IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v175y2022ics0040162521008210.html
   My bibliography  Save this article

What influences algorithmic decision-making? A systematic literature review on algorithm aversion

Author

Listed:
  • Mahmud, Hasan
  • Islam, A.K.M. Najmul
  • Ahmed, Syed Ishtiaque
  • Smolander, Kari

Abstract

With the continuing application of artificial intelligence (AI) technologies in decision-making, algorithmic decision-making is becoming more efficient, often even outperforming humans. Despite this superior performance, people often consciously or unconsciously display reluctance to rely on algorithms, a phenomenon known as algorithm aversion. Viewed as a behavioral anomaly, algorithm aversion has recently attracted much scholarly attention. With a view to synthesize the findings of existing literature, we systematically review 80 empirical studies identified through searching in seven academic databases and using the snowballing technique. We inductively categorize the influencing factors of algorithm aversion under four main themes: algorithm, individual, task, and high-level. Our analysis reveals that although algorithm and individual factors have been investigated extensively, very little attention has been given to exploring the task and high-level factors. We contribute to algorithm aversion literature by proposing a comprehensive framework, highlighting open issues in existing studies, and outlining several research avenues that could be handled in future research. Our model could guide developers in designing and developing and managers in implementing and using of algorithmic decision.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521008210
    DOI: 10.1016/j.techfore.2021.121390
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162521008210
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2021.121390?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. Sanders, Nada R. & Manrodt, Karl B., 2003. "The efficacy of using judgmental versus quantitative forecasting methods in practice," Omega, Elsevier, vol. 31(6), pages 511-522, December.
    2. Yaniv, Ilan & Kleinberger, Eli, 2000. "Advice Taking in Decision Making: Egocentric Discounting and Reputation Formation," Organizational Behavior and Human Decision Processes, Elsevier, vol. 83(2), pages 260-281, November.
    3. Whitecotton, Stacey M., 1996. "The Effects of Experience and a Decision Aid on the Slope, Scatter, and Bias of Earnings Forecasts," Organizational Behavior and Human Decision Processes, Elsevier, vol. 66(1), pages 111-121, April.
    4. Hal R. Arkes & Victoria A. Shaffer & Mitchell A. Medow, 2007. "Patients Derogate Physicians Who Use a Computer-Assisted Diagnostic Aid," Medical Decision Making, , vol. 27(2), pages 189-202, March.
    5. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    6. Arkes, Hal R. & Dawes, Robyn M. & Christensen, Caryn, 1986. "Factors influencing the use of a decision rule in a probabilistic task," Organizational Behavior and Human Decision Processes, Elsevier, vol. 37(1), pages 93-110, February.
    7. Newman, David T. & Fast, Nathanael J. & Harmon, Derek J., 2020. "When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 149-167.
    8. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    9. Yasuhiro Yamakawa & Mike W. Peng & David L. Deeds, 2008. "What Drives New Ventures to Internationalize from Emerging to Developed Economies?," Entrepreneurship Theory and Practice, , vol. 32(1), pages 59-82, January.
    10. Paul Slovic & Melissa L. Finucane & Ellen Peters & Donald G. MacGregor, 2004. "Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality," Risk Analysis, John Wiley & Sons, vol. 24(2), pages 311-322, April.
    11. 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.
    12. Kohei Kawaguchi, 2021. "When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business," Management Science, INFORMS, vol. 67(3), pages 1670-1695, March.
    13. Cao, Guangming & Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K., 2021. "Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making," Technovation, Elsevier, vol. 106(C).
    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. Jones, Marian V. & Coviello, Nicole & Tang, Yee Kwan, 2011. "International Entrepreneurship research (1989–2009): A domain ontology and thematic analysis," Journal of Business Venturing, Elsevier, vol. 26(6), pages 632-659.
    16. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    17. Jane M. Mackay & Joyce J. Elam, 1992. "A Comparative Study of How Experts and Novices Use a Decision Aid to Solve Problems in Complex Knowledge Domains," Information Systems Research, INFORMS, vol. 3(2), pages 150-172, June.
    18. Bigdeli, Alinaghi Ziaee & Kamal, Muhammad Mustafa & de Cesare, Sergio, 2013. "Electronic information sharing in local government authorities: Factors influencing the decision-making process," International Journal of Information Management, Elsevier, vol. 33(5), pages 816-830.
    19. 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.
    20. Tsang, Eric W. K., 2014. "Old and New," Management and Organization Review, Cambridge University Press, vol. 10(03), pages 390-390, November.
    21. Dale L. Goodhue, 1995. "Understanding User Evaluations of Information Systems," Management Science, INFORMS, vol. 41(12), pages 1827-1844, December.
    22. Kaur, Puneet & Dhir, Amandeep & Tandon, Anushree & Alzeiby, Ebtesam A. & Abohassan, Abeer Ahmed, 2021. "A systematic literature review on cyberstalking. An analysis of past achievements and future promises," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    23. 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.
    24. Bhimani, Hardik & Mention, Anne-Laure & Barlatier, Pierre-Jean, 2019. "Social media and innovation: A systematic literature review and future research directions," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 251-269.
    25. Zahedi, Mansooreh & Shahin, Mojtaba & Ali Babar, Muhammad, 2016. "A systematic review of knowledge sharing challenges and practices in global software development," International Journal of Information Management, Elsevier, vol. 36(6), pages 995-1019.
    26. 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.
    27. Goodwin, Paul & Sinan Gönül, M. & Önkal, Dilek, 2013. "Antecedents and effects of trust in forecasting advice," International Journal of Forecasting, Elsevier, vol. 29(2), pages 354-366.
