Mapping Global Trends in AI and Human Resources Management Based on Bibliometric Analysis: Using Publish or Perish and Vosviewer

Authors

RR Alfiatunn Sarasati

Faculty of Psycology, Gunadarma University (Indonesia)

Trini Saptariani

Faculty of Computer Sciense, Gunadarma University (Indonesia)

Noversyah

Faculty of Economy Gunadarma University (Indonesia)

Dharma TE

Faculty of Economy Gunadarma University (Indonesia)

Article Information

DOI: 10.47772/IJRISS.2026.100700771

Subject Category: Management

Volume/Issue: 10/7 | Page No: 11437-11454

Publication Timeline

Submitted: 2026-07-29

Accepted: 2026-08-03

Published: 2026-08-12

Abstract

This empirical study aims to analyze the green financial investment disclosure index (GFDI) for the 2020-2025 period for public entities in Indonesia. The unit of analysis is the annual reports of 335 issuers, sourced from the Indonesian Stock Exchange (IDX), which are grouped into real and manufacturing sectors. The data was analyzed descriptively qualitatively and verified using the two independent sample T test. The results of the analysis showed the following: 1) GFDI of annual reports for the 2020-2025 period of companies listed on the JSE averaged 0.50378; 2) The average GFDI of annual reports for the 2020-205 period of manufacturing sector issuers is 0.48426, while the average GFDI of annual reports for the 2020-2025 period of real sector issuers is 0.52204; Furthermore, the results of the verification analysis show that there are differences in the annual reports for the 2020-2025 period between issuers in the manufacturing sector and the real sector.

Keywords

item disclosure, disclosure index, green economic, annual report

Downloads

References

1. 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. [Google Scholar] [Crossref]

2. Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamberger, P., Bozkurt, Ö., ... & Cooke, F. L. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions. Human Resource Management Journal, 33(3), 606–659. [Google Scholar] [Crossref]

3. Setiawan, R., & Rahmawati, D. (2023). Dampak kecerdasan buatan terhadap efektivitas keputusan manajerial dan tantangan etika SDM. Jurnal Manajemen Indonesia (Sinta 2), 23(2), 112–126. [Google Scholar] [Crossref]

4. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. [Google Scholar] [Crossref]

5. Prasetyo, A., & Santoso, B. (2024). Transformasi digital dan kesiapan kompetensi teknis pengelola SDM di industri perbankan Indonesia. Jurnal Dinamika Manajemen (Sinta 2), 15(1), 45–58. [Google Scholar] [Crossref]

6. Malik, A., De Silva, M. T., Budhwar, P., & Srikanth, N. R. (2023). High-performance HR practices and artificial intelligence adoption: The role of HR capability and dynamic capabilities. Human Resource Management, 62(5), 635–655. [Google Scholar] [Crossref]

7. Bankins, S., Formosa, P., Richards, D., & Ryan, A. (2024). AI in the workplace: Implications for employee well-being, ethics, and human resource management. Human Resource Management Review, 34(1), 101002. [Google Scholar] [Crossref]

8. Chowdhury, S., Dey, P., Rodriguez-Espindola, O., Abadie, A., & Zhang, A. (2023). Impact of Artificial Intelligence on organizational performance: The mediating role of AI-enabled capabilities and human resource management. Journal of Business Research, 162, 113889. [Google Scholar] [Crossref]

9. Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A systematic literature review on business model innovation in the International Human Resource Management context in the AI era. Journal of International Management, 29(4), 101032. [Google Scholar] [Crossref]

10. Samuel, A.L., 1959. Machine learning, the. Technol. Rev. 62 (1), 42–45. [Google Scholar] [Crossref]

11. Andrew Ng. (2020). AI For Everyone. Retrieved September 15, 2021, from https://www. coursera.org/learn/ai-for-everyone. [Google Scholar] [Crossref]

12. Wang, S.C., 2003. Artificial neural network. In: Interdisciplinary Computing in Java Programming. The Springer International Series in Engineering and Computer Science, vol. 743. Springer, Boston, MA. [Google Scholar] [Crossref]

13. de Visser, E.J., Awad, E., Kuppens, T., 2018. From “automation” to “autonomy”: the importance of trust repair in human–machine interaction. Ergonomics 61 (11), 1432–1450. [Google Scholar] [Crossref]

14. Chowdhury, G., 2003. Natural language processing. Annu. Rev. Inf. Sci. Technol. 37, 51–89. [Google Scholar] [Crossref]

