Investigating the factors affecting e-procurement adoption in supply chain performance: An empirical study on Malaysia manufacturing industry
Bibliographic record
Abstract
Global business is getting more and more cutthroat. Digital technology plays a significant role in giving companies a competitive advantage and improving the effectiveness of corporate processes. The Malaysian government has advanced by implementing e-government to use digital technologies to improve operations. In line with the objective of Malaysia’s government, this study examines the impact of e-procurement adoption and e-procurement determinants on supply chain performance among Malaysian manufacturing companies. Using a quantitative research design with an online survey questionnaire, 99 responses from manufacturers listed in the Federation of Malaysian Manufacturers directory were obtained, representing 19.41% of the response rates. It fulfilled the minimum sample size of 92, and the data were examined using PLS-SEM. A total of 13 hypotheses are supported, accepted hypotheses one and two in which top management support and information communication technology infrastructure do not directly affect supply chain management. Besides, the findings prove this study's mediating effect on e-procurement adoption. It indicates that most Malaysian manufacturing companies have embraced e-procurement to support their supply chain operations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".