Abstract
Products are now developed based on what customers desire, and thus attractive quality creation has become crucial. In studies on customer satisfaction, methods for analyzing quality attributes and enhancing customer satisfaction have been proposed to facilitate product development. Although substantial studies have performed to assess the impact of the attributes on customer satisfaction, little research has been conducted that quantitatively calculate the odds of customer satisfaction for the Kano classification, fitting a nonlinear relationship between attribute-level performance and customer satisfaction. In the present study, the odds of customer satisfaction were determined to identify the classification of quality attributes, and took customer psychology into account to suggest how decision-makers should prioritize the allocation of resources. A novel method for quantitatively assessing quality attributes was proposed to determine classification criteria and fit the nonlinear relationship between quality attributes and customer satisfaction. Subsequently, a case study was conducted on bicycle user satisfaction to verify the novel method. The concept of customer satisfaction odds was integrated with the value function from prospect theory to understand quality attributes. The results of this study can serve as a reference for product designers to create attractive quality attributes in their products and thus enhance customer satisfaction.
Highlights
With respect to improving Q8, improvement in Q1 attained a greater increase in customer satisfaction odds ratio than did improvement in Q2, indicating that improving Q1 performance was more useful than improving Q2 performance
A novel method was proposed to quantitatively examine the asymmetrical and nonlinear relationship between quality attributes and customer satisfaction, and to classify quality attributes on the basis of this relationship. These findings are that the influences of some attributes on customer satisfaction are significant, but that the logistic regression models the probability of customer satisfaction to fit the nonlinear relationship
Examining the cyclists confirmed that quality attributes and customer satisfaction were in an asymmetric and nonlinear relationship, as found in previous studies [51, 20, 24]
Summary
This study was aimed to explore the odds on customer satisfaction due to high quality performance and the risk of customer dissatisfaction due to low quality performance, enhancing quantitative assessment and
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