Mission Driven and Data Averse: How Empathy Fosters Resistance to Algorithms and Hard Data

Published Online:https://doi.org/10.1287/mnsc.2024.07098

References

  • Abelson B, Varshney KR, Sun J (2014) Targeting direct cash transfers to the extremely poor. Proc. 20th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1563–1572.Google Scholar
  • Ahn SK, Choi J, Thomas T (2023) Going for broke to help the cause: When does a non-profit mission lead to riskier investment decisions? Working paper, Wisconsin School of Business, Madison, WI.Google Scholar
  • Aiken E, Bellue S, Karlan D, Udry C, Blumenstock JE (2022) Machine learning and phone data can improve targeting of humanitarian aid. Nature 603(7903):864–870.CrossrefGoogle Scholar
  • Angwin J, Larson J, Mattu S, Kirchner L (2022) Machine bias. Ethics of Data and Analytics (Auerbach Publications, New York), 254–264.CrossrefGoogle Scholar
  • Arkes HR, Dawes RM, Christensen C (1986) Factors influencing the use of a decision rule in a probabilistic task. Organ. Behav. Human Decision Processes 37(1):93–110.CrossrefGoogle Scholar
  • Arya A, Mittendorf B, Ramanan RN (2019) Beyond profits: The rise of dual-purpose organizations and its consequences for disclosure. Accounting Rev. 94(1):25–43.CrossrefGoogle Scholar
  • Asay HS, Guggenmos RD, Kadous K, Koonce L, Libby R (2022) Theory testing and process evidence in accounting experiments. Accounting Rev. 97(6):23–43.CrossrefGoogle Scholar
  • Barnard RT, Turnbull DJ (2019) Discrimination and social justice: Questions of diversity, plurality, representativeness, measurability, and doublespeak. Internat. J. Interdisciplinary Civic Political Stud. 14(2):21–34.CrossrefGoogle Scholar
  • Bean R (2021) Why is it so hard to become a data-driven company. Harvard Bus. Rev. (February 5), https://hbr.org/2021/02/why-is-it-so-hard-to-become-a-data-driven-company.Google Scholar
  • Belmi P, Schroeder J (2021) Human “resources”? Objectification at work. J. Personality Soc. Psych. 120(2):384–417.CrossrefGoogle Scholar
  • Berenguer J (2007) The effect of empathy in proenvironmental attitudes and behaviors. Environ. Behav. 39(2):269–283.CrossrefGoogle Scholar
  • Berger L, Guo L, Presslee A (2023) Motivating employees with goal‐based prosocial rewards. Contemporary Accounting Res. 40(1):231–256.CrossrefGoogle Scholar
  • Berman JZ, Barasch A, Levine EE, Small DA (2018) Impediments to effective altruism: The role of subjective preferences in charitable giving. Psych. Sci. 29(5):834–844.CrossrefGoogle Scholar
  • Bloom P (2017) Empathy and its discontents. Trends Cognitive Sci. 21(1):24–31.CrossrefGoogle Scholar
  • Bode C, Singh J, Rogan M (2015) Corporate social initiatives and employee retention. Organ. Sci. 26(6):1702–1720.LinkGoogle Scholar
  • Bol JC, Kramer S, Maas VS (2016) How control system design affects performance evaluation compression: The role of information accuracy and outcome transparency. Accounting Organ. Soc. 51:64–73.CrossrefGoogle Scholar
  • Bonezzi A, Ostinelli M (2021) Can algorithms legitimize discrimination? J. Experiment. Psych. Appl. 27(2):447–459.CrossrefGoogle Scholar
  • Bradford T (2023) “Give me some credit!”: Using alternative data to expand credit access. Payments System Res. Briefing, https://www.kansascityfed.org/research/payments-system-research-briefings/give-me-some-credit-using-alternative-data-to-expand-credit-access/.Google Scholar
  • Brynjolfsson E, McElheran K (2016) The rapid adoption of data-driven decision-making. Amer. Econom. Rev. 106(5):133–139.CrossrefGoogle Scholar
