Utilizing Artificial Intelligence in Human Resources: A Qualitative Study in the Philippines
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Abstract
Background/ problem: Artificial intelligence (AI) is increasingly being utilized in human resource management (HRM), thereby transforming its practice. However, nuanced literature on its use in developing contexts remains limited.
Objective/ purpose: This exploratory study examined how human resources (HR) practitioners in the Philippines perceive and experience the utilization of AI in their work.
Design and Methodology: Guided by the unified theory of acceptance and use of technology (UTAUT), it adopted a qualitative research design. Eleven practitioners from diverse industries were interviewed, and data were analyzed through reflexive thematic analysis.
Findings: Reflexive thematic analysis identified specific themes across four UTAUT dimensions: (1) social influences (leadership influence, workforce openness/resistance, institutional influences); (2) facilitating conditions (structural barriers, change management, AI-friendly ecosystem); (3) effort expectancy (perceived tool flexibility, competency gaps, output unreliability); and (4) performance expectancy (operational efficiency, functional efficiency, strategic capacity). Findings highlight how social and organizational drivers enable AI usability to elevate HR’s strategic role.
Conclusion and Implications: This study highlights the interdisciplinary nature of technology adoption in HRM by bridging topics in work psychology, management, organization development, and AI. It demonstrates that the utilization of AI’s transformative potential depends not only on technical capacity but also on addressing psychological and social barriers. Implications point to the need for greater structural support from organizations in the use of AI in HR as well as resources to develop the AI capabilities of HR practitioners.
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References
Asfahani, A. M. (2022). The impact of artificial intelligence on industrial-organizational psychology: A systematic review. The Journal of Behavioral Science, 17(3), 125–139. https://so06.tci-thaijo.org/index.php/IJBS/article/view/259402
Braun, V., & Clarke, V. (2021a). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238
Braun, V., & Clarke, V. (2021b). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Research in Sport, Exercise and Health, 13(2), 201–216. https://doi.org/10.1080/2159676X.2019.1704846
Bronfenbrenner, U. (1995). Developmental ecology through space and time: A future perspective. In P. Moen, G. H. Elder, Jr., & K. Luscher (Eds.), Examining lives in context (pp. 619–647). American Psychological Association. https://doi.org/10.1037/10176-018
Brougham, D., & Haar, J. (2020). Technological disruption and employment: The influence on job insecurity and turnover intentions: A multi-country study. Technological Forecasting and Social Change, 161, 120276. https://doi.org/10.1016/j.techfore.2020.120276
Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022). Artificial intelligence – challenges and opportunities for international HRM: A review and research agenda. The International Journal of Human Resource Management, 33(6), 1065–1097. https://doi.org/10.1080/09585192.2022.2035161
Caringal-Go, J. F., Teng-Calleja, M., Franco, E. P., Manaois, J. O. & Zantua, R. M. S. (2021). Crisis leadership from the perspective of employees during the COVID-19 pandemic. Leadership & Organization Development Journal, 42(4), 630–643. https://doi.org/10.1108/LODJ-07-2020-0284
Csaszar, F. A., Jacobides, M. G., & Zemsky, P. (2026). The effects of artificial intelligence on management education. Strategic Organization, 24(2), 440–462. https://doi.org/10.1177/14761270251385484
Cucio, M., & Hennig, T. (2025). Artificial intelligence and the Philippine labor market: Mapping occupational exposure and complementarity (IMF Working Paper No. 2025/043). International Monetary Fund. https://doi.org/10.5089/9798229001977.001
Ekuma, K. (2023). Artificial intelligence and automation in human resource development: A systematic review. Human Resource Development Review, 23(2), 199–229. https://doi.org/10.1177/15344843231224009
García-Madurga, M. Á., Gil-Lacruz, A. I., Saz-Gil, I., & Gil-Lacruz, M. (2024). The role of artificial intelligence in improving workplace well-being: A systematic review. Businesses, 4(3), 389–410. https://doi.org/10.3390/businesses4030024
Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 105–117). SAGE Publications.
