Data Mining to Capture User-Experience: A Case Study in Notebook Product Appearance Design
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Data Mining to Capture User-Experience: A Case Study in Notebook Product Appearance Design

Authors: Rhoann Kerh, Chen-Fu Chien, Kuo-Yi Lin

Abstract:

In the era of rapidly increasing notebook market, consumer electronics manufacturers are facing a highly dynamic and competitive environment. In particular, the product appearance is the first part for user to distinguish the product from the product of other brands. Notebook product should differ in its appearance to engage users and contribute to the user experience (UX). The UX evaluates various product concepts to find the design for user needs; in addition, help the designer to further understand the product appearance preference of different market segment. However, few studies have been done for exploring the relationship between consumer background and the reaction of product appearance. This study aims to propose a data mining framework to capture the user’s information and the important relation between product appearance factors. The proposed framework consists of problem definition and structuring, data preparation, rules generation, and results evaluation and interpretation. An empirical study has been done in Taiwan that recruited 168 subjects from different background to experience the appearance performance of 11 different portable computers. The results assist the designers to develop product strategies based on the characteristics of consumers and the product concept that related to the UX, which help to launch the products to the right customers and increase the market shares. The results have shown the practical feasibility of the proposed framework.

Keywords: Consumers Decision Making, Product Design, Rough Set Theory, User Experience.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1091798

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References:


[1] Hoegg, J. A., and J. W. Alba, "Seeing is believing (too much): The influence of product form on perceptions of functional performance,” Journal of Product Innovation Management, 28 (3), 346–359 (2011).
[2] Creusen, M. E. H. "Research Opportunities Related to Consumer Response to Product Design,” Journal of product innovation management, 28(3), 405–408 (2011).
[3] Hassenzahl, M. and N. Tractinsky, "User Experience – a Research Agenda,” Behaviour and Information Technology, 25( 2), 91-97 (2006).
[4] Veryzer, R. W. and de B. B. Mozota, "The Impact of User-Oriented Design on New Product Development: An Examination of Fundamental Relationships,” Journal of Product Innovation Management, 22(2), 128–143 (2005).
[5] Berry M. J.and G.S. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Support, John Wiley & Sons, Inc, (1997).
[6] Pawlak, Z., "Rough Sets,” International Journal of Computer and Information Sciences, 11(5), 341-356 (1982).
[7] Greco, S., B. Matarazzo and R. Słowiński, "Rough sets theory for multicriteria decision analysis,” European Journal of Operational Research, 129 (1), 1–47 (2001).
[8] Yang, X., J. Yang, C. Wu, and D. Yu, "Dominance-based rough set approach and knowledge reductions in incomplete ordered information system,” Information Sciences, 178(4), 1219-1234 (2008).
[9] Hsu C.Y., C.F. Chien, K.Y. Lin and C.Y. Chien "Data mining for yield enhancement in TFT-LCD manufacturing: an empirical study,” Journal of the Chinese Institute of Industrial Engineers, 27(2), 140-156 (2010).
[10] Chien, C. F. and L. Chen, "Using Rough Set Theory to Recruit and Retain High-Potential Talents for Semiconductor Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, 20(4), 528-541 (2007).
[11] Peng, J., C.-F. Chien and B. Tseng, "Rough set theory for data mining for fault diagnosis on distribution feeder,” IEE Proceedings- Generation, Transmission, and Distributions, 151(6), 689-697 (2004).
[12] Pawlak, Z., "Rough set approach to knowledge-based decision support,” European Journal of Operational Research, 99(1), 48–57 (1997).
[13] Hang, J. and M. Kamber, Data Mining Concepts and Techniques, Elsevier, (2006).
[14] Harding, J. A., M. Shahbaz, S. Srinivas and A. Kusiak, "Data Mining in Manufacturing:A Review,” Journal of Manufacturing Science and Engineering, 128, 969-976 (2006).
[15] Chien, C. F., W. C. Wang and J. C. Cheng, "Data mining for yield enhancement in semiconductor manufacturing and an empirical study,” Expert Systems with Applications, 33(1), 192-198 (2007).
[16] Ngai, E.W.T., L. Xiu and D. C. K. Chau, "Application of data mining techniques in customer relationship management: A literature review and classification,” Expert Systems with Applications, 36(2-2), 2592-2602 (2009).
[17] Braha, D., Y. Elovici, M. Last, "Theory of actionable data mining with application to semiconductor manufacturing control,” International Journal of Production Research, 45(13), 3059–3084 (2007).
[18] Bloch, P. H., F. F. Brunel and T. J. Arnold, "Individual differences in the centrality of visual product aesthetics: Concept and measurement,” Journal of Consumer Research, 29(4), 551–565 (2003).