Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67781
Volume:
10 |
10 |
Oct 2026
Pages:
1-16
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Management FMC
Ramaiah University of Applied Sciences
Department of Management FMC
Ramaiah University of Applied Sciences
Generative Artificial Intelligence (GenAI) tools such as ChatGPT, Gemini and Copilot have entered everyday office work quickly, but little is known about what the professionals that run hospital recruitment have to say about them. This study evaluates the awareness, perception and readiness of human resource (HR) professionals towards GenAI adoption in healthcare recruitment. A qualitative, descriptive and exploratory design was followed. Open-ended questionnaire and interview responses were collected from one HR or recruitment professional, per each of 30 hospitals in Kerala, India and were analysed in NVivo using a four-parent, seventeen-child codebook, word frequency queries, word clouds and objective-wise thematic interpretations. The respondents were generally aware of GenAI and familiar with the common tools, although awareness had not yet become an organisational adoption. The potential utility of GenAI were seen to be in organisation of applications, preparation of candidate summaries, qualification matching and reduction of repetitive administrative work, although gains in hiring time had not been consistently reported. Concerns were raised around accuracy, privacy, biases, ethics, trust and organisational barriers. The respondents expect adoption to be a gradual and human-led process, contingent upon AI-literacy, training and governance. The study contributes context-specific qualitative evidence on this topic that has mostly been explored in clinical settings and in other industries, and proposes a phased, governance-first approach to adoption.
KEYWORDS
HR professionals in the hospitals studied are aware of Generative AI and familiar with common tools, but this awareness has not yet become formal adoption by recruitment. They see its value primarily in administrative and information-handling work, and they regard its benefits as more potential than shown. Their concerns are specific and consistent in their nature: accuracy and context, privacy and data security, bias and ethics, trust and the need for human review, and organisational barriers. They expect adoption to be gradual and conditional on training, governance and human oversight. The central message is neither enthusiasm nor resistance: hospitals that build AI literacy, set out clear rules and keep humans accountable for decisions about people will be best positioned to benefit from Generative AI in recruitment.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., … Albekairy, A. M. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23, Article 689 Ayorinde, A., Mensah, D. O., Walsh, J., Ghosh, I., Ibrahim, S. A., Hogg, J., Peek, N., & Griffiths, F. (2024). Health care professionals' experience of using AI: Systematic review with narrative synthesis. Journal of Medical Internet Research, 26, e55766. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. Bazeley, P., & Jackson, K. (2013). Qualitative data analysis with NVivo (2nd ed.). Sage Publications Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63(2), 215–226. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901 Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., … Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659.
Publication Date
10/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
5
Total views since publication
PDF Downloads
13
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...