The DOI matches its Crossref record; 147 of the 147 references it cites were found in a registry or an index.
Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations
Abstract
Purpose: Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition. Design/methodology/approach: This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM. Findings: This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition. Practical implications: This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition. Originality/value: This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.
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“As a result, how people perceive AI is determined by how it works, how much it costs, and what features it includes (Pillai and Sivathanu, 2020).”
From Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector (2023) · cites this paper as Pillai and Sivathanu, 2020
In the citing paper
“Recruiters can now delegate these mundane tasks to AI systems, freeing up their time and resources for more strategic issues (Pillai and Sivathanu, 2020).”
From Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector (2023) · cites this paper as Pillai and Sivathanu, 2020
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BackgroundResultInfluentialArtificial Intelligence (AI) for Talent Acquisition
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Kukreti, R., & Yadav, M. (2026) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2026.104725 · Cited by 10
Artificial Intelligence and its impact on different business functions
Mishra, A. A., & Sharma, M. (2023) · Management Dynamics · doi:10.57198/2583-4932.1328 · Cited by 7
Use of Information Technologies in Strategic Human Resource Management
Mızrak, F. (2023) · Advances in human resources management and organizational development book series · doi:10.4018/978-1-6684-9172-0.ch017 · Cited by 9
Sandeep, M. M., & Lavanya, V. (2026) · Journal of Management Development · doi:10.1108/jmd-03-2024-0082 · Cited by 8
Chauhan, V., Joshi, R., & Choudhary, V. (2023) · Journal of Financial Services Marketing · doi:10.1057/s41264-023-00253-5 · Cited by 6
“Users' privacy concerns have been shown to decrease their satisfaction in online settings, and this is a key deterrent to the widespread use of new technologies (Shankar et al. 2003; Pillai and Sivathanu 2020a, b).”
Rana, R., & Kumar, S. (2025) · Human Systems Management · doi:10.1177/01672533251365116 · Cited by 8
Al‐Khatib, A. W., Zaid, M. A. A., & Issa, A. (2025) · Information Discovery and Delivery · doi:10.1108/idd-04-2025-0076 · Cited by 8
Managing Human Resources in Artificial Intelligence Era 5.0
Oswal, J., Rajput, N., & Seth, S. (2021) · Advances in logistics, operations, and management science book series · doi:10.4018/978-1-7998-8497-2.ch010 · Cited by 6
Yadav, R. S., Balasubramanian, G., & Dash, S. S. (2024) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-09-2023-0338 · Cited by 7
YAPAY ZEKÂ TEKNOLOJİLERİ ARACILIĞIYLA YETENEK EDİNİMİ, YETENEĞİ GELİŞTİRME ve ELDE TUTMA
Demir, Ç. (2021) · the Journal of Academic Social Sciences · doi:10.29228/asos.51053 · Cited by 6
Wadhwa, S., & Wadhwa, K. (2023) · Review of Artificial Intelligence in Education · doi:10.37497/rev.artif.intell.educ.v4i00.9 · Cited by 6
Transforming Human Resources With AI
Massoud, M. F., Maaliky, B., Fawal, A. E., Mawlawi, A. H., & Yahkni, F. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1046-5.ch011 · Cited by 6
Abdelraouf, M., Emad, A., & Kadry, M. A. (2024) · المجلة العلمية للدراسات التجارية والبيئية · doi:10.21608/jces.2024.354124 · Cited by 11
Alsamawi, F. N., & Kurnaz, S. (2021) · Applied Nanoscience · doi:10.1007/s13204-021-01909-1 · Cited by 8
RetractedAdoption Factors of Artificial intelligence in Human Resource Management
Tuffaha, M. (2022) · RiuNet (Universitat Politècnica de València) · doi:10.4995/thesis/10251/185909 · Cited by 11
Riyath, M. I. M., & Eid, R. (2025) · Benchmarking An International Journal · doi:10.1108/bij-01-2025-0005 · Cited by 6
Chen, X., Martínez‐Ruiz, M. P., Bulmer, E., & Yáñez‐Araque, B. (2025) · Information · doi:10.3390/info16080705 · Cited by 7
“To measure human resource functions (the dependent variable), the five items of Shahzad et al. [60] from the original scale of Pillai and Sivathanu [72] were employed.”
Sivathanu, B., Pillai, R., & Rana, N. P. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2025.2554850 · Cited by 7
Self-citationAnshima, Bhardwaj, B., & Sharma, D. (2026) · Management Review Quarterly · doi:10.1007/s11301-025-00581-x · Cited by 10
Chang, T., & Lan-gui, H. (2026) · Information & Management · doi:10.1016/j.im.2026.104306 · Cited by 7
Islam, T., & Zhou, E. (2024) · Advances in management and applied economics · doi:10.47260/amae/1457 · Cited by 5
Althawadi, A., & Alastal, A. Y. M. (2024) · Studies in systems, decision and control · doi:10.1007/978-3-031-54379-1_15 · Cited by 5
Metadata
- Authors
- Pillai, R., & Sivathanu, B.
- Affiliations
- Sri Balaji University, Pune (IN)
- Venue
- Benchmarking: An International Journal
- Publisher
- Emerald
- Published
- 2020-08-14
- Volume
- 27
- Issue
- 9
- Pages
- 2599-2629
- ISSN
- 1463-5771 (ISSN-L)
- OpenAlex
- W3049223401
- Cited by
- 422
- References
- 146
- Citations by year
- 2026: 67 · 2025: 104 · 2024: 116 · 2023: 72 · 2022: 41 · 2021: 22
- FWCI
- 36.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Benchmarking An International Journal (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- artificial intelligence adoption, talent acquisition, technology adoption, task-technology fit, technology-organization-environment framework, cost-effectiveness, relative advantage, top management support, competitive pressure, privacy and security concerns, human resource management
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