A Model of 3G Adoption

In this paper we develop a model of end users’ 3G adoption behavior based on the premise that 3G is not an entirely new innovation but rather an upgrade of mobile data services (MDS). Drawing theoretical foundations from the knowledge transfer paradigm in marketing research and based on the technological architecture of MDS, we hypothesize how beliefs about upgrading benefits and costs are formed and how they will influence 3G adoption intention. The empirical results support our hypotheses, based on which theoretical and managerial implications are discussed.

[1]  William B. Dodds,et al.  Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations , 1991 .

[2]  Detmar W. Straub,et al.  Validating Instruments in MIS Research , 1989, MIS Q..

[3]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[4]  Rolph E. Anderson,et al.  Multivariate Data Analysis with Readings , 1979 .

[5]  Deborah Roedder John,et al.  Consumer Learning by Analogy: A Model of Internal Knowledge Transfer , 1997 .

[6]  Allen M. Weiss,et al.  Marketing in Technology-Intensive Markets: Toward a Conceptual Framework , 1999 .

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

[8]  Detmar W. Straub,et al.  Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs , 1999, MIS Q..

[9]  Wendy K. Smith,et al.  A Structural Approach to Assessing Innovation: Construct Development of Innovation Locus, Type, and Characteristics , 2002, Manag. Sci..

[10]  Kalle Lyytinen,et al.  The Disruptive Nature of Information Technology Innovations: The Case of Internet Computing in Systems Development Organizations , 2003, MIS Q..

[11]  F. Bass,et al.  A diffusion theory model of adoption and substitution for successive generations of high-technology products , 1987 .

[12]  A. Markman,et al.  Entrenched Knowledge Structures and Consumer Response to New Products , 2001 .

[13]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[14]  L. Hayduk.  Structural equation modeling with LISREL , 1987 .

[15]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[16]  Mohanbir Sawhney,et al.  Leveraged high-variety strategies: From portfolio thinking to platform thinking , 1998 .

[17]  M. Mcgrath.  Product Strategy for High Technology Companies , 2000 .

[18]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[19]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[20]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[21]  Robert J. Meyer,et al.  The Enhancement Bias in Consumer Decisions to Adopt and Utilize Product Innovations , 2003 .

[22]  Ashesh Mukherjee,et al.  The Effect of Novel Attributes on Product Evaluation : Explaining Consumer Resistance to Technological Innovation , 2001 .

[23]  Geoffrey A. Moore.  Crossing the chasm : marketing and selling technology products to mainstream customers , 1991 .

[24]  Detmar W. Straub,et al.  Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model , 1997, MIS Q..

[25]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..