Skip to main content
SpringerLink
Log in

Seeming Ethical Makes You Attractive: Unraveling How Ethical Perceptions of AI in Hiring Impacts Organizational Innovativeness and Attractiveness

Journal of Business Ethics Aims and scope Submit manuscript

Cite this article

Abstract

More organizations use AI in the hiring process than ever before, yet the perceived ethicality of such processes seems to be mixed. With such variation in our views of AI in hiring, we need to understand how these perceptions impact the organizations that use it. In two studies, we investigate how ethical perceptions of using AI in hiring are related to perceptions of organizational attractiveness and innovativeness. Our findings indicate that ethical perceptions of using AI in hiring are positively related to perceptions of organizational attractiveness, both directly and indirectly via perceptions of organizational innovativeness, with variations depending on the type of hiring method used. For instance, we find that individuals who consider it ethical for organizations to use AI in ways often considered to be intrusive to privacy, such as analyzing social media content for traits and characteristics, view such organizations as both more innovative and attractive. Our findings trigger a timely discussion about the critical role of ethical perceptions of AI in hiring on organizational attractiveness and innovativeness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data Availability

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

Notes

  1. For a review see John-Matthews et al. (2022).

  2. Shilton et al. (2013) provides a framework for “Values in Design” (VID) which can be found in emerging technologies, including “privacy, trust, security, safety, community, freedom from bias, autonomy, freedom of expression, identity, dignity, calmness, compassion, and respect” (p. 5).

  3. Since we collected data from both job seekers and employed individuals with hiring experience, we ran additional analyses to examine whether and how the hypothesized structural model varied depending on whether respondents were job seekers or not. We found that the results were equivalent for both types of participants.

  4. While we cannot completely rule out reverse causality, we tested an alternative model, in which organizational attractiveness would influence ethical perceptions of using AI in hiring both directly and indirectly via organizational innovativeness. We found that organizational attractiveness was positively related to these ethical perceptions, but only directly. The indirect relationship via organizational innovativeness was not significant, neither was the relationship between innovativeness and ethical perceptions of using AI in hiring. These findings provide further support to our theory-driven model.

  5. As for Study 1, we ran additional analyses to examine whether the hypothesized structural model varied depending on whether respondents were job seekers or not. We found that the results were equivalent for both types of participants.

References

  • Aiken, J. R., & Hanges, P. J. (2017). The Sum of the Parts: Methods of Combining Assessments for Employment Decisions. In Handbook of employee selection (pp. 388–396). Routledge.

  • Albert, E. (2019). AI in talent acquisition: A review of AI-applications used in recruitment and selection. Strategic HR Review, 18, 215–221.

    Google Scholar 

  • Alder, G. S., & Gilbert, J. (2006). Achieving ethics and fairness in hiring: Going beyond the law. Journal of Business Ethics, 68(4), 449–464.

    Google Scholar 

  • Alniacik, U., Cigerim, E., Akcin, K., & Bayram, O. (2011). Independent and joint effects of perceived corporate reputation, affective commitment, and job satisfaction on turnover intentions. Procedia— Social and Behavioral Sciences, 24, 1177–1189.

    Google Scholar 

  • 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, 611–623.

    Google Scholar 

  • Arthur, W., Glaze, R. M., Villado, A. J., & Taylor, J. E. (2009). Unproctored Internet-based tests of cognitive ability and personality: Magnitude of cheating and response distortion. Industrial and Organizational Psychology, 2, 39–45.

    Google Scholar 

  • Azadegan, A., & Dooley, K. J. (2010). Supplier innovativeness, organizational learning styles and manufacturer performance: An empirical assessment. Journal of Operations Management, 28, 488–505.

    Google Scholar 

  • Bangerter, A., Roulin, N., & König, C. J. (2012). Personnel selection as a signaling game. Journal of Applied Psychology, 97, 719–738.

    Google Scholar 

  • Bansal, G., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138–150.

    Google Scholar 

  • BBC. (2019). Apple's 'sexist' credit card investigated by US regulator. Retrieved from: https://www.bbc.com/news/business-50365609

  • Belinda, C. D., Westerman, J. W., & Bergman, S. M. (2018). Recruiting with ethics in an online era: Integrating corporate social responsibility with social media to predict organizational attractiveness. Journal of Vocational Behavior, 109, 101–117.

