The impact of reading a web site's privacy statement on perceived control over privacy and perceived trust
Bibliographic record
Abstract
Purpose The purpose of this research is to study the impact of reading a web site's privacy statement on the perceptions of control over privacy and trust in a cyber merchant. Design/methodology/approach Two experiments were designed to monitor the actual reading of the privacy statement. Study one compares the influence of actual reading with self‐reported claims. Study two manipulated the format of the privacy statement (opt‐in or opt‐out) and included a control condition to assess the influence of the presence of a privacy statement and the influence of the format on the dependent variables. Findings The findings show that the mere presence of a privacy statement has a positive influence on perceived control. However, reading the privacy statement does not necessarily have a positive influence on perceived control and trust, contrary to commonly held assumptions. Participants who read the opt‐in format felt significantly more control and trust than the participants who read the opt‐out format. The opt‐out format decreases perceived control compared with the group that did not read the privacy statement when it was available. Research limitations/implications The sample size for both experiments was relatively modest, which limits the generalisability of the findings. Practical implications Cyber merchants should devote particular attention to the strategic role of the format of the privacy statement. Originality/value In contrast to other studies that relied on surveys, this paper assesses the impact of the actual reading of the privacy statement via an experimental approach. Moreover, the impact of the format of the privacy statement has been empirically tested.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".