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Record W3155371861 · doi:10.5267/j.ac.2021.3.020

Financial services authority on profitability with external factors as moderating variables at regional development banks in Indonesia

2021· article· en· W3155371861 on OpenAlexvenueno aff
Reslianty Rachim, Sukisno S. Riadi, Ardi Paminto, Felisitas Defung, Rahcmad Budi Suharto, Made Setini

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexBusinessModerationLocal governmentSample (material)FinanceFinancial systemAccountingPolitical science

Abstract

fetched live from OpenAlex

Analyzing the effects of Internal Factors, Local Government Interventions, External Factors and Policies of Bank Indonesia and the Financial Services Authority on Profitability with External Factors as Moderating Variables at Regional Development Banks in Indonesia. Sample this research is a Regional Development Bank of 24 Banks with research data from 2010 to 2018. A total of 24 Regional Development Banks throughout Indonesia were sampled from 2010 to 2018, so the observation data in this study includes 216 research data and there are 80 outlier data so that the data processed in this study with 136 data processed in the study. Analysis of the data in this study used Structural Equation Modeling with Warp PLS Program and internal actors gave a significant influence on profitability, Intervenes local government gave insignificant influence on profitability, External actors gave a significant influence on profitability, policy Bank Indonesia and financial services authority gave insignificant influence on profitability, Policy Bank Indonesia and financial services authority gave a positive and insignificant influence on profitability with external factors as moderation at the Regional Development Bank in Indonesia.

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)0.000
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.015
GPT teacher head0.218
Teacher spread0.203 · 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
Published2021
Admission routes1
Has abstractyes

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