The effects of production and operational costs, capital structure and company growth on the profitability: Evidence from manufacturing industry
Bibliographic record
Abstract
The purpose of this research is to analyze the effects of production and operational costs, capital structure and company growth on profitability. The method used in this research is quantitative method, data collection is performed by distributing questionnaires among employees of packaging industry. The population in this study are industrial employees in Jabodetabek whose numbers have not been identified with certainty. The questionnaire is distributed electronically using a simple random sampling technique. The results of the questionnaire returned are 180 respondents. Based on the results of data analysis, it is concluded that Capital structure has a significant effect on profitability. An increase in the capital structure variable will be followed by an increase in profitability and a decrease in variable capital structure will be followed by a decrease in profitability. Company growth has no significant effect on profitability. An increase in the company growth variable will not be followed by an increase in profitability and a decrease in variable company growth will not be followed by a decrease in profitability. Operational cost has a significant effect on profitability. An increase in the operational cost variable will be followed by an increase in profitability and a decrease in variable operational cost will be followed by a decrease in profitability. Production cost has no significant effect on profitability. An increase in the production cost variable will not be followed by an increase in profitability and a decrease in variable production cost will not be followed by a decrease in profitability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".