Motivation Factors Toward Knowledge Sharing Intentions and Attitudes
Bibliographic record
Abstract
Employees’ knowledge is a fundamental and valuable resource for the organization, and if it is used and shared properly among employees, the organization will gain a competitive edge. However, knowledge sharing does not occur definitely; instead, it is an individual choice that cannot be compulsory. This research tackles a critical issue, which is motivating employees toward knowledge sharing. The aim of this study is to examine the impact of the antecedents of motivation, which consists of (organizational commitment, environmental dynamism, reward, and job-related factors), to determine and explain the knowledge sharing intentions and attitudes. This will be along with examining organizational climate effect on the intentions of knowledge sharing. A total of 283 questionnaires were submitted to Arab Open University employees, and 221 valid questionnaires were considered in this study. The findings revealed that organizational commitment and intrinsic reward have a significant influence on intrinsic motivation. Moreover, it was found that extrinsic reward has a positive impact on extrinsic motivation. In addition, the findings revealed that extrinsic motivation has a positive influence on knowledge sharing intentions and attitudes, however, intrinsic motivation has a positive impact only on attitudes toward knowledge sharing. Also, attitudes toward knowledge sharing positively and highly influence knowledge sharing intentions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".