MétaCan
Menu
Back to cohort
Record W4400439613 · doi:10.5465/amproc.2024.180bp

CEO Compensation and Firm Performance: A Study on the Moderating Roles of CEO Origin and Firm Size

2024· article· en· W4400439613 on OpenAlexaboutno aff
Rida Elias, Najoie Nasr, Bassam Farah

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessExecutive compensationCompensation (psychology)Industrial organizationPsychologyCorporate governanceSocial psychologyFinance

Abstract

fetched live from OpenAlex

This study delves into the critical role of Chief Executive Officers (CEOs) in organizational performance, with a specific focus on the impact of CEO origin (internal vs. external) on the relationship between CEO compensation and firm financial performance. CEOs often draw significant attention and controversy, especially regarding their compensation and the succession process. Our research investigates these aspects, contributing novel insights to both the Resource-Based View (RBV) and CEO succession literature. We analyze a comprehensive dataset of publicly traded companies in the US and Canada, examining the relationship between CEO compensation and firm performance moderated by CEO origin. The study reveals that internally promoted CEOs, compared to externally hired counterparts, differently influence the relationship between CEO compensation and firm performance. Our findings indicate that internally promoted CEOs strengthen the positive relationship between compensation and ROA. In contrast, externally recruited CEOs present a different scenario, often involving higher compensation but with a less straightforward impact on firm performance. We also explore how firm size moderates this relationship, uncovering complex dynamics that challenge traditional assumptions in strategic management.

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 score0.406
Threshold uncertainty score0.538

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 science0.0000.000
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 head0.030
GPT teacher head0.246
Teacher spread0.217 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAcademy of Management ProceedingsSame topicCorporate Finance and GovernanceFrench-language works237,207