Skip to main content

Advertisement

Springer Nature Link
Log in

Eliciting design knowledge from affective responses using rough sets and Kansei engineering system

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

In the last 20 years, the Kansei engineering system (KES) has employed a variety of mathematical models to overcome design problems in consumer products. However, the increasing globalization of consumer markets has made the acquisition of market knowledge more competitive than ever; therefore, a strong focus has recently been placed on developing the means to capture consumer affective responses and obtain comprehensive data related to preferences in product exterior features. Rough set theory (RST) is a rule-based knowledge acquisition method capable of targeting imprecise, non-linear human perceptions. Surprisingly, little research has been conducted into the development of KES combined with RST. Therefore, this study used a systemic approach to perform a visual design of a toothbrush by combining the Kansei engineering and RST for exploring the relationship between form and color during a product evaluation. We also provide a point-by-point comparison of KES with RST to provide a reference for the merging of these two techniques. These findings will be of considerable interest to marketing researchers, artists, product designers, and color scientists as well as manufacturers and research centers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Akcay O, Sable P, Dalgin MH (2012) The importance of color in product choice among young Hispanic, Caucasian, and African-American groups in the USA. Int J Bus Soc Sci 3:1–6

    Google Scholar 

  • Bloch PH (1995) Seeking the ideal form: product design and consumer response. J Mark 59:16–29

    Article  Google Scholar 

  • Choo S, Kim Y (2003) Effect of color on fashion fabric image. Color Res Appl 28:221–226

    Article  Google Scholar 

  • Coates D (2003) Watches tell more than time. Mc. Graw Hill, New York

    Google Scholar 

  • Craven MW, Shavlik JW (1997) Using neural networks for data mining. Future Gener Comput Syst 13:211–229

    Article  Google Scholar 

  • Demirbilek O, Sener B (2003) Product design, semantics and emotional response. Ergonomics 46:1346–1360

    Article  Google Scholar 

  • Han SH, Yun MH, Kim KJ, Kwahk J (2000) Evaluation of product usability: development and validation of usability dimensions and design elements based on empirical models. Int J Ind Ergon 26:477–488

    Article  Google Scholar 

  • Hotta H, Hagiwara M (2006) A fuzzy rule based personal Kansei modeling system 2006. In: IEEE International Conference on Fuzzy Systems Sheraton Vancouver Wall Centre Hotel Vancouver, BC, Canada

  • Hsiao SW, Huang HC (2002) A neural network based approach for product form design. Des Stud 23:67–84

    Article  Google Scholar 

  • Hsiao SW, Tsai HC (2005) Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. Int J Ind Ergon 35:411–428

    Article  Google Scholar 

  • Hsu SH, Chuang MC, Chang CC (2000) A semantic differential study of designers’ and users’ product form perception. Int J Ind Ergon 25:375–391

    Article  Google Scholar 

  • Inuiguchi M, Miyajima T (2007) Rough set based rule induction from two decision tables. Eur J Oper Res 181:1540–1553

    Article  MATH  Google Scholar 

  • Jindo T, Hirasago K, Nagamachi M (1995) Development of a design support system for office chairs using 3-D graphics. Int J Ind Ergon 15:49–62

    Article  Google Scholar 

  • Jiro K (1986) KJ method. Chuokoron-Sha, Tokyo

    Google Scholar 

  • Jones JC (1992) Design methods. Van Nostrand Reinhold, New York

    Google Scholar 

  • Kim HS, Cho SB (2000) Application of interactive genetic algorithm to fashion design. Eng Appl Artif Intell 13:635–644

    Article  Google Scholar 

  • Kwahk J, Han SH (2002) A methodology for evaluating the usability of audiovisual consumer electronic products. Appl Ergon 33:419–431

    Article  Google Scholar 

  • Mak B, Munakata T (2002) Rule extraction from expert heuristics: a comparative study of rough sets with neural networks and ID3. Eur J Oper Res 136:212–229

    Article  MATH  Google Scholar 

  • Nagamachi M (1995) Kansei Engineering: a new ergonomic consumer-oriented technology for product development. Int J Ind Ergon 15:3–11

