My Advisor, Her AI, and Me: Evidence from a Field Experiment on Human–AI Collaboration and Investment Decisions

References

• Abdel-Karim BM, Pfeuffer N, Carl KV, Hinz O (2023) How AI-based systems can induce reflections: The case of AI-augmented diagnostic work. MIS Quart. 47(4):1395–1424.Crossref, Google Scholar
• Acemoglu D, Restrepo P (2019) Automation and new tasks: How technology displaces and reinstates labor. J. Econom. Perspect. 33(2):3–30.Crossref, Google Scholar
• Agrawal A, Gans JS, Goldfarb A (2024) Prediction machines, insurance, and protection: An alternative perspective on AI’s role in production. J. Japanese Internat. Econom. 72:101307.Crossref, Google Scholar
• Angrist JD, Pischke JS (2009) Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Asch SE (1955) Opinions and social pressure. Sci. Amer. 193(5):31–35.Crossref, Google Scholar
• Avramov D, Cheng S, Metzker L (2023) Machine learning vs. economic restrictions: Evidence from stock return predictability. Management Sci. 69(5):2587–2619.Link, Google Scholar
• Awad E, Dsouza S, Kim R, Schulz J, Henrich J, Shariff A, Bonnefon JF, Rahwan I (2018) The moral machine experiment. Nature 563(7729):59–64.Crossref, Google Scholar
• Bauer K, von Zahn M, Hinz O (2023) Expl(AI)ned: The impact of explainable artificial intelligence on users’ information processing. Inform. Systems Res. 34(4):1582–1602.Link, Google Scholar
• Bauer K, Heigl R, Hinz O, Kosfeld M (2024a) Feedback loops in machine learning: A study on the interplay of continuous updating and human discrimination. J. Assoc. Inform. Systems 25(4):804–866.Google Scholar
• Bauer K, Jussupow E, Heigl R, Vogt B, Hinz O (2024b) All just in your head? Unraveling the side effects of generative AI disclosure in creative task. Preprint, submitted April 15, https://doi.org/10.2139/ssrn.4782554.Google Scholar
• Bergstein B (2020) What AI still can’t do. MIT Tech. Rev. 123(2):1–7.Google Scholar
• Berk R (2017) An impact assessment of machine learning risk forecasts on parole board decisions and recidivism. J. Experiment. Criminology 13(2):193–216.Crossref, Google Scholar
• Bonaccio S, Dalal RS (2006) Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences. Organ. Behav. Human Decision Processes 101(2):127–151.Crossref, Google Scholar
• Brynjolfsson E, Mitchell T, Rock D (2018) What can machines learn, and what does it mean for occupations and the economy? AEA Papers Proc. 108(1):43–47.Crossref, Google Scholar
• Buckley RP, Zetzsche DA, Arner DW, Tang BW (2021) Regulating artificial intelligence in finance: Putting the human in the loop. Sydney Law Rev. 43(1):43–81.Google Scholar
• Cao S, Jiang W, Wang J, Yang B (2024) From man vs. machine to man + machine: The art and AI of stock analyses. J. Financial Econom. 160:103910.Crossref, Google Scholar
• Castelo N, Bos MW, Lehmann DR (2019) Task-dependent algorithm aversion. J. Marketing Res. 56(5):809–825.Crossref, Google Scholar
• Chang AH, Yang LK, Tsaih RH, Lin SK (2022) Machine learning and artificial neural networks to construct P2P lending credit-scoring model: A case using lending club data. Quant. Finance Econom. 6(2):303–325.Crossref, Google Scholar
• Cooper AC, Woo CY, Dunkelberg WC (1988) Entrepreneurs’ perceived chances for success. J. Bus. Venturing 3(2):97–108.Crossref, Google Scholar
• Costa P, Henshaw JE (2022) Quantifying the investor’s view on the value of human and robo-advice. Report, Vanguard Group, Inc., Malvern, PA.Google Scholar
• Council of the European Union (2024) Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. Accessed August 12, 2024, https://data.consilium.europa.eu/doc/document/ST-5662-2024-INIT/en/pdf.Google Scholar
