Age and entrepreneurship: Mapping the scientific coverage and future research directions
Bibliographic record
Abstract
Abstract Researchers’ interest in studying the relationship between age and entrepreneurship has mushroomed in the last decade. While over a hundred articles are published and indexed in the Scopus database alone with varying and fragmented results, there has been a lack of effort in reviewing, integrating, and classifying the literature. This article offers a framework-based systematic review of 174 articles to comprehend the relationship and influencing factors related to an individual's age and entrepreneurship. Bibliographic coupling is used to identify the prominent clusters in the literature on this topic and the most influential articles. Also, the TCCM review framework is adopted to provide a comprehensive insight into dominant theories applied, contexts (geographic regions and industries) incorporated, characteristics (antecedents, consequences, mediating and moderating variables, and their relationships) investigated, and research methods employed in age and entrepreneurship research over the last fifteen (2007–2022). Though the literature covers an array of industries, to better understand the age-entrepreneurship correlation, we need to investigate the new-age technologically driven business sectors further to expand our knowledge. Furthermore, we detect that the Theory of Planned Behavior mostly dominates the literature, with other theories trivially employed. Finally, we apply the TCCM framework to suggest fertile areas for future research.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.037 | 0.048 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".