Treść głównego artykułu
Abstrakt
The purpose of the paper is to determine the role of the phenomenon of algorithmic discrimination in the processes of implementing smart technologies in HR, particularly in the context of sustainable management. To accomplish this task, the author conducted a scoping review of the literature. The study indicated a significant role of the described phenomenon in shaping employee opinions about artificial intelligence and emphasised the importance of sustainable people management in its utilisation. The research results call for deeper reflection on how to assess the performance of artificial intelligence and highlight that attempting to replicate human abilities in machines not only offers new possibilities but also carries the risk of perpetuating human imperfections. The limitations of the study arise from the small number of available empirical studies in this area. The article helps to understand the essence of artificial intelligence and contributes to filling the knowledge gap regarding methods of managing people in the process of implementing smart technologies.
Szczegóły artykułu
Autor (Autorzy) artykułu oświadcza, że przesłane opracowanie nie narusza praw autorskich osób trzecich. Wyraża zgodę na poddanie artykułu procedurze recenzji oraz dokonanie zmian redakcyjnych. Przenosi nieodpłatnie na Oficynę Wydawniczą SGH autorskie prawa majątkowe do utworu na polach eksploatacji wymienionych w art. 50 Ustawy z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych – pod warunkiem, że praca została zaakceptowana do publikacji i opublikowana.
Oficyna Wydawnicza SGH posiada autorskie prawa majątkowe do wszystkich treści czasopisma. Zamieszczenie tekstu artykuły w repozytorium, na stronie domowej autora lub na innej stronie jest dozwolone o ile nie wiąże się z pozyskiwaniem korzyści majątkowych, a tekst wyposażony będzie w informacje źródłowe (w tym również tytuł, rok, numer i adres internetowy czasopisma).
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Referencje
- Arias, M., Rivero, C., Márquez, O. (2023). Artificial Intelligence to Manage Workplace Bullying. Journal of Business Research, 160. Retrieved from: https://doi.org/10.1016/j.jbusres.2023.113813
- Arksey, H., O’Malley, L. (2005). Scoping Studies: Towards a Methodological Framework. International Journal of Social Research Methodology, 8 (1), p. 19–32. Retrieved from: https://doi.org/10.1080/1364557032000119616.
- Aust-Before Ehnert, I., Matthews, B., Muller-Camen, M. (2019). Common Good HRM: A Paradigm Shift in Sustainable HRM? Human Resource Management Review, 30, p. 100705. Retrieved from: https://doi.org/10.1016/j.hrmr.2019.100705
- Baldegger, R., Caon, M., Sadiku, K. (2020). Correlation Between Entrepreneurial Orientation and Implementation of AI in Human Resource Management (HRM). Technology Innovation Management Review, 10 (4), p. 72–79. Retrieved from: https://doi.org/10.22215/TIMREVIEW/1348.
- Bartosiak, M. L., Modlinski, A. (2022). Fired by an Algorithm? Exploration of Conformism with Biased Intelligent Decision Support Systems in the Context of Workplace Discipline. Career Development International, 27 (6–7), p. 601–615. Retrieved from:
- https://doi.org/10.1108/CDI-06-2022-0170.
- Boudreau, J. W., Ramstad, P. M. (2005). Talentship, Talent Segmentation, and Sustainability: A New HR Decision Science Paradigm for a New Strategy Definition. Human Resource Management, 44 (2), p. 129–136. Retrieved from: https://doi.org/10.1002/hrm.20054
- Colquhoun, H., Levac, D., O’Brien, K., Straus, S., Tricco, A., Perrier, L., Kastner, M., Moher, D. (2014). Scoping Reviews: Time for Clarity in Definition, Methods, and Reporting. Journal of Clinical Epidemiology, 67. Retrieved from: https://doi.org/10.1016/j.jclinepi.2014.03.013.
