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Record W4382281274 · doi:10.47700/jiefes.v4i1.5861

Determinants of the Profitability of Islamic Rural Banks During Covid-19 in Indonesia

2023· article· en· W4382281274 on OpenAlexaboutno aff
Deni Lubis, Dini Arsyi Anjani, Tita Nursyamsiah

Bibliographic record

VenueJournal of Islamic Economics and Finance Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexReturn on assetsIslamBusinessPanel dataQuarter (Canadian coin)Financial systemAccountingEconomicsFinanceEconometricsGeography

Abstract

fetched live from OpenAlex

The Return on Asset (ROA) value of Islamic rural banks (BPRS) keeps decreasing during the Covid-19 pandemic, specifically from 2020 to 2021. The ROA value of Sharia Commercial Bank and Sharia Business Unit also decreased in 2020 but increased in 2021. During the pandemic, many financial institutions were in trouble, but BPRS was still able to survive in the midst of a crisis. This phenomenon attracted some scholars to study the factors that made BPRS survive during a pandemic. This study aims to analyze the effect of internal and external factors on Islamic rural banks’ profitability from the second quarter of 2020 to the first quarter of 2022. The sample used consisted of 134 Islamic rural banks with complete data to be analyzed. This study used panel data regression with ROA as the dependent variable. The result of regression shows that partially, FDR has a positive impact on ROA, whereas NPF and OER hurt ROA. On the other hand, CAR, GDP, and CPI have no impact on ROA. The finding shows that the internal factors of the BPRS have an important role in dealing with crises during a pandemic. The government is expected to support the digital transformation of BPRS in order to increase the efficiency and convenience of BPRS, so the public is attracted to join BPRS.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.329

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.321
Teacher spread0.291 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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