The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services
Why this work is in the frame
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Bibliographic record
Abstract
The study’s aim is to investigate how FinTech users’ perceived risk influences their continuance intention to use FinTech services. The new model, which was based on the Expectation Confirmation Model, was created to achieve the study’s aim. The Partial Least Square Structural Equation Model was used to investigate the proposed model and the relationship between the adopted constructs. The sample consists of 802 individual survey responses from northern India from April to June 2022. The proposed model explains 45.4% of the variance in the continuance intention of FinTech users, which is significantly influenced by perceived usefulness and satisfaction. Furthermore, perceived risk, as a moderator, significantly moderates continuance intention through satisfaction and satisfaction through confirmation. However, perceived risk was found to have an insignificant moderating effect on the relationship between perceived usefulness and satisfaction as well as perceived usefulness and continuance intention. The findings provide insights to FinTech service providers about the factors that influence users’ intent to continue using FinTech 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 | 0.004 | 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 it