Government institutional support, entrepreneurial orientation, strategic renewal, and firm performance in transitional China
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how entrepreneurial orientation (EO) and strategic renewal (as a critical dimension of corporate entrepreneurship) might transmit government institutional support and thereby enhance firm performance in a transition economy. Design/methodology/approach Multi-respondent data were collected from 230 Chinese-based firms. The hypotheses were tested with structural equation modeling, in combination with a bias-corrected bootstrap method, to assess the significance of the theorized direct and indirect relationships. Findings Government institutional support enhances EO and strategic renewal individually, yet EO also fully mediates the relationship between government institutional support and strategic renewal. Moreover, strategic renewal fully mediates the relationship between EO and firm financial performance, and it partially mediates the relationship between EO and firm reputation. Originality/value This study contributes to entrepreneurship literature by testing an organization-level model of entrepreneurial phenomena in established firms that identifies EO and strategic renewal as two distinct mechanisms through which government institutional support in a transition economy can enhance organizational effectiveness, which entails the firm’s financial performance and reputation. In doing so, this study provides an extended understanding of how EO and strategic renewal might influence a firm’s financial and nonfinancial outcomes in different ways.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".