Designing a Balanced Scorecard to Measure a Bank's Performance: A Case Study
Bibliographic record
Abstract
Performance measurement systems play a key role in evaluating the strategic performance of an organization, but many managers agree that their evaluation systems do not adequately fulfill this function. Hence, in recent years a shift towards the Balanced Scorecard (BSC) has emerged as a managerial approach to evaluate the strategic performance of the organization. The purpose of this study is to contribute to the understanding of how BSC is developed and applied in evaluating the performance of a Large Local Bank (LLB) in Iraq. Using the concepts of Kaplan and Norton, and the data made available from the bank, a BSC was derived to measure the performance of the bank between 2006-2009. The analysis assisted the cause-effect relationships between the non-financial, and the financial dimensions of the BSC. Due to lack of research work, in this area, in the banking sector in Iraq, this study shall contribute to the knowledge on how banks in Iraq may apply the BSC to evaluate their performance, and how they might turn strategic vision into potential performance. The authors proposed some future research needs required in this area. The use of the BSC developed here is limited to the bank studied; however, the approach could trigger off reflections among policy makers and other banks to start using the BSC.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".