Top

Journal of Business Ethics

Published in:

16-02-2023 | Original Paper

When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company

Authors: Chenfeng Yan, Quan Chen, Xinyue Zhou, Xin Dai, Zhilin Yang

Published in: Journal of Business Ethics | Issue 4/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The growing uses of algorithm-based decision-making in human resources management have drawn considerable attention from different stakeholders. While prior literature mainly focused on stakeholders directly related to HR decisions (e.g., employees), this paper pertained to a third-party observer perspective and investigated how consumers would respond to companies’ adoption of algorithm-based HR decision-making. Through five experimental studies, we showed that the adoption of algorithm-based (vs. human-based) HR decision-making could induce consumers’ unfavorable ethicality inferences of the company (study 1); because implementing a calculative and data-driven approach (i.e. algorithm-based) to make employee-related decisions violates the deontological principles of respectful employee treatment (study 2). However, this effect was attenuated when consumers had high (vs. low) power distance beliefs (study 3); the algorithm served as assistance (vs. replacement) for human decisions (study 4); or the adoption was framed as employee-oriented (vs. company-oriented) motivated (study 5). Our findings suggested that consumers are aversive to algorithm-based HR decision-making because it is deontologically problematic regardless of its decision quality (i.e. accuracy). This paper contributes to the extant understanding of stakeholders’ responses to algorithm-based HR decision-making and consumers’ attitudes toward algorithm users.

previous article Impact of Peer Unethical Behaviors on Employee Silence: The Role of Organizational Identification and Emotions

next article How Much Does Workplace Sexual Harassment Hurt Firm Value?

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

The project received IRB at School of Management, Huazhong University of Science and Technology (IRB #: 2020.04.23, Study Title: AI-HRM).

We used MTurk's Qualification Type function to avoid any overlapping of participants across our studies. After each experiment, we granted the participants the same qualification. The granted population did not receive further invitations.

Alge, B. J. (2001). Effects of computer surveillance on perceptions of privacy and procedural justice. Journal of Applied Psychology, 86(4), 797–804. https://doi.org/10.1037/0021-9010.86.4.797CrossRef

Arnold, D. G., & Bowie, N. E. (2007). Respect for workers in global supply chains: Advancing the debate over sweatshops. Business Ethics Quarterly, 17(1), 135–145. https://doi.org/10.5840/beq200717121CrossRef

Belmi, P., & Schroeder, J. (2021). Human “resources”? objectification at work. Journal of Personality and Social Psychology, 120(2), 384–417. https://doi.org/10.1037/pspi0000254CrossRef

Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: Amazon.com’s mechanical turk. Political Analysis, 20(3), 351–368. https://doi.org/10.1093/pan/mpr057CrossRef

Boeuf, B., & Darveau, J. (2019). An ethical perspective on necro-advertising: The moderating effect of brand equity. Journal of Business Ethics, 155(4), 1077–1099. https://doi.org/10.1007/s10551-017-3490-xCrossRef

Brunk, K. H. (2010). Exploring origins of ethical company/brand perceptions—A consumer perspective of corporate ethics. Journal of Business Research, 63(3), 255–262. https://doi.org/10.1016/j.jbusres.2009.03.011CrossRef

Brunk, K. H. (2012). Un/ethical company and brand perceptions: Conceptualising and operationalising consumer meanings. Journal of Business Ethics, 111(4), 551–565. https://doi.org/10.1007/s10551-012-1339-xCrossRef

Brunk, K., & Blümelhuber, C. (2011). One strike and you’re out: Qualitative insights into the formation of consumers’ ethical company or brand perceptions. Journal of Business Research, 64(2), 134–141. https://doi.org/10.1016/j.jbusres.2010.02.009CrossRef

Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? workforce implications: Profound change is coming, but roles for humans remain. Science, 358(6370), 1530–1534. https://doi.org/10.1126/science.aap8062CrossRef

Burton, J. W., Stein, M. K., & Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220–239. https://doi.org/10.1002/bdm.2155CrossRef

