E-procurement in small and medium sized enterprises; facilitators, obstacles and effect on performance
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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