Improving the Innovation Performance for Vietnamese Firm Based on Practices of Idealized Influence and Individualized Consideration: The Mediating Role of Knowledge Sharing
Bibliographic record
Abstract
Innovation performance is the fundamental factor for firms to survive and achieve competitive advantage in the context of increasing competitive pressure. The purpose of this paper is to investigate the impacts of two key components of transformational leadership (TL) namely idealized influence and individualized consideration on knowledge sharing (KS), as well as their influences on firm’s innovation performance. The paper used Structural Equations Modeling (SEM) to elaborate the relationship among these latent factors through using survey data gathered from 235 participants of 60 medium and small-sized firms in Vietnam. The findings reveal that KS activities of employees play a crucial role in improving firm’s innovation performance, and serve as a mediating role in the effects of TL on innovation performance. Moreover, the findings highlight the impact of individualized consideration on innovation performance in comparison with the impact of idealized influence on innovation performance. In general, the findings of this study have advanced the understanding and brought new initiatives for Vietnamese firms to follow and improve its innovation performance.
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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 | 0.005 |
| 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 | 0.002 | 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".