The performance impact of digital technology adoption in procurement: A case study of the manufacturing industry in the Eastern Economic Corridor, Thailand
Bibliographic record
Abstract
The procurement process is part of Purchase to pay (P2P) that consumes huge company costs. There are many procedures that need to be performed related to data. Without digital technology it might cause human error which will lead to issues in higher cost and will create a lot of delay in the procurement process. To improve procurement performance, selecting the right technologies that will create performance impact is quite a challenging task for the company. This study aims to examine the impact of digital technology adoption on procurement performance and to examine the mechanism through which digital technologies impact procurement performance. Target groups are medium and large manufacturing companies in the Eastern economic corridor, Thailand. In research findings, there is data supporting our hypotheses that digital technology adoption in procurement has a positive impact on procurement performance in cost and cycle time reduction. As a result of the moderation effect, reducing human errors, data availability and responsiveness moderated the positive impact between technology adoption and procurement cost reduction. The effect between technology adoption and procurement cycle time reduction has positively been mediated by reducing human errors and data availability. However, responsiveness rejected the hypothesis that mediates the relationship between technology adoption and procurement cycle time reduction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".