Appropriation of wireless technology: Direct impacting factors on the youth's adoption intention and usage of the wireless application protocol phone

Information Technology Journal

Volumn 7, Issue 8, 2008, Pages 1116-1124

  Kim, Sanghyun ^a  

^a Kyungpook National University   (South Korea)

Author keywords

Behaviors; Individuals; Technology adoption; WAP phone

Indexed keywords

CELLULAR TELEPHONE SYSTEMS; DATA STRUCTURES; TECHNOLOGY; TELEPHONE SETS;

ADOPTION INTENTIONS; BEHAVIORS; INDIVIDUALS; RESEARCH MODELS; TECHNOLOGY ACCEPTANCE MODELS; TECHNOLOGY ADOPTION; WAP PHONE; WAP PHONES; WIRELESS APPLICATION PROTOCOLS; WIRELESS TECHNOLOGIES;

WIRELESS TELECOMMUNICATION SYSTEMS;

EID: 57249084628     PISSN: 18125638     EISSN: 18125646     Source Type: Journal    
DOI: 10.3923/itj.2008.1116.1124     Document Type: Article

References (43)

1. Agrawal, R., V. Sambamurthy and R. Stair, 2000. Research report: The evolving relationship between general and specific computer self-efficacy: An empirical assessment. Inform. Syst. Res., 11: 418-430.
2. Anandarajan, M., M. Igbaria and U. Anakwe, 2002. IT acceptance in a less-developed country: A motivational factor perspective. Int. J. Inform. Manage., 22: 47-65.
3. Barclay, D., R. Thompson and C. Higgins, 1995. The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration Technol. Stud., 2: 285-309.
4. Bentler, P.M., 1990. Comparative fit indexes in structural models. Psychol. Bull., 107: 238-246.
5. Browne, M.W. and R. Cudeck, 1993. Alternative Ways of Assessing Model Fit. In: Testing Structural Equation Models, Bollen, K.A. and IS. Long (Eds.). Sage, California, ISBN: 08039-4506-X, pp: 136-162.
6. Carmines, E.G. and J.P. McIver, 1981. Analyzing Models with Unobserved Variables. In: Social Measurement: Current Issues, Bohrnstedt, G.W. and E.F. Borgatta (Eds.). Sage,BeverlyHills,ISBN-13: 978-0803915954, pp: 65-115.
7. Carroll, J., S. Howard, J. Peck and J. Murphy, 2002. A field study of perceptions and use of mobile telephones by 16 to 22 Year old. J. Inform. Technol. Theory Application, 4: 49-62.
8. Carroll, J. and E. Hartnell-Young, 2004. Available just enough: The mobile phone as a safety net for parents and their teenage children. The Life of Mobile Data: Technology, Mobility and Data Subjectivity conference, University of Surrey.
9. Cheung, W., M. Chang and V. Lai, 2000. Prediction of internet and world wide web usage at work: A test of an extended Triandis model. Decision Support Syst., 30: 83-100.
10. Chiasson, M. and C. Lovato, 2001. Factors influencing the formation of a user's perceptions and use of a dss software innovation. Database Adv. Inform. Syst., 32: 16-35.
11. Clarke, C, 2000. Coming attraction. Wireless Rev., 17: 12-16.
12. Constantiou, I.D., J. Damsgaard and L. Knutsen, 2004. Investigating mobile services adoption and diffusion in the Danish market. IRIS27 Working Conference: Learn IT, Know IT, Move IT, August 14-17, Falkenberg, Sweden, pp: 1-1.
13. Danaher, P., B. Hadie and W. Putsis, 2001. Marketing-Mix variables and the diffusion of successive generations of a technological innovation. I. Market. Res., 38: 501-514.
14. Davis, D., 1995. Cellular Telephones and Pagers in Hawaii: Cultural Influences on Personal Use Patterns. 1st Edn, Department of Communication Working Paper, University of Hawaii, Hawaii.
15. Davis, F., 1989. Perceived Usefulness, perceived ease of use interface and user acceptance of information technology. MIS Q., 13: 319-340.
16. Davis, F. and V. Venkatesh, 1996. A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. Int. I. Human Comput. Stud., 45: 19-45.
17. Davis, F.D., R.P. Bagozzi and P.R. Warshaw, 1989. User acceptance of computer technology: A comparison of two theoretical models. Manage. Sci., 35: 982-1003.