Carlson, R. W., Bigman, Y. E., Gray, K., Ferguson, M. J., & Crockett, M. J. (2022). How inferred motives shape moral judgements. Nature Reviews Psychology, 1(8), 468–478. https://doi.org/10.1038/s44159-022-00071-xCrossRef

Castelvecchi, D. (2016). Can we open the black box of AI? Nature, 538(7623), 20–23. https://doi.org/10.1038/538020aCrossRef

Chamorro-Premuzic, T., Akhtar, R., Winsborough, D., & Sherman, R. A. (2017). The datafication of talent: How technology is advancing the science of human potential at work. Current Opinion in Behavioral Sciences, 18, 13–16. https://doi.org/10.1016/j.cobeha.2017.04.007CrossRef

Cheung, M. F. Y., & To, W. M. (2021). The effect of consumer perceptions of the ethics of retailers on purchase behavior and word-of-mouth: The moderating role of ethical beliefs. Journal of Business Ethics, 171(4), 771–788. https://doi.org/10.1007/s10551-020-04431-6CrossRef

Cohn, D. Y. (2010). Commentary essay on “exploring origins of ethical company/brand perceptions—A consumer perspective of corporate ethics.” Journal of Business Research, 63(12), 1267–1268. https://doi.org/10.1016/j.jbusres.2009.03.011CrossRef

Cowgill, B. (2020). Bias and Productivity in Humans and Machines: Theory and Evidence from Resume Screening (pp. 1–35). Columbia Business School.

Cropanzano, R., Goldman, B., & Folger, R. (2003). Deontic justice: The role of moral principles in workplace fairness. Journal of Organizational Behavior, 24(8), 1019–1024. https://doi.org/10.1002/job.228CrossRef

Davenport, T. H., & Kirby, J. (2016). Just how smart are smart machines? MIT Sloan Management Review, 1(3), 7–7.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0CrossRef

Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34(7), 571–582. https://doi.org/10.1037/0003-066X.34.7.571CrossRef

Dean, D. H. (2004). Consumer reaction to negative publicity: Effects of corporate reputation, response, and responsibility for a crisis event. Journal of Business Communication, 41(2), 192–211. https://doi.org/10.1177/0021943603261748CrossRef

Dietvorst, B. J., & Bartels, D. M. (2021). Consumers object to algorithms making morally relevant tradeoffs because of algorithms’ consequentialist decision strategies. Journal of Consumer Psychology. https://doi.org/10.1002/jcpy.1266CrossRef

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994–101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002CrossRef

Gill, T. (2020). Blame it on the self-driving car: how autonomous vehicles can alter consumer morality. Journal of Consumer Research, 47(2), 272–291. https://doi.org/10.1093/jcr/ucaa018CrossRef

Greene, J. D., Morelli, S. A., Lowenberg, K., Nystrom, L. E., & Cohen, J. D. (2008). Cognitive load selectively interferes with utilitarian moral judgment. Cognition, 107(3), 1144–1154. https://doi.org/10.1016/j.cognition.2007.11.004CrossRef

Greenwood, M. R. (2002). Ethics and HRM: A review and conceptual analysis. Journal of Business Ethics, 36(3), 261–278. https://doi.org/10.1023/A:1014090411946CrossRef

Gwinn, J. D., Judd, C. M., & Park, B. (2013). Less power = less human? effects of power differentials on dehumanization. Journal of Experimental Social Psychology, 49(3), 464–470. https://doi.org/10.1016/j.jesp.2013.01.005CrossRef

Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review, 108(4), 814–834. https://doi.org/10.1037/0033-295X.108.4.814CrossRef

Han, D., Lalwani, A. K., & Duhachek, A. (2017). Power distance belief, power, and charitable giving. Journal of Consumer Research, 44(1), 182–195. https://doi.org/10.1093/jcr/ucw084CrossRef

Haslam, N. (2006). Dehumanization: An integrative review. Personality and Social Psychology Review, 10(3), 252–264. https://doi.org/10.1207/s15327957pspr1003_4CrossRef

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis. Guilford.

