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Understanding and Improving Consumer Reactions to Service Bots

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  • Noah Castelo
  • Johannes Boegershausen
  • Christian Hildebrand
  • Alexander P Henkel
  • June Cotte
  • Klaus Wertenbroch

Abstract

Many firms are beginning to replace customer service employees with bots, from humanoid service robots to digital chatbots. Using real human–bot interactions in lab and field settings, we study consumers’ evaluations of bot-provided service. We find that service evaluations are more negative when the service provider is a bot versus a human—even when the provided service is identical. This effect is explained by consumers’ belief that service automation is motivated by firm benefits (i.e., cutting costs) at the expense of customer benefits (such as service quality). The effect is eliminated when firms share the economic surplus derived from automation with consumers through price discounts. The effect is reversed when service bots provide unambiguously superior service to human employees—a scenario that may soon become reality. Consumers’ default reactions to service bots are therefore largely negative but can be equal to or better than reactions to human service providers if firms can demonstrate how automation benefits consumers.

Suggested Citation

  • Noah Castelo & Johannes Boegershausen & Christian Hildebrand & Alexander P Henkel & June Cotte & Klaus Wertenbroch, 2023. "Understanding and Improving Consumer Reactions to Service Bots," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 50(4), pages 848-863.
  • Handle: RePEc:oup:jconrs:v:50:y:2023:i:4:p:848-863.
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    File URL: http://hdl.handle.net/10.1093/jcr/ucad023
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    References listed on IDEAS

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    1. Zeelenberg, M. & Pieters, R., 2004. "Beyond valence in customer dissatisfaction : A review and new findings on behavioral responses to regret and disappointment in failed services," Other publications TiSEM 7bfb4aa9-cba7-4786-850d-1, Tilburg University, School of Economics and Management.
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    2. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    3. Celiktutan, Begum & Klesse, Anne-Kathrin & Tuk, Mirjam A., 2024. "Acceptability lies in the eye of the beholder: Self-other biases in GenAI collaborations," International Journal of Research in Marketing, Elsevier, vol. 41(3), pages 496-512.
    4. Kirshner, Samuel N., 2024. "GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    5. Vomberg, Arnd & Schauerte, Nico & Krakowski, Sebastian & Ingram Bogusz, Claire & Gijsenberg, Maarten J. & Bleier, Alexander, 2023. "The cold-start problem in nascent AI strategy: Kickstarting data network effects," Journal of Business Research, Elsevier, vol. 168(C).
    6. Zheng, Chundong & Zhang, Lan & Bian, Xuemei, 2024. "A showdown in the kitchen: Exploring consumers’ preferences for robot-made versus human-made foods at different stages of dietary restraint," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    7. Ulrich Gnewuch & Stefan Morana & Oliver Hinz & Ralf Kellner & Alexander Maedche, 2024. "More Than a Bot? The Impact of Disclosing Human Involvement on Customer Interactions with Hybrid Service Agents," Information Systems Research, INFORMS, vol. 35(3), pages 936-955, September.

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