Contemporary Perspectives for Technological Entrepreneurship in the Age of Change: Between Success and Resilience
Bibliographic record
Abstract
Problem: why are some technology entrepreneurship projects successfully, resilient and others not when they are executed in the same ecosystem? Research objectives: to revise conceptual and theoretical portraits of the process of technological entrepreneurship; propose a model that incorporates multidimensional factors that can effectively contribute to the success and resilience of technology entrepreneurship. Methodology: we used the inductive approach and a qualitative exploratory strategy. Private and public companies are our sample for convenience. Results: at the design stage, human capital and relationship capital identify market issues and opportunities. at the implementation and development stage, human, relational, structural, and technological capital are effective levers to generate performance and resilience. Finally, at the marketing and consolidation stage, human, structural, relational, financial, and technological capital have an undeniable contribution. But it is above all the integration of all these factors that generates success and resilience. Implications and limitations: the chapter is useful for researchers, entrepreneurs and governments who will find strategies to enhance the success and resilience of technological entrepreneurship. This research is part of the theory of artificial science. The adoption of an inductive approach and a qualitative strategy is one of its limitations. Future research could use the mixed strategy to extrapolate results.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".