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What (de) motivates customers to use AI-powered conversational agents for shopping? The extended behavioral reasoning perspective

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  • Jan, Ihsan Ullah
  • Ji, Seonggoo
  • Kim, Changju

Abstract

Artificial Intelligence (AI)-powered conversational agents have become ubiquitous tools in the digital transformation of conventional customer-company interactions. Despite the widespread implementation of Artificial Intelligence (AI)-powered conversational agents, there is still a limited understanding of how customers use and resist these technologies for shopping. To address this gap, this study investigates factors that influence the usage and resistance of AI-based conversational agents for shopping using the extended behavioral reasoning theory (BRT) and partial least squares-based structural equation modeling (PLS-SEM). To test the proposed framework, this study conducted two empirical studies in South Korea. Study 1 focused on text-based chatbots with a sample of 232 participants, while Study 2 examined voice-based chatbots with a sample of 234 participants. The results of the both studies mainly supported the hypotheses driven by the extended BRT. Theoretically, this study contributes by offering a comprehensive understanding of customer motivation, attitudes, and behavioral intentions toward the use of AI-powered conversational agents in shopping. Managerially, this study provides important insights for retail managers and developers of AI-powered conversational agents for shopping. By understanding the factors that drive customer usage and resistance, managers, and developers can better design and deploy these innovative technologies to enhance the customer experience and improve business outcomes.

Suggested Citation

  • Jan, Ihsan Ullah & Ji, Seonggoo & Kim, Changju, 2023. "What (de) motivates customers to use AI-powered conversational agents for shopping? The extended behavioral reasoning perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:joreco:v:75:y:2023:i:c:s096969892300187x
    DOI: 10.1016/j.jretconser.2023.103440
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