MétaCan
Menu
← Back to cohort
Record W2344605784 · doi:10.5539/ijef.v8n5p190

Moderating Effects of Media Coverage and Corporate Governance on CSR-CFP Nexus-Evidence from Listed Companies on Taiwan Stock Exchange

2016· article· en· W2344605784 on OpenAlexvenueno aff
Yuan Chang

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityNexus (standard)Corporate governanceBusinessAccountingIncentiveExecutive compensationStock exchangePropensity score matchingRobustness (evolution)FinanceEconomicsPublic relationsMicroeconomics

Abstract

fetched live from OpenAlex

Media coverage helps firm’s benevolent action under the sunlight (well-known by the public). Effective CEO incentive compensation and sound corporate governance align the interest of management with the firm by forming correct and efficient decision on positive-feedback social activities. This paper examines whether media coverage, compensation and corporate governance act as positive moderators for the relationship between corporate social responsibility (CSR) and corporate financial performance (CFP), namely, CSR-CFP nexus. Based on data of TWSE-listed companies during 2005-2009, regression result generally shows that higher CEO compensation strengthen positive relationship between firm’s CSR engagement and financial performance. Weaker corporate governance deteriorates positive CSR-CFP relationship. Media coverage has little influence on the relationship between CSR and CFP. Robustness checks such as fixed/random effect estimation, two-stage estimation and propensity score matching to control for selection bias yield similar outcome.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.032
GPT teacher head0.213
Teacher spread0.182 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Economics and Finance→Same topicCorporate Finance and Governance→French-language works237,207→