Algorithm Reliance: Fast and Slow
References
- (2018) Behavioral foundations of queueing systems. Donohue K, Katok E, Leider S, eds. Handbook of Behavioral Operations (John Wiley & Sons, Inc., Hoboken, NJ), 325–366.Crossref, Google Scholar
- (2025) Improving human-algorithm collaboration: Causes and mitigation of over- and under-adherence. Management Sci., ePub ahead of print March 24, https://doi.org/10.1287/mnsc.2022.03850.Google Scholar
- (2024) Improving human sequential decision-making with reinforcement learning. Preprint, submitted March 19, https://arxiv.org/abs/2108.08454.Google Scholar
- (2017) Early task initiation and other load-adaptive mechanisms in the emergency department. Management Sci. 63(11):3531–3551.Link, Google Scholar
- (2022) Behavioral externalities of process automation. Preprint, submitted November 17, https://dx.doi.org/10.2139/ssrn.4295527.Google Scholar
- (2024) Human and machine: The impact of machine input on decision making under cognitive limitations. Management Sci. 70(2):1258–1275.Link, Google Scholar
- (2018) The parts of customer service that should never be automated. Harvard Bus. Rev. (February), https://hbr.org/2018/02/the-parts-of-customer-service-that-should-never-be-automated.Google Scholar
- (2020) The impact of forced intervention on ai adoption. Preprint, submitted November 10, https://dx.doi.org/10.2139/ssrn.3640862.Google Scholar
- (2023) Believing in analytics: Managers’ adherence to price recommendations from a DSS. Manufacturing Service Oper. Management 25(2):524–542.Link, Google Scholar
- (2019) Task-dependent algorithm aversion. J. Marketing Res. 56(5):809–825.Crossref, Google Scholar
- (2023) Incorporating artificial intelligence into healthcare workflows: Models and insights. Tutorials Opera. Res. Adv. Frontiers OR/MS: From Methodologies Appl. (INFORMS, Catonsville, NY), 133–155.Google Scholar
- (2019) Load effect on service times. Eur. J. Oper. Res. 279(3):673–686.Crossref, Google Scholar
- (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych. General 144(1):114–126.Crossref, Google Scholar
- (2018) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.Link, Google Scholar
- (2018) Impact of behavioral factors on performance of multi-server queueing systems. Production Oper. Management 27(8):1553–1573.Crossref, Google Scholar
- (2020) z-Tree unleashed: A novel client-integrating architecture for conducting z-tree experiments over the internet. J. Behav. Experiment. Finance 28:100400.Crossref, Google Scholar
- (2024) Reup education: Can AI help learners return to college? Harvard Bus. School (June), https://www.hbs.edu/faculty/Pages/item.aspx?num=64835.Google Scholar
- (2021) Reducing algorithm aversion through experience. J. Behav. Experiment. Finance 31:100524.Crossref, Google Scholar
- (2007) z-tree: Zurich toolbox for ready-made economic experiments. Experiment. Econom. 10:171–178.Crossref, Google Scholar
- (2010) Service times in call centers: Agent heterogeneity and learning with some operational consequences. Borrowing Strength: Theory Powering Applications: A Festschrift for Lawrence D. Brown, vol. 6 (Institute of Mathematical Statistics, Beachwood, OH), 99–124.Crossref, Google Scholar
- (2023) On the fairness of machine-assisted human decisions: Theory and experimental evidence. Preprint, submitted September 24, https://arxiv.org/abs/2110.15310.Google Scholar
- (2012) Automation bias: A systematic review of frequency, effect mediators, and mitigators. J. Amer. Medical Inform. Assoc. 19(1):121–127.Crossref, Google Scholar
- (2004) The Online Recruitment System ORSEE 2.0: A guide for the organization of experiments in economics. Working paper, University of Cologne, Department of Economics, Cologne, Germany.Google Scholar
- (2023) The gatekeeper’s dilemma: “When should I transfer this customer?” Oper. Res. 71(3):843–859.Link, Google Scholar
- (2018) Discretion in hiring. Quart. J. Econom. 133(2):765–800.Crossref, Google Scholar
