An uncertain Kansei Engineering methodology for behavioral service design

Abstract

To perfect a service, service providers must understand the fundamental emotional effects that a service may invoke. Kansei Engineering (KE) has been recently adapted to service industries to realize the relationships between service design elements and customers’ emotional perceptions. However, effective service design based on KE is still seriously challenged by the uncertainty and behavioral biases of customers’ emotions. This article tries to propose an uncertain KE methodology for behavioral service design. To do so, an integrative framework is first proposed by linking design attributes, emotional needs, and overall satisfaction, so as to design services best satisfying customers’ emotional needs. Second, multinomial logistic regression is used to build the uncertain relationships between design attributes and emotional attributes. Third, a quantitative Kano model is proposed to model the asymmetric and nonlinear satisfaction functions reflecting the “gains and losses” effect of positive emotions and negative emotions. Next, the Prospect Theory is used to derive customer overall satisfaction by distinguishing the “gains and losses”. Finally, the proposed methodology is applied to a case study of the campus express delivery service in China. An independent tracking study shows that the results are consistent with service acceptance and provide valuable insights.

Keywords:

• Service design
• Kansei Engineering
• uncertainty
• behavioral biases
• express delivery service

Acknowledgments

We appreciate the constructive comments and valuable suggestions from the anonymous referees and Area Editor, which have helped improve the quality of this paper.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 71871093, 71471063, and 71961137012.

Notes on contributors

Hong-Bin Yan

Hong-Bin Yan is a professor of management science and engineering in the School of Business at East China University of Science and Technology, China. He received his PhD degree in knowledge sciences from the Japan Advanced Institute of Science and Technology in Japan, and M.Sc. & B.Sc. in management from Dalian University of Technology in China, respectively. His current research interests include service management, new product development, Kansei/sensory Engineering, technology innovation management, and decision analysis. He has published research papers in IISE Transactions, Decision Sciences, Omega-International Journal of Management Science, European Journal of Operational Research, Annals of Operations Research, International Journal of Production Research, IEEE Transactions on Engineering Management, Information Sciences, Knowledge-Based Systems, Computers & Industrial Engineering, and Expert Systems with Applications.

Ming Li

Ming Li is a PhD candidate of management science and engineering in the School of Business at East China University of Science and Technology, China. She received her BSc in industrial engineering from Zhengzhou University in China. Her current research interests include service management, Kansei/sensory Engineering and technology innovation management.