Management of the Company’s Innovation Development: The Case for Polish Enterprises
Bibliographic record
Abstract
Management of innovation processes in a company is a field that is still not sufficiently researched and applied in practice. Managers in companies often do not know about modern techniques and design tools for creating innovation processes and about the possibility of their effective usage for management and in decision-making conditions. Therefore the main aim of the paper is to present contemporary theoretical and practical achievements in the field of innovation management, which focus on the area of innovation processes and emphasize the possibilities of managing innovation processes in business. The practical purpose of this study was to analyze the state and development of innovativeness of a selected group of Polish enterprises. The following methods were used in the work: a critical analysis of the literature, deductive methods, CAWI method (Computer Assisted Web Interview), and synthesis of measurement results of analytical indicators in selected functional areas of the studied enterprises. The conclusion was that for several years, it is clearly visible that a small group of innovative companies has formed in Poland that constantly increases its expenditure on innovative activities including research and development. In addition, the expenditures incurred are at a very decent level when compared to the European Union (EU) average, which suggests that these companies are competitive not only at the country level, but also outside it.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".