Knowledge Management and Organizational Learning from the Employee Perspectives: A Study from Saudi Arabia Context
Bibliographic record
Abstract
This paper investigates the relationship between Knowledge Management (KM) and Organizational Learning (OL) from the employee perspective.KM has emerged as one of the most important areas in management practices and established as a basic resource for firms and economies. KM is an area of research and practice that is still searching for a stable set of core concepts and practical applications. OL is considered to be one of the most important issues in modern managerial literature. Also, OL is one of the most important organizational factors that can direct the behavior and attitudes of the employees in the organizations. This study was conducted at Saudi banks in Al-Taif Governorate. It is practical, according to its purpose, and descriptive according to its data collection method. The present study investigates the evaluative attitudes of the employees towards KM and OL. It will also illustrate the relationship between KM and OL. Two groups of employees were examined. Of the 350 questionnaires that were distributed, 285 usable questionnaires were returned, a response rate of 81%.The finding reveals that there are differences among the employees regarding their evaluative attitudes towards KM and OL. Also, this study reveals that there is a statistically significant relationship between KM and OL. Accordingly, the study provided a set of recommendations including the necessity to pay more attention to KM and OL. This will achieve its success currently and in the future, besides attaining a competitive advantage.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".