Decision Making Case Study

Topics: Mobile phone, Case study, Decision making Pages: 25 (6669 words) Published: June 17, 2013
CASE STUDY ANALYSIS OF CORPORATE DECISIONMAKING FOR CELL PHONE DEPLOYMENT Roberts, G. Keith1 [email protected] Pick, James B. [email protected] ABSTRACT The wireless cell phone market has experienced phenomenal growth over the last decade. This paper studies the factors that five companies considered important in deciding to deploy wireless cell phone devices, the extent of current use of wireless cell phones, the extent of existing utilization and/or planning for web-enabled cell phone use, the constraining factors in their deployment decisions, how such decisions are made, and how regulation of the wireless industry has affected their decision-making process. The conceptual model combines the TAM and innovation diffusion models, adding the factors of security/privacy and web connectivity. Case study methodology is utilized for five manufacturing and technology firms. A key finding is that the most important decision factors are security/privacy, provision of quality service to customers, web connectivity, and, for one firm, productivity. Many other findings are presented, and the conceptual model is supported by the findings. The study’s practical implications are examined. BACKGROUND AND LITERATURE REVIEW This study focuses on identifying factors that corporations consider important in their decision to deploy devices designed for mobile telephony and mobile data services. We also consider the approval steps in decision-making, the extent and importance of web-enabled cell phones, and the functional areas of use of cell phones. There has been little research regarding corporate adoption of wireless (mobile) devices, but there is a solid foundation of theories and previous studies on technology adoption for this case study. The decision to adopt a wireless device, especially if the alternative is a wireline device, is in essence a technology adoption issue (Kleijnen and de Ruyter 2003, Van Akkeren and Harker 2003). A number of theories have been developed to help explain the concept of technology adoption (Mennecke and Strader 2003; Kleijnen and de Ruyter 2003). One widely accepted model is the Technology Acceptance Model (TAM) (Davis 1989, Davis 1993). Davis (1989) emphasized the theoretical constructs of perceived usefulness and perceived ease of use as a means of predicting user acceptance of information technology. Adams et al. (1992) replicated Davis’ research for fixed voice and e-mail. They refined the measurement scales and utilized structured equation modeling to explain interactions. In later research using the TAM model, Davis’ results in 1993 indicated that while ease of use is clearly important, usefulness is even 1

To be presented at Americas Conference on Information Systems (AMCIS) and published in the proceedings, August 2003.

more important in determining user acceptance. Lederer, Maupin, Sena, and Zhuang (2000) investigated TAM for work-related tasks involving the web. Their findings provided support for TAM and also showed that usefulness has a stronger effect than ease of use. Rogers (1995) identifies six attributes of an innovation that help to explain the rate of technology adoption: (1) Relative Advantage (degree to which innovation is perceived as being better than the idea it supersedes), (2) Compatibility (degree to which innovation is perceived as consistent with existing values, past experiences, and needs of potential adopters), (3) Complexity (degree to which innovation is perceived as relatively difficult to understand and use), (4) Trialability (degree to which innovation may be experimented with on limited basis), (5) Observability (degree to which results of innovation are visible to others), and (6) Communication. In his discussion of the attributes of innovation, Rogers states “Cellular phones have an almost ideal set of perceived attributes, and this is undoubtedly one reason for the innovation’s very rapid rate of adoption in the U.S.” (Rogers, 1995, p. 245)...

References: Adams, D.A., Nelson, R.R., and Todd, P.A. 1992. “Perceived Usefulness, Ease of use, and Usage of Information Technology: A Replication,” MIS Quarterly (16:2), 1992, pp. 227247. Agarwal, R. and Prasad, J. “The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies,” Decision Sciences (28:3), 1997, pp. 557-582. Black, S.K. Telecommunications Law in the Internet Age, San Francisco, California: Morgan Kaufmann, 2002. Clarke III, I. “Emerging Value Propositions for M-Commerce,” Journal of Business Strategies (18:2), 2001, pp. 133-148. Davis, F.D. “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly (13:3), 1989, pp. 319-340.. Davis, F.D. “User Acceptance of Information Technology: System Characteristics, User Perceptions, and Behavioral Impacts,” International Journal of Man-Machine Studies (38), 1993, pp. 318-339. Davis, G.B. “Anytime/Anyplace Computing and the Future of Knowledge Work,” Communications of the ACM, 43(12), 2002, pp. 67-73. Dodd, A.Z. The Essential Guide to Telecommunications. 3rd Edition. Upper Saddle River, New Jersey: Prentice Hall, 2002. Eastlick, M.A. and Lotz, S. “Profiling Potential Adopters and Non-adopters of an Interactive Electronic Shopping Medium,” International Journal of Retail and Distribution Management 26(6), 1999, pp. 209-223. Federal Communications Commission. 7th Annual Commissioners Competition Report, Retrieved January 26, 2003, from, 2003. Gefen, D. and Straub, D.W. “Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model,” MIS Quarterly (December), 1997, pp. 389-400. Kleijnen, M. and de Ruyter, K. “Factors Influencing the Adoption of Mobile Gaming Services,” in Mennecke, B.J. and Strader, T.J. (eds.), Mobile Commerce: Technology, Theory, and Applications, Hershey, Pennsylvania, Idea Group Publishing, 2003, pp. 202-217. Lederer, A.L., Maupin, D.J., Sena, M.P., and Zhuang, Y. “The Technology Acceptance Model and the World Wide Web.” Decision Support Systems (29), 2000, pp. 269-282. Mennecke, B.J., and Strader, T.J. Mobile Commerce Technology, Theory and Applications, Hershey, Pennsylvania: Idea Group, 2003. Palen, L “Mobile Telephone in a Connected Life,” Communications of the ACM 45(3), 2002, pp. 78-82. Rogers, E. Diffusion of Innovations, 4th edition, New York, New York: Free Press, 1995. Stake, R.E. The Art of Case Study Research, Thousand Oaks, California: SAGE Publications, 1995. Van Akkeren, J. and Harker, D. “Mobile Data Technologies and Small Business Adoption and Diffusion: An Empirical Study of Barriers and Facilitators,” in Mennecke, B.J. and Strader, T.J. (eds.), Mobile Commerce: Technology, Theory, and Applications, Hershey, Pennsylvania, Idea Group Publishing, 2003, pp. 218-244. Yin, R.K. Case Study Research: Design and Methods. 2nd Edition, Thousand Oaks, California: SAGE Publications, 1994. Yin, R.K. Applications of Case Study Research, Thousand Oaks, California: SAGE Publications, 1993.
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