Human-Algorithm Collaboration in Gig Work: The Role of Experience, Skill Level, and Task Complexity

References

• Acemoglu D (2002) Technical change, inequality, and the labor market. J. Econom. Lit. 40(1):7–72.Crossref, Google Scholar
• Agarwal N, Moehring A, Rajpurkar P, Salz T (2023) Combining human expertise with artificial intelligence: Experimental evidence from radiology. NBER Working Paper No. 31422, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Allen R, Choudhury P (2022) Algorithm-augmented work and domain experience: The countervailing forces of ability and aversion. Organ. Sci. 33(1):149–169.Link, Google Scholar
• Aral S, Brynjolfsson E, Van Alstyne M (2012) Information, technology, and information worker productivity. Inform. Systems Res. 23(3-part-2):849–867.Link, Google Scholar
• Autor DH (2015) Why are there still so many jobs? The history and future of workplace automation. J. Econom. Perspect. 29(3):3–30.Crossref, Google Scholar
• Autor DH, Levy F, Murnane RJ (2003) The skill content of recent technological change: An empirical exploration. Quart. J. Econom. 118(4):1279–1333.Crossref, Google Scholar
• Ba S, He S, Lee S-Y (2022) Mobile app adoption and its differential impact on consumer shopping behavior. Production Oper. Management 31(2):764–780.Crossref, Google Scholar
• Bai B, Dai H, Zhang DJ, Zhang F, Hu H (2022) The impacts of algorithmic work assignment on fairness perceptions and productivity: Evidence from field experiments. Manufacturing Service Oper. Management 24(6):3060–3078.Link, Google Scholar
• Bapna R, Gupta A, Ray G, Singh S (2023) Single-sourcing vs. multisourcing: An empirical analysis of large information technology outsourcing arrangements. Inform. Systems Res. 34(3):1109–1130.Link, Google Scholar
• Bell JJ, Pescher C, Tellis GJ, Füller J (2024) Can AI help in ideation? A theory-based model for idea screening in crowdsourcing contests. Marketing Sci. 43(1):54–72.Link, Google Scholar
• Berente N, Gu B, Recker J, Santhanam R (2021) Managing artificial intelligence. MIS Quart. 45(3):1433–1450.Crossref, Google Scholar
• Bhandari A, Scheller-Wolf A, Harchol-Balter M (2008) An exact and efficient algorithm for the constrained dynamic operator staffing problem for call centers. Management Sci. 54(2):339–353.Link, Google Scholar
• Brynjolfsson E, Hitt L (1996) Paradox lost? Firm-level evidence on the returns to information systems spending. Management Sci. 42(4):541–558.Link, Google Scholar
• Burbano VC, Chiles B (2022) Mitigating gig and remote worker misconduct: Evidence from a real effort experiment. Organ. Sci. 33(4):1273–1299.Link, Google Scholar
• Burtch G, Carnahan S, Greenwood BN (2018) Can you gig it? An empirical examination of the gig economy and entrepreneurial activity. Management Sci. 64(12):5497–5520.Link, Google Scholar
• Card D, DiNardo JE (2002) Skill-biased technological change and rising wage inequality: Some problems and puzzles. J. Labor Econom. 20(4):733–783.Crossref, Google Scholar
• Chen Z, Chan J (2024) Large language model in creative work: The role of collaboration modality and user expertise. Management Sci. 70(12):9101–9117.Link, Google Scholar
• Choudhary V, Shunko M, Netessine S (2021) Does immediate feedback make you not try as hard? A study on automotive telematics. Manufacturing Service Oper. Management 23(4):835–853.Link, Google Scholar
• Dai T, Singh S (2020) Conspicuous by its absence: Diagnostic expert testing under uncertainty. Marketing Sci. 39(3):540–563.Link, Google Scholar
• Dauod H, Won D (2022) Real-time order picking planning framework for warehouses and distribution centres. Internat. J. Production Res. 60(18):5468–5487.Crossref, 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.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):1155–1170.Link, 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
• Ge R, Zheng Z, Tian X, Liao L (2021) Human–robot interaction: When investors adjust the usage of robo-advisors in peer-to-peer lending. Inform. Systems Res. 32(3):774–785.Link, Google Scholar
• Girotra K, Meincke L, Terwiesch C, Ulrich KT (2024) Using large language models for idea generation in innovation. Preprint, submitted September 7, https://doi.org/10.2139/ssrn.4526071.Google Scholar
• Grover V, Zhan X, Sun H, Jiang D (2025) Fashionable consumer technology, IT fashion, and consumer behavior. Inform. Systems Res. 36(3):1293–1313.Link, Google Scholar
• Gu GY (2024) Technology and disintermediation in online marketplaces. Management Sci. 70(11):7868–7891.Link, Google Scholar
• Haidt J, Allen N (2020) Scrutinizing the effects of digital technology on mental health. Nature 578(7794):226–227.Crossref, Google Scholar
• Hitt LM, Brynjolfsson E (1996) Productivity, business profitability, and consumer surplus: Three different measures of information technology value. MIS Quart. 20(2):121–142.Crossref, Google Scholar
