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Estimating the Impact of “Humanizing” Customer Service Chatbots

Author

Listed:
  • Scott Schanke

    (Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53202)

  • Gordon Burtch

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

  • Gautam Ray

    (Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455)

Abstract

We study the impacts of “humanizing” artificial intelligence (AI)-enabled autonomous customer service agents (chatbots). Implementing a field experiment in collaboration with a dual channel clothing retailer based in the United States, we automate a used clothing buy-back process, such that individuals engage with the retailer’s autonomous chatbot to describe the used clothes they wish to sell, obtain a cash offer, and (if they accept the offer) print a shipping label to finalize the transaction. We causally estimate the impact of chatbot anthropomorphism on transaction conversion by randomly exposing consumers to exogenously varied levels of chatbot anthropomorphism, operationalized by incorporating a random draw from a set of three anthropomorphic features: humor, communication delays, and social presence. We provide evidence that, in this retail setting, anthropomorphism is beneficial for transaction outcomes, but that it also leads to significant increases in offer sensitivity. We argue that the latter effect occurs because, as a chatbot becomes more human-like, consumers shift to a fairness evaluation or negotiating mindset. We also provide descriptive evidence suggesting that the benefits of anthropomorphism for transaction conversion may derive, at least in part, from consumers’ increased willingness to disclose personal information necessary to complete the transaction.

Suggested Citation

  • Scott Schanke & Gordon Burtch & Gautam Ray, 2021. "Estimating the Impact of “Humanizing” Customer Service Chatbots," Information Systems Research, INFORMS, vol. 32(3), pages 736-751, September.
    • Handle: RePEc:inm:orisre:v:32:y:2021:i:3:p:736-751
    DOI: 10.1287/isre.2021.1015
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    References listed on IDEAS

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    1. van Dolen, Willemijn M. & de Ruyter, Ko & Streukens, Sandra, 2008. "The effect of humor in electronic service encounters," Journal of Economic Psychology, Elsevier, vol. 29(2), pages 160-179, April.
    2. Guest Editors: Hemant Jain & Balaji Padmanabhan & Paul A. Pavlou & Raghu T. Santanam, 2018. "all for Papers—Special Issue of Information Systems Research —Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society," Information Systems Research, INFORMS, vol. 29(1), pages 250-251, March.
    3. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    4. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    5. Moon, Youngme, 2000. "Intimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 26(4), pages 323-339, March.
    6. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    7. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    8. Guth, Werner & Schmittberger, Rolf & Schwarze, Bernd, 1982. "An experimental analysis of ultimatum bargaining," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 367-388, December.
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    Cited by:

    1. Martin Adam & Konstantin Roethke & Alexander Benlian, 2023. "Human vs. Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages," Information Systems Research, INFORMS, vol. 34(3), pages 1148-1168, September.

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