Artificial Intelligence Adoption in Human Resources Management Processes: A Corporate Start-Up Context in Developing Economies
DOI:
https://doi.org/10.19166/ff.v6i2.11699Trefwoorden:
Artificial Intelligence, Human Resources Management, Corporate Start-up, Developing Economies, Technology AdoptionSamenvatting
This study examined the role of AI adoption in Human Resources Management (HRM) processes within corporate start-ups in developing economies. Adopting a mixed-methods survey research design, the study sampled 293 Limited Liability SME start-ups across five states in Nigeria, collecting quantitative data via primary questionnaires and qualitative insights through a thematic analysis of 12 peer-reviewed articles. A total of 215 valid responses were used for the analysis, representing a 73% response rate. Thematic findings revealed that AI plays a vital strategic role across key HRM functions, including talent acquisition, personalised onboarding, real-time performance tracking, employee retention analytics, and administrative task automation. Conversely, the empirical analysis indicated that the most critical challenges hindering AI adoption are practical constraints such as the perception that their businesses are too small to implement AI, a severe shortage of AI-skilled personnel, and a limited understanding of how AI functions in HR operations, among others. The study also revealed that AI adoption offers corporate start-ups in developing economies opportunities for efficient recruitment, personalised training, improved performance management, employee retention, administrative automation, and enhanced global competitiveness. The study concluded that AI is significant for the competitive agility and sustainable growth of corporate start-ups in emerging economies. It recommended, among others, a phased technology adoption approach alongside proactive management and government support such as training workshops, grants, and subsidised digital infrastructure to mitigate cost barriers and enhance AI literacy. The study’s focus on start-ups in five Nigerian states limits its generalisability and therefore becomes a limitation to the study.
Referenties
Abrokwah-Larbi, K., & Awuku-Larbi, Y. (2024). The impact of artificial intelligence in marketing on the performance of business organisations: Evidence from SMEs in an emerging economy. Journal of Entrepreneurship in Emerging Economies, 16(4), 1090–1117. https://doi.org/10.1108/JEEE-07-2022-0207
Agwu, D. J. (2026). Artificial intelligence and human resource management: HR analytics, talent management, and ethical AI. International Journal of Applied Resilience and Sustainability, 2(2), 989–1013. https://doi.org/10.70593/deepsci.0202041
Ali, I., Mughal, N., Khan, Z. H., Ahmed, J., & Mujtaba, G. (2022). Resume classification system using natural language processing and machine learning techniques. Mehran University Research Journal of Engineering and Technology, 41(1), 65–79. https://doi.org/10.22581/muet1982.2201.07
Alkudah, N. M., Almomani, T. O., & Sarayrah, S. A. (2024). Incorporating artificial intelligence in human resources management in small and medium companies: Descriptive study. International Journal of Academic Research in Business and Social Sciences, 14(3), 146–169. https://dx.doi.org/10.6007/IJARBSS/v14-i3/20946
Almousa, O., Prates, J., Yeslam, N., Mac Gregor, D., Zhang, J., Phan, V., Nielsen, M., Smith, R., & Qayumi, K. (2019). Virtual reality simulation technology for cardiopulmonary resuscitation training: An innovative hybrid system with haptic feedback. Simulation & Gaming, 50(1), 6–22. https://doi.org/10.1177/1046878118820905
Al-Onizat, H. H. (2023). Analysis of recent advances in artificial intelligence for human resource management. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 268–275. https://doi.org/10.17762/ijritcc.v11i10.8943
Aydın, E., & Turan, M. (2023). An AI-based shortlisting model for sustainability of human resource management. Sustainability, 15(3), 1–15. https://doi.org/10.3390/su15032737
Badghish, S., & Soomro, Y. A. (2024). Artificial intelligence adoption by SMEs to achieve sustainable business performance: Application of Technology–Organisation–Environment framework. Sustainability, 16(5), 1–24. https://doi.org/10.3390/su16051864
Bandari, V. (2019). Exploring the transformational potential of emerging technologies in human resource analytics: A comparative study of the applications of IoT, AI, and cloud computing. Journal of Humanities and Applied Science Research, 2(1), 15–27.
