Relationship between Human Resources Management Practices, Transformational Leadership, and Knowledge Sharing on Innovation in Iranian Electronic Industry
Bibliographic record
Abstract
Electronic industry needs innovation to survive, and also to compete internationally. This study examines factors that can enhance technical innovation of companies in the electronic industry of Iran. The main purpose of this study is to examine the relationship between human resource management practices, transformational leadership, knowledge sharing, and innovation of the large and major electronic companies.More specifically, the research attempts to examine whether knowledge sharing mediates the relationship between human resource management practices and transformational leadership with innovation. A quantitative research approach was used in this study. A cross-sectional correlational research design was used.The sample for this study was drawn from a population of 23,704 employees (managers, engineers, and expert technicians) of eight largest electronic companies in Iran using stratified sampling method. The sample size was 376.After exploratory Factor Analysis (EFA) and confirmatory factor analysis (CFA), structural equation modeling (SEM) technique was used to test the hypothetical model. The Findings asserts that only two HRM practices (training and participation) and three transformational leadership components (vision, intellectual stimulation and personal recognition) have significant impacts on innovation. Besides, knowledge sharing has significant and positive impact on innovation. Out of five HRM practices, training, staffing, participation have significant and positive impacts on knowledge sharing while intellectual stimulation, and personal recognition(as transformational leadership components) have significant and positive impacts.Finally, knowledge sharing merely mediated the relationships of training, participation, vision and personal recognition with innovation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".