Opracowanie metodologii badawczej w celu oceny gotowości młodzieży do praktyk HR opartych na sztucznej inteligencji na Łotwie
DOI:
https://doi.org/10.18778/0208-6018.372.04Słowa kluczowe:
artificial intelligence, human resource management, youth readiness, mixed-methods, LatviaAbstrakt
Celem prezentowanego badania jest opracowanie kompleksowej metodologii badawczej, która pozwoli ocenić gotowość młodych profesjonalistów na Łotwie do funkcjonowania w systemach zarządzania zasobami ludzkimi wspomaganych przez sztuczną inteligencję. Ponieważ sztuczna inteligencja jest coraz częściej wykorzystywana w procesach rekrutacji i zarządzania talentami, zrozumienie stopnia przygotowania młodzieży do korzystania z takich systemów jest obecnie konieczne.
W badaniu zastosowano metodę mieszaną, łączącą badania ilościowe z jakościowymi wywiadami częściowo ustrukturyzowanymi i grupami fokusowymi. Narzędzie badawcze zostało skonstruowane tak, aby ocenić kompetencje cyfrowe, świadomość roli odgrywanej przez sztuczną inteligencję w zarządzaniu zasobami ludzkimi, zaufanie do systemów algorytmicznych oraz zdolność adaptacji. Komponent jakościowy zapewnia kontekstowy wgląd w percepcję i osobiste doświadczenia związane z rolą sztucznej inteligencji w rekrutacji. Rekrutację uczestników badania wspiera łotewska agencja rekrutacyjna, która zapewnia dostęp do odpowiedniej i zróżnicowanej bazy kandydatów.
Spodziewane wyniki obejmują identyfikację odrębnych profili gotowości łotewskiej młodzieży i ujawniają zarówno obszary kompetencji, jak i istotne luki w wiedzy lub przekonaniu o posiadaniu takich kompetencji. Przewiduje się również odkrycie różnic w postawach i nierówności w dostępie do zasobów cyfrowych.
Proponowana metodologia oferuje powtarzalne ramy do oceny gotowości do współpracy ze sztuczną inteligencją na poziomie krajowym i ma na celu pomoc specjalistom w zarządzaniu zasobami ludzkimi, edukatorom i decydentom w opracowywaniu skutecznych strategii wspierających adaptację młodzieży do transformacji miejsc pracy spowodowanych przez sztuczną inteligencję.
Pobrania
Bibliografia
Almalki M., Alkhamis M., Khairallah F., Choukou M. (2025), Perceived artificial intelligence readiness in medical and health sciences education: a survey study of students in Saudi Arabia, “BMC Medical Education”, vol. 25, 439, https://doi.org/10.1186/s12909-025-06995-1
Google Scholar
DOI: https://doi.org/10.1186/s12909-025-06995-1
Arslan A., Cooper C., Khan Z., Golgeci I., Ali I. (2021), Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies, “International Journal of Manpower”, vol. 43(1), pp. 75–88, https://doi.org/10.1108/ijm-01-2021-0052
Google Scholar
DOI: https://doi.org/10.1108/IJM-01-2021-0052
Bainbridge H.T.J., Lee I. (2014), Mixed methods in HRM research, [in:] K. Sanders, J. Cogin, H.T.J. Bainbridge (eds.), Research Methods for Human Resource Management, Routledge, London, pp. 15–33.
Google Scholar
Ben-Gal H. (2023), Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients’ gender, age and health awareness? A two-phase pilot study, “Frontiers in Public Health”, vol. 10, 931225, https://doi.org/10.3389/fpubh.2022.931225
Google Scholar
DOI: https://doi.org/10.3389/fpubh.2022.931225
Bergdahl J., Latikka R., Celuch M., Savolainen I., Mantere E., Savela N., Oksanen A. (2023), Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives, “Telematics Informatics”, vol. 82, 102013, https://doi.org/10.1016/j.tele.2023.102013
Google Scholar
DOI: https://doi.org/10.1016/j.tele.2023.102013
Bikse V., Lūsēna-Ezera I., Rivža P., Rivža B. (2021), The Development of Digital Transformation and Relevant Competencies for Employees in the Context of the Impact of the COVID-19 Pandemic in Latvia, “Sustainability”, vol. 13(16), 9233, https://doi.org/10.3390/su13169233
Google Scholar
DOI: https://doi.org/10.3390/su13169233
Braun V., Clarke V. (2006), Using thematic analysis in psychology, “Qualitative Research in Psychology”, vol. 3(2), pp. 77–101, https://doi.org/10.1191/1478088706qp063oa
Google Scholar
DOI: https://doi.org/10.1191/1478088706qp063oa
Brown P., Parker K., Newlyn H. (2024), How young workers can thrive with AI when they have the right skills, https://www.weforum.org/stories/2024/07/how-young-workers-can-thrive-with-ai-when-they-have-the-right-skills/ [accessed: 6.05.2025].
