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Ethical Perceptions of AI in Hiring and Organizational Trust: The Role of Performance Expectancy and Social Influence

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Abstract

The use of artificial intelligence (AI) in hiring entails vast ethical challenges. As such, using an ethical lens to study this phenomenon is to better understand whether and how AI matters in hiring. In this paper, we examine whether ethical perceptions of using AI in the hiring process influence individuals’ trust in the organizations that use it. Building on the organizational trust model and the unified theory of acceptance and use of technology, we explore whether ethical perceptions are shaped by individual differences in performance expectancy and social influence and how they, in turn, impact organizational trust. We collected primary data from over 300 individuals who were either active job seekers or who had recent hiring experience to capture perceptions across the full range of hiring methods. Our findings indicate that performance expectancy, but not social influence, impacts the ethical perceptions of AI in hiring, which in turn influence organizational trust. Additional analyses indicate that these findings vary depending on the type of hiring methods AI is used for, as well as on whether participants are job seekers or individuals with hiring experience. Our study offers theoretical and practical implications for ethics in HRM and informs policy implementation about when and how to use AI in hiring methods, especially as it pertains to acting ethically and trustworthily.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. “We have high-level machine intelligence when machines are able to perform almost all tasks that are economically relevant today better than the median human (today) at each task.” (Zhang and Dafoe, 2019, p. 34).

References

  • Accenture. (2015). U.S. consumers want more personalized retail experience and control over personal information, Accenture Survey Shows. Retrieved May 3, 2021, from https://newsroom.accenture.com/industries/retail/us-consumers-want-more-personalized-retail-experience-and-control-over-personal-information-accenture-survey-shows.htm

  • Adell, E., Várhelyi, A., & Nilsson, L. (2018). The definition of acceptance and acceptability. In Driver acceptance of new technology (pp. 11–22). CRC Press.

  • Anderson, N. (2003). Applicant and recruiter reactions to new technology in selection: A critical review and agenda for future research. International Journal of Selection and Assessment, 11(2–3), 121–136.

    Google Scholar 

  • Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623.

    Google Scholar 

  • Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104, 671–732.

    Google Scholar 

  • Bauer, T. N., Truxillo, D. M., Mansfield, L. R., & Erdogan, B. (2012). Contingent workers: Who are they and how can we select them for success?. In The Oxford handbook of personnel assessment and selection.

  • BBC. (2018). Artificial Intelligence: Morality in the 21st century. Retrieved June 7, 2021, from https://www.bbc.co.uk/programmes/b0bgrw3k

  • Biswas, M. K., & Suar, D. (2016). Antecedents and consequences of employer branding. Journal of Business Ethics, 136(1), 57–72.

    Google Scholar 

  • Bloomberg, J. (2018). Don’t Trust Artificial Intelligence? Time to Open the AI Black Box. Forbes. Retrieved May 3, 2021, from https://www.forbes.com/sites/jasonbloomberg/2018/09/16/dont-trust-artificialintelligence-time-to-open-the-ai-black-box/#577a14153b4a

  • Bozan, K., Parker, K., & Davey, B. (2016). A closer look at the social influence construct in the UTAUT Model: An institutional theory-based approach to investigate health IT adoption patterns of the elderly. In Proceedings of the 49th Hawaii international conference on system sciences (pp. 3105–3114).

  • Brooksbank, R., Fullerton, S., & Miller, S. (2019). Technology-based marketing strategies through the consumer lens: How might perceptions of ethicality and effectiveness interrelate? International Journal of Technology Marketing, 13(3–4), 428–451.

    Google Scholar 

  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.

  • Brynjolfsson, E., Rock, D., & Syverson, C. (2019). A clash of expectations and statistics. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), Artificial intelligence and the modern productivity paradox (pp. 23–60). University of Chicago Press.

    Google Scholar 

  • Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77–91).

  • Callan, M. J., Kim, H., Gheorghiu, A. I., & Matthews, W. J. (2017). The interrelations between social class, personal relative deprivation, and prosociality. Social Psychological and Personality Science, 8(6), 660–669.

    Google Scholar 

  • CareerBuilder. (2017, May 18). More than half of HR managers say AI will become a regular part of HR in next 5 years. Retrieved June 15, 2021, from http://press.careerbuilder.com/2017-05-18-More-Than-Half-of-HR-Managers-Say-Artificial-Intelligence-Will-Become-a-Regular-Part-of-HR-in-Next-5-Years

  • Chamorro-Premuzic, T., Polli, F., & Dattner, B. (2019). Building ethical AI for talent management. Harvard Business Review, 21.