    Google Scholar 

  • Berthon, P., Ewing, M., & Hah, L. L. (2005). Captivating company: Dimensions of attractiveness in employer branding. International Journal of Advertising, 24, 151–172.

    Google Scholar 

  • Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review, 94(4), 991–1013.

    Google Scholar 

  • Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63, 215–226.

    Google Scholar 

  • Bogle, S., & Sankaranarayanan, S. (2012). Job search system in Android environment: Application of intelligent agents. International Journal of Information Sciences and Techniques, 2, 1–17.

    Google Scholar 

  • Bologna, M. J. (2019). ‘Hiring Robots’ Restrictions Passed by Illinois Legislature. Bloomberg Law News. Retrieved from https://news.bloomberglaw.com/daily-labor-report/hiring-robots-restrictions-passed-by-illinois-legislature

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

    Google Scholar 

  • Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of machine learning research (Vol. 81, pp. 1–15). http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf

  • Büschgens, T., Bausch, A., & Balkin, D. B. (2013). Organizational culture and innovation: A meta-analytic review. Journal of Product Innovation Management, 30, 763–781.

    Google Scholar 

  • Cable, D. M., & Graham, M. E. (2000). The determinants of job seekers’ reputation perceptions. Journal of Organizational Behavior, 21, 929–947.

    Google Scholar 

  • Chapman, D. S., Uggerslev, K. L., Carroll, S. A., Piasentin, K. A., & Jones, D. A. (2005). Applicant attraction to organizations and job choice: A meta-analytic review of the correlates of recruiting outcomes. Journal of Applied Psychology, 90, 928–944.

    Google Scholar 

  • Crockett, K., Colyer, E., & Latham, A. (2021, December). The Ethical Landscape of Data and Artificial Intelligence: Citizen Perspectives. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–9). IEEE.

  • Das, P., Pandey, M., & Rautaray, S. S. (2018). A CV parser model using entity extraction process and big data tools. International Journal of Information Technology and Computer Science, 9, 21–31.

    Google Scholar 

  • Dattner, B., Chamorro-Premuzic, T., Buchband, R., & Schettler, L. (2019). The legal and ethical implications of using AI in hiring. Harvard Business Review, 25, 1–7.

    Google Scholar 

  • Dawson, L. M. (1997). Ethical differences between men and women in the sales profession. Journal of Business Ethics, 16, 1143–1152.

    Google Scholar 

  • Derous, E., & de Fruyt, F. (2016). Developments in recruitment and selection research. International Journal of Selection and Assessment, 24, 1–3.

    Google Scholar 

  • Dineen, B. R., Noe, R. A., & Wang, C. (2004). Perceived fairness of web-based applicant screening procedures: Weighing the rules of justice and the role of individual differences. Human Resource Management, 43, 127–145.

    Google Scholar 

  • Edwards, C., Edwards, A., Stoll, B., Lin, X., & Massey, N. (2019). Evaluations of an artificial intelligence instructor’s voice: Social identity theory in human–robot interactions. Computers in Human Behavior, 90, 357–362.

    Google Scholar 

  • Ehrhart, K. H., & Ziegert, J. C. (2005). Why are individuals attracted to organizations? Journal of Management, 31(6), 901–919.

    Google Scholar 

  • Esmaeilzadeh, P. (2020). Use of AI-based tools for healthcare purposes: A survey study from consumers’ perspectives. BMC Medical Informatics and Decision Making, 20(1), 1–19.

    Google Scholar 

  • Ewing, M. T., Pitt, L. F., De Bussy, N. M., & Berthon, P. (2002). Employment branding in the knowledge economy. International Journal of Advertising, 21, 3–22.

    Google Scholar 

  • Figueroa-Armijos, M., Clark, B. B., & da Motta Veiga, S. P. (2022). Ethical perceptions of AI in hiring and organizational trust: The role of performance expectancy and social influence. Journal of Business Ethics. https://doi.org/10.1007/s10551-022-05166-2

    Article  Google Scholar 

  • Franklin, S., & Graesser, A. (1996). Is it an agent, or just a program? A taxonomy for autonomous agents. International workshop on agent theories, architectures, and languages (pp. 21–35). Berlin: Springer.