• Cowgill B, Tucker CE (2019) Algorithmic fairness and economics. Preprint, submitted April 4, https://doi.org/10.2139/ssrn.3361280.Google Scholar
• Curran PG (2016) Methods for the detection of carelessly invalid responses in survey data. J. Experiment. Soc. Psych. 66:4–19.Crossref, Google Scholar
• Dalal RS, Bonaccio S (2010) What types of advice do decision-makers prefer? Organ. Behav. Human Decision Processes 112(1):11–23.Crossref, Google Scholar
• Darda KM, Cross ES (2023) The computer, a choreographer? Aesthetic responses to randomly-generated dance choreography by a computer. Heliyon 9(1):e12750.Crossref, Google Scholar
• Dell’Acqua F (2022) Falling asleep at the wheel: Human/AI collaboration in a field experiment on HR recruiters. Working paper, Laboratory for Innovation Science, Harvard Business School, Boston.Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych.: General 144(1):114–126.Crossref, Google Scholar
• Dohmen T, Falk A, Huffman D, Sunde U (2012) The intergenerational transmission of risk and trust attitudes. Rev. Econom. Stud. 79(2):645–677.Crossref, Google Scholar
• Dong H, Liu R, Tham AW (2024) Accuracy comparison between five machine learning algorithms for financial risk evaluation. J. Risk Financial Management 17(2):50.Crossref, Google Scholar
• European Commission (2019) Communication: Building trust in human centric artificial intelligence. Accessed August 29, 2024, https://digital-strategy.ec.europa.eu/en/library/communication-building-trust-human-centric-artificial-intelligence.Google Scholar
• Faisal MF, Saqlain MNU, Bhuiyan MAS, Miraz MH, Patwary MJ (2021) Credit approval system using machine learning: Challenges and future directions. Proc. 2021 Internat. Conf. Comput., Networking, Telecommunications Engrg. Sci. Appl.(Institute of Electrical and Electronics Engineers, Piscataway, NJ), 76–82.Google Scholar
• Feuerriegel S, Shrestha YR, von Krogh G, Zhang C (2022) Bringing artificial intelligence to business management. Nature Machine Intelligence 4(7):611–613.Crossref, Google Scholar
• Friestad M, Wright P (1994) The persuasion knowledge model: How people cope with persuasion attempts. J. Consumer Res. 21(1):1–31.Crossref, Google Scholar
• Fügener A, Grahl J, Gupta A, Ketter W (2021) Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. MIS Quart. 45(3):1527–1556.Crossref, Google Scholar
• Fügener A, Grahl J, Gupta A, Ketter W (2022) Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Inform. Systems Res. 33(2):678–696.Link, Google Scholar
• Goh E, Gallo R, Hom J, Strong E, Weng Y, Kerman H, Cool JA, et al. (2024) Large language model influence on diagnostic reasoning: A randomized clinical trial. JAMA Network Open 7(10):e2440969.Crossref, Google Scholar
• Greene W (2004) Fixed effects and bias due to the incidental parameters problem in the tobit model. Econometric Rev. 23(2):125–147.Crossref, Google Scholar
• Harvey N, Fischer I (1997) Taking advice: Accepting help, improving judgment, and sharing responsibility. Organ. Behav. Human Decision Processes 70(2):117–133.Crossref, Google Scholar
• Hayes AF (2017) Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (Guilford Publications, New York).Google Scholar
• Hermosilla M, Gutiérrez-Navratil F, Prieto-Rodríguez J (2018) Can emerging markets tilt global product design? Impacts of Chinese colorism on Hollywood castings. Marketing Sci. 37(3):356–381.Link, Google Scholar
• Kahneman D, Rosenfield AM, Gandhi L, Blaser T (2016) Noise: How to overcome the high, hidden cost of inconsistent decision making. Harvard Bus. Rev. 94(10):38–46.Google Scholar
• Kellogg KC, Valentine MA, Christin A (2020) Algorithms at work: The new contested terrain of control. Acad. Management Ann. 14(1):366–410.Crossref, Google Scholar
