Knowledge Management Capabilities and Organizational Performance in Roads and Transport Authority of Dubai: The mediating role of Learning Organization
Bibliographic record
Abstract
The purpose of this study is to examine the effect of knowledge management capabilities on organizational performance in the public sector. Learning organization was included as a mediator to investigate its effect on the relationship between knowledge management capabilities and organizational performance. The conceptual framework provided a useful perspective to study knowledge management capabilities in a government setting in Dubai. Two hundred and fifty‐five usable questionnaires were collected from the survey. The respondents were executives, managers and directors of the Roads and Transport Authority of Dubai, United Arab Emirates. SPSS version 21 and amos version 20 ( IBM Corporation , Armonk , NY , USA ) were utilized to test the conceptual model. The findings show that knowledge management capabilities have a positive and significant relationship with organizational performance. Learning organization fully mediates the relationship between knowledge management capabilities and organizational performance. The study only focuses on the Roads and Transport Authority, which is one of the government agencies in Dubai. Recommendations are provided to offer practitioners alternative solutions to their weaknesses and set strategies to improve the effectiveness of their knowledge management capabilities to promote continuous learning in the organization. This is the first study of knowledge management and learning organization carried out in Dubai or the United Arab Emirates in the public sector. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".