Abstract
Drawing on Rogers' diffusion of innovations theory, specifically his innovation decision process model, this paper develops a research framework that identifies the accelerators and inhibitors of virtual world technology adoption. Categorized by level of analysis, specific factors influencing the adoption of virtual world technology by organizations are identified. At the individual level of analysis, factors such as the technology's ease of use and usefulness, as well as the individual's computer self-efficacy, trust, and enjoyment are discussed in terms of their "bottom-up" influence on technology diffusion. At the group level of analysis, different forms of group efficacy (such as computer collective efficacy and virtual team efficacy) are suggested to play a role in virtual world technology adoption and diffusion. Further, group-level technology acceptance factors (including a priori beliefs and attitudes toward the technology, as well as psychosocial variables) are proposed to influence a group's overall valence toward the technology, driving the adoption of the technology Finally, at the organizational level of analysis, technical compatibility, technical complexity, and relative advantage are explored as major factors influencing an organization's willingness to adopt virtual world technology through "top-down" diffusion that follows specific isomorphic processes. Finally, the role of organizational culture is also discussed. Through the identification and discussion of innovation diffusion factors which occur at different levels of analysis and at different stages of the innovation decision process, this study provides a framework for studying the adoption, spread, and continual use of virtual world technology.
- Agarwal, R., and Prasad, J. (1998). "A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology," Information Systems Research, Vol. 9, No.2, pp. 204--215. Google ScholarDigital Library
- Ajzen, I., and Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior, Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
- Compeau, D.R., & Higgins, C. A. (1995). "Computer Self-Efficacy: Development of a Measure and Initial Test," MIS Quarterly, Vol. 19, No.2, pp. 189--211. Google ScholarDigital Library
- Cooper, R.B., and Zmud, R.W. (1990). "Information Technology Implementation Research: A Technological Diffusion Approach," Management Science, Vol. 36, No.2, pp. 123--139. Google ScholarDigital Library
- Davis, F.D., Bagozzi, R.P., & Warshaw, P. R. (1989). "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, Vol. 35, No.8, pp. 982--1003. Google ScholarDigital Library
- DiMaggio, P.J., and Powell, W.W. (1983). "The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields," American Sociological Review, Vol. 48, No.2, pp.147--160.Google ScholarCross Ref
- Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.Google Scholar
- Fuller, M. A., Hardin, A.M., and Davison, R.M. (2007). "Efficacy in Technology-Mediated Distributed Teams," Journal of Management Information Systems, Vol. 23, No.3, pp. 209--235. Google ScholarDigital Library
- Hardin, A. M., Fuller, M. A., and Davison, R. M. (2007). "I Know I Can, but Can We? Culture and Efficacy Beliefs in Global Virtual Teams," Small Group Research, Vol. 38, No.1, pp. 1--25.Google ScholarCross Ref
- Hardin, A.M., Fuller, M.A., and Valacich, J. (2006). "Group Efficacy in Virtual Teams: New Questions in an Old Debate," Small Group Research, Vol. 37, No.1, pp. 65--85.Google ScholarCross Ref
- Hof, R.D. (2006). "My Virtual Life," Business Week Retrieved May 15, 2007, from http://www.businessweek.com/magazine/content/06_18/b3982001.htm?chan=searchGoogle Scholar
- McKnight, D.H., Choudhury, V., and Kacmar, C. (2002). "Developing and Validating Trust Measures for E-Commerce: An Integrative Typology," Information Systems Research, Vol. 13, No.3, pp. 334--359. Google ScholarDigital Library
- Reuters, A. (2006), "IBM accelerates push into 3D virtual worlds," Reuters, Retrieved April 19, 2007, from http://secondlife.reuters.com/stories/2006/11/09/ibm-accelerates-pushinto-3d-virtual-worlds/Google Scholar
- Rogers, E.M. (1995). Diffusion of Innovations. New York: Free Press.Google Scholar
- Sarker, S., Valacich, J.S., and Sarker, S. (2005). "Technology Adoption by Groups: A Valence Perspective," Journal of the Association for Information Systems, Vol. 6, No.2, pp. 37--71.Google ScholarCross Ref
- Tierney, P., and Farmer, S.M. (2002). "CreativeGoogle Scholar
- Self-Efficacy: Its Potential Antecedents and Relationship to Creative Performance," Academy of Management Journal, Vol. 45, No.6, pp. 1137--1148.Google Scholar
Index Terms
- Diffusion of virtual innovation
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