The Role of Digital Banking Services on Commercial Banks Performance in Somalia: A Descriptive and OLS Approach
Bibliographic record
Abstract
The main objective of this study is to examine the role of digital banking services on commercial banks’ performance in Somalia. A descriptive research design was used in this study to illustrate the relationship between variables. The study selected 300 participants using stratified random sampling. The study employed the Kobo-collect tool to collect data from the field. SPSS v28 and Eviews12 data analysis tools were employed in this study. The ADF test results show that all variables are stationary in the first difference with a constant and trend for each of the three critical levels. Pearson chi-square statistics showed that the association between explanatory variables and commercial banking performance is statistically significant. Descriptive statistics showed that the skewness and kurtosis of the normal distribution of probability are best to fit and close to zero using the Jarque-Bera test. Positive kurtosis values suggest a peaked distribution with long, fatty tails, and it proposes that a large proportion of numbers are concentrated in its tails rather than its centre. The regression result gives the impression that the explanatory variables determine 86% of the overall variance in commercial banks’ performance. The correlation results showed a strong and significant positive correlation between digital banking service delivery and commercial banks' performance in Mogadishu, Somalia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".