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Impact of Gen-AI chatbots on consumer services experiences and behaviors: Focusing on the sensation of awe and usage intentions through a cybernetic lens

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  • Pathak, Kanishka
  • Prakash, Gyan
  • Samadhiya, Ashutosh
  • Kumar, Anil
  • Luthra, Sunil

Abstract

Generative AI-based chatbots have surfaced as a magical tool, revolutionizing the service industry. Equipped with superior conversational traits and the ability to attune with the conversant, chatbots have forged a coherent bond with the conversant. The untapped potential of chatbots deserves a better understanding of how consumers interact with them. This study recruits the theory of cybernetics to shed light upon the mechanism of chatbot consumer interaction. The theory helps to explain the psychological perspectives of chatbot users and answers the all-important question as to how to craft a coherent communication by enhancing the antecedents to chatbot efficiency. A mixed-method approach is used to identify factors that add to the dynamics of chatbot efficiency, a sensation of awe, and usage intentions. In method 1, the grounded theory approach is used to identify themes and factors to develop a conceptual model. In method 2, the conceptual model devised is subjected to empirical testing through Partial Least Square Structural Equation Modeling. The theory of cybernetics highlights the importance of information exchange and attunement which leads to higher usage intention in chatbots. The results disseminate the indispensability of AI-enabled chatbots for retailing businesses to attain commercial competency.

Suggested Citation

  • Pathak, Kanishka & Prakash, Gyan & Samadhiya, Ashutosh & Kumar, Anil & Luthra, Sunil, 2025. "Impact of Gen-AI chatbots on consumer services experiences and behaviors: Focusing on the sensation of awe and usage intentions through a cybernetic lens," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:joreco:v:82:y:2025:i:c:s0969698924004168
    DOI: 10.1016/j.jretconser.2024.104120
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    Cited by:

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    2. Chihli Hung & Ming-Hsuan Wu, 2025. "A Classification Intelligent Question Answering Model for Retrieval-Based Chatbots," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 15(1), pages 1-3.

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