Bankins, S. (2021). The ethical use of artificial intelligence in human resource management: A decision-making framework. Ethics and Information Technology, 23(4), 841–854. https://doi.org/10.1007/s10676-021-09619-6
Basiouny, H. M. E. S. A. (2025). The role of artificial intelligence in improving human resource management performance: (Case study on FAB Egypt). Journal of Financial and Commercial Research, 26(4), 870–905.
Bersin, J. (2019). AI in HR: The new frontier. SHRM Executive Network. https://www.shrm.org/content/dam/en/shrm/executivenetwork/AI%20in%20HR-%20The%20New%20Frontier.pdf.
Bharadiya, J. P., Tzenios, N. T., & Reddy, M. (2023). Predicting crop yield using deep learning and remote sensing. Journal of Engineering Research and Reports, 24(12), 29–44. https://doi.org/10.9734/jerr/2023/v24i12858
Bhardwaj, G., Singh, S. V., & Kumar, V. (2020). An empirical study of artificial intelligence and its impact on human resource functions. In 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. 47–51). IEEE. https://doi.org/10.1109/ICCAKM46823.2020.9051544
Bhutoria, A. (2022). Personalised education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 1–18. https://doi.org/10.1016/j.caeai.2022.100068
de Laat, M., Joksimovic, S., & Ifenthaler, D. (2020). Artificial intelligence, real-time feedback, and workplace learning analytics to support in situ complex problem-solving: A commentary. The International Journal of Information and Learning Technology, 37(5), 267–277. https://doi.org/10.1108/IJILT-03-2020-0026
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, 15, 1–15. https://doi.org/10.3389/fpsyg.2024.1360401
Farhan, M., Alhazmi, A. K., & Alsakkaf, N. (2025). A model for AI-driven employee performance enhancement. Journal of Science and Technology, 30(2), 107–113. https://doi.org/10.20428/jst.v30i2.2760
Forrester. (2023). How AI improves HR functions and employee engagement. Forrester Insight.
González, C. A. C., Benítez, H. A. M., Montiel, E. G. M., Amaya, L. E. Y., & Ortega, R. V. (2025). The artificial intelligence adoption in human talent management among SMEs in emerging economies: Evidence from Ecuador. Journal of Technology Management and Innovation, 20(4), 88–100. https://doi.org/10.4067/s0718-27242025000300088
Gryncewicz, W., Zygała, R., & Pilch, A. (2023). AI in HRM: Case study analysis. Preliminary research. Procedia Computer Science, 225, 2351–2360. https://doi.org/10.1016/j.procs.2023.10.226
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
Halid, H., Ravesangar, K., Mahadzir, S. L., & Halim, S. N. A. (2024). Artificial intelligence (AI) in human resource management (HRM). In C. Machado (Ed.), Building the future with human resource management (pp. 37–70). Springer. https://doi.org/10.1007/978-3-031-52811-8_2
Hinge, P., Salunkhe, H., & Boralkar, M. (2023). Artificial intelligence (AI) in HRM (human resources management): A sentiment analysis approach. In Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022) (pp. 557–568). Atlantis Press. https://doi.org/10.2991/978-94-6463-136-4_47
Holmström, J., & Carroll, N. (2024). How organisations can innovate with generative AI. Business Horizons, 68(5), 559–573. https://doi.org/10.1016/J.BUSHOR.2024.02.010
Kabir, M. E. (2020). Artificial intelligence in business and prospects. IAR Journal of Entrepreneurship, Innovation & Design Thinking, 1(1), 28–31. https://doi.org/10.47310/iarjeidt.2020.v01i01.007
Kaushal, N., Kaurav, R. P. S., Sivathanu, B., & Kaushik, N. (2023). Artificial intelligence and HRM: Identifying future research agenda using systematic literature review and bibliometric analysis. Management Review Quarterly, 73(2), 455–493. https://doi.org/10.1007/s11301-021-00249-2
