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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/. [ABSTRACT FROM AUTHOR]

Owing to the rapidly changing customer preferences and demands, the e-commerce industry encounters various uncertainties and risks to enhance competitiveness. Quality function deployment (QFD) is a commonly used model, which can translate customer requirements (CRs) into products or service design requirements (DRs), to improve competitiveness by launching a new business. To measure the uncertainties and behavioral risk factors in e-commerce service design, we propose a new QFD model based on the Kano model and TOPSIS method by considering the behavior of experts with prospect theory under interval type-2 fuzzy linguistic environment. The categories of CRs are identified using the Kano model and the weights of CRs are determined dynamically according to the development stages of enterprises. The priorities of DRs are ranked using the extended TOPSIS method with prospect theory. A case study of China's e-commerce service design is used to show the application of the proposed QFD model. The prioritizing results show the flexibility of the proposed model on determining the weights of CRs and the priorities of DRs for enterprises at different development stages. • A quality function deployment model is proposed under linguistic environment. • Identify the attribute category of CRs using the interval type-2 fuzzy Kano model. • Determine the weights of CRs based on the development stage of enterprises. • Rank DRs by an extended TOPSIS method considering the behavioral factors. [ABSTRACT FROM AUTHOR]