Supply chain performance: Investigating the role of compensation and organizational support in the government organization
Bibliographic record
Abstract
This research aims to analyze the relationship between compensation and supply chain performance and to analyze the relationship between organizational support and supply chain performance at the immigration office. The research method uses a quantitative associative survey method. The analysis used in this research is partial least squares-structural equation modeling (PLS-SEM). The population of this study were senior employees of government organization or immigration offices in Indonesia and the research respondents were 467 senior employees who were selected using a simple random sampling method. Research data was obtained by distributing online questionnaires via social media. The online questionnaire contains statement items and is designed using a 7 Likert scale. The Likert scale used in this research is (1) strongly disagree, (2) disagree, (3) quite disagree, (4) Neutral, (5) quite agree, (6) agree, (7) strongly agree. Data processing uses SmartPLS 4.0 software, and the data analysis stages are testing the outer model and inner model, testing the inner model by carrying out validity tests, reliability tests while the inner model tests hypothesis or significance tests. The results of this research are that compensation has a positive and significant relationship to supply chain performance at the immigration office and organizational Support has a positive and significant relationship to supply chain performance at the immigration office. By implementing a fair and good compensation system, it will encourage supply chains to improve their performance. Supply chains will try to improve their performance because the better their performance, the supply chain will receive better compensation. Work motivation has a positive and significant effect on supply chain performance. Organizational support is very important for supply chain behavior. The organization has an obligation to develop a climate that supports consumer orientation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".