Abstract
This study examines how the nature of technology affects users’ intention to continue using information technologies. It proposes an extended technology acceptance model, with perceived ease of use, perceived usefulness and pleasure affecting the intention to continue using a technology. We hypothesized that these effects are moderated by the technology’s utilitarian and hedonic values. The model was validated for smartphone functions. A user survey showed that perceived ease of use significantly affected the intention to continue using only for high-utilitarian functions, whereas pleasure affected the intention to continue using only for high-hedonic functions. The effect of perceived ease of use on perceived usefulness was stronger for high-utilitarian than for low-utilitarian functions. The effect of pleasure on perceived usefulness was stronger for high-hedonic than for low-hedonic functions. The results suggest that marketing should consider the nature of the functions.
- Adams, D., Nelson, R. R., and Todd, P. A. 1992. Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Q. 16, 227--247. Google ScholarDigital Library
- Agarwal, R. and Karahanna, E. 2000. Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Q. 24, 665--694. Google ScholarDigital Library
- Atkinson, M. and Kydd, C. 1997. Individual characteristics associated with World Wide Web use: An empirical study of playfulness and motivation. Datab. Adv. Inf. Syst. 28, 53--62. Google ScholarDigital Library
- Bagozzi, R., Gopinath, M., and Nyer, P. 1999. The role of emotions in marketing. J. Acad. Market. Sci. 27, 184--206.Google ScholarCross Ref
- Barclay, D., Higgins, C., and Thompson, R. 1995. The partial least squares approach to causal modeling: Personal computer adoption and use as an illustration. Tech. Stud. 2, 285--308.Google Scholar
- Batra, R. and Ahtola, O. 1991. Measuring the hedonic and utilitarian sources of consumer choice. Market. Lett. 2, 159.Google ScholarCross Ref
- Batra, R. and Ray, M. 1986. Affective responses mediating acceptance of advertising. J. Consum. Resear. 13, 234--249.Google ScholarCross Ref
- Bush, A. J. and Hair, J. F. 1985. An assessment of the mall intercept as a data collection method. J. Market. Resear. 22, 158--167.Google ScholarCross Ref
- Candel, J. M. and Pennings, J. M. E. 1999. Attitude-based models for binary choices: A test for choices involving an innovation. J. Econ. Psych. 20, 481--569.Google ScholarCross Ref
- Chi, M., Bassok, M., Lewis, M., Reimamt, P., and Glaser, R. 1989. Self-explanations: How students study and use examples in learning to solve problems. Cogn. Sci. 18, 145--182.Google ScholarCross Ref
- Chin, W. 1998. The partial least squares approach to structural equation modeling. In Modern Methods for Business Research, G. Marcoulides Ed., Lawrence Erlbaum Associates, Mahwah, NJ, 295--333.Google Scholar
- Chin, W. W., Marcolin, B. L., and Newsted, P. R. 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inf. Syst. Resear. 14, 189--217. Google ScholarDigital Library
- Davis, F. 1986. A technology acceptance model for empirically testing new end-user information systems: Theory and results. Ph.D. dissertation, Sloan School of Management, MIT.Google Scholar
- Davis, F. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 318--340. Google ScholarDigital Library
- Davis, F., Bagozzi, R., and Warshaw, P. 1989. User acceptance of computer-technology: A comparison of two theoretical models. Manage. Sci. 35, 982--1003. Google ScholarDigital Library
- Davis, F., Bagozzi, R., and Warshaw, P. 1992. Extrinsic and intrinsic motivation to use computers in the workplace. J. Appl. Soc. Psych. 22, 1111--1132.Google ScholarCross Ref
- Deci, E. 1975. Intrinsic Motivation. Plenum, New York.Google Scholar
- DeLone, W. H. and McLean, E. R. 2003. The DeLone and McLean model of information systems success: A ten-year update. J. Manage. Inf. Syst. 19, 9--30. Google ScholarDigital Library
- Egham. 2010. Gartner says worldwide mobile phone sales grew 17 per cent in first quarter 2010. Gartner Newsroom.Google Scholar
- Fang, X., Chan, S., Brzezinski, J., and Xu, S. 2005. Moderating effects of task type on wireless technology acceptance. J. Manage. Inf. Syst. 22, 123--157. Google ScholarDigital Library
- Festinger, L. 1962. A Theory of Cognitive Dissonance. Stanford University Press.Google Scholar
- Fornell, C. and Bookstein, F. 1982. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. J. Market. Resear. 19, 440--452.Google ScholarCross Ref
