Financial and Non-Financial Measures in Evaluating Performance: The Role of Strategic Intelligence in the Context of Commercial Banks in Kenya
Bibliographic record
Abstract
This study provides comprehensive discussion on role of strategic intelligence in commercial banks, in Kenyan context. The primary focus was to evaluate the performance of commercial banks using both financial and non-financial performance measurers. The financial measurers comprised return on equity (ROE), while non-financial measures were customer satisfaction, learning and growth, and internal processes. The study was anchored on resource-based view and balanced scorecard model. The target population comprised 40 commercial banks. Additionally, the sample size 181 was selected proportionately through stratified sampling procedure. Data collection instruments comprised closed and open -ended questionnaires and online review. The study used both primary and secondary data, where primary data was obtained from Kenya commercial banks head offices, while secondary data, for the year 2016 – 2018, was obtained from the annual reports of the central bank of Kenya. Data analysis was done using descriptive statistics and linear multiple regression analysis. Findings of the study indicate that strategic intelligence has a statistically significance on the performance of commercial banks in Kenya. Moreover, both financial and non-financial measures of performance are relevant in the banking sector and growth of Kenyan economy. The study recommends that commercial bank in Kenya should integrate their training focus and strategy implementation with investors interests based on balanced score card.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".