Cited by 927AI and HR TechnologiesImpact of AI and Big Data on Business and SocietyHuman Resource and Talent Management

The DOI matches its Crossref record; 201 of the 201 references it cites were found in a registry or an index; 43 of the 206 sources its claims cite were checked for support.

Unlocking the value of artificial intelligence in human resource management through AI capability framework

Open access
Chowdhury, Soumyadeb, Dey, Prasanta, Joel-Edgar, Sian, Bhattacharya, Sudeshna, Rodriguez-Espindola, Oscar, Abadie, Amelie, Truong, Linh·2023·Human Resource Management Review

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This systematic review synthesizes multidisciplinary literature to identify the organizational resources needed to build AI capability in HRM, arguing that technical infrastructure alone is insufficient. It proposes an AI capability framework centred on non-technical resources and AI-employee integration, and offers research propositions to advance AI scholarship in HRM.

Key findings

  • The review distilled five themes: AI applications in HRM, collective intelligence, AI and employment/skills, drivers to AI adoption, and barriers to AI adoption.
  • Organizations need complementary non-technical resources—human skills and competencies, leadership, team coordination, culture, innovation mindset, governance, knowledge management, and AI-employee integration—alongside technical resources such as data, infrastructure, and AI transparency.
  • The review process began with 14,376 records, removed 1,564 duplicates, screened 12,812 records, retained 216 after title/abstract screening, identified 72 eligible articles, added 12 through cross-referencing, and included 84 studies; 18 topics were grouped into five themes.
  • The proposed framework integrates resource-based view, knowledge-based view, and knowledge management strategies to explain how organizational readiness and context of AI use shape business and employee outcomes.

Methods

  • Systematic literature review following established protocols, searching SCOPUS and EBSCO across 98 journals ranked 3, 4, or 4* in the ABS Journal Guide 2020, spanning HRM, general management, international business, information management, and operations management; last search date June 2021.
  • Used an R script to remove duplicates, Latent Dirichlet Allocation topic modelling on titles and abstracts, team-based coding of topics, a document extraction table, and thematic synthesis to develop the AI capability framework.
  • Final synthesis drew on 84 articles, organized into five research themes and used to propose research priorities and practical implications.

Limitations

  • Stated: the review is limited to top-tier peer-reviewed journals (ABS 3–4*), so lower-ranked journals, non-peer-reviewed articles, and practitioner reports were excluded; the keyword and Boolean search may have missed relevant articles due to database unavailability or human error; future work should include Web of Science, DBLP, SSRN, SpringerLink, and law journals.
  • Stated: selection of themes, research propositions, and resources relied on academic expert judgement and existing literature, so future studies should validate them with HR practitioners through interviews, Delphi studies, or AHP analysis.
  • Evident: the framework is conceptual and not empirically tested in the paper; the conclusion reports reviewing 62 journals and 69 relevant articles, while the methodology reports 98 journals and 84 final articles, indicating an internal inconsistency in the reported review scope.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.hrmr.2022.100899

Cites

201 of 201 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

43 of 206 cited sources checked · 17 supported · 0 flagged · accuracy not yet measured

Show all 68 claims

  • Probably not a substantial claim. 98
  • It may state two separate claims as one. 80
148 of 148
  • The paper relies on this without support.

    Popular press and technology vendors have overinflated expectations about benefits and drivers to AI adoption and implementation.

    “The popular press and technology vendors have overinflated expectations about benefits and drivers to AI adoption and implementation”
  • The authors state this as an assumption.

    AI systems in HRM are manmade (manufactured by human) and not developed by AI itself.

    “While calling AI as manmade may be debatable (Steels and Brooks, 2018), however we are yet to come across an AI system which is developed by AI itself in HRM.”
  • The authors state this as an assumption.

    There is a way of controlling the input data and changing the outcome.

    “It is assumed in this argument that there is a way of controlling the input data and changing the outcome.”
  • The authors state this as an assumption.

    AI has shortcomings in creative and social intelligence.

