The attraction of personalized service for users in mobile commerce: an empirical study

ABSTRACT

There has been a notable increase in consumer use of mobile applications. Consumers begin to adopt mobile commerce applications. In response, firms have been investing billions of dollars in order to enhance the hardware and software platforms for mobile commerce. Consequently, with such large investments, firms are highly motivated to attract new clients and retain their old customers. In the present study, the strategic parameters have been studied in order to determine the ways in which mobile service providers acquire new customers. For the purpose of analysis, the dependent variable is the service subscribers' intention to switch to a new service provider with personalized services. Four main constructs have been studied - the amount and the perceived usefulness of general advertisements, the perceived usefulness and privacy issues about personalized advertisements. This empirical study indicates that all four constructs are significant in affecting the decision by subscribers to change to a new mobile service provider.

REFERENCES

• 1: {1} AL-GAHTANI, S., AND KING M. 1999. Attitudes, satisfaction and usage: factors contributing to each in the acceptance of information technology. Behavior & Information Technology, 18(4), 277-297
• 2: Liliana Ardissono , Anna Goy , Giovanna Petrone , Marino Segnan, Personalization in business-to-customer interaction, Communications of the ACM, v.45 n.5, May 2002 [doi>10.1145/506218.506245]
• 3: Akhilesh Bajaj , Sarma R. Nidumolu, A feedback model to understand information system usage, Information and Management, v.33 n.4, p.213-224, March 25, 1998 [doi>10.1016/S0378-7206(98)00026-3]
• 4: {4} CAMPBELL, D.T. AND FISKE, D.W. 1959. Convergent and Discriminant Validation by the Multitrait-Multimethod Matrix. Psychological Bulletin, 56(1), 81-105.
• 5: Investingating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories, Information and Management, v.39 n.4, p.297-311, January, 2002 [doi>10.1016/S0378-7206(01)00098-2]
• 6: Pei-Yu (Sharon) Chen , Lorin M. Hitt, Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses: A Study of the Online Brokerage Industry, Information Systems Research, v.13 n.3, p.255-274, September 2002 [doi>10.1287/isre.13.3.255.78]
• 7: Wynne W. Chin , Abhijit Gopal, Adoption intention in GSS: relative importance of beliefs, ACM SIGMIS Database, v.26 n.2-3, p.42-64, May/Aug. 1995 [doi>10.1145/217278.217285]
• 8: {8} DAVIS F.D. (1989), Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology, MIS Quarterly, 13(3), 319-339.
• 9: Josef Fink , Jürgen Koenemann , Stephan Noller , Ingo Schwab, Putting personalization into practice, Communications of the ACM, v.45 n.5, May 2002 [doi>10.1145/506218.506242]
• 10: {10} MORGAN, B. 2002. Mobile Provisioning: Mobile Data's Next Step. Wireless Week, 8(18), 38.
• 11: {11} NASH ,K. S. 2000. Personalization: Clash of the Killer Ps. Computerworld, 34(25), 66-67.
• 12: {12} NUNNALLY, J.C. (1978). Psychometric Theory (2nd ed.). New York: McGraw-Hill.
• 13: Mike Perkowitz , Oren Etzioni, Adaptive Web sites: automatically synthesizing Web pages, Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, p.727-732, July 1998, Madison, Wisconsin, United States
• 14: Mike Perkowitz , Oren Etzioni, Adaptive Web sites, Communications of the ACM, v.43 n.8, p.152-158, Aug. 2000 [doi>10.1145/345124.345171]
• 15: James Pitkow , Hinrich Schütze , Todd Cass , Rob Cooley , Don Turnbull , Andy Edmonds , Eytan Adar , Thomas Breuel, Personalized search, Communications of the ACM, v.45 n.9, September 2002 [doi>10.1145/567498.567526]
• 16: {16} PORTER, M. 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press.
• 17: Naren Ramakrishnan, PIPE: Web Personalization by Partial Evaluation, IEEE Internet Computing, v.4 n.6, p.21-31, November 2000 [doi>10.1109/4236.895012]
• 18: {18} RUPP, W.T. 2002. Mobile Commerce: New Revenue Machine or Black Hole? Business Horizons, 45(4), 26.
• 19: {19} STAHL, S. 2002. M-commerce is Still More Myth Than Reality. InformationWeek, 893, 8-9.
• 20: {20} XIA, L. AND SUDHARSHAN, D. 2002. Effects of Interruptions on Consumer Online Decision Processes. Journal of Consumer Psychology, 12(3), 265-280.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.7 Digital Libraries
          Subjects: User issues

Additional Classification:
  C. Computer Systems Organization
  C.1 PROCESSOR ARCHITECTURES
      C.1.3 Other Architecture Styles
          Subjects: Cellular architecture (e.g., mobile)
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.5 Local and Wide-Area Networks
          Subjects: Internet (e.g., TCP/IP)

  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.4 Systems and Software
          Subjects: User profiles and alert services
      H.3.5 On-line Information Services
          Subjects: Web-based services

  K. Computing Milieux
  K.4 COMPUTERS AND SOCIETY
      K.4.4 Electronic Commerce
          Subjects: Security

General Terms:
Experimentation, Management, Measurement, Reliability, Standardization

Keywords:
mobile commerce, personalization, privacy