Evaluating user perceptions of trust in persuasive technology: A comparative study
Bibliographic record
Abstract
Persuasive technologies are increasingly used across digital platforms to influence user behavior in areas such as health, education, and commerce. However, many of these systems are built without fully considering ethical design principles, which can impact user trust. This study investigates how ethical features, including transparency, autonomy, consent, and data privacy and security shape trust perceptions across different demographic groups. Two mobile application prototypes were developed, one with ethically informed design elements (UI A) and one with standard design features (UI B). A survey of 449 participants showed that UI A was perceived as more trustworthy than UI B in terms of transparency and autonomy, while no significant differences were found for consent or data privacy and security. Trust perceptions also varied by gender, age, and technological expertise. These findings highlight that ethical design must be paired with user-centered approaches that account for diverse experiences and expectations to build trustworthy, persuasive technologies. The primary contribution of this work lies in demonstrating how ethically informed UI design elements influence user trust, and in revealing how these effects differ across gender, age, and technological expertise.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".