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Cited by 422AI and HR TechnologiesEmployer Branding and e-HRMTechnology Adoption and User Behaviour

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

Pillai, Rajasshrie, Sivathanu, Brijesh·2020·Benchmarking: An International Journal
DOI 10.1108/bij-04-2020-0186
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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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Crossref · 10.1108/bij-04-2020-0186

Cites

147 of 147 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 146 references

Research works printed but not linked: 25

  1. 1980, Understanding Attitudes and Predicting Behavior
  2. 2012, Conceptual model for examining knowledge management system (KMS) adoption in public sector organizations in Saudi Arabia, Gov Workshop, 2, 1
  3. 2001, Social cognitive theory of mass communication media psychology, Psychology, 3, 265
  4. 2017, Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation, 1
  5. 2018, Insight - Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women
  6. 1990, The context for change: organization, technology and environment. The processes of technological innovation, The Processes of Technological Innovation, 199, 151
  7. 2017, Biometrics won't solve our data-security crisis, Harvard Business Review
  8. 2016, Impact of technology on recruitment process and its impact on service quality of HR service provider, BIMS Journal of Management, 3, 26
  9. 2000, Web portals usher in, drive away business, National Underwriter/Property and Casualty Risk and Benefits, 104, 19
  10. 1955, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
  11. 2001, The War for Talent
  12. 1978, Psychometric Theory
  13. 2011, Literature Review of Information Technology Adoption Models at Firm Level
  14. 2017, A Multidimensional Framework for Human Resource Information Systems Adoption and Use in a South African University
  15. 2016, E-recruitment–an organizational change. International, Journal of Multidisciplinary Approach and Studies, 3, 78
  16. 2017, Factors influencing E-HRM implementation in government organisations, Journal of Enterprise Information Management, 31, 247
  17. 2003, Diffusion of Innovations
  18. 1983, Diffusion of. Innovations. Glencoe
  19. 2006, Information System Success: Individual and Organizational Determinants
  20. 2007, Research in E-HRM: Review and Implications, 19
  21. 2009, Trust and Electronic Government Success: An Empirical Study, 99
  22. 1991, An integrated model of information system Adoption in small business, Journal of Managemnet Inforamtion System, 15, 187
  23. 1990, The Process of Technology Innovation
  24. 2005, Adoption of Electronic Government Services Among Business Organizations in Singapore, 417
  25. 2013, Computers in Human Behavior Why Aren' T Organizations Adopting Virtual Worlds?, 772

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Metadata

Authors
Pillai, R., & Sivathanu, B.
Affiliations
Sri Balaji University, Pune (IN)
ORCID
Venue
Benchmarking: An International Journal
Publisher
Emerald
Published
2020-08-14
Volume
27
Issue
9
Pages
2599-2629
ISSN
1463-5771 (ISSN-L)
DOI
10.1108/bij-04-2020-0186
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
Licence
www.emerald.com/insight/site-policies (tdm)
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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