Factors influencing the adoption of mobile gaming services

The current chapter focuses the adoption process of mobile gaming. After providing a brief introduction to the topic of re-commerce and m-services, several relevant adoption factors are highlighted. These factors have been researched empirically, via a conjoint study conducted in the Netherlands. The results illustrated a hierarchical importance of the factors identified, whereby perceived risk, complexity, and compatibility were identified as the three main regarded as the factors that are mainly influencing the adoption of mobile gaming applications. Based on these findings, we have provided several managerial implications.

[1]  P. Verhoef,et al.  Possible determinants of consumers’ adoption of electronic grocery shopping in the Netherlands , 2001 .

[2]  Lyman E. Ostlund Perceived Innovation Attributes as Predictors of Innovativeness , 1974 .

[3]  Ritu Agarwal,et al.  Are Individual Differences Germane to the Acceptance of New Information Technologies , 1999 .

[4]  Thomas C. Kinnear,et al.  Exploring the Consumer Decision Process in the Adoption of Solar Energy Systems , 1981 .

[5]  Gary L. Lilien,et al.  Marketing Engineering: Computer-Assisted Marketing Analysis and Planning , 1998 .

[6]  Peter Tarasewich,et al.  Issues in wireless E-commerce , 2000, SECO.

[7]  Paul E. Green,et al.  Conjoint Internal Validity Under Alternative Profile Presentations , 1988 .

[8]  Daniel P. Dolan The Big Bumpy Shift: Digital Music via Mobile Internet , 2000 .

[9]  Peter Enderwick,et al.  A cognitive model on attitude towards technology adoption , 2000 .

[10]  Moez Limayem,et al.  E-Mail and V-Mail Usage: Generalizing Across Technologies , 2000, J. Organ. Comput. Electron. Commer..

[11]  F. Pons,et al.  Values and adoption of innovations: a cross‐cultural study , 1999 .

[12]  Young-Gul Kim,et al.  Extending the TAM for a World-Wide-Web context , 2000, Inf. Manag..

[13]  Albert L. Lederer,et al.  The technology acceptance model and the World Wide Web , 2000, Decis. Support Syst..

[14]  F. Bass A new product growth model for consumer durables , 1976 .

[15]  Paul May,et al.  Mobile Commerce: Mobile Commerce Services Directory , 2001 .

[16]  Jung P. Shim,et al.  Factors influencing corporate web site adoption: a time-based assessment , 2001, Inf. Manag..

[17]  D. Lehmann,et al.  Purchase Intentions and the Dimensions of Innovation: An Exploratory Model , 1990 .

[18]  E. Rogers,et al.  The diffusion of interactive communication innovations and the critical mass: the adoption of telecommunications services by German banks , 1999 .

[19]  Mary Ann Eastlick,et al.  Profiling potential adopters and non‐adopters of an interactive electronic shopping medium , 1999 .

[20]  Paul E. Green,et al.  Segmenting Markets with Conjoint Analysis , 1991 .

[21]  Peter A. Todd,et al.  Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication , 1992, MIS Q..

[22]  Timo Laakko,et al.  Two approaches to bringing Internet services to WAP devices , 2000, Comput. Networks.

[23]  Joseph F. Hair,et al.  An Assessment of the Mall Intercept as a Data Collection Method , 1985 .

[24]  Christopher R. Plouffe Intermediating technologies and multi‐group adoption: A comparison of consumer and merchant adoption intentions toward a new electronic payment system , 2001 .

[25]  Detmar W. Straub,et al.  The psychological origins of perceived usefulness and ease-of-use , 1999, Inf. Manag..

[26]  Fred D. Davis User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts , 1993, Int. J. Man Mach. Stud..

[27]  Paul E. Green,et al.  Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice , 1990 .

[28]  K. Fang,et al.  An Analysis of Electronic-Mail Usage. , 1998 .

[29]  Fred D. Davis A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[30]  E. Rogers Diffusion of Innovations , 1962 .