Creative Industries’ Entrepreneurial Success: Social Capital, Networks, and Internationalization Strategy
Bibliographic record
Abstract
The purpose of this study is to explain the success factors of business ventures in creative industries. By analyzing three types of festivals in three countries through the perspectives of entrepreneurial success, internationalization, and management, the paper explains how business ventures in creative industries from developing economies mobilized key factors to succeed. The study particularly focuses on identifying the types of partners, channels, and strategies that entrepreneurs in creative industries mobilized to achieve international success. Using data from publicly available online sources to categorize important factors for success, the paper argues that social capital-based view may explain success in creative industries better than resource-based or knowledge-based views although the combination of the three perspectives is deemed necessary. Founders’ personal social capital (connections and networks) appeared particularly important for entrepreneurial success in creative industries. The importance of social capital is perceivable in national and international success and is also applicable, to some degree, to the contexts of both developing and developed economies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".