The growth paradox: mental health perceptions, ecosystem support, and growth aspirations of ADHD entrepreneurs
Bibliographic record
Abstract
Abstract The paradoxical nature of ADHD in entrepreneurship presents a unique challenge: traits that spark venture creation often impede business growth. Through person–environment fit theory (P-E-Fit) and the underdog theory of entrepreneurship, we examine how perceived mental health and ecosystem support influence ADHD entrepreneurs’ growth aspirations. The underdog theory suggests that systemic adversities foster adaptive capabilities, while P-E-Fit shows how environmental support enhances alignment between personal characteristics and entrepreneurial demands. In a study of 1160 Quebec entrepreneurs, including 167 with high ADHD levels, findings reveal that while ADHD initially constrains growth aspirations, this relationship shifts through the interaction of mental health perceptions and financial support. Financial support emerges as the crucial mechanism for translating positive mental health perceptions into growth aspirations. These findings demonstrate how underdog attributes can become entrepreneurial advantages through proper environmental fit, while showing how targeted financial support can transform ADHD-related challenges into sources of innovation. Plain English Summary Grit emerged as the missing catalyst that transforms psychological resources cinto entrepreneurial performance during polycrisis. This longitudinal study compared entrepreneurs across two distinct crisis periods to understand how internal mechanisms sustain business performance when external support fails. While entrepreneurs possessed psychological capital and community belonging throughout both periods, grit functioned as an amplifier that activated these resources differently over time. As crises persisted without recovery intervals, high-performing entrepreneurs demonstrated that grit strengthened relationships between psychological strengths and three key outcomes: crisis management capabilities, innovation and technology, and funding acquisition. This study challenges conventional theory by showing that internal resources require emotional activation through grit to translate into sustained performance. Thus, the principal implication is that policymakers must incentivize and educational institutions must redesign entrepreneurship programs to cultivate grit as a core competency in our perpetually crisis-driven landscape.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".