Regulatory Co-Creation and Institutional Uncertainty: Entrepreneurial Agency in Emerging Industries
Bibliographic record
Abstract
This study examines how entrepreneurs in Canada’s recreational cannabis industry actively shape regulatory frameworks within a context of institutional uncertainty. Drawing on institutional theory, we argue that regulations in emerging, heavily regulated sectors are not fixed constraints but dynamic constructs subject to negotiation. Through semi-structured interviews with cannabis executives across multiple provinces, we find that unclear and evolving policies can foster bottom-up innovations. Entrepreneurs leverage gaps, inconsistencies, and ambiguities in the regulatory system to adapt or circumvent restrictive rules, often justifying rule-bending as a response to illogical or unfair frameworks. Their actions both expose regulatory shortcomings and prompt incremental adaptations, underscoring an ongoing dialogic process between entrepreneurs and regulators. Using the Gioia Methodology, we develop a data structure revealing three key themes: navigating regulatory ambiguity, exerting agency in co-creating policy, and challenging existing constraints. Our findings reposition entrepreneurs from passive adopters of regulation to active institutional entrepreneurs who transform emerging industries by co-creating their governing frameworks.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".