- Fornell, C. and Larcker, D. 1981. Structural equation models with unobservable variables and measurement errors. J. Market. Resear. 18, 39--50.Google ScholarCross Ref
- Gefen, D. and Straub, D. W. 2000. The relative importance of perceived ease of use in IS adoption: A study of ecommerce adoption. J. Assoc. Inf. Syst. 1, 1--28.Google ScholarCross Ref
- Goodhue, D. and Thompson, R. 1995. Task-technology fit and individual performance. MIS Q. 19, 213--236. Google ScholarDigital Library
- Heider, F. 1946. Attitudes and cognitive organizations. J. Psych. 21, 107--112.Google ScholarCross Ref
- Heider, F. 1958. The Psychology of Interpersonal Relations. Wiley, New York.Google Scholar
- Hoffman, D. and Novak, T. 1996. Marketing in hypermedia computer-mediated environments: conceptual foundations. Amer. Market. Assoc. 60, 50--68.Google Scholar
- Holbrook, M. B. and Hirschman, E. C. 1982. The experiential aspects of consumption: Consumer fantasies, feelings and fun. J. Consum. Resear. 9, 132--140.Google ScholarCross Ref
- Hong, S., Thong, J., and Tam, K. 2006. Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decis. Support Syst. 42, 1819--1834. Google ScholarDigital Library
- Karahanna, E., Straub, W., and Chervany, N. 1999. Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Q. 23, 183--213. Google ScholarDigital Library
- Keil, M., Tan, B. C. Y., Wei, K. K., Saarinen, T., Tuunainen, V., and Wassenaar, A. 2000. A cross-cultural study on escalation of commitment behaviour in software projects. MIS Q. 24, 299--325. Google ScholarDigital Library
- Kempf, D. 1999. Attitude formation from product trial: Distinct roles of cognition and affect for hedonic and functional products. Psych. Market. 16, 35--50.Google ScholarCross Ref
- Kim, J. and Forsythe, S. 2007. Hedonic usage of product virtualization technologies in online apparel shopping. Int. J. Retail Distrib. Manage. 35, 502--514.Google ScholarCross Ref
- Kim, H., Chan, H., and Chan, Y. 2007. A balanced thinking-feelings model of information systems continuance. Int. J. Hum.-Comput. Stud. 65, 511--525. Google ScholarDigital Library
- Konana, P. and Balasubramanian, S. 2005. The social-economic-psychological model of technology adoption and usage: An application to online investing. Decis. Support Syst. 39, 505--524. Google ScholarDigital Library
- Laroche, M. 2002. Selected issues in modeling consumer brand choice: The extended competitive vulnerability model. In Essays by Distinguished Marketing Scholars of the Society for Marketing Advances, A. G. Woodside and E. Moore Eds., Elsevier, New York, 69--114.Google Scholar
- Lazarus, R. 1991. Emotion and Adaptation. Oxford University Press, New York.Google Scholar
- Lee, Y., Kozar, K., and Larsen, K. 2003. The technology acceptance model: Past, present, and future. Comm. Assoc. Inf. Syst. 12, 752--780.Google ScholarCross Ref
- Legris, P., Ingham, J., and Collerette, P. 2003. Why do people use information technology? A critical review of the technology acceptance model. Inf. Manag. 40, 191--204. Google ScholarDigital Library
- Li, D., Chau, P., and Lou, H. 2005. Understanding individual adoption of instant messaging: An empirical investigation. J. Assoc. Inf. Syst. 6, 102--129.Google ScholarCross Ref
- Limayem, M. and Hirt, S. G. 2003. Force of habit and information systems usage: Theory and initial validation. J. Assoc. Inf. Syst. 4, 65--97.Google ScholarCross Ref
- Lin, J. and Chan, H. 2009. Understanding the beliefs and intentions in search and purchase functions in an E-commerce web site. IEEE Trans. Engin. Manage. 56, 106--114.Google ScholarCross Ref
- Lucas, H. and Spitler, V. 1999. Technology use and performance: A field study of broker workstations. Decis. Sci. 30, 291--311.Google ScholarCross Ref
- Mano, H. and Oliver, R. L. 1993. Assessing the dimensionality and structure of the consumption experience: evaluation, feeling, and satisfaction. J. Consum. Resear. 20, 451--466.Google ScholarCross Ref
- Mathieson, K. 1991. Predicting user intentions: comparing the technology acceptance model with the theory of planned behaviour. Inf. Syst. Resear. 2, 173--191.Google ScholarDigital Library
- Mehrabian, A. and Russell, J. 1974. An Approach to Environmental Psychology. MIT Press, Cambridge, MA.Google Scholar
- Moon, J. and Kim, Y. 2001. Extending the TAM for a world wide web context. Inf. Manage. 38, 217--230. Google ScholarDigital Library
