Bibliographic record
Abstract
Purpose – After presenting a brief review of the Schumpeterian and Kirznerian views on entrepreneurship, the purpose of this paper is aims to measure the two views within the economic and institutional contexts of emerging economies. Design/methodology/approach – Configurations of innovative entrepreneurship, opportunity entrepreneurship and contextual variables were assessed using cluster analysis on 16 emerging countries. Findings – Four profiles were found: innovative entrepreneurship of the Schumpeter Mark I type, innovative entrepreneurship of the Schumpeter Mark II type, opportunity entrepreneurship of the Kirznerian form and a fourth cluster described as a potentially emerging Schumpeter Mark II profile. The economic and governance indicators were favorable in the two innovative entrepreneurship clusters, whereas the contextual indicators of innovation were particularly favorable in the Schumpeter Mark II group. Research limitations/implications – The study demonstrated the importance of aligning theory, methods and context in comparative entrepreneurship research. Profiling countries on theory-based entrepreneurial dimensions appears as a viable approach. However, the results also pointed to the need for more attention to the dynamic aspects of country entrepreneurial activity. Another limitation lies in the low number of emerging countries for which complete comparable data are available. Practical implications – For policy makers, it may be interesting to examine our results showing that the economic and governance correlates are more favorable in the two innovative clusters. Originality/value – The study is one of the few recent attempts to clarify the relationship between entrepreneurship and innovation in the context of emerging economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".