Bibliometric Mapping Of The Literature On Ai In Workforce Recruitment: Visualization Of Trends And Scientific Collaborations
Pemetaan Bibliometrik Literatur tentang Kecerdasan Buatan (AI) dalam Rekrutmen Tenaga Kerja: Visualisasi Tren dan Kolaborasi Ilmiah
DOI:
https://doi.org/10.21070/jbmp.v11i2.2192Keywords:
artificial intelligence, recruitment, Bibliometric Analysis, algorithmic bias, human resource managementAbstract
This study aims to explore the research landscape of artificial intelligence (AI) in recruitment and selection within human resource management by mapping publication trends, intellectual structures, and emerging themes. Using a bibliometric analysis of 404 Scopus-indexed documents and tools such as Bibliometrix and VOSviewer, the study examines scholarly output, collaboration networks, and thematic clusters from global research. Results show a rapid increase in publications since 2018, with a peak in 2024, driven by key technologies including machine learning, natural language processing, and predictive analytics. Emerging areas such as generative AI (e.g., ChatGPT), blockchain, and virtual reality are gaining traction, while ethical concerns around fairness, transparency, and algorithmic bias are becoming increasingly prominent. The research highlights South Asia, particularly India, as a leading contributor, though the field remains globally diverse. These findings provide a comprehensive overview of the evolving AI in recruitment domain, offering theoretical insights and practical guidance for HR professionals and policymakers. The study underscores the need for multidisciplinary, ethically informed approaches to ensure AI-driven recruitment is both efficient and equitable.
References
Aydin, O., Karaarslan, E., & Narin, N. G. (2024). Artificial Intelligence, VR, AR and Metaverse Technologies for Human Resources Management. ArXiv Preprint. https://arxiv.org/abs/2406.15383 DOI: https://doi.org/10.2139/ssrn.4480626
Black, J. S., & van Esch, P. (2020a). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63(2), 215–226. https://doi.org/10.1016/j.bushor.2019.12.001
Black, J. S., & van Esch, P. (2020b). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63(2), 215–226. https://doi.org/10.1016/j.bushor.2019.12.001 DOI: https://doi.org/10.1016/j.bushor.2019.12.001
Bogen, M., & Rieke, A. (2018). Help wanted: An examination of hiring algorithms, equity, and bias. Upturn. https://doi.org/10.2139/ssrn.3316912
Bondarouk, T., Meijerink, J., & Lepak, D. P. (2023). Emerging perspectives on HRM and artificial intelligence: A bibliometric review. International Journal of Human Resource Management, 34(5), 811–835. https://doi.org/10.1080/09585192.2022.2102741
Cuesta-Valiño, P., Gázquez-Abad, J. C., & Rodríguez, P. G. (2024). The application of artificial intelligence in recruitment, training, and employee onboarding HR practices. In Contributions to Management Science (pp. 211–228). Springer. https://doi.org/10.1007/978-3-031-58704-7_13 DOI: https://doi.org/10.1007/978-3-031-58704-7_13
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070 DOI: https://doi.org/10.1016/j.jbusres.2021.04.070
Hamouche, S., Pavlopoulos, D., Daskalakis, S., & Lepenioti, K. (2023). Artificial intelligence in human resource management research: A review and research agenda. The International Journal of Human Resource Management, 34(11), 2230–2259. https://doi.org/10.1080/09585192.2021.2004608
Jha, S., & Kumar, R. (2023). Organizational readiness factors for artificial intelligence adoption in human resource management. Journal of Organizational Change Management, 36(5), 799–814. https://doi.org/10.1108/JOCM-08-2022-0231
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392. https://doi.org/10.1007/s10551-019-04204-w DOI: https://doi.org/10.1007/s10551-019-04204-w
Meijerink, J., Bondarouk, T., & Lepak, D. P. (2021). When and why digital HRM technologies augment HRM service provision: Theoretical perspectives and empirical evidence. Human Resource Management Review, 31(4). https://doi.org/10.1016/j.hrmr.2020.100789 DOI: https://doi.org/10.1016/j.hrmr.2020.100789
Mendling, J., Pentland, B. T., & Recker, J. (2020). Building a complementary agenda for business process management and digital innovation. European Journal of Information Systems, 29(3), 208–219. https://doi.org/10.1080/0960085X.2020.1755207 DOI: https://doi.org/10.1080/0960085X.2020.1755207
Mollah, M. A., Rahman, M., & Islam, M. (2024). Examining the role of AI-augmented HRM for sustainable performance: Evidence from emerging markets. Sustainability, 16(24), 10843. https://doi.org/10.3390/su162410843 DOI: https://doi.org/10.3390/su162410843
Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5 DOI: https://doi.org/10.1007/s11192-015-1765-5
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Savani, W., Williams, J., & Suresh, R. (2023). Applicants’ fairness perceptions of algorithm-driven hiring procedures. Journal of Business Ethics. https://doi.org/10.1007/s10551-022-05320-w DOI: https://doi.org/10.1007/s10551-022-05320-w
Strohmeier, S., & Piazza, F. (2022a). Fairness perceptions of AI-based selection: Comparing applicant experiences with algorithmic and human decision-making. Journal of Business Ethics, 179(3), 735–752. https://doi.org/10.1007/s10551-021-05035-6
Strohmeier, S., & Piazza, F. (2022b). Fairness perceptions of AI-based selection: Comparing applicant experiences with algorithmic and human decision-making. Journal of Business Ethics, 179(3), 735–752. https://doi.org/10.1007/s10551-021-05035-6
Tambe, P., Cappelli, P., & Yakubovich, V. (2019a). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
Tambe, P., Cappelli, P., & Yakubovich, V. (2019b). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910 DOI: https://doi.org/10.1177/0008125619867910
Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224–247. https://doi.org/10.1016/j.ijinfomgt.2018.08.002 DOI: https://doi.org/10.1016/j.ijinfomgt.2018.08.002
Upadhyay, A. K., & Khandelwal, K. (2019). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 18(2), 94–98. https://doi.org/10.1108/SHR-01-2019-0009
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3 DOI: https://doi.org/10.1007/s11192-009-0146-3
Van Esch, P., Black, J. S., & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215–222. https://doi.org/10.1016/j.chb.2018.09.009 DOI: https://doi.org/10.1016/j.chb.2018.09.009
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), 1237–1266. https://doi.org/10.1080/09585192.2020.1871398 DOI: https://doi.org/10.1080/09585192.2020.1871398
Wamba, S. F., Queiroz, M. M., Guthrie, C., & Braganza, A. (2022). Industry experiences of artificial intelligence (AI): benefits and challenges in operations and supply chain management. Production Planning & Control, 33(16), 1493–1497. https://doi.org/10.1080/09537287.2021.1937951 DOI: https://doi.org/10.1080/09537287.2021.1882695
Zhou, Y., Luo, X., & Tang, J. (2021a). Algorithmic fairness in recruitment: Impacts on applicant trust and justice perceptions. AI & Society, 36(4), 1149–1164. https://doi.org/10.1007/s00146-020-01040-5
Zhou, Y., Luo, X., & Tang, J. (2021b). Algorithmic fairness in recruitment: Impacts on applicant trust and justice perceptions. AI & Society, 36(4), 1149–1164. https://doi.org/10.1007/s00146-020-01040-5
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629 DOI: https://doi.org/10.1177/1094428114562629
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Rizky Kurnia Manggala, Priyo Yulianto, Dhyah Setyorini
This work is licensed under a Creative Commons Attribution 4.0 International License.
