M&A Efficiency Islamic Bank Pre-Merger Analysis: Does Control Covid-19 Matter?
Bibliographic record
Abstract
Analysis of pre-mergers is important to do as an effort for Islamic banks to increase competitiveness. This study aims to analyze the determinants of the efficiency of pre-merger Islamic banking in Indonesia. This study uses a quantitative research design through the two-stage banking data envelopment analysis (DEA) model. The input variables for the first stage are third party funds (DPK), operational costs (BIOP), total financing (TFIN), and the output variables are operating income (PENDOP), total assets (TASSET). Then in the second stage with multivariate tobit regression, using the dependent variable the efficiency score obtained through the results of the first stage and in the first model the independent variables are total assets, bank size, ROA, NPF, CAR and then measurements are made on additional Covid-19 controls for the second model. The results showed that total assets, bank size, ROA, and CAR significantly affected efficiency scores. NPF has no significant effect. The second model with the Covid-19 control variable produced a more substantial empirical model influence than non-control Covid-19. Overall, the performance of Islamic commercial banks increased until the fourth quarter of 2020, seen from the improving quality of financing, which was also marked by the decline in NPF and quite good intermediation. Limitations of this study include the limited update of the Covid-19 control data.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".