Moderating the role of the perceived security and endorsement on the relationship between per-ceived risk and intention to use the artificial intelligence in financial services
Bibliographic record
Abstract
Advancement of banking and financial investment has led to the rapid expansion of services automation. The consistent increase of Artificial Intelligence (AI) usage in investment management implies the impending popularity of technology-based service. This study examined influencer endorsement and perceived security benefits as moderators to the relationship between perceived risk and financial AI services. Questionnaires were disseminated to 300 respondents who were customers with experience of using financial AI services in Jordan, and they were chosen through purposive sampling method. Structural equation modeling run using Smart-partial least squares (PLS 3.3.6) was employed in analyzing the data obtained from 220 completed questionnaires. The results show that perceived risk negatively affects financial AI services, while influencer endorsement and perceived security moderate the relationship between perceived risk and financial AI services. This study provides insight to companies on how to reduce perceived risk to encourage people to use business intelligence applications, as in the use of financial technology services.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".