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

In this study, a two-stage integrated approach is proposed and implemented to explore user perceptions about kitchen faucet styles and to find optimal levels of design parameters related to product appearance. At the first stage, a group of representative users have been asked to judge 38 systematically selected different faucet designs by using a semantic differential (SD) scale for 11 image (kansei) words about their visual perceptions. Then the relations between overall preference and kansei word scores of users are investigated by Ordinal Logistic Regression (OLOGREG). The model obtained helps understand how customers describe highly preferred designs. It can also be used to confirm if a product design will be highly preferred. At the second stage, the relations between overall preference scores and design parameter levels related to the product appearance are modeled using again OLOGREG, and then the best design parameter levels for visual perceptions are found by maximizing the overall preference scores. The results are confirmed and discussed. They are also compared to those of a commonly used approach in the literature, Conjoint Analysis (CA). This comparison has showed that the OLOGREG approach is superior to traditional CA. Finally, it is discussed how the product style design optimization approach presented and demonstrated in this study for the case of some kitchen faucets can be used in general for other products. Relevance to industry: Developing a user-centered product is an important policy of an enterprise in today's highly competitive marketplace. To reach this objective, a systematic method is proposed by integrating several techniques. This method will be useful to any industry that designs and produces consumer products. These products more fit the consumer needs and the competitiveness of them are improved.