At the moment, when mobile phone users are demanding more mobile phone features as well as broader bandwidth, the 4G wireless telecommunication standard is emerging. However, how to define appropriate mobile phone features toward various market segmentations to fulfill customers’ needs and minimize the manufacturing cost has become one of the most important issues for 4G mobile phone manufacturers. Thus, a rule based consumer behavior forecast mechanism will be very helpful for marketers and designers of the mobile phone manufacturers to understand and realize. Moreover, precise prediction rules for consumer behavior being derived by the forecast mechanism can be very useful for marketers and designers to define the features of the next generation mobile phones. Therefore, in the pre-process, we utilized Rough Set Theory (RST) that found decision rules to construct the decision table, and approach to data mining and knowledge discovery based on information flow distribution in a flow graph. The post-process applied the formal concept analysis (FCA) from these suitable rules to explore the attribute relationship and the most important factors affecting the preference of customers for the 4G mobile phone features. An empirical study on Taiwanese mobile phone users will be leveraged for verifying the feasibility of the proposed forecast mechanism. Meanwhile, the proposed consumer behavior forecast mechanism can be leveraged on defining features of other high technology products/services.

Ajzen, I. and Fishbein, M. (1975), Understanding attitudes and predicting social behavior, Englewood Cliffs, NJ: Prentice-Hall, Inc
Barak, B. and Gould, S. (1985). Alternative age measures: a research agenda. Advances in Consumer Research, 12, 53-58
Brüggemann, R., Voigt, K., and Steinberg, C.E.W. (1997), “Application of formal concept analysis to evaluate environmental databases,” Pergamon, 35(3), 479-486.
Coley, A. and Burgess, B. (2003). Gender differences in cognitive and affective impulse buying, Journal of Fashion Marketing and Management, 7 (3), 282-295.
Curry, B. (2003). “Rough sets: current and future developments,” Expert Systems, 20(5), 247-250.
Darley, W. K. and Smith, R.E. (1995). Gender differences in information processing strategies: an empirical test of the selectivity model in advertising response.Journal of Advertising, 24 (1), 41-56.
Dimitras, A. I., Slowinski, R., Susmaga, R., and Zopounidis, C. (1999). “Business failure prediction using rough sets,” European Journal of Operational Research, 114(2), 263-280.
Dittmar, H., Beattie, J. and Friese, S. (1996). Objects, decisions, considerations and self-images in men’s and women’s impulse purchases. Acta Psychologica, 93 (1-3), 187-206.
Dawe, M. (2007). “Understanding Mobile Phone Requirements for Young,” In 9th international ACM SIGACCESS conference on Computers and accessibility, 179 - 186, October 14-17, AZ, USA
Formica, A. (2008), “Concept similarity an formal concept analysis: An information content approach,” Knowledge-Based Systems, 21 (1), 80-87
Frattasi, S., Fathi, H., Fitzek, F. H., Prasad, R., Katz, M. D. (2006), “Defining 4G Technology from the User's Perspective,” In IEEE network, 20(1), 35-41.
Goldsmith, R. E. (2002). Some personality traits of frequent clothing buyers. Journal of Fashion Marketing and Management, 6 (3), 303-316.
Graham, J.F., Stendardi (Jr.), E.J., Myers, J.K., and Graham, M.J. (2002). Gender differences in investment strategies: an information processing perspective.
International Journal of Bank Marketing, 20 (1), 17-26.
Greco, S., Pawlak, Z., Slowi´nski, R. (2002), “Generalized decision algorithms, rough inference rules and flow graphs,” Rough Sets and Current Trends in Computing. 2475, 93-104
Hair, J. F., Anderson, R. E., Tatham, R. L., and Black., W. C. (1997), Multivariate data analysis : with readings, 4th ed., New Jersey: Prentice-Hall, Inc., New Jersey, USA..
Hodgin, R. C. (2009), 60% of world's population now has cell phone, highest ever. Retrieved 12 25, 2009, from TGdaily: http://www.tgdaily.com/trendwatch-features/41586-60-of-worlds-population-now-has-cell-phone-highest-ever
Kotler, P. and Keller, K. L. (2006). Identifying Market Segments and Targets, Marketing management, 12th edition, Pearson Education Singapore.
Kumar, A., and Agrawal, D. P. (2005), Advertising data analysis using rough sets model. International Journal of Information Technology and Decision Making, 4(2), 263-276.
Ibrahim, J. (2002, December). 4G Features, Bechtel telecommunications Technical Journal . 1(1), 11-14
Jhieh-Yu S., How-Ming S., Gwo-Hshiung T., Shu-Huei H. (2009),Using FSBT Technique with Rough Set Theory for Personal Investment Portfolio Analysis, European Journal of Operational Research, 20(2), 601-607
Laroche, M., Saad, G., Cleveland, M. and Browne, E. (2000). Gender differences in information search strategies for a Christmas gift. Journal of Consumer Marketing, 17 (6), 500-524.
Law, R., and Au, N. (2000), “Relationship modeling in tourism shopping: a decision rules induction approach. Tourism Management,” 21(3), 241-249.
