Barriers to scale: the effect of regulations on entrepreneurial strategies in a nascent industry
Bibliographic record
Abstract
Purpose This paper aims to discuss the effect of Canada’s regulatory framework on the strategies of entrepreneurial businesses during the first phase of legalization of the recreational cannabis industry. Decriminalization of cannabis required a host of regulatory changes at the federal, provincial and municipal levels. Each province developed legal markets independently, differentially impacting entrepreneurial strategies. This paper describes the value chain that emerged in the first phase of the nascent industry, focusing on the actions of the businesses. Design/methodology/approach The authors develop a qualitative narrative analysis using government publications, press articles (especially from the business press) and personal communications of industry insiders speaking in public settings. The paper includes four short case studies to illustrate the emerging value chain of the nascent industry. Findings The study’s findings highlight the effect of regulatory frameworks on entrepreneurial strategies. We find that public policies had a significant impact on entrepreneurs and startup strategies. Inter-jurisdictional differences limited expansion into different provinces, with implications for regional economic development. Achieving public policy goals was delayed as a result of regulatory challenges that impacted industry development. Practical implications The authors’ findings show enterprises may develop growth strategies that comply with regulations when participating in nascent industries, but they must cope with extra risks, capital costs and uncertainty. The analysis also illustrates the value of engaging in government-industry collaboration to improve emerging regulatory frameworks. Originality/value The originality of this research consists of the detailed description of the first phase of Canada’s legalized recreational cannabis industry and the insight gained into the dynamics of nascent industries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".