How knowledge sharing mediates the influence of high-performance work systems on employee intrepreneurial behavior: A moderation role of entrepreneurial leadership
Bibliographic record
Abstract
This study offers a comprehensive investigation of the complex interconnections between High-Performance Work Systems, Knowledge Sharing, Entrepreneurial Leadership, and Employee Intrapreneurial Behavior in the telecommunications sector of Jordan. By using a quantitative method, this research employs structured questionnaires to gather comprehensive empirical findings from a sample of industry specialists. With 312 verified replies providing a solid framework, advanced analytical methods such as Structural Equation Modeling (SEM) and Partial Least Squares (PLS) were utilized to clarify the complex paths and linkages of the proposed hypothesis. The main findings of this study reveal that Entrepreneurial Leadership plays a crucial role in enhancing the influence of High-Performance Work Systems in fostering a dynamic intrapreneurial culture. It acts as a catalyst that magnifies the intrapreneurial inclinations among workers. Furthermore, knowledge sharing has arisen as a mediator, facilitating the influence of High-Performance Work Systems in fostering EIB. The research offers a thorough and intricate analysis that enriches our understanding of the diverse elements and mechanisms at play. The acquisition of this invaluable knowledge can be effectively employed to enlighten organizational strategies and policies, with the ultimate objective of cultivating an atmosphere that is conducive to innovation and intrapreneurial triumph within the swiftly evolving telecommunications sector of Jordan.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".