Google Scholar
Budhwar P., Malik A., Thedushika De Silva M., Thevisuthan P. (2022), Artificial intelligence – challenges and opportunities for international HRM: a review and research agenda, “The International Journal of Human Resource Management”, vol. 33(6), pp. 1065–1097, https://doi.org/10.1080/09585192.2022.2035161
Google Scholar
DOI: https://doi.org/10.1080/09585192.2022.2035161
Charlwood A., Guenole N. (2022), Can HR adapt to the paradoxes of artificial intelligence?, “Human Resource Management Journal”, vol. 32(4), pp. 729–742, https://doi.org/10.1111/1748-8583.12433
Google Scholar
DOI: https://doi.org/10.1111/1748-8583.12433
Chen Z. (2022), Collaboration among recruiters and artificial intelligence: removing human prejudices in employment, “Cognition, Technology & Work”, vol. 25, pp. 135–149, https://doi.org/10.1007/s10111-022-00716-0
Google Scholar
DOI: https://doi.org/10.1007/s10111-022-00716-0
Chen Z. (2023), Ethics and discrimination in artificial intelligence-enabled recruitment practices, “Humanities and Social Sciences Communications”, vol. 10(1), 567, https://doi.org/10.1057/s41599-023-02079-x
Google Scholar
DOI: https://doi.org/10.1057/s41599-023-02079-x
Choung H., Seberger J.S., David P. (2023), When AI is Perceived to Be Fairer than a Human: Understanding Perceptions of Algorithmic Decisions in a Job Application Context, “International Journal of Human–Computer Interaction”, vol. 40(22), pp. 7451–7468, https://doi.org/10.1080/10447318.2023.2266244
Google Scholar
DOI: https://doi.org/10.1080/10447318.2023.2266244
Chun J.S., De Cremer D., Kim Y. (2024), What algorithmic evaluation fails to deliver: respectful treatment and individualized consideration, “Scientific Reports”, vol. 14(1), 25996, https://doi.org/10.1038/s41598-024-76320-1
Google Scholar
DOI: https://doi.org/10.1038/s41598-024-76320-1
Cohen L., Manion L., Morrison K. (2018), Research Methods in Education, Routledge, London, https://doi.org/10.4324/9781315456539
Google Scholar
DOI: https://doi.org/10.4324/9781315456539
Creswell J.W. (2014), Research design: Qualitative, quantitative, and mixed methods approaches, Sage Publications, Thousand Oaks.
Google Scholar
Creswell J.W., Plano Clark V.L. (2018), Designing and Conducting Mixed Methods Research, Sage Publications, Thousand Oaks, https://books.google.com/books/about/Designing_and_Conducting_Mixed_Methods_R.html?id=eTwmDwAAQBAJ [accessed: 6.05.2025].
Google Scholar
Dai Y., Chai C., Lin P., Jong M., Guo Y., Qin J. (2020), Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age, “Sustainability”, vol. 12(16), 6597, https://doi.org/10.3390/su12166597
Google Scholar
DOI: https://doi.org/10.3390/su12166597
Dalain A., Yamin M. (2025), Examining the Influence of AI-Supporting HR Practices Towards Recruitment Efficiency with the Moderating Effect of Anthropomorphism, “Sustainability”, vol. 17(6), 2658, https://doi.org/10.3390/su17062658
Google Scholar
DOI: https://doi.org/10.3390/su17062658
Dastin J. (2018), Insight – Amazon scraps secret AI recruiting tool that showed bias against women, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G [accessed: 6.05.2025].