- Conte, F., Siano, A. (2023). Data-driven Human Resource and Data-driven Talent Management in Internal and Recruitment Communication Strategies: An Empirical Survey on Italian Firms and Insights for European Context. Corporate Communications, 28 (4), p. 618–637. Retrieved from: https://doi.org/10.1108/CCIJ-02-2022-0012.
- Ćwiklicki, M. (2020). Metodyka przeglądu zakresu literatury (scoping review). In: Sopińska, A., Modliński, A. (Eds.), Współczesne zarządzanie – koncepcje i wyzwania. Warsaw: Oficyna Wydawnicza SGH, p. 53–68.
- Daudt, H., Mossel, C., Scott, S. (2013). Enhancing the Scoping Study Methodology: A Large, Interprofessional Team’s Experience with Arksey and O’Malley’s Framework. BMC Medical Research Methodology, 13. Retrieved from: https://doi.org/10.1186/14712288-13-48.
- Dyllick, T., Muff, K. (2016). Clarifying the Meaning of Sustainable Business: Introducing a Typology From Business-as-Usual to True Business Sustainability.
- Organization & Environment, 29 (2), p. 156–174. Retrieved from: https://doi. org/10.1177/1086026615575176.
- Hofeditz, L., Clausen, S., Riess, A., Mirbabaie, M., Stieglitz, S. (2022). Applying XAI to an AI-Based System for Candidate Management to Mitigate Bias and Discrimination in Hiring. Electronic Markets, 32 (4), p. 2207–2233. Retrieved from: https://doi. org/10.1007/s12525-022-00600-9.
- Ivaschenko, A., Diyazitdinova, A. R., Nikiforova, T. (2021). Optimisation of the Rational Proportion of Intelligent Technologies Application in Service Organisations. Organizacija, 54 (2), p. 162–177. Retrieved from: https://doi.org/10.2478/orga2021-0011.
- Kramar, R. (2014). Beyond Strategic Human Resource Management: Is Sustainable
- Human Resource Management the Next Approach? The International Journal of Human Resource Management, 25 (8), p. 1069–1089. Retrieved from: https://doi.org/10.1080/09585192.2013.816863.
- Kshetri, N. (2021). Evolving Uses of Artificial Intelligence in Human Resource Management in Emerging Economies in the Global South: Some Preliminary Evidence. Management Research Review, 44 (7), p. 970–990. Retrieved from: https://doi.org/10.1108/MRR03-2020-0168.
- Malik, N., Tripathi, S., Kar, A., Gupta, S. (2022). Impact of Artificial Intelligence on Employees Working in Industry 4.0 Led Organizations. International Journal of Manpower, 43 (2), p. 334–354. Retrieved from: https://doi.org/10.1108/IJM-032021-0173.
- Malin, C., Kupfer, C., Fleiss, J., Kubicek, B., Thalmann, S. (2023). In the AI of the Beholder-A Qualitative Study of HR Professionals’ Beliefs about AI-Based Chatbots and Decision Support in Candidate Pre-Selection. Administrative Sciences, 13 (11). https://doi.org/10.3390/admsci13110231.
- Mays, N., Roberts, E., Popay, J. (2001). Studying the Organisation and Delivery of Health Services. London: Routlege.
- Mirowska, A., Mesnet, L. (2022). Preferring the Devil You Know: Potential Applicant Reactions to Artificial Intelligence Evaluation of Interviews. Human Resources Management Journal, 32 (2), p. 364–383. Retrieved from: https://doi.org/10.1111/17488583.12393.
- Olajide, O., Sposato, M. (2022). Opportunities and Risks of Artificial Intelligence in Recruitment and Selection. International Journal of Organizational Analysis, 30 (6), p. 1771–1782. Retrieved from: https://doi.org/10.1108/IJOA-07-2020-2291.
