Assessing the evolution of banking reputation literature: a bibliometric analysis
Bibliographic record
Abstract
Purpose The concept of banking reputation has gained significant attention due to its relevance in the banking industry. A strong reputation has become crucial for a bank’s success, as it affects trust, credibility and stakeholders' perceptions. However, understanding and managing reputation in the banking sector involves several challenges. This study aims to analyze the field of banking reputation research through bibliometric analysis. Design/methodology/approach It explores the evolution of research in this area, identifies key journals, articles and authors, examines the main research streams, and identifies research fronts and opportunities for future advancement. Findings The findings reveal that banking reputation research has evolved over time, with multiple perspectives and viewpoints. Key journals and authors in the field are identified, and leading research streams are highlighted. The study also uncovers the conceptual and intellectual structure of the research domain, providing insights into the complex and multidimensional nature of banking reputation. Furthermore, the study emphasizes the importance of corporate social responsibility, sustainability practices and gender diversity in shaping a bank’s reputation. These factors play a significant role in attracting and retaining customers, accessing financial markets and securing funding. Research limitations/implications The results contribute to the existing body of knowledge and provide researchers and practitioners with valuable insights for further exploration. Originality/value The paper concludes by outlining potential avenues for future research in the field of banking reputation.
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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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.195 | 0.186 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".