Google Scholar
Deepa R., Sekar S., Malik A., Kumar J., Attri R., Gupta V. (2024), Impact of AI-focussed technologies on social and technical competencies for HR managers – A systematic review and research agenda, “Technological Forecasting and Social Change”, vol. 202, 123301, https://doi.org/10.1016/j.techfore.2024.123301
Google Scholar
DOI: https://doi.org/10.1016/j.techfore.2024.123301
Dima J., Gilbert M.-H., Dextras-Gauthier J., Giraud L. (2024), The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges, “Frontiers in Psychology”, vol. 15, 1360401, https://doi.org/10.3389/fpsyg.2024.1360401
Google Scholar
DOI: https://doi.org/10.3389/fpsyg.2024.1360401
Drage E., Mackereth K. (2022), Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”, “Philosophy & Technology”, vol. 35, 89, https://doi.org/10.1007/s13347-022-00543-1
Google Scholar
DOI: https://doi.org/10.1007/s13347-022-00543-1
Einola K., Khoreva V. (2022), Best friend or broken tool? Exploring the co‐existence of humans and artificial intelligence in the workplace ecosystem, “Human Resource Management”, vol. 62(1), pp. 117–135, https://doi.org/10.1002/hrm.22147
Google Scholar
DOI: https://doi.org/10.1002/hrm.22147
European Commission (2021), Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act), COM(2021) 206 final, Brussels, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206 [accessed: 6.05.2025].
Google Scholar
EY Foundation (2024), Empowering young people in the age of AI: Understanding and addressing digital exclusion in recruitment, https://www.eyfoundation.com/en_uk/news/reports-and-resources [accessed: 6.05.2025].
Google Scholar
França T., Mamede H., Barroso J., Santos V. (2023), Artificial intelligence applied to potential assessment and talent identification in an organisational context, “Heliyon”, vol. 9(4), e14694, https://doi.org/10.1016/j.heliyon.2023.e14694
Google Scholar
DOI: https://doi.org/10.1016/j.heliyon.2023.e14694
Fritts M., Cabrera F. (2021), AI recruitment algorithms and the dehumanization problem, “Ethics and Information Technology”, vol. 23, pp. 791–801, https://doi.org/10.1007/s10676-021-09615-w
Google Scholar
DOI: https://doi.org/10.1007/s10676-021-09615-w
Hashid A., Almaqtari F. (2024), The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices, “Journal of Open Innovation: Technology, Market, and Complexity”, vol. 10(1), 100218, https://doi.org/10.1016/j.joitmc.2024.100218
Google Scholar
DOI: https://doi.org/10.1016/j.joitmc.2024.100218
Holmström J. (2021), From AI to digital transformation: The AI readiness framework, “Business Horizons”, vol. 65(3), pp. 329–339, https://doi.org/10.1016/J.BUSHOR.2021.03.006
Google Scholar
DOI: https://doi.org/10.1016/j.bushor.2021.03.006
Horodyski P. (2023), Applicants’ perception of artificial intelligence in the recruitment process, “Computers in Human Behavior Reports”, vol. 11, 100303, https://doi.org/10.1016/j.chbr.2023.100303
Google Scholar
DOI: https://doi.org/10.1016/j.chbr.2023.100303
Hradecky D., Kennell J., Cai W., Davidson R. (2022), Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe, “International Journal of Information Management”, vol. 65, 102497, https://doi.org/10.1016/j.ijinfomgt.2022.102497
Google Scholar
DOI: https://doi.org/10.1016/j.ijinfomgt.2022.102497
Hunkenschroer A., Luetge C. (2022), Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda, “Journal of Business Ethics”, vol. 178, pp. 977–1007, https://doi.org/10.1007/s10551-022-05049-6
Google Scholar
DOI: https://doi.org/10.1007/s10551-022-05049-6
International Labour Organization (2022), Global Employment Trends for Youth 2022: Investing in transforming futures for young people, https://www.ilo.org/publications/major-publications/global-employment-trends-youth-2022-investing-transforming-futures-young [accessed: 6.05.2025].
Google Scholar
Jeffe S. (2024), Employers and Students Are Concerned About Being Ready for an AI Workplace – Where Are Colleges?, https://www.ruffalonl.com/blog/artificial-intelligence/employers-and-students-are-concerned-about-being-ready-for-an-ai-workplace-where-are-colleges/ [accessed: 6.05.2025].
