Can the Media Richness of a Privacy Disclosure Enhance Outcome? A Multifaceted View of Trust in Rich Media Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Trust in rich media environments is conceptualized as comprising both trust in the retailer and trust in the others represented virtually during on-line interaction. More specifically, the authors posit that media richness (manipulated by the modality of the privacy disclosure) affects the e-store social presence that drives retailer trust and behavioral intentions, and that in rich media environments, agent trust (trustworthiness of the virtually represented agent communicating the disclosure) (1) mediates the relation between social presence and retailer trust, and (2) shapes consumer judgments of retailer trustworthiness and purchase intentions. These hypotheses are supported by the results of an experiment with 423 consumers, which show that there is a hierarchical order for social influence in rich media environments that entices retailers to manage not only the media richness of their B2C messages but also the social actors communicating these messages at their e-stores.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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