- (2007) Operations systems with discretionary task completion. Management Sci. 53(1):61–77.Link, Google Scholar
- (2018) Artificial intelligence in service. J. Service Res. 21(2):155–172.Crossref, Google Scholar
- (2018) Discretionary task ordering: Queue management in radiological services. Management Sci. 64(9):4389–4407.Link, Google Scholar
- (2021) Eliciting human judgment for prediction algorithms. Management Sci. 67(4):2314–2325.Link, Google Scholar
- (2021) Towards a better understanding on mitigating algorithm aversion in forecasting: An experimental study. J. Management Control 32(4):495–516.Crossref, Google Scholar
- (2020) Why are we averse towards algorithms? A comprehensive literature review on algorithm aversion. Proc. 28th Eur. Conf. Inform. Systems (AIS, Atlanta), 4171–4186.Google Scholar
- (2025) AI chatbots in customer service: Adoption hurdles and simple remedies. Preprint, submitted April 8, https://arxiv.org/abs/2504.06145.Google Scholar
- (2021) When will workers follow an algorithm? A field experiment with a retail business. Management Sci. 67(3):1670–1695.Link, Google Scholar
- (2009) Impact of workload on service time and patient safety: An econometric analysis of hospital operations. Management Sci. 55(9):1486–1498.Link, Google Scholar
- (2012) An econometric analysis of patient flows in the cardiac intensive care unit. Manufacturing Service Oper. Management 14(1):50–65.Link, Google Scholar
- (2020) Field experiment on the profit implications of merchants’ discretionary power to override data-driven decision-making tools. Management Sci. 66(11):5182–5190.Link, Google Scholar
- (2023) Mismanaging diagnostic accuracy under congestion. Oper. Res. 71(3):895–916.Link, Google Scholar
- (2022) Human-computer interactions in demand forecasting and labor scheduling decisions. Preprint, submitted December, https://dx.doi.org/10.2139/ssrn.4296344.Google Scholar
- (2022) The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice. Production Oper. Management 31(9):3419–3434.Crossref, Google Scholar
- (2021) Running online experiments using web-conferencing software. J. Econom. Sci. Assoc. 7(2):167–183.Crossref, Google Scholar
- (2021) Revisiting the cause of algorithm aversion: Algorithm feedback asymmetry in the field and lab. Preprint, submitted August 25, https://dx.doi.org/10.2139/ssrn.3891832.Google Scholar
- (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
- (2016) Five keys to understanding algorithmic business. Gartner (April 15), https://www.gartner.com/smarterwithgartner/five-keys-to-understanding-algorithmic-business.Google Scholar
- (2001) Organizational differences in rates of learning: Evidence from the adoption of minimally invasive cardiac surgery. Management Sci. 47(6):752–768.Link, Google Scholar
- (2001) Supporting decision making and action selection under time pressure and uncertainty: The case of in-flight icing. Human Factors 43(4):573–583.Crossref, Google Scholar
- (1998) Modeling and worker motivation in JIT production systems. Management Sci. 44(12-part-1):1595–1607.Link, Google Scholar
- (2018) Humans are not machines: The behavioral impact of queueing design on service time. Management Sci. 64(1):453–473.Link, Google Scholar
- (2018) Public attitudes toward computer algorithms. Pew Res. Center (November 16), https://www.pewresearch.org/internet/2018/11/16/public-attitudes-toward-computer-algorithms/.Google Scholar
- (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
- (2014) When does the devil make work? An empirical study of the impact of workload on worker productivity. Management Sci. 60(6):1574–1593.Link, Google Scholar
- (2010) Ordering behavior in retail stores and implications for automated replenishment. Management Sci. 56(5):766–784.Link, Google Scholar
- (1977) Speed-accuracy tradeoff and information processing dynamics. Acta Psych. 41(1):67–85.Crossref, Google Scholar
- (2019) Making sense of recommendations. J. Behav. Decision Making 32(4):403–414.Crossref, Google Scholar