- Oswald, F. L., Behrend, T. S., Putka, D. J., Sinar, E. (2020). Big Data in IndustrialOrganizational Psychology and Human Resource Management: Forward Progress for Organizational Research and Practice. Annual Review of Organizational Psychology and Organizational Behavior, 7, p. 505–533. Retrieved from: https://doi.org/10.1146/annurev-orgpsych-032117-104553.
- Soleimani, M., Intezari, A., Pauleen, D. (2022). Mitigating Cognitive Biases in Developing AI-Assisted Recruitment Systems: A Knowledge-Sharing Approach. International
- Journal of Knowledge Management, 18 (1). Retrieved from: https://doi.org/10.4018/ IJKM.290022.
- Suseno, Y., Chang, C., Hudik, M., Fang, E. S. (2022). Beliefs, Anxiety and Change Readiness for Artificial Intelligence Adoption Among Human resource Managers: The Moderating Role of High-performance Work Systems. International Journal of Human Resource Management, 33 (6), p. 1209–1236. Retrieved from: https://doi.org/10.1080/09585192.2021.1931408.
- Tranfield, D., Denyer, D., Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British
- Journal of Management, 14 (3), p. 207–222. Retrieved from: https://doi.org/10.1111/ 1467-8551.00375.
- Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., Lewin, S., Godfrey, C. M., Macdonald, M. T., Langlois, E. V., Soares-Weiser, K., Moriarty, J., Clifford, T.,
- Tunçalp, Ö., Straus, S. E. (2018). PRISMA Extension for Scoping Reviews ( PRISMAScR): Checklist and Explanation. Annals of Internal Medicine, 169 (7), p. 467–473. Retrieved from: https://doi.org/10.7326/M18-0850.
- Trocin, C., Hovland, I., Mikalef, P., Dremel, C. (2021). How Artificial Intelligence Affords Digital Innovation: A Cross-case Analysis of Scandinavian Companies. Technological Forecasting and Social Change, 173. Retrieved from: https://doi.org/10.1016/j. techfore.2021.121081.
- 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 (6), p. 1237–1266. Retrieved from: https://doi.org/10.1080/09585192.2020.1871398.
- Weber, P. (2023). Unrealistic Optimism Regarding Artificial Intelligence Opportunities in Human Resource Management. International Journal of Knowledge Management, 19 (1). Retrieved from: https://doi.org/10.4018/IJKM.317217.
- Zawiła-Niedźwiecki, J. (2014), Operacjonalizacja zarządzania wiedzą w świetle badań Wydziału Zarzadzania Politechniki Warszawskiej, Informatyka Ekonomiczna, 1 (31), p. 91–100. Retrieved from: https://doi.org/10.15611/ie.2014.1.08.
- Zhou, Y., Wang, L., Chen, W. (2023). The Dark Side of AI-enabled HRM on Employees Based on AI Algorithmic Features. Journal of Organizational Change Management, 36 (7), p. 1222–1241. Retrieved from: https://doi.org/10.1108/JOCM-10-2022-0308.
Referencje
Arias, M., Rivero, C., Márquez, O. (2023). Artificial Intelligence to Manage Workplace Bullying. Journal of Business Research, 160. Retrieved from: https://doi.org/10.1016/j.jbusres.2023.113813
Arksey, H., O’Malley, L. (2005). Scoping Studies: Towards a Methodological Framework. International Journal of Social Research Methodology, 8 (1), p. 19–32. Retrieved from: https://doi.org/10.1080/1364557032000119616.
Aust-Before Ehnert, I., Matthews, B., Muller-Camen, M. (2019). Common Good HRM: A Paradigm Shift in Sustainable HRM? Human Resource Management Review, 30, p. 100705. Retrieved from: https://doi.org/10.1016/j.hrmr.2019.100705
Baldegger, R., Caon, M., Sadiku, K. (2020). Correlation Between Entrepreneurial Orientation and Implementation of AI in Human Resource Management (HRM). Technology Innovation Management Review, 10 (4), p. 72–79. Retrieved from: https://doi.org/10.22215/TIMREVIEW/1348.
