Empirical Evidence of a Changing Operating Cost Structure and Its Impact on Banks’ Operating Profit: The Case of Germany
Bibliographic record
Abstract
The financial sector is undergoing extensive changes and challenges that affect the entire market and infrastructure of financial service providers. Technological development leads to increased digitalisation and allows new business models to emerge. With regard to the banking sector, it is evident that this sector is characterized by employees and associated services. However, due to changing conditions, a decline in personnel has been recorded for many years. This raises the question as to what extent—based on contrary assumptions of the principle agency theory and the expense preference hypothesis—personnel changes influence the operational success of banks. On this basis, six hypotheses were formulated and tested. The principal component analysis method was applied to prepare the data. Afterwards, the actual analysis was carried out using a mixed method approach. The results on the basis of the years 2013–2017 showed a negative personnel development, which contributed to the improvement of the operating results of banks. Hereby it becomes evident that the business model design of savings and cooperative banks is of secondary importance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".