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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]

In minimum cost consensus problems, to accomplish the group consensus, decision makers accommodate their initial discrepant opinions based on the unit adjustment costs. During this process, decision makers may exhibit loss aversion, which results in extra expenses when participants feel loss in contrast to their reference opinion adjustments. Existing minimum cost consensus models pay little attention to the loss-averse preference. Hence, to fill up this gap, a minimum cost consensus model with loss aversion (MCCM-LA) is established and the desired properties are analyzed. To manage large-scale group decision making problems, we first propose an integrated opinion similarity, connectivity similarity and behavior similarity clustering algorithm to divide decision makers into multiple subgroups. Balancing the individual adjustment willingness and consensus reaching efficiency, a two-stage consensus reaching mechanism is further designed based on MCCM-LA to realize the accordant opinion. Finally, the effectiveness and feasibility of the proposed method are demonstrated by sensitivity and comparative analyses with an illustrative example. [ABSTRACT FROM AUTHOR]

In modern logistics, port transportation plays an important role in the development of the surrounding economy. However, to determine the level of port group competitiveness, regional ports must be classified. To realize this classification, this paper introduces a novel multiple criteria sorting method, ORESTE-SORT, together with its main characteristics and properties. This approach can help in handling three types of preference relationships among alternatives and profiles: preference, indifference, and incomparability. In addition, a novel sorting rule, the assignment rule driven by attitudes (ARDA), is introduced to allocate alternatives to predefined categories based on the generalization of two classical sorting rules: optimistic and pessimistic rules. We also perform a case study to illustrate the suitability of the proposed sorting method for sorting port group competitiveness. Finally, sensitivity analysis and comparative analysis are performed with the ELECTRE-SORT method to determine the effectiveness of ORESTE-SORT, following which the capability of this new sorting methodology is further discussed. [ABSTRACT FROM AUTHOR]

In group decision-making processes, decision makers may have different risk preference types, which usually make them take various responses to the same problem. In minimum cost consensus problems, risk seekers, risk neutrals and risk averters may exhibit individual cost adjustment characteristics, which require aggregating their opinions in a model. To solve this problem, this paper presents a minimum cost consensus model to obtain group consensus preferences using a general optimization model, which can handle three types of risk preferences in decision making. In addition, a data-driven weighting scheme for the degrees of importance of decision makers is developed, and a generalized group consensus opinion aggregation function is designed. The proposed group consensus opinion aggregation function extends the existing developed opinion production method, which is more compatible than other aggregation functions. Finally, the proposed minimum cost consensus with risk preferences model is verified through an illustrative example. [ABSTRACT FROM AUTHOR]