18. Davis, F.D., 1993. User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. Int. I. Man Mach. Stud., 38: 475-487.
19. Dowling, G. and R. Staelin, 1994. A model of perceived risk and intended risk-handling activity. J. Consumer Res., 21: 119-134.
20. Fishbein, M. and I. Ajzen, 1975. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. 1st Edn., Addison-Wesley, Reading, MA.
21. Heijden, H., 2003. Factors influencing the usage of Websites: The Case of a Generic Portal in the Netherlands. Inform. Manage., 40: 541-549.
22. Hendrickson, A., P. Massey and T. Cronan, 1993. On the test-retest reliability of perceived usefulness and perceived ease of use scales. MIS Q., 17: 227-230.
23. Horton, R., T. Buck, P. Waterson and C. Clegg, 2001. Explaining intranet use with the technology acceptance model. J. Inform. Technol., 16: 237-249.
24. Hu, P., P. Chau, O. Sheng and K. Tam, 1999. Examining the technology acceptance model using physician acceptance of telemedicine. J. Manage. Inform. Syst, 16: 91-112.
25. Jarvenpaa, S. and N. Tractinsky, 1999. Consumer trust in an Internet store: A cross-cultural validation. J. Comput. Mediated Commun., 5: 1-35.
26. Kauffman, R. and A. Techatassanasoontorn, 2005. Is there a global digital divide for digital wireless phone technologies? J. Assoc. Inform. Syst, 6: 338-382.
27. Kwon, H. and L. Chidambaram, 2000. A test of the technology acceptance model the case of cellular telephone adoption. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000, IEEE Compute Society, Press, Los Alamitos, pp:1-1.
28. Liang, H., Y. Xue and T. Bryd, 2003. PDA usage in healthcare professionals: Testing an extended technology acceptance model. Int. J. Mobile Commun., 1: 372-389.
29. Mathieson, K., 1991. Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Inform. Syst. Res., 2: 173-191.
30. Morris, M. and V. Venkatesh, 2000. Age differences in technology adoption decisions: Implications for a changing work force Durham. Personnel Psychol., 53: 375-403.
31. Parasuraman, A., 2000. Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. J. Serv. Res., 2: 307-320.
32. Pedersen, P., 2005. Adoption of mobile Internet services: An exploratory study of mobile commerce early adopters. J. Organizational Comput. Elect. Commerce, 15: 203-222.
33. Pedersen, P., H. Nysveen and H. Thorbjonsen, 2003. Identity expression in the adoption of mobile services: The case of multimedia messaging services. http://ikt.hia.no/perep/mms-study.pdf.
34. Sweeney, J.C. and G.N. Soutar, 2001. Consumer perceived value: The development of multiple item scale. J. Retailing, 77: 203-220.
35. Szajna, B., 1994. Software evaluation and choice: Predictive evaluation of the technology acceptance instrument MIS Q., 18: 319-324.
36. Taylor, S. and P. Todd, 1995. Understanding information technology usage: A test of competing models. Inform. Syst. Res., 6: 144-176.
37. Teo, T., V. Lim and R. Lai, 1999. Intrinsic and extrinsic motivation in Internet usage. Omega, 2: 25-37.
38. Venkatesh, V., 1999. Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Q., 23: 239-260.
39. Venkatesh, V. and F.D. Davis, 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manage. Sci., 46: 186-204.
40. Venkatesh, V.A. and S.A. Brown, 2001. Longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Q., 25: 71-102.
41. Virki, T., 2007. Global cellphone penetration reaches 50 pet. Reuters.
42. Waverman, L., M. Meschi and M. Fuss, 2005. The Impact of Telecoms on Economic Growth in Developing Countries. IstEdn., Vodafone Policy Paper Series, 2, London, United Kingdom.
43. Webster, E.J. and J.J. Martocchio, 1992. Microcomputer playfulness: Development of a measure with workplace implications. MIS Q., 6: 201-216.