15. Biswas, J., 2018. November 8. Top Six Use-Cases of AI In Human Resources Department, Analytics India Magazine. https://analyticsindiamag.com/top-use-cases-ai-human-r esources/. [Google Scholar] [Crossref]

16. Guenole, N., Feinzig, S., 2018. The business case for AI in HR—with insights and tips on getting started. IBM Smarter Workforce Institute 1–36. [Google Scholar] [Crossref]

17. Garavan, T., Morley, M., Gunnigle, P., McGuire, D., 2016. Strategic human resource development: towards a conceptual framework to understand its contribution to dynamic capabilities. Hum. Resour. Dev. Int. 19 (4), 289–306. [Google Scholar] [Crossref]

18. Kaplan, A., Haenlein, M., 2019. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus. Horiz. 62 (1), 15–25. [Google Scholar] [Crossref]

19. Sunder, M. Vijaya, Ganesh, L.S., 2021. Identification of the dynamic capabilities ecosystem—A systems thinking perspective. Group Org. Manag. 46 (5), 893–930. [Google Scholar] [Crossref]

20. Ching, E., 2020. Understanding the 6 major capabilities of AI. medium.com (May 12). https://medium.com/qavar/understanding-the-6-major-capabilities-of-ai-efea8e361d06. [Google Scholar] [Crossref]

21. Basu, S., Majumdar, B., Mukherjee, K., Munjal, S., Palaksha, C., 2023. Artificial intelligence–HRM interactions and outcomes: A systematic review and causal configurational explanation. Human Resource Management Review 33 (1), 100893. [Google Scholar] [Crossref]

22. Teece, D.J., 2018. Dynamic capabilities as (workable) management systems theory. Journal of Management and Organization 24 (3), 359–368. [Google Scholar] [Crossref]

23. Helfat, C.E., Martin, J.A., 2015. Dynamic managerial capabilities: review and assessment of managerial impact on strategic change. Journal of Management 41 (5), 1281–1312. [Google Scholar] [Crossref]

24. Jong, J.C. De, 2020. AI (appreciative inquiry) + AI (artificial intelligence) = SFL (sustainable future leadership). AI Practitioner 22 (1), 45–51. [Google Scholar] [Crossref]

25. Ambrosini, V., Altintas, G., 2019. Dynamic Managerial Capabilities, Subject: Business Policy and Strategy, Oxford Research Encyclopedia, Business and Management (Oxfordre.Com/Business). (c), Oxford University Press USA, pp. 1–18. https://doi. org/10.1093/acrefore/9780190224851.013.20. [Google Scholar] [Crossref]

26. Pedron, C.D., Caldeira, M., 2011. Customer relationship management adoption: using a dynamic capabilities approach. International Journal of Internet Marketing and Advertising 6 (3), 265–281. [Google Scholar] [Crossref]

27. Hmoud, B., 2021. The adoption of artificial intelligence in human resource management. Forum Scientiae Oeconomia 9 (1), 105–118. [Google Scholar] [Crossref]

28. Khanra, S., Dhir, A., M¨ antym¨ aki, M., 2020. Big data analytics and enterprises: a bibliometric synthesis of the literature. Enterprise Information Systems 14 (6), 737–768. [Google Scholar] [Crossref]

29. Khanra, S., Dhir, A., Kaur, P., M¨ antym¨ aki, M., 2021. Bibliometric analysis and literature review of ecotourism: toward sustainable development. Tour. Manag. Perspect. 37. 100777 https://doi.org/10.1016/j.tmp.2020. [Google Scholar] [Crossref]

30. Tandon, A., Kaur, P., Mantym ¨ ¨ aki, M., Dhir, A., 2021. Blockchain applications in management: A bibliometric analysis and literature review. Technological Forecasting and Social Change 166, 120649. [Google Scholar] [Crossref]

31. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. and Lim, W.M., 2021. How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, pp.285–296. Available at: https://doi.org/10.1016/j.jbusres.2021.04.070. [Google Scholar] [Crossref]

32. Harzing, A.W., 2021. Publish or Perish. Available at: https://harzing.com/resources/publish-or-perish [Accessed 15 June 2026]. [Google Scholar] [Crossref]

33. Bhatt, P., 2022. AI adoption in the hiring process–important criteria and extent of AI adoption. foresight 25 (1), 144–163. [Google Scholar] [Crossref]

34. Van Eck, N.J. and Waltman, L., 2020. VOSviewer Manual: Manual for VOSviewer Version 1.6.16. Leiden: Leiden University. [Google Scholar] [Crossref]