Khan, M. I., Parahyanti, E., & Hussain, S. (2024). The role of generative AI in human resource management: Enhancing operational efficiency, decision-making, and addressing ethical challenges. Asian Journal of Logistics Management, 3(2), 104–125. https://doi.org/10.14710/ajlm.2024.24671
Madanchian, M., Taherdoost, M., & Mohamed, N. (2023). AI-based human resource management tools and techniques: A systematic literature review. Procedia Computer Science, 229(2), 367–377. https://doi.org/10.1016/j.procs.2023.12.039
Meijerink, J., Boons, M., Keegan, A. E., & Marler, J. H. (2021). Algorithmic human resource management: Synthesising developments and cross-disciplinary insights on digital HRM. The International Journal of Human Resource Management. 32(12), 2545–2562. https://doi.org/10.1080/09585192.2021.1925326
Menaka, R. (2023). Role of artificial intelligence (AI) in human resource management (HRM) in recent era. Shanlax International Journal of Management, 11(2), 32–38. https://doi.org/10.34293/management.v11i2.6664
Morgenstern, J. D., Rosella, L. C., Daley, M. J., Goel, V., Schünemann, H. J., & Piggott, T. (2021). AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: A fundamental qualitative descriptive study of the implications of artificial intelligence for public health. BMC Public Health, 21(1), 1–14. https://doi.org/10.1186/s12889-020-10030-x
Muralidhar, S., Bharadwaj, A. & Bhat, P. (2023). Coalesce of artificial intelligence and human resource management: A conceptual study. BOHR International Journal of Advances in Management Research, 2(1), 72–80. https://doi.org/10.54646/bijamr.2023.20
Murire, O. T. (2024). Artificial intelligence and its role in shaping organisational work practices and culture. Administrative Sciences, 14(12), 1–16. https://doi.org/10.3390/admsci14120316
Nawaz, N., Arunachalam, H., Pathi, B. K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1), 1–11. https://doi.org/10.1016/j.jjimei.2023.100208
Norman, E., & Pahlawati, E. (2024). Peran artificial intelligence dalam rekrutmen dan seleksi: Meningkatkan efisiensi dan akurasi dalam MSDM. Sci-Tech Journal, 3(1), 45–59. https://doi.org/10.56709/stj.v3i1.320
Nwosu, C., Westcott, S. C., Arachie, A. E., & Oke, G. D. (2026). Optimising the marketing performance of small businesses through the instrumentality of artificial intelligence. Business and Economics in Developing Countries, 4(1), 10–15. https://doi.org/10.26480/bedc.01.2026.10.15
Nwosu, K. C., Westcott, S. C., Arachie, A. E., & Okafor, N. M. (2026). Artificial intelligence democratisation in developing economies through the lenses of small business operations. African Journal of Management and Business Research, 22(1), 242–255. https://doi.org/10.62154/ajmbr.2026.022.01022
Nyathani, R. (2023). AI in performance management: Redefining performance appraisals in the digital age. Journal of Artificial Intelligence & Cloud Computing, 2(2), 1–4. https://doi.org/10.47363/JAICC/2023(2)134
Okereke, C. C., Arachie, A., Onah, F. S. & Ndum, N. B. (2025). Artificial intelligence (AI) adoption in supply chain management dynamics of manufacturing firms in emerging markets. African Journal of Management and Business Research, 21(1), 129–147. https://doi.org/10.62154/ajmbr.2025.021.01017
Olaniyan, O. P., Elufioye, O. A., Okonkwo, F. C., Udeh, C. A., Eleogu, T. F., & Olatoye, F. O. (2022). AI-driven talent analytics for strategic HR decision-making in the United States of America: A review. International Journal of Management & Entrepreneurship Research, 4(12), 607–622. https://doi.org/10.51594/ijmer.v4i12.674
Popo-Olaniyan, O. (2022). Artificial intelligence and analytics: Transforming HR decision-making in American organisations. International Journal of Management & Entrepreneurship Research, 4(12), 607–622.
