Can Providing Algorithmic Performance Information Facilitate Humans’ Inventory Ordering Behaviors?

Published Online:https://doi.org/10.1287/isre.2022.0621

References

  • AI100 (2021) Gathering strength, gathering storms: The one hundred year study on Artificial Intelligence (AI100) 2021 study panel report. Accessed April 7, 2025, https://ai100.stanford.edu/gathering-strength-gathering-storms-one-hundred-year-study-artificial-intelligence-ai100-2021-study.Google Scholar
  • Akash K, Jain N, Misu T (2020) Toward adaptive trust calibration for level 2 driving automation. Proc. Internat. Conf. Multimodal Interaction (Association for Computing Machinery, New York), 538–547.Google Scholar
  • AWS (2021) From forecasting demand to ordering: An automated machine learning approach with amazon forecast to decrease stockouts, excess inventory, and costs. Accessed April 6, 2025, https://aws.amazon.com/blogs/machine-learning/from-forecasting-demand-to-ordering-an-automated-machine-learning-approach-with-amazon-forecast-to-decrease-stock-outs-excess-inventory-and-costs/.Google Scholar
  • Bai X, Marsden JR, Ross WT, Wang G (2020) A note on the impact of daily deals on local retailers’ online reputation: Mediation effects of the consumer experience. Inform. Systems Res. 31(4):1037–1492.LinkGoogle Scholar
  • Barberis NC (2013) Thirty years of prospect theory in economics: A review and assessment. J. Econom. Perspective 27(1):173–196.CrossrefGoogle Scholar
  • Bigman YE, Gray K (2018) People are averse to machines making moral decisions. Cognition 181:21–34.CrossrefGoogle Scholar
  • Brynjolfsson E, Mitchell T (2017) What can machine learning do? Workforce implications. Science 358(6370):1530–1534.CrossrefGoogle 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. 108:43–47.CrossrefGoogle Scholar
  • Burton JW, Stein M, Jensen TB (2020) A systematic review of algorithm aversion in augmented decision making. Behavioral Decision Making 33(2):220–239.CrossrefGoogle Scholar
  • Cachon GP, Olivares M (2010) Drivers of finished-goods inventory in the U.S. automobile industry. Management Sci. 56(1):202–216.LinkGoogle Scholar
  • Caplin A, Dean M (2015) Revealed preference, rational inattention, and costly information acquisition. Amer. Econom. Rev. 105(7):2183–2203.CrossrefGoogle Scholar
  • Cao S, Gomez C, Huang CM (2023) How time pressure in different phases of decision-making influences human-AI collaboration. Proc. ACM Human-Comput. Interaction 7(CSCW2):1–26.CrossrefGoogle Scholar
  • Carver CS, Scheier MF (1998) On the Self Regulation of Behavior (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Castelo N, Bos MW, Lehmann DR (2019) Task-dependent algorithm aversion. J. Marketing Res. 56(5):809–825.CrossrefGoogle Scholar
  • Costello AM, Down AK, Mehta MN (2020) Machine+ man: A field experiment on the role of discretion in augmenting AI-based lending models. J. Accounting Econom. 70(2–3):101360.CrossrefGoogle Scholar
  • Cui R, Li M, Zhang S (2022) AI and procurement. Manufacturing Service Oper. Management 24(2):691–706.LinkGoogle Scholar
  • Davis AM, Tong J (2021) Behavioral inventory management. Preprint, submitted May 18, http://dx.doi.org/10.2139/ssrn.3848040.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.CrossrefGoogle Scholar
  • Dietvorst B, Simmons JP, Massey C (2018) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.LinkGoogle Scholar
  • Donohue K, Özer Ö, Zheng Y (2020) Behavioral operations: Past, present, and future. Manufacturing Service Oper. Management 22(1):191–202.LinkGoogle Scholar
  • Feiler D, Tong J (2021) From noise to bias: Overconfidence in new product forecasting. Management Sci. 68(6):4685–4702.LinkGoogle Scholar
  • Feng X, Gao J (2020) Is optimal recommendation the best? A laboratory investigation under the newsvendor problem. Decision Support Systems 131:113251.Google Scholar
