Growth of Venture Firms under State Capitalism with Chinese Characteristics: Qualitative Comparative Analysis of Fuzzy Set
Bibliographic record
Abstract
This study builds upon the venture growth literature and venture legitimation mechanisms and investigates how venture firms in China can acquire legitimacy and necessary resources from state stakeholders for venture growth during the COVID-19 pandemic. To offer a context-specific perspective of Chinese ventures’ legitimation strategies, we discuss that under Chinese state capitalism, these ventures need to follow lingering socialist values, such as equality and social stability, to be recognized as appropriate business operations by state audiences. Furthermore, we discuss that access to necessary resources for venture growth is limited during crises. Based on the understanding of particular contexts of Chinese state capitalism and the COVID-19 pandemic, we examine how various sets of a venture’s identity, associative, and organizational mechanisms influence venture growth during crises in China. In addition, we consider serial entrepreneurship as a contextual factor affecting the effectiveness of causal effects. This study applies the fuzzy-set qualitative comparative analysis method to take a configurational approach and identify multiple concurrent causality of legitimacy mechanisms on venture growth. We conduct a survey and analyze data from 107 entrepreneurs of Chinese technology ventures during the COVID-19 pandemic. Findings show that Chinese ventures with or without repeat entrepreneurs can actively utilize various sets of legitimation mechanisms to acquire legitimacy and necessary resources from Chinese state audiences for venture growth during adversity. This study provides comprehensive understanding and practical implications on Chinese ventures’ legitimation strategies for venture growth during crises.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".