Google Scholar
Jöhnk J., Weißert M., Wyrtki K. (2020), Ready or Not, AI Comes – An Interview Study of Organizational AI Readiness Factors, “Business & Information Systems Engineering”, vol. 63, pp. 5–20, https://doi.org/10.1007/s12599-020-00676-7
Google Scholar
DOI: https://doi.org/10.1007/s12599-020-00676-7
Kalnina D., Nīmante D., Baranova S. (2024), Artificial intelligence for higher education: benefits and challenges for pre-service teachers, “Frontiers in Education”, vol. 9, 1501819, https://doi.org/10.3389/feduc.2024.1501819
Google Scholar
DOI: https://doi.org/10.3389/feduc.2024.1501819
Karaca O., Çalışkan S., Demir K. (2021), Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study, “BMC Medical Education”, vol. 21, 112, https://doi.org/10.1186/s12909-021-02546-6
Google Scholar
DOI: https://doi.org/10.1186/s12909-021-02546-6
Khajeali N., Kohan N., Rezaei S., Saberi A. (2025), Psychometric assessment of the Persian translated version of the “medical artificial intelligence readiness scale for medical students”, “PLOS One”, vol. 20(5), e0323543, https://doi.org/10.1371/journal.pone.0323543
Google Scholar
DOI: https://doi.org/10.1371/journal.pone.0323543
Köchling A., Wehner M.C. (2020), Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development, “Business Research”, vol. 13(3), pp. 795–848, https://doi.org/10.1007/s40685-020-00134-w
Google Scholar
DOI: https://doi.org/10.1007/s40685-020-00134-w
Kotp M., Ismail H., Basyouny H., Aly M., Hendy A., Nashwan A., Hendy A., Elmoaty A. (2025), Empowering nurse leaders: readiness for AI integration and the perceived benefits of predictive analytics, “BMC Nursing”, vol. 24, 56, https://doi.org/10.1186/s12912-024-02653-x
Google Scholar
DOI: https://doi.org/10.1186/s12912-024-02653-x
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”, vol. 44(7), pp. 970–990, https://doi.org/10.1108/MRR-03-2020-0168
Google Scholar
DOI: https://doi.org/10.1108/MRR-03-2020-0168
Malik A., Budhwar P., Mohan H., Srikanth N.R. (2022), Employee experience – the missing link for engaging employees: Insights from an MNE’s AI‐based HR ecosystem, “Human Resource Management”, vol. 62(1), pp. 97–115, https://doi.org/10.1002/hrm.22133
Google Scholar
DOI: https://doi.org/10.1002/hrm.22133
Malik A., Budhwar P., Patel C., Srikanth N.R. (2020), May the bots be with you! Delivering HR cost-effectiveness and individualised employee experiences in an MNE, “The International Journal of Human Resource Management”, vol. 33(6), pp. 1148–1178, https://doi.org/10.1080/09585192.2020.1859582
Google Scholar
DOI: https://doi.org/10.1080/09585192.2020.1859582
Murugesan U., Subramanian P., Srivastava S., Dwivedi A. (2023), A study of Artificial Intelligence impacts on Human Resource Digitalization in Industry 4.0, “Decision Analytics Journal”, vol. 7, 100249, https://doi.org/10.1016/j.dajour.2023.100249
Google Scholar
DOI: https://doi.org/10.1016/j.dajour.2023.100249
Mykhailenko O., Blayone T., Ušča S., Kvasovskyi O., Desyatnyuk O. (2020), Optimism, interest and gender equality: comparing attitudes of university students in Latvia and Ukraine toward IT learning and work, “Compare: A Journal of Comparative and International Education”, vol. 52(6), pp. 895–913, https://doi.org/10.1080/03057925.2020.1843999
Google Scholar
DOI: https://doi.org/10.1080/03057925.2020.1843999
Nasution M., Elveny M., Pamučar D., Popovic M., Gušavac B. (2024), Uncovering the Hidden Insights of the Government AI Readiness Index: Application of Fuzzy LMAW and Schweizer-Sklar Weighted Framework, “Decision Making: Applications in Management and Engineering”, vol. 7(2), pp. 443–468, https://doi.org/10.31181/dmame7220241221
Google Scholar
DOI: https://doi.org/10.31181/dmame7220241221
Ng D., Wu W., Leung J., Chiu T., Chu S. (2023), Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach, “British Journal of Educational Technology”, vol. 55(3), pp. 1082–1104, https://doi.org/10.1111/bjet.13411
Google Scholar
DOI: https://doi.org/10.1111/bjet.13411
