Explore the relationship between Transformational Leadership, Social Interaction and Knowledge Management among Banking Sector of Pakistan
Bibliographic record
Abstract
The intention of current empirical research is to explore the relationship between transformational leadership, social interaction and knowledge management among banking sector of Pakistan. The said sector is selected as population of the research. With the help of simple random sampling, different branches of public banks and private banks are selected as a sample. 270 questionnaires were circulated to top level and middle level managers. 230 questionnaires were filled by employee and used for analysis. The overall response rate was 85%. Different statistical methods i.e. Reliability analysis, Pearson’s correlation analysis and multiple regression analysis are applied on collected data. The results of Person’s correlation analysis shows that there is positive relationship between transformational leadership, social interaction, knowledge management and its dimensions i.e. knowledge sharing and knowledge application. Moreover, regression analysis’s results explains that social interaction is strong predictor of knowledge management as compare to transformational leadership. From the managerial viewpoint, the results give rational direction to banking sector of Pakistan to understand the significance of knowledge and its management as well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".