Analysis of the Factor Affected Chinese Audience Choice Behavior between Traditional TV and Network Video in PLS-SEM

Abstract

Since the technical barriers of media industry were resolved, audience has become the determinant that impacted the development of media industry who would choose different media products depending on their perception. In order to find the key factors that impact Chinese audience’s choice behavior and provide practical guide for media industry to improve its service, the paper studies influence of audience’s perception on their choice between traditional TV and network video by building PLS-SEM. And then, partners and age which were proved to be the most important demographic characteristics affecting audience choice of video terminal in author’s previous studies are selected as moderator variables to explore how demographic characteristics influence the different paths of assumptions. The statistical results indicate that relative performance expectancy, relative effect expectancy, relative social influence and habit have significant positive effects on choice intention, relative time-related risk has no significantly negative effects on choice intention, relative physical risk has significant negative impacts on choice intention, habit and choice intention have significant positive effects on choice behavior. In the different paths of assumptions, partners and age exist significant differences.

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Tan, D. and Bai, J. (2015) Analysis of the Factor Affected Chinese Audience Choice Behavior between Traditional TV and Network Video in PLS-SEM. Modern Economy, 6, 833-845. doi: 10.4236/me.2015.67078.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Haiting, Y.U. (2007) Internet TV Is Not a Traditional TV Terminator. Commercial Culture (Academic), 8, 233, 244.
[2] Yi, S.H. (2009) The Research on the Internet Survival Status of TV Media in the Digital Background in China. Wuhan University, Wuhan.
[3] Lai, X.X. (2010) TV Programs Orientation and Development Strategy on the Challenge of Network Video. Central South University, Changsha.
[4] He, H.M. and Xue, M. (2012) The Linkage between TV and Network Video. TV Research, 11, 29-31.
[5] Jiang, Y. (2008) Study on the Audience of Network TV in China. Nanjing Normal University, Nanjing.
[6] Fang, X.Q. (2008) Research on the Consumer Behavior of IPTV Audience. Huazhong University of Science and Technology, Wuhan.
[7] Cha, M., Rodriguez, P., Crowcroft, J., Moon, S. and Amatriain, X. (2008) Watching Television over an IP Network. Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement, 71-84.
[8] Hou, D.L. (2012) Empirical Study on the User Behavior Intentions in Network Video Services. Huazhong University of Science and Technology, Wuhan.
[9] Xiao, Q. (2011) Study on Uer’s Behavior of Web TV Based on Innovation Diffusion Theory. Southwest University, Chongqing.
[10] Peng, D.L., Huang, F.M. and He, F. (1990) The Research on the TV Audience’s Behavior and Psychology. Journal of Beijing Normal University, 59-67.
[11] Zhang, T.D. and Li, Y. (2003) The Viewing Behavior and the Viewing Mode of Beijingese. Beijing Social Sciences, No. 2, 137-142.
[12] Ying, M. (2007) Analysis the Audience Choosing the TV Program. Journal of Liaoning Institute of Science and Technology, No. 9, 73-74.
[13] Zhai, X.W. (1993) The Characteristics of Interpersonal Relationship in China. Sociology Research, No. 4, 74-83.
[14] Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003) User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27, 425-478.
[15] Gefen, D. (2004) TAM or Just Plain Habit. Advanced Topics in End User Computing, 3, 1-15. http://dx.doi.org/10.4018/978-1-59140-257-2.ch001
[16] Chen, Y., Mao, S.S., Pan, X.Y. and Xu, Y.H. (2014) Understanding the Post-Adoption Behavior: The Influence of User’s Habits on Continuance Usage. Chinese Journal of Management, No. 3, 408-415.
[17] Stone, R.N. and Gronhaug, K. (1993) Perceived Risk: Further Considerations for the Marketing Discipline. European Journal of Marketing, 27, 39-50. http://dx.doi.org/10.1108/03090569310026637
[18] Agarwal, R. and Karahanna, E. (2000) Relative Time-Related Flies When You’re Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage. MIS Quarterly, 24, 665-694. http://dx.doi.org/10.2307/3250951
[19] Almousa, M. (2011) Perceived Risk in Apparel Online Shopping: A Multi Dimensional Perspective. Canadian Social Science, 7, 23-31.
[20] Bauer, R.A. (1960) Consumer Behavior as Risk. In: Hancock, R.S., Ed., Dynamic Marketing for a Changing World, American Marketing Association, Chicago, 389-398.
[21] Verplanken, B. and Orbell, S. (2003) Reflections on Past Behavior: A Self-Report Index of Habit Strength. Journal of Applied Social Psychology, 33, 1313-1330. http://dx.doi.org/10.1111/j.1559-1816.2003.tb01951.x
[22] Zhu, M.T. (2010) Research of Formation Mechanism of Use Habit in Internet Banking Context. Dalian University of Technology, Dalian.
[23] Venkatesh, V. and Davis, F.D.A. (2000) Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 186-204. http://dx.doi.org/10.1287/mnsc.46.2.186.11926
[24] Davis, F.D. (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-340. http://dx.doi.org/10.2307/249008
[25] Ajzen, I. (1991) The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. http://dx.doi.org/10.1016/0749-5978(91)90020-T
[26] Limayem, M. and Hirt, S.G. (2003) Force of Habit and Information Systems Usage: Theory and Initial Validation. Journal of the Association for Information Systems, 4, Article 3.
[27] Triandis, H.C. (1980) Values, Attitudes, and Interpersonal Behavior. Nebraska Symposium on Motivation, University of Nebraska Press, Lincoln.
[28] Sharifonnasabi, F. and bin Marsuki, M.Z. (2014) Influence of Perception of Internet Usage and SME’s Activity on Organizational Performance among Malaysia Tourism Small and Medium Enterprises.
[29] Banovic, M., Grunert, K.G., Barreira, M.M. and Fontes, M.A. (2010) Consumers’ Quality Perception of National Branded, National Store Branded, and Imported Store Branded Beef. Meat Science, 84, 54-65. http://dx.doi.org/10.1016/j.meatsci.2009.08.037
[30] Zhao, F.Q., Liu, J.L. and Peng, Y. (2012) Customer Satisfaction Index Model Based on PLS Algorithm. Journal of Beijing Institute of Technology (Social Sciences Edition), No. 1, 56-59, 65.
[31] Chin, W.W. (1998) The Partial Least Squares Approach to Structural Equation Modeling. Modern Methods for Business Research, 295, 295-336.

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