Avoiding uncertainty by measuring the impact of perceived risk on the intention to use financial artificial intelligence services
Why this work is in the frame
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Bibliographic record
Abstract
The moderating role of influencer endorsement and perceived monetary benefits on the relationship between perceived risk and financial artificial intelligence services was explored in this study. Data were obtained through questionnaires distributed to 200 respondents who were selected using a purposive sampling method. The respondents were customers receiving financial artificial intelligence services in Jordan. Analysis was performed using a structural equation modeling approach run by Smart-partial least squares (PLS) 3.2.9 involving data from 138 returned questionnaires. The results show a negative impact of perceived risk on financial artificial intelligence services, and a moderation effect of influencer endorsement and perceived monetary benefits on the relationship between perceived risk and financial artificial intelligence services. The findings can assist companies in their strategies of decreasing perceived risks that individuals could be encouraged to utilize business intelligence applications, for instance, financial technology services.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it