The impact of e-leadership, e-work environment and e-job satisfaction on employee commitment at the immigration organization
Bibliographic record
Abstract
This research aims to analyze the influence of e-leadership behavior on employee commitment at the immigration office, the influence of the e-Work Environment on employee commitment at the immigration office, and the influence of e-Job satisfaction on employee commitment at the immigration office. The research method used in this research is associative research. Associative research is research that aims to determine the relationship between two or more variables. In this way we can build a theory that functions to predict and control a phenomenon. The population in this study were all immigration office employees. In this research, an analysis model is used, namely Structural Equation Modeling (SEM). The respondents for this research were 678 immigration office employees who were determined using a simple random sampling method. Research data was obtained by distributing online questionnaires via social media. The instrument used in this research uses a Likert scale 7 scale. The data analysis stage of this research is testing the outer model and inner model. The outer model test consists of convergent validity, discriminant validity and composite reliability and the inner model test, namely hypothesis testing or significance testing. The results of data analysis show that e-leadership behavior has a positive and significant relationship to employee commitment at the immigration office, the e-Work Environment has a positive and significant relationship to employee commitment, and e-Job satisfaction has a positive and significant relationship to employee commitment at the immigration office.
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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 | 0.004 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".