Difficulties Experienced during Implementation of an Adapted Quality Management System in Incubated Companies
Bibliographic record
Abstract
The purpose of this paper is presents a study to find out the main difficulties experienced by nine micro and small enterprises throughout the implementation of a quality management system adapted to the reality of companies incubated. According to the objective presented, the article used the case study technique applied in nine incubated company, emphasizing the difficulties observed during the implementation of quality management system adapted to their realities. After the implementation, those companies showed significant progress in their management models. However we observed some gaps to conquer better results, such as difficulties in establishing long-term goals; non-financial targets; the idea that employees can’t help in the improvement company, among other difficulties reported in this paper. In the literature there are a lot of papers about quality management in micro and small enterprises, but this work stands out for analyze a specific kind of company, micro and small companies incubated. This is the main difference and value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".