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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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Smart HRM 4.0 practices for organizational performance: the role of dynamic capabilities
Pillai, R., & Srivastava, K. B. L. (2023) · Benchmarking An International Journal · doi:10.1108/bij-05-2023-0288 · Cited by 42
Self-citationSalah, O. H., & Ayyash, M. M. (2024) · VINE Journal of Information and Knowledge Management Systems · doi:10.1108/vjikms-03-2023-0055 · Cited by 30
AI adoption in the hiring process – important criteria and extent of AI adoption
Bhatt, P. (2022) · foresight · doi:10.1108/fs-07-2021-0144 · Cited by 33
Fareri, S., Apreda, R., Mulas, V., & Alonso, R. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122844 · Cited by 44
What Hinders Adoption of Artificial Intelligence for Cybersecurity in the Banking Sector
Ali, A. G., & Shah, M. H. (2024) · Information · doi:10.3390/info15120760 · Cited by 28
Assessing the Determinants of Metaverse Adoption for E-Commerce Retailing
Pillai, R., Sivathanu, B., Rana, N. P., & Niemann‐Struweg, I. (2024) · Journal of Computer Information Systems · doi:10.1080/08874417.2024.2416452 · Cited by 32
“54,112 Stickiness to traditional e-commerce platforms may prevent consumers from using the metaverse for shopping because they are comfortable with and used to traditional e-commerce.”
Self-citationPenpokai, S., Vuthisopon, S., & Saengnoree, A. (2023) · International Journal of Professional Business Review · doi:10.26668/businessreview/2023.v8i3.971 · Cited by 37
Arora, R., & Damarla, R. B. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.247 · Cited by 26
Artificial Intelligence and the UK Construction Industry – Empirical Study
Jallow, H., Renukappa, S., Suresh, S., & Rahimian, F. P. (2022) · Engineering Management Journal · doi:10.1080/10429247.2022.2147381 · Cited by 26
“AI can be said to be a disruptive innovation which is within the digital transformation going on in the world currently for organizations to enable advancements that could have been predicted impossible (Pillai & Sivathanu, 2020; Sanni-Anibire et al., 2020).”
BackgroundBig Data Analytics Adoption in Manufacturing Companies: The Contingent Role of Data-Driven Culture
Thanabalan, P., Zadeh, A. V., Hanifah, H. M., & Ramayah, T. (2024) · Information Systems Frontiers · doi:10.1007/s10796-024-10491-0 · Cited by 31
A classification framework for generative artificial intelligence for social good
Crumbly, J., Pal, R., & Nezih Altay, A. (2024) · Technovation · doi:10.1016/j.technovation.2024.103129 · Cited by 28
Kaur, M., & Gandolfi, F. (2023) · Review of International Comparative Management · doi:10.24818/rmci.2023.3.382 · Cited by 41
Al‐Khatib, A. W. (2024) · Management Decision · doi:10.1108/md-09-2023-1525 · Cited by 29
Yu, Z., Hu, N., Zhao, Q., Hu, X. Y., Jia, C., Zhang, C., Liu, B., & Li, Y. (2024) · Journal of Medical Internet Research · doi:10.2196/62768 · Cited by 28
“Perceived risk refers to the degree of insecurity that doctors perceive when they are using technology to execute tasks and exchange data [34].”
Abdulkareem, A. K., Oladimeji, K. A., Ishola, A. A., Lawan, B. M., Umar, A. Y., & Adejumo, A. (2024) · International Journal of Public Sector Management · doi:10.1108/ijpsm-10-2023-0307 · Cited by 25
Chapano, M., Mey, M. R., & Werner, A. (2023) · SA Journal of Human Resource Management · doi:10.4102/sajhrm.v21i0.2085 · Cited by 23
Ioakeimidou, D., Chatzoudes, D., Symeonidis, S., & Chatzoglou, P. (2023) · International Journal of Manpower · doi:10.1108/ijm-10-2022-0496 · Cited by 29
Yang, Y. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.600218 · Cited by 22
“Research shows that the use of AI technology in talent training has a positive impact, which can effectively reduce costs and effectively improve teaching efficiency (Pillai and Sivathanu, 2020).”
BackgroundBenabou, A., & Touhami, F. (2025) · International journal of production management and engineering · doi:10.4995/ijpme.2025.21900 · Cited by 38
“As AI takes a central role in the strategic initiatives of HR departments, the ability to attract and secure top talent has become a critical responsibility for HR managers (Pillai & Sivathanu, 2020).”
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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