  • Burton JW, Stein MK, Jensen TB (2020) A systematic review of algorithm aversion in augmented decision making. J. Behav. Decision Making 33(2):220–239.CrossrefGoogle Scholar
  • Burum B, Nowak MA, Hoffman M (2020) An evolutionary explanation for ineffective altruism. Nature Human Behav. 4(12):1245–1257.CrossrefGoogle Scholar
  • Campbell D, Loumioti M, Wittenberg-Moerman R (2019) Making sense of soft information: Interpretation bias and loan quality. J. Accounting Econom. 68(2–3):101240.CrossrefGoogle Scholar
  • Carman JG, Fredericks KA (2008) Nonprofits and evaluation: Empirical evidence from the field. New Directions Evaluation 2008(119):51–71.CrossrefGoogle Scholar
  • Carman JG, Fredericks KA (2010) Evaluation capacity and nonprofit organizations: Is the glass half-empty or half-full? Amer. J. Evaluation 31(1):84–104.CrossrefGoogle Scholar
  • Casas-Arce P, Cheng MM, Grabner I, Modell S (2022) Managerial accounting for decision-making and planning. J. Management Accounting Res. 34(1):1–7.CrossrefGoogle Scholar
  • Cassar L (2019) Job mission as a substitute for monetary incentives: Benefits and limits. Management Sci. 65(2):896–912.LinkGoogle Scholar
  • Cassar L, Meier S (2021) Intentions for doing good matter for doing well: The negative effects of prosocial incentives. Econom. J. 131(637):1988–2017.Google Scholar
  • Caviola L, Schubert S, Greene JD (2021) The psychology of (in) effective altruism. Trends Cognitive Sci. 25(7):596–607.CrossrefGoogle Scholar
  • Chan EW, Zhang X (2021) Understanding and deterring misreporting in nonprofits: The joint effects of pay level and penalty type. Accounting Rev. 96(4):157–177.CrossrefGoogle Scholar
  • Chen CX, Hudgins R, Wright WF (2022) The effect of advice valence on the perceived credibility of data analytics. J. Management Accounting Res. 34(2):97–116.CrossrefGoogle Scholar
  • Chen CX, Pesch HL, Wang LW (2020) Selection benefits of below-market pay in social-mission organizations: Effects on individual performance and team cooperation. Accounting Rev. 95(1):57–77.CrossrefGoogle Scholar
  • Christensen PO, Frimor H, Şabac F (2020) Real incentive effects of soft information. Contemporary Accounting Res. 37(1):514–541.CrossrefGoogle Scholar
  • Christin A (2017) Algorithms in practice: Comparing web journalism and criminal justice. Big Data Soc. 4(2):1–14.CrossrefGoogle Scholar
  • Commerford BP, Dennis SA, Joe JR, Ulla JW (2022) Man versus machine: Complex estimates and auditor reliance on artificial intelligence. J. Accounting Res. 60(1):171–201.CrossrefGoogle Scholar
  • Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. Proc. 23rd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Halifax, NS, Canada), 797–806.Google Scholar
  • Dastin J (2022) Amazon scraps secret AI recruiting tool that showed bias against women. Ethics of Data and Analytics (Auerbach Publications, New York), 296–299.CrossrefGoogle Scholar
  • Datar SM, Rajan MV (2018) Horngren’s Cost Accounting: A Managerial Emphasis (Pearson, London).Google Scholar
  • De Waal FB (2008) Putting the altruism back into altruism: The evolution of empathy. Annual Rev. Psych. 59(1):279–300.CrossrefGoogle Scholar
  • De‐Arteaga M, Feuerriegel S, Saar‐Tsechansky M (2022) Algorithmic fairness in business analytics: Directions for research and practice. Production Oper. Management 31(10):3749–3770.CrossrefGoogle Scholar
  • Decety J, Cowell JM (2014) The complex relation between morality and empathy. Trends Cognitive Sci. 18(7):337–339.CrossrefGoogle Scholar
  • Díaz A, Rowshankish K, Saleh T (2018) Why data culture matters. McKinsey Quart. 3(1):36–53.Google Scholar
