The influence of market power and revenue diversification on the profitability and stability of Indonesian banking during the COVID-19 pandemic
Bibliographic record
Abstract
The present study aims to assess and scrutinize the impact of market power and revenue diversification on the level of Non-Performing Loans (NPL), which serves as an indicator of banking stability, through profitability during the COVID-19 pandemic. The population of interest includes all non-Sharia commercial banking institutions listed on the Indonesia Stock Exchange (IDX) from 2020 to 2022. A purposive sampling method was employed, resulting in a total of 264 observations. The data analysis was performed using panel data regression with the assistance of EViews version 10 software. The findings of this research reveal a direct positive and significant influence of market power and revenue diversification on bank profitability, as well as a direct negative and significant impact of market power, revenue diversification, and bank profitability on NPL. A noteworthy result derived from this study is the partial mediating role of profitability in the relationship between market power, revenue diversification, and NPL. Consequently, it is concluded that market power and revenue diversification play a pivotal role in enhancing profitability, mitigating credit risk, and ultimately improving banking stability. This study lends support to the non-structural approach of NEIO (New Empirical Industrial Organization), the Competition Fragility theory, and the Product Portfolio Theory. However, it is important to acknowledge the limitations of this research, such as the focus solely on non-Sharia banking institutions due to their distinct characteristics compared to conventional commercial banks, as well as data constraints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".