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Chatbot commerce—How contextual factors affect Chatbot effectiveness

Author

Listed:
  • Pei-Fang Hsu

    (National Tsing Hua University)

  • Tuan (Kellan) Nguyen

    (Middlesex University)

  • Chen-Ya Wang

    (National Tsing Hua University)

  • Pei-Ju Huang

    (National Tsing Hua University)

Abstract

The emergence of Chatbots has attracted many firms to sell their merchandise via chats and bots. Although Chatbots have received tremendous interest, little is understood about how different usage contexts affect Chatbots’ effectiveness in mobile commerce. Due to differences in their nature, not all shopping contexts are suitable for Chatbots. To address this research gap, this study examines how contextual factors (i.e., intrinsic task complexity that embraces shopping task attributes and group shopping environment, and extrinsic task complexity that entails information intensity) affect user perceptions and adoption intentions of Chatbots as recommendation agents in mobile commerce. Applying the lenses of cognitive load theory (CLT) and common ground theory (CGT), we perform an experiment and apply quantitative analytical approaches. The results show that Chatbots are more suitable in the context of one-attribute, information-light, and group-buying tasks, whereas traditional Apps are suitable for multi-attribute, information-intensive, and single-buying scenarios. These findings make important theoretical contributions to the IT adoption literature as well as to CLT and CGT theory by contextualizing the evolving state of Chatbot commerce and providing guidelines for designing better Chatbot user experiences, thereby enhancing user perceptions and adoption intentions.

Suggested Citation

  • Pei-Fang Hsu & Tuan (Kellan) Nguyen & Chen-Ya Wang & Pei-Ju Huang, 2023. "Chatbot commerce—How contextual factors affect Chatbot effectiveness," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    • Handle: RePEc:spr:elmark:v:33:y:2023:i:1:d:10.1007_s12525-023-00629-4
    DOI: 10.1007/s12525-023-00629-4
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    References listed on IDEAS

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    1. Kasilingam, Dharun Lingam, 2020. "Understanding the attitude and intention to use smartphone chatbots for shopping," Technology in Society, Elsevier, vol. 62(C).
    2. Young Eun Lee & Izak Benbasat, 2011. "Research Note ---The Influence of Trade-off Difficulty Caused by Preference Elicitation Methods on User Acceptance of Recommendation Agents Across Loss and Gain Conditions," Information Systems Research, INFORMS, vol. 22(4), pages 867-884, December.
    3. Sylvie Borau & Tobias Otterbring & Sandra Laporte & Samuel Fosso Wamba, 2021. "The most human bot: Female gendering increases humanness perceptions of bots and acceptance of AI," Post-Print hal-03648092, HAL.
    4. Wood, Robert E., 1986. "Task complexity: Definition of the construct," Organizational Behavior and Human Decision Processes, Elsevier, vol. 37(1), pages 60-82, February.
    5. Lin Li & Kyung Young Lee & Emmanuel Emokpae & Sung-Byung Yang, 2021. "What makes you continuously use chatbot services? Evidence from chinese online travel agencies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 575-599, September.
    6. V. Kumar & Ashutosh Dixit & Rajshekar (Raj) G. Javalgi & Mayukh Dass, 2016. "Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 24-45, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Recommendation agent; Innovation adoption; Chatbot; Cognitive load theory; Common ground theory;
    All these keywords.

    JEL classification:

    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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