Does Organizational Learning Affect R&D Engineers’ Creativity?
Bibliographic record
Abstract
R&D engineers are viewed as the vital source of creativity and innovation for the companies to improve its business values, in particular in the original design manufacturer (ODM) companies. In order to promote R&D engineers’ creativity within the organization, organizational learning is viewed to be well shaped to enhance creativity of the engineers. Therefore, this study aims to determine the effect of perceived organizational learning towards the research and development (R&D) engineers’ creativity in the ODM companies in Malaysia. The perceived organizational learning was conceptualized into four dimensions of managerial commitment, systems perspective, openness and experimentation, and knowledge transfer and integration. Structural Equation Modeling (SEM) was employed to test the model using SMART-PLS version 2.0 package, drawing on a sample of 140 R&D engineers working in the ODM companies in Malaysia. Data analysis revealed that, all four dimensions of organizational learning contribute the significant impact on R&D engineers’ creativity. The results offer a number of suggestions to ODM firms in Malaysia. Specifically, organization can establish a platform to enable its R&D engineers to acquire, exchange and apply knowledge within the team or even organization. In doing so, the employees can utilise the knowledge to create new solutions, improve efficiency and solve issues encountered in the organization and this definitely will increase the R&D engineers’ creativity.
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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 | 0.003 | 0.013 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".