MétaCan
Menu
Back to cohort
Record W3107556111 · doi:10.38043/jmb.v17i4.2718

Corporate Governance dan Kinerja Keuangan

2020· article· en· W3107556111 on OpenAlexaboutno aff
Haniatus Sa’diyah

Bibliographic record

VenueJurnal Manajemen Bisnis · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Technology, Consumer Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceNonprobability samplingBusinessAccountingQuarter (Canadian coin)Sample (material)Path analysis (statistics)Panel dataSupervisory boardPopulationShariaFinanceIslamEconomicsStatistics

Abstract

fetched live from OpenAlex

This study aims to determine the effect of corporate governance as proxied by the Board of Commissioners, the Board of Independent Commissioners, the Board of Directors and the Sharia Supervisory Board on Financial Performance, through a connecting variable, namely Non Performing Financing (NPF). The sample of this research is using purposive sampling method. The population is 13 Islamic Commercial Banks in Indonesia. The samples obtained were 8 Islamic Commercial Banks. The data is obtained from the quarterly reports of each bank, namely the first quarter of 2017 to the second quarter of 2020. Data analysis and hypothesis testing methods use path analysis using panel data. The results of this study indicate that corporate governance as proxied by the Board of Commissioners, the Independent Commissioner, the Board of Directors and the Sharia Supervisory Board has no effect on financial performance and non-performing financing. This means that higher corporate governance does not affect financial performance or non-performing financing. In this study it was also found that non-performing financing has an effect on financial performance. If non-performing financing decreases, financial performance will increase. In addition, non-performing financing in this study cannot be an intervening variable for corporate governance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.166
GPT teacher head0.402
Teacher spread0.237 · 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 teacher head, not a consensus.

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

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJurnal Manajemen BisnisSame topicHealth, Technology, Consumer BehaviorFrench-language works237,207