Leadership Styles and Organizational Learning An Empirical Study on Saudi Banks in Al-Taif Governorate Kingdom of Saudi Arabia
Bibliographic record
Abstract
This paper investigates how two important research streams, namely Leadership Styles (LS) and Organizational Learning (OL), might be related. In other words, LS and OL represent two rich lines of research: one is about how people lead and the other is about how people learn. Specifically, this contribution addresses two issues (1) the evaluative attitudes of the employees towards LS and OL and (2) the relationship between LS and OL. This study was conducted at Saudi banks in Al-Taif Governorate, Kingdom of Saudi Arabia. This research is practical, according to its purpose, and descriptive according to its data collection method. Three groups of employees at Saudi banks were reviewed. Of the 335 questionnaires that were distributed, 285 usable questionnaires were returned, a response rate of 85%. The finding reveals that there are differences among the three groups of employees regarding their evaluative attitudes towards LS and OL. Also, this study reveals that the aspects of LS have a significantly direct effect on OL. Accordingly, the study provides a set of recommendations that included the need for Transactional Leadership Styles (TALS) in general, and Transformational Leadership Style (TFLS) in particular, in order to achieve the best response to the needs and wishes of the workers at Saudi banks to increase their contribution to the achievement of OL on the one hand, and raise the level of their performance and enhance competitive advantage of these organizations on the other hand.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".