Knowledge Management in Collaborative Manufacturing Food Companies Performances: Twin Impacts of Learning and Innovation
Bibliographic record
Abstract
The purpose of this approach is to establish the twin impacts of organizational innovation (OI) with organizational learning (OL) in the relation between knowledge management (KM) and organizational performance (OP). 168 companies of manufacturing Food Company were chosen from Malaysia, Taiwan, China and path analysis is used to analyse the underlying hypotheses. The research framework under study contain four latent variables (OP; OL; KM; OI) and three observed indicators (firm type; firm size; firm age). Structural equation modelling include mediation and moderation analysis were used in this study. The obtained results support the literature regarding the relationship among these four constructs and prove that the combination of OI and OL is mediator in the linkage between KM and firm performance. Moreover, firm age, size and type are acting as moderators among the research latent variables. The introduced model can be consider as a basic framework for technology management modelling studies. Limitation and implications for future studies are discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".