Understanding the Discovery and Creation of Entrepreneurial Opportunity through Alliances
Bibliographic record
Abstract
Our study builds on the recent discussions on the varied conceptualizations of entrepreneurial opportunities and its implications on entrepreneurial actions. In our study, we examine how entrepreneurial perceptions of opportunity affects the sub-processes of opportunity recognition and entrepreneurial alliances. We employ an inductive multiple case study design. The findings from our study underscore and extend the claims of the discovery and creation approaches to conceptualize entrepreneurial opportunity. Our study offers a unique contribution to theory by identifying the influence of specific entrepreneurial perceptions of opportunities on the order and purpose of processes employed in recognizing opportunity. Further, our study adds to the current debates on entrepreneurial opportunity by proposing that entrepreneurial perceptions of opportunity influences the sequence and intent of alliances employed in recognizing opportunity. The study guides entrepreneurs in their choice of processes and alliances best suited for their stages of opportunity recognition. Moreover, it provides insights to policy makers on how and when specific alliances can aid particular enterprises. Our study provides a point of departure and invigorates further discussion on how entrepreneurial perceptions of opportunities impact entrepreneurial actions and processes.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".