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Customer behavior in the presence of algorithmic marketing agents: The role of hedonic values

Author

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  • Chacon, Alvaro
  • Martínez-Troncoso, Carolina
  • Kausel, Edgar E.

Abstract

Artificial intelligence (AI) marketing agents have increasingly emerged as a viable alternative to human representatives for direct customer interactions. In this research, we investigated customer behaviors in response to sales scenarios managed by AI agents considering individual customers' values. In three pre-registered studies, we examined the willingness of 1417 participants to engage in promotional activities related to purchasing real estate and vehicles. Using regression and simple slope analyses, we examined how the interaction among agents (human or algorithm), response types (negative or positive), and customers' hedonic values influence the likelihood of becoming promoters. Our results revealed a moderation effect in which the relationship between the type of marketing agent and the response type was influenced by customers' hedonic values. We found a positive relationship between hedonic values and promotion behavior when negative feedback was delivered by a human agent and when positive feedback came from an algorithmic agent. In contrast, algorithmic agents tend to elicit flatter responses across hedonic levels when delivering negative feedback, indicating reduced emotional engagement but also less potential for dissatisfaction. These insights emphasize the importance of aligning the source of communication with individual consumer characteristics to enhance customer promotion.

Suggested Citation

  • Chacon, Alvaro & Martínez-Troncoso, Carolina & Kausel, Edgar E., 2025. "Customer behavior in the presence of algorithmic marketing agents: The role of hedonic values," Technological Forecasting and Social Change, Elsevier, vol. 220(C).
  • Handle: RePEc:eee:tefoso:v:220:y:2025:i:c:s0040162525003531
    DOI: 10.1016/j.techfore.2025.124322
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    References listed on IDEAS

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    1. Alvaro Chacon & Tomas Reyes & Edgar E. Kausel, 2025. "Are engineers more likely to avoid algorithms after they see them err? A longitudinal study," Behaviour and Information Technology, Taylor & Francis Journals, vol. 44(4), pages 789-804, February.
    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. Thomas Niemand & Sascha Kraus & Sophia Mather & Antonio C. Cuenca-Ballester, 2020. "Multilevel marketing: optimizing marketing effectiveness for high-involvement goods in the automotive industry," International Entrepreneurship and Management Journal, Springer, vol. 16(4), pages 1367-1392, December.
    4. Meuter, Matthew L. & Ostrom, Amy L. & Bitner, Mary Jo & Roundtree, Robert, 2003. "The influence of technology anxiety on consumer use and experiences with self-service technologies," Journal of Business Research, Elsevier, vol. 56(11), pages 899-906, November.
    5. 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.
    6. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.
    7. Agag, Gomaa & Ali Durrani, Baseer & Hassan Abdelmoety, Ziad & Mostafa Daher, Maya & Eid, Riyad, 2024. "Understanding the link between net promoter score and e-WOM behaviour on social media: The role of national culture," Journal of Business Research, Elsevier, vol. 170(C).
    8. Zehnle, Meike & Hildebrand, Christian & Valenzuela, Ana, 2025. "Not all AI is created equal: A meta-analysis revealing drivers of AI resistance across markets, methods, and time," International Journal of Research in Marketing, Elsevier, vol. 42(3), pages 729-751.
    9. 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.
    10. Lourenço, Carlos J.S. & Dellaert, Benedict G.C. & Donkers, Bas, 2020. "Whose Algorithm Says So: The Relationships Between Type of Firm, Perceptions of Trust and Expertise, and the Acceptance of Financial Robo-Advice," Journal of Interactive Marketing, Elsevier, vol. 49(C), pages 107-124.
    11. Melanie Clegg & Reto Hofstetter & Emanuel de Bellis & Bernd H Schmitt, 2024. "Unveiling the Mind of the Machine," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 51(2), pages 342-361.
    12. Chacon, Alvaro & Kaufmann, Esther, 2025. "An overview of the effects of algorithm use on judgmental biases affecting forecasting," International Journal of Forecasting, Elsevier, vol. 41(2), pages 424-439.
    13. Karine Picot-Coupey & Nina Krey & Elodie Huré & Claire-Lise Ackermann, 2021. "Still work and/or fun? -Corroboration of the hedonic and utilitarian shopping value scale," Post-Print hal-02572817, HAL.
    14. Chacon, Alvaro & Kausel, Edgar E. & Reyes, Tomas & Trautmann, Stefan, 2025. "Preventing algorithm aversion: People are willing to use algorithms with a learning label," Journal of Business Research, Elsevier, vol. 187(C).
    15. Achiel Fenneman & Joern Sickmann & Thomas Pitz & Alan G Sanfey, 2021. "Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-20, February.
    16. Picot-Coupey, Karine & Krey, Nina & Huré, Elodie & Ackermann, Claire-Lise, 2021. "Still work and/or fun? Corroboration of the hedonic and utilitarian shopping value scale," Journal of Business Research, Elsevier, vol. 126(C), pages 578-590.
    17. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    18. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    19. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    20. Gavan J. Fitzsimons & Donald R. Lehmann, 2004. "Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses," Marketing Science, INFORMS, vol. 23(1), pages 82-94, September.
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