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Record W4407411676 · doi:10.1002/ijfe.3131

The Impact of Say‐On‐Pay on Firm Efficiency in Anglo‐Saxon Economies—Do <scp>CEO</scp> Personal Traits and <scp>CG</scp> Mechanisms Matter?

2025· article· en· W4407411676 on OpenAlexaboutno aff
Essam Joura, Ali Meftah Gerged, Qin Xiao, Subhan Ullah

Bibliographic record

VenueInternational Journal of Finance & Economics · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsMonetary economicsMicroeconomics

Abstract

fetched live from OpenAlex

ABSTRACT In this study, we explore how the personal traits of CEOs and corporate governance mechanisms moderate the link between say‐on‐pay (SOP) votes and various aspects of firm efficiency. Our sample consists of 1931 firms listed in four Anglo‐Saxon economies (i.e., USA, UK, Canada and Australia) during a period of notable regulatory changes. Our findings reveal a significant and positive impact of SOP votes on firm efficiency. This suggests that company executives recognise that lower efficiency leads to lower pay or even job loss. Interestingly, our analysis indicates that younger managers can contribute more to creating value and improving business performance compared with their older counterparts. However, the relationship between gender and firm efficiency remains inconclusive. Furthermore, our study highlights the limited involvement of the board of directors in driving firm efficiency. This could be attributed to inadequate monitoring, cooperation and communication among board members, particularly in the case of audit committees, which seem to have less skilled members. Alternatively, this lack of board engagement may be due to the influence of powerful managers within the company. This paper also offers practical implications to policymakers and practitioners and suggests avenues for future research that can build upon our evidence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.225
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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