  • Chattaraman, V., Kwon, W. S., Gilbert, J. E., & Li, Y. (2014). Virtual shopping agents. Journal of Research in Interactive Marketing, 8(2), 144–162.

    Google Scholar 

  • Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st machine learning for healthcare conference (Vol. 56, pp. 301–318).

  • Clark, B. B., Robert, C., & Hampton, S. A. (2016). The technology effect: How perceptions of technology drive excessive optimism. Journal of Business and Psychology, 31(1), 87–102.

    Google Scholar 

  • Cordeiro, W. P. (1997). Suggested management responses to ethical issues raised by technological change. Journal of Business Ethics, 16, 1393–1400.

    Google Scholar 

  • Currall, S. C., & Inkpen, A. C. (2002). A multilevel approach to trust in joint ventures. Journal of International Business Studies, 33(3), 479–495.

    Google Scholar 

  • Daniels, N. (1979). Wide reflective equilibrium and theory acceptance in ethics. The Journal of Philosophy, 76(5), 256–282.

    Google Scholar 

  • Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. Retrieved June 15, 2021, from https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G

  • Dattner, B., Chamorro-Premuzic, T., Buchband, R., & Schettler, L. (2019). The legal and ethical implications of using AI in hiring. Harvard Business Review, 25.

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1002.

    Google Scholar 

  • Davis, F. D., Schoorman, F. D., Mayer, R. C., & Tan, H. H. (2000). The trusted general manager and business unit performance: Empirical evidence of a competitive advantage. Strategic Management Journal, 21(5), 563–576.

    Google Scholar 

  • Du, S. (2021). Reimagining the future of technology: The Social dilemma review. Journal of Business Ethics, 177(1), 213–215.

    Google Scholar 

  • Fan, X., Oh, S., McNeese, M., Yen, J., Cuevas, H., Strater, L., & Endsley, M. R. (2008). The influence of agent reliability on trust in human–agent collaboration. In Proceedings of the 15th European conference on cognitive ergonomics: The ergonomics of cool interaction (Vol. 369, pp. 1–8).

  • Fatma, M., & Rahman, Z. (2017). An integrated framework to understand how consumer-perceived ethicality influences consumer hotel brand loyalty. Service Science, 9(2), 136–146.

    Google Scholar 

  • Ferrario, A., Loi, M., & Viganò, E. (2020). In AI we trust Incrementally: A Multi-layer model of trust to analyze Human-Artificial intelligence interactions. Philosophy & Technology, 33(3), 523–539.

    Google Scholar 

  • Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2), 130–132.

    Google Scholar 

  • Gaudiello, I., Zibetti, E., Lefort, S., Chetouani, M., & Ivaldi, S. (2016). Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to iCub answers. Computers in Human Behavior, 61, 633–655.

    Google Scholar 

  • Gibney, E. (2016). Google AI algorithm masters ancient game of Go. Nature News, 529(7587), 445.

    Google Scholar 

  • Gill, H., Boies, K., Finegan, J. E., & McNally, J. (2005). Antecedents of trust: Establishing a boundary condition for the relation between propensity to trust and intention to trust. Journal of Business and Psychology, 19(3), 287–302.

    Google Scholar 

  • Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660.

    Google Scholar 

  • Gonzalez-Garcia, C. G., Meana-Llorian, D., & Lovelle, J. M. C. (2017). A review about smart objects, sensors, and actuators. International Journal of Interactive Multimedia & Artificial Intelligence, 4(3), 7–10.

    Google Scholar 

  • Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018). When will AI exceed human performance? Evidence from AI experts. Journal of Artificial Intelligence Research, 62, 729–754.

    Google Scholar 

  • Greenwood, M., & Van Buren III, H. J. (2010). Trust and stakeholder theory: Trustworthiness in the organisation–stakeholder relationship. Journal of Business Ethics, 95(3), 425–438.

  • Gunz, S., & Thorne, L. (2020). Thematic Symposium: The Impact of Technology on Ethics, Professionalism and Judgement in Accounting. Journal of Business Ethics, 167, 153–155.

