Human-Centered Artificial Intelligence: A Field Experiment

References

• Abadie A, Athey S, Imbens GW, Wooldridge JM (2023) When should you adjust standard errors for clustering? Quart. J. Econom. 138(1):1–35.Crossref, Google Scholar
• Agrawal A, Gans J, Goldfarb A (2018) Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, Boston).Google Scholar
• Ahearne M, Mackenzie SB, Podsakoff PM, Mathieu JE, Lam SK (2010) The role of consensus in sales team performance. J. Marketing Res. 47(3):458–469.Crossref, Google Scholar
• Allen RT, 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
• Allinson CW, Hayes J (1996) The cognitive style index: A measure of intuition-analysis for organizational research. J. Management Stud. 33(1):119–135.Crossref, Google Scholar
• Amabile TM, Patterson C, Mueller J, Wojcik T, Odomirok PW, Marsh M, Kramer SJ (2001) Academic-practitioner collaboration in management research: A case of cross-profession collaboration. Acad. Management J. 44(2):418–431.Crossref, Google Scholar
• Angrist JD, Pischke JS (2009) Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Ångström RC, Björn M, Dahlander L, Mähring M, Wallin MW (2023) Getting AI implementation right: Insights from a global survey. Calif. Management Rev. 66(1):5–22.Crossref, Google Scholar
• Arellano M (1993) On the testing of correlated effects with panel data. J. Econom. 59(1):87–97.Crossref, Google Scholar
• Armstrong SJ, Cools E, Sadler-Smith E (2012) Role of cognitive styles in business and management: Reviewing 40 years of research. Internat. J. Management Rev. 14(3):238–262.Crossref, Google Scholar
• Athey S, Imbens GW (2017) The econometrics of randomized experiments. Banerjee A, Duflo E, eds. Handbook of Economic Field Experiments, vol. 1 (North-Holland, Amsterdam), 73–140.Crossref, Google Scholar
• Baer M, Oldham GR, Cummings A (2003) Rewarding creativity: When does it really matter? Leadership Quart. 14(4–5):569–586.Crossref, Google Scholar
• Bagozzi RP, Foxall GR (1995) Construct validity and generalizability of the Kirton Adaption-Innovation Inventory. Eur. J. Personality 9(3):185–206.Crossref, Google Scholar
• Baron RM, Kenny DA (1986) The moderator-mediator variable distinction in social Psychological research: Conceptual, strategic, and statistical considerations. J. Personality Soc. Psych. 51(6):1173–1182.Crossref, Google Scholar
• Baum CF, Wiggins V, Stillman S, Schaffer ME, Windmeijer F (1999) OVERID: Stata module to conduct postestimation tests of overidentification. (S396902). https://ideas.repec.org/c/boc/bocode/s396802.html.Google Scholar
• Benbya H, Davenport TH, Pachidi S (2020) Artificial intelligence in organizations: Current state and future opportunities. MIS Quart. Executive 19(4):ix–xxi.Google Scholar
• Berry WD, Feldman S (1985) Multiple Regression in Practice (Sage, Thousand Oaks, CA).Crossref, Google Scholar
• Boyacı T, Canyakmaz C, de Véricourt F (2024) Human and machine: The impact of machine input on decision making under cognitive limitations. Management Sci. 70(2):1258–1275.Link, Google Scholar
• Brown SP, Peterson RA (1994) The effect of effort on sales performance and job satisfaction. J. Marketing 58(2):70–80.Crossref, Google Scholar
• Brynjolfsson E, Mitchell T (2017) What can machine learning do? Workforce implications. Science 358(6370):1530–1534.Crossref, Google Scholar
• Cable DM, Edwards JR (2004) Complementary and supplementary fit: A theoretical and empirical integration. J. Appl. Psych. 89(5):822–834.Crossref, Google Scholar
• Capel T, Brereton M (2023) What is human-centered about human-centered AI? A map of the research landscape. Proc. 2023 CHI Conf. Hum. Factors Comput. Syst. (Association for Computing Machinery, New York).Google Scholar
• Carnabuci G, Diószegi B (2015) Social networks, cognitive style, and innovative performance: A contingency perspective. Acad. Management J. 58(3):881–905.Crossref, Google Scholar
• Chalmers D, MacKenzie NG, Carter S (2021) Artificial intelligence and entrepreneurship: Implications for venture creation in the fourth industrial revolution. Entrep. Theory Pract. 45(5):1028–1053.Crossref, Google Scholar
• Chen L, Hsieh JJPA, Rai A (2022) How does intelligent system knowledge empowerment yield payoffs? Uncovering the adaptation mechanisms and contingency role of work experience. Inform. Systems Res. 33(3):1042–1071.Link, Google Scholar