    28. Khan, Anupriya & Krishnan, Satish & Dhir, Amandeep, 2021. "Electronic government and corruption: Systematic literature review, framework, and agenda for future research," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    29. Gao, Fangjian & Sunyaev, Ali, 2019. "Context matters: A review of the determinant factors in the decision to adopt cloud computing in healthcare," International Journal of Information Management, Elsevier, vol. 48(C), pages 120-138.
    30. Maranda McBride & Lemuria Carter & Celestine Ntuen, 2012. "The impact of personality on nurses' bias towards automated decision aid acceptance," International Journal of Information Systems and Change Management, Inderscience Enterprises Ltd, vol. 6(2), pages 132-146.
    31. 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.
    32. Guckenbiehl, Peter & Corral de Zubielqui, Graciela & Lindsay, Noel, 2021. "Knowledge and innovation in start-up ventures: A systematic literature review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    33. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    34. Gogoll, Jan & Uhl, Matthias, 2018. "Rage against the machine: Automation in the moral domain," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 74(C), pages 97-103.
    35. Gino, Francesca, 2008. "Do we listen to advice just because we paid for it? The impact of advice cost on its use," Organizational Behavior and Human Decision Processes, Elsevier, vol. 107(2), pages 234-245, November.
    36. 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.
    37. 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.
    38. Efendić, Emir & Van de Calseyde, Philippe P.F.M. & Evans, Anthony M., 2020. "Slow response times undermine trust in algorithmic (but not human) predictions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 157(C), pages 103-114.
    39. Laurie Swinney, 1999. "Consideration of the social context of auditors’ reliance on expert system output during evaluation of loan loss reserves," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 8(3), pages 199-213, September.
    40. Tommaso Minola & Giuseppe Criaco & Lucio Cassia, 2014. "Are youth really different? New beliefs for old practices in entrepreneurship," International Journal of Entrepreneurship and Innovation Management, Inderscience Enterprises Ltd, vol. 18(2/3), pages 233-259.
    41. Collins, Christopher & Dennehy, Denis & Conboy, Kieran & Mikalef, Patrick, 2021. "Artificial intelligence in information systems research: A systematic literature review and research agenda," International Journal of Information Management, Elsevier, vol. 60(C).
    42. Paweł Niszczota & Dániel Kaszás, 2020. "Robo-investment aversion," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
    43. Ujwal Kayande & Arnaud De Bruyn & Gary L. Lilien & Arvind Rangaswamy & Gerrit H. van Bruggen, 2009. "How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations," Information Systems Research, INFORMS, vol. 20(4), pages 527-546, December.
    44. 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.
    45. Litterscheidt, Rouven & Streich, David J., 2020. "Financial education and digital asset management: What's in the black box?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 87(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. Manav Raj & Justin Berg & Rob Seamans, 2023. "Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content," Papers 2303.06217, arXiv.org, revised Jun 2024.
    2. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    3. Lukas-Valentin Herm & Theresa Steinbach & Jonas Wanner & Christian Janiesch, 2022. "A nascent design theory for explainable intelligent systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2185-2205, December.
    4. Brice Corgnet, 2023. "An Experimental Test of Algorithmic Dismissals," Working Papers 2302, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    5. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    6. Arsenyan, Jbid & Mirowska, Agata & Piepenbrink, Anke, 2023. "Close encounters with the virtual kind: Defining a human-virtual agent coexistence framework," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    7. 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).
    8. Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    9. Jonas Wanner & Lukas-Valentin Herm & Kai Heinrich & Christian Janiesch, 2022. "The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2079-2102, December.
    10. Haque, AKM Bahalul & Islam, A.K.M. Najmul & Mikalef, Patrick, 2023. "Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    11. 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).
    12. Ivanova-Stenzel, Radosveta & Tolksdorf, Michel, 2023. "Measuring Preferences for Algorithms - Are people really algorithm averse after seeing the algorithm perform?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277692, Verein für Socialpolitik / German Economic Association.
    13. Jan René Judek, 2024. "Willingness to Use Algorithms Varies with Social Information on Weak vs. Strong Adoption: An Experimental Study on Algorithm Aversion," FinTech, MDPI, vol. 3(1), pages 1-11, January.
    14. Baabdullah, Abdullah M. & Alalwan, Ali Abdallah & Algharabat, Raed S. & Metri, Bhimaraya & Rana, Nripendra P., 2022. "Virtual agents and flow experience: An empirical examination of AI-powered chatbots," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    15. 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).
    16. 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.
    17. Bachler, Sebastian & Haeussler, Stefan & Momsen, Katharina & Stefan, Matthias, 2024. "Do people willfully ignore decision support? Evidence from an online experiment," VfS Annual Conference 2024 (Berlin): Upcoming Labor Market Challenges 302404, Verein für Socialpolitik / German Economic Association.
    18. Mathieu Chevrier & Brice Corgnet & Eric Guerci & Julie Rosaz, 2024. "Algorithm Credulity: Human and Algorithmic Advice in Prediction Experiments," GREDEG Working Papers 2024-03, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    19. Gehrlein, Jonas & Miebs, Grzegorz & Brunelli, Matteo & Kadziński, Miłosz, 2023. "An active preference learning approach to aid the selection of validators in blockchain environments," Omega, Elsevier, vol. 118(C).
    20. 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.
    21. Michael Pin-Chuan Lin & Arita Li Liu & Eric Poitras & Maiga Chang & Daniel H. Chang, 2024. "An Exploratory Study on the Efficacy and Inclusivity of AI Technologies in Diverse Learning Environments," Sustainability, MDPI, vol. 16(20), pages 1-19, October.
    22. 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.

