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Record W4360983107 · doi:10.1002/cjas.1716

CEO turnover, stakeholder management, and dynamics between corporate social responsibility and firm performance

2023· article· en· W4360983107 on OpenAlexvenueno aff
Yankuo Qiao

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)Corporate social responsibilityStakeholderStakeholder theoryHeuristicBusinessEnterprise valuePower (physics)Transition (genetics)Value (mathematics)Empirical evidenceAccountingCorporate governanceIndustrial organizationMicroeconomicsEconomicsPublic relationsManagementFinancePolitical scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Focusing on the CEO transition period, this paper documents that the variation of CSR is highly associated with the fluctuation of firm performance. Moreover, it is found that the strong association is undermined for the CEO transition period during which ample variations of value relevant firm policies take place. Furthermore, the empirical evidence suggests that CEOs whose predecessors relinquish power involuntarily are more likely to adjust CSR activities for the purpose of optimal stakeholder management. The paper presents a heuristic and parsimonious model to guide the empirical investigation and conducts a system of simultaneous equations to ameliorate the endogenous nexus of firm performance, CEO turnover and CSR adjustment. The results are also robust to a battery of additional checks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.169
GPT teacher head0.293
Teacher spread0.124 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2023
Admission routes1
Has abstractyes

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