• Choudhary V, Marchetti A, Shrestha YR, Puranam P (2023) Human-AI ensembles: When can they work? J. Management 51(2):536–569.Google Scholar
• Choudhury P, Starr E, Agarwal R (2020) Machine learning and human capital complementarities: Experimental evidence on bias mitigation. Strategic Management J. 41(8):1381–1411.Crossref, Google Scholar
• Chung DJ, Kim B, Park BG (2019) How do sales efforts pay off? Dynamic panel data analysis in the Nerlove–Arrow framework. Management Sci. 65(11):5197–5218.Link, Google Scholar
• Clapp RG (1993) Stability of cognitive style in adults and some implications, a longitudinal study of the Kirton Adaption-Innovation Inventory. Psych. Rep. 73(3):1235–1245.Crossref, Google Scholar
• Cools E, Van Den Broeck H (2007) Development and validation of the cognitive style indicator. J. Psych. 141(4):359–387.Crossref, Google Scholar
• de Véricourt F, Gurkan H (2023) Is your machine better than you? You may never know. Management Sci., ePub ahead of print May 25, https://doi.org/10.1287/mnsc.2023.4791.Link, Google Scholar
• Dennis AR, Lakhiwal A, Sachdeva A (2023) AI agents as team members: Effects on satisfaction, conflict, trustworthiness, and willingness to work with. J. Management Inform. Systems 40(2):307–337.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):983–1476.Google Scholar
• Dylst P, Vulto A, Simoens S (2011) Tendering for outpatient prescription pharmaceuticals: What can be learned from current practices in Europe? Health Policy 101(2):146–152.Crossref, Google Scholar
• Fortnow L (2013) The Golden Ticket: P, NP, and the Search for the Impossible (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Foxall GR (1995) Cognitive styles of consumer initiators. Technovation 15(5):269–288.Crossref, Google Scholar
• Frick TW, Belo R, Telang R (2023) Incentive misalignments in programmatic advertising: Evidence from a randomized field experiment. Management Sci. 69(3):1665–1686.Link, 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
• Gaessler F, Piezunka H (2023) Training with AI: Evidence from chess computers. Strategic Management J. 44(11):2724–2750.Crossref, Google Scholar
• Green GI, Hughes CT (1986) Effects of decision support systems training and cognitive style on decision process attributes. J. Management Inform. Systems 3(2):83–93.Crossref, Google Scholar
• Gujarati D (1995) Basic Econometrics 3rd ed. (McGraw-Hill, New York).Google Scholar
• Hair JF, Anderson RE, Tatham RL, Black WC (1998) Multivariate Data Analysis (Prentice Hall, Upper Saddle River, NJ).Google Scholar
• Hansen LP (1982) Large sample properties of generalized method of moments estimators. Econometrica 50(4):1029–1054.Crossref, Google Scholar
• Hausman JA (1978) Specification tests in econometrics. Econometrica 46(6):1251–1271.Crossref, Google Scholar
• Hayes J, Allinson CW (1994) Cognitive style and its relevance for management practice. British J. Management 5(1):53–71.Crossref, Google Scholar
• Hillebrand L, Raisch S, Schad J (2025) Managing with artificial intelligence: An integrative framework. Acad. Management Ann. 19(1):343–375.Google Scholar
• Hunt RG, Krzystofiak FJ, Meindl JR, Yousry AM (1989) Cognitive style and decision making. Organ. Behav. Human Decision Process. 44(3):436–453.Crossref, Google Scholar
• Ibrahim R, Kim SH, Tong J (2021) Eliciting human judgment for prediction algorithms. Management Sci. 67(4):2314–2335.Link, Google Scholar
• Keller RT, Holland WE (1978) A cross-validation study of the Kirton Adaption-Innovation Inventory in three research and development organizations. Appl. Psych. Measurement 2(4):563–570.Crossref, Google Scholar
• Kemp A (2024) Competitive advantage through artificial intelligence: Toward a theory of situated AI. Acad. Management Rev. 49(3):618–635.Crossref, Google 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.Link, Google Scholar
• Kim H, Glaeser EL, Hillis A, Kominers SD, Luca M (2024) Decision authority and the returns to algorithms. Strategic Management J. 45(4):619–648.Crossref, Google Scholar
• Kirton M (1976) Adaptors and innovators: A description and measure. J. Appl. Psych. 61(5):622–629.Crossref, Google Scholar
• Kirton M, Bailey A, Glendinning W (1991) Adaptors and innovators: Preference for educational procedures. J. Psych. 125(4):445–455.Crossref, Google Scholar
