Bibliographic record
Abstract
The reason of conducting this research is that there is no reliable study measures entrepreneurial orientation (EO) at government entities. EO among commercial organisations is widely addressed in previous research and literature due the intense market competition where this type of organisations should invigorate and accelerate EO to lead markets. Nevertheless, there is quite small number of research works that examine EO among semi-governmental organisations. However, those research works are not sophisticated enough to measure the impact of adopting and implementing EO within governmental organisations. The four principles of EO, which are innovation, autonomy, risk-taking and pro-activeness are applied to examine the extent of implications among governmental sectors and impact of EO on an organisation’s performance. Continually, organisations’ performance is been identified by three elements. These are stimulating internal environmental effectiveness, ability to achieve the organisation’s goals and individual ability for self-development. The research is applied among Saudi governmental sectors in Tabuk City. Measurement items and instruments adopted from previous studies. Then, the exploratory factor analysis performed to determine applicability of EO factors among governmental sectors. Exploratory factor analysis proves that some factors, such as innovation and autonomy have a significant reason to be applied. The research results show that innovation has an impact on the internal environment to stimulate internal environmental effectiveness. However, the research could not find any link between innovation, ability to achieve organisation’s goals and individual ability for self-development. On the other hand, the internal environment’s effectiveness and ability to achieve an organisation’s goals can be increased if there is quite good margin of autonomy. Furthermore, whenever risk-taking attitude is high, individual development ability is clearly high as well. Finally, pro-activeness has a slight link with ability to achieve organisation’s goals.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".