Hill, T. E. (1980). Humanity as an end in itself. Ethics, 91(1), 84–99. https://doi.org/10.1086/292205CrossRef

Kaibel, C., Koch-Bayram, I., Biemann, T., & Mühlenbock, M. (2019). Applicant perceptions of hiring algorithms - Uniqueness and discrimination experiences as moderators. 79th Annual Meeting of the Academy of Management 2019: Understanding the Inclusive Organization, AoM 2019, 2019(1), 18172–18172. Doi: https://doi.org/10.5465/AMBPP.2019.210

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004CrossRef

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174CrossRef

Klink, R. R., & Wu, L. (2017). Creating ethical brands: The role of brand name on consumer perceived ethicality. Marketing Letters, 28(3), 411–422. https://doi.org/10.1007/s11002-017-9424-7CrossRef

Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: A systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 13(3), 795–848. https://doi.org/10.1007/s40685-020-00134-wCrossRef

Laczniak, G. R., & Murphy, P. E. (2012). Stakeholder theory and marketing: Moving from a firm-centric to a societal perspective. Journal of Public Policy and Marketing, 31(2), 284–292. https://doi.org/10.1509/jppm.10.106CrossRef

Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2966–2981. https://doi.org/10.1287/mnsc.2018.3093CrossRef

Langer, M., König, C. J., & Fitili, A. (2018). Information as a double-edged sword: The role of computer experience and information on applicant reactions towards novel technologies for personnel selection. Computers in Human Behavior, 81, 19–30. https://doi.org/10.1016/j.chb.2017.11.036CrossRef

Lecher, C. (2019). [How Amazon automatically tracks and fires warehouse workers for ‘productivity’].

Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data and Society, 5(1), 1–16. https://doi.org/10.1177/2053951718756684CrossRef

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392. https://doi.org/10.1007/s10551-019-04204-wCrossRef

Lena, A., & Christoph, H. (2022). Ethics of AI-enabled recruiting and selection: A review and research agenda. Journal of Business Ethics. https://doi.org/10.1007/s10551-022-05049-6CrossRef

Levine, E. E., & Schweitzer, M. E. (2015). Prosocial lies: When deception breeds trust. Organizational Behavior and Human Decision Processes, 126, 88–106. https://doi.org/10.1016/j.obhdp.2014.10.007CrossRef

Li, X., & Li, K. J. (2022). Beating the algorithm: Consumer manipulation, personalized pricing, and big data management. Manufacturing & Service Operations Management. https://doi.org/10.1287/msom.2022.1153CrossRef

Lin, Y. T., Hung, T. W., & Huang, L. T. L. (2021). Engineering equity: How AI can help reduce the harm of implicit bias. Philosophy and Technology, 34, 65–90. https://doi.org/10.1007/s13347-020-00406-7CrossRef

Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005CrossRef

Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013CrossRef

Love, L. F., & Singh, P. (2011). Workplace branding: Leveraging human resources management practices for competitive advantage through “best employer” surveys. Journal of Business and Psychology, 26(2), 175–181. https://doi.org/10.1007/s10869-011-9226-5CrossRef

Luo, X., Qin, M. S., Fang, Z., & Qu, Z. (2021). Artificial intelligence coaches for sales agents: Caveats and solutions. Journal of Marketing, 85(2), 14–32. https://doi.org/10.1177/0022242920956676CrossRef

Martin, K. (2019). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160(4), 835–850. https://doi.org/10.1007/s10551-018-3921-3CrossRef

Martin, K., & Waldman, A. (2022). Are algorithmic decisions legitimate? The effect of process and outcomes on perceptions of legitimacy of AI decisions. Journal of Business Ethics. Doi: https://doi.org/10.1007/s10551-021-05032-7

Melé, D. (2014). “Human quality treatment”: Five organizational levels. Journal of Business Ethics, 120(4), 457–471. https://doi.org/10.1007/s10551-013-1999-1CrossRef

Mirowska, A., & Mesnet, L. (2022). Preferring the devil you know: Potential applicant reactions to artificial intelligence evaluation of interviews. Human Resource Management Journal, 32(2), 364–383. https://doi.org/10.1111/1748-8583.12393CrossRef

