Abstract
Kansei and usability are two important aspects that should be considered in product development. In order to design the product that not only meets consumer’s affective needs but also is easy to use, this paper presents a novel approach based on data mining to reveal the relationship between kansei and usability. Firstly, kansei image and usability evaluation indexes were determined. Secondly, within-subjects experimental design was applied to test kansei image, usability, and user’s satisfaction. Finally, association rule and decision tree were utilized to mine the experimental data so as to discover the rules hidden in the data. A case study of mobile phones was conducted based on the proposed method. The results suggest that there is a significant relationship between kansei and usability which together influence user’s satisfaction with product. This approach can provide designers with useful suggestions and solutions for product design.
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Yanagisawa, H., Murakami, T.: Factors Affecting Viewpoint Shifts When Evaluating Shape Aesthetics towards Extracting Customer’s Latent Needs of Emotional Quality. In: 2008 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 3, pp. 791–800. ASME, New York City (2008)
ISO 9241-11: Ergonomic Requirements for Office Work with Visual Display Terminals (VDTs). Part 11 - Guidelines for Specifying and Measuring Usability. International Standards Organization, Geneva (1998)
Sonderegger, A., Sauer, J.: The Influence of Design Aesthetics in Usability Testing: Effects on User Performance and Perceived Usability. Applied Ergonomics 41, 403–410 (2010)
Seva, R.R., Gosiaco, K.G.T., Santos, M.C.E.D., Pangilinan, D.M.L.: Product Design Enhancement Using Apparent Usability and Affective Quality. Applied Ergonomics 42, 511–517 (2011)
Vergara, M., Mondragón, S., Sancho-Bru, J.L., Company, P., Agost, M.-J.: Perception of Products by Progressive Multisensory Integration. A Study on Hammers. Applied Ergonomics 42, 652–664 (2011)
Anand, S.S., Büchner, A.G.: Decision Support Using Data Mining. Financial Times Pitman, London (1998)
Xia, S.S., Wang, L.Y.: Customer Requirements Mapping Method Based on Association Rule Mining for Mass Customization. Journal of Shanghai Jiaotong University (Science) 13, 291–296 (2008)
Bae, J.K., Kim, J.: Product Development with Data Mining Techniques: A Case on Design of Digital Camera. Expert Systems with Applications 38, 9274–9280 (2011)
Lin, P., Yang, C.: Impact of Product Pictures and Brand Names on Memory of Chinese Metaphorical Advertisements. International Journal of Design 4, 57–70 (2010)
Liao, S.-H., Chen, Y.-J., Deng, M.-Y.: Mining Customer Knowledge for Tourism New Product Development and Customer Relationship Management. Expert Systems with Applications 37, 4212–4223 (2010)
Horner, S.B., Fireman, G.D., Wang, E.W.: The Relation of Student Behavior, Peer Status, Race, and Gender to Decisions about School Discipline Using CHAID Decision Trees and Regression Modeling. Journal of School Psychology 48, 135–161 (2010)
Smith, S., Fu, S.-H.: Factor Analysis of Head-Up Display Presentation Images. In: 2009 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 2, pp. 967–974. ASME, San Diego (2010)
Norman, D.A.: Emotional Design: Why We Love (or Hate) Everyday Things. Basic Books, New York (2004)
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Li, Y., Zhu, L. (2012). A Data Mining Based Approach to Research the Relationship between Kansei and Usability: A Case Study of Mobile Phones. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_4
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DOI: https://doi.org/10.1007/978-3-642-33478-8_4
Publisher Name: Springer, Berlin, Heidelberg
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