Share This Article:

Market Segmentation for Mobile TV Content on Public Transportation by Integrating Innovation Adoption Model and Lifestyle Theory

Abstract Full-Text HTML Download Download as PDF (Size:300KB) PP. 244-250
DOI: 10.4236/jssm.2008.13026    7,484 Downloads   13,846 Views   Citations
Author(s)    Leave a comment

ABSTRACT

An integrated approach based on innovation adoption model and lifestyle theory for customer segmentation of mobile TV content on public transportation using multivariate statistical analysis is proposed. Due to high daily trips and dif-ferent train types Taiwan Railway Administration is chosen as the case study. Firstly, the content of mobile TV on the train are identified as the segmentation variable and key factor facets for mobile TV content are renamed by using fac-tor analysis. Then, the cluster analysis is used to classify customer groups which are named by analysis of variance (ANOVA) and market segmentations are described with demographic, lifestyle and train patronage variables by using cross analysis and Chi-squared independence tests. Finally, this paper discusses empirical results to provide valuable implications for better mobile TV content marketing strategies in the future.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

C. Tao, "Market Segmentation for Mobile TV Content on Public Transportation by Integrating Innovation Adoption Model and Lifestyle Theory," Journal of Service Science and Management, Vol. 1 No. 3, 2008, pp. 244-250. doi: 10.4236/jssm.2008.13026.

References

[1] N. Holland, et al., “Rescuing 3G with Mobile TV: Buins-ess Models and Monetizing 3G,” Pyramid Research, March 2006.
[2] Wireless World Forum, “Mobile Youth 06 Video,” mobileYouth06–part one, July 2006.
[3] J. Trefzger, “Mobile TV-launch in germany-challenges and implications,” working paper No. 209, Institute for Broadcasting Economics, Cologne University, Germany, 2005.
[4] S. Orgad, “How will mobile tv transform viewer’s experi-ence and change advertising,” Final report, Dept. of Me-dia and Communications, London School of Economics and Political Science, November 2006.
[5] QuickPlayMedia, “Mobile TV and video survey 2008,” Toronto, Canada, 2008.
[6] M. P. Shih, “Analysis of mobile TV and its key success factors: from the perspective of mobile operator,” Pro-ceedings of International Symposium on HDTV and Mo-bile TV, Taipei, Taiwan, 2007.
[7] C. Carlsson and P. Walden, “ Mobile TV-to live or die by content,” Proceedings of the 40th Hawaii International Conference on System Sciences, IEEE, Hawaii, USA, 2007.
[8] C. M. Tan and C. C. Wong, “Mobile broadband race: Friend or foe,” Proceedings of the International Confer-ence on Mobile Business, IEEE, ICMB’06, 2006.
[9] G. Wang, “ Mobile TV value chain and operator strate-gies,” Master’s Thesis, Dept. of Communication Systems, School of Information and Communication Technology, KTH, Finland, February 2007.
[10] T. M. Lee and J. K. Jun, “Contextual perceived usefulness? toward an understanding of mobile commerce acceptance”, Proceedings of the International Conference on Mobile Business, IEEE, ICMB’05, 2005.
[11] J. T. Plummer, “The concept and application of lifestyle segmentation,” Journal of Marketing, pp. 33-74, January 1974.
[12] Y. Wind and P. E. Green, “Some conceptual measurement and analytical problem in life style research,” Life style and Psychographics, Chicago, AMA, 1974.
[13] E. Rogers, Diffusion of Innovation, Free Press, New York, 1962.
[14] J. H. Wu and S. C. Wang, “What drives mobile commerce? An empirical evaluation of the revised technology accep-tance model,” Information & Management 42, pp.719–729, 2005.
[15] Y. Shin, H. Jeon, and M. Choi, “Analysis of the consumer preferences toward m-commerce applications based on an empirical study,” Proceedings of International Conference on Hybrid Information Technology, IEEE, ICHIT’06, 2006.
[16] S. L. Holak and D. R. Lehmann, “Purchase intentions and the dimensions of innovation: An exploratory model,” Journal of Product Innovation Management, 7 (1), pp. 59-73, 1990.
[17] R. N. Miller, “Target marketing,” Multinational Market-ing , Vol. 13, No. 10, 1993.
[18] A. L. Gilbert and J. D. Kendall, “A marketing model for mobile wireless services,” Proceedings of the 36th Hawaii International Conference on System Science (HICSS’03), 2003.
[19] H. H. Lin and Y. S. Wang, “Predicting consumer inten-tion to use mobile commerce in taiwan,” Proceedings of the International Conference on Mobile Business (ICMB’05), 2005.
[20] H. Feng, T. Hoegler, and W. Stucky, “Exploring the criti-cal success factors for mobile commerce,” Proceedings of the International Conference on Mobile Business (ICMB’06), 2006.
[21] C. C. Aggarwal, C. Procopiuc, J. S. Wolf, P. S. Yu, and J. S. Park, “Fast algorithms for projected clustering,” Pro-ceedings of SIGMOD Conference, Philadelphia, 1999.
[22] A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: A review,” ACM Computing Surveys, Vol. 31, No. 3, September 1999.
[23] M. Zait and H. Messatfa, “A comparative study of cluster-ing methods,” FGCS Journal, Special Issue on Data Min-ing, 1997.
[24] P. V. Balakrishnan, M. C. Cooper, V. S. Jacob, and P. A. Lewis, “Comparative performance of the fscl neural net and k-means algorithm for market segmentation,” Euro-pean Journal of Operational Research, No. 93, 1996.
[25] H. Hruschka and M. Natter, “Comparing performance of feedforward neural nets and k-means for cluster-based market segmentation,” European Journal of Operational Research, No. 114, 1999.
[26] C. Y. Tsai and C. C. Chiu, “A purchased-based market segmentation methodology”, Expert Systems with Appli-cations, No. 27, 2004.
[27] D. Zakrzewska and J. Murlewski, “Clustering algorithms for bank customer segmentation”, Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA’05), 2005.
[28] J. C. Nunnally, Psychometric Theory, McGraw-Hill, New York, 1967.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.