    Article  Google Scholar 

  • Nayatani Y, Komatsubara H (2005) Relationships among chromatic tone, perceived lightness, and degree of vividness. Color Res Appl 30:221–234

    Article  Google Scholar 

  • Øhrn A (1999) Discernibility and Rough Sets in Medicine: Tools and Applications. Norwegian University of Science and Technology

  • Park J, Han SH (2004) A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design. Int J Ind Ergon 34:31–47

    Article  Google Scholar 

  • Pawar KS, Driva H (1999) Performance measurement for product design and development in a manufacturing environment. Int J Prod Econ 60–61:61–68

    Article  Google Scholar 

  • Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci 177:3–27

    Article  MathSciNet  MATH  Google Scholar 

  • Schütte S, Eklund J (2005) Design of rocker switches for work-vehicles—an application of Kansei Engineering. Appl Ergon 36:557–567

    Article  Google Scholar 

  • Shieh MD, Hsu FC (2013) Using FCM clustering for consumer segmentation in Kansei engineering system. Int J Digit Content Technol Appl 7:563–571

    Article  Google Scholar 

  • Shieh MD, Yang CC (2008) Classification model for product form design using fuzzy support vector machines. Comput Ind Eng 55:150–164

    Article  Google Scholar 

  • Shimizu Y, Jindo T (1995) A fuzzy logic analysis method for evaluating human sensitivities. Int J Ind Ergon 15:39–47

    Article  Google Scholar 

  • Singh S (2006) Impact of color on marketing. Manag Decis 44:783–789

    Article  Google Scholar 

  • Tanoue C, Ishizaka K, Nagamachi M (1997) Kansei Engineering: a study on perception of vehicle interior image. Int J Ind Ergon 19:115–128

    Article  Google Scholar 

  • Tsai JL, Knutson B, Fung HH (2006) Cultural variation in affect valuation. J Pers Soc Psychol 90:288–307

    Article  Google Scholar 

  • Tsuchiya T, Maeda T, Matsubara Y, Nagamachi M (1996) A fuzzy rule induction method using genetic algorithm. Int J Ind Ergon 18:135–145

    Article  Google Scholar 

  • Wallace DR (1991) A computer model of aesthetic industrial design. Massachusetts Institute of Technology

  • Yang CC (2011) Constructing a hybrid Kansei engineering system based on multiple affective responses: application to product form design. Comput Ind Eng 60:760–768

    Article  Google Scholar 

  • Yang SM, Nagamachi M, Lee SY (1999) Rule-based inference model for the Kansei Engineering System. Int J Ind Ergon 24:459–471

    Article  Google Scholar 

  • Yang BS, Lim DS, Tan ACC (2005) VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table. Expert Syst Appl 28:735–742

    Article  Google Scholar 

  • Yang CC, Shieh MD, Chen KH, Lin PJ (2011a) Product form feature selection for mobile phone design using LS-SVR and ARD. J Converg Inf Technol 6:138–150

    Article  Google Scholar 

  • Yang CC, Sun CC, Lin PJ, Tsai CC (2011b) A general framework for Kansei engineering system. Int J Digit Content Technol Appl 5:173–180

    Google Scholar 

  • Yun MH, Han SH, Hong SW, Kim J (2003) Incorporating user satisfaction into the look-and-feel of mobile phone design. Ergonomics 46:1423–1440

    Article  Google Scholar 

  • Zhai LY, Khoo LP, Zhong ZW (2009) A dominance-based rough set approach to Kansei Engineering in product development. Expert Syst Appl 36:393–402

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Design, National Cheng Kung University, No. 1, University Rd., Tainan, 70101, Taiwan, ROC

    Meng-Dar Shieh & Chih-Lung Huang

  2. Department of Multimedia Animation Design, TransWorld University, No. 1221, Zhennan Rd., Douliu, Yunlin, 640, Taiwan, ROC

    Yu-En Yeh

Authors
  1. Meng-Dar Shieh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-En Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chih-Lung Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chih-Lung Huang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shieh, MD., Yeh, YE. & Huang, CL. Eliciting design knowledge from affective responses using rough sets and Kansei engineering system. J Ambient Intell Human Comput 7, 107–120 (2016). https://doi.org/10.1007/s12652-015-0307-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-015-0307-6

Keywords