Humans’ Use of AI Assistance: The Effect of Loss Aversion on Willingness to Delegate Decisions

References

• Armantier O, Boly A (2015) Framing of incentives and effort provision. Internat. Econom. Rev. 56(3):917–938.Crossref, Google Scholar
• Baird A, Maruping LM (2021) The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quart. 45(1):315–341.Crossref, Google Scholar
• Canessa N, Crespi C, Baud-Bovy G, Dodich A, Falini A, Antonellis G, Cappa SF (2017) Neural markers of loss aversion in resting-state brain activity. Neuroimage 146:257–265.Crossref, Google Scholar
• Babic B, Gerke S, Evgeniou T, Cohen IG (2021) Beware explanations from AI in health care. Science 373(6552):284–286.Crossref, Google Scholar
• Bansal D, Nushi B, Kamar E, Lasecki WS, Weld DS, Horvitz E (2019a) Beyond accuracy: The role of mental models in human-AI team performance. Proc. AAAI Conf. Human Computation Crowdsourcing 7(1):2–11.Google Scholar
• Bansal G, Nushi B, Kamar E, Weld DS, Lasecki WS, Horvitz E (2019b) Updates in human-AI teams: Understanding and addressing the performance/compatibility trade-off. Proc. AAAI Conf. Artificial Intelligence 33(1):2429–2437.Crossref, Google Scholar
• Bauer K, Gill A (2024) Mirror, mirror on the wall: Algorithmic assessments, transparency, and self-fulfilling prophecies. Inform. Systems Res. 35(1):226–248.Link, 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
• Beam EA, Masatioglu Y, Watson T, Yang D (2023) Loss aversion or lack of trust: Why does loss framing work to encourage preventive health behaviors? J. Behav. Exp. Econom. 104:1–17.Google Scholar
• Bigman YE, Gray K (2018) People are averse to machines making moral decisions. Cognition 181:21–34.Crossref, Google Scholar
• Breiter HC, Aharon I, Kahneman D, Dale A, Shizgal P (2001) Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron 30(2):619–639.Crossref, Google Scholar
• Brynjolfsson E, McAfee A (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (W. W. Norton, New York).Google Scholar
• Brynjolfsson E, Li D, Raymond L (2025) Generative AI at work. Quart. J. Econom. 140(2):889–942.Google Scholar
• Burton JW, Stein MK, Jensen TB (2019) A systematic review of algorithm aversion in augmented decision making. Behav. Decis. Making 33(2):220–239.Crossref, Google Scholar
• Bussone A, Stumpf S, O’Sullivan D (2015) The role of explanations on trust and reliance in clinical decision support systems. 2015 Internat. Conf. Healthcare Informatics (Dallas), 160–169.Google Scholar
• Camerer C (2000) Prospect theory in the wild. Kahneman D, Tversky A, eds. Choices, Values, and Frames (Russell Sage, New York), 288–300.Crossref, Google Scholar
• Castelo N, Bos MW, Lehmann DR (2019) Task-dependent algorithm aversion. J. Marketing Res. 56(5):809–825.Crossref, Google Scholar
• Cheng L, Chouldechova A (2023) Overcoming algorithm aversion: A comparison between process and outcome control. Proc. 2023 CHI Conf. Human Factors Computing Systems (ACM, Hamburg, Germany), 1–27.Google Scholar
• Dietvorst BJ, Bharti S (2020) People reject algorithms in uncertain decision domains because they have diminishing sensitivity to forecasting error. Psychol. Sci. 31(10):1302–1314.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Exp. Psychol. Gen. 144(1):114–126.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2018) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):115–1170.Google Scholar
• Durso FT, Hackworth CA, Truitt TR, Crutchfield J, Nikolic D, Manning CA (1998) Situation awareness as a predictor of performance en route air traffic controllers. Air Traffic Control Quart. 6(1):1–20.Crossref, Google Scholar
• Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Human Factors 37(1):32–64.Crossref, Google Scholar
• Endsley MR (1988) Situation awareness global assessment technique (SAGAT). Proc. IEEE 1988 National Aerospace Electronics Conf. (IEEE, Piscataway, NJ), 789–795.Google Scholar
• Fildes R, Goodwin P, Lawrence M, Nikolopoulos K (2009) Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. Internat. J. Forecast. 25(1):3–23.Crossref, Google Scholar
• Filiz I, Judek JR, Lorenz M, Spiwoks M (2021) Reducing algorithm aversion through experience. J. Behav. Exp. Finance 31:1–8.Crossref, Google Scholar
• Filiz I, Judek JR, Lorenz M, Spiwoks M (2023) The extent of algorithm aversion in decision-making situations with varying gravity. PLoS One 18(2):e0278751.Crossref, Google Scholar
• Fracker ML (1989) Attention allocation in situation awareness. Proc. Human Factors Soc. Annual Meeting 33(20):1396–1400.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
• Ganju KK, Atasoy H, McCullough J, Greenwood B (2020) The role of decision support systems in attenuating racial biases in healthcare delivery. Management Sci. 66(11):5171–5181.Link, Google Scholar
• Gartner (2023) Gartner experts answer the top generative AI questions for your enterprise. Accessed January 4, 2024, https://www.gartner.com/en/topics/generative-ai.Google Scholar
• Green EA, Armstrong ME, Mantua J (2023) Scientific measurement of situation awareness in operational testing. ITEA J. 44(3):1–19.Google Scholar
• Gwenuch U, Morana S, Hinz O, Kellner R, Maedche A (2024) More than a bot? The impact of disclosing human involvement on customer interactions with hybrid service agents. Inform. Systems Res. 35(3):936–955.Link, Google Scholar
• Han S, Lerner JS, Keltner D (2007) Feelings and consumer decision making: The appraisal-tendency framework. J. Consum. Psychol. 17(3):158–168.Crossref, Google Scholar
• Hannan RL, Hoffman VB, Moser DV (2005) Bonus versus penalty: Does contract frame affect employee effort? Rapoport A, Zwick R, eds. Experimental Business Research, vol. II (Springer, Dordrecht, Netherlands), 151–169.Google Scholar
• Hossain T, List JA (2012) The behavioralist visits the factor: Increasing productivity using simple framing manipulations. Management Sci. 58(12):2151–2167.Link, Google Scholar
• Howell A (2021) How to build confidence at work. Harvard Bus. Rev. (August 9), https://www.hbr.org/2021/08/how-to-build-confidence-at-work.Google Scholar
• Imas A, Sadoff S, Samek A (2017) Do people anticipate loss aversion? Management Sci. 63(5):1271–1284.Link, Google Scholar
• Iqbal K, Islam A, List JA, Nguyen V (2021) Myopic loss aversion and investment decisions: From the laboratory to the field. NBER Paper No. 28730, National Bureau of Economic Research, Washington, DC.Google Scholar
• Jussupow E, Benbasat I, Heinzl A (2020) Why are we averse towards algorithms? A comprehensive literature review on algorithm aversion. Proc. 28th Eur. Conf. Inform. Systems (ECIS2020), An Online AIS Conference, June 15–17, 2020.Google Scholar
• Jussupow E, Spohrer K, Heinzl A, Gawlitza J (2021) Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Inform. Systems Res. 32(3):713–735.Link, Google Scholar
• Kahn I, Yeshurun Y, Rotshtein P, Fried I, Ben-Bashat D, Hendler T (2002) The role of the amygdala in signaling prospective outcome of choice. Neuron 33(6):983–994.Crossref, Google Scholar
• Kahneman D, Tversky A (1979) Prospect theory: An analysis of decision under risk. Econometrica J. Econometric Soc. 47(2):263–291.Crossref, Google Scholar
• Kahneman D, Tversky A (1984) Choices, Values, and Frames (Cambridge University Press, New York).Google Scholar