Bartosiak, M. L., Modlinski, A. (2022). Fired by an Algorithm? Exploration of Conformism with Biased Intelligent Decision Support Systems in the Context of Workplace Discipline. Career Development International, 27 (6–7), p. 601–615. Retrieved from:
https://doi.org/10.1108/CDI-06-2022-0170.
Boudreau, J. W., Ramstad, P. M. (2005). Talentship, Talent Segmentation, and Sustainability: A New HR Decision Science Paradigm for a New Strategy Definition. Human Resource Management, 44 (2), p. 129–136. Retrieved from: https://doi.org/10.1002/hrm.20054
Colquhoun, H., Levac, D., O’Brien, K., Straus, S., Tricco, A., Perrier, L., Kastner, M., Moher, D. (2014). Scoping Reviews: Time for Clarity in Definition, Methods, and Reporting. Journal of Clinical Epidemiology, 67. Retrieved from: https://doi.org/10.1016/j.jclinepi.2014.03.013.
Conte, F., Siano, A. (2023). Data-driven Human Resource and Data-driven Talent Management in Internal and Recruitment Communication Strategies: An Empirical Survey on Italian Firms and Insights for European Context. Corporate Communications, 28 (4), p. 618–637. Retrieved from: https://doi.org/10.1108/CCIJ-02-2022-0012.
Ćwiklicki, M. (2020). Metodyka przeglądu zakresu literatury (scoping review). In: Sopińska, A., Modliński, A. (Eds.), Współczesne zarządzanie – koncepcje i wyzwania. Warsaw: Oficyna Wydawnicza SGH, p. 53–68.
Daudt, H., Mossel, C., Scott, S. (2013). Enhancing the Scoping Study Methodology: A Large, Interprofessional Team’s Experience with Arksey and O’Malley’s Framework. BMC Medical Research Methodology, 13. Retrieved from: https://doi.org/10.1186/14712288-13-48.
Dyllick, T., Muff, K. (2016). Clarifying the Meaning of Sustainable Business: Introducing a Typology From Business-as-Usual to True Business Sustainability.
Organization & Environment, 29 (2), p. 156–174. Retrieved from: https://doi. org/10.1177/1086026615575176.
Hofeditz, L., Clausen, S., Riess, A., Mirbabaie, M., Stieglitz, S. (2022). Applying XAI to an AI-Based System for Candidate Management to Mitigate Bias and Discrimination in Hiring. Electronic Markets, 32 (4), p. 2207–2233. Retrieved from: https://doi. org/10.1007/s12525-022-00600-9.
Ivaschenko, A., Diyazitdinova, A. R., Nikiforova, T. (2021). Optimisation of the Rational Proportion of Intelligent Technologies Application in Service Organisations. Organizacija, 54 (2), p. 162–177. Retrieved from: https://doi.org/10.2478/orga2021-0011.
Kramar, R. (2014). Beyond Strategic Human Resource Management: Is Sustainable
Human Resource Management the Next Approach? The International Journal of Human Resource Management, 25 (8), p. 1069–1089. Retrieved from: https://doi.org/10.1080/09585192.2013.816863.
Kshetri, N. (2021). Evolving Uses of Artificial Intelligence in Human Resource Management in Emerging Economies in the Global South: Some Preliminary Evidence. Management Research Review, 44 (7), p. 970–990. Retrieved from: https://doi.org/10.1108/MRR03-2020-0168.
Malik, N., Tripathi, S., Kar, A., Gupta, S. (2022). Impact of Artificial Intelligence on Employees Working in Industry 4.0 Led Organizations. International Journal of Manpower, 43 (2), p. 334–354. Retrieved from: https://doi.org/10.1108/IJM-032021-0173.
