The role of perceived usefulness in moderating the relationship between the DeLone and McLean model and user satisfaction
Bibliographic record
Abstract
The aim of this paper is to examine one of the most crucial factors in the “Technology Acceptance Model” proposed by Davis (perceived usefulness) in moderating the “DeLone and McLean” success model in the context of educational portal in Higher Education. Questionnaires were distributed online to 200 respondents and deserved to be analyzed. The respondents were regular students at the University of Bengkulu. Data analysis used Smart-PLS version 3.2.9. The research findings indicated an influence of “system quality, information quality, and service quality partially on user satisfaction” of the educational portal information systems. The result shows that perceived usefulness can strengthen the relationship between system quality, information quality, and service quality to the satisfaction of customer. This research contributes to the development of perceived usefulness variable as a moderating variable affecting the quality of a system, quality of information, and quality of service partially on user satisfaction and finding strategies needed by the University of Bengkulu effective and efficient information system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".