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Record W2087574492 · doi:10.1080/09638180701707045

The Effects of Corporate Governance on the Relationship between Innovative Efforts and Performance1

2007· article· en· W2087574492 on OpenAlexaff
Johnny Jermias

Bibliographic record

VenueEuropean Accounting Review · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCorporate governanceIncentiveIndependence (probability theory)BusinessPosition (finance)Positive relationshipMarketingAccountingIndustrial organizationMicroeconomicsEconomicsPsychologySocial psychologyFinance

Abstract

fetched live from OpenAlex

The purpose of this study is to examine the effects of managerial share ownership, CEO duality and board independence on the relationship between innovative efforts and performance. The study is motivated by the observation that despite the widely held belief that innovative efforts are crucial to firms' survival, previous studies were unable to provide any evidence in support of this belief. It addresses this incongruity by focusing on the effects of corporate governance on the relationship between innovative efforts and performance. Specifically, this study predicts and finds that managerial share ownership has a positive effect on this relationship while CEO duality has a negative effect. Contrary to the hypothesis, this study finds that board independence also has a negative effect on the relationship between innovative efforts and performance. This contradictory result is, however, consistent with the managerial-incentive theory, which proposes that inside directors are in a better position than outside directors to motivate managers to undertake profitable projects because they have superior access to firms' specific information.

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)0.004
metaresearch head score (Gemma)0.026
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 score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.238
Teacher spread0.200 · 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

Citations17
Published2007
Admission routes1
Has abstractyes

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