• Krakowski S (2025) Human-AI agency in the age of generative AI. Inform. Organ. 35(1):100560.Crossref, Google Scholar
• Krakowski S, Luger J, Raisch S (2023) Artificial intelligence and the changing sources of competitive advantage. Strategic Management J. 44(6):1425–1452.Crossref, 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
• Li M, Armstrong SJ (2015) The relationship between Kolb’s experiential learning styles and Big Five personality traits in international managers. Personality Individual Differences 86:422–426.Crossref, Google Scholar
• Likert R (1932) A technique for the measurement of attitudes. Arch. Psych. 140:1–55.Google Scholar
• Liu M, Tang X, Xia S, Zhang S, Zhu Y, Meng Q (2023) Algorithm aversion: Evidence from ridesharing drivers. Management Sci., ePub ahead of print October 3, https://doi.org/10.1287/mnsc.2022.02475.Google Scholar
• Logg JM, Minson JA, Moore DA (2019) Algorithm appreciation: People prefer algorithmic to human judgment. Organ. Behav. Human Decision Process. 151:90–103.Crossref, Google Scholar
• McKenzie D (2012) Beyond baseline and follow-up: The case for more T in experiments. J. Dev. Econom. 99(2):210–221.Crossref, Google Scholar
• Mintzberg H, Raisinghani D, Théorêt A (1976) The structure of “unstructured” decision processes. Admin. Sci. Quart. 21(2):246–275.Crossref, Google Scholar
• Mitchell TM (1997) Machine Learning (McGraw-Hill, Boston).Google Scholar
• Murray A, Rhymer J, Sirmon DG (2021) Humans and technology: Forms of conjoined agency in organizations. Acad. Management Rev. 46(3):552–571.Crossref, Google Scholar
• Neter J, Kutner MH, Nachtsheim CJ, Wasserman W (1990) Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental Designs, 3rd ed. (McGraw-Hill Irwin, Boston).Google Scholar
• Ostroff C (2012) Person-environment fit in organizational settings. Kozlowski SWJ, ed. The Oxford Handbook of Organizational Psychology, vol. 1 (Oxford University Press, New York), 373–408.Crossref, Google Scholar
• Puranam P (2021) Human-AI collaborative decision-making as an organization design problem. J. Organ. Design 10(2):75–80.Crossref, Google Scholar
• Rai A, Constantinides P, Sarker S (2019) Next-generation digital platforms: Toward human-AI hybrids. MIS Quart. 43(1):iii–ix.Crossref, Google Scholar
• Raisch S, Fomina K (2025) Combining human and artificial intelligence: Hybrid problem-solving in organizations. Acad. Management Rev. 50(2):441–464.Crossref, 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
• Sargan JD (1958) The estimation of economic relationships using instrumental variables. Econometrica 26(3):393–415.Crossref, Google Scholar
• Scott SG, Bruce RA (1994) Determinants of innovative behavior: A path model of individual innovation in the workplace. Acad. Management J. 37(3):580–607.Crossref, Google Scholar
• Shneiderman B (2022) Human-Centered AI (Oxford University Press, Oxford).Crossref, Google Scholar
• Shrestha YR, Ben-Menahem SM, von Krogh G (2019) Organizational decision-making structures in the age of artificial intelligence. California Management Rev. 61(4):66–83.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
• Tarafdar M, Page X, Marabelli M (2023) Algorithms as co-workers: Human algorithm role interactions in algorithmic work. Inform. Systems J. 33(2):232–267.Crossref, Google Scholar
• Te’eni D, Yahav I, Zagalsky A, Schwartz D, Silverman G, Cohen D, Mann Y, Lewinsky D (2023) Reciprocal human-machine learning: A theory and an instantiation for the case of message classification. Management Sci., ePub ahead of print November 14, https://doi.org/10.1287/mnsc.2022.03518.Link, Google Scholar
• Townsend DM, Hunt RA, Rady J, Manocha P, Hyeong Jin J (2025) Are the futures computable? Knightian uncertainty and artificial intelligence. Acad. Management Rev. 50(2):415–440.Crossref, Google Scholar
• Vomberg A, Schauerte N, Krakowski S, Ingram Bogusz C, Gijsenberg MJ, Bleier A (2023) The cold-start problem in nascent AI strategy: Kickstarting data network effects. J. Bus. Res. 168:114236.Crossref, 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
• Wing C, Simon K, Bello-Gomez RA (2018) Designing difference in difference studies: Best practices for public health policy research. Annual Rev. Public Health 39(1):453–469.Crossref, Google Scholar