Qualitative Analysis of Effects Managerial Ability and Environmental Industry to Performance of the Firm
Bibliographic record
Abstract
This study aimed to analyze the effect of managerial capacity and industry environment to performance of companies in the small industrial of teak wood furniture in Southeast Sulawesi. The research used census sampling of 143 managers or owners of the company as respondents. The analysis in this research is descriptive and qualitative. The result of the analysis showed that high managerial skills in specialized skills and moral values of trust can anticipate industrial environmental uncertainty by implementing alliances strategies to improve company performance. Specialized expertise and high moral values are essential to managerial skills in order to improve company’s responsiveness to enhance company’s capacity resource and cost production efficiency. Firstly, it can be more responsive to customer need, create quality in product or service, imitating product, and accelerate system to speed-up production process. Then, secondly, being efficient in cost production to formulate and implement proper competitive strategic to improve sales volume, profit and asset. It is suggested that owner and manager of small industrial in teak furniture firstly need to improve managerial skills in term of conceptual abilities, interpersonal skills, and technical skills. Then second important thing is trust, in term of moral values to make cooperation to third parties and formulating strategic to improve the performance of the firm.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".