• Kingston JK (2016) Artificial intelligence and legal liability. Bramer M, Petridis M, eds. Research and Development in Intelligent Systems XXXIII (Springer, Cham, Switzerland), 269–279.Google Scholar
• Koestner R, Gingras I, Abutaa R, Losier GF, DiDio L, Gagné M (1999) To follow expert advice when making a decision: An examination of reactive versus reflective autonomy. J. Personality 67(5):851–872.Crossref, Google Scholar
• Komiak SY, Benbasat I (2006) The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quart. 30(4):941–960.Crossref, Google Scholar
• Kumar V, Natarajan S, Keerthana S, Chinmayi K, Lakshmi N (2016) Credit risk analysis in peer-to-peer lending system. 2016 IEEE Internat. Conf. Knowledge Engrg. Appl. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 193–196.Google Scholar
• Lebovitz S, Lifshitz-Assaf H, Levina N (2022) To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organ. Sci. 33(1):126–148.Link, Google Scholar
• Lessmann S, Baesens B, Seow HV, Thomas LC (2015) Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. Eur. J. Oper. Res. 247(1):124–136.Crossref, Google Scholar
• Li X, Grahl J, Hinz O (2022) How do recommender systems lead to consumer purchases? A causal mediation analysis of a field experiment. Inform. Systems Res. 33(2):620–637.Link, Google Scholar
• Loewenstein GF, Weber EU, Hsee CK, Welch N (2001) Risk as feelings. Psych. Bull. 127(2):267.Crossref, Google Scholar
• Longoni C, Bonezzi A, Morewedge CK (2019) Resistance to medical artificial intelligence. J. Consumer Res. 46(4):629–650.Crossref, Google Scholar
• Lu T, Zhang Y (2025) 1 + 1 > 2? Information, humans, and machines. Inform. Systems Res. 36(1):394–418.Link, Google Scholar
• MacCrimmon KR, Wehrung DA (1990) Characteristics of risk taking executives. Management Sci. 36(4):422–435.Link, Google Scholar
• Malekipirbazari M, Aksakalli V (2015) Risk assessment in social lending via random forests. Expert Systems Appl. 42(10):4621–4631.Crossref, Google Scholar
• March JG, Shapira Z (1987) Managerial perspectives on risk and risk taking. Management Sci. 33(11):1404–1418.Link, Google Scholar
• MarketsandMarkets (2025) AI in finance market size, share, growth report—2030. Accessed March 3, 2025, https://www.marketsandmarkets.com/Market-Reports/ai-in-finance-market-90552286.html.Google Scholar
• McKnight DH, Choudhury V, Kacmar C (2002) Developing and validating trust measures for e-commerce: An integrative typology. Inform. Systems Res. 13(3):334–359.Link, Google Scholar
• Meade AW, Craig SB (2012) Identifying careless responses in survey data. Psych. Methods 17(3):437–455.Crossref, Google Scholar
• Millet K, Buehler F, Du G, Kokkoris MD (2023) Defending humankind: Anthropocentric bias in the appreciation of AI art. Comput. Human Behav. 143:107707.Crossref, Google Scholar
• Mishra K, Metilda MJ (2015) A study on the impact of investment experience, gender, and level of education on overconfidence and self-attribution bias. IIMB Management Rev. 27(4):228–239.Crossref, Google Scholar
• Önkal D, Goodwin P, Thomson M, Gönül S, Pollock A (2009) The relative influence of advice from human experts and statistical methods on forecast adjustments. J. Behav. Decision Making 22(4):390–409.Crossref, Google Scholar
• Petty RE, Cacioppo JT (1986) The elaboration likelihood model of persuasion. Communication and Persuasion (Springer, New York), 1–24.Google Scholar
• Pfeuffer N, Baum L, Stammer W, Abdel-Karim BM, Schramowski P, Bucher AM, Hügel C, Rohde G, Kersting K, Hinz O (2023) Explanatory interactive machine learning: Establishing an action design research process for machine learning projects. Bus. Inform. Systems Engrg. 65(6):677–701.Crossref, Google Scholar
