Development of Digital Innovations in Company Financial Management
Bibliographic record
Abstract
Relevance of the research.Digital innovations are increasingly penetrating all areas of business operations and management.Today's companies are often faced with the choice of introducing digital innovations or ceasing to exist.Therefore, the study of how to make the implementation of digital innovations in the financial management of the company as effective as possible is a modern and relevant topic of research.The purpose of the study: The article aims to investigate the role, advantages, and disadvantages of digital innovations in the financial management of the company, as well as to determine current trends in digital innovations and the degree of their integration into the activities of companies.Methodology: Used methods: analysis and synthesis, the method of economic-statistical analysis, graphic methods.As a result of the study, the necessity of implementing digital innovations in the financial management of the company was established, the advantages and disadvantages of this process were identified, the priority areas of innovation implementation, and the main barriers to their implementation were outlined.The range of modern digital innovations in company finance was presented and the percentage of companies that use them was determined.The novelty of the research consists in comparing the theory and practice of implementing digital innovations in the management of company finances by determining the share of companies that use new technologies.The practical significance lies in the possibility of applying the results in the activities of companies planning to introduce digital innovations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".