An empirical study on measurement of efficiency of digital transformation by using data envelopment analysis
Bibliographic record
Abstract
Nowadays digitalization is an important topic for businesses and government agencies. There are important reports publishing about digitalization or digital transformation. This study aims to measure the relative efficiency of digital transformation among EU Countries based on data envelopment analysis (DEA). The necessary data are extracted from Digital Transformation Scoreboard 2018 published by European Commission. DEA is one of popular methods for measuring the relative efficiency of similar units. This study empirically proposes an alternative ranking for countries with respect to digital transformation efficiency by using "enablers and output" approach of Digital Transformation Scoreboard. Digital Infrastructure, Investment and Access to Finance, Supply and Demand of Digital Skills, E-Leadership and Entrepreneurial Culture are considered as input while ICT start-ups and Digital Transformation are considered as the output of DEA model. The results indicate that while some countries like Denmark, Italy and United Kingdom are considered relatively efficient, Netherland and Germany are not very efficient according to our results.
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".