Pratama, A. S., Sari, S. M., Hj, M. F., Badwi, M., & Anshori, M. I. (2023). Pengaruh artificial intelligence, big data dan otomatisasi terhadap kinerja SDM di era digital. Jurnal Publikasi Ilmu Manajemen, 2(4), 108–123. https://doi.org/10.55606/jupiman.v2i4.2739
Prikshat, V., Malik, A., & Budhwar, P. (2021). AI-augmented HRM: Antecedents, assimilation and multilevel consequences. Human Resource Management Review, 33(1), 1–18. https://doi.org/10.1016/j.hrmr.2021.100860
Qin, C., Zhang, L., Cheng, Y., Zha, R., Shen, D., Zhang, Q., Chen, X., Sun, Y., Zhu, C., Zhu, H., & Xiong, H. (2025). A comprehensive survey of artificial intelligence techniques for talent analytics. Proceedings of the IEEE, 113(2), 125–171. https://doi.org/10.1109/JPROC.2025.3572744
Rathore, S. P. S. (2023). The impact of AI on recruitment and selection processes: Analysing the role of AI in automating and enhancing recruitment and selection procedures. International Journal for Global Academic & Scientific Research, 2(2), 78–93. https://doi.org/10.55938/ijgasr.v2i2.50
Saini, H., & Tarkar, P. (2022). Artificial intelligence in human resource practices with challenges and future directions. In Handbook of research on innovative management using AI in Industry 5.0 (pp. 222–230). IGI Global. https://doi.org/10.4018/978-1-7998-8497-2.ch015
Sánchez, E., Calderón, R., & Herrera, F. (2025). Artificial intelligence adoption in SMEs: Survey based on TOE–DOI framework, primary methodology and challenges. Applied Sciences, 15(12), 1–43. https://doi.org/10.3390/app15126465
Scully, J., Crawshaw, J., Fullard, A., Gregson, M., Clegg, B., & Turner, P. (2020). Workforce (Artificial) intelligence planning. In J. Crawshaw, P. Budhwar, & A. Davis (Eds.), Human resource management: Strategic and international perspectives (3rd ed., pp. 170–197). SAGE Publications.
Srivastava, S. P., & Himanshu. (2026). Examining antecedents of AI-integrated digital HRM adoption in Indian SMEs. Millennial Asia. Advance online publication. https://doi.org/10.1177/09763996251409324
Sundari, S., Silalahi, V. A. J. M., Wardani, F. P., Siahaan, R. S., Sacha, S., Krismayanti, Y., & Anjarsari, N. (2024). Artificial intelligence (AI) and automation in human resources: Shifting the focus from routine tasks to strategic initiatives for improved employee engagement. East Asian Journal of Multidisciplinary Research, 3(10), 4983–4996. https://doi.org/10.55927/eajmr.v3i10.11758
Thangaraju, K., & Palani, P. (2025). Influence of AI-driven sustainable human resource management on employee creative performance. Acta Tecnología, 11(4), 153–163. https://doi.org/10.22306/atec.v11i4.284
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Triveni, S. (2025). Leveraging AI technologies for effective employee engagement and retention in human resources. International Research Journal of Modernisation in Engineering Technology and Science, 7(1), 1–6.
Vanisree, D., & Shree, D. V. (2024). Role of AI in human resource management. International Journal of Scientific Research in Engineering and Management, 8(4), 1–4. https://doi.org/10.55041/IJSREM31361
Vincent-Lancrin, S., & van der Vlies, R. (2020). Trustworthy artificial intelligence (AI) in education: Promises and challenges (OECD Education Working Papers No. 218). OECD Publishing. https://doi.org/10.1787/a6c90fa9-en
Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Wang, S., & Zhang, H. (2025). Leveraging generative artificial intelligence for sustainable business model innovation in production systems. International Journal of Production Research, 63(18), 6732–6757. https://doi.org/10.1080/00207543.2025.2485318
World Economic Forum. (2021, December 7). Human-centred artificial intelligence for human resources: A toolkit for human resources professionals. https://www3.weforum.org/docs/WEF_Human_Centred_Artificial_Intelligence_for_Human_Resources_2021.pdf
Zafar, A. (2023). Unleashing AI potential in human resource management (A case study of corporate sector in Karachi). Journal of Usman Institute of Technology University, 4(1), 65–89. https://doi.org/10.46568/gjmas.v4i1.182
Zhu, C. (2025). The role of AI tools in optimising human resource management practices. Advances in Curriculum Design & Education, 1(2), 1–13. https://doi.org/10.63808/acde.v1i2.132
Žibret, K. (2024). The transformative role of artificial intelligence in human resources. Mednarodno Inovativno Poslovanje Journal of Innovative Business and Management, 16(1), 1–15. https://doi.org/10.32015/JIBM.2024.16.1.5
##submission.downloads##
Gepubliceerd
Nummer
Sectie
Licentie
Copyright (c) 2026 Arachie Augustine Ebuka, Nwafor Angela Ebele, Obiekezie Alphonsus Emenike, Ukadike Ifeanyichukwu Stewarts
Dit werk wordt verdeeld onder een Naamsvermelding-GelijkDelen 4.0 Internationaal licentie.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website). The final published PDF should be used and bibliographic details that credit the publication in this journal should be included.
