Human Behavior
Computers in Human Behavior 23 (2007) 1642–1659 www.elsevier.com/locate/comphumbeh Consumer behavior in online game communities:
A motivational factor perspective
Chin-Lung Hsu
a,*
, Hsi-Peng Lu
b
a
b
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: alung@mail.dyu.edu.tw (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
1643
in
References: Al-Gahtani, S. S., & King, M. (1999). Attitudes, satisfaction and usage: factors contributing to each in the acceptance of information technology 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 1657 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 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 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 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. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace Deci, E. L., & Ryan, R. M. (1987). Accessibility and stability of predictors in the theory of planned behaviour. Dermatis, H., Salke, M., Galanter, M., & Bunt, G. (2001). The role of social cohesion among residents in a therapeutic community DFC Intelligence (2004). Available from: http://www.find.org.tw/0105/news/0105_news_disp.asp?news_id=3316. 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 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 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. 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 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 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 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 Hunton, J. E., Arnold, V., & Gibson, D. (2001). Collective user participation: a catalyst for group cohesion and perceived respect Igbaria, M., Schiffman, S. J., & Wieckowshi, T. S. (1994). The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology 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 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 Kwon, O. B., Kim, C.-R., & Lee, E. J. (2002). Impact of website information design factors on consumer ratings of web-based auction sites 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 Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model Liaw, S.-S., & Huang, H.-H. (2003). An investigation of user attitude toward search engines as an information retrieval tool