IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2504.06145.html

Deploying Chatbots in Customer Service: Adoption Hurdles and Simple Remedies

Author

Listed:
  • Evgeny Kagan
  • Brett Hathaway
  • Maqbool Dada

Abstract

Despite recent advances in Artificial Intelligence, the use of chatbot technology in customer service continues to face adoption hurdles. This paper explores reasons for these adoption hurdles and tests several service design levers to increase chatbot uptake. We use incentivized online experiments to study chatbot uptake in a variety of scenarios. The results of these experiments are threefold. First, people respond positively to improvements in chatbot performance; however, the chatbot channel is utilized less frequently than expected-time minimization would predict. A key driver of this underutilization is the reluctance to engage with a gatekeeper process, i.e., a process with an imperfect initial service stage and possible transfer to a second, expert service stage -- a behavior we term "gatekeeper aversion". We show that gatekeeper aversion can be further amplified by a secondary hurdle, algorithm aversion. Second, chatbot uptake can be increased by providing customers with average waiting times in the chatbot channel, as well as by being more transparent about chatbot capabilities and limitations. Third, methodologically, we show that chatbot adoption can depend on experimental implementation. In particular, chatbot adoption decreases further as (i) stakes are increased, (ii) the human/algorithmic nature of the server is manipulated with more realism. Our results suggest that firms should continue to prioritize investments in chatbot technology. However, less expensive, process-related interventions can also be effective. These may include being more transparent about the types of queries that are (or are not) suitable for chatbots, emphasizing chatbot reliability and quick resolution times, as well as providing faster live agent access to customers who experienced chatbot failure.

