PENGARUH BUDAYA ORGANISASI DAN KEPEMIMPINAN TERHADAP KINERJA PEGAWAI DENGAN MOTIVASI PEGAWAI SEBAGAI VARIABEL INTERVENING DI BADAN PERENCANAAN PEMBANGUNAN RISET DAN INOVASI DAERAH (BAPPERIDA) PROVINSI PAPUA BARAT DAYA
Bibliographic record
Abstract
This research aims to test and analyze the influence of organizational culture and leadership on employee performance with employee motivation as an intervening variable. The research uses a quantitative approach. The research population was 60 employees of the Regional Research and Innovation Development Planning Agency (BAPPERIDA) of Southwest Papua Province. Based on the purposive sampling technique, the sample taken was 50 ASNs with the consideration that ASNs have clearer and more structured roles and responsibilities in the BAPPERIDA of Southwest Papua Province so that they are relevant to this research. The data analysis technique uses a Structural Equation Model based on Partial Least Square (SEM-PLS). The research results show that organizational culture has a significant effect on employee performance. Leadership has a significant effect on employee performance. Organizational culture has a significant effect on employee motivation. Leadership has a significant effect on employee motivation. Employee motivation has a significant effect on employee performance. Organizational culture has a significant effect on employee performance which is mediated by employee motivation. Leadership has a significant effect on employee performance which is mediated by employee 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 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.009 | 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".