  • Finkelstein SR, Fishbach A (2012) Tell me what I did wrong: Experts seek and respond to negative feedback. J. Consumer Res. 39(1):22–38.CrossrefGoogle 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.LinkGoogle Scholar
  • Gabaix X (2019) Behavioral inattention. Bernheim BD, DellaVigna S, Laibson D, eds. Handbook of Behavioral Economics: Foundations and Applications (Elsevier, Amsterdam), 261–343.CrossrefGoogle Scholar
  • Glikson E, Woolley AW (2020) Human trust in artificial intelligence: Review of empirical research. Ann. Management Rev. 14(2):627–660.Google Scholar
  • Hayes AF (2015) An index and test of linear moderated mediation. Multivariate Behav. Res. 50(1):1–22.CrossrefGoogle Scholar
  • Heart T, Zucker A, Parmet Y, Pliskin JS, Pliskin N (2011) Investigating physicians’ compliance with drug prescription notifications. J. Assoc. Inform. Systems 12(3):235–254.Google Scholar
  • Ibrahim R, Kim S-H, Tong J (2021) Eliciting human judgment for prediction algorithms. Management Sci. 67(4):2314–2325.LinkGoogle Scholar
  • Jacobs M, He J, Pradier F, Lam B, Ahn AC, McCoy TH, Gajos KZ, et al. (2021) Designing AI for trust and collaboration in time-constrained medical decisions: A sociotechnical lens. Proc. CHI Conf. Human Factors Comput. Systems, 1–14.Google Scholar
  • Jarvenpaa SL (1989) The effect of task demands and graphical format on information processing strategies. Management Sci. 35(3):285–303.LinkGoogle Scholar
  • Jones GR, George JM (1998) The experience and evolution of trust: Implications for cooperation and teamwork. Acad. Management Rev. 23(3):531–546.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.LinkGoogle Scholar
  • Karlinsky-Shichor Y, Netzer O (2024) Automating the B2B salesperson pricing decisions: A human-machine hybrid approach. Marketing Sci. 43(1):138–157.LinkGoogle Scholar
  • Kesavan S, Kushwaha T (2020) Field experiment on the profit implications of merchants’ discretionary power to override data-driven decision-making tools. Management Sci. 66(11):5182–5190.LinkGoogle Scholar
  • Koo M, Fishbach A (2008) Dynamics of self regulation: How (un)accomplished goal actions affect motivation. J. Personality Soc. Psych. 194(2):83–95.Google Scholar
  • Kremer M, Moritz B, Siemsen E (2011) Demand forecasting behavior: System neglect and change detection. Management Sci. 57(10):1827–1843.Google Scholar
  • Liang H, Xue Y (2022) Save face or save life: Physicians’ dilemma in using clinical decision support systems. Inform. Systems Res. 33(2):737–758.Google Scholar
  • Lebovitz S, Levina N, Lifshitz-Assaf H (2021) Is AI ground truth really true? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quart. 45(3):1501–1525.CrossrefGoogle Scholar
  • Locke EA, Latham GP (1990) A Theory of Goal Setting and Task Performance (Prentice Hall, Upper Saddle River, NJ).Google Scholar
  • Logg JM, Minson JA, Moore DA (2019) Algorithm appreciation: People prefer algorithmic to human judgment. Organ. Behav. Human Decision Processes 151:90–103.CrossrefGoogle Scholar
  • Longoni C, Bonezzi A, Morewedge CK (2019) Resistance to medical artificial intelligence. J. Consumer Res. 46(4):629–650.CrossrefGoogle Scholar
  • Luo X, Qin MS, Fang Z, Qu Z (2021) Artificial intelligence coaches for sales agents: Caveats and solutions. J. Marketing 85(2):14–32.CrossrefGoogle Scholar
  • Luo X, Tong S, Lin Z, Zhang C (2021) The impact of platform protection insurance on buyers and sellers in the sharing economy: A natural experiment. J. Marketing 85(2):50–69.CrossrefGoogle Scholar
  • Martelaro N, Nneji VC, Ju W, Hinds P (2016) Tell me more designing HRI to encourage more trust, disclosure, and companionship. Proc. 11th ACM/IEEE Internat. Conf. Human-Robot Interaction (IEEE Press, Piscataway, NJ), 181–188.Google Scholar
