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Record W3084600133 · doi:10.5267/j.ac.2020.8.023

The role of the board of director with political connection for increasing the firm value

2020· article· en· W3084600133 on OpenAlexvenueno aff
Fahmi Idris, Agung Dharmawan Buchdadi, Muhammad Rizqi Muttaqien, Taqwa Hariguna

Bibliographic record

VenueAccounting · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Governance and Financial Management
Canadian institutionsnot available
Fundersnot available
KeywordsConnection (principal bundle)PoliticsValue (mathematics)BusinessManagementPolitical scienceEconomicsMathematicsStatisticsLawGeometry

Abstract

fetched live from OpenAlex

This study examines the impact of independent board of directors, executive compensation, and political connections on firm performance in one of the biggest democracy countries in South East Asia.Company performance is measured by return on assets (ROA), and firm value is measured by Tobin's Q.Meanwhile, as an independent variable we use several good corporate governance variables, namely the board of directors, executive compensation variable and political connections.Company data are from companies listed in LQ45 index which means the best stock performance in Indonesia capital market.The finding shows the crucial role of independent board of director and political connection for enhancing the firm value.Findings indicate an independent board of director can improve company performance.Meanwhile, executive compensation can provide motivation to increase company value, but has other motivations in improving company performance.Meanwhile, political connections can improve company performance and value.Thus, this study emphasizes the crucial role of the experience and the connection of the board of directors in improving the value of the firm.

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)0.009
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.010
GPT teacher head0.188
Teacher spread0.177 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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