The Pursuit of Legitimacy: How do AI Startups Navigate Between Institutional Environments?
Bibliographic record
Abstract
Startups need legitimacy to acquire resources from their environment to survive and thrive. In a born-global industry such as artificial intelligence (AI), startups' legitimacy building is subject to pressures from institutions that are constantly evolving at both macro and meso levels due to AI's huge potentials for creating positive and negative impacts. Yet, we do not know how AI startups deal with such an institutional context because prior studies mostly explored startups in established or static institutional environments. From our embedded multiple-case study of six AI startups based in Montreal (Canada) and Kuala Lumpur (Malaysia), we found that informal norms largely guide AI entrepreneurs despite governments' efforts to influence startup operating frameworks. In the case of weak formal regulations, AI startups purposefully rely on industry norms over other institutions. When corporate social responsibility (CSR) is embedded in meso-level norms, startups are more likely to proactively engage in CSR activities. We also found that industry leaders play a primary role in establishing the governing rules in AI, which in turn inform national policy development. Our study results provide interesting insights for players in emerging industries who want to know about CSR strategies when the industry is writing its rules. Furthermore, our study has implications that help policymakers in their efforts to influence normative institutions with the aim of creating an environment conducive to responsible practices. All in all, our study contributes to the literature a better understanding of entrepreneurs' legitimacy building through CSR in dynamic institutional settings.
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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 | 0.006 | 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 | 0.006 | 0.008 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".