Growth of Digital Entrepreneurship in Academic Literature: A Bibliometric Analysis
Bibliographic record
Abstract
Entrepreneurial activity is considered the driving force for modern economies and societal development through economic growth, employment generation and the promotion of innovation. This paper seeks to study the growth of the available literature in the academic world and to highlight the trends regarded as 'key' in the realm of digital entrepreneurship by means of the conduct of bibliometric analysis concerning the conceptual background, the assumptions that lie under, the designs of the research along with an analysis of what was contributed to the field and the direction road map pointing out topic areas for further research. An in-depth bibliometric and systematic literature analysis is conducted in accordance with the objectives of the study. As we know the bibliometric analysis of literature can identify research clusters based on the quantity and the quality of the research conducted. Through the use of Vosviewer 1.6.10 software, the authors analyzed 122 articles from the Scopus database. The progress of research on digital entrepreneurship has been studied from 1970 to 2022. It is found that digital entrepreneurship research has gained encouragement after the year 2018. By means of cluster analysis, the authors identified three clusters which revealed a number of closely associated key words. The findings further revealed that the synthesis of topics of recent date which were of interest to scholars have led about the evolution of a large number of topical clusters along with the identification of a change in interest over the days gone past. From a study whose aim was the various economic issues, in the direction of an analysis that has deepened the factors which have led to a number of factors that have contributing for the development of digital entrepreneurial platforms.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.179 | 0.196 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".