A study on the success factors for knowledge management in supply chains of electronics industry
Bibliographic record
Abstract
This research aimed to 1) study the success factors for knowledge management through electronics industry supply chains, and 2) study guidelines and recommendations regarding the success factors for knowledge management through electronics industry supply chains. The study employed the quantitative research methodology. The statistical devices used included frequency, percentage and Structure Equation Modeling (SEM). The population and sample group comprised executives of the electronics industry in the electronics and electrical appliances sector in Thailand. The results revealed that the factors regarding the information technology system, leadership support and knowledge management had positive effects on the success of the knowledge management through electronics industry supply chains with the statistical significance (β) of 0.519, 0.621 and 0.448, respectively. However, the factors regarding human resource management affected the success of the knowledge management negatively at the statistical significance of 0.323. As for the effects of variables on the success of the knowledge management, it was found that the factors with the most positive indirect effects (IE) and total effects (TE) were those regarding 1) leadership support (IE = 0.278, TE = 0.278), 2) information technology system (IE = 0.233, TE = 0.233), and 3) knowledge management, which had a positive direct effect (DE) at 0.448 and a total effect (TE) at 0.448. However, the factors regarding human resource management had a negative indirect effect (IE) on the success of the knowledge management at -0.145.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".