E-Procurement Systems: Examining the Effect of End-User Satisfaction on Individual Performance
Bibliographic record
Abstract
End-user satisfaction and individual performance have been identified by many researchers as critical determinants of the success of information systems. As an escalating number of organizations now utilize e-procurement systems, there is a desire to understand their effect on individual end-user’s performance. Therefore, this research attempts to empirically examine a framework identifying the relationships between end-user satisfaction, and individual end-user performance, in addition to assessing the impact of three proposed antecedents of end-user satisfaction: professionalism, training and usability. Data gathered from 432 end-users of ePerolehan system in the Malaysian government agencies were utilized to examine the relationships proposed in the framework using the Partial least square (PLS) approach. The findings provide strong support for our model. Our results indicate three factors professionalism, training and usability significantly affect end-user satisfaction, while the higher levels of end-user satisfaction leads to improved individual performance.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".