A Comparative Study of Ordinal Probit and Logistic Regression for Affective Product Design

Zhen He; Du Wu

doi:10.4028/www.scientific.net/AMR.452-453.642

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 452-453A Comparative Study of Ordinal Probit and Logistic...

A Comparative Study of Ordinal Probit and Logistic Regression for Affective Product Design

Abstract:

Affective product design, which concentrates on customers’ affective responses and aspirations, is arousing increasing attention. In this paper, ordinal probit regression (OPR) is introduced into this field to discover mapping knowledge from design elements to customer affect, and a comparative study is always recommended between OPR and ordinal logistic regression (OLR) for available data to choose a better fitted model. The discovered mapping relations could facilitate the handling of affective information and assist the designer to make trade-off decisions. A case study of cell phone design was conducted. Four generic affective dimensions and six key product attributes of the cell phone were identified. OPR and OLR were applied successively to reveal the quantitative relations from design elements to customer affect. For the two models, five classes of indexes were compared. The findings show that OLR is superior to OPR to fit the collected data.

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Periodical:

Advanced Materials Research (Volumes 452-453)

Pages:

642-647

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https://doi.org/10.4028/www.scientific.net/AMR.452-453.642

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Online since:

January 2012

Authors:

Zhen He, Du Wu

Keywords:

Affective Product Design, Discrete Choice Mode, Ordinal Logistic Regression, Ordinal Probit Regression

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References

[1] H.H. Lai, Y.M. Chang and H.C. Chang: A robust design approach for enhancing the feeling quality of a product: a car profile case study. International Journal of Industrial Ergonomics. Vol. 35 (2005), pp.445-460.

DOI: 10.1016/j.ergon.2004.10.008

Google Scholar

[2] P. Nurkka, S. Kujala and K. Kemppainen: Capturing users' perceptions of valuable experience and meaning. Journal of Engineering Design. Vol. 20 (2009), pp.449-465.

DOI: 10.1080/09544820903158835

Google Scholar

[3] K.Y. Chanb, C.K. Kwonga, T.S. Dillonb and K.Y. Funga: An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness. Journal of Engineering Design. Vol. 22 (2011), pp.523-542.

DOI: 10.1080/09544820903550924

Google Scholar

[4] M.C. Chuang and Y.C. Ma: Expressing the expected product images in product design of micro-electronic products. International Journal of Industrial Ergonomics. Vol. 27 (2001), pp.233-245.

DOI: 10.1016/s0169-8141(00)00053-6

Google Scholar

[5] J. Kuang and P. Jiang: Product platform design for a product family based on Kansei engineering. Journal of Engineering Design. Vol. 20 (2009), pp.589-607.

DOI: 10.1080/09544820802132410

Google Scholar

[6] T. Jindo, K. Hirasago and M. Nagamachi: Development of a design support system for office chairs using 3-D graphics. International Journal of Industrial Ergonomics. Vol. 15 (1995), pp.49-62.

DOI: 10.1016/0169-8141(94)00056-9

Google Scholar

[7] H. You, T. Ryu, K. Oh, M.H. Yun and K.J. Kim: Development of customer satisfaction models for automotive interior materials. International Journal of Industrial Ergonomics. Vol. 36 (2006), pp.323-330.

DOI: 10.1016/j.ergon.2005.12.007

Google Scholar

[8] S. Ishiharaa, K. Ishiharab, M. Nagamachi and Y. Matsubarac: An automatic builder for a Kansei Engineering expert system using self-organizing neural networks. International Journal of Industrial Ergonomics. Vol. 15 (1995), pp.13-24.

DOI: 10.1016/0169-8141(94)15053-8

Google Scholar

[9] S.W. Hsiao and H.C. Huang: A neural network based approach for product form design. Design Studies. Vol. 23 (2002), pp.67-84.

DOI: 10.1016/s0142-694x(01)00015-1

Google Scholar

[10] J.R. Jiao, Y. Zhang and M. Helander: A Kansei mining system for affective design. Expert Systems with Applications. Vol. 30 (2006), pp.658-673.

DOI: 10.1016/j.eswa.2005.07.020

Google Scholar

[11] S. Barone, A. Lombardo and P. Tarantino: A weighted logistic regression for conjoint analysis and Kansei engineering. Quality and Reliability Engineering International. Vol. 23 (2007), pp.689-706.

DOI: 10.1002/qre.866

Google Scholar

[12] F. Zhou, D. Wu, X. Yang and J. Jiao, Ordinal logistic regression for affective product design, in: IEEM 2008, Singapore, (2008).

DOI: 10.1109/ieem.2008.4738219

Google Scholar

[13] A. Agresti: Categorical Data Analysis (John Wiley & Sons, Hoboken, New Jersey 2002).

Google Scholar

[14] T.F. Liao: Interpreting probability models: Logit, probit, and other generalized linear models (Sage, Thousand Oaks, CA 1994).

Google Scholar