The Relevant Human Resource Professional in Relation to Artificial Intelligence—Constructing Human Agency
DOI:
https://doi.org/10.18291/njwls.166522Abstract
As artificial intelligence (AI) is introduced in knowledge work, boundaries of professional agency change. Human resources management professionals in Sweden have historically been struggling with claiming relevance in organizations. Through a qualitative analysis of articles from Swedish HR magazines, this study explores how the relevant HR professional is discursively constructed in relation to the increasing use of AI, and the ramifications of these constructions for the agency of HR professionals. The study shows that the HR professional is left to reconcile conflicting portrayals of herself—as both more and less biased than AI—and contradictory expectations to both fearlessly embrace and question AI. The relevant HR professional is thus one who successfully navigates these tensions, operating within the boundaries of agency shaped by these opposing constructions. This research contributes to the algorithmic HRM literature and the literature on AI and agency.
Author Biographies
Anna Launberg, Mälardalen University
Senior lecturer, Mälardalens University
Eva Lindell, Mälardalen University
Associate Professor, Mälardalens University
References
Alon-Barkat, S., & Busuioc, M. (2023). Human–AI interactions in public sector decision making:“automation bias” and “selective adherence” to algorithmic advice. Journal of Public Administration Research and Theory, 33(1), 153–169.
Alsos, K., & Dølvik, J. E. (2021). The future of work in the Nordic countries: Opportunities and challenges for the Nordic working life models. Nordic Council of Ministers.
Anderson, J., & Rainie, L. (2023). The future of human agency. Pew Research Center.
Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human resource management journal, 26(1), 1–11. https://doi.org/10.1111/1748-8583.12090
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022). Ethical framework for artificial intelligence and digital technologies. International Journal of Information Management, 62, 102433. https://doi.org/10.1016/j.ijinfomgt.2021.102433
Baakeel, O. A. (2020). The Impact of Human Resource Information Systems on Organizational Performance. The International Journal of Business & Management. Nordic journal of working life studies 19
Berglund, J. (2002). De otillräckliga: en studie av personalspecialisternas kamp för erkän¬nande och status [The Insufficient: A Study of HR Specialists’ Struggle for Recognition and Status] Stockholm School of Economics].
Blomberg, H. (2016). Nurses’ blogs as part of a political process–Professional identity as a rhetorical resource for negotiating responsibility and blame. Discourse, Context & Media, 13, 82–88. https://doi.org/10.1016/j.dcm.2016.07.001
Boon, C., Paauwe, J., Boselie, P., & Den Hartog, D. (2009). Institutional pressures and HRM: developing institutional fit. Personnel Review, 38(5), 492–508. https://doi. org/10.1108/00483480910978018
Bursell, M., & Roumbanis, L. (2024). After the algorithms: A study of meta-algorithmic judgments and diversity in the hiring process at a large multisite company. Big Data & Society, 11(1). https://doi.org/10.1177/20539517231221
Carter, L., & Liu, D. (2025). How was my performance? Exploring the role of anchoring bias in AI-assisted decision making. International Journal of Information Management, 82, 102875.
Claydon, T., & Doyle, M. (1996). Trusting me, trusting you? The ethics of employee empow¬erment. Personnel Review, 25(6), 13–25. https://doi.org/10.1108/00483489610148509
Demetis, D., & Lee, A. S. (2018). When humans using the IT artifact becomes IT using the human artifact. Journal of the Association for Information Systems, 19(10), 5. https:// aisel.aisnet.org/jais
Dewandre, N. (2020). Big data: From modern fears to enlightened and vigilant embrace of new beginnings. Big Data & Society, 7(2), 2053951720936708.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114. https://doi.org/10.1037/xge0000033
Edwards, D., & Potter, J. (1992). Discursive psychology (Vol. 8). Sage.
Espegren, Y., & Hugosson, M. (2023). HR analytics-as-practice: A systematic literature review. Journal of Organizational Effectiveness: People and Performance, 12(5), 83–111. https://doi.org/10.1108/JOEPP-11-2022-0345
Eteläpelto, A., Vähäsantanen, K., Hökkä, P., & Paloniemi, S. (2013). What is agency? Conceptualizing professional agency at work. Educational Research Review, 10, 45–65. https://doi.org/10.1016/j.edurev.2013.05.001
Fanni, R., Steinkogler, V. E., Zampedri, G., & Pierson, J. (2023). Enhancing human agency through redress in Artificial Intelligence Systems. AI & Society, 38(2), 537–547. https:// doi.org/10.1007/s00146-022-01454-7
Foucault, M. (1980). Two lectures. In C. Gordon (Ed.), Power/knowledge: Selected Interviews and Other Writings 1972-1977 by Michel Foucault (pp. 78–108). Pantheon Books.
Gal, U., Jensen, T. B., & Stein, M.-K. (2020). Breaking the vicious cycle of algorithmic man¬agement: A virtue ethics approach to people analytics. Information and Organization, 30(2), 100301. https://doi.org/10.1016/j.infoandorg.2020.100301
Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022). The dark sides of people analytics: Reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410–435. https://doi.org/10.1080/096 0085X.2021.1927213
Greasley, K., & Thomas, P. (2020). HR analytics: The onto‐epistemology and politics of metricised HRM. Human Resource Management Journal, 30(4), 494–507. https://doi. org/10.1111/1748-8583.12283
Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8
Häll, A. (2024). Lost in translation: The human resource business partner in a Scandinavian context. Nordic Journal of Working Life Studies, 15(1). https://orcid. org/0000-0002-9673-6084
Isaac, M. S., Wang, R. J.-H., Napper, L. E., & Marsh, J. K. (2024). To err is human: Bias salience can help overcome resistance to medical AI. Computers in Human Behavior, 161, 108402.
Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8(2). https://doi. org/10.1177/20539517211020332
Johnson, R. D., Stone, D. L., & Lukaszewski, K. M. (2020). The benefits of eHRM and AI for talent acquisition. Journal of Tourism Futures, 7(1), 40–52. https://doi.org/10.1108/ JTF-02-2020-0013
Jorgensen, M. W., & Philips, L. (2002). Discourse analysis as theory and method. SAGE publications.
Jussupow, E., Benbasat, I., & Heinzl, A. (2020). Why are we averse towards algorithms? A comprehensive literature review on algorithm aversion. ECIS 2020 Proceedings,
Komp-Leukkunen, K., & Poli, A. (2024). An introduction to digitalizing work in the Nordics. Nordic Journal of Working Life Studies, 14(S12). https://doi.org/10.18291/njwls.145237
Lelebina, O., & Gand, S. (2018). Beyond “Hero-based” management: Revisiting HRM prac¬tices for managing collective expertise. Relations Industrielles, 73(1), 39–66. https://doi. org/10.7202/1044426ar
Madill, A., & Doherty, K. (1994). ‘So you did what you wanted then’: Discourse analysis, per¬sonal agency, and psychotherapy. Journal of Community & Applied Social Psychology, 4(4), 261–273. https://doi.org/10.1002/casp.2450040406
Mahadevan, J., & Schmitz, A. P. (2020). HRM as an ongoing struggle for legitimacy: A critical discourse analysis of HR managers as “employee-experience designers”. Baltic Journal of Management, 15(4), 515–532. https://doi.org/10.1108/BJM-10-2018-0368
Malik, A., Thevisuthan, P., & De Sliva, T. (2022). Artificial intelligence, employee engage¬ment, experience, and HRM. In Strategic human resource management and employment relations: An international perspective (pp. 171–184). Springer.
Mehrabad, M. S., & Brojeny, M. F. (2007). The development of an expert system for effective selection and appointment of the jobs applicants in human resource manage¬ment. Computers & Industrial Engineering, 53(2), 306–312. https://doi.org/10.1016/j. cie.2007.06.023
Meijerink, J., Boons, M., Keegan, A., & Marler, J. (2021). Algorithmic human resource man¬agement: Synthesizing developments and cross-disciplinary insights on digital HRM. In (Vol. 32, pp. 2545–2562): Taylor & Francis.
Miceli, M. P., Near, J. P., & Dworkin, T. M. (2009). A word to the wise: How managers and policy-makers can encourage employees to report wrongdoing. Journal of Business Ethics, 86, 379–396. https://doi.org/10.1007/s10551-008-9853-6
Noe, R. A., Hollenbeck, J. R., Gerhart, B., & Wright, P. M. (2015). Human Resources Management. 9th edition. Mc Graw Hill Education.
Potter, J. (1996). Representing reality: Discourse, rhetoric and social construction. Sage.
Potter, J., & Wetherell, M. (1987). Discourse and social psychology: Beyond attitudes and behaviour. Sage.
Prikshat, V., Islam, M., Patel, P., Malik, A., Budhwar, P., & Gupta, S. (2023). AI-Augmented HRM: Literature review and a proposed multilevel framework for future research. Technological fFrecasting and Social Change, 193. https://doi.org/10.1016/j.techfore.2023.122645
Reed, M. I. (1988). The problem of human agency in organizational analysis. Organization Studies, 9(1), 33–46. https://doi.org/10.1177/017084068800900
Seeck, H., & Parzefall, M. R. (2008). Employee agency: Challenges and opportunities for psychological contract theory. Personnel Review, 37(5), 473–489. https://doi. org/10.1108/00483480810891637
Sekerka, L. E., Bagozzi, R. P., & Charnigo, R. (2009). Facing ethical challenges in the work¬place: Conceptualizing and measuring professional moral courage. Journal of Business Ethics, 89, 565–579. https://doi.org/10.1007/s10551-008-0017-5
Silberg, J., & Manyika, J. (2019). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute, 1(6), 1–31.
Strohmeier, S., & Piazza, F. (2015). Artificial intelligence techniques in human resource management—A conceptual exploration. In Intelligent Techniques in Engineering Management: Theory and Applications (pp. 149–172). Springer.
Tengblad, S., & Nord, D. (2024). Betyder AI alls inte? [Does AI mean nothing at all?].
Timmons, K. C. (2020). Pre-Employment personality tests, algorithmic bias, and the Americans with Disabilities Act. Penn St. L. Rev., 125, 389.
Ulrich, D., Younger, J., Brockbank, W., & Ulrich, M. (2012). HR talent and the new HR competencies. Strategic HR Review, 11(4), 217–222. https://doi.org/10. 1108/14754391211234940
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A sys¬tematic review. In Artificial intelligence and International HRM (pp. 172–201).
Wetherell, M. (1998). Positioning and interpretative repertoires: Conversation analysis and post-structuralism in dialogue. Discourse & Society, 9(3), 387–412. https://doi.org/10.1 177%2F0957926598009003005
Wetherell, M., & Potter, J. (1988). Discourse analysis and the identification of interpretative repertoires. Analysing everyday explanation: A casebook of methods, 1688183.
Wilcox, T. (2012). Human resource management in a compartmentalized world: Whither moral agency? Journal of Business Ethics, 111, 85–96. https://doi.org/10.1007/ s10551-012-1440-1
Wright, C. (2008). Reinventing human resource management: Business partners, internal consultants and the limits to professionalization. Human Relations, 61(8), 1063–1086. https://doi.org/10.1177/0018726708094860
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Author/Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
