Predicting resilience in retailing using grey theory and moving probability based Markov models. (September 2021)

• Record Type: Journal Article
• Authors: Rajesh, R.; Agariya, Arun Kumar; Rajendran, Chandrasekharan
• Is Part Of: Journal of retailing and consumer services. Volume 62(2022)
• Publication Date: 2021-09
• Subjects: Retail town center -- Resilience -- Grey theory -- Grey prediction -- Markov models; Retail trade -- Periodicals; Service industries -- Periodicals; Customer services -- Periodicals; Commerce de détail -- Périodiques; Service à la clientèle -- Périodiques; Customer services; Retail trade; Periodicals; 658.87
• DOI: 10.1016/j.jretconser.2021.102599
• Languages: English
• ISSNs: 0969-6989

Abstract

Abstract: The level of resilience for an urban retail system is referred to as the ability of diverse types of retailing to adjust to any modifications, crises or shocks, which can adversely influence the system equilibrium, without compromising on performing its' functions in a viable way. We use the case of retailing in an urban environment, considering a town center, and observed the resilience factors in retailing. Apart from that, we propose a methodology to measure and predict the level of retail resilience of urban town centers. The idea and theory of grey prediction models and moving probability based Markov models are used in this research for predicting the retail resilience of town centers using several identified indicators. Here, the retail resilience of a case town center, which is located in an Indian city, is evaluated based on the five indicators of retail resilience. From the results of prediction, an increasing trend in the level of retail resilience is observed for the case during 2020. This is perceived as per the results of predictions from the grey model of the first order and with one variable (GM (1, 1) model) and the grey moving probability state Markov model-based error correction. Managers can acknowledge the level of retail resilience and the stage of the adaptive cycle, where the town center stands in resilience, for improving the future trends in the resilience of the town center. Also, the policy implications points in the direction to mend or amend strategies to fit the town center within the adaptive cycle of resilience, as discussed in the paper. Highlights: The resilience factors in retailing were observed by considering the case of a town center. A methodology is proposed to predict the level of retail resilience of an urban town center. Grey prediction models and moving probability Markov models were used in this research. Five indicators of retail resilience were used to evaluate the stages of the adaptive cycle. Managers can realize the position of town center in resilience to improving future performances.