The Effect of Branchless Banking Strategy on the Financial Performance of Commercial Banks in Kenya
Bibliographic record
Abstract
The Banking sector acts as the life blood of modern trade and economic development. Commercial banks influence, facilitate and integrate the economic activities like resources mobilization, poverty elimination, production, and distribution of public finance. The financial performance of commercial banks has great implications in the financial sector and in the country at large, and will still remain an important subject of concern by all the stakeholders in the banking industry. In the last two decades, a lot of banking innovation has taken place in order to improve commercial banks financial performance. Branchless banking which involves the use of agency banking and electronic banking channels in the distribution of banking products and services is one such innovation. This study purpose was to evaluate the effect of branchless banking on the financial performance of commercial banks in Kenya. The specific objectives of the study were to analyze the individual effects of agency banking and electronic banking channels on the financial performance of commercial banks in Kenya and the combined effect of both agency and electronic banking on the financial performance of commercial banks in Kenya. The study adopted an exploratory research design. A survey of all the 42 licensed commercial banks in Kenya was done. Both primary and secondary data on branchless banking and financial performance of banks was obtained from the individual commercial banks, Central Bank of Kenya banking annual supervision reports respectively. Return on Assets (ROA) was used as the main indicator of commercial banks financial performance. The amount of investment in agency and electronic banking was used as indicator for agency and electronic banking. Data analysis was done using SPSS and STATA statistical softwares. Descriptive statistics, diagnostic tests and tests of hypothesis were done. Data was presented using tables and charts. Study findings indicated that when used in isolation; both agency and electronic banking had a significant negative effect on the financial performance of commercial banks at 5 percent significance level. However, when agency and electronic banking channels were used together as a multichannel strategy, they had a significant positive effect on bank’s financial performance at 5 percent significance level. The study recommends that for positive returns, commercial banks should invest in both agency and electronic banking as a multichannel strategy since these channels are complimentary to each other.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".