An extension of interpretive structural modeling using linguistic term sets for business decision-making

Sanjay Kumar Tyagi (), Sujeet Kumar Sharma (), R. Krishankumar () and K. S. Ravichandran ()
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Sanjay Kumar Tyagi: Higher College of Technology
Sujeet Kumar Sharma: Indian Institute of Management
R. Krishankumar: Amrita School of Engineering, Coimbatore
K. S. Ravichandran: Rajiv Gandhi National Institute of Youth Development

OPSEARCH, 2022, vol. 59, issue 3, No 16, 1158-1177

Abstract: Abstract This paper presents a new interpretive structural modeling (ISM) technique based on linguistic term sets. In the proposed approach, the linguistic terms will replace binary numbers 0 and 1, representing one attribute's influence on another. The main objective is to introduce the concept of linguistic term sets to the ISM and develop a linguistic interpretive structural modeling (LISM), where the decision-makers (DM) would use linguistic terms such as very high (VH), high (H), low (L), very low (VL) and, no influence (N) to measure the strength of the impact of an attribute on other attributes. Since the linguistic terms are closer to the human cognitive process, it is more convenient and realistic for the decision-makers to use linguistic terms instead of binary numbers to express the pairwise relationship between different attributes. The integration of fuzzy linguistic terms and the ISM would enhance the consistency level and reduce the uncertainty inherent in the decision-maker's choice. The proposed LISM has been demonstrated by identifying the inter-relationships among the key attributes of business analytics methodology (BAM) acceptance in the industry settings.

Keywords: Business analytics; Fuzzy numbers; ISM; Linguistic terms; Multi-criteria decision-making (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12597-021-00565-x

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