Customer-oriented product design: an integrated decision framework with sentiment analysis and optimisation model

Abstract

In the context of Big Data and Business Intelligence, how to timely mine customer requirements (CRs) and accurately incorporate them into product design is of great significance, while the research concerning this issue is relatively scarce. The purpose of this study is to develop an integrated decision framework with sentiment analysis and optimisation model for customer-oriented product design. In the framework, CRs are firstly extracted by the proposed crawling and mining algorithms, and then they are transferred into corresponding product features (PFs) and product feature specifications (PFSs) based on the idea of QFD. Next, through the consideration of the customer sentiments and the psychological and behavioural factors, the achievements of PFSs towards CRs are measured. Further, a multi-objective product design optimisation model is constructed to generate desirable design schemes. The proposed framework is applied in the design of elderly smart bracelets. The feasibility and effectiveness of the framework are proved.

Keywords:

• Product design decision method
• customer requirements mining
• sentiment analysis
• optimisation model
• QFD

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This work was partly supported by the China Postdoctoral Science Foundation [Project No. 2019M653794], the Philosophy and Social Science Innovative Talent Support Program of Henan [Project No. 2022-CXRC-27], [Project No. 2021GGJS060], and the Logistics Research Center Project of Key Research Base of Humanities and Social Sciences in Henan Province [Project No. 2015-JD-04].

Notes on contributors

Yong-Hai Li

Yong-Hai Li received the Ph.D. degree from School of Business Administration, Northeastern University, China in 2014. Since 2014, he has been with the School of Management, Henan University of Technology, China, where he is currently a Professor. His research interests include big data and management decision analysis, e-commerce and business intelligence and rural development management.

Shan-Tao Yue

Shan-Tao Yue received his B.S. degree from School of Management, Henan University of Technology, China in 2020. He is currently pursuing his M.S. degree at School of Management, Henan University of Technology, China with Prof. Yong-Hai Li. His current research is focussed on big data management decision analysis and new product development.

Jin Zheng

Jin Zheng received her B.S. degree and M.S. degree from School of Management, Henan University of Technology, China in 2019 and 2022, respectively. She is a Ph.D. candidate in School of Business, Liaoning University, China. Her current research is focussed on rural development management. She has published papers in journals including Computers and Electronics in Agriculture and Kybernetes.

Weiwei Wang

Weiwei Wang received her B.S. degree from School of Management, Henan University of Technology, China in 2020. She is currently pursuing her M.S. degree at School of Management, Henan University of Technology, China with Prof. Yong-Hai Li. Her current research is focussed on e-commerce and business intelligence and rural development management.