数据驱动的设计优化:结合计划行为理论和贝叶斯网络的需求发现实证研究

IF 7 2区 工程技术 Q1 ENGINEERING, INDUSTRIAL International Journal of Production Research Pub Date : 2023-10-26 DOI:10.1080/00207543.2023.2271093
Yitian Liu, Kang Hu, Ruifeng Zhou, Xianfeng Ai, Yunqing Chen
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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. 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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. 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引用次数: 0

摘要

摘要许多理论方法被用于研究用户行为和需求。然而,与顾客特征相关的不确定性往往会使顾客研究得出的结论产生偏差,并影响产品设计的有效性。本文在计划行为理论(TPB)的基础上,将贝叶斯网络(BN)引入到客户行为分析的研究中,从用户行为意向的角度,建立了由客户研究数据驱动的分析模型,指导设计优化。该模型结合用户背景因素和TPB因素,分析了两者之间关联的不确定性,并通过结构学习修正了设计者先验知识中的错误。通过案例研究,本文发现,增强顾客主观规范和感知行为控制的评价会导致更大的购买或使用概率。此外,具有特定特征的顾客更容易产生行为意向。论文最后根据研究结果提出了设计优化方案,并对研究方法的优势和未来的研究方向进行了讨论。关键词:产品设计设计优化计划行为理论贝叶斯网络客户需求披露声明作者未报告潜在的利益冲突。本研究还得到了上海海洋大学信息技术学院和华中师范大学的支持和帮助。数据可用性声明基于对受试者的保护,本研究中所有样本的序列文件和注释数据已存入Figshare (https://doi.org/10.6084/m9.figshare.21118321.v1)。数据包括剔除参与者信息后的统计数据表、R运算生成的贝叶斯网络图集、SPSSAU平台处理的数据表、Netica处理文件。刘一田:监督、构思、研究、实验、设计、写作-原稿、写作-审编、写作-翻译、持续修改。康虎:监督、构思、研究、实验、设计、写作——初稿、不断修改。周瑞峰:写作-审编,写作-翻译,持续修改。艾先丰:监督、构思、研究、实验、设计、写作-原稿。陈云青:概念、研究、写作—原稿、写作—审校、写作—翻译。额外的informationFunding。作者刘奕天是一名研究生。2020年毕业于武汉科技大学艺术与设计学院工业设计专业,正在攻读工业工程专业博士学位。专注于工业设计研究和客户研究课题。现专注于高温彩釉陶瓷艺术创作与技术研发。康虎是副院长;副教授。2004年7月加入武汉科技大学,从教近18年。2017年任武汉科技大学育才(儿童)教育装备设计研究院副院长,2019年任武汉科技大学艺术与设计学院副院长。同时担任教育部学位与研究生教育发展中心学术论文评审专家;中国机械工程学会工业设计分会委员;武汉科技大学青年科技协会常务理事。周瑞峰是一名研究生。周瑞峰于2022年获得英国华威大学可持续汽车电气化专业理学硕士学位。他于2023年开始在纽卡斯尔大学攻读博士学位。他目前的研究重点是电动汽车用锂离子电池。艾先峰,副教授。先锋艾主要专注于可持续产品服务体系设计和设备产品设计研究。2018年,他指导学生获得国际红点设计奖。陈云青是一名研究生。研究方向为产品设计方法与理论。
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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.
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来源期刊
International Journal of Production Research
International Journal of Production Research 管理科学-工程:工业
CiteScore
19.20
自引率
14.10%
发文量
318
审稿时长
6.3 months
期刊介绍: The International Journal of Production Research (IJPR), published since 1961, is a well-established, highly successful and leading journal reporting manufacturing, production and operations management research. IJPR is published 24 times a year and includes papers on innovation management, design of products, manufacturing processes, production and logistics systems. Production economics, the essential behaviour of production resources and systems as well as the complex decision problems that arise in design, management and control of production and logistics systems are considered. IJPR is a journal for researchers and professors in mechanical engineering, industrial and systems engineering, operations research and management science, and business. It is also an informative reference for industrial managers looking to improve the efficiency and effectiveness of their production systems.
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