Malin, C., Kupfer, C., Fleiss, J., Kubicek, B., Thalmann, S. (2023). In the AI of the Beholder-A Qualitative Study of HR Professionals’ Beliefs about AI-Based Chatbots and Decision Support in Candidate Pre-Selection. Administrative Sciences, 13 (11). https://doi.org/10.3390/admsci13110231.
Mays, N., Roberts, E., Popay, J. (2001). Studying the Organisation and Delivery of Health Services. London: Routlege.
Mirowska, A., Mesnet, L. (2022). Preferring the Devil You Know: Potential Applicant Reactions to Artificial Intelligence Evaluation of Interviews. Human Resources Management Journal, 32 (2), p. 364–383. Retrieved from: https://doi.org/10.1111/17488583.12393.
Olajide, O., Sposato, M. (2022). Opportunities and Risks of Artificial Intelligence in Recruitment and Selection. International Journal of Organizational Analysis, 30 (6), p. 1771–1782. Retrieved from: https://doi.org/10.1108/IJOA-07-2020-2291.
Oswald, F. L., Behrend, T. S., Putka, D. J., Sinar, E. (2020). Big Data in IndustrialOrganizational Psychology and Human Resource Management: Forward Progress for Organizational Research and Practice. Annual Review of Organizational Psychology and Organizational Behavior, 7, p. 505–533. Retrieved from: https://doi.org/10.1146/annurev-orgpsych-032117-104553.
Soleimani, M., Intezari, A., Pauleen, D. (2022). Mitigating Cognitive Biases in Developing AI-Assisted Recruitment Systems: A Knowledge-Sharing Approach. International
Journal of Knowledge Management, 18 (1). Retrieved from: https://doi.org/10.4018/ IJKM.290022.
Suseno, Y., Chang, C., Hudik, M., Fang, E. S. (2022). Beliefs, Anxiety and Change Readiness for Artificial Intelligence Adoption Among Human resource Managers: The Moderating Role of High-performance Work Systems. International Journal of Human Resource Management, 33 (6), p. 1209–1236. Retrieved from: https://doi.org/10.1080/09585192.2021.1931408.
Tranfield, D., Denyer, D., Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British
Journal of Management, 14 (3), p. 207–222. Retrieved from: https://doi.org/10.1111/ 1467-8551.00375.
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., Lewin, S., Godfrey, C. M., Macdonald, M. T., Langlois, E. V., Soares-Weiser, K., Moriarty, J., Clifford, T.,
Tunçalp, Ö., Straus, S. E. (2018). PRISMA Extension for Scoping Reviews ( PRISMAScR): Checklist and Explanation. Annals of Internal Medicine, 169 (7), p. 467–473. Retrieved from: https://doi.org/10.7326/M18-0850.
Trocin, C., Hovland, I., Mikalef, P., Dremel, C. (2021). How Artificial Intelligence Affords Digital Innovation: A Cross-case Analysis of Scandinavian Companies. Technological Forecasting and Social Change, 173. Retrieved from: https://doi.org/10.1016/j. techfore.2021.121081.
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 (6), p. 1237–1266. Retrieved from: https://doi.org/10.1080/09585192.2020.1871398.
Weber, P. (2023). Unrealistic Optimism Regarding Artificial Intelligence Opportunities in Human Resource Management. International Journal of Knowledge Management, 19 (1). Retrieved from: https://doi.org/10.4018/IJKM.317217.
Zawiła-Niedźwiecki, J. (2014), Operacjonalizacja zarządzania wiedzą w świetle badań Wydziału Zarzadzania Politechniki Warszawskiej, Informatyka Ekonomiczna, 1 (31), p. 91–100. Retrieved from: https://doi.org/10.15611/ie.2014.1.08.
Zhou, Y., Wang, L., Chen, W. (2023). The Dark Side of AI-enabled HRM on Employees Based on AI Algorithmic Features. Journal of Organizational Change Management, 36 (7), p. 1222–1241. Retrieved from: https://doi.org/10.1108/JOCM-10-2022-0308.