Conceptualising the panic buying phenomenon during COVID-19 as an affective assemblage

Purpose: This study aims to conceptualise the panic buying behaviour of consumers in the UK during the novel COVID-19 crisis, using the assemblage approach as it is non-deterministic and relational and affords new ways of understanding the phenomenon. Design/methodology/approach: The study undertakes a digital ethnography approach and content analysis of Twitter data. A total of 6,803 valid tweets were collected over the period when panic buying was at its peak at the beginning of the first lockdown in March 2020. Findings: The panic buying phase was a radical departure from the existing linguistic, discursive, symbolic and semiotic structures that define routine consumer behaviour. The authors suggest that the panic buying behaviour is best understood as a constant state of becoming, whereby stockpiling, food waste and a surge in cooking at home emerged as significant contributors to positive consumer sentiments. Research limitations/implications: The authors offer unique insights into the phenomenon of panic buying by considering DeLanda’s assemblage theory. This work will inform future research associated with new social meanings of products, particularly those that may have been (re)shaped during the COVID-19 crisis. Practical implications: The study offers insights for practitioners and retailers to lessen the intensity of consumers’ panic buying behaviour in anticipation of a crisis and for successful crisis management. Originality/value: Panic buying took on a somewhat carnivalesque hue as consumers transitioned to what we consider to be atypical modes of purchasing that remain under-theorised in marketing. Using the conceptual lenses of assemblage, the authors map bifurcations that the panic buyers’ assemblages articulated via material and immaterial bodies. © 2022, Emerald Publishing Limited.

Bayesian inference of dynamic cumulative residual entropy from pareto ii distribution with application to covid-19

Dynamic cumulative residual entropy is a recent measure of uncertainty which plays a substantial role in reliability and survival studies. This article comes up with Bayesian estimation of the dynamic cumulative residual entropy of Pareto II distribution in case of non-informative and informative priors. The Bayesian estimator and the corresponding credible interval are obtained under squared error, linear exponential (LINEX) and precautionary loss functions. The Metropolis-Hastings algorithm is employed to generate Markov chain Monte Carlo samples from the posterior distribution. A simulation study is done to implement and compare the accuracy of considered estimates in terms of their relative absolute bias, estimated risk and the width of credible intervals. Regarding the outputs of simulation study, Bayesian estimate of dynamic cumulative residual entropy under LINEX loss function is preferable than the other estimates in most of situations. Further, the estimated risks of dynamic cumulative residual entropy decrease as the value of estimated entropy decreases. Eventually, inferential procedure developed in this paper is illustrated via a real data. © 2021 the Author(s), licensee AIMS Press.