Entrepreneurship and organizational performance: Empirical insight into the role of entrepreneurial training, culture and government funding across higher education institutions in Pakistan
Bibliographic record
Abstract
This paper examines the relationship between entrepreneurial training, entrepreneurial culture and organizational performance. Accordingly, the study also investigates the moderating effect of government funding on the relationship between entrepreneurial culture, entrepreneurial training and business performance among the public universities in Pakistan. The respondents include 415 heads of department out of 1100 identified in the study. Structural equation modeling using PLS 2.0 M3 reports significant relationship between entrepreneurial training and organizational performance. Likewise, entrepreneurial culture is also reported to be significantly important for boosting organizational performance. Importantly, the study reports significant moderation of government funding on the relationship between entrepreneurial culture and organizational performance. However, no moderation is reported on the entrepreneurial training and organizational performance relationship. The findings of this study have enhanced the understanding regarding government funding in the prospect of improving the performance of the HEIs in Pakistan. The study recommends that the state government and higher education institutions in Pakistan allow room for entrepreneurial activities, and development of entrepreneurial principles and opportunities, and further encourage entrepreneurial practices through the development of public entrepreneurial orientation and entrepreneurial training on innovation to generate/create positive effects on the organizational performance of HEIs.
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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 | 0.003 |
| 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 | 0.001 |
| 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".