Exploring circular supply chain practices from a dual perspective: using a hybrid method under uncertainty

Building risk mitigation strategies for circularity adoption in Indian textile supply chains

Textile industries are among the most polluting and demand urgent management measures to mitigate their negative environmental impact. Thus, it is imperative to incorporate the textile industry into the circular economy and to foster sustainable practices. This study aims to establish a comprehensive, compliant decision framework to analyse risk mitigation strategies for circular supply chain (CSC) adoption in India's textile industries. The Situations Actors Processes and Learnings Actions Performances (SAP-LAP) technique analyses the problem. However, interpreting the interacting associations between the SAP-LAP model-based variables is somewhat lacking in this procedure, which might skew the decision-making process. As a result, in this study, the SAP-LAP method is accompanied by a novel ranking technique, namely, the Interpretive Ranking Process (IRP), which reduces decision-making issues in the SAP-LAP method and aids in evaluating the model by determining the ranks of variables; furthermore, the study also offers causal relationships among the various risks and risk factors and various identified risk-mitigation actions by constructing Bayesian Networks (BN) based on conditional probabilities. The study's originality represents the findings using an instinctive and interpretative choice approach to address significant concerns in risk perception and mitigation techniques for CSC adoption in the Indian textile industries. The suggested SAP-LAP and the IRP-based model would assist firms in addressing risk mitigation techniques for CSC adoption concerns by providing a hierarchy of the various risks and mitigation strategies to cope with. The simultaneously proposed BN model will help visualise the conditional dependency of risks and factors with proposed mitigating actions.

Opening the black box of big data sustainable value creation: the mediating role of supply chain management capabilities and circular economy practices

Purpose

This article examines the mechanisms through which big data analytics capabilities (BDAC) contribute to creating sustainable value and analyzes the mediating roles that supply chain management capabilities (SCMC), as well as circular economy practices (CEP), play through their impact on sustainable performance.

Design/methodology/approach

Following a literature review, a serial mediation model is presented. Hypotheses regarding direct and mediating relationships are tested to determine their potential for sustainability impact and circularity. Partial least squares structural equation modeling (PLS-SEM) has been applied for causal and predictive purposes.

Findings

The results indicate that big data analytics capabilities do not have a direct positive impact on sustainable performance but influence indirectly through SCMC and CEP.

Originality/value

Although some authors have addressed the associations between IT business value, supply chain (SC), and sustainability, this paper provides empirical evidence related to these relationships. Additionally, this study performs novel predictive analyses.