  • Friedman, B., & Nissenbaum, H. (1997). Bias in computer systems. In B. Friedman (Ed.), Human values and the design of computer technology (pp. 21–40). Cambridge University Press.

    Google Scholar 

  • Fuller, J. B., Raman, M., Sage-Gavin, E., & Hines, K. (2021). Hidden workers: Untapped talent. Harvard Business School Project on Managing the Future of Work and Accenture. HBR Reprint F2203A.

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

    Google Scholar 

  • Gonzalez, M. F., Capman, J. F., Martin, N. R., Justenhoven, R., & Preuss, A. (2019b). Rage against the machine: Reactions to artificial intelligence in selection systems. In: Poster presented at the 34th annual meeting of the Society for Industrial and Organizational Psychology, National Harbor, MD.

  • Gonzalez, M. F., Capman, J. F., Oswald, F. L., Theys, E. R., & Tomczak, D. L. (2019a). Where’s the IO? Artificial intelligence and machine learning in talent management systems. Personnel Assessment and Decisions, 5, 33–44.

    Google Scholar 

  • Goodman, E. (2017). Biometrics won't solve our data-security crisis. Harvard Business Review. Retrieved from https://hbr.org/2017/12/biometrics-wontsolve-our-data-security-crisis

  • Gruzd, A., & Hernandez-Garcia, A. (2018). Privacy concerns and self-disclosure in private and public uses of social media. Cyberpsychology, Behavior, and Social Networking, 21(7), 418–428.

    Google Scholar 

  • Haenlein, M., Huang, M. H., & Kaplan, A. (2022). Guest editorial: Business ethics in the era of artificial intelligence. Journal of Business Ethics, 178, 867–869.

    Google Scholar 

  • Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36–S40.

    Google Scholar 

  • Hermann, E. (2022). Leveraging artificial intelligence in marketing for social good—An ethical perspective. Journal of Business Ethics, 179(1), 43–61.

    Google Scholar 

  • Highhouse, S., Lievens, F., & Sinar, E. F. (2003). Measuring attraction to organizations. Educational and Psychological Measurement, 63, 986–1001.

    Google Scholar 

  • Highhouse, S., Thornbury, E. E., & Little, I. S. (2007). Social-identity functions of attraction to organizations. Organizational Behavior and Human Decision Processes, 103, 134–146.

    Google Scholar 

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

    Google Scholar 

  • Holm, A. B. (2014). Institutional context and e-recruitment practices of Danish organizations. Employee Relations, 36, 432–455.

    Google Scholar 

  • Houser, K. A. (2019). Can AI solve the diversity problem in the tech industry: Mitigating noise and bias in employment decision-making. Stanford Technology Law Review, 22, 290–351.

    Google Scholar 

  • Howardson, G. N., & Behrend, T. S. (2014). Using the Internet to recruit employees: Comparing the effects of usability expectations and objective technological characteristics on Internet recruitment outcomes. Computers in Human Behavior, 31, 334–342.

    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, 1–55.

    Google Scholar 

  • Hunkenschroer, A. L., & Kriebitz, A. (2022). Is AI recruiting (un) ethical? A human rights perspective on the use of AI for hiring. AI and Ethics, 3, 199–213.

    Google Scholar 

  • Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection: A review and research agenda. Journal of Business Ethics, 178, 977–1007.

    Google Scholar 

  • IBM. (2018). 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/

  • Islam, G., & Greenwood, M. (2022). The metrics of ethics and the ethics of metrics. Journal of Business Ethics, 175(1), 1–5.

    Google Scholar 

  • Jacobson, J., Gruzd, A., & Hernandez-Garcia, A. (2020). Social media marketing: Who is watching the watchers? Journal of Retailing and Consumer Services, 53, 101774.

    Google Scholar 

  • Johnson, D. G. (2000). Computer Ethics (3rd ed.). Prentice Hall.

    Google Scholar 

  • Johnson, D. G. (2015). Technology with no human responsibility? Journal of Business Ethics, 127, 707–715.

    Google Scholar 

  • Johnson, D. G., & Verdicchio, M. (2017). Reframing AI discourse. Minds & Machines, 27, 575–590.