    Google Scholar 

  • Gupta, R., Jain, K., & Jajodia, I. (2021). Determinants of smart speaker adoption intention: Extending the theory of planned behaviour. International Journal of Technology Marketing, 15(2–3), 181–202.

    Google Scholar 

  • Haenlein, M., Huang, M. H., & Kaplan, A. (2022). Guest Editorial: Business ethics in the era of artificial intelligence. Journal of Business Ethics. https://doi.org/10.1007/s10551-022-05060-x

    Article  Google Scholar 

  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (Fifth edition). Spain Prentice Hall.

    Google Scholar 

  • Hermann, E. (2021). Leveraging artificial intelligence in marketing for social good—An ethical perspective. Journal of Business Ethics. https://doi.org/10.1007/s10551-021-04843-y

    Article  Google Scholar 

  • Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104–121.

    Google Scholar 

  • Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917–926.

    Google Scholar 

  • Hrubes, D., Ajzen, I., & Daigle, J. (2001). Predicting hunting intentions and behavior: An application of the theory of planned behavior. Leisure Sciences, 23(3), 165–178.

    Google Scholar 

  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

    Google Scholar 

  • Hume, D. (2000). An enquiry concerning human understanding: A critical edition (Vol. 3). Oxford University Press.

    Google Scholar 

  • Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection: A review and research agenda. Journal of Business Ethics. https://doi.org/10.1007/s10551-022-05049-6

    Article  Google Scholar 

  • IBM. (2018a). Power your candidate experience with AI. Retrieved September 23, 2018a, from https://twitter.com/IBMWatsonTalent?lang=en

  • IBM. (2018b). Bias in AI: How we build fair AI systems and less-biased humans. Retrieved April 29, 2021, from https://www.ibm.com/blogs/policy/bias-in-ai/

  • Jagger, S., Siala, H., & Sloan, D. (2016). It’s all in the game: A 3D learning model for business ethics. Journal of Business Ethics, 137(2), 383–403.

    Google Scholar 

  • Jan, P. T., Lu, H. P., & Chou, T. C. (2012). The adoption of e-learning: An institutional theory perspective. Turkish Online Journal of Educational Technology—TOJET, 11(3), 326–343.

    Google Scholar 

  • Jasanoff, S. (2016). The ethics of invention: Technology and the human future. W. W. Norton & Company.

    Google Scholar 

  • Johnson, D. G. (2015). Technology with no human responsibility? Journal of Business Ethics, 127(4), 707.

    Google Scholar 

  • Kaplan, F. (2004). Who is afraid of the humanoid? Investigating cultural differences in the acceptance of robots. International Journal of Humanoid Robotics, 1(3), 465–480.

    Google Scholar 

  • Keh, H. T., & Xie, Y. (2009). Corporate reputation and customer behavioral intentions: The roles of trust, identification and commitment. Industrial Marketing Management, 38(7), 732–742.

    Google Scholar 

  • Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–416.

    Google Scholar 

  • Klotz, A. C., da Motta Veiga, S. P., Buckley, M. R., & Gavin, M. B. (2013). The role of trustworthiness in recruitment and selection: A review and guide for future research. Journal of Organizational Behavior, 34(S1), S104–S119.

    Google Scholar 

  • Knight, W. (2016). Tougher Turing test exposes Chatbots’ stupidity. Retrieved July 8, 2021, from https://www.technologyreview.com/2016/07/14/7797/tougher-turing-test-exposes-chatbots-stupidity/

  • 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.

    Google Scholar 

  • Laurim, V., Arpaci, S., Prommegger, B., & Krcmar, H. (2021, January). Computer, whom should I hire? Acceptance criteria for artificial intelligence in the recruitment process. In Proceedings of the 54th Hawaii international conference on system sciences (pp. 5495–5504).

  • Leclercq-Vandelannoitte, A. L. (2017). An ethical perspective on emerging forms of ubiquitous IT-based control. Journal of Business Ethics, 142(1), 139–154.

    Google Scholar 

  • Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5(1), 2053951718756684.

    Google Scholar 

  • 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.

    Google Scholar 

  • Levy, D. (2009). Love and sex with robots: The evolution of human–robot relationships (p. 352). Harper.

    Google Scholar 

  • Lewin, K. (1943). Forces behind food habits and methods of change. Bulletin of the National Research Council, 108, 35–65.

    Google Scholar 

  • Li, P. P., Bai, Y., & Xi, Y. (2012). The contextual antecedents of organizational trust: A multidimensional cross-level analysis. Management and Organization Review, 8(2), 371–396.