• Preacher KJ, Zhang Z, Zyphur MJ (2016) Multilevel structural equation models for assessing moderation within and across levels of analysis. Psych. Methods 21(2):189–205.Crossref, Google Scholar
• Rahwan I, Cebrian M, Obradovich N, Bongard J, Bonnefon JF, Breazeal C, Crandall JW, et al. (2019) Machine behaviour. Nature 568(7753):477–486.Crossref, Google Scholar
• Rohaan D, Topan E, Groothuis-Oudshoorn CG (2022) Using supervised machine learning for B2B sales forecasting: A case study of spare parts sales forecasting at an after-sales service provider. Expert Systems Appl. 188:115925.Crossref, Google Scholar
• Singh R, Ayyar MP, Pavan TVS, Gosain S, Shah RR (2019) Automating car insurance claims using deep learning techniques. 2019 IEEE Fifth Internat. Conf. Multimedia Big Data(Institute of Electrical and Electronics Engineers, Piscataway, NJ), 199–207.Google Scholar
• Slovic P (1987) Perception of risk. Science 236(4799):280–285.Crossref, Google Scholar
• Sniezek JA, Buckley T (1995) Cueing and cognitive conflict in judge-advisor decision making. Organ. Behav. Human Decision Processes 62(2):159–174.Crossref, Google Scholar
• Sniezek JA, Van Swol LM (2001) Trust, confidence, and expertise in a judge-advisor system. Organ. Behav. Human Decision Processes 84(2):288–307.Crossref, Google Scholar
• Steffel M, Williams EF, Perrmann-Graham J (2016) Passing the buck: Delegating choices to others to avoid responsibility and blame. Organ. Behav. Human Decision Processes 135:32–44.Crossref, Google Scholar
• Strickland LH, Lewicki RJ, Katz AM (1966) Temporal orientation and perceived control as determinants of risk-taking. J. Experiment. Soc. Psych. 2(2):143–151.Crossref, Google Scholar
• Sun J, Zhang DJ, Hu H, Van Mieghem JA (2022) Predicting human discretion to adjust algorithmic prescription: A large-scale field experiment in warehouse operations. Management Sci. 68(2):846–865.Link, Google Scholar
• Taudien A, Fuegener A, Gupta A, Ketter W (2022) Calibrating users’ mental models for delegation to AI. Proc. 2022 Internat. Conf. Inform. Systems (Association for Information Systems, Atlanta).Google Scholar
• Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–1131.Crossref, Google Scholar
• von Zahn M, Bauer K, Mihale-Wilson C, Jagow J, Speicher M, Hinz O (2025) Smart green nudging: Reducing product returns through digital footprints and causal machine learning. Marketing Sci. 44(4):954–969.Link, Google Scholar
• Wasserbacher H, Spindler M (2022) Machine learning for financial forecasting, planning and analysis: Recent developments and pitfalls. Digital Finance 4(1):63–88.Crossref, Google Scholar
• Weber EU, Milliman RA (1997) Perceived risk attitudes: Relating risk perception to risky choice. Management Sci. 43(2):123–144.Link, Google Scholar
• Weber EU, Blais AR, Betz NE (2002) A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. J. Behav. Decision Making 15(4):263–290.Crossref, Google Scholar
• Wooldridge JM (2010) Econometric Analysis of Cross Section and Panel Data (MIT Press, Cambridge, MA).Google Scholar
• Yaniv I (2004) Receiving other people’s advice: Influence and benefit. Organ. Behav. Human Decision Processes 93(1):1–13.Crossref, Google Scholar
• Yin M, Wortman Vaughan J, Wallach H (2019) Understanding the effect of accuracy on trust in machine learning models. Proc. 2019 CHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 1–12.Google Scholar
• You S, Yang CL, Li X (2022) Algorithmic versus human advice: Does presenting prediction performance matter for algorithm appreciation? J. Management Inform. Systems 39(2):336–365.Crossref, Google Scholar