Profiling the application of the 2-tuple linguistic model in marketing: a comprehensive analysis and future directions

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2026

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Springer Nature
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Shu, Z., Galán Hernández, J.J. & Carrasco-Aguilar, Á. Profiling the application of the 2-tuple linguistic model in marketing: a comprehensive analysis and future directions. Fuzzy Optim Decis Making (2026). https://doi.org/10.1007/s10700-026-09477-1

Abstract

Customer opinions, preferences, and decision-makers’ insights play a vital role in marketing by influencing product development, strategy, and customer experience. Computing with Words (CWW) offers an effective method for processing this qualitative information using linguistic terms instead of numerical values. This work aims to explore the application of one of the most widely used methodologies in CWW—the 2-tuple linguistic model—in marketing. It employs a combination of bibliometric analysis and a systematic literature review for a comprehensive analysis. Articles published between 2000 and 2024 on the Web of Science database are analyzed, incorporating Scopus´s Field-Weighted Citation Impact (FWCI) metrics to assess the impact of individual studies and the average influence of research in marketing-related areas. From an initial sample of 165 peer-reviewed articles, 90 were selected for analysis. The findings indicate that the 2-tuple linguistic model is primarily applied to decision-making problems, with Multiple Attribute Group Decision Making emerging as the most prominent theme due to its high centrality and high density in the strategic diagram, as well as its strong connections to various marketing-related areas. The 2-tuple linguistic model is widely applied in areas such as supplier management and product development and innovation, with digital transformation and consumer behavior representing potential directions for applying the model to address their respective marketing challenges. Using the FWCI, this work also identifies marketing-related areas within decision-making themes where the 2-tuple linguistic model is already applied but has limited influence, revealing opportunities for development and future research.

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