The Role of Transformational Leadership on Employee Performance Through Organizational Learning Culture and Intrinsic Work Motivation
Bibliographic record
Abstract
When organizations devote various resources to increase its effectiveness and growth, leaders continue to think about the best ways to motivate employees to work more effectively and efficiently. Therefore, this study examines the following questions: (a) the extent to which transformational leadership affects organizational learning culture, intrinsic work motivation, and employee performance? (b) the extent to which organizational learning culture affects employee performance? and (c) the extent to which intrinsic work motivation affects employee performance? Using a questionnaire distributed online to employees at a community health center in Riau island – Indonesia, data were collected and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) employing SmartPLS 3.0 software. The results of the study prove that (a) transformational leadership directly and significantly affects organizational learning culture, intrinsic work motivation, and employee performance, (b) organizational learning culture directly has no significant effect on employee performance, and (c) intrinsic work motivation directly and significantly affects employee performance. The findings and implications of this study indicate that intrinsic work motivation indirectly plays an important role in linking transformational leadership and employee performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".