Work environment and entrepreneurship orientation towards MSME performance through organizational commitment
Bibliographic record
Abstract
This research aims to analyze and develop a theoretical framework, built with a performance model influenced by the work environment and entrepreneurial orientation, and mediated by organizational commitment. This research used a quantitative approach as the methodology. The survey was distributed using a questionnaire instrument. The population in this study were 6,708 MSME owners in Brebes Regency, Central Java, Indonesia, and the sample in this study were 377 MSME owners, which was determined using the Slovin formula. The sampling technique in this research used a non-random sampling with proportional sampling. Simultaneously, the statistical method used to test the hypotheses in this study is a multivariate Structural Equation Modelling (SEM). Based on the results of statistical tests, it can be known partially that 1) Work environment has a significant effect on the performance of MSMEs, 2) Entrepreneurial orientation has a significant effect on the performance of MSMEs, 3) Organizational commitment has a significant effect on the performance of MSMEs. 4) Work environment has a significant effect on organizational commitment, 5) Entrepreneurial orientation has a significant effect on organizational commitment, 6) Work environment has a significant effect on the performance of MSMEs through organizational commitment, and 7) Entrepreneurial orientation has a significant effect on the performance of MSMEs through organizational commitment. The novelty in this study is that the empirically constructed model of organizational commitment has proven to be significant as a mediation of the work environment and entrepreneurial orientation towards performance. with the strongest influence of work environment and entrepreneurial orientation on organizational commitment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".