The effect of digital supply chain on organizational performance: An empirical study in Malaysia manufacturing industry
Bibliographic record
Abstract
Nowadays, global technologies, especially digital things, have become an important tool for businesses to maintain feasible partnerships and build a great value connection with other companies. New digital technologies that are emerging every day are on their way to affect nearly all business processes and activities. This study investigates the effect of the digital supply chain on the supply chain and organization performance in the Malaysia manufacturing industry. This paper also further assesses the mediating effect of supply chain performance in the relationship between the digital supply chain and the organizational performance in the Malaysia manufacturing industry. The objectives are achieved via quantitative research design. The researchers emailed the online survey questionnaire to 1160 manufacturing companies listed in the Federation of Malaysian Manufacturers (FMM) directory via stratified sampling technique and received 63 responses. 7 incomplete responses have been deleted and 56 usable responses representing 5.43% of the response rate used for data analysis. The data was analyzed by using the Partial Least Square Structural Equation Modeling (PLS-SEM). Three hypotheses are not supported and seven hypotheses are supported, which includes all the hypotheses of moderating effect. The manufacturing companies in Malaysia can consider adopting the DSC in the business process to remain reliable in the competitive market by providing good supply chain performance and best organizational performance as a whole. The implication of the study is given to academics and practitioners, specifically manufacturing companies. The limitations and the recommendation for future study have been highlighted.
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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 | 0.003 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".