Effect of power source mismatch on new venture performance
Bibliographic record
Abstract
Purpose The venture capital syndication brings in various resources for the portfolio firms, which positively affects those firms’ performance, while conflicts within syndicates also have negative impact on the portfolio firms’ performance. This study aims to explore the two opposite effects of the venture capital syndication on the portfolio firms’ operations. Drawing on Ma et al.’s (2013) power source match perspective, the authors examine the effect of (mis)match of power source between ownership and status on the portfolio firms’ performance. Design/methodology/approach The study uses panel data from two professional databases containing information about the venture capital-backed firms in China. The fixed effect model is applied to analyze the data. Findings This study found that power source match in the venture capital syndicates works positively on the portfolio firms’ performance. This positive relationship is weakened when there is ownership-dominated power source mismatch present. Practical implications This study suggests that when new ventures search for venture capital, it is better to allocate greater ownership to the venture capital providers with high-status power, so that ownership power and status power can have a proper match to increase the coordination among venture capital providers, thereby helping portfolio firms perform better. Originality/value This study looks into the performance of a portfolio firm when there is power a (mis)match in a venture capital syndication, extending the current literature in this area where only the performance of the venture syndications is examined.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".