Pension and retirement intentions as determinants of employee engagement and productivity
Bibliographic record
Abstract
The issue of pension funds is not only a financial matter, but also a human resource. Pension funds do not stand alone, but are assumed to be related to other human resource (HR) variables. Starting from this background, this study aims to examine the effect of the occupational pension scheme (OPS) and retirement intentions (RI) variables partially on employee productivity (EP) directly or through employee engagement (EE), the effect of OPS on RI, and the effect of OPS on EP through RI. Post-positivist is the research paradigm, with a quantitative research approach, with explanatory causal types and statistical studies. Dapenma-Pamsi is selected as the location of this research and we choose the Joint Pension Fund of municipal waterwork which are located in six provinces in Java Island. The sampling technique for this study was proportionate stratified random sampling, with a total sample of 500 active Dapenma-Pamsi participants in six provinces in Java. The research instrument was a questionnaire with a Likert scale of 1-7. The data analysis technique used SEM-AMOS. The results of the study are supported by ten research hypotheses. The novelty of this research is the integration of variables rooted in the discipline of financial management and variables from the discipline of human resource management. This research is also could help Indonesia Government foster the growth of Private Pension Fund Program in Indonesia.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".