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Cited by 4Corporate Finance and GovernanceFirm Innovation and GrowthOrganizational Management and Leadership

The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.

Artificial intelligence (AI) and corporate governance: Evidence from board size

Open access
Chatjuthamard, Pattanaporn, Jiraporn, Pornsit, Lee, Sang Mook·2026·Journal of Behavioral and Experimental Finance

Overview

Integrity signals

Registry

Crossref · 10.1016/j.jbef.2026.101144

Cites

60 of 60 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Show all 60 references

Research works printed but not linked: 14

  1. Babina, 2024, Artificial intelligence, firm growth, and product innovation, J. Financ. Econ., 151
  2. Cath, 2018, Artificial intelligence and the “good society”: The US, EU, and UK approach, Sci. Eng. Ethics, 24, 505
  3. Chatjuthamard, 2023, Corporate culture, innovation, and board size: Recent evidence from machine learning and earnings conference calls, SSRN Electron. J.
  4. Cockburn, 2018, Impact Artif. Intell. Innov., 24449
  5. Hermalin, 2001, Boards Dir. Endog. determined Inst. A Surv. Econ. Lit.
  6. Jensen, 1993, The modern industrial revolution, exit, and the failure of internal control systems, J. Finance, 48, 831, 10.1111/j.1540-6261.1993.tb04022.x · DOI
  7. Jenter, 2023, Does board size matter?, SSRN Electron. J., 916
  8. Knyazeva, 2013, The supply of corporate directors and board independence, Rev. Financ. Stud., 26, 1561, 10.1093/rfs/hht020 · DOI · Paper page
  9. Konadu, 2022, Board gender diversity, environmental innovation and corporate carbon emissions, Technol. Forecast. Soc. Change, 174
  10. Li, 2010, Textual analysis of corporate disclosures: A survey of the literature, J. Account. Lit., 29, 143
  11. Lipton, 1992, A Modest Proposal for Improved Corporate Governance, Bus. Lawyer, 48, 59
  12. Lu, 2022, Board of directors’ attributes and corporate outcomes: A systematic literature review and future research agenda, Int. Rev. Financ. Anal., 84
  13. Ongsakul, 2024, Corporate culture and shareholder litigation rights: evidence from a quasi-natural experiment and textual analysis, Rev. Behav. Financ.
  14. Wongsinhirun, 2023, Corporate culture and board gender diversity: Evidence from textual analysis, Int. Rev. Financ. Anal., 86

Metadata

Authors
Chatjuthamard, P., Jiraporn, P., & Lee, S. M.
Affiliations
  • Chulalongkorn University (TH)
  • Pennsylvania State University (US)
Corresponding author
  • Chatjuthamard, Pattanaporn
  • Jiraporn, Pornsit
  • Lee, Sang Mook
Venue
Journal of Behavioral and Experimental Finance
Publisher
Elsevier BV
Published
2026-03
Volume
49
Pages
101144
ISSN
2214-6350 (ISSN-L)
OpenAlex
W7124161722
Cited by
4
References
60
Citations by year
2026: 4
FWCI
73.58
Type
journal article
Language
en
Open access
Locations
  • Journal of Behavioral and Experimental Finance (publishedVersion)
  • SSRN Electronic Journal (acceptedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Finance › Corporate Finance and Governance
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Firm Innovation and Growth
  • Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Organizational Management and Leadership
Keywords
artificial intelligence adoption, board size, corporate governance, board governance, R&D-intensive firms, cash reserves, firm adaptability, strategic decision-making

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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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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.