An integrated waterfall–DEMATEL–fuzzy TOPSIS approach to post-COVID-19 customer demand resilience: Evidence from fast-fashion MSMEs

Fares, Naila, Jaime, Lloret, Kumar, Vikas, Frederico, Guilherme, Kumar, Anil and Garza-Reyes, Jose Arturo (2023) An integrated waterfall–DEMATEL–fuzzy TOPSIS approach to post-COVID-19 customer demand resilience: Evidence from fast-fashion MSMEs. Benchmarking: an International Journal, 30 (6). pp. 2012-2039. ISSN 1463-5771

Abstract

Purpose:
The purpose of this paper is to analyze the resilience of customer demand management post-COVID-19, using fast-fashion MSMEs as an example. Precisely, this paper investigates how waterfall project management can enable stakeholders to build survival operations managerial decisions.
Design/methodology/approach:
Based on interviews and surveys with ten fast-fashion operations experts, and an integrated waterfall DEMATEL Fuzzy TOPSIS methodology of the fuzzy multi-criteria decision making (FMCDM), we explored and prioritized the enablers of resilience management in fast-fashion retail area.
Findings:
Results reveal that the highest priority enabler is maintaining customer loyalty. Other enablers are associated with e-commerce endorsement, customer-focused assortment, and flexible store operations.
Research /implications:
The findings of this paper will enable fast-fashion MSMEs to develop effective actions and prioritize the operations efforts to foster the post-pandemic recovery.
Originality/value:
Despite the importance of the resilience project and the changing fastfashion customer patterns with covid, only a handful of research has explored how resilience can be managed in this field. This research thus can help close this gap for operations resilience research and retail context

Documents
7860:40755
[thumbnail of Integrated-waterfall–DEMATEL–fuzzy-TOPSIS-approach_Updated-1.pdf]
Preview
Integrated-waterfall–DEMATEL–fuzzy-TOPSIS-approach_Updated-1.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial 4.0.

Download (581kB) | Preview
Details
Record
View Item View Item