In this study, we consider the food and beverage categories, which are the most widespread in terms of market share. In knowledge management, the analysis of customer behaviour leads to the proliferation of a heated and still lively debate ( Wilhelm et al., 2013 Gaviria-Marin et al., 2018 Del Vecchio et al., 2020) in light of current consumption trends, this strand of studies should deepen the interconnections between customer behaviour and the AVMs context.ĪVMs may offer several products, including food, beverages, drugs, newspapers, cigarettes and other sundries. These new shopping trends combined with the consolidation of non-store retail channels, such as automatic vending machines (AVMs), present researchers and practitioners with new challenges in acquiring customer knowledge. There is also increased consolidation of new shopping habits consumers are more confident than ever about purchasing products in automated human–machine environments ( Wenshan et al., 2015).

Retailers must therefore pay attention to technology-driven innovations ( Inman and Nikolova, 2017 Bradlow et al., 2017) and the new sources of data-driven marketing knowledge that are coming from increased diffusion of big data and real-time consumer information ( Kawaguchi et al., 2019). Over the past few decades, understanding consumer purchasing behaviour and decision-making processes has become crucial both for researchers and retailers who are searching for innovative and stimulating ways to present products in order to increase shopping activity ( Bäckström and Johansson, 2006 Grewal et al., 2017). The full terms of this licence may be seen at

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Planograms for retail stores