E-procurement in small and medium sized enterprises; facilitators, obstacles and effect on performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze e-procurement in small and medium-sized enterprises (SMEs) and its relationship with top management support, IT obstacles and strategic purchasing and the effect of e-procurement on performance (procurement performance and business performance). Design/methodology/approach The hypotheses were tested using a sample of 199 managers from SMEs in manufacturing. Findings The results indicated a significant relationship between e-procurement in SMEs and top management support, IT obstacles and strategic purchasing. Similarly, the authors found a positive relationship between e-procurement and procurement process performance and business performance. Practical implications The findings stress to SME managers, the need to pay attention to top management support, IT obstacles and strategic purchasing when implementing e-procurement. Similarly, it provides evidence of the benefits of e-procurement on procurement process performance and business performance. Originality/value This study fills a gap in the literature regarding e-procurement in SMEs and its impact on performance. SMEs constitute a significant part of today’s economies and e-procurement can significantly impact the performance of these organizations.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".