A weighted logistic regression for conjoint analysis and Kansei engineering

Abstract

Customer needs for emotional satisfaction are increasingly being considered by product and service designers. While several existing methods such as conjoint analysis (CA), Kano model and quality function deployment support the translation of customer requirements into technical specifications, researchers are now working to develop methods aimed at integrating affective aspects into product design. Kansei engineering (KE) is a design philosophy that considers customer perceptions and emotions by adopting a multi-disciplinary approach. CA is a useful tool within a KE project. This article presents a methodology for conducting a KE project in early development phases. This methodology is based on two new procedures. The first one is aimed at calculating attribute importance weights by using respondent choice time in controlled interviews. The second procedure allows the exploitation of such weights in an ordinal logistic regression model for analysing the results of CA experiments. By using the proposed methodology, it is possible to identify product/service attributes able to induce specific emotions and feelings in customers and consequently choose the right development strategy. An application of the method for the design of mobile phones is presented.

Determination of optimal product styles by ordinal logistic regression versus conjoint analysis for kitchen faucets

“…OLOGREG can be used when dependent variables are measured on an ordinal scale, and it allows taking interaction effects into account. Recently, Barone et al (2007) have used a weighted OLOGREG procedure to improve CA results. As a result they have obtained significant design parameters for each kansei word more accurately compared to traditional CA based on LREG.…”

A methodology of integrating affective design with defining engineering specifications for product design

“…One important task of the Kansei engineering framework is the evaluation of relationships between the defined affective dimensions and the design attributes. Various approaches have been attempted in previous studies on modelling the affective relationships such as quantification theory I (Chang 2008), ordinal logistic regression (Barone, Lombardo, and Tarantino 2007), partial least-squares analysis (Nagamachi 2008), artificial neural network (Lai, Lin, and Yeh 2005;Chen, Khoo, and Yan 2006), fuzzy logic approach (Lau et al 2006;Lin, Lai, and Yeh 2007), FR (Sekkeli et al 2010), genetic programming-based FR , support vector regression model (Yang and Shieh 2010) and ANFIS (Kwong, Wong, and Chan 2009). Orsborn, Cagan, and Boatwright (2009) quantified aesthetic form preference using utility functions.…”

Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis

“…doi:10.1016/j.ins.2008.06.023 are also used [28,36] in Kansei evaluation. Moreover, Barone et al [2] proposed a weighted regression approach by means of conjoint analysis, in which attribute importance weights are estimated by using respondent choice time in controlled interviews. Petiot and Yannou [39] proposed an integrated approach which rates and ranks the new product prototypes according to their closeness to the specified ''ideal product", in which three types of satisfaction utility functions are defined and a multi-additive model is used to obtain the global satisfaction utility.…”

“…Moreover, with the development of global markets and modern technologies, it is likely that many similar products will be functionally equivalent [19], thus consumers may find that it is difficult to distinguish and choose their desired product(s). In this regard, consumers' psychological needs and feelings must be considered in choice of products [2].…”