A mediated moderated analysis of knowledge management and stakeholder relationships between open innovation and performance of entrepreneurial firms
Bibliographic record
Abstract
Even though entrepreneurial firms make substantial contributions to both domestic and global economies and innovations, there is disagreement in the literature regarding how open innovation affects these firms' ability to succeed. The current research is an attempt to address the gaps by analyzing the intricacies and dynamics of entrepreneurial firms’ involvement in Open Innovation. Furthermore, the impact on performance is examined from a knowledge perspective. In Jordanian context, this study analyzed the link between stakeholder interactions, knowledge management, open innovation, and the performance of entrepreneurial firms. The findings demonstrated that open innovation activities are statistically significant to the overall performance of entrepreneurial firms. However, since it has an unintentional detrimental effect over the performance of entrepreneurial firms in Jordan, the moderating effect of stakeholder relations and the mediation effect of knowledge management has been analyzed. The moderating role of stakeholder relationships has been proven statistically which enriched the theoretical foundations of RBV and contingency theory by adding stakeholders’ theory into the combination of the two theories, at the end limitations and guidelines for future research along with practical implications are emphasized.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".