The effects of digital transformational leadership, work environment and motivation on reinforc-ing job satisfaction: Evidence from vocational schools
Bibliographic record
Abstract
The purpose of this study was to analyze the effect of job satisfaction, motivation, Digital Transformational Leadership, work on jobs and teachers' performance of vocational schools. The study uses quantitative methods to test and prove the hypotheses made through various analyses and data processing. The research hypothesis testing was carried out by using the Structural Equation Model (SEM) approach based on Partial Least Square (PLS). The population used in this study is vocational schools’ teachers in Pati Central Java, Indonesia. The number of respondents in this study is 110 respondents of vocational teachers who have worked at least 1 years in their schools and data obtained from the distribution of online questionnaires with snowball sampling method. Based on the results of hypothesis testing data processing using SmartPLS software, the results obtained that job satisfaction had a positive and significant effect on teachers' performance of vocational schools, motivation had a positive and significant effect on job satisfaction of vocational schools, motivation had a positive and significant effect on teachers' performance of vocational schools. Digital Transformational Leadership also had a positive and significant effect on job satisfaction of vocational schools. Digital Transformational Leadership had a positive and significant effect on teachers' performance of vocational schools, work environment had a positive and not significant effect on job satisfaction of vocational schools, work environment had a positive and significant effect on teachers' performance of vocational schools. The novelty of this research is the relationship model of the role of digital transformational leadership, work environment, motivation on job satisfaction and teachers’ performance of vocational schools.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".