An Empirical Investigation of the Antecedents and Consequences of Privacy Uncertainty in the Context of Mobile Apps
Bibliographic record
Abstract
When using mobile apps that extensively collect user information, privacy uncertainty, which is consumers’ difficulty in assessing the privacy of the data they entrust to others, is a major concern. Using a simulated app-buying experiment, we find that privacy uncertainty, which is mainly driven by uncertainty about what data are collected and how they are used and protected, is indeed a significant influencer of one’s intentions to use a mobile app and the perceived risk associated with that use, as well as the price a potential consumer is willing to pay for an app. Our results further show that the uncertainty concerning the data collected while using a mobile app drives consumers’ decisions more than the uncertainty regarding data that are collected at the time an app is downloaded. To investigate whether privacy uncertainty continues to be a factor after a consumer has already started using an app, we conducted a survey of users of wellness and personal finance apps. The results indicate that privacy uncertainty is a lingering concern because it continues to influence a user’s intention to continue using an app and the perceived risk associated with that continued use.
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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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".