Relative Privacy Valuations Under Varying Disclosure Characteristics
Bibliographic record
Abstract
Our study investigates relative changes to the value individuals place on the disclosure of their private information. We use an incentive compatible mechanism to capture an individual’s willingness-to-accept (WTA) for a privacy disclosure in three randomized experiments. Characteristics of the required privacy disclosures are manipulated by altering information context, intended secondary use of the disclosed private information, and the requirement to disclose personally identifying information. We consistently find across the experiments that the information context influences the relative value of a disclosure but we observe null effects for external secondary use and the inclusion of identifying information. We increase the saliency of the privacy disclosure characteristics in the second experiment, which is found to increase participants’ average WTA but does not change relative values across the varying disclosure characteristics. In the third experiment, we train participants on the potential consequences of disclosing private information, which does not increase participants WTA beyond simply increasing the saliency of the privacy factors in a disclosure. Training participants of the consequences also does not influence the relative value of privacy disclosures across the varying disclosure characteristics. However, through a post-experiment survey we find that participants do acknowledge the increased risk introduced through external secondary use and providing identifying information. Additionally, we find that the participants’ internet privacy concerns increase when they are trained on the consequences of disclosing private information online. These results provide a unique perspective on privacy valuations. They fill a gap in the literature by clarifying privacy factors that influence the value of a multidimensional disclosure and provide further evidence of the privacy paradox, where consumers claim a desire for privacy but their economic actions suggest otherwise.
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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 | 0.013 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".