Charting the future of entrepreneurship: a roadmap for interdisciplinary research and societal impact
Bibliographic record
Abstract
The entrepreneurship field is increasingly interlaced with diverse disciplines, tackling complex societal issues from sustainability to digitalization and family business dynamics. Recognizing the necessity to steer future research, the editorial team of Cogent Business and Management’s Entrepreneurship and Innovation section present ten research domains identified through collective expertise. These areas, ranging from corporate innovation to entrepreneurship education and transitional entrepreneurship, are critical for academic investigation and hold potential for significant societal impact. These domains are not intended to constitute a ‘top 10’ list, nor are they exhaustive; rather, they are intended to help guide scholars toward research domains we believe are ripe for exploration and with the potential to be highly impactful. These domains embody the field’s ever-evolving nature, encapsulating the entrepreneurial spirit as a quilt of interconnected patches rather than isolated pieces. They encourage an interdisciplinary approach, highlighting the need for a comprehensive understanding of entrepreneurial activity. As the entrepreneurship literature grows, its adaptability will be crucial for theoretical advancement and practical applications. The proposed research roadmap aims to ignite cross-disciplinary dialogue, driving the impact of entrepreneurship research beyond academic circles and into the realms of policy and practice.
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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 | 0.039 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.031 | 0.048 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".