  • McKinsey (2020) Global survey: The state of AI in 2020. Accessed April 7, 2025, https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020#.Google Scholar
  • Olivares M, Cachon GP (2009) Competing retailers and inventory: An empirical investigation of General Motors’ dealerships in isolated US markets. Management Sci. 55(9):1586–1604.Google Scholar
  • Perera HN, Fahimnia B, Tokar T (2020) Inventory and ordering decisions: A systematic review on research driven through behavioral experiments. Internat. J. Oper. Production Management 40(7/8):997–1039.CrossrefGoogle Scholar
  • Phillips R, Şimşek AS, Van Ryzin G (2015) The effectiveness of field price discretion: Empirical evidence from auto lending. Management Sci. 61(8):1741–1759.LinkGoogle Scholar
  • Rai A (2020) Explainable AI: From black box to glass box. J. Acad. Marketing Sci. 48:137–141.CrossrefGoogle Scholar
  • Rajagopalan S (2013) Impact of variety and distribution system characteristics on inventory levels at U.S. retailers. Manufacturing Service Oper. Management 15(2):191–204.LinkGoogle Scholar
  • Rice S, Keller D (2009) Automation reliance under time pressure. Cognitive Tech. 14(1):36–44.Google Scholar
  • Schweitzer ME, Cachon GP (2000) Decision bias in the newsvendor problem with a known demand distribution: Experimental evidence. Management Sci. 46(3):404–420.LinkGoogle Scholar
  • Sebo S, Traeger M, Jung M, Scassellati B (2018) The ripple effects of vulnerability: The effects of a robot’s vulnerable behavior on trust in human-robot teams. Proc. 2018 ACM/IEEE Internat. Conf. Human-Robot Interaction (Association for Computing Machinery, New York), 178–186.Google Scholar
  • Simon HA (1978) Information-processing theory of human problem solving. Handbook of Learning and Cognitive Processes, 271–295.Google Scholar
  • Solyalı O, Cordeau JF, Laporte G (2016) The impact of modeling on robust inventory management under demand uncertainty. Management Sci. 62(4):1188–1201.LinkGoogle Scholar
  • Sun J, Zhang D, 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.LinkGoogle Scholar
  • Traeger ML, Strohkorb Sebo S, Jung M, Scassellati B, Christakis NA (2020) Vulnerable robots positively shape human conversational dynamics in a human–robot team. Proc. Natl. Acad. Sci. USA 117(12):6370–6375.CrossrefGoogle Scholar
  • Tunstall C, Rice S, Mehta R, Dunbar V, Oyman K (2014) Time pressure has limited benefits for human-automation performance. Proc. Human Factors Ergonomics Soc. Annual Meeting 58(1):1043–1046.Google Scholar
  • Van Donselaar KH, Gaur V, Van Woensel T, Broekmeulen RA, Fransoo JC (2010) Ordering behavior in retail stores and implications for automated replenishment. Management Sci. 56(5):766–784.Google Scholar
  • Venkatesh V, Thong JY, Chan FK, Hu PJ (2016) Managing citizens’ uncertainty in e-government services: The mediating and moderating roles of transparency and trust. Inform. Systems Res. 27(1):87–111.LinkGoogle Scholar
  • Wall Street Journal (2021) Retail set to overtake banking in AI spending. Wall Street Journal (September 7), https://www.wsj.com/articles/retail-set-to-overtake-banking-in-ai-spending-11631007001.Google Scholar
  • Wilson HJ, Daugherty PR (2018) Collaborative intelligence: Humans and AI are joining forces. Harvard Bus. Rev. 96(4):114–123.Google Scholar
  • Xu J, Benbasat I, Cenfetelli RT (2014) The nature and consequences of trade-off transparency in the context of recommendation agents. MIS Quart. 38(2):379–406.CrossrefGoogle Scholar
  • Yin M, Wortman Vaughan J, Wallach H (2019) Understanding the effect of accuracy on trust in machine learning models. Proc. CHI Conf. Human Factors Comput. Systems, 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.CrossrefGoogle Scholar
  • Zhao H, Xu L, Siemsen E (2021) Inventory sharing and demand-side underweighting. Manufacturing Service Oper. Management 23(5):1217–1236.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.