  • Dietvorst BJ, Bartels DM (2022) Consumers object to algorithms making morally relevant tradeoffs because of algorithms’ consequentialist decision strategies. J. Consumer Psych. 32(3):406–424.CrossrefGoogle 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
  • Douthit JD, Martin PR, McAllister M (2022) Charitable contribution matching and effort-elicitation. Accounting Rev. 97(1):213–232.CrossrefGoogle Scholar
  • Dykes B (2019) The four key pillars to fostering a data-driven culture. Forbes.com (March 28), https://www.forbes.com/sites/brentdykes/2019/03/28/the-four-key-pillars-tofostering-a-data-driven-culture.Google Scholar
  • Eisenberg N, Miller PA (1987) The relation of empathy to prosocial and related behaviors. Psych. Bull. 101(1):91–119.CrossrefGoogle Scholar
  • Emett SA, Kaplan SE, Mauldin EG, Pickerd JS (2023) Auditing with data and analytics: External reviewers’ judgments of audit quality and effort. Contemporary Accounting Res. 40(4):2314–2339.CrossrefGoogle Scholar
  • Everett JA, Faber NS, Savulescu J, Crockett MJ (2018) The costs of being consequentialist: Social inference from instrumental harm and impartial beneficence. J. Experiment. Soc. Psych. 79:200–216.CrossrefGoogle Scholar
  • Friedman JN, Sacerdote B, Staiger DO, Tine M (2025) Standardized test scores and academic performance at Ivy Plus Colleges. AEA Papers Proc. 115:676–681.Google Scholar
  • Gomila R (2021) Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. J. Experiment. Psych. General 150(4):700–709.CrossrefGoogle Scholar
  • Graeber T, Roth C, Zimmermann F (2024) Stories, statistics, and memory. Quart. J. Econom. 139(4):2181–2225.CrossrefGoogle Scholar
  • Hayes AF (2022) Introduction to Mediation, Moderation, and Conditional Process Analysis: Regression-Based Approach (The Guilford Press, New York).Google Scholar
  • Hedblom D, Hickman BR, List JA (2019) Toward an understanding of corporate social responsibility: Theory and field experimental evidence. National Bureau of Economic Research Cambridge, MA.Google Scholar
  • Heβler PO, Pfeiffer J, Hafenbrädl S (2022) When self-humanization leads to algorithm aversion: What users want from decision support systems on prosocial microlending platforms. Bus. Inform. Systems Engrg. 64(3):275–292.CrossrefGoogle Scholar
  • Hobson JL, Sommerfeldt RD, Wang LW (2021) Cheating for the cause: The effects of performance-based pay on socially oriented misreporting. Accounting Rev. 96(5):317–336.CrossrefGoogle Scholar
  • Hurley M, Adebayo J (2016) Credit scoring in the era of big data. Yale J. Law Tech. 18:148–216.Google Scholar
  • Ijiri Y (1975) Theory of Accounting Measurement (American Accounting Association, Sarasota, FL).Google Scholar
  • Ijiri Y, Jaedicke RK (1966) Reliability and objectivity of accounting measurements. Accounting Rev. 41(3):474–483.CrossrefGoogle Scholar
  • Independent Sector (2024) Health of the U.S. Nonprofit Sector: Annual Review. Washington, DC, 1–23. Accessed September 10, 2025, https://independentsector.org/wp-content/uploads/2024/12/annual-health-report-dec2024_v4.pdf.Google Scholar
  • Janus K (2018) Creating a data culture: How nonprofit organizations can do a better job with their data. Accessed April 11, 2024, https://ssir.org/articles/entry/creating_a_data_culture.Google Scholar
  • Jung M, Seiter M (2021) Towards a better understanding on mitigating algorithm aversion in forecasting: An experimental study. J. Management Control 32(4):495–516.CrossrefGoogle Scholar
  • Jung S-M, Shin JY (2022) Social performance incentives in mission-driven firms. Management Sci. 68(10):7631–7657.LinkGoogle Scholar
