Understanding the Impact of Emerging Technologies on Entrepreneurial Ventures
Bibliographic record
Abstract
Abstract In an era characterized by rapid technological advancement, the intersection of emerging technologies and entrepreneurial ventures has become a focal point of inquiry for scholars, policymakers, and practitioners alike. This qualitative research seeks to explore the multifaceted impact of emerging technologies on entrepreneurial endeavors, shedding light on the motivations, challenges, strategies, and outcomes associated with technology-driven entrepreneurship. Through in-depth interviews with entrepreneurs operating in diverse industries and geographical regions, this study elucidates the complex dynamics shaping the relationship between emerging technologies and entrepreneurial ventures. The findings reveal a diverse array of motivations driving entrepreneurs to adopt and integrate emerging technologies into their ventures, including economic incentives, social impact objectives, and environmental sustainability goals. However, the integration of emerging technologies is not without its challenges, as entrepreneurs encounter regulatory uncertainties, organizational resistance, and resource constraints in their quest to innovate. Despite these challenges, entrepreneurs employ various strategies, such as forming strategic partnerships, fostering innovation culture, and embracing customer-centric approaches, to successfully integrate emerging technologies into their ventures and drive innovation. The implications of these findings extend to both theory and practice in technology-driven entrepreneurship, highlighting the need for policymakers, investors, and practitioners to create an enabling environment that supports innovation and entrepreneurship. By understanding the drivers, barriers, and strategies associated with technology adoption in entrepreneurial ventures, stakeholders can better support and capitalize on the transformative potential of emerging technologies to drive economic growth and societal progress. In summary, this research contributes to our understanding of the complex interplay between emerging technologies and entrepreneurial ventures, offering insights for researchers, policymakers, and practitioners seeking to navigate the evolving landscape of technology-driven entrepreneurship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".