Can the Media Richness of a Privacy Disclosure Enhance Outcome? A Multifaceted View of Trust in Rich Media Environments
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.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".