  • Kallus N, Zhou A (2018) Residual unfairness in fair machine learning from prejudiced data. Dy J, Krause A, eds. Proc. 35th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 80 (PMLR, New York), 2439–2448.Google Scholar
  • Kim M, Charles C, Pettijohn SL (2019) Challenges in the use of performance data in management: Results of a national survey of human service nonprofit organizations. Public Performance Management Rev. 42(5):1085–1111.CrossrefGoogle Scholar
  • Kleinberg J, Ludwig J, Mullainathan S, Rambachan A (2018) Algorithmic fairness. AEA Papers Proc. 108:22–27.Google Scholar
  • Kuhn PJ, Osaki TT (2022) When is discrimination unfair? National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Levine EE, Barasch A, Rand D, Berman JZ, Small DA (2018) Signaling emotion and reason in cooperation. J. Experiment. Psych. General 147(5):702–719.CrossrefGoogle Scholar
  • Liang A, Lu J (2024) Algorithmic fairness and social welfare. AEA Papers Proc. 114:628–632.Google Scholar
  • Liberti JM, Petersen MA (2019) Information: Hard and soft. Rev. Corporate Financial Stud. 8(1):1–41.CrossrefGoogle Scholar
  • Liu M (2022) Assessing human information processing in lending decisions: A machine learning approach. J. Accounting Res. 60(2):607–651.CrossrefGoogle 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
  • Mandinach EB (2012) A perfect time for data use: Using data-driven decision making to inform practice. Ed. Psych. 47(2):71–85.CrossrefGoogle Scholar
  • Mattiassi AD, Sarrica M, Cavallo F, Fortunati L (2021) What do humans feel with mistreated humans, animals, robots, and objects? Exploring the role of cognitive empathy. Motivation Emotion 45(4):543–555.CrossrefGoogle Scholar
  • Maxwell NL, Rotz D, Garcia C (2016) Data and decision making: Same organization, different perceptions; different organizations, different perceptions. Amer. J. Evaluation 37(4):463–485.CrossrefGoogle Scholar
  • Mayer DJ, Fischer RL (2023) Exploring data use in nonprofit organizations. Evaluation Program Planning 97:102197.CrossrefGoogle Scholar
  • Mitchell S, Potash E, Barocas S, D’Amour A, Lum K (2021) Algorithmic fairness: Choices, assumptions, and definitions. Annual Rev. Statist. Appl. 8(1):141–163.CrossrefGoogle Scholar
  • Molecke G, Pinkse J (2017) Accountability for social impact: A bricolage perspective on impact measurement in social enterprises. J. Bus. Venturing 32(5):550–568.CrossrefGoogle Scholar
  • Montealegre A, Bush LS, Moss D, Pizarro DA, Jimenez-Leal W (2025) Does maximizing good make people look bad? Personality Soc. Psych., ePub ahead of print November 3, https://doi.org/10.1177/01461672251361210.Google Scholar
  • Montemayor C, Halpern J, Fairweather A (2022) In principle obstacles for empathic AI: Why we can’t replace human empathy in healthcare. AI Soc. 37(4):1353–1359.CrossrefGoogle Scholar
  • Morse L, Teodorescu MHM, Awwad Y, Kane GC (2022) Do the ends justify the means? Variation in the distributive and procedural fairness of machine learning algorithms. J. Bus. Ethics 181(4):1083–1095.CrossrefGoogle 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.CrossrefGoogle Scholar
  • Niessen ASM, Kausel EE, Neumann M (2022) Using narratives and numbers in performance prediction: Attitudes, confidence, and validity. Internat. J. Selection Assessment 30(2):216–229.CrossrefGoogle Scholar
  • O’Neil C (2017) Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown Publishing Group, New York).Google Scholar
  • Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453.CrossrefGoogle Scholar
  • Olavsrud T (2022) Feeding America turns to data to feed the hungry. Accessed September 14, 2025, https://www.cio.com/article/403152/feeding-america-turns-to-data-to-feed-the-hungry.html.Google Scholar
