Top Management Support, Organizational Learning, Innovative Behavior, Employee Commitment and Organizational Performance of Manufacturing Companies in Hai Phong
Bibliographic record
Abstract
Many organizational researchers consider innovative behavior to be an important work related factor (Fex & Spector, 2006). Researchers have found strong links between innovative behavior and organizational performance in the workplace. Jex, Beehr and Roberts (1992) found innovative behavior of employees as direct outcomes of organizational performance. Similarly, innovative behavior has been identified as a major effect for financial outcomes by many researchers (Dewe, 2003). Innovative behavior was found to be another major effect for employee satisfaction (Fox and Spector, 2006). Fox and Spector (2006) identified positive work behavior as a behavioral response caused by innovative behavior of employees (Noe, 2000). Top management support is another highly researched organizational factor that has been found to affect job attitudes and work behavior (Weiss, 2002). Top management support has been found to affect behaviors such as organizational citizenship behavior, absenteeism, turnover, and work performance (Feather & Rauter, 2004). In a HRD related topic, Egan, Yang and Barlett (2004) examined the relationship between top management support and organizational performance and shown that there is a positive relationship between these two variables. The factors discussed above that is organizational learning, top management support, innovative behavior, employee commitment, and organizational performance are the focus of this study. Exploration of these variables was based on a systematic examination of literature, a unique contribution toward elaborating upon the elements impacting organizational 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".