customer loyalty to online contents. CyberPsychology and Behavior, 7(1), 11–24. Chin, W. W., & Gopal, A. (1995). Adoption intention in GSS: relative importance of beliefs. The Data Base for Advances in Information Systems, 26(2&3), 42–63. David, O. S., Jonathan, L. F., & Letitia, A. P. (1986). Social psychology. Los Angels: University of California. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–339. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. Deci, E. L., & Ryan, R. M. (1987). Accessibility and stability of predictors in the theory of planned behaviour. Journal of Personality and Social Psychology, 63(5), 754–765. Dermatis, H., Salke, M., Galanter, M., & Bunt, G. (2001). The role of social cohesion among residents in a therapeutic community. Journal of Substance Abuse Treatment, 21, 105–110. DFC Intelligence (2004). Available from: Dick, A. S., & Basu, K. (1994). Customer loyalty: an integrated conceptual framework. Journal of Academy of Marketing Science, 22(2), 99–113. Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information and Management, 36(1), 9–21. Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using DavisÕs perceived usefulness and ease-of-use instruments for decision making: a confirmatory and multigroup invariance Analysis. Decision Sciences, 29(4), 839–869. Evans, C. R., & Dion, K. L. (1991). Group cohesion and performance: a meta-analysis. Small Group Research, 22(2), 203–216. Evans, N. J., & Jarvis, P. A. (1980). Group cohesion: a review and reevaluation. Small Group Behavior, 11, 359–370. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Reading, MA: Addison-Wesley. Fornell, C. R. (1982). A second generation of multivariate analysis methods (Vols. I and II). New York: Praeger Special Studies. Fornell, C. R., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. Gefen, D. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51–90. Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: an extension to the technology acceptance model. MIS Quarterly, 21(4), 389–400. Goodman, P. S., Ravlin, E., & Schminke, M. (1987). Understanding groups in organizations. Research Organization Behaviour, 9, 121–173. Hagel, J., III, & Armstrong, G. A. (1997). Net gain: expanding markets through virtual communities. Boston, MA: Harvard Business School Press. Hayduck, L. A. (1987). Structural equation modeling with LISREL. Baltimore, MD: Johns Hopkings University Press. Hsu, C. L., & Lu, H. P. (2004). Why do people play online games? An extended TAM with social influences and flow experience. Information and Management, 41(7), 853–868. Hogg, M. A. (1992). The social psychology of group cohesiveness: from attraction to social identity. New York: New York University Press. Hu, P. J., Chau, P. K. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91–112.


C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659

Hunton, J. E., Arnold, V., & Gibson, D. (2001). Collective user participation: a catalyst for group cohesion and perceived respect. International Journal of Accounting Information Systems, 2(1), 1–17. Igbaria, M., Schiffman, S. J., & Wieckowshi, T. S. (1994). The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology. Behaviour and Information Technology, 13(6), 349–361. Igbaria, M., Parasuraman, S., & Baroudi, J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13(1), 127–143. Joreskog, K. G., & Sorbom, D. (1996). LISREL 8: UsersÕ reference guide. Chicago: Scientific Software International. Kardaras, D., Karakostas, B., & Papathanassiou, E. (2003). The potential of virtual communities in the insurance industry in the UK and Greece. International Journal of Information Management, 23, 41–53. Kaasinen, E. (2005). User acceptance of location-aware mobile guides based on seven field studies. Behaviour and Information Technology, 24(1), 37–49. Klein, H. J., & Mulvey, P. W. (1995). Two investigations of the relationships among group goals, goal commitment, cohesion, and performance. Organizational Behavior and Human Decision Processes, 61(1), 44–53. Kwon, O. B., Kim, C.-R., & Lee, E. J. (2002). Impact of website information design factors on consumer ratings of web-based auction sites. Behaviour and Information Technology, 21(6), 387–402. Laka, C. (1996). Relational development in computer-supported groups. MIS Quarterly. Lee, G. G., & Pai, J.-C. (2003). Effects of organizational context and inter-group behavior on the success of strategic information systems planning: an empirical study. Behaviour and Information Technology, 1–18. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40, 191–204. Liaw, S.-S., & Huang, H.-H. (2003). An investigation of user attitude toward search engines as an information retrieval tool. Computers in Human Behavior, 19, 751–765. Liker, J. K., & Sindi, A. A. (1997). User acceptance of expert systems: a test of the theory of reasoned action. Journal of Engineering and Technology Management, 14, 147–173. Lin, J. C.-C., & Lu, H. (2000). Toward an understanding of the behavioural intention to use a web site. International Journal of Information Management, 20, 197–208. Loebbecke, C., & Powell, P. (2002). E-business in the entertainment sector: the Egmont case. International Journal of Information Management, 22(4), 307–322. Lu, H., & Lin, J. C.-C. (2003). Predicting customer behavior in the market-space: a study of Rayport and SvioklaÕs framework. Information and Management, 40, 1–10. Lucas, H. C., & Spitler, V. K. (2000). Implementation in a world of workstations and networks. Information and Management, 38(2), 119–128. Luo, W., & Strong, D. (2000). Perceived critical mass effect on groupware acceptance. European Journal of Information Systems, 9(2), 91–103. Mathieson, K. E. P., & Chin, W. C. (2001). Extending the technology acceptance model: the influence of perceived user resources. The Data Base for Advances in Information Systems, 32(3), 86–113. Methlie, L. B. v., & Nysveen, H. (1999). Loyalty of online bank customers. Journal of Information Technology, 14, 375–386. MIC, Analysis of demographic of the online game users, (2003). Available from focus/0105_focus_disp.asp?focus_id=241. Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a world-wide-web context. Information and Management, 38(4), 217–230. Narayanan, V. K., & Nath, R. (1984). The influence of group cohesiveness on some changes induced by flexitime: a quasi-experiment. Journal of Applied Behavioral Science, 20(3), 265–276. NetValue (2002). Available from Pin, L., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891. Piper, W. E., Marrache, M., LaCroix, R., Richardsen, M., & Jones, B. D. (1983). Cohesion and a basic bond in groups. Human Relations, 36, 93–108. Plouffe, C. R., Hulland, J., & Vandenbosch, M. (2001). Richness versus parsimony in modeling technology adoption decisions: Understanding merchant adoption of a smart cart-based payment system. Information Systems Research, 12(2), 208–222. Podsakoff, P. M., MacKenzie, S. B., & Ahearne, M. (1997). Moderating effects of goal acceptance on relationship between group cohesiveness and productivity. Journal of Applied Psychology, 82(6), 974–983.