- Neuman, W. L. 2006. Social Research Methods: Qualitative and Quantitative Approaches. Allyn and Bacon, Boston, MA.Google Scholar
- Newcomb, T. M. 1953. An approach to the study of communicative acts. Psych. Rev. 60, 393--404.Google ScholarCross Ref
- Newsland. 2007. Newsland published its regular survey of smart mobile device users in USA, Europe and Russia for the first half of 2006. http://www.newsland.net/UserFiles/File/research_060816-2-eng.pdf.Google Scholar
- Nysveen, H., Pedersen, P., and Thorbjørnsen, H. 2005. Intentions to use mobile services: antecedents and cross-service comparisons. J. Acad. Market. Sci. 33, 1--17.Google ScholarCross Ref
- Osgood, C. E. and Tannenbaum, P. H. 1955. The principle of congruity in the prediction of attitude change. Psych. Rev. 62, 42--55.Google ScholarCross Ref
- Pavlou, P. A. and Fygenson, M. 2006. Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Q. 30, 115--143. Google ScholarDigital Library
- Pirolli, P. and Reeker, M. 1994. Learning strategies and transfer in the domain of programming. Cogn. Instr. 12, 235--275.Google ScholarCross Ref
- Podsakoff, P., Mackenzie, S., Lee, J., and Podsakoff, N. 2003. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psych. Market. 88, 879--903.Google ScholarCross Ref
- Roca, J., Chiu, C., and Martinez, F. 2006. Understanding e-learning intention: an extension of the Technology Acceptance Model. Int. J. Hum.-Comput. Stud. 64, 628--696. Google ScholarDigital Library
- Rosenberg, M. J. 1956. Cognitive structure and attitudinal affect. J Abnormal Soc. Psych. 53, 367--372.Google ScholarCross Ref
- Russell, J. A. 1980. A circumplex model of affect. J. Person. Soc. Psych. 39, 1160--1178.Google ScholarCross Ref
- Segars, A. H. and Grover, V. 1993. Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Q. 17, 517--525. Google ScholarDigital Library
- Starbuck, W. and Webster, J. 1991. When is play productive? Account. Manage. Inf. Techn. 1, 71--90.Google ScholarCross Ref
- Sun, H. and Zhang, P. 2006a. The role of moderating factors in user technology acceptance. Int. J. Hum.-Comput. Stud. 64, 53--78. Google ScholarDigital Library
- Sun, H. and Zhang, P. 2006b. Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. J. Assoc. Inf. Syst. 7, 618--645.Google ScholarCross Ref
- Taylor, S. and Todd, P. 1995a. Assessing IT usage: The role of prior experience. MIS Q. 19, 561--570. Google ScholarDigital Library
- Taylor, S. and Todd, P. 1995b. Understanding information technology usage: A test of competing models. Inf. Syst. Resear. 6, 144--176.Google ScholarDigital Library
- Thong, J., Hong, S., and Tam, K. 2006. The effects of post-adoption beliefs on the expectation-disconfirmation model for information technology continuance. Int. J. Hum.-Comput. Stud. 64, 799--810. Google ScholarDigital Library
- Valacich, J. S., Parboteeah, D. V., and Wells, J. D. 2007. The online consumer’s hierarchy of needs. Comm. ACM 50, 84--90. Google ScholarDigital Library
- van der Heijden, H. 2002. On the cognitive-affective structure of attitudes toward information systems. In Proceedings of the 23rd International Conference on Information Systems. 803--806.Google Scholar
- van der Heijden, H. 2004. User acceptance of hedonic information systems. MIS Q. 28, 695--704. Google ScholarCross Ref
- Venkatesh, V. 2000. Determinants of perceived ease of use: integrating perceived behavioral control, computer anxiety and enjoyment into the Technology Acceptance Model. Inf. Syst. Resear. 11, 342--365. Google ScholarDigital Library
- Venkatesh, V. and Davis, F. D. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manage. Sci. 46, 186--204. Google ScholarDigital Library
- Venkatesh, V., Morris, M., Davis, G., and Davis, F. 2003. User acceptance of information technology: Toward a unified view. MIS Q. 27, 425--478. Google ScholarCross Ref
- Venkatesh, V., Speier, C., and Morris, M. 2002. User acceptance enablers in individual decision making about technology: Toward an integrated model. Decis. Sci. 33, 297--316.Google ScholarCross Ref
- Wakefield, R. L. and Whitten, D. 2006. Mobile computing: a user study on hedonic/utilitarian mobile device usage. Euro. J. Inf. Syst. 15, 292--300.Google ScholarCross Ref
- Yi, M. and Hwang, Y. 2003. Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. Int. J. Hum.-Comput. Stud. 59, 431--449. Google ScholarDigital Library
Index Terms
- The Moderating Effects of Utilitarian and Hedonic Values on Information Technology Continuance
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