Title: Sentiment analysis of behavioural attributes for effective supply chain relationships: a fuzzy goal-setting approach

Authors: Margaret F. Shipley; Ray Qing Cao; Rob Austin McKee

Addresses: University of Houston Downtown, 326 N. Main Street, Houston, Texas 77002, USA ' University of Houston Downtown, 326 N. Main Street, Houston, Texas 77002, USA ' University of Houston Downtown, 326 N. Main Street, Houston, Texas 77002, USA

Abstract: Sentiments expressed through social media can reflect behavioural attributes of trust, commitment, collaboration, and information sharing between or among actors in supply chain management (SCM) relationships. These four attributes are generally considered to enhance SCM performance if each is at a high level. In this study, sentiment mining was undertaken using three different web crawler algorithms focusing on blog, forum, and Twitter sources. After classifying the mined sentiment data, scores were evaluated using a fuzzy model to address uncertainty and ambiguity. The least degree of fit of each attribute and combination of attributes was determined by industry for pharmaceuticals, software, retailing, and healthcare. Results indicate that the importance attributed to levels of interactions for the behavioural attributes necessitated in SCM relationships differs for the industries studied. However, overall, the most consequential attribute seems to be trust between the individuals involved. This work contributes to SCM research through the utilisation of techniques to focus on human attributes for decision making that may improve SCM performance; specifically for the industries studied.

Keywords: sentiment analysis; supply chain management; SCM; fuzzy logic; social capital; social media; trust; information sharing; collaboration; commitment.

DOI: 10.1504/IJBPSCM.2020.109202

International Journal of Business Performance and Supply Chain Modelling, 2020 Vol.11 No.2, pp.128 - 151

Received: 22 Aug 2019
Accepted: 10 Apr 2020
Published online: 01 Sep 2020 *

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