    Google Scholar 

  • 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, 15–25.

    Google Scholar 

  • Kashive, N., & Khanna, V. T. (2017). Study of early recruitment activities and employer Brand knowledge and its effect on organization attractiveness and firm performance. Global Business Review, 18, 172–190.

    Google Scholar 

  • Kasy, M., & Abebe, R. (2021). Fairness, equality, and power in algorithmic decision-making. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp.576–586).

  • Kekäle, T., & Kola‐Nyström, S. (2007). Successful innovations from an established company. Business Strategy Series.

  • Kelley, S. (2022). Employee perceptions of the effective adoption of AI principles. Journal of Business Ethics, 178, 871–893.

    Google Scholar 

  • Khalil, O. E. (1993). Artificial decision-making and artificial ethics: A management concern. Journal of Business Ethics, 12(4), 313–321.

    Google Scholar 

  • Konradt, U., Warszta, T., & Ellwart, T. (2013). Fairness perceptions in web-based selection: Impact on applicants’ pursuit intentions, recommendation intentions, and intentions to reapply. International Journal of Selection and Assessment, 21, 155–169.

    Google Scholar 

  • Kristof, A. L. (1996). Person-organization fit: An integrative review of its conceptualizations, measurement, and implications. Personnel Psychology, 49, 1–49.

    Google Scholar 

  • Kristof-Brown, A. L., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of individuals’ fit at work: A meta-analysis of person–job, person–organization, person–group, and person–supervisor fit. Personnel Psychology, 58, 281–342.

    Google Scholar 

  • 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 

  • Lavanchy, M., Reichert, P., Narayanan, J., & Savani, K. (2023). Applicants’ fairness perceptions of algorithm-driven hiring procedures. Journal of Business Ethics. https://doi.org/10.1007/s10551-022-05320-w

    Article  Google Scholar 

  • Levashina, J., Hartwell, C. J., Morgeson, F. P., & Campion, M. A. (2014). The structured employment interview: Narrative and quantitative review of the research literature. Personnel Psychology, 67, 241–293.

    Google Scholar 

  • Leventhal, G. S. (1980). What should be done with equity theory? New approaches to the study of fairness in social relationships. In K. J. Gergen, M. S. Greenberg, & R. H. Willis (Eds.), Social exchange: Advances in theory and research (pp. 27–55). Plenum.

    Google Scholar 

  • Liem, C. C., Langer, M., Demetriou, A., Hiemstra, A. M., Wicaksana, A. S., Born, M. P., & König, C. J. (2018). Psychology meets machine learning: Interdisciplinary perspectives on algorithmic job candidate screening. In: Explainable and interpretable models in computer vision and machine learning (pp. 197–253). Springer, Cham.

  • Lievens, F., & Highhouse, S. (2003). The relation of instrumental and symbolic attributes to a company’s attractiveness as an employer. Personnel Psychology, 56, 75–102.

    Google Scholar 

  • Lievens, F., Van Hoye, G., & Schreurs, B. (2005). Examining the relationship between employer knowledge dimensions and organizational attractiveness: An application in a military context. Journal of Occupational and Organizational Psychology, 78, 553–572.

    Google Scholar 

  • Luthar, H. K., & Karri, R. (2005). Exposure to ethics education and the perception of linkage between organizational ethical behavior and business outcomes. Journal of Business Ethics, 61(4), 353–368.

    Google Scholar 

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

    Google Scholar 

  • 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. https://doi.org/10.1007/s10551-021-05032-7

    Article  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 

  • Martins, E. C., & Terblanche, F. (2003). Building organizational culture that stimulates creativity and innovation. European Journal of Innovation Management, 6, 64–74.

    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, 1693–1725.

    Google Scholar 

  • McDonald, K., Fisher, S., & Connelly, C. E. (2017). e-HRM systems in support of “smart” workforce management: an exploratory case study of system success. In T. Bondarouk, H. J. M. Ruël, & E. Parry (Eds.), Electronic HRM in the smart era, the changing context of managing people (pp. 87–108). Emerald Publishing Limited.