• Hong Y, Pavlou PA (2017) On buyer selection of service providers in online outsourcing platforms for IT services. Inform. Systems Res. 28(3):547–562.Link, Google Scholar
• Huang N, Burtch G, Hong Y, Pavlou PA (2020) Unemployment and worker participation in the gig economy: Evidence from an online labor market. Inform. Systems Res. 31(2):431–448.Link, 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
• Kim DY, Knight B, Mitrofanov D (2025) Nudging customers to select high stock delivery windows via information sharing. Preprint, submitted March 5, https://doi.org/10.2139/ssrn.5166995.Google Scholar
• Kim Y, Knight B, Mitrofanov D, Xu Y (2024) Algorithm-enabled decision support and worker learning: Evidence from a large-scale field experiment. Preprint, submitted October 4, https://doi.org/10.2139/ssrn.4976809.Google Scholar
• Knight B, Mitrofanov D (2022) Why you should warn customers when you’re running low on stock. Harvard Bus. Rev. (September 5), https://hbr.org/2022/09/why-you-should-warn-customers-when-youre-running-low-on-stock.Google Scholar
• Knight B, Mitrofanov D (2025) Disclosing low product availability: An online platform’s strategy for mitigating stockout risk. Management Sci., ePub ahead of print June 23, https://doi.org/10.1287/mnsc.2022.01808.Link, Google Scholar
• Kudyba S, Diwan R (2002) Increasing returns to information technology. Inform. Systems Res. 13(1):104–111.Link, 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
• Leung E, Paolacci G, Puntoni S (2018) Man versus machine: Resisting automation in identity-based consumer behavior. J. Marketing Res. 55(6):818–831.Crossref, Google Scholar
• Li C, Wang H, Jiang S, Gu B (2024) The effect of AI-enabled credit scoring on financial inclusion: Evidence from an underserved population of over one million. MIS Quart. 48(4):1803–1834.Crossref, Google Scholar
• Liang C, Peng J, Hong Y, Gu B (2023) The hidden costs and benefits of monitoring in the gig economy. Inform. Systems Res. 34(1):297–318.Link, Google Scholar
• Liu M, Tang X, Xia S, Zhang S, Zhu Y, Meng Q (2026) Algorithm aversion: Evidence from ridesharing drivers. Management Sci. 72(1):193–203.Link, Google Scholar
• Löffler M, Boysen N, Schneider M (2022) Picker routing in AGV-assisted order picking systems. INFORMS J. Comput. 34(1):440–462.Link, Google Scholar
• Lou B, Wu L (2021) AI on drugs: Can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-pharma firms. MIS Quart. 45(3):1451–1482.Crossref, Google Scholar
• McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, et al. (2020) International evaluation of an AI system for breast cancer screening. Nature 577(7788):89–94.Crossref, Google Scholar
• Newman DT, Fast NJ, Harmon DJ (2020) When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions. Organ. Behav. Human Decision Processes 160:149–167.Crossref, Google Scholar
• Ozdemir Z, Barron J, Bandyopadhyay S (2011) An analysis of the adoption of digital health records under switching costs. Inform. Systems Res. 22(3):491–503.Link, Google Scholar
• Pang K-W, Chan H-L (2017) Data mining-based algorithm for storage location assignment in a randomised warehouse. Internat. J. Production Res. 55(14):4035–4052.Crossref, Google Scholar
• Parasurama P, Ipeirotis P (2023) Hiring with algorithmic fairness constraints: Theory and empirics. Working paper, New York University, New York.Google Scholar
• Raisch S, Krakowski S (2021) Artificial intelligence and management: The automation–augmentation paradox. Acad. Management Rev. 46(1):192–210.Crossref, Google Scholar
• Schiffer M, Boysen N, Klein PS, Laporte G, Pavone M (2022) Optimal picking policies in e-commerce warehouses. Management Sci. 68(10):7497–7517.Link, Google Scholar
• Sodiya EO, Umoga UJ, Oladipupo Amoo O, Atadoga A (2024) AI-driven warehouse automation: A comprehensive review of systems. GSC Advanced Res. Rev. 18(2):272–282.Crossref, Google Scholar
• Sturm T, Gerlach JP, Pumplun L, Mesbah N, Peters F, Tauchert C, Nan N, Buxmann P (2021) Coordinating human and machine learning for effective organizational learning. MIS Quart. 45(3):1581–1602.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
• Tambe P, Hitt LM (2012) The productivity of information technology investments: New evidence from it labor data. Inform. Systems Res. 23(3-part-1):599–617.Link, Google Scholar
• Wang W, Gao G, Agarwal R (2024) Friend or foe? Teaming between artificial intelligence and workers with variation in experience. Management Sci. 70(9):5753–5775.Abstract, Google Scholar
• Wiles E, Horton JJ (2024) More, but worse: The impact of AI writing assistance on the supply and quality of job posts. Working paper, Boston University, Boston.Google Scholar
• Wiles E, Munyikwa Z, Horton J (2025) Algorithmic writing assistance on jobseekers’ resumes increases hires. Management Sci. 71(12):10144–10164.Link, Google Scholar