  • Petersen MA (2004) Information: Hard and soft. Working paper, Kellogg School of Management, Evanston, IL.Google Scholar
  • Piercey MD (2023) “Throw it in as a covariate?” Common problems using measured control variables in experimental research. Auditing 42(2):183–205.CrossrefGoogle Scholar
  • Rambachan A, Kleinberg J, Ludwig J, Mullainathan S (2020) An economic perspective on algorithmic fairness. AEA Papers Proc. 110:91–95.CrossrefGoogle Scholar
  • Salesforce (2022) Nonprofit trends. (October 27), https://www.salesforce.com/news/stories/nonprofit-statistics-trends-2022/.Google Scholar
  • Schuhmacher K, Towry KL, Zureich J (2022) Leading by example in socially driven organizations: The effect of transparent leader compensation contracts on following. Accounting Rev. 97(3):373–393.CrossrefGoogle Scholar
  • Shepherd DA, Williams TA, Zhao EY (2019) A framework for exploring the degree of hybridity in entrepreneurship. Acad. Management Perspect. 33(4):491–512.CrossrefGoogle Scholar
  • Small DA, Loewenstein G (2003) Helping a victim or helping the victim: Altruism and identifiability. J. Risk Uncertainty 26:5–16.CrossrefGoogle Scholar
  • Sprinkle GB (2003) Perspectives on experimental research in managerial accounting. Accounting, Organ. Soc. 28(2–3):287–318.CrossrefGoogle Scholar
  • Stevens A, Deruyck P, Van Veldhoven Z, Vanthienen J (2020) Explainability and fairness in machine learning: Improve fair end-to-end lending for Kiva. 2020 IEEE Sympos. Series Comput. Intelligence (SSCI) (Canberra, ACT), 1241–1248.Google Scholar
  • Tableau (2020) Feeding America fights hunger with data. Accessed March 28, 2025, https://www.tableau.com/solutions/customer/feeding-america-uses-data-close-hunger-gap.Google Scholar
  • Tawakuli A, Engel T (2025) Make your data fair: A survey of data preprocessing techniques that address biases in data towards fair AI. J. Engrg. Res. 13(3):2362–2369.CrossrefGoogle Scholar
  • Tonin M, Vlassopoulos M (2015) Corporate philanthropy and productivity: Evidence from an online real effort experiment. Management Sci. 61(8):1795–1811.LinkGoogle Scholar
  • Tripp W, Gage D, Williams H (2020) Addressing the data analytics gap: A community-university partnership to enhance analytics capabilities in the non-profit sector. Collaborations 3(1):1–10.Google Scholar
  • U.S. Bureau of Labor Statistics (2014) Nonprofits account for 11.4 million jobs, 10.3 percent of all private sector employment. (October 21), https://www.bls.gov/opub/ted/2014/ted_20141021.htm.Google Scholar
  • Van den Steen E (2010) Culture clash: The costs and benefits of homogeneity. Management Sci. 56(10):1718–1738.LinkGoogle Scholar
  • Waller D (2020) 10 steps to creating a data-driven culture. Harvard Bus. Rev. (February 6), https://hbr.org/2020/02/10-steps-to-creating-a-data-driven-culture.Google Scholar
  • West A (2019) Data-driven decision making for not-for-profit organizations. CPA J. 89(4):10–12.Google Scholar
  • Williams BA, Brooks CF, Shmargad Y (2018) How algorithms discriminate based on data they lack: Challenges, solutions, and policy implications. J. Inform. Policy 8:78–115.CrossrefGoogle Scholar
  • Wooldridge JM (2010) Econometric Analysis of Cross Section and Panel Data (MIT Press, Cambridge, MA).Google Scholar
  • World Economic Forum (2024) The state of social enterprise: A review of global data 2013–2023. Insight Report (World Economic Forum, Schwab Foundation for Social Entrepreneurship, Cologny/Geneva, Switzerland), 1–32.Google 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.