Leadership Is a Driving Factor: Financial Technology Effect in Rural Bank Performance
Bibliographic record
Abstract
The study examined the mediating roles of adaptive leadership in the relationship between financial technology innovation and rural bank performance. A survey-based research approach was applied, and the hypotheses were tested using a sample of 305 respondents. A measurement model was employed to evaluate the validity and reliability of the scales used in the study. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the hypothesized relationships and test the mediation effects. The empirical findings support the hypothesized relationship between both financial technology innovation and the director’s individual performance on rural bank performance. Adaptive leadership was found not to mediate the association between financial technology innovation and rural bank performance. The research highlights the importance of financial technology innovation and individual director performance in enhancing rural bank performance. Furthermore, the findings support the notion that developing financial technology innovation is crucial for fostering adaptive leadership. Additionally, adaptive leadership contributes to strengthening director individual performance, which ultimately drives overall performance improvements in rural banks. The integration of these variables offers new empirical insights. It expands the understanding of rural bank performance, highlighting how internal capabilities can be optimized to improve organizational outcomes in this under-researched sector.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".