IDEAS home Printed from https://ideas.repec.org/a/eee/jouret/v99y2023i2p280-296.html
   My bibliography  Save this article

Should service firms introduce algorithmic advice to their existing customers? The moderating effect of service relationships

Author

Listed:
  • von Walter, Benjamin
  • Wentzel, Daniel
  • Raff, Stefan

Abstract

An increasing number of service firms are introducing algorithmic advice to their customers. In this research, we examine the introduction of such tools from a relational perspective and show that the type of relationship a customer has with a service firm moderates his or her response to algorithmic advice. Studies 1 and 2 find that customers in communal relationships are more reluctant to use algorithmic advice instead of human advice than customers in exchange relationships. Study 3 shows that offering customers algorithmic advice may harm communal relationships but not exchange relationships. Building on these findings, Studies 4, 5, and 6 examine how firms can mitigate the potentially negative relational consequences of algorithmic advice. While a fallback option that signals that customers can request additional human advice if needed is effective in preventing relational damages in communal relationships, this same intervention backfires in exchange relationships. These findings have important implications by showing that managers need to consider the relational consequences of introducing algorithmic advice to existing customers.

Suggested Citation

  • von Walter, Benjamin & Wentzel, Daniel & Raff, Stefan, 2023. "Should service firms introduce algorithmic advice to their existing customers? The moderating effect of service relationships," Journal of Retailing, Elsevier, vol. 99(2), pages 280-296.
    • Handle: RePEc:eee:jouret:v:99:y:2023:i:2:p:280-296
    DOI: 10.1016/j.jretai.2023.05.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0022435923000179
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretai.2023.05.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Doeglas, Dirk & Suurmeijer, Theo & Briançon, Serge & Moum, Torbjørn & Krol, Boudien & Bjelle, Anders & Sanderman, Robbert & van den Heuvel, Wim, 1996. "An international study on measuring social support: Interactions and satisfaction," Social Science & Medicine, Elsevier, vol. 43(9), pages 1389-1397, November.
    3. Huang, Ming-Hui & Rust, Roland T., 2022. "A Framework for Collaborative Artificial Intelligence in Marketing," Journal of Retailing, Elsevier, vol. 98(2), pages 209-223.
    4. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    5. Christian Hildebrand & Anouk Bergner, 2021. "Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 659-676, July.
    6. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    7. 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.
    8. Gelbrich, Katja & Hagel, Julia & Orsingher, Chiara, 2021. "Emotional support from a digital assistant in technology-mediated services: Effects on customer satisfaction and behavioral persistence," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 176-193.
    9. Pankaj Aggarwal & Meng Zhang, 2006. "The Moderating Effect of Relationship Norm Salience on Consumers' Loss Aversion," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 33(3), pages 413-419, October.
    10. Dalal, Reeshad S. & Bonaccio, Silvia, 2010. "What types of advice do decision-makers prefer?," Organizational Behavior and Human Decision Processes, Elsevier, vol. 112(1), pages 11-23, May.
    11. Lisa C. Wan & Michael K. Hui & Robert S. Wyer Jr., 2011. "The Role of Relationship Norms in Responses to Service Failures," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 38(2), pages 260-277.
    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. Broekhuizen, Thijs & Dekker, Henri & de Faria, Pedro & Firk, Sebastian & Nguyen, Dinh Khoi & Sofka, Wolfgang, 2023. "AI for managing open innovation: Opportunities, challenges, and a research agenda," Journal of Business Research, Elsevier, vol. 167(C).
    2. Huang, Xiaozhi & Wu, Xitong & Cao, Xin & Wu, Jifei, 2023. "The effect of medical artificial intelligence innovation locus on consumer adoption of new products," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    3. 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.
    4. Benjamin von Walter & Dietmar Kremmel & Bruno Jäger, 2022. "The impact of lay beliefs about AI on adoption of algorithmic advice," Marketing Letters, Springer, vol. 33(1), pages 143-155, March.
    5. Lauren Skinner Beitelspacher & Thomas L. Baker & Adam Rapp & Dhruv Grewal, 2018. "Understanding the long-term implications of retailer returns in business-to-business relationships," Journal of the Academy of Marketing Science, Springer, vol. 46(2), pages 252-272, March.
    6. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    7. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    8. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    9. Freddy Gabbay & Rotem Lev Aharoni & Ori Schweitzer, 2022. "Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    10. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.
    11. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    12. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    13. repec:cup:judgdm:v:15:y:2020:i:3:p:449-451 is not listed on IDEAS
    14. Dario Sipari & Betsy D. M. Chaparro-Rico & Daniele Cafolla, 2022. "SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis," IJERPH, MDPI, vol. 19(16), pages 1-27, August.
    15. Park, Sehoon & Kim, Chaeyeong & Park, Jane, 2023. "How power distance belief, self-construal, and relationship norms impact conspicuous consumption," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    16. Chiara Longoni & Andrea Bonezzi & Carey K. Morewedge, 2020. "Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Becksted (2020)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(3), pages 446-448, May.
    17. Chen Yang & Jing Hu, 2022. "When do consumers prefer AI-enabled customer service? The interaction effect of brand personality and service provision type on brand attitudes and purchase intentions," Journal of Brand Management, Palgrave Macmillan, vol. 29(2), pages 167-189, March.
    18. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.
    19. Hsin-Hsien Liu & Jung-Hua Chang, 2017. "Relationship type, perceived trust, and ambiguity aversion," Marketing Letters, Springer, vol. 28(2), pages 255-266, June.
    20. Oded Rotem & Tamar Schwartz & Ron Maor & Yishay Tauber & Maya Tsarfati Shapiro & Marcos Meseguer & Daniella Gilboa & Daniel S. Seidman & Assaf Zaritsky, 2024. "Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    21. Jochen Wirtz & Valentina Pitardi, 2023. "How intelligent automation, service robots, and AI will reshape service products and their delivery," Italian Journal of Marketing, Springer, vol. 2023(3), pages 289-300, September.

    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:eee:jouret:v:99:y:2023:i:2:p:280-296. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-retailing .

    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.