A New Business Design Tool for Digital Business Model Innovation: DEA Approach
Bibliographic record
Abstract
The new economic structures characterized by the growing dynamics of the economic context and a greater influence of the market on entrepreneurial activities requires companies to generate and adopt more and more competitive innovations, to maintain and develop a high level of innovative activity. Innovations are perceived as a necessity, they are a factor, and they are the mobile strength of companies in the 21st century. Every modern manager should recognize the role of innovations for the existence of companies and aim for innovative development. The news in various sectors must be followed. In this sense, it is necessary to look for options and overcome the obstacles that stand in the way of innovations. This paper attempts to assess the level of innovativeness of 26 European economies in the years 2016–2018 by using the Data Envelopment Analysis (DEA). To perform this study, the evaluation used was carried out on the basis of a summary index constructed with the use of statistical methods of non linear ordering; in particular. In the analysis carried out, the statistics of the Global Innovation Index were used to describe the innovative capacity of economies in two areas: (a) science and technology; and (b) education and training. The evolved classification of innovativeness of these countries, built on the basis of a synthetic index, will allow to create a ranking that will lead to comparative analysis among these countries.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".