A study on the success factors for knowledge management in supply chains of electronics industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it