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This research delves into the diverse responses of individuals to the challenges of the COVID-19 pandemic and subsequent shopping behaviour. Based on the K-means clustering algorithm, a comprehensive analysis of survey data identifies distinct consumer clusters based on their adaptive capacities, resilience, and shopping preferences. The study encompasses a broad demographic spectrum, examining various aspects such as age, urban or rural residence, education, household size, and income. The findings identify several clusters of consumers showing specific behaviour during the COVID-19 pandemic and their subsequent shopping behaviours, unveiling distinct profiles of adaptation, resilience, and shopping preferences. The K-means clustering algorithm allows the identification of four distinct consumer clusters. The results have significant implications for understanding consumer behaviour, tailoring marketing strategies, and shaping the future of retail under the New Normal. Each cluster's characteristics provide valuable insights for businesses to align strategies with evolving consumer needs, considering both demographic factors and shopping preferences. The research contributes to the understanding of consumer behaviour during times of crisis, providing relevant insights from a theoretical perspective based on the K-means clustering algorithm.
Stanca et al. (Wed,) studied this question.