35. Zehir, C., Karabog˘a, T., Bas¸ar, D., 2020. The transformation of human resource management and its impact on overall business performance: Big data analytics and AI Technologies in Strategic HRM. In: Hacioglu, [Google Scholar] [Crossref]

36. U. (Ed.), Digital Business Strategies in Blockchain Ecosystems. Contributions to Management Science. Springer, Cham, pp. 265–279. https://doi.org/10.1007/978-3-030-29739-8_12. [Google Scholar] [Crossref]

37. Wamba, S.F., 2022. Humanitarian supply chain: a bibliometric analysis and future research directions. Ann. Oper. Res. 319, 937–963. https://doi.org/10.1007/ s10479-020-03594-9. [Google Scholar] [Crossref]

38. Hamilton, R.H., Davison, H.K., 2022. Legal and ethical challenges for HR in machine learning. Empl. Responsib. Rights J. 34 (1), 19–39. [Google Scholar] [Crossref]

39. Prikshat, V., Islam, M., Patel, P., Malik, A., Budhwar, P., Gupta, S., 2023a. AI-augmented HRM: literature review and a proposed multilevel framework for future research. Technological Forecasting and Social Change 193, 122645. [Google Scholar] [Crossref]

40. Zhou, X., Zhou, M., Huang, D., Cui, L., 2022. A probabilistic model for co-occurrence analysis in bibliometrics. J. Biomed. Inform. 128, 104047. [Google Scholar] [Crossref]

41. Pan, Y., Froese, F.J., 2022. An interdisciplinary review of AI and HRM: challenges and future directions. Human Resource Management Review 33 (1), 100881. [Google Scholar] [Crossref]

42. Pan, Y., Froese, F., Liu, N., Hu, Y., Ye, M., 2021. The adoption of artificial intelligence in employee recruitment: the influence of contextual factors. International Journal of Human Resource Management 33 (6), 1125–1147. [Google Scholar] [Crossref]

43. Venkatesh, V., Davis, Fred D., 2000. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46 (2), 186–204. [Google Scholar] [Crossref]

44. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., 2003. User acceptance of information technology: toward a unified view. MIS Q. 27 (3), 425–478. [Google Scholar] [Crossref]

45. Venkatesh, V., Thong, J.Y., Xu, X., 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36 (1), 157–178. [Google Scholar] [Crossref]

46. Guenduez, A.A., Mergel, I., 2022. The role of dynamic managerial capabilities and organizational readiness in smart city transformation. Cities 129, 103791. [Google Scholar] [Crossref]

47. Dwivedi, Y.K., Sharma, A., Rana, N.P., Giannakis, M., Goel, P., Dutot, V., 2023. Evolution of artificial intelligence research in technological forecasting and social change: research topics, trends, and future directions. Technological Forecasting and Social Change 192, 122579. [Google Scholar] [Crossref]

48. Shet, S.V., Pereira, V., 2021. Proposed managerial competencies for industry 4.0 – implications for social sustainability. Technological Forecasting and Social Change 173, 121080. [Google Scholar] [Crossref]

49. Jha, S., 2022. Data privacy and security issues in HR analytics: challenges and the road ahead. In: Expert Clouds and Applications: Proceedings of ICOECA 2021. Springer, Singapore, pp. 199–206. [Google Scholar] [Crossref]

50. Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Brennan, S.E., 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj 372. [Google Scholar] [Crossref]

51. Charlwood, A., & Guenole, N. (2022). Can artificial intelligence make human resources more strategic? Human Resource Management Journal, 32(4), 743–756. https://doi.org/10.1111/1748-8583.12457 [Google Scholar] [Crossref]

52. Koştı, G. (2025). A bibliometric analysis of artificial intelligence and machine learning applications for human resource management. Future Business Journal, 11(1), 10. https://doi.org/10.1186/s43093-025-00602-x [Google Scholar] [Crossref]

53. Rodgers, W., Murray, J. M., Akeel, U., & Youssef, A. (2023). Artificial intelligence in human resource management: Ethical, legal, and socio-technical perspectives. Computers in Human Behavior, 142, 107652. https://doi.org/10.1016/j.chb.2022.107652 [Google Scholar] [Crossref]

54. Strohmeier, S. (2020). Smart HRM and AI: The digital transformation of human resource management. Journal of General Management, 46(1), 22–33. https://doi.org/10.1177/0306307020942008 [Google Scholar] [Crossref]

55. Zinke-Wehlmann, C., & Friedrich, J. (2024). Designing human-AI collaboration in human resource management: A socio-technical approach. Computers in Industry, 155, 104044. https://doi.org/10.1016/j.compind.2023.104044 [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles