BANKING SECTOR PERFORMANCE, PROFITABILITY, AND EFFICIENCY: A CITATION‐BASED SYSTEMATIC LITERATURE REVIEW
Bibliographic record
Abstract
Abstract This study presents a citation‐based systematic literature review on banking sector performance, particularly in terms of profitability, productivity, and efficiency. Specifically, the study aims to identify the leading sources of knowledge in terms of the most influential journals, authors, and papers. The paper presents a content analysis of the 100 most cited papers. In total, 1996 peer‐review papers were found relevant in the Scopus database by using a comprehensive list of keywords. The results show that the Journal of Banking & Finance appears to be the leading journal in terms of publication count and citations. Based on total citations, Allen Berger is the most prolific author. The most cited paper is “Problem loans and cost efficiency in commercial banks” by Allan Berger and Robert DeYoung. The content analysis of the top 100 papers identifies five essential themes: determinants of efficiency, methodology, ownership, financial crises, and scale economies. In terms of estimation approaches, 74% of papers employed frontier analysis, which includes 34% parametric and 40% nonparametric methods, and remaining 26% have used financial ratio analysis. Additionally, stochastic frontier and data envelopment analysis are widely used in parametric and nonparametric methods, respectively. An intermediate approach is extensively adopted for the specification of inputs and outputs.
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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 | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.064 | 0.053 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".