IDEAS home Printed from https://ideas.repec.org/a/eee/joreco/v72y2023ics0969698923000061.html
   My bibliography  Save this article

Is algorithm aversion WEIRD? A cross-country comparison of individual-differences and algorithm aversion

Author

Listed:
  • Liu, Nicole Tsz Yeung
  • Kirshner, Samuel N.
  • Lim, Eric T.K.

Abstract

Although algorithms offer superior performance over humans across many tasks, individuals often exhibit algorithm aversion, resisting algorithmic advice in favour of human recommendations. However, most algorithm aversion studies rely on American samples, potentially limiting the generalisability of the findings. Given the increasing adoption of algorithms globally, we explore if the impact of two crucial factors driving algorithm aversion, uniqueness neglect and familiarity, differ between culturally different countries. Drawing on the individualism-collectivism cultural dimension, we conducted two online studies comparing algorithm aversion between people in India and the United States in medical and financial services scenarios. While our results suggest that there is no difference in the degree of algorithm aversion between Indians and Americans at an aggregate level, we find important cross-cultural differences: Uniqueness neglect strengthens algorithm aversion for Americans more than Indians, while familiarity weakens algorithm aversion more for Indians than Americans. Thus, our results reveal generalisability issues within the algorithm aversion literature, as factors influencing algorithm aversion can be culturally dependent.

Suggested Citation

  • Liu, Nicole Tsz Yeung & Kirshner, Samuel N. & Lim, Eric T.K., 2023. "Is algorithm aversion WEIRD? A cross-country comparison of individual-differences and algorithm aversion," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:joreco:v:72:y:2023:i:c:s0969698923000061
    DOI: 10.1016/j.jretconser.2023.103259
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969698923000061
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretconser.2023.103259?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. Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K, 2019. "Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda," International Journal of Information Management, Elsevier, vol. 48(C), pages 63-71.
    3. Prentice, Catherine & Nguyen, Mai, 2020. "Engaging and retaining customers with AI and employee service," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    4. Song, Mengmeng & Xing, Xinyu & Duan, Yucong & Cohen, Jason & Mou, Jian, 2022. "Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    5. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    6. 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.
    7. Trainor, Kevin J. & Andzulis, James (Mick) & Rapp, Adam & Agnihotri, Raj, 2014. "Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM," Journal of Business Research, Elsevier, vol. 67(6), pages 1201-1208.
    8. Joseph Henrich & Steven J. Heine & Ara Norenzayan, 2010. "Most people are not WEIRD," Nature, Nature, vol. 466(7302), pages 29-29, July.
    9. Hallikainen, Heli & Laukkanen, Tommi, 2018. "National culture and consumer trust in e-commerce," International Journal of Information Management, Elsevier, vol. 38(1), pages 97-106.
    10. Nabi, Nazia & Siahtiri, Vida & O'Cass, Aron, 2019. "In search of status: Unpacking the relationship of status with individualism-collectivism, counterconformity motivations and life satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 378-386.
    11. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    12. Yuan, Chunlin & Zhang, Chenlei & Wang, Shuman, 2022. "Social anxiety as a moderator in consumer willingness to accept AI assistants based on utilitarian and hedonic values," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    13. 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.
    14. Zhang, Yun & Weng, Qingxiong & Zhu, Nan, 2018. "The relationships between electronic banking adoption and its antecedents: A meta-analytic study of the role of national culture," International Journal of Information Management, Elsevier, vol. 40(C), pages 76-87.
    15. Park, Jungkun & Hong, EunPyo & Le, Hoang TPM, 2021. "Adopting autonomous vehicles: The moderating effects of demographic variables," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    16. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brüns, Jasper David & Meißner, Martin, 2024. "Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).

    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. Wang, Cuicui & Li, Yiyang & Fu, Weizhong & Jin, Jia, 2023. "Whether to trust chatbots: Applying the event-related approach to understand consumers’ emotional experiences in interactions with chatbots in e-commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    2. Peng, Leiqing & Luo, Mengting & Guo, Yulang, 2023. "Deposit AI as the “invisible hand†to make the resale easier: A moderated mediation model," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    3. Li, Sixian & Peluso, Alessandro M. & Duan, Jinyun, 2023. "Why do we prefer humans to artificial intelligence in telemarketing? A mind perception explanation," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    4. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    5. 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.
    6. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    7. repec:cup:judgdm:v:15:y:2020:i:3:p:449-451 is not listed on IDEAS
    8. 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.
    9. 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).
    10. 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.
    11. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
    12. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    13. 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.
    14. Benedikt Berger & Martin Adam & Alexander Rühr & Alexander Benlian, 2021. "Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 55-68, February.
    15. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    16. Alexia GAUDEUL & Caterina GIANNETTI, 2023. "Trade-offs in the design of financial algorithms," Discussion Papers 2023/288, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    17. Zulia Gubaydullina & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2022. "Comparing Different Kinds of Influence on an Algorithm in Its Forecasting Process and Their Impact on Algorithm Aversion," Businesses, MDPI, vol. 2(4), pages 1-23, October.
    18. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    19. Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    20. Alabed, Amani & Javornik, Ana & Gregory-Smith, Diana, 2022. "AI anthropomorphism and its effect on users' self-congruence and self–AI integration: A theoretical framework and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    21. Pascal Oliver Heßler & Jella Pfeiffer & Sebastian Hafenbrädl, 2022. "When Self-Humanization Leads to Algorithm Aversion," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 275-292, June.

    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:joreco:v:72:y:2023:i:c:s0969698923000061. 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-and-consumer-services .

    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.