    Google Scholar 

  • Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management. MIS Quarterly, 31(1), 59–87.

    Google Scholar 

  • Liao, S. M. (2020). Ethics of artificial intelligence. Oxford University Press.

    Google Scholar 

  • Lin, C. P. (2010). Modeling corporate citizenship, organizational trust, and work engagement based on attachment theory. Journal of Business Ethics, 94(4), 517–531.

    Google Scholar 

  • Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600–610.

    Google Scholar 

  • Lockey, S., Gillespie, N., Holm, D., & Someh, I. A. (2021). A review of trust in artificial intelligence: Challenges, vulnerabilities and future directions. In Proceedings of the 54th Hawaii international conference on system sciences (pp. 5463–5472).

  • 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.

    Google Scholar 

  • Loureiro, S. M., Cavallero, L., & Miranda, F. J. (2018). Fashion brands on retail websites: Customer performance expectancy and e-word-of-mouth. Journal of Retailing and Consumer Services, 41, 131–141.

    Google Scholar 

  • Madhavan, R., & Grover, R. (1998). From embedded knowledge to embodied knowledge: New product development as knowledge management. Journal of Marketing, 62(4), 1–12.

    Google Scholar 

  • Margolis, J. D., Grant, A. M., & Molinsky, A. L. (2007). Expanding ethical standards of HRM: Necessary evils and the multiple dimensions of impact. In A. H. Pinnington, R. Macklin, & T. Campbell (Eds.), Human resource management: Ethics and employment (pp. 237–251). Oxford University Press.

    Google Scholar 

  • Marin, L., Ruiz, S., & Rubio, A. (2009). The role of identity salience in the effects of corporate social responsibility on consumer behavior. Journal of Business Ethics, 84(1), 65–78.

    Google Scholar 

  • Marreiros, H., Tonin, M., Vlassopoulos, M., & Schraefel, M. C. (2017). Now that you mention it: A survey experiment on information, inattention and online privacy. Journal of Economic Behavior & Organization, 140, 1–17.

    Google Scholar 

  • Martin, K. (2019). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160, 835–850.

    Google Scholar 

  • Martin, K. E., & Freeman, R. E. (2004). The separation of technology and ethics in business ethics. Journal of Business Ethics, 53(4), 353–364.

    Google Scholar 

  • Martin, K., Shilton, K., & Smith, J. (2019). Business and the ethical implications of technology. Journal of Business Ethics, 160, 307–317.

    Google Scholar 

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

    Article  Google Scholar 

  • Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734.

    Google Scholar 

  • McCarthy, J. M., Bauer, T. N., Truxillo, D. M., Anderson, N. R., Costa, A. C., & Ahmed, S. M. (2017). Applicant perspectives during selection: A review addressing So what? What’s new? And where to next? Journal of Management, 43(6), 1693–1725.

    Google Scholar 

  • Meyer, D. (2018). Amazon reportedly killed an AI recruitment system because it couldn’t stop the tool from discriminating against women. Fortune. Retrieved May 3, 2021, from http://fortune.com/2018/10/10/amazon-ai-recruitment-bias-women-sexist/

  • Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38.

    Google Scholar 

  • Müller-Abdelrazeq, S. L., Schönefeld, K., Haberstroh, M., & Hees, F. (2019). Interacting with collaborative robots—a study on attitudes and acceptance in industrial contexts. In O. Korn (Ed.), Social robots: Technological, societal and ethical aspects of human–robot interaction (pp. 101–117). Springer.

    Google Scholar 

  • Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167, 209–234.

    Google Scholar 

  • Nawaz, N. (2019). How far have we come with the study of artificial intelligence for recruitment process. International Journal of Scientific & Technology Research, 8(07), 488–493.

    Google Scholar 

  • Nikolaou, I., Georgiou, K., Bauer, T. N., & Truxillo, D. M. (2019). Applicant reactions in employee recruitment and selection: The role of technology. In R. N. Landers (Ed.), The Cambridge handbook of tech and employee behavior (pp. 100–130). Cambridge University Press.

    Google Scholar 

  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.

    Google Scholar 

  • North-Samardzic, A. (2020). Biometric technology and ethics: Beyond security applications. Journal of Business Ethics, 167(3), 433–450.

    Google Scholar 

  • O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.

