Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method
提出一种利用在线评论文本的建模框架，提取产品特征并分析其重要性、性能和竞争力，生成改进策略，帮助产品经理和设计师进行产品重新设计与创新。
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. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Project 72071151] and the Natural Science Foundation of Hubei Province, China [Grant 2023CFB712]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0333 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0333 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .