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The purpose of this paper is to persuade a hybrid framework, which can be used to assess the performance of various supply chains and can be further used to segregate supply chains concerning critical KPMs. The KPMs have been selected in the COVID-19 pandemic condition.

A real case of e-commerce is presented to illustrate the working of the proposed framework comprising a hybrid methodology of BWM and Fuzzy TOPSIS to measure the performance of the e-commerce supply chains by identifying the critical key performance metrics (KPMs) and measuring the performance of the considered supply chains against these.

The proposed framework is illustrated using real-time data from experts, collected through interviews and discussions. It is found that rate of return on investment (SCPM 27), flexibility of service systems to meet particular customer needs (SCPM 23) and supplier lead time against industry norm (SCPM 33) are significantly weighed in assessing performance of the selected supply chains, with weights 0.07764, 0.06863 and 0.0547, respectively. Amazon and Flipkart are seen to stand out among the other supply chains taken for the present study with closeness coefficients as 0.945 and 0.516, respectively.

The contemporary world has seen the drastic attack of COVID-19 on many firms worldwide, and hence measuring the performance of the supply chains has become necessary so as to understand the critical factors affecting performance, their relative importance and the firm's relative standings. There have been studies in the recent past where researchers worked on similar motives to generate a framework to measure performance of supply chains, but it is seen that the methodologies lack flexibility with respect to effectively handling large data, uncertainty in human emotions, consistency, etc. This is where the current study stands out in effectively measuring the performance of supply chains so as to aid many firms affected by the pandemic.

Smart devices like fully automated smart refrigerators can simplify customers' shopping processes. However, despite the advantages, such as saving time and effort, these devices are rarely used in private households. Therefore, this study aims to investigate the influence of various aspects on the usage intention of automated purchase processes and how these can be established in the long term.

For examining the given topic, an online experiment was conducted in which the experimental factors automation (not given vs given) and product type (utilitarian vs hedonic) were systematically manipulated.

The findings show a decreasing usage intention for automated shopping processes and no significant results for the utilitarian and hedonic product types. In addition, trust and behavioural control mediate the effect of automation on usage intention, and this effect is further moderated by inherent novelty seeking.

The study investigates the usage intention of automated buying processes in a business-to-consumer retail context and highlights the importance of trust and behavioural control. Retailers offering automated buying processes should further focus on customers that seek novelty. In general, this study offers new insights into establishing automated buying processes in the long term.