Ling, C., Hwang, W., and Salvendy, G. (2007), “A survey of what customers want in a cell phone design,” Behaviour and Information Technology , 26 (2), 149 – 163.
Liu, M., Shao, M., Zhang, W., and Wu, C. (2007), “Reduction method for concept lattices based on rough set theory and its application”, Computer and Mathematics with applications, 53 (9), 1390-1410.
Nam, J., Hamlin, R., Gam, H. J. Kang, J. H., Kim, J., Kumphai, P., Starr, C. and Richards, L. (2007). The fashion-conscious behaviours of mature female consumers. International Journal of Consumer Studies, 31 (1), 102-108.
Rocha, M.A.V., Hammond, L. and Hawkins D. (2005). Age, gender and national factors in fashion consumption. Journal of Fashion Marketing and Management,9 (4), 380-390.
Norman, D. (1988), The Design of Everyday Things, New York: Doubleday.
Olson, L.and Delen, D (2008), Advanced Data Mining Techniques, Springer.
Parasuraman, A. (2000), “Technology Readiness Index (TRI): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies”, Journal of Service Research, 2(4), 307-320.
Pawlak, Z. (1982), “Rough sets”, International Journal of Computer and Information Sciences, 11(5), 341-356.
Pawlak, Z. (2002),“ Rough sets and intelligent data analysis”, Information Sciences, 147(4), 1-12.
Perner, L., Alpert, F., and Kamins. (2007), “How do consumers know which brand is the market leader or market pioneer? Consumers' inferential processes, confidence and accuracy”, Journal of Marketing Management, 7 (8), 590-611.
Petiot, J.-F., and Yannou, B. (2004), “Measuring consumer perceptions for a better comprehension, specification and assessment of product semantics”, International Journal of Industrial Ergonomics, 33(6), 507-525
Priss, U. (2006), “Formal concept analysis in information science”, Annual Review of Information Science and Technology, 40 (1), 521-543.
Romahi, Y., and Shen, Q. (2000), “Dynamic financial forecasting with automatically induced fuzzy associations”, In Proceedings of the 9th international conference on fuzzy systems, 493-498, Sofia, Bulgaria.
Sabnavis, M. (2002). Getting A fix on the New Middle-Class Consumer, Indian Management, 41 (7), 52-54.
Sandhusen, R.L. (2000), Marketing. 3rd ed., Hauppauge, New York, USA Sharma, S. (2004). Understanding urban youth-instant karma. Indian Management, 43, (4), 72-81.
Shuai, J. J., and Li, H. L. (2005), “Using rough set and worst practice DEA in business failure prediction. ” Lecture Notes in Computer Science, 3642, 503-510.
Sun, J., Z., Sauvola, J., & Howie, D. (2010, November). Features in Future: 4G Visions From a Technical Perspective. Proc. IEEE Global Communications Conference , 6, 3533–3537.
Szimigin, I. and Carrigan, M. (2001). Learning to love the older consumer. Journal of Consumer Behavior, 1 (1), 22-34.
Venkatesh, V. and Morris, M. G. (2000), Why don’t men Ever Stop to Ask for Directions? Gender, Social Influence and their role in Technology Acceptance and Usage Behaviour. MIS Quarterly, 24 (1), 115-139.
Wang, C.H., Chin, Y.C. and Tzeng, G.H. (2010), “Mining the R&D innovation performance processes for high-tech firms based on rough set theory”, Technovation, 30(7-8), 447-458
Walczak, B., and Massart, D. L. (1999), “Rough set theory”, Chemometrics and Intelligent Laboratory Systems, 47(1), 1-16.
Winer, R. S. (2000), Marketing Management , the United State: Prentice-Hall, Inc., New Jersey, USA.
Wille, R. (2005), “Formal concept analysis as methodical theory of concepts and concept hierarchies, in: B. Ganter et al. (Eds.)”, Formal concept analysis, LNAI, 1(3), 26-36.
Williams, T. G. (2002). Social class influences on purchase evaluation criteria.Journal of Consumer Marketing, 19 (3), 249-276.
Wormuth, B., and Becker, P. (2004), “Introduction to formal concept analysis”, 2nd International Conference of Formal Concept Analysis, February 23-February 27. Sydney, Australia.
Yao, Y.Y. (2004), “Concept lattices in rough set theory”, In Proceedings of 23rd International Meeting of the North American Fuzzy Information Processing Society, USA.
Yao, J. T., and Herbert, J. P. (2009), “Financial time-series analysis with rough sets. ” Applied Soft Computing, 9(3), 1000-1007.
Yu-Ping O.Y, How-Ming S., Gwo-Hshiung T., Leon Y., and Chien-Chung C. (2008), Business Aviation Decision-Making using Rough Sets, RSCTC 2008, Springer-Verlag in the Lecture Notes on Artificial Intelligence (LNAI 5306) series. EI
Yun, M. H., Han, S. H., and Kim, K. J. (2000), “Consumer Preference Survey for Telecom Products (In Korean)”, Research Report for Samsung Engineering, Ministry of Science and Technology, ROK Government.