The Role of Privacy Fatigue in Privacy Paradox: A PSM and Heterogeneity Analysis
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
Powerful rising trends of mobile media platforms have also resulted in the escalation of users’ privacy concerns. However, there is a paradox