The Moderating Effect of Subjective Norm on Cloud Computing Users’ Perceived Risk and Usage Intention
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
The high popularity and high demand of internet applications lead to faster development of cloud computing services that make network access become more convenient. However, using a new technology can cause users’ perceived risk because they worry about internet security and invasion of privacy. The study aims to explore the influence of end users’ perceived risk on usage intention of cloud computing services and the interactive effect of perceived risk and subjective norm on usage intention. The study dispatched 350 questionnaires to internet users. The amount of valid questionnaires is 238 and the effective response rate is 68%. The results showed that both perceived risk and subjective norm are significantly affected to usage intention, and subjective norm has a moderating effect on the relationship between perceived risk and usage intention.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it