Investigating consumers’ intention of using contactless logistics technology in COVID-19 pandemic: a Copula-Bayesian Network approach

Abstract

Understanding consumers’ intention of using contactless logistics technology is necessary for logistics service providers to make value-added business strategies in the COVID-19 pandemic. Previous studies have suggested that multiple factors shall be considered when investigating the intention, but few have attempted to comprehensively explain the causality of the intention. To bridge this gap, this study develops a Genetic Algorithm (GA)-based structure learning method and constructs a Copula-Bayesian Network (Copula-BN) upon questionnaire survey data to explore the causation of the intention. Based on the derived Copula-BN, we identify the key factors that contribute to the intention and reveal the causal relations along the main branch composed of those factors. Several findings provide theoretical and practical insights into the consumer-technology interaction under the pandemic context. Besides, this study demonstrates that the structure of the Copula-BN is rational and reasonable, which provides a solid basis for investigating the causality of the intention.

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