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Record W2971974189 · doi:10.36407/serambi.v1i2.68

Analisis Determinasi Tingkat Net Core Operational Margin Pada Bank Umum Syariah di Indonesia

2019· article· en· W2971974189 on OpenAlexaboutno aff
Komarudin Komarudin, Ayus Ahmad Yusuf

Bibliographic record

VenueSERAMBI Jurnal Ekonomi Manajemen dan Bisnis Islam · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsNet interest marginBusinessCapital adequacy ratioNonprobability samplingOperational riskMargin (machine learning)Quarter (Canadian coin)Panel dataFinanceFinancial systemAccountingEconomicsReturn on assetsRisk managementPopulationEconometricsIncentive

Abstract

fetched live from OpenAlex

Purpose- The purpose of this study is to explore the impact of a bank’s internal and external factors on net core operating margin in Indonesia’s Islamic commercial bank's companies.
 Methods- A quantitative approach by using panel data regression with a period from 2nd quarter 2010 to 2nd quarter 2015. The object of this study is eight Islamic commercial banks companies which were listed in sharia bank directory of Central Bank of Indonesia and Financial Services Authority of the Republic of Indonesia, those selected by purposive sampling technique with specific criteria.
 Findings- This study can be concluded that capital adequacy ratio and average operational cost have a positive and significant impact on the net core operational margin. On the contrary, financing to deposit ratio, bank size, operational efficiency ratio, and BI rate have a negative and significant impact on net core operational margin.
 Research implications- The management needs to create an efficient banking system by reducing operational costs and improving management quality. Banks are also advised to increase the prudential principle in managing finance so that the risk of default can be minimized.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.277
Teacher spread0.258 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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