DOI QR코드

DOI QR Code

An Empirical Study on Mobile Web Browsing Service Adoption

모바일 웹 브라우징 서비스 수용에 관한 연구

  • Published : 2009.02.28

Abstract

Mobile web browsing services that bring the full PC browsing experience to customer's mobile handset have been emerged. This study is to investigate the intention to use mobile web browsing based on Technology Acceptance Model(TAM) which has been widely used to explain and predict the IT acceptance and incorporated with self-efficacy which was identified as an important determinant of user's new technology adoption in recent literature on technology acceptance. Specifically, from a theoretical perspective, this study not only clarifies mobile self-efficacy, but also develops an instrument to measure the concept of mobile self-efficacy. The results indicate that both computer self-efficacy and mobile self-efficacy directly influence perceived ease of use, and that perceived ease of use enhance perceived usefulness. And the findings indicate that perceived ease of use and usefulness have direct effects on attitude and then it is positively associated with intention to use mobile web browsing. Therefore, the findings imply that mobile self-efficacy can be employed as an important variable in examining user's intention for various mobile services to come in future.

Keywords

Mobile Web Browsing;Technology Acceptance Model;Self-efficacy;Mobile Self-efficacy

References

  1. 전종홍, "모바일 웹 브라우징 기술 및 표준화 동향," 정보처리학회지, 제15권, 제4호, pp.23-32, 2008.
  2. F. D. Davis, "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology," Mis Quarterly, Vol.13, No.3, pp.319-340, 1989. https://doi.org/10.2307/249008
  3. V. Venkatesh and F. D. Davis, "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,“ Management Science, Vol.46, No.2, pp.186-204, 2000. https://doi.org/10.1287/mnsc.46.2.186.11926
  4. A. Bandura, Self-efficacy: The exercise of control, W.H. Freeman: New York, 1997.
  5. I. Ajzen and M. Fishbein, Understanding Attitudes and Predicting Social Behavior, Prentice-Hall: Englewood Cliffs, NJ, 1980.
  6. Y. F. Kuo and S. N. Yen, "Towards an understanding of the behavioral intention to use 3G mobile value-added services," Computers in Human Behavior, Vol.25, pp.103-110. 2009. https://doi.org/10.1016/j.chb.2008.07.007
  7. V. S. Lai and H. Li, "Technology Acceptance Model for Internet Banking: an Invariance Analysis," Information & Management, Vol.42, No.2, pp.373-386, 2005. https://doi.org/10.1016/j.im.2004.01.007
  8. J. R. Fu, C. K. Farn, and W. P. Chao, "Acceptance of electronic tax filing: A study of taxpayer intentions," Information & Management, Vol.43, No.1, pp.109-126, 2006. https://doi.org/10.1016/j.im.2005.04.001
  9. Y. Jung, B. Perez-Mira, and S. Wiley-Patton, "Consumer adoption of mobile TV: Examining psychological flow and media content," Computers in Human Behavior, Vol.25, No.1, pp.123-129, 2009. https://doi.org/10.1016/j.chb.2008.07.011
  10. R. Agarwal and E. Karahanna, "Time Flies When You're Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage," MIS Quarterly, Vol.24, No.4, pp.665-694, 2000. https://doi.org/10.2307/3250951
  11. P. Luarn and H. H. Lin, "Toward an understanding of the behavioral intention to use mobile banking," Computers in Human Behavior, Vol.21, No.6, pp.873-891. 2005. https://doi.org/10.1016/j.chb.2004.03.003
  12. R. Agarwal, V. Sambamurthy, and R. M. Stair, "Research report: The evolving relationship between general and specific computer self-efficacy - An empirical assessment," Information Systems Research, Vol.11, No.4, pp.418-430, 2000. https://doi.org/10.1287/isre.11.4.418.11876
  13. D. Compeau and C. Higgins, "Computer self-efficacy: Development of a measure and initial test," MIS Quarterly, Vol.19, pp.189-211, 1995. https://doi.org/10.2307/249688
  14. G. M. Marakas, M. Y. Yi, and R. D. Johnson, "The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research," Information Systems Research, Vol.9, No.2, pp.126-163, 1998. https://doi.org/10.1287/isre.9.2.126
  15. M. S. Eastin and R. L. Rose, "Internet Self-Efficacy and the Psychology of the Digital Devide," J. of Computer mediated Communication, Vol.6, No.1, pp.24-45, 2000.
  16. M. H. Hsu and C. M. Chiu, "Internet self-efficacy and electronic service acceptance," Decision Support Systems, Vol.38, No.3, pp.369-381, 2004. https://doi.org/10.1016/j.dss.2003.08.001
  17. G. Torkzadeh, J. C. J. Chang, and D. Demirhan, "A contingency model of computer and Internet self-efficacy," Information & Management, Vol.43, No.4, pp.541-550, 2006. https://doi.org/10.1016/j.im.2006.02.001
  18. M. Igbaria and J. Iivari, "The effects of self-efficacy on computer usage," Omega: International J. of Management Science, Vol.23, No.6, pp.587-605, 1995. https://doi.org/10.1016/0305-0483(95)00035-6
  19. B. Hasan, "Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance," Information & Management, Vol.43, No.5, pp.565-571, 2006. https://doi.org/10.1016/j.im.2005.11.005
  20. F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, "User Acceptance of Computer Technology: A Comparison of Two Theoretical Model," Management Science, Vol.35, No.8, pp.982-1003, 1989. https://doi.org/10.1287/mnsc.35.8.982
  21. R. Agarwal and J. Prasad, "Are individual differences germane to the acceptance of new information technologies?," Decision Sciences, Vol.30, No.2, pp.361-391, 1999. https://doi.org/10.1111/j.1540-5915.1999.tb01614.x
  22. S. Taylor and P. A. Todd, "Understanding information technology usage: A test of competing models," Information Systems Research, Vol.6, No.2, pp.144-176, 1995. https://doi.org/10.1287/isre.6.2.144
  23. S. Y. Hung, C. Y. Ku, and C.-M. Chang, "Critical factors of WAP services adoption: an empirical study," Electronic Commerce Research and Applications, Vol.2, No.1, pp.42-60, 2003. https://doi.org/10.1016/S1567-4223(03)00008-5
  24. C. H. Liao, C. W. Tsou, and M. F. Huang, "Factors influencing the usage of 3G mobile services in Taiwan," Online Information Review, Vol.31, No.6, pp.759-774, 2007. https://doi.org/10.1108/14684520710841757
  25. R. Agarwal and J. Prasad, "A conceptual and operational definition of person innovativeness in the domain of information technology," Information Systems Research, Vol.9, No.2, pp.204-215, 1998. https://doi.org/10.1287/isre.9.2.204
  26. D. Gefen, D. W. Straub, and M. C. Boudreau, "Structural equation modelling and regression: Guidelines for research practice," Communications of the AIS, Vol.4, No.7, pp.1-79, 2000.
  27. C. Fornell and D. Larcker, "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error," J. of Marketing Research, Vol.18, No.1, pp.39-50, 1981. https://doi.org/10.2307/3151312

Cited by

  1. Analyzing Impact Factors of User Resistance to Accepting Paid Mobile Application vol.13, pp.4, 2013, https://doi.org/10.5392/JKCA.2013.13.04.361
  2. An Empirical Study on the Differences of Relationship between Content Quality Factors and User Satisfaction on Mobile Contents Based on User Characteristics vol.14, pp.4, 2013, https://doi.org/10.5762/KAIS.2013.14.4.1957