C.-L. Hsu, H.-P. Lu / Computers in Human Behavior 23 (2007) 1642–1659


Preece, J. (2000). Online communities: designing usability, support sociability. Chichester, UK: Wiley. Preece, J. (2001). Sociability and usability in online communities: determining and measuring success. Behaviour and Information Technology, 20(5), 347–356. Preece, J., Nonnecke, B., & Andrews, D. (2004). The top five reasons for lurking: improving community experiences for everyone. Computers in Human Behavior, 20, 201–223. Riemenschneider, C. K., Harrison, D. A., jr, & Mykytyn, P. P (2003). Understanding it adoption decisions in small business: Integrating current theories. Information and Management, 40, 269–285. Romm, C., Pliskin, N., & Clarke, R. (1997). Virtual communities and society: toward and integrative three phase model. International Journal of Information Management, 17(4), 261–270. Scott, J. E. (1994). The measurement of information systems effectiveness: evaluating a measuring instrument. In Proceedings of the fifteenth international conference on information systems (pp. 111–128). Vancouver, BC. Stafford, M. R., & Stern, B. (2002). Consumer bidding behavior on internet auction sites. International Journal of Electronic Commerce, 7(1), 135–150. Takahashi, D. (2000). E-Commerce (A Special Report): industry by industry – donÕt shoot! everybody figured game sites would be hot; the surprise is whoÕs playing –and what. Wall Street Journal, New York, 17. Taylor, S., & Todd, P. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176. Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in Internet usage. OMEGA International Journal of Management Science, 27(1), 25–37. Vellerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. Advance Experiment Social Psychology, 29, 271–360. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204. Venkatesh, V., & Morris, M. G. (2000). Why donÕt men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139. Wachter, R. M., Gupta, J. N. D., & Quaddus, M. A. (2000). IT takes a village: Virtual communities in support of education. International Journal of Information Management, 20, 437–489. Wasko, M. M., & Faraj, S. (2000). It is what one does: Why people participate and help others in electronic communities of practice. Journal of Strategic Information Systems, 9, 155–173. Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42, 719–729. Yoo, Y., & Alavi, M. (2001). Media and group cohesion: relative influences on social presence, task participation, and group consensus. MIS Quarterly, 25(3), 371–390. Yu, J., Ha, I., Choi, M., & Rho, J. (2005). Extending the TAM for a t-commerce. Information and Management, 42, 965–976. Chin-Lung Hsu is an assistant professor of Information Management at Da-Yeh University, Changhua, Taiwan. He received a B.S., an M.B.A and a Ph.D. from National Taiwan University of Science and Technology, Taipei, Taiwan, in 1997, 1999 and 2004, respectively. Dr. HsuÕs articles have appeared in Information and Management and International Journal of Computer Application in Technology. His research interests include electronic commerce and user behavior. Hsi-Peng Lu is a Professor of Information Management at National Taiwan University, Taichung, Taiwan, in 1985, an M.S. from National Tsing-Hua University, Taiwan, in 1987, and the an M.S. and a Ph.D. in Industrial Engineering from the University of Wisconsin – Madison in 1991 and 1992, respectively. His research interests are in electronic commerce, managerial decision making, and information systems management. His work has appeared in journals such as Information and Management, Omega, European Journal of Operational Research, Journal of Computer Information Systems, Management Decision, Information Systems Management, International Journal of Information Management, and International Journal of Technology Management. He also works as a TV host and is a consultant for many organizations in Taiwan.