    Google Scholar 

  • Minge, M., & Thüring, M. (2018). Hedonic and pragmatic halo effects at early stages of user experience. International Journal of Human-Computer Studies, 109, 13–25.

    Google Scholar 

  • Morris, M. G., Venkatesh, V., & Ackerman, P. L. (2005). Gender and age differences in employee decisions about new technology: An extension to the theory of planned behavior. IEEE Transactions on Engineering Management, 52(1), 69–84.

    Google Scholar 

  • Mosley, R. (2012). Employer brand management: Practical lessons from the new yorkworld’s leading employers. New York: Wiley.

    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 

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

    Google Scholar 

  • Nisbett, R. E., & Wilson, T. D. (1977). The halo effect: Evidence for unconscious alteration of judgments. Journal of Personality and Social Psychology, 35, 250–256.

    Google Scholar 

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

  • Oracle (2019). State of Artificial Intelligence in talent acquisition. Retrieved from https://www.oracle.com/a/ocom/docs/artificial-intelligence-intalent-acquisition.pdf.

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

    Google Scholar 

  • Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33, 1125–1147.

    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 

  • Pistono, F., & Yampolskiy, R. V. (2016). Unethical research: how to create a malevolent artificial intelligence. Presented at 25th International Joint Conference on Artificial Intelligence (IJCAI-16). Ethics for Artificial Intelligence Workshop. https://arxiv.org/ftp/arxiv/papers/1605/1605.02817.pdf

  • Plummer, J. T. (2000). How personality makes a difference. Journal of Advertising Research, 40, 79–83.

    Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63(1), 539–569.

    Google Scholar 

  • Pučėtaitė, R., Novelskaitė, A., Lämsä, A. M., & Riivari, E. (2016). The relationship between ethical organisational culture and organisational innovativeness: Comparison of findings from Finland and Lithuania. Journal of Business Ethics, 139, 685–700.

    Google Scholar 

  • Pulakos, E. D. (2005). Selection assessment methods. SHRM Foundation.

  • Riivari, E., & Lämsä, A. M. (2019). Organizational ethical virtues of innovativeness. Journal of Business Ethics, 155, 223–240.

    Google Scholar 

  • Riivari, E., Lämsä, A. M., Kujala, J., & Heiskanen, E. (2012). The ethical culture of organisations and organisational innovativeness. European Journal of Innovation Management, 15, 310–331.

    Google Scholar 

  • Ritson, M. (2002). Marketing and HR collaborate to harness employer Brand power. Retrieved from: www.campaignlive.co.uk/article/opinionmarketing-hr-collaborate-harness-employer-brand-power/162068?src_site=marketingmagazine

  • Ryan, A. M., Inceoglu, I., Bartram, D., Golubovich, J., Grand, J., Reeder, M., Derous, E., Nikolaou, I., & Yao, X. (2015). Trends in testing: Highlights of a global survey. In I. Nikolaou & J. K. Oostrom (Eds.), Employee recruitment, selection, and assessment: Contemporary issues for theory and practice (pp. 136–153). Psychology 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, 565–606.

    Google Scholar 

  • Sarros, J. C., Cooper, B. K., & Santora, J. C. (2008). Building a climate for innovation through transformational leadership and organizational culture. Journal of Leadership and Organizational Studies, 15, 145–158.

    Google Scholar 

  • Schneider, B. (1987). The people make the place. Personnel Psychology, 40, 437–453.

    Google Scholar 

  • Schreurs, B., Druart, C., Proost, K., & De Witte, K. (2009). Symbolic attributes and organizational attractiveness: The moderating effects of applicant personality. International Journal of Selection and Assessment, 17, 35–46.

    Google Scholar 

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

    Google Scholar 

  • Slaughter, J. E., & Greguras, G. J. (2009). Initial attraction to organizations: The influence of trait inferences. International Journal of Selection and Assessment, 17, 1–18.

    Google Scholar 

  • Slaughter, J. E., Zickar, M. J., Highhouse, S., & Mohr, D. C. (2004). Personality trait inferences about organizations: Development of a measure and assessment of construct validity. Journal of Applied Psychology, 89, 85–102.

    Google Scholar 

  • Sommer, L. P., Heidenreich, S., & Handrich, M. (2017). War for talents—How perceived organizational innovativeness affects employer attractiveness. R&D Management, 47, 299–310.

