Do Users Always Want to Know More? Investigating the Relationship between System Transparency and Users' Trust in Advice-Giving Systems.
Bibliographic record
Abstract
Users’ adoptions of online-shopping advice-giving systems (AGSs) are crucial for e-commerce web-sites to attract users and increase profits. Users’ trust in AGSs influences them to adopt AGSs. While previous studies have demonstrated that AGS transparency increases users’ trust through enhancing users’ understanding of AGSs’ reasoning, hardly any attention has been paid to the possible inconsistency between the level of AGS transparency and the extent to which users feel they understand the logic of AGSs’ inner working. We argue that the relationship between them may not always be positive. Specifically, we posit that providing information regarding how AGSs work can enhance users’ trust only when users have enough time and ability to process and understand the information. Moreover, providing excessively detailed information may even reduce users’ perceived understanding of AGSs, and thus hurt users’ trust. In this research, we will use a lab experiment to explore how providing in-formation with different levels of detail will influence users’ perceived understanding of and trust in AGSs. Our study would contribute to the literature by exploring the potential inverted U-shape relationship among AGS transparency, users’ perceived understanding of and trust in AGSs, and contribute to the practice by offering suggestions for designing trustworthy AGSs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".