IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 10

Published on: October 2026

AI-BASED HIRING VS TRADITIONAL HIRING

Kamalesh R

Dr. Madhu B K

Faculty of Management and Commerce, M. S. Ramaiah University of Applied Sciences, Bengaluru, India

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

Artificial intelligence (AI) is increasingly embedded in recruitment activities such as candidate sourcing, resume screening, shortlisting, communication and initial assessment. This study examines whether AI-supported recruitment practices are associated with perceived recruitment effectiveness, whether transparency and human oversight are associated with procedural fairness, and whether AI-supported practices and human interaction affect candidate experience. A quantitative, descriptive, explanatory and cross-sectional survey design was used. Primary data were collected through a structured closed-ended questionnaire from 101 respondents with awareness, experience or exposure to recruitment and selection. A five-point Likert scale was used. Data were coded in Microsoft Excel and analysed using Jamovi through descriptive statistics, Cronbach's alpha, Pearson correlation, simple linear regression and multiple linear regression. The results did not support any of the three alternative hypotheses. AI-practices/integration showed an almost zero association with perceived recruitment effectiveness (r = .011, p = .910; R² = .000129). Transparency/human oversight showed no significant relationship with procedural fairness (r = −.084, p = .406; R² = .00699). AI-supported practices and human interaction together explained only 1.05% of the variance in candidate experience (R² = .0105), and neither predictor was significant. The findings should be interpreted cautiously because several composite measures showed very low internal consistency. The study therefore suggests that AI should not be evaluated simply as a replacement for traditional recruitment; instead, individual AI practices, human safeguards, transparency and interaction should be assessed separately using stronger measurement scales.

Index Terms - Artificial Intelligence Recruitment, Traditional Hiring, Recruitment Effectiveness, Procedural Fairness, Human Oversight, Candidate Experience, Human–AI Recruitment

How to Cite this Paper

R, K. (2026). AI-Based Hiring Vs Traditional Hiring. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(10), 1-9. https://doi.org/10.55041/ijcope.v2i10.053

R, Kamalesh. "AI-Based Hiring Vs Traditional Hiring." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 10, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i10.053.

R, Kamalesh. "AI-Based Hiring Vs Traditional Hiring." International Journal of Creative and Open Research in Engineering and Management 02, no. 10 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i10.053.

Search & Index

References


  • Bhatt, (2023). AI adoption in the hiring process – important criteria and extent of AI adoption. Foresight, 25(1), 144–163. doi:10.1108/FS-07-2021-0144.

  • Fumagalli, E., Rezaei, S., & Salomons, A. (2022). OK computer: Worker perceptions of algorithmic Research Policy, 51(2), Article 104420. doi:10.1016/j.respol.2021.104420.

  • Gonzalez, F., Liu, W., Shirase, L., Tomczak, D. L., Lobbe,E., Justenhoven, R., & Martin, N. R. (2022). Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Computers in Human Behavior, 130, 107179. doi:10.1016/j.chb.2022.107179.



  • Horodyski, P. (2023). Applicants’ perception of artificial intelligence in the recruitment process. Computers in Human Behavior Reports, 11, doi:10.1016/j.chbr.2023.100303.

  • Hosain, S., Amin, M. B., Debnath, G. C., & Rahaman, M.(2025). The use of artificial intelligence (AI) in the hiring process: Job applicants’ perceptions of procedural justice. Computers in Human Behavior Reports, 19, 100713. doi:10.1016/j.chbr.2025.100713.



  • Hunkenschroer, L., & Kriebitz, A. (2023). Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring. AI and Ethics, 3, 199–213. doi:10.1007/s43681-022-00166-4.

  • Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection: A review and research Journal of Business Ethics, 178(4), 977–1007. doi:10.1007/s10551-022-05049-6.

  • Itam, J., Shukla, N., Toni, M., Kesavan, K. R., & Mishra, (2026). AI-driven talent acquisition in Indian IT firms: A qualitative study of automation, impacts, and skill requirements. SN Business & Economics, 6, Article 347. doi:10.1007/s43546-026-01325-z.



  • Lee, H., & Cha, K. J. (2023). FAT-CAT—Explainability and augmentation for an AI system: A case study on AI recruitment-system adoption. International Journal of Human-Computer Studies, 171, 102976. doi:10.1016/j.ijhcs.2022.102976.

  • Mehrotra, , & Khanna, A. (2022). Recruitment through AI in selected Indian companies. Metamorphosis, 21(1), 31–39. doi:10.1177/09726225211066220.

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Oct 15 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Scroll to Top