Turning Lead into Gold: How Do Entrepreneurs Mobilize Resources to Exploit Opportunities?
Bibliographic record
Abstract
The mobilization of resources is a central and defining feature of entrepreneurship. As the body of empirical research on entrepreneurial resource mobilization has grown, the literature has become increasingly fragmented. We review the literature on entrepreneurs’ mobilization of resources, spanning human, social, financial, and other forms of capital. We identify five critical issues that hold back progress in resource mobilization research. We then propose a path ahead for future research guided by two overarching goals. First, we advocate for a process perspective, focusing attention on how an individual actor’s disposition and situation shape her responses, how these responses interact with those of other actors, and how these individual and collective responses unfold over time to generate outcomes. Second, we call for stronger unification of theory within the entrepreneurial resource mobilization literature and across contiguous conversations in strategy and organization theory. Theoretical consilience will enable the accumulation of empirical research into a cohesive body of knowledge on entrepreneurial resource mobilization.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".