    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. 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).
    2. 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.
    3. 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).
    4. 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.
    5. Christiane B. Haubitz & Cedric A. Lehmann & Andreas Fügener & Ulrich W. Thonemann, 2021. "The Risk of Algorithm Transparency: How Algorithm Complexity Drives the Effects on Use of Advice," ECONtribute Discussion Papers Series 078, University of Bonn and University of Cologne, Germany.
    6. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    7. Merle, Aurélie & St-Onge, Anik & Sénécal, Sylvain, 2022. "Does it pay to be honest? The effect of retailer-provided negative feedback on consumers’ product choice and shopping experience," Journal of Business Research, Elsevier, vol. 147(C), pages 532-543.
    8. 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.
    9. Kevin Bauer & Andrej Gill, 2024. "Mirror, Mirror on the Wall: Algorithmic Assessments, Transparency, and Self-Fulfilling Prophecies," Information Systems Research, INFORMS, vol. 35(1), pages 226-248, March.
    10. 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).
    11. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    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. Back, Camila & Morana, Stefan & Spann, Martin, 2023. "When do robo-advisors make us better investors? The impact of social design elements on investor behavior," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 103(C).
    14. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    15. Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.
    16. Hélène Laurell & Leona Achtenhagen & Svante Andersson, 2017. "The changing role of network ties and critical capabilities in an international new venture’s early development," International Entrepreneurship and Management Journal, Springer, vol. 13(1), pages 113-140, March.
    17. 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.
    18. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    19. Markus Jung & Mischa Seiter, 2021. "Towards a better understanding on mitigating algorithm aversion in forecasting: an experimental study," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 32(4), pages 495-516, December.
    20. Tse, Tiffany Tsz Kwan & Hanaki, Nobuyuki & Mao, Bolin, 2024. "Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment," Journal of Economic Psychology, Elsevier, vol. 102(C).

    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:tefoso:v:175:y:2022:i:c:s0040162521008210. 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: http://www.sciencedirect.com/science/journal/00401625 .

    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.