Modeling the Enablers of Consumers’ E-Shopping Behavior: A Multi-Analytic Approach

Abstract

The evolution of e-commerce amid the positive growth forecast of the e-commerce market has sparked scholarly interest in e-shopping antecedents to better understand customer behavior and ensure sustainable e-shopping services. The purpose of this study is to investigate the relationship between the enablers of customers’ e-shopping intention and e-shopping behavior in the post-pandemic period. Personal innovativeness, service quality, perceived risk, and trust were incorporated into the Unified Theory of Technology Acceptance and Usage (UTAUT) original framework and UTAUT 2 in this study. To explore the relationship among the study variables, data were collected from 420 shoppers via an online survey using a convenience sampling technique. The obtained data were analyzed using a multi-analytic approach, such as structural equation modeling and artificial neural networks (SEM-ANN). The empirical findings showed that trust, habit, and e-shopping intention significantly influence consumers’ e-shopping behavior. Furthermore, the results indicated that personal innovativeness, facilitating conditions, performance expectancy, habit, effort expectancy, perceived risk, price value, hedonic motivation, service quality, and trust were all significantly linked to e-shopping intention. The study revealed that effort expectancy acts as a mediator between service quality and e-shopping behavior. This research provides valuable insights into e-shopping behavior in developing countries during the post-pandemic era. By providing a more comprehensive and accurate understanding of the factors that influence e-shopping behavior, hybrid SEM-ANN analysis can help managers and policymakers arrive at better-informed decisions to promote and encourage e-shopping.