The Impact of Digitalization in Supporting the Performance of Circular Economy: A Case Study of Greece
Bibliographic record
Abstract
Digitalization has the potential to hasten the economic transition towards a more resource-efficient as well as robust circular production system. However, there is a paucity of empirical research on the influence that digitalization has on the ability of a circular economy to function effectively. The objective of this study was to investigate the effect that digitalization has on the performance of the circular economy. The research was based on an empirical analysis of quantitative data obtained from a sample size of 200 investors and entrepreneurs in the financial sector of Kozani, Greece. Regression results showed that there is a positive relationship between digital practices and performance of a circular economy, and that digital business innovations have a positive effect on performance of a circular economy. Even while a sizeable proportion of Greek companies apply new business innovations to support the strategy of resource efficiency, it is abundantly obvious that this percentage is far higher among industrial organizations that place a heavy focus on digitalization. According to the findings of the research, there is a favorable correlation between the adoption of digital business practices and innovations and the success of circular economies. This demonstrates very clearly that digitalization has the potential to function as a driving force behind the development of circular business models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".