Suggested Citation

  • Evgeny Kagan & Brett Hathaway & Maqbool Dada, 2025. "Deploying Chatbots in Customer Service: Adoption Hurdles and Simple Remedies," Papers 2504.06145, arXiv.org.
    • Handle: RePEc:arx:papers:2504.06145
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2504.06145
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Robert A. Shumsky & Edieal J. Pinker, 2003. "Gatekeepers and Referrals in Services," Management Science, INFORMS, vol. 49(7), pages 839-856, July.
    2. Kumar, Piyush & Dada, Maqbool, 2021. "Investigating the impact of service line formats on satisfaction with waiting," International Journal of Research in Marketing, Elsevier, vol. 38(4), pages 974-993.
    3. Jiankun Sun & Dennis J. Zhang & Haoyuan Hu & Jan A. Van Mieghem, 2022. "Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations," Management Science, INFORMS, vol. 68(2), pages 846-865, February.
    4. Ryan W. Buell, 2021. "Last-Place Aversion in Queues," Management Science, INFORMS, vol. 67(3), pages 1430-1452, March.
    5. Catherine C. Eckel & Philip J. Grossman, 2002. "Sex Differences and Statistical Stereotyping in Attitudes Toward Financial Risk," Monash Economics Working Papers archive-03, Monash University, Department of Economics.
    6. James C. Cox & Glenn W. Harrison, 2008. "Risk aversion in experiments: An introduction," Research in Experimental Economics, in: Risk Aversion in Experiments, pages 1-7, Emerald Group Publishing Limited.
    7. Ryan W. Buell & Tami Kim & Chia-Jung Tsay, 2017. "Creating Reciprocal Value Through Operational Transparency," Management Science, INFORMS, vol. 63(6), pages 1673-1695, June.
    8. Leclerc, France & Schmitt, Bernd H & Dube, Laurette, 1995. "Waiting Time and Decision Making: Is Time like Money?," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 22(1), pages 110-119, June.
    9. Michael Freeman & Nicos Savva & Stefan Scholtes, 2017. "Gatekeepers at Work: An Empirical Analysis of a Maternity Unit," Management Science, INFORMS, vol. 63(10), pages 3147-3167, October.
    10. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    11. Naor, P, 1969. "The Regulation of Queue Size by Levying Tolls," Econometrica, Econometric Society, vol. 37(1), pages 15-24, January.
    12. Amos Arieli & Yaniv Ben-Ami & Ariel Rubinstein, 2011. "Tracking Decision Makers under Uncertainty," American Economic Journal: Microeconomics, American Economic Association, vol. 3(4), pages 68-76, November.
    13. Dilip Soman & Mengze Shi, 2003. "Virtual Progress: The Effect of Path Characteristics on Perceptions of Progress and Choice," Management Science, INFORMS, vol. 49(9), pages 1229-1250, September.
    14. Eike B. Kroll & Bodo Vogt, 2008. "Loss Aversion for time: An experimental investigation of time preferences," FEMM Working Papers 08027, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    15. John A. List & Azeem M. Shaikh & Yang Xu, 2019. "Multiple hypothesis testing in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 773-793, December.
    16. Sheehan, Ben & Jin, Hyun Seung & Gottlieb, Udo, 2020. "Customer service chatbots: Anthropomorphism and adoption," Journal of Business Research, Elsevier, vol. 115(C), pages 14-24.
    17. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    18. Ryan W. Buell & Michael I. Norton, 2011. "The Labor Illusion: How Operational Transparency Increases Perceived Value," Management Science, INFORMS, vol. 57(9), pages 1564-1579, February.
    19. 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.
    20. Festjens, Anouk & Bruyneel, Sabrina & Diecidue, Enrico & Dewitte, Siegfried, 2015. "Time-based versus money-based decision making under risk: An experimental investigation," Journal of Economic Psychology, Elsevier, vol. 50(C), pages 52-72.
    21. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    22. 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.
    23. Piyush Kumar & Manohar U. Kalwani & Maqbool Dada, 1997. "The Impact of Waiting Time Guarantees on Customers' Waiting Experiences," Marketing Science, INFORMS, vol. 16(4), pages 295-314.
    24. Andrew Prahl & Lyn Van Swol, 2017. "Understanding algorithm aversion: When is advice from automation discounted?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(6), pages 691-702, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Doll, Monika & Seebauer, Michael & Tonn, Maren, 2017. "Bargaining over waiting time in gain and loss framed ultimatum games," FAU Discussion Papers in Economics 15/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Festjens, Anouk & Bruyneel, Sabrina & Diecidue, Enrico & Dewitte, Siegfried, 2015. "Time-based versus money-based decision making under risk: An experimental investigation," Journal of Economic Psychology, Elsevier, vol. 50(C), pages 52-72.
    3. Ruijie Zhang & Xiaohua Han & Rowan Wang & Jianghua Zhang & Yinghao Zhang, 2023. "Please don't make me wait! Influence of customers' waiting preference and no‐show behavior on appointment systems," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1597-1616, June.
    4. Sezer Ülkü & Chris Hydock & Shiliang Cui, 2020. "Making the Wait Worthwhile: Experiments on the Effect of Queueing on Consumption," Management Science, INFORMS, vol. 66(3), pages 1149-1171, March.
    5. Qiuping Yu & Yiming Zhang & Yong-Pin Zhou, 2022. "Delay Information in Virtual Queues: A Large-Scale Field Experiment on a Major Ride-Sharing Platform," Management Science, INFORMS, vol. 68(8), pages 5745-5757, August.
    6. Tinglong Dai & Sridhar Tayur, 2022. "Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4443-4451, December.
    7. Moshe Glickman & Orian Sharoni & Dino J Levy & Ernst Niebur & Veit Stuphorn & Marius Usher, 2019. "The formation of preference in risky choice," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-25, August.
    8. Bryce McLaughlin & Jann Spiess, 2022. "Algorithmic Assistance with Recommendation-Dependent Preferences," Papers 2208.07626, arXiv.org, revised Oct 2025.
    9. Jonathan Chapman & Erik Snowberg & Stephanie Wang & Colin Camerer, 2018. "Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)," NBER Working Papers 25072, National Bureau of Economic Research, Inc.
    10. Ryan W. Buell, 2017. "Last Place Aversion in Queues," Harvard Business School Working Papers 18-053, Harvard Business School, revised Oct 2019.
    11. Zhu, Yimin & Zhang, Jiemin & Wu, Jifei & Liu, Yingyue, 2022. "AI is better when I'm sure: The influence of certainty of needs on consumers' acceptance of AI chatbots," Journal of Business Research, Elsevier, vol. 150(C), pages 642-652.
    12. Michael H. Birnbaum & Jeffrey P. Bahra, 2012. "Separating response variability from structural inconsistency to test models of risky decision making," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 7(4), pages 402-426, July.
    13. Kuhberger, Anton, 1998. "The Influence of Framing on Risky Decisions: A Meta-analysis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 75(1), pages 23-55, July.
    14. Topi Miettinen & Olli Ropponen & Pekka Sääskilahti, 2020. "Prospect Theory, Fairness, and the Escalation of Conflict at a Negotiation Impasse," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(4), pages 1535-1574, October.
    15. Wang, Xun & Rodrigues, Vasco Sanchez & Demir, Emrah & Sarkis, Joseph, 2024. "Algorithm aversion during disruptions: The case of safety stock," International Journal of Production Economics, Elsevier, vol. 278(C).
    16. Oualid Jouini & Zeynep Akşin & Yves Dallery, 2011. "Call Centers with Delay Information: Models and Insights," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 534-548, October.
    17. Schipper, Burkhard C., 2023. "Sex hormones and choice under risk," Journal of Economic Psychology, Elsevier, vol. 96(C).
    18. Kroll, Eike B. & Morgenstern, Ralf & Neumann, Thomas & Schosser, Stephan & Vogt, Bodo, 2014. "Bargaining power does not matter when sharing losses – Experimental evidence of equal split in the Nash bargaining game," Journal of Economic Behavior & Organization, Elsevier, vol. 108(C), pages 261-272.
    19. Tamás Csermely & Alexander Rabas, 2016. "How to reveal people’s preferences: Comparing time consistency and predictive power of multiple price list risk elicitation methods," Journal of Risk and Uncertainty, Springer, vol. 53(2), pages 107-136, December.
    20. Liu, Liqun & Meyer, Jack, 2025. "Almost stochastic dominance: Magnitude constraints on risk aversion," Insurance: Mathematics and Economics, Elsevier, vol. 122(C), pages 82-90.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2504.06145. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.