The effect of e-procurement on financial performance: Moderating the role of competitive pressure
Bibliographic record
Abstract
The importance of E-procurement is significant for the development of nations. Findings of previous studies in terms of predictors and consequences of using E-procurement are inconsistent and most prior literature were conducted in developed countries. The purpose of this study is to examine the predictors and consequences of using e-procurement. Based on resource-based view (RBV) and Technology-Organization-Environment framework (TOE), the study proposed that technological (relative advantage, compatibility, and complexity) and organizational factor (top management support, organizational readiness, and Information System (IS) committee) will have significant effect on e-procurement which in turn expected to affect the firm performance. Competitive pressure is proposed as a moderating variable between technological and organizational factors, and e-procurement. The population of the study includes large companies in Jordan. Purposive sampling was deployed to collect the data using a questionnaire. The findings were derived from 221 responses. Data analysis was conducted using Smart PLS. The findings showed that technological (relative advantage, compatibility, and complexity) and organizational (top management support and organizational readiness) have significant effect on e-procurement which in turn affected firm performance. Competitive pressure did not moderate the effect of technological and organizational factors on e-procurement. The findings help the policy makers in Jordan to increase the usage of e-procumbent and firm performance by focusing on the benefits and reducing the complexity of using a new technology.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".