Pengaruh Kompensasi dan Beban Kerja Terhadap Kinerja Pegawai Honorer dengan Motivasi Sebagai Variabel Intervening pada Sekretariat Daerah Kota Solok
Bibliographic record
Abstract
The background of this study is based on the phenomenon of low performance of honorary employees which is allegedly influenced by lack of compensation and unbalanced workload. This study aims to analyze the effect of compensation and workload on the performance of honorary employees with work motivation as an intervening variable in the Solok City Regional Secretariat. This study uses a quantitative method with a survey approach, where data is collected through the distribution of questionnaires to 103 honorary employees who are the research samples. The data analysis technique used is Structural Equation Modeling (SEM) to test the relationship between the research variables. The results of the study indicate that compensation has a positive and significant effect on work motivation and employee performance. In contrast, workload shows a negative influence on work motivation but is not significant on employee performance. Additionally, work motivation is proven to be a significant intervening variable in mediating the effect of compensation on the performance of honorary employees. This study implies that increasing compensation can enhance employee motivation and performance, while excessive workload may decrease work motivation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".