Palinkas L., Horwitz S., Green C., Wisdom J., Duan N., Hoagwood K. (2015), Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research, “Administration and Policy in Mental Health and Mental Health Services Research”, vol. 42, pp. 533–544, https://doi.org/10.1007/s10488-013-0528-y
Google Scholar
DOI: https://doi.org/10.1007/s10488-013-0528-y
Pereira V., Hadjielias E., Christofi M., Vrontis D. (2023), A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective, “Human Resource Management Review”, vol. 33(1), 100857, https://doi.org/10.1016/j.hrmr.2021.100857
Google Scholar
DOI: https://doi.org/10.1016/j.hrmr.2021.100857
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 Forecasting and Social Change”, vol. 193, 122645, https://doi.org/10.1016/j.techfore.2023.122645
Google Scholar
DOI: https://doi.org/10.1016/j.techfore.2023.122645
Raghavan M., Barocas S., Kleinberg J., Levy K. (2020), Mitigating bias in algorithmic hiring: evaluating claims and practices, [in:] FAT* ‘20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Association for Computing Machinery, New York, pp. 469–481, https://doi.org/10.1145/3351095.3372828
Google Scholar
DOI: https://doi.org/10.1145/3351095.3372828
Rahman A., Raj A., Tomy P., Hameed M. (2024), A comprehensive bibliometric and content analysis of artificial intelligence in language learning: tracing between the years 2017 and 2023, “Artificial Intelligence Review”, vol. 57, 107, https://doi.org/10.1007/s10462-023-10643-9
Google Scholar
DOI: https://doi.org/10.1007/s10462-023-10643-9
Rigotti C., Fosch-Villaronga E. (2024), Fairness, AI & recruitment, “Computer Law & Security Review”, vol. 53, 105966, https://doi.org/10.1016/j.clsr.2024.105966
Google Scholar
DOI: https://doi.org/10.1016/j.clsr.2024.105966
Rožman M., Oreški D., Tominc P. (2022), Integrating artificial intelligence into a talent management model to increase work engagement and performance, “Frontiers in Psychology”, vol. 13, 1014434, https://doi.org/10.3389/fpsyg.2022.1014434
Google Scholar
DOI: https://doi.org/10.3389/fpsyg.2022.1014434
Rubin A. (2025), Gen Z is still anxiously using AI: Poll, https://www.axios.com/2025/04/08/gen-z-artificial-intelligence-gallup-feelings [accessed: 6.05.2025].
Google Scholar
Solyst J., Yang E., Xie S., Ogan A., Hammer J., Eslami M. (2023), The Potential of Diverse Youth as Stakeholders in Identifying and Mitigating Algorithmic Bias for a Future of Fairer AI, [in:] J. Nichols (ed.), Proceedings of the ACM on Human-Computer Interaction, vol. 7, Association for Computing Machinery, New York, pp. 1–27, https://doi.org/10.1145/3610213
Google Scholar
DOI: https://doi.org/10.1145/3610213
Tambe P., Cappelli P., Yakubovich V. (2019), Artificial Intelligence in Human Resources Management: Challenges and a Path Forward, “California Management Review”, vol. 61(4), pp. 15–42, https://doi.org/10.1177/0008125619867910
Google Scholar
DOI: https://doi.org/10.1177/0008125619867910
Teddlie C., Yu F. (2016), WITHDRAWN – Mixed Methods Sampling: A Typology With Examples, “Journal of Mixed Methods Research”, vol. 1, pp. 77–100, https://doi.org/10.1177/2345678906292430
Google Scholar
DOI: https://doi.org/10.1177/1558689806292430
United Nations (2023), United Nations – World Youth Report (WYR), https://www.un.org/development/desa/youth/world-youth-report.html [accessed: 6.05.2025].
Google Scholar
Upadhyay A.K., Khandelwal K. (2018), Applying artificial intelligence: implications for recruitment, “Strategic HR Review”, vol. 17(5), pp. 255–258, https://doi.org/10.1108/SHR-07-2018-0051
Google Scholar
DOI: https://doi.org/10.1108/SHR-07-2018-0051
Uren V., Edwards J. (2023), Technology readiness and the organizational journey towards AI adoption: An empirical study, “International Journal of Information Management”, vol. 68, 102588, https://doi.org/10.1016/j.ijinfomgt.2022.102588
Google Scholar
DOI: https://doi.org/10.1016/j.ijinfomgt.2022.102588
Pobrania
Opublikowane
Jak cytować
Numer
Dział
