Women executives and financing pecking order of GEM-listed companies: Moderating roles of social capital and regional institutional environment
Bibliographic record
Abstract
This paper investigates the financing options of female executives within China’s unique environment. We examined 154 GEM enterprises listed on the Shenzhen Stock Exchange from 2009 to 2016. The data were analyzed using statistical procedures including multilevel regression analysis based on the existing financing pecking order models. Empirical evidence shows that women executives are less likely to use internal and debt financing. In terms of internal financing willingness, social capital and external institutional environment have negative and positive moderating effects respectively. In terms of debt financing willingness, social capital has a positive moderating effect. In addition, a poor external institutional environment has an amplifying effect on the moderating role of women executives’ social capital. Our study enriches current research on women executives' financing preferences which are limited to areas such as debt, equity financing and risk appetite and provides enlightenment for companies and women executives to improve their competitive advantages, and for government to optimize the external institutional environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".