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Record W4376646987 · doi:10.1111/1911-3846.12874

<scp>CEO</scp> career concerns in early tenure and corporate social responsibility reporting

2023· article· en· W4376646987 on OpenAlexvenueno aff
Long Chen, Chih‐Hsien Liao, Albert Tsang, Li Yu

Bibliographic record

VenueContemporary Accounting Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
FundersGeorge Mason University
KeywordsCorporate social responsibilityBusinessReputationAccountingIntermediaryIncentiveTurnoverCompensation (psychology)Voluntary disclosurePublic relationsMarketingPolitical scienceEconomicsPsychologyManagementSocial psychologyMarket economy

Abstract

fetched live from OpenAlex

Abstract The literature on corporate social responsibility (CSR) disclosure focuses on its economic consequences, but little is known about motivations—especially CEO personal incentives—behind such disclosure. Using an array of CSR reporting measures, we find that career concerns of CEOs early in their tenure motivate them to use voluntary CSR reporting as a signaling mechanism. The negative association between CEO tenure and CSR reporting is more pronounced in firms with stronger information intermediaries—that is, a higher level of socially responsible investors, a higher number of analysts following, and a higher level of media coverage. We also find that CEOs early in their tenure receive more personal benefits after voluntary CSR reporting, in terms of higher total compensation, better reputation, and less turnover, than CEOs later in their tenure. Taken together, the findings of our study lend support to the conjecture that CEO career concerns early in their tenure can be an important determinant of firms' voluntary CSR reporting.

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)0.036
metaresearch head score (Gemma)0.058
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0360.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0000.002
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 head0.238
GPT teacher head0.374
Teacher spread0.136 · 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; both teacher heads agree on what is shown here.

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

Citations63
Published2023
Admission routes1
Has abstractyes

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