While many companies have benefited from online sales as their sole sales channel with the rapid growth of online retailing, this approach has limitations, especially for products that contain non-digital information and require a complementary service to fully attract customers. Sellers of these types of products are actively considering or have already adopted a multichannel strategy, which includes maintaining the existing online channel and opening physical brick-and-mortar stores. To stimulate sales, the service operator may consider offering a product bundle with the product manufacturer by providing subsidies to them. Decisions on product bundling could potentially facilitate or pose barriers to channel expansion. This study employs a game-theoretic model to explore the optimal pricing, multichannel and bundling strategies for a product manufacturer and a service operator who offer the core products with ancillary services in either bundled or non-bundled format. Our equilibrium analysis yields several insights. First, the manufacturer’s offline expansion allows customers who visit in-store to try and inspect the product, which raises not only the offline price but also the manufacturer’s online price. Interestingly, this price increase is more significant for the bundled format compared to the non-bundled format. Second, the bundling strategy influences the manufacturer’s decision to expand into multichannel operations. Specifically, product bundling incentivises multichannel expansion if the newly added physical stores can attract a significant number of new customers, indicating that demand spillover is significant. Conversely, product bundling may deter multichannel expansion if the online hassle cost is moderate.

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/ .

Operational decisions made by manufacturers and retailers are often affected by the power relationships of both vertical and horizontal competitions in a supply chain. Although many studies have been carried to assess how the power structure impacts supply chains, most of them only focus on the power dynamics in the vertical competition or horizontal competition. This paper systematically examines the impacts of bidimensional power structure on a supply chain. Considering a triadic setting that is made of two rival retailers and a common manufacturer, we compare the equilibriums for the supply chain and individual firms with five different possible power structures. The analysis results show that the bidimensional power dynamics have a significant impact. Furthermore, the impacts of the bidimensional power structure are also dependent on the nature of customer demand. This paper provides important management implications that are supportive for companies to make strategic and operational decisions.