Predicting the Use of Chatbots for Consumer Channel Selection in Multichannel Environments: An Exploratory Study

Abstract

Online consumers are increasingly looking for more convenient ways to purchase products and services, and chatbots are becoming increasingly popular in multichannel environments due to their ability to provide an efficient service. In this context, managing digital complexity with the help of artificial intelligence and supporting decisions in a multichannel context is an appealing perspective for the retailer, who must find the right strategy to win and keep customers online. The present empirical study aims to better understand consumer behaviour in the multichannel environment in the context of four categories of products and services (retail banking, mobile communications, fashion, and consumer electronics) from the perspective of identifying determinants of channel selection when the consumer uses chatbots. Data were collected from 936 respondents with multichannel retail experience to conduct an empirical investigation on social media platforms, including Twitter, Facebook, and Instagram; these data were then analysed using structural equation modelling (SEM). We found that the online consumer’s multichannel behaviour was not only a reality in the field of broad purchasing decisions but already a norm, and consumers had good reasons to use more channels in the context of chatbots. Research results suggest that chatbots can represent a decision-making aid for managers in retail companies who want to develop an efficient and optimal logistics service strategy in multichannel environments.