The Work Commitment of Construction Project Managers in Indonesia Using the Structural Equation Modelling Method
Bibliographic record
Abstract
The aims of this research were to determine how much influence the independent variables have on the dependent variable and to identify the variable with the dominant influence on the dependent variable the work commitment of construction project managers. The independent variable is a variable that can affect the value of other variables. The dependent variable is influenced by the independent variable either directly or indirectly. After data collection, the research method used was data analysis using the Structural Equation Modeling (SEM) Amos program. The results showed that the following independent variables have a positive effect on the dependent variable work commitment: leadership, organizational climate, organizational culture, communication climate, trust, work motivation, work experience, salary, and job satisfaction. The variable with the dominant influence on the work commitment of construction project managers was found to be salary. The salary variable is the variable that has the highest influence on work commitment and is a motivator to achieve high performance. Good culture management will encourage the achievement of high productivity and the project can be completed according to plan. The contribution that can be made through this research is that salary is the main need that must receive attention from the company, and is a work motivator to achieve high productivity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".