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Record W2887785833 · doi:10.5539/ijef.v10n9p46

Executive Incentives and Maximization of the Value of Stakeholders ——Regulating Effects Based on the Independent Director

2018· article· en· W2887785833 on OpenAlexvenueno aff
Qitong Yu, Zili Lin, Chang Deng

Bibliographic record

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveSalaryCorporate governanceExecutive compensationMaximizationEquity (law)ShareholderBusinessEnterprise valueCashValue (mathematics)AccountingPrincipal–agent problemCompensation (psychology)Agency costMicroeconomicsEconomicsFinanceMarket economy

Abstract

fetched live from OpenAlex

Salary incentives mechanism is the main approach used by corporations to solve the agency issues between the management and shareholders. From the perspective of maintaining the maximum of stakeholders’ value, this paper, using the data of A-share listed companies between 2012 and 2016, examines the effectiveness of cash compensation and equity-based incentives. Whether the relationship between the two can be regulated by the introduction of the independent director is further discussed. The empirical results indicate that cash compensation effectively increase the executive’s concern about the interests of stakeholders, while equity-based incentives do just the opposite. In addition, regardless of the proportion of independent director, its regulating effects on the relationship between the two above is negative, causing an adverse effect on the executive’s corporate governance in the light of maximization of the value of stakeholders.

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)0.006
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 score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.198
Teacher spread0.179 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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