Hofstede, G. (1980). Motivation, leadership, and organization: Do American theories apply abroad? Organizational Dynamics, 9(1), 42–63. https://doi.org/10.1016/0090-2616(80)90013-3
Jaiswal, A., Arun, C. J., & Varma, A. (2022). Rebooting employees: Upskilling for artificial intelligence in multinational corporations. The International Journal of Human Resource Management, 33(6), 1179–1208. https://doi.org/10.1080/09585192.2021.1891114
Jibril, H., & Roper, S. (2025). Factors influencing firms’ adoption of advanced technologies: A rapid evidence review (Research report). Department for Science, Innovation & Technology. https://www.gov.uk/government/publications/barriers-and-enablers-to-advanced-technology-adoption-for-uk-businesses/factors-influencing-firms-adoption-of-advanced-technologies-a-rapid-evidence-review
Kaufman, M. R., Eschliman, E. L., & Karver, T. S. (2023). Differentiating sex and gender in health research to achieve gender equity. Bulletin of the World Health Organization, 101(10), 666–671. https://doi.org/10.2471/BLT.22.289310
Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: developing framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965. https://doi.org/10.1111/jan.13031
Koştı, G., & Kayadibi, İ. (2025). A bibliometric analysis of artificial intelligence and machine learning applications for human resource management. Future Business Journal, 11, 179. https://doi.org/10.1186/s43093-025-00602-x
Lee, K., & Joshi, K. (2020). Understanding the role of cultural context and user interaction in artificial intelligence based systems. Journal of Global Information Technology Management, 23(3), 171–175. https://doi.org/10.1080/1097198X.2020.1794131
Lichtenthaler, U. (2020). Extremes of acceptance: Employee attitudes toward artificial intelligence. Journal of Business Strategy, 41(5), 39–45. https://doi.org/10.1108/JBS-12-2018-0204
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, 7, 100249. https://doi.org/10.1016/j.dajour.2023.100249
Nankervis, A., Connell, J., Cameron, R., Montague, A., & Prikshat, V. (2021). ‘Are we there yet?’ Australian HR professionals and the fourth industrial revolution. Asia Pacific Journal of Human Resources, 59(1), 3–19. https://doi.org/10.1111/1744-7941.12245
Palos-Sánchez, P. R., Baena-Luna, P., Badicu, A., & Infante-Moro, J. C. (2022). Artificial intelligence and human resources management: A bibliometric analysis. Applied Artificial Intelligence, 36(1). https://doi.org/10.1080/08839514.2022.2145631
Park, S., Chai, D. S., Park, J. J., & Oh, J. (2025). Exploring opportunities for artificial intelligence in organization development. Human Resource Development Review, 24(1), 3–36. https://doi.org/10.1177/15344843241273231
Pedrami, M., & Vaezi, S.K. (2025). Factors influencing artificial intelligence adoption in human resource management: A meta-synthesis and systematic review of multidimensional considerations. Journal of Work-Applied Management, 18(1), 47–64. https://doi.org/10.1108/JWAM-10-2024-0158
Presbitero, A., & Teng-Calleja, M. (2023). Job attitudes and career behaviors relating to employees’ perceived incorporation of artificial intelligence in the workplace: A career self-management perspective. Personnel Review, 52(4), 1169–1187. https://doi.org/10.1108/PR-02-2021-0103
Prikshat, V., Kumar, S., Patel, P., & Varma, A. (2025). Impact of organisational facilitators and perceived HR effectiveness on acceptance of AI-augmented HRM: An integrated TAM and TPB perspective. Personnel Review, 54(3), 879–912. https://doi.org/10.1108/PR-04-2023-0303
Revillod, G. (2025). Exploring the acceptability of artificial intelligence in human resources management: Insights from Swiss organizations. Systems Research and Behavioral Science, 42(4), 1061–1084. https://doi.org/10.1002/sres.3140
Tanantong, T., & Wongras, P. (2024). A UTAUT-based framework for analyzing users’ intention to adopt artificial intelligence in human resource recruitment: A case study of Thailand. Systems, 12(1), 28. https://doi.org/10.3390/systems12010028
Teng-Calleja, M., Caringal-Go, J. F., Manaois, J. O., Isidro, M. Q. Y., & Zantua, R. M. S. (2020). Examining organizational response and employee coping behaviors amid the COVID-19 pandemic. The Journal of Behavioral Science, 15(3), 34–50. https://so06.tci-thaijo.org/index.php/IJBS/article/view/242518
Teng-Calleja, M., Presbitero, A., & Franco, E. P. (2024). Strategic human resource management. In M. Teng-Calleja, J. F. Caringal-Go, M. T. D. Mactal, & M. R. M. Hechanova (Eds.), Evolving HR: Knowledge and stories from Philippine organizations. Ateneo de Manila University Press.
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3) 443–488. https://doi.org/10.1108/JEIM-09-2014-0088
Willig, C. (2021). From recipes to adventures. In C. Willig (Ed.), Introducing qualitative research in psychology: Adventures in theory and method (4th ed., pp. 3–16). Open University Press.
World Bank (2024). World Bank East Asia and the Pacific economic update, October 2024: Jobs and technology. World Bank. http://hdl.handle.net/10986/41998
Ulrich, D. (1997). Human resource champions: The next agenda for adding value and delivering results. Harvard Business School Press.
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. https://doi.org/10.2307/30036540
Vongurai, R. (2025). Examining organizational and human drivers of artificial intelligence adoption for enhancing operational performance in Thai technology enterprises. The Journal of Behavioral Science, 20(2), 1–14. https://doi.org/10.69523/tjbs.2025.282283
Xue, L., Rashid, A. M., & Ouyang, S. (2024). The unified theory of acceptance and use of technology (UTAUT) in higher education: A systematic review. Sage Open, 14(1). https://doi.org/10.1177/21582440241229570
Zirar, A., Ali, S. I. & Islam, N. (2023). Worker and workplace artificial intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/10.1016/j.technovation.2023.102747
