The Influence of Digitalization on the Innovative Strategy of the Industrial Enterprises Development in the Context of Ensuring Economic Security
Bibliographic record
Abstract
The purpose of the article is to study the impact of digitalization on the innovative strategy of the enterprise development in the context of ensuring economic security.Within the article, peculiarities of the digitalization development in Ukraine are examined.The main obstacles that hold back the development of innovative technologies and digital platforms in Ukraine are identified.The problems of the country's industrial development and the difficulties of implementing the principles of the digital economy in the existing conditions are substantiated.It was determined that industrial development of the country is decreasing, which leads to a low level of digitalization and innovative development of industrial productions and reduces their level of economic security and competitiveness.The impact of digitization on the innovative development of industrial enterprises was considered based on the calculation of efficiency indicators of the innovative activity for selected systems of the production process.Based on the calculations made using the correlation-regression analysis, the influence of the selected factors on the resulting indicator was determined.The areas that influence slowing down of the digital development process of industrial enterprises are singled out.
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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 | 0.004 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".