• Khan WU, Shachak A, Seto E (2022) Understanding decision-making in the adoption of digital health technology: The role of behavioral economics’ prospect theory. J. Med. Internet Res. 24(2):e32714.Crossref, Google Scholar
• Kuhnen CM, Knutson B (2005) The neural basis of financial risk taking. Neuron 45(5):763–770.Crossref, Google Scholar
• Lerner JS, Tiedens LZ (2006) Portrait of the angry decision maker: How appraisal tendencies shape anger’s influence on cognition. Behavioral Decision Making 19(2):115–137.Crossref, Google Scholar
• Li Y, Li Y, Chen Q, Chang Y (2024) Humans as teammates: The signal of human-AI teaming enhances consumer acceptance of chatbots. Internat. Inform. Management 76:1–12.Google Scholar
• Lin T, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, Perona P, Ramanan D, Zitnick CL, Dollár P (2014) Microsoft COCO: Common objects in context. Preprint, submitted May 1, https://arxiv.org/abs/1405.0312.Google Scholar
• Longoni C, Bonezzi A, Morewedge CK (2019) Resistance to medical artificial intelligence. J. Consumer Res. 46(4):629–650.Crossref, Google Scholar
• Luo X, Tong S, Fang Z, Qu Z (2019) Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Sci. 38(6):937–947.Abstract, Google Scholar
• Madrian BC (2014) Applying insights from behavioral economics to policy design. Annu. Rev. Econom. 6(1):663–688.Crossref, Google Scholar
• Manktelow K, Jones J (1987) Principles from the psychology of thinking and mental models. Gardiner MM, Christie B, eds. Applying Cognitive Psychology to User-Interface Design (Wiley and Sons, Chichester, UK), 83–117.Google Scholar
• Mayer H, Yee L, Chui M, Roberts R (2025) Superagency in the workplace empowering people to unlock AI’s full potential. McKinsey & Company Report (January 28), https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work#/.Google Scholar
• Mrkva K, Johnson EJ, Gachter S, Herrmann A (2020) Moderating loss aversion: Loss aversion has moderators, but reports of its death are greatly exaggerated. J. Consum. Psychol. 30(3):407–428.Crossref, Google Scholar
• Pope DG, Schweitzer ME (2011) Is Tiger Woods loss averse? Persistent bias in the face of experience, competition, and high stakes. Amer. Econom. Rev. 101(1):129–157.Crossref, Google Scholar
• Ross SL, Zhou T (2024) Loss aversion and focal point bias: Empirical evidence from housing markets. J. Behav. Experiment Finance 42:1–14.Crossref, Google Scholar
• Schanke S, Burtch G, Ray G (2021) Estimating the impact of “humanizing” customer service chatbots. Inform. Systems Res. 32(3):736–751.Link, Google Scholar
• She S, Eimontaite I, Zhang D, Sun Y (2017) Fear, anger, and risk preference reversals: An experimental study on a Chinese sample. Frontiers Psychol. 8:1–8.Crossref, Google Scholar
• Senoner J, Netland T, Feuerriegel S (2022) Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing. Management Sci. 68(8):5704–5723.Link, Google Scholar
• Tom SM, Fox CR, Trepel C, Poldrack RA (2007) The neural basis of loss aversion in decision-making under risk. Science 315(5811):515–518.Crossref, Google Scholar
• Tong S, Jia N, Luo X, Fang Z (2021) The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management J. 42(9):1600–1631.Crossref, Google Scholar
• Tversky A, Kahneman D (1991) Loss aversion in riskless choice: A reference-dependent model. Quart. J. Econom. 106(4):1039–1061.Crossref, Google Scholar
• Vishwanath A (2009) From belief-importance to intention: The impact of framing on technology adoption. Commun. Monogr. 76(2):177–206.Crossref, Google Scholar
• Wilson JH, Daugherty PR (2018) Collaborative intelligence: Humans and AI are joining forces. Harvard Bus. Rev. 96(4):114–123.Google Scholar