Potential user factors driving adoption of IPTV. What are customers expecting from IPTV?

Technological Forecasting and Social Change

Volumn 74, Issue 8, 2007, Pages 1446-1464

  Shin, Dong Hee ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

IPTV; Logistic model; South Korea; User analysis

Indexed keywords

DIGITAL TELEVISION; ECONOMIC AND SOCIAL EFFECTS; MATHEMATICAL MODELS; REGRESSION ANALYSIS; TECHNOLOGICAL FORECASTING;

INTERNET PROTOCOL TELEVISION; LOGISTIC MODEL; USER ANALYSIS; USER FACTORS;

INTERNET PROTOCOLS;

CONCEPTUAL FRAMEWORK; INTERNET; LOGISTICS; REGRESSION ANALYSIS; TECHNOLOGY ADOPTION; TELEVISION;

ASIA; EURASIA; FAR EAST; KOREA; SOUTH KOREA;

EID: 34548312519     PISSN: 00401625     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.techfore.2006.05.007     Document Type: Article

References (41)

1. Scherer F.M. Demand-pull and technological opportunity: Schmookler Revisited. J. Ind. Econ. 30 (1982) 225-237
2. Fadlalla A. An experimental investigation of the impact of aggregation on the performance of data mining with logistic regression. Inf. Manage. 42 5 (2005) 695-707
3. A. Harris, White Paper of Enabling IPTV: What carriers need to know to succeed, May 2005, IDC Analyze the Future Series Report. http://www.emc.com/analyst/pdf/IDC_IPTV_WhitePaper_Jun_9_05.pdf (2005).
4. Taplin J. The IPTV Revolution, a Conference Paper Presented at The Network Society and the Knowledge Economy: Portugal in the Global Context, May, Portugal (2005)
5. Yankee Group. IPTV Outlook: Cross Countries Overview. Yankee Group Research Publication Series (2005)
6. Song, K: Prospectus of IPTV. Electronics and Telecommunications Research Institute Research Publication, Special Issue on IPTV, Summer (2005).
7. Gartner Group. IPTV prediction. Gartner Group Research Report (Winter 2005)
8. Day G., and Shoemaker P. Managing Emerging Technologies (2000), John Wiley and Sons, Inc. 86
9. Klein R., and Sherman R. Estimating new product demand from biased survey data. J. Econom. 76 1-2 (1997) 53-76
10. Y. Cheong, and J. Leckenby, SEM versus regression for modeling of attitude toward the internet retailer, Paper to be presented at the 2004 Annual Conference American Academy of Advertising, Baton Rouge, LA.
11. Kalwani M.U., and Silk A.J. On the reliability and predictive validity of purchase intention measures. Mark. Sci. 1 (Summer 1982) 243-286
12. Shocker A.D., and Srinivasan V. Multiattribute approaches for product concept evaluation and generation: a critical review. J. Mark. Res. 16 (1979) 159-180
13. Smith S. New product pricing in quality sensitive markets. Mark. Sci. 5 1 (1986) 70-87
14. Rogers E.M. Diffusion of Innovation. 4th ed. (1995), The Free Press, New York 124
15. In: Blumler J., and Katz E. (Eds). The Uses of Mass Communications (1974), Sage, Beverly Hills, CA
16. S.P. Schnaars, L.C. Swee, and C.M. Malole, Five Modern Lessons from a 55 Year-Old Technological Forecast. J. Prod. Innov. Manag., 10 66-74
17. Moore G.C., and Benbasat I. Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Sys. Res. 2 3 (1991) 192-222
18. Igbaria M. User acceptance of microcomputer technology: an empirical test. Omega 21 1 (1993) 73-90
19. Kwon H.S., and Chidambaram L. A test of the technology acceptance model: the case of cellular telephone adoption. Proceedings of the HICSS-34, Hawaii, January 3-6 (2000)
20. S. Aydin, G. Özer, The analysis of antecedents of customer loyalty in the Turkish mobile telecommunication market, Eur. J. Mark. Bradford: 2005. vol. 39, Iss. 7/8; pg. 910, 16 pgs (2005).
21. Anon, On the mobile: groundbreaking global study explores behavioral effects of mobile phone use Mobile Guy.com. http://www.mobileguy.com/publish/article70.shtml (2002).
22. Venkatesh V. Creation of favorable user perceptions: exploring the role of intrinsic motivation. MISQ 23 2 (1999)
23. Gill T.G. Expert systems usage: task change and intrinsic motivation. MISQ 20 3 (1996)
24. Schmidt A., et al. There is more to context than location. Proc. Int'l Wkshp. on Interactive Applications of Mobile Computing (IMC98) (1998), Rostock, Germany
25. Nakanishi Y., et al. Estimating communication context through location information and schedule information-a study with home office workers. Conf. Human Factors and Computing Systems Extended Abstract (CHI 2002) (2002), ACM Press 642-643
26. Tang J., et al. ConNexus to Awarenex: extending awareness to mobile users. Proceedings of Conference Human Factors and Computing Systems (CHI 2001) (2001), ACM Press 221-228
27. Rekimoto J., Ayatsuka Y., and Kohno M. SyncTap: an interaction technique for mobile networking. Proc. of the fifth ACM International Symposium on Human Computer Interaction with Mobile Devices and Services, Udine, Italy, LNCS (2003), Springer-Verlag 104-115
28. Cheverst K., Dix A., Fitton D., Friday A., and Rouncefield M. Exploring the utility of remote messaging and situated office door displays. Proc. of the Fifth ACM International Symposium on Human Computer Interaction with Mobile Devices and Services, Udine, Italy, LNCS (Sept. 2003), Springer-Verlag 336-341
29. Sawng Y.W., et al. Market acceptance for the satellite DMB (Digital Multimedia Broadcasting) services in Korea. International Conference on Mobile Business (ICMB'05) (2005) 413-419
30. The American Association for Public Opinion Research: Standard definitions: Final dispositions of case codes and outcome rates for surveys. Retrieved from June 15, 2005 http://www.aapor.org/ethics/stddef.html.
31. IEEE 90: Institute of Electrical and Electronics Engineers. IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries (1990), New York, NY
32. Arnold M. On the phenomenology of technology: The Janus-faces of mobile phones. Information and Organization 13 (2003) 231-256
33. Burgoon J., Bonito K., Bengtsson A., Ramirez B., Dunbar A., and Miczo R. Testing the interactivity model: communication processes, partner assessments, and the quality of collaborative work. J. Manage. Inf. Sys. 16 3 (2000) 33-56
34. Cox D.R., and Snell E. Analysis of Binary Data. 2nd ed. (1989), Chapman and Hall, London, U.K.
35. Hosmer D.W., and Lemeshow S. Applied Logistic Regression. 2nd ed. (2000), Wiley, New York
36. Kim M., and Jee K. Characteristics of individuals influencing adoption intentions for portable Internet service. ETRI J. 2 1 (2005) 67-76
37. Polit D. Data Analysis and Statistics for Nursing Research (1996), Appleton & Lange, Stamford, Connecticut
38. Limi A. Estimating demand for cellular phone services in Japan. Telecommun. Policy 29 (2005) 3-23
39. Shurmer M. Future demand for pay-TV in the UK. Telecommun. Policy 21 7 (1997) 611-618
40. Kim W.C., and Mauborgne R. Blue Ocean Strategy. Harvard Bus. Rev. (October 2004) 76-85
41. Gandal N., Kende M., and Rob R. The dynamics of technological adoption in hardware/software system: The case of compact disc players. Rand J. Econ. 31 1 (2000) 43-61