Examining the barriers to operationalization of humanitarian supply chains: lessons learned from COVID-19 crisis

Analyzing the enablers of circular supply chain using Neutrosophic-ISM method: lessons from the Indian apparel industry

Purpose

This research identifies the enablers for implementing circular supply chains (CSCEs) and analyzes interrelationships between them to quantify their driving and dependence power to understand the critical CSCEs.

Design/methodology/approach

Initially, 10 CSCEs are identified for the Indian apparel industries through an extant literature review and validated using the Delphi method by seeking experts' opinions. The identified CSCEs are subjected to a novel neutrosophic interpretive structural modeling (N-ISM) method to capture the interrelationships between CSCEs and compute the driving and dependence power of CSCEs.

Findings

The findings of the present research work revealed that “supportive legislative framework, awareness of circular economy's potential for revenue gain and availability of trained research and development (R&D) team” are the critical CSCEs that need to be considered while implementing a circular supply chain in apparel industries.

Research limitations/implications

This study offers insightful implications to guide practitioners in implementing the circular economy in apparel supply chains.

Originality/value

This research work is one of the earlier studies to analyze the enablers for implementing circular supply chains. This study has explored CSCEs in the context of apparel industries. From a methodological perspective, the novel N-ISM method is worth highlighting as the originality of the work.

Developing an evidence-based TISM: an application for the success of COVID-19 Vaccination Drive

The study illustrates an application of evidence data for performing Total Interpretive Structural Modeling (TISM). TISM is widely used to analyze the critical success factors or inhibitors and their interlinkages. This study uses learning from evidence data, specifically social media analytics, to identify the relationship between the elements. Thus, it leads to the advancement of the TISM-P methodology with evidence-based TISM (TISM-E). This study uses Twitter as a source of evidence data. Further, 2,60,297 tweets were used to illustrate the process of TISM-E. The paper demonstrates the application of TISM-E for the success of the COVID-19 vaccination drive. The pandemic effects are long-term; therefore, the hierarchical model developed shows a sustainable approach for vaccinating maximum population. Further, the framework developed will ensure the distribution efficacy of vaccines. In addition, it will aid practitioners in developing future vaccination policies. The enhanced model provides evidence for polarity (positive/negative) of relationships and can help to build propositions for theory development. The study contributes to healthcare, modeling research, and graph-theoretic literature.