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Human vs. Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages

Author

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  • Martin Adam

    (Information Systems and E-Services, Technical University of Darmstadt, 64293 Darmstadt, Germany)

  • Konstantin Roethke

    (Information Systems and E-Services, Technical University of Darmstadt, 64293 Darmstadt, Germany)

  • Alexander Benlian

    (Information Systems and E-Services, Technical University of Darmstadt, 64293 Darmstadt, Germany)

Abstract

Customers in sales processes increasingly encounter automated sales agents (ASAs) that complement or replace human sales agents (HSAs). Yet, little is known about whether, how, and why customers respond to ASAs in contrast to HSAs across successive decision stages of the same sales process. Even less is known about customer responses to HSA-ASA combinations, where both agents assume distinct roles and focus on complementary tasks that are traditionally performed by only one single sales agent. Against this backdrop, this paper explores the influence of increasingly common sales representative (rep) types (i.e., ASA, HSA, and HSA-ASA) on customer decisions across sales stages. Drawing on information processing theory and the literature on hedonic-utilitarian decision making, we investigate customer responses to text-based ASAs from vendor companies in two common early stages of email sales processes (i.e., sales initiation stages) when customers successively decide whether to indicate their initial interest in an offer and then, whether to provide their contact information. Specifically, we conducted two complementary multi-decision experiments, namely (1) a randomized field experiment in a high-stakes sales initiation setting ( n = 325) and (2) a subsequent randomized online experiment to complement the real-world insights ( n = 408). Our core findings reveal reversing effect patterns of sales rep types across stages: although customers are more likely to indicate their initial interest to HSAs (versus ASAs) because of HSAs’ higher levels of social presence, they are less likely to provide contact information to HSAs because of HSAs’ lower levels of performance expectancy and effort expectancy. We also show that HSA-ASA combinations can be reasonable options for single ASAs, yet contextual features of the sales setting may affect differential customer responses to HSA-ASA combinations (versus ASAs) in each sales stage. Taken together, we uncover shifting effect patterns in customer responses to sales rep types across successive sales stages and shed light on the consecutive underlying mechanisms that explain these shifts. These findings have significant implications for vendor companies seeking to allocate HSAs and/or ASAs effectively across various decision stages in sales processes and beyond.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:orisre:v:34:y:2023:i:3:p:1148-1168
    DOI: 10.1287/isre.2022.1171
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    as
    1. Andreas Fügener & Jörn Grahl & Alok Gupta & Wolfgang Ketter, 2022. "Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation," Information Systems Research, INFORMS, vol. 33(2), pages 678-696, June.
    2. 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.
    3. Ben Q. Liu & Dale L. Goodhue, 2012. "Two Worlds of Trust for Potential E-Commerce Users: Humans as Cognitive Misers," Information Systems Research, INFORMS, vol. 23(4), pages 1246-1262, December.
    4. 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.
    5. Hemant Jain & Balaji Padmanabhan & Paul A. Pavlou & T. S. Raghu, 2021. "Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society," Information Systems Research, INFORMS, vol. 32(3), pages 675-687, September.
    6. Weiyin Hong & Frank K. Y. Chan & James Y. L. Thong & Lewis C. Chasalow & Gurpreet Dhillon, 2014. "A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research," Information Systems Research, INFORMS, vol. 25(1), pages 111-136, March.
    7. Ronald T. Cenfetelli & Izak Benbasat & Sameh Al-Natour, 2008. "Addressing the What and How of Online Services: Positioning Supporting-Services Functionality and Service Quality for Business-to-Consumer Success," Information Systems Research, INFORMS, vol. 19(2), pages 161-181, June.
    8. 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.
    9. Alexander Bleier & Maik Eisenbeiss, 2015. "Personalized Online Advertising Effectiveness: The Interplay of What, When, and Where," Marketing Science, INFORMS, vol. 34(5), pages 669-688, September.
    10. Xue (Jane) Tan & Youwei Wang & Yong Tan, 2019. "Impact of Live Chat on Purchase in Electronic Markets: The Moderating Role of Information Cues," Information Systems Research, INFORMS, vol. 30(4), pages 1248-1271, December.
    11. Paul A. Pavlou & David Gefen, 2004. "Building Effective Online Marketplaces with Institution-Based Trust," Information Systems Research, INFORMS, vol. 15(1), pages 37-59, March.
    12. Paschen, Jeannette & Wilson, Matthew & Ferreira, João J., 2020. "Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel," Business Horizons, Elsevier, vol. 63(3), pages 403-414.
    13. Simona Botti & Ann L. McGill, 2011. "The Locus of Choice: Personal Causality and Satisfaction with Hedonic and Utilitarian Decisions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(6), pages 1065-1078.
    14. 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.
    15. Bettman, James R & Luce, Mary Frances & Payne, John W, 1998. "Constructive Consumer Choice Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 187-217, December.
    16. Anja Lambrecht & Katja Seim & Catherine Tucker, 2011. "Stuck in the Adoption Funnel: The Effect of Interruptions in the Adoption Process on Usage," Marketing Science, INFORMS, vol. 30(2), pages 355-367, 03-04.
    17. Xinshu Zhao & John G. Lynch & Qimei Chen, 2010. "Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(2), pages 197-206, August.
    18. Ni Huang & Tianshu Sun & Peiyu Chen & Joseph M. Golden, 2019. "Word-of-Mouth System Implementation and Customer Conversion: A Randomized Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 805-818, September.
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