Determining (Identifying) the Effect of Sub Ordinates Motivation and their Budget Participation on Budget Targets
Bibliographic record
Abstract
Organizational behavior management thinkers have reported a positive relationship between staffs performances and budget participation and attaining to institution goals. This work aimed to identify the effect of subordinates motivation and their participation on commitment on budget targets. This work was applicable in purpose, and it was a causal research. To study the proposed hypotheses and statistical analyses, field method and to gather data, tool of questionnaire were used. Statistical society of this research included Markazi provinces welfare organization staff that research collected the required data to test the hypotheses of the research using the questionnaire from this society, in this regard and with respect to the limited society. Sampling wasnâÂÂt performed and the questionnaire was not distributed among all members that finally, 228 questionnaires were completed. To test hypotheses, structural equations method and LISREL software were used. Considering data analysis the result indicated participation in budget has effects on staffs motivation, distributive justice and procedural justice. Staffs motivation has effects on distributive justice and management performance and also distributive justice has effects on procedural justice and management performance, and finally, procedural justice has effect on management performance.
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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 | 0.011 |
| 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 | 0.000 |
| 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".