Abstract
This paper delves into the realm of human-chatbot interaction and aims to enhance consumers’ experience with chatbots in products recommendation. Through a situational experimental study, we examine the interaction effect of chatbots’ conversational interactivity and recommended product relevance on recommendation effectiveness. The results indicate that when recommended product relevance is high, conversational interactivity has a positive effect on recommendation effectiveness, while this effect is reversed when recommended product relevance is low. Moreover, we intend to further examine the emotional appeal and expectations discrepancy consumers form with chatbots in a future study to explain the interplay between conversational interactivity and product relevance.
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Notes
- 1.
Chatbot Market Growth is projected to reach USD 3.62 (globenewswire.com).
- 2.
Chatbot Market Growth is projected to reach USD 3.62 (globenewswire.com).
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Acknowledgments
This work was supported by the National Natural Science Foundation of China [grant number: 72002193], the Fundamental Research Funds for the Central Universities [grant number: S20230031], and Zhejiang University-The Hong Kong Polytechnic University Joint Center.
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Peng, X., Zhou, L., Tang, Q., Liu, Y. (2023). Effects of the Conversation and Recommendation Mechanism on Chatbots’ Recommendation Effectiveness. In: Zaphiris, P., et al. HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14060. Springer, Cham. https://doi.org/10.1007/978-3-031-48060-7_38
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