INFLUENCE OF FINANCING INNOVATIONS ON INFORMATION SYSTEMS MANAGEMENT IN THE DIGITAL ECONOMY
Bibliographic record
Abstract
In this article, we elaborated on the features and effectiveness of financial support for innovations, which are connected with the improvement of information systems management in the digital economy.We considered the key aspects of the influence of funding for innovation on this process on the example of the leading companies in the sphere of innovative technologies (German SAP and Canadian Celestica).We established approaches of each studied company to the management of these processes and revealed characteristics and opportunities for achieving economic effectiveness within the directions for funding for innovation.The purpose of this research was to determine the character of the influence of financial support for innovations on the level of the management of information systems of the subjects.For this, we analysed the financial reporting of the companies and utilised the methods of comparative analysis, factor analysis, and correlation analysis.The novelty of this research lies in establishing the directions for the improvement of financing of innovations to raise the level of the management of digital information systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".