Using E-Leadership as a Strategic Tool in Enhancing Organizational Commitment of Virtual Teams in Foreign Commercial Banks in North West Bank -Palestine
Bibliographic record
Abstract
Using information technologies in organizations has been one of the most important topics for numerous scholars in the business field. This manuscript aims to investigate the impact of using E-leadership as a strategic tool in enhancing the organizational commitment of virtual teams in foreign commercial banks in Northern West Bank in Palestine. Primary data was collected via questionnaires which distributed on virtual teams in foreign commercial banks in North West Bank - Palestine. Researchers used a purposive sample which is suitable to study objectives. The empirical model in this study illustrates the relationship among E leadership dimensions (i.e. Envision, engage, energize, empower, execute, and elastic), perceptions of organizational commitment. This manuscript employs simple regression analysis to examine the impact of E-leadership on organizational commitment. In addition, researchers use Pearson correlation matrix to identify the relationship between the six dimensions of E-leadership and organizational commitment. The result proves that positive impact of using E-leadership as a strategic tool in enhancing the organizational commitment of virtual teams in foreign commercial banks is significant. Also, finds that there is a positive significant relationship between E-leadership and its six dimensions with organizational commitment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".