Factors affecting e-supply chain management systems adoption in Jordan: An empirical study
Bibliographic record
Abstract
Recently, there have been a growing number of articles focusing on the benefits of adopting e-SCM systems and the value of such systems in supply chain performance. However, less academic research was devoted to understanding factors affecting the adoption intention of such systems. This study uses the technology, organization, and environment (TOE) framework to examine factors that affect the adoption of e-SCM systems in Jordan, where limited research has been conducted in this country. Through an online survey filled by 251 participants via the LinkedIn website, the study shows that perceived relative advantage, financial resources, employee competency, top management support, competitive pressures, and customer pressure positively impact the adoption intention of e-SCM systems. The findings confirm the association between variables embedded in the TOE framework and the adoption intention of innovative supply chain systems and solutions and support earlier findings. According to the study findings, e-SCM systems providers should focus on the relative advantage these systems offer to increase the likelihood of their adoption.
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How this classification was reachedexpand
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".