Managing the Retail Operations in the COVID-19 Pandemic: Evidence from Morocco

S. Benchekroun, V.G. Venkatesh, I. Dkhissi, D.J. Persis, A. Manimuthu, M. Suresh and V.R. Sreedharan
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S. Benchekroun: University of Ottawa [Ottawa]
V.G. Venkatesh: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
I. Dkhissi: UIR - Université Internationale de Rabat
D.J. Persis: IIMK - Indian Institute of Management Kozhikode [Inde]
A. Manimuthu: NTU - Nanyang Technological University [Singapour]
M. Suresh: Amrita Vishwa Vidyapeetham
V.R. Sreedharan: University of Bradford

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Abstract: Novel coronavirus disease (COVID-19) and resulting lockdowns have contributed to major retail operational disturbances around the globe, forcing retail organizations to manage their operations effectively. The impact can be measured as a black swan event (BSE). Therefore, to understand its impact on retail operations and enhance operational performance, the study attempts to evaluate retail operations and develop a decision-making model for disruptive events in Morocco. The study develops a three-phase evaluation approach. The approach involves fuzzy logic (to measure the current performance of retail operations), graph theory (to develop an exit strategy for retail operations based on different scenarios), and ANN and random forest-based prediction model with K-cross validation (to predict customer retention for retail operations). This methodology is preferred to develop a unique decision-making model for BSE. From the analysis, the current retail performance index has been computed as ``Average'' level and the graph-theoretic approach highlighted the critical attributes of retail operations. Further, the study identified triggering attributes for customer retention using machine learning-based prediction models (MLBPM) and develops a contactless payment system for customers' safety and hygiene. The framework can be used on a periodic basis to help retail managers to improve their operational performance level for disruptive events. \textcopyright 2022 John Wiley & Sons Ltd.

Date: 2023-01-01
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Published in Managerial and Decision Economics, 2023, 44 (1), pp.424-447. ⟨10.1002/mde.3691⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04433050

DOI: 10.1002/mde.3691

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