The effects of profit volatility, income smoothing, good corporate governance and non-performing financing on profit quality of sharia commercial banks
Bibliographic record
Abstract
The purpose of this study was to analyse the effects of profit volatility (X1), income smoothing (X2), corporate governance (X3), and non-performing financing (X4) on profit quality (Y) of sharia banks in Indonesia. The samples of this study were 10 sharia commercial banks in the period of 2012-2018 with 66 panel data that had been tested for outliers and normality. This study used a purposive sampling method, and it used the classical assumption tests, namely multicollinearity, autocorrelation, heteroscedasticity, and normality tests. This study used panel data regression analysis. The results of the study showed that profit volatility was detrimental to profit quality as evidenced by a beta coefficient of 0.0929 and the significance level of 0.1100, income smoothing was detrimental to profit quality as evidenced by a beta coefficient of -0.015 and the significance level of 0.1009, corporate governance had a negative influence on profit quality as evidenced by a beta coefficient of 0.0468 and the significance level of 0.293, non-performing financing was detrimental to profit quality as evidenced by a beta coefficient of -0.0096 and the significance level of 0.9139. The predictive ability of the four variables on profit quality was 16.34% while the remaining 83.66% was influenced by other factors not included in the research model.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".