The Moderating Role of Staff Efficiency in the Relationship between Bank’s Specific Variables and Liquidity Risk in Islamic Banks of Gulf Cooperation Council (GCC) Countries
Bibliographic record
Abstract
The efficiency of bank’s staff plays a crucial role in managing and mitigating the financial risks like liquidity risk. The aim of this paper is to propose a conceptual model/framework for investigating the moderating role of staff efficiency on the relationship between bank’s specific variables and liquidity risk in Islamic banks in Gulf Cooperation Council (GCC). GCC economies depend heavily on oil revenues which makes it subject to oil prices fluctuations. Therefore, liquidity in GCC banks, especially Islamic banks almost always suffers liquidity pressure. Thus, the issue of liquidity in this region has grown in importance in light of recent oil decline. Several attempts have been made to investigate the determinants of liquidity risk, yet the findings lack consistency. Most of the previous studies have ignored GCC region and have focused on other environments like credit risk but gave less attention to the moderating role of staff efficiency function in the Islamic banks with respect to liquidity risk. This paper offers a framework by adding a moderator of staff efficiency to the existing models of the bank’s specific determinants of liquidity risk with a particular attention to the GCC countries which are heavily dependent on oil revenues and always are subject to the impact of oil prices instabilities. Many stakeholders should benefit from the outcomes of this study. It should pave the way for bankers, regulators, investors and researchers to have a better understanding and insight about the factors that affect liquidity risk in the aforesaid banks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".