Data driven design optimisation: an empirical study of demand discovery combining theory of planned behaviour and Bayesian networks

AbstractMany theoretical methods have been applied to research user behaviour and requirements. However, the uncertainty associated with customer characteristics often biases the conclusions drawn from customer research and affects the effectiveness of product design. In this paper, Bayesian networks (BN) are introduced into the research on customer behaviour analysis based upon theory of planned behaviour (TPB), and an analysis model driven by customer research data is established from the perspective of user behaviour intention to guide design optimisation. Combining the User background Factor with the TPB Factor, the model analyses the uncertainty of the association between the two, and corrects the errors in the designer's prior knowledge through structural learning. By a case study the paper finds that the evaluations that enhance customers’ subjective norms and perceived behavioural control lead to a greater probability of purchase or use. In addition, customers with specific characteristics are more inclined to generate behaviour intention. The paper finally provides a design optimisation plan based upon the result of the research and discusses about the advantages of the research approaches and the directions of future researches.KEYWORDS: Product designdesign optimisationtheory of planned behaviourBayesian networkscustomer requirements Disclosure statementNo potential conflict of interest was reported by the author(s).AcknowledgmentsThe research also received support and assistance from College of Information Technology Shanghai Ocean University and Central China Normal University.Data availability statementBased on the protection of human subjects, the sequence files and note data for all samples used in this study have been desposited in Figshare (https://doi.org/10.6084/m9.figshare.21118321.v1). The data includes the statistical data table with the information of the participants removed, the Bayesian network graph collection generated by the R operation, the data table processed by the SPSSAU platform, and the Netica processing file.CRediT authorship contribution statementYitian Liu: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft, Writing – review & editing, Writing – translate, Continuous modification. Kang Hu: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft, Continuous modification. Ruifeng Zhou: Writing – review & editing, Writing – translate and Continuous modification. Xianfeng Ai: Supervision, Conceptualisation, Research, Experiment, Design, Writing – original draft. Yunqing Chen: Conceptualisation, Research, Writing – original draft, Writing – review & editing, Writing – translate.Additional informationFunding.Notes on contributorsYitian LiuYitian Liu is a graduate student. Graduated from the School of Art and Design, Wuhan University of Science and Technology in 2020, majoring in industrial Design, and is studying for a doctorate degree in industrial engineering. Focused on industrial design research and customer research topics. Now focus on high temperature colour glaze ceramic art creation and technology research and development.Kang HuKang Hu is a subdean; associate professor. He joined Wuhan University of Science and Technology in July 2004 and has been teaching for nearly 18 years. In 2017, he served as the vice president of YuCai (Children's) Educational Equipment Design and Research Institute of Wuhan University of Science and Technology, and in 2019, he served as the vice president of the Art and Design College of Wuhan University of Science and Technology. At the same time as the academic dissertation review expert of the Center for Academic Degree and Graduate Education Development, Ministry of Education; Member of Industrial Design Branch of China Mechanical Engineering Society; Executive Director of Youth Science and Technology Association, Wuhan University of Science and Technology.Ruifeng ZhouRuifeng Zhou is a graduate student. Ruifeng Zhou received his Master of Science degree (Sustainable Automotive Electrification) from the University of Warwick in 2022. He starts pursuing PhD degree in 2023 at Newcastle University. His current research focuses on lithium-ion batteries for electric vehicles.Xianfeng AiXianfeng Ai is an associate professor. Xianfeng Ai mainly focuses on sustainable product service system design and equipment product design research. In 2018, he guided students to win the International Red Dot Design Award.Yunqing ChenYunqing Chen is a graduate student. The research direction is product design method and theory.