    “Additionally, AI is assumed to have shortcomings in creative and social intelligence (Mak et al., 2020).”
4 of 4

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Conclusion gives a different journal count

    Minor

    The methodology says the final journal list contained 98 journals, while the conclusion describes the review as covering 62 journals.

  • Research-priority count changes from five to four

    Minor

    The discussion introduces five research priorities, whereas the conclusion says the review proposes four.

  • Conclusion gives a different review size

    Minor

    The selection process reports 84 studies in the final review, but the conclusion says 69 articles were relevant. Both statements describe the completed review.

  • Two different questions carry the A8 label

    Minor

    The skills-development question and the later ethics-and-governance question are both numbered A8, so the proposition labels are not unique.

4 of 4

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 18

  1. Amabile, 2020, Creativity, artificial intelligence, and a world of surprises, Academy of Management Discoveries, 6, 351
  2. Babic, 2021, Onboarding AI, Harvard Business Review, 98, 56
  3. Barro, 2019, People and machines: Partners in innovation, MIT Sloan Management Review, 60, 22
  4. Budhwar, 2020, Call for papers: Artificial intelligence challenges and opportunities for international HRM. The, International Journal of Human Resource Management.
  5. Bughin, 2018, Skill shift: Automation and the future of the workforce, McKinsey Global Institute, 1, 3
  6. Chesney, 2019, Deepfakes and the new disinformation war: The coming age of post-truth geopolitics, Foreign Aff., 98, 147
  7. Chui, 2018, Notes from the AI frontier: Insights from hundreds of use cases, McKinsey Global Institute, 1
  8. Davenport, 2017, How P&G and American Express are approaching AI, Harvard Business Review (blog)
  9. Fountaine, 2019, Building the AI-powered organization, Harvard Business Review, 97, 62
  10. Ghosh, 2019, Taking a Systems Aproach to Adopting AI, Harvard Business Review, Technology & Operations
  11. IEEE 7010, 2020
  12. Kakkar, 2019, Technology driven human resource measurement–A strategic perspective, International Journal on Emerging Technologies, 10, 179
  13. Ransbotham, 2018, Artificial intelligence in business gets real, MIT Sloan Management Review, 60280
  14. Ransbotham, 2019, Winning with AI, MIT Sloan Management Review and Boston Consulting Group
  15. Ransbotham, 2017, Reshaping business with artificial intelligence: Closing the gap between ambition and action, MIT Sloan Management Review, 59, 1
  16. Satell, 2019, We need AI that is explainable, auditable, and transparent, Harvard Business Review
  17. Singh, 2020, Workforce analytics: Increasing managerial efficiency in human resource, International Journal of Scientific and Technology Research, 9, 3260
  18. Wilson, 2018, Collaborative intelligence: humans and AI are joining forces, Harvard Business Review, 96, 114

Cited by

Sorted by citations

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Metadata

Authors
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L.
Affiliations
  • TBS Education (FR)
  • Aston University (GB)
  • Northeastern University London (GB)
Corresponding author
Chowdhury, Soumyadeb
Venue
Human Resource Management Review
Publisher
Elsevier BV
Published
2023-03
Volume
33
Issue
1
Pages
100899
ISSN
1053-4822 (ISSN-L)
OpenAlex
W4293229829
Cited by
927
References
156
Citations by year
2026: 230 · 2025: 311 · 2024: 257 · 2023: 118
FWCI
164.25
Type
journal article
Language
en
Open access
Locations
  • Human Resource Management Review (publishedVersion)
  • Aston Publications Explorer (Aston University) (acceptedVersion)
  • Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
Funders
Aston University (2020-21)
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Human Resource and Talent Management
Keywords
artificial intelligence in human resource management, organizational resources, artificial intelligence adoption, organizational readiness, non-technical skills, leadership, organizational culture, AI governance, resource-based view, knowledge-based view, research propositions

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Cited sources are checked with a language model’s help (gpt-6-sol).

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