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学术前沿讲座——Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method



Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method


秦晋栋  (武汉理工大学)








秦晋栋,武汉理工大学管理学院副教授、博士生导师、数据科学与智能决策研究中心主任、数字治理与管理决策创新研究院副院长。新加坡国立大学和欧洲工商管理学院(INSEAD)访问学者。主要研究方向为数据驱动的决策分析及其商务智能应用。在INFORMS Journal on Computing (UTD24)IISE TransactionsEuropean Journal of Operations ResearchOmegaDecision Support SystemsInternational Journal of Production EconomicsAnnals of Operations ResearchJournal of the Operational Research SocietyIEEE TransactionsInformation Sciences、系统工程理论与实践、中国管理科学等管理科学与信息科学领域重要期刊发表SCI/SSCI论文80余篇,出版中英文学术专著3部,入选斯坦福大学发布的全球前2%顶尖科学家榜单(2021-2023)、爱思维尔中国高被引学者(管理科学与工程20212022)、湖北省楚天学者计划,湖北省青年科技晨光计划。研究成果入选中国百篇最具国际学术影响力论文,获省部级科研奖励5项。现任Springer期刊《Management System Engineering》执行主编,Information Sciences等四个SCI/SSCI期刊副主编及中国系统工程学会数据科学专委会等5个学会的常务理事或理事。


Online reviews published on the e-commerce platform provide a new source of information for designers to develop new products. Past research on new product development (NPD) using user-generated textual data commonly focused solely on extracting and identifying product features to be improved. However, the competitive analysis of product features and more specific improvement strategies have not been explored deeply. This study fully uses the rich semantic attributes of online review texts and proposes a novel online review–driven modeling framework. This new approach can extract fine-grained product features; calculate their importance, performance, and competitiveness; and build a competitiveness network for each feature. As a result, decision-making is assisted, and specific product improvement strategies are developed for NPD beyond existing modeling approaches in this domain. Specifically, online reviews are first classified into redesign- and innovation-related themes using a multiple embedding model, and the redesign and innovation product features can be extracted accordingly using a mutual information multilevel feature extraction method. Moreover, the importance and performance of features are calculated, and the competitiveness and competitiveness network of features are obtained through a personalized unidirectional bipartite graph algorithm. Finally, the importance—performance–competitiveness analysis plot is constructed, and the product improvement strategy is developed via a multistage combined search algorithm. Case studies and comparative experiments show the effectiveness of the proposed method and provide novel business insights for stakeholders, such as product providers, managers, and designers.