Digital transformation in the Indonesia manufacturing industry: The effect of e-learning, e-task and leadership style on employee engagement
Bibliographic record
Abstract
In facing business competition in the manufacturing industry, it continues to adapt. Demands start from employees who are expected to continue to grow and leaders who are also changing. This is aimed at staying in business and also retaining the best employees by planning some changes in how to train and assign employees electronically as well as changing leadership styles to adapt to today's digital era. This study aims to determine the influence of E-learning, e-task and leadership style in the manufacturing industry in Indonesia. The data collection method in this study uses a questionnaire with 130 respondents. in this study using four variables, namely thirteen dimensions and twenty-six indicators. The analytical method used is descriptive analysis, and the test instrument uses SEM AMOS. The results showed that e-learning organization and e-task as well as leadership style had a significant and significant effect on Employee Engagement. the most factor great influence is the leadership style; This means that employees expect to get a new style in accordance with this digital era since there has been a change in the concept of employee engagement, where employees will feel they do not have a sense of engagement with the company if the attitude of the leader who is not sensitive to all aspects of changes in the effects of the digital era is caused by changes in employee behavior in this era where information is very easy to obtain for employees to know the conditions anywhere else that offers an advantage. compared to where they work now.
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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 | 0.002 |
| 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 | 0.000 |
| 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".