    Google Scholar 

  • OECD. (2019). Artificial Intelligence on Society (Vol. 58(3), pp. 377–400). Retrieved from https://www.oecd-ilibrary.org/.ors

  • OED. (2021). Oxford University Press. OED. Retrieved April 29, 2021, from www.oxfordreference.com

  • Oshlyansky, L., Cairns, P., & Thimbleby, H. (2007). Validating the unified theory of acceptance and use of technology (UTAUT) tool cross-culturally. In Proceedings of the 21st British HCI group annual conference. University of Lancaster (Vol. 21, pp. 1–4).

  • Palan, S., & Schitter, C. (2018). Prolific.ac—A subject pool for online experiments. Journal of Behavioral and Experimental Finance, 17, 22–27.

    Google Scholar 

  • Parmigiani, A., & Mitchell, W. (2005). How buyers shape supplier performance: Can governance skills substitute for technical expertise in managing out-sourcing relationships? Academy of Management Proceedings, 2005(1), C1–C6.

    Google Scholar 

  • Parry, K. W., Cohen, M., & Bhattacharya, S. (2016). Rise of the machines: A critical consideration of automated leadership decision making in organizations. Group & Organization Management, 41(5), 571–594.

    Google Scholar 

  • Pasquale, F. (2015). The Black Box Society: The Secret Algorithms that Control Money and Information. Harvard University Press.

    Google Scholar 

  • Peck, D. (2013). They’re watching you at work. The Atlantic, 312(5), 72–84.

    Google Scholar 

  • Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017). Beyond the Turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70, 153–163.

    Google Scholar 

  • Pirson, M., & Malhotra, D. (2011). Foundations of organizational trust: What matters to different stakeholders? Organization Science, 22(4), 1087–1104.

    Google Scholar 

  • Pirson, M., Martin, K., & Parmar, B. (2019). Public trust in business and its determinants. Business & Society, 58(1), 132–166.

    Google Scholar 

  • Polli, F. (2019). Using AI to eliminate bias from hiring. Harvard Business Review, 29.

  • Prpic, N. (2020). The AI recruitment evolution—from Amazon’s biased algorithm to contextual understanding. Retrieved May 3, 2021, from https://www.talentlyft.com/en/blog/article/414/the-ai-recruitment-evolution-from-amazons-biased-algorithm-to-contextual-understanding

  • Pulakos, E. D. (2005). Selection assessment methods. United stated of America: Society for Human Resource Management (SHRM) Foundation.

    Google Scholar 

  • Rąb-Kettler, K., & Lehnervp, B. (2019). Recruitment in the times of machine learning. Management Systems in Production Engineering, 27, 105–109.

    Google Scholar 

  • Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 469–481).

  • Ramchurn, S. D., Wu, F., Jiang, W., Fischer, J. E., Reece, S., Roberts, S., Rodden, T., Greenhalgh, C., & Jennings, N. R. (2016). Human–agent collaboration for disaster response. Autonomous Agents and Multi-Agent Systems, 30(1), 82–111.

    Google Scholar 

  • Rawls, J. (2001). Justice as fairness: A restatement. Harvard University Press.

    Google Scholar 

  • Robinson, L., Gibson, G., Kingston, A., Newton, L., Pritchard, G., Finch, T., & Brittain, K. (2013). Assistive technologies in caring for the oldest old: A review of current practice and future directions. Aging and Health, 9(4), 365–375.

    Google Scholar 

  • Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Free Press.

    Google Scholar 

  • Ryan, A. M., & Ployhart, R. E. (2000). Applicants’ perceptions of selection procedures and decisions: A critical review and agenda for the future. Journal of Management, 26(3), 565–606.

    Google Scholar 

  • Sanchez-Monedero, J., Dencik, L., & Edwards, L. (2020, January). What does it mean to ‘solve’ the problem of discrimination in hiring? In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 458–468).

  • Schoorman, F. D., Mayer, R. C., & Davis, J. H. (2007). An integrative model of organizational trust: Past, present, and future. Academy of Management Review, 32(2), 344–354.

    Google Scholar 

  • Schwoerer, C. E., May, D. R., Hollensbe, E. C., & Mencl, J. (2005). General and specific self-efficacy in the context of a training intervention to enhance performance expectancy. Human Resource Development Quarterly, 16(1), 111–129.