    Google Scholar 

  • Srinivasan, V. (2014). A call for artificial intelligence tools to support manufacturing innovation. International Journal on Artificial Intelligence Tools, 23, 1460025.

    Google Scholar 

  • Stefanowicz, B. (2022, March 23). AI recruitment: The future of hiring or HR’s nightmare? Tidio. Retrieved from https://www.tidio.com/blog/ai-recruitment/

  • Stephan, M., Brown, D., & Erickson, R. (2017). Talent acquisition: Enter the cognitive recruiter. Retrieved from https://www2.deloitte.com/insights/us/en/focus/human-capital-trends/2017/predictive-hiring-talent-acquisition.html

  • Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Monterey, CA: Brooks/Cole.

    Google Scholar 

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

    Google Scholar 

  • 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, 178, 961–976.

    Google Scholar 

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

    Google Scholar 

  • Tom, V. R. (1971). The role of personality and organizational images in the recruiting process. Organizational Behavior and Human Performance, 6, 573–592.

    Google Scholar 

  • Tonidandel, S., King, E. B., & Cortina, J. M. (2018). Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21, 525–547.

    Google Scholar 

  • Tsang, L., Kracov, D. A., Mulryne, J., Strom, L., Perkins, N., Dickinson, R., et al. (2017). The impact of artificial intelligence on medical innovation in the European Union and United States. Intellectual Property Technology Law Journal, 29, 3–12.

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

    Google Scholar 

  • van Esch, P., Black, J. S., & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215–222.

    Google Scholar 

  • van Esch, P., Black, J. S., & Arli, D. (2020). Job candidates’ reactions to AI-Enabled job application processes. AI and Ethics, 1, 119–130.

    Google Scholar 

  • Van Hoye, G., Bas, T., Cromheecke, S., & Lievens, F. (2013). The instrumental and symbolic dimensions of organisations' image as an employer: A large‐scale field study on employer branding in Turkey. Applied Psychology, 62, 543–557.

    Google Scholar 

  • Vanderstukken, A., Van den Broeck, A., & Proost, K. (2016). For love or for money: Intrinsic and extrinsic value congruence in recruitment. International Journal of Selection and Assessment, 24, 34–41.

    Google Scholar 

  • Varsha, P. S., Akter, S., Kumar, A., Gochhait, S., & Patagundi, B. (2021). The impact of artificial intelligence on branding: A bibliometric analysis (1982–2019). Journal of Global Information Management, 29, 221–246.

    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, 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, 328–376.

    Google Scholar 

  • Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33, 1237–1266.

    Google Scholar 

  • Weinert, S., Gunther, E., Ruger-Muck, E., & Raab, G. (2020). Artificial intelligence in personnel selection and its influence on employer attractiveness. Marketing Science & Inspirations, 15, 22–35.

    Google Scholar 

  • Weintraub, A. (2017). Consider HIPAA when using A.I. & machine learning. MedStack. Retrieved from https://medstack.co/blog/consider-hipaa-using-machine-learning/

  • Whittaker, M., Crawford, K., Dobbe, R., Fried, G., Kaziunas, E., Mathur, V., et al. (2018). AI now report 2018. Retrieved from: www.ainowinstitute.org

  • Yeung, A. C., Lai, K. H., & Yee, R. W. (2007). Organizational learning, innovativeness, and organizational performance: A qualitative investigation. International Journal of Production Research, 45, 2459–2477.

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Serge P. da Motta Veiga & Maria Figueroa-Armijos

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

    Brent B. Clark

Authors
  1. Serge P. da Motta Veiga
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria Figueroa-Armijos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brent B. Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Serge P. da Motta Veiga.

Ethics declarations

Conflicts of Interest

We have no conflict of interest to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

da Motta Veiga, S.P., Figueroa-Armijos, M. & Clark, B.B. Seeming Ethical Makes You Attractive: Unraveling How Ethical Perceptions of AI in Hiring Impacts Organizational Innovativeness and Attractiveness. J Bus Ethics 186, 199–216 (2023). https://doi.org/10.1007/s10551-023-05380-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10551-023-05380-6

Keywords

search

Navigation