Morse, L., Teodorescu, M. H. M., Awwad, Y., & Kane, G. C. (2021). Do the ends justify the means? Variation in the distributive and procedural fairness of machine learning algorithms. Journal of Business Ethics. Doi: https://doi.org/10.1007/s10551-021-04939-5

Newlands, G. (2021). Algorithmic surveillance in the gig economy: The organization of work through lefebvrian conceived space. Organization Studies, 42(5), 719–737. https://doi.org/10.1177/0170840620937900CrossRef

Newman, D. T., Fast, N. J., & Harmon, D. J. (2020). When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 160, 149–167. https://doi.org/10.1016/j.obhdp.2020.03.008CrossRef

O’Reilly, J., & Aquino, K. (2011). A model of third parties’ morally motivated responses to mistreatment in organizations. Academy of Management Review, 36(3), 526–543. https://doi.org/10.5465/amr.2009.0311CrossRef

Oswald, F. L., Behrend, T. S., Putka, D. J., & Sinar, E. (2020). Big data in industrial-organizational psychology and human resource management: Forward progress for organizational research and practice. Annual Review of Organizational Psychology and Organizational Behavior, 7, 505–533. https://doi.org/10.1146/annurev-orgpsych-032117-104553CrossRef

Palmeira, M., & Spassova, G. (2015). Consumer reactions to professionals who use decision aids. European Journal of Marketing, 49(3–4), 302–326. https://doi.org/10.1108/EJM-07-2013-0390CrossRef

Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184–188. https://doi.org/10.1177/0963721414531598CrossRef

Pirson, M. (2019). A humanistic perspective for management theory: Protecting dignity and promoting well-being. Journal of Business Ethics, 159(1), 39–57. https://doi.org/10.1007/s10551-017-3755-4CrossRef

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/AMR.2018.0072CrossRef

Rank-Christman, T., Morrin, M., & Ringler, C. (2017). R-E-S-P-E-C-T Find out what my name means to me: The effects of marketplace misidentification on consumption. Journal of Consumer Psychology, 27(3), 333–340. https://doi.org/10.1016/j.jcps.2016.12.002CrossRef

Reed, A., Aquino, K., & Levy, E. (2007). Moral identity and judgments of charitable behaviors. Journal of Marketing, 71(1), 178–193. https://doi.org/10.1509/jmkg.71.1.178CrossRef

Rifon, N. J., Choi, S. M., Trimble, C. S., & Li, H. (2004). Congruence effects in sponsorship: The mediating role of sponsor credibility and consumer attributions of sponsor motive. Journal of Advertising, 33(1), 30–42. https://doi.org/10.1080/00913367.2004.10639151CrossRef

Robert, L. P., Pierce, C., Marquis, L., Kim, S., & Alahmad, R. (2020). Designing fair AI for managing employees in organizations: A review, critique, and design agenda. Human-Computer Interaction, 35(5–6), 545–575. https://doi.org/10.1080/07370024.2020.1735391CrossRef

Rowan, J. R. (2000). The moral foundation of employee rights. Journal of Business Ethics, 24(4), 355–361. https://doi.org/10.1023/A:1006286315756CrossRef

Sampson, S. E. (2021). A strategic framework for task automation in professional services. Journal of Service Research, 24(1), 122–140. https://doi.org/10.1177/1094670520940407CrossRef

Shrestha, Y. R., Krishna, V., & von Krogh, G. (2021). Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. Journal of Business Research, 123, 588–603. https://doi.org/10.1016/j.jbusres.2020.09.068CrossRef

Skarlicki, D. P., & Kulik, C. T. (2004). Third-party reactions to employee (mis)treatment: A justice perspective. Research in Organizational Behavior, 26(04), 183–229. https://doi.org/10.1016/S0191-3085(04)26005-1CrossRef

Sparks, J. R., & Pan, Y. (2010). Ethical judgments in business ethics research: Definition, and research agenda. Journal of Business Ethics, 91(3), 405–418. https://doi.org/10.1007/s10551-009-0092-2CrossRef

Srinivasan, R., & Sarial-Abi, G. (2021). When algorithms fail: Consumers’ responses to brand harm crises caused by algorithm errors. Journal of Marketing. https://doi.org/10.1177/0022242921997082CrossRef

Su, Y., & Jin, L. (2021). The impact of online platforms’ revenue model on consumers’ ethical inferences. Journal of Business Ethics. https://doi.org/10.1007/s10551-021-04798-0CrossRef

Suen, H. Y., Chen, M. Y. C., & Lu, S. H. (2019). Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes? Computers in Human Behavior, 98(43), 93–101. https://doi.org/10.1016/j.chb.2019.04.012CrossRef

Telkamp, J. B., & Anderson, M. H. (2022). The implications of diverse human moral foundations for assessing the ethicality of artificial intelligence. Journal of Business Ethics. https://doi.org/10.1007/s10551-022-05057-6CrossRef

Teodorescu, M. H. M., Morse, L., Awwad, Y., & Kane, G. C. (2021). Failures of fairness in automation require a deeper understanding of human–ML augmentation. MIS Quarterly, 45(3), 1483–1499. https://doi.org/10.25300/MISQ/2021/16535CrossRef

Wessling, K. S., Huber, J., & Netzer, O. (2017). MTurk character misrepresentation: Assessment and solutions. Journal of Consumer Research, 44(1), 211–230. https://doi.org/10.1093/jcr/ucx053CrossRef

Winterich, K. P., & Zhang, Y. (2014). Accepting inequality deters responsibility: How power distance decreases charitable behavior. Journal of Consumer Research, 41(2), 274–293. https://doi.org/10.1086/675927CrossRef

Xie, W., Yu, B., Zhou, X., Sedikides, C., & Vohs, K. D. (2014). Money, moral transgressions, and blame. Journal of Consumer Psychology, 24(3), 299–306. https://doi.org/10.1016/j.jcps.2013.12.002CrossRef

Xu, H. F., Bolton, L. E., & Winterich, K. P. (2021). How do consumers react to company moral transgressions? The role of power distance belief and empathy for victims. Journal of Consumer Research, 48(1), 77–101. https://doi.org/10.1093/jcr/ucaa067CrossRef

Yoo, B., Donthu, N., & Lenartowicz, T. (2011). Measuring Hofstede’s five dimensions of cultural values at the individual level: Development and validation of CVSCALE. Journal of International Consumer Marketing, 23(3–4), 193–210. https://doi.org/10.1080/08961530.2011.578059CrossRef

Young, L., & Saxe, R. (2009). Innocent intentions: A correlation between forgiveness for accidental harm and neural activity. Neuropsychologia, 47(10), 2065–2072. https://doi.org/10.1016/j.neuropsychologia.2009.03.020CrossRef

Zollo, L. (2021). The consumers’ emotional dog learns to persuade its rational tail: Toward a social intuitionist framework of ethical consumption. Journal of Business Ethics, 168(2), 295–313. https://doi.org/10.1007/s10551-019-04420-4CrossRef

Title: When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company
Authors: Chenfeng Yan
Quan Chen
Xinyue Zhou
Xin Dai
Zhilin Yang
Publication date: 16-02-2023
Publisher: Springer Netherlands
Published in: Journal of Business Ethics / Issue 4/2024
Print ISSN: 0167-4544
Electronic ISSN: 1573-0697
DOI: https://doi.org/10.1007/s10551-023-05351-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2024

Workplace Ostracism and Helping Behavior: A Cross-Level Investigation

Impact of Peer Unethical Behaviors on Employee Silence: The Role of Organizational Identification and Emotions

How Much Does Workplace Sexual Harassment Hurt Firm Value?

The Effects of CEO Awards on Corporate Social Responsibility Focus

When Is CEO Activism Conducive to the Democratic Process?

“Articulating Cognizance About What to Hide What not": Insights into Why and When Ethical Leadership Regulates Employee Knowledge-Hiding Behaviors

Premium Partner