    Google Scholar 

  • Sheeran, P., & Webb, T. L. (2016). The intention–behavior gap. Social and Personality Psychology Compass, 10(9), 503–518.

    Google Scholar 

  • Sheppard, B. H., & Sherman, D. M. (1998). The grammars of trust: A model and general implications. Academy of Management Review, 23(3), 422–437.

    Google Scholar 

  • Shilton, K., Koepfler, J. A., & Fleischmann, K. R. (2013). Charting sociotechnical dimensions of values for design research. The Information Society, 29(5), 259–271.

    Google Scholar 

  • Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47–53.

    Google Scholar 

  • Singh, J. J., Iglesias, O., & Batista-Foguet, J. M. (2012). Does having an ethical brand matter? The influence of consumer perceived ethicality on trust, affect and loyalty. Journal of Business Ethics, 111(4), 541–549.

    Google Scholar 

  • Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324.

    Google Scholar 

  • Speicher, T., Heidari, H., Grgic-Hlaca, N., Gummadi, K. P., Singla, A., Weller, A., & Zafar, M. B. (2018, July). A unified approach to quantifying algorithmic unfairness: Measuring individual &group unfairness via inequality indices. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2239–2248).

  • StataCorp. (2019). Stata 16 Base Reference Manual. College Station, TX: Stata Press.

    Google Scholar 

  • Surowiecki, J. (2005). The wisdom of crowds. Anchor.

    Google Scholar 

  • Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42.

    Google Scholar 

  • Teo, H. H., Wei, K. K., & Benbasat, I. (2003). Predicting intention to adopt interorganizational linkages: An institutional perspective. MIS Quarterly, 27(1), 19–49.

    Google Scholar 

  • Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124–143.

    Google Scholar 

  • Tilcsik, A. (2021). Statistical discrimination and the rationalization of stereotypes. American Sociological Review, 86(1), 93–122.

    Google Scholar 

  • Turkle, S. (Ed.). (2011). The inner history of devices. MIT Press.

    Google Scholar 

  • Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255–258.

    Google Scholar 

  • Van de Poel, I. (2016). An ethical framework for evaluating experimental technology. Science and Engineering Ethics, 22(3), 667–686.

    Google Scholar 

  • van den Broek, E., Sergeeva, A., & Huysman, M. (2019). Hiring algorithms: an ethnography of fairness in practice. In ICIS Proceedings, 6. https://aisel.aisnet.org/icis2019/future_of_work/future_work/6

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Google Scholar 

  • Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

    Google Scholar 

  • Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376.

    Google Scholar 

  • Weber, J., & Gillespie, J. (1998). Differences in ethical beliefs, intentions, and behaviors: The role of beliefs and intentions in ethics research revisited. Business & Society, 37(4), 447–467.

    Google Scholar 

  • Why, M. (2018). 4 reasons why an automated hiring process will help your company. In Select international, a PSI business (Vol. 2018). Select International.

  • Wright, S. A., & Schultz, A. E. (2018). The rising tide of artificial intelligence and business automation: Developing an ethical framework. Business Horizons, 61(6), 823–832.

    Google Scholar 

  • Yampolskiy, R. V. (2019). Predicting future AI failures from historic examples. Foresight, 21(1), 138–152.

    Google Scholar 

  • Zaheer, A., McEvily, B., & Perrone, V. (1998). Does trust matter? Exploring the effects of interorganizational and interpersonal trust on performance. Organization Science, 9(2), 141–159.

    Google Scholar 

  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. SSRN 3312874.

  • Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224.

    Google Scholar 

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Authors and Affiliations

  1. EDHEC Business School, 24 avenue Gustave Delory, CS 50411, 59057, Roubaix Cedex 1, France

    Maria Figueroa-Armijos & Serge P. da Motta Veiga

  2. College of Business Administration, University of Nebraska Omaha, 6708 Pine Street, Omaha, NE, 68182, USA

    Brent B. Clark

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  1. Maria Figueroa-Armijos
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  2. Brent B. Clark
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  3. Serge P. da Motta Veiga
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Appendix

Appendix

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Fig. 2
figure 2

SEM results for overarching research model

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Figueroa-Armijos, M., Clark, B.B. & da Motta Veiga, S.P. Ethical Perceptions of AI in Hiring and Organizational Trust: The Role of Performance Expectancy and Social Influence. J Bus Ethics 186, 179–197 (2023). https://doi.org/10.1007/s10551-022-05166-2

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