Artificial Intelligence (AI) in Human Resource Management (HRM): A Bibliometric Research
Keywords:
Artificial Intelligence, HRM, bibliometric analysis, trends, research themeAbstract
Purpose: The purpose of this study is to conduct a bibliometric analysis to explore the scholarly literature on the integration of Artificial Intelligence (AI) in Human Resource Management (HRM), by focusing on identifying trends, key contributors, research themes, and emerging areas of interest.
Methodology/Approach: The research employs systematic bibliometric analysis, utilizing academic databases such as Scopus to collect relevant literature. Keywords including "AI," "Artificial Intelligence," "HRM," and "Human Resource Management" were used to retrieve documents. Bibliometric software such as bibliophily is utilized to visualize co-authorship networks, citation patterns, keyword co-occurrence, and publication trends.
Findings: The analysis shows how AI applications in HRM have developed over time, moving from theoretical explorations to empirical studies and the incorporation of cutting-edge AI technologies. Important researchers and research clusters are highlighted, along with field-wide collaborative networks and key contributors. Personalized learning, employee performance prediction, AI-based hiring, and ethical issues are among the often-studied topics. Emerging fields include cross-cultural research, ethical frameworks for AI deployment, and the integration of AI with emerging technologies.
Originality: By providing an organized bibliometric overview of the academic environment on AI in HRM and offering insights into trends, important contributors, and developing research areas, this study adds to the body of current literature. The methodical approach used in this study guides future research and practice while also improving our grasp of the revolutionary potential of AI in HRM practices.
References
Agnihotri, A., Mathad, K., & Kadry, S. (2023). The new HR inclination for artificial intelligence aiding talent acquisition. In Disruptive artificial intelligence and sustainable human resource management.
Anshima, B., Bhardwaj, B., & Sharma, D. (2023). Artificial intelligence: Shifting the landscape of the human resources ecosystem. In Leveraging AI and emotional intelligence in contemporary business organizations (pp. 371–381). https://doi.org/10.4018/979-8-3693-1902-4.ch022
Balasubramanian, P., & Balakrishna, D. R. (2023). Case study: Infosys—Talent management processes automation with AI. In Springer handbooks (Part F674, pp. 1451–1458). https://doi.org/10.1007/978-3-030-96729-1_70
Bankar, S., & Shukla, K. (2023). Performance management and artificial intelligence: A futuristic conceptual framework. In Contemporary studies of risks in emerging technology: Part B (pp. 341–360). https://doi.org/10.1108/978-1-80455-566-8-2023-1019
Bhatt, P. (2023). AI adoption in the hiring process—Important criteria and extent of AI adoption. Foresight, 25(1), 144–163. https://doi.org/10.1108/FS-07-2021-0144
Bhupathi, P., Prabu, S., & Goh, A. P. I. (2023). Artificial intelligence-enabled knowledge management using a multidimensional analytical framework of visualizations. International Journal of Cognitive Computing in Engineering, 4, 240–247. https://doi.org/10.1016/j.ijcce.2023.06.003
Chitrao, P., Bhoyar, P. K., Divekar, R., & Bhatt, P. (2022). Study on the use of artificial intelligence in talent acquisition. In 2022 International Conference on Interdisciplinary Research in Technology and Management (IRTM 2022)—Proceedings. IEEE. https://doi.org/10.1109/IRTM54583.2022.9791659
Dixit, C. K., Somani, P., Gupta, S. K., & Pathak, A. (2023). Data-centric predictive modelling of turnover rate and a new hire in workforce management system. In Designing workforce management systems for Industry 4.0: Data-centric and AI-enabled approaches (pp. 121–137). https://doi.org/10.1201/9781003357070-8
Dutta, D., & Mishra, S. K. (2023). Bots for mental health: The boundaries of human and technology agencies for enabling mental well-being within organizations. Personnel Review. https://doi.org/10.1108/PR-11-2022-0832
Dutta, D., Mishra, S. K., & Tyagi, D. (2023). Augmented employee voice and employee engagement using artificial intelligence-enabled chatbots: A field study. The International Journal of Human Resource Management, 34(12), 2451–2480. https://doi.org/10.1080/09585192.2022.2085525
Kaushik, P., Miglani, S., Shandilya, I., Singh, A., Saini, D., & Singh, A. (2023). HR functions productivity boost by using AI. International Journal on Recent and Innovation Trends in Computing and Communication, 11(8s), 701–713. https://doi.org/10.17762/ijritcc.v11i8s.7672
Kiran, P. B. N., Sireesha, K., Donthu, S., Vali, S. K. M., Pushpalatha, S., & Madhan, P. (2023). Framework analysing the impact of artificial intelligence on human resource management using AMO framework. In 2023 4th International Conference on Intelligent Engineering and Management (ICIEM). IEEE. https://doi.org/10.1109/ICIEM59379.2023.10165698
Paigude, S., Pangarkar, S. C., Hundekari, S., Mali, M., Wanjale, K., & Dongre, Y. (2023). The potential of artificial intelligence in boosting employee retention in the human resource industry. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 1–10. https://doi.org/10.17762/ijritcc.v11i3s.614
Pavitra, K. H., & Agnihotri, A. (2023). Artificial intelligence in corporate learning and development: Current trends and future possibilities. In 2023 2nd International Conference on Smart Technologies for Smart Nation (SmartTechCon 2023) (pp. 688–693). IEEE. https://doi.org/10.1109/SmartTechCon57526.2023.10391698
Rai, A., & Singh, L. B. (2023). Artificial intelligence-based people analytics transforming human resource management practices. In The adoption and effect of artificial intelligence on human resources management: Part A (pp. 229–244). https://doi.org/10.1108/978-1-80382-027-9-2023-1012
Rudra Kumar, M., & Gunjan, V. K. (2022). Machine learning-based solutions for human resource systems management. In Lecture notes in electrical engineering (Vol. 828, pp. 1239–1249). Springer. https://doi.org/10.1007/978-981-16-7985-8_129
Sonawane, A., Loomba, A., & Paluchova, J. (2022). An influence of artificial intelligence on jobs and HRM in small manufacturing units. In Managing human resources in SMEs and start-ups: International challenges and solutions (pp. 223–251). World Scientific. https://doi.org/10.1142/9789811239212_0011
Yadav, M., Kakkar, M., & Kaushik, P. (2023). Harnessing artificial intelligence to empower HR processes and drive enhanced efficiency in the workplace to boost productivity. International Journal on Recent and Innovation Trends in Computing and Communication, 11(8s), 381–390. https://doi.org/10.17762/ijritcc.v11i8s.7218
Yadav, P. V., Kollimath, U. S., Giramkar, S. A., Pisal, D. T., Badave, S. S., & Swamy, S. M. (2023). HR 4.0: Role of AI in transforming HRM. In 2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA 2023). IEEE. https://doi.org/10.1109/eSmarTA59349.2023.10293704
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