Who enters, where and why? The influence of capabilities and initial resource endowments on the location choices of de novo enterprises
Bibliographic record
Abstract
Some geographical locations have characteristics that create opportunities for de novo enterprises, but not all new firms can access the benefits presented by a potential location. The ability of new firms to appropriate benefit and avoid risk depends on the resources that entrepreneurs can marshal for their enterprise. This article develops a model of the interplay between the attributes of de novo entrants and their founding locations. The model assumes that de novo entrants tend to appear in the region where their founders live, but that founders choose among locations within their regions.The test of the model, using data on all de novo entrants in the Canadian manufacturing sector during 1984—98, reveals that entrants with greater resource and capability endowments are more likely to locate in areas with an agglomeration of similar firms, but this effect reverses at high endowment levels. Additionally, larger entrants are less likely to locate in areas characterized by intense local competition and potential entry deterrence, while smaller and well-endowed entrants tend to locate in areas where entry barriers are lower and asset turnover higher. These findings suggest that entrants choose locations strategically within their founding regions.They also indicate that the strategic imperatives of de novo entrants differ significantly from those of geographically diversifying firms, and thus suggest amendments to theories of location choice when modeling the decisions of new ventures.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".