Growing gains and growing pains: Examining the growth intentions of established entrepreneurs
Bibliographic record
Abstract
Abstract Research Summary Following a growing body of research indicating that most high‐growth entrepreneurial firms are “one hit wonders,” this article leverages Canadian survey and administrative data to investigate the relationship between recent entrepreneurial income and growth barriers, on the one hand, and the growth intentions of established firms, on the other. We draw on the theory of planned behavior to develop hypotheses on how salient information resulting from entrepreneurial experience may shape growth intentions. As anticipated, we find that higher incomes negatively associate with intentions. The picture for barriers is more mixed, such that recently experienced human resources and financial barriers positively associate with growth intentions and barriers related to competition and regulations negatively associate with intentions. The implications for policy and for further research are discussed. Managerial Summary Our study investigates factors associated with growth expectations among small firms, utilizing descriptive and multivariate analyses. In doing this, we extend the theory of planned behavior into a study of established firms, recognizing that intentions are dynamic and will be shaped by experience of entrepreneurship. Key findings indicate that past performance significantly affects future growth expectations, while higher personal income correlates with lower growth intentions, suggesting entrepreneurs become “satisficers” as income increases. In addition, perceptions of external barriers are negatively associated with future expectations, while internal barriers do not significantly hinder them. This result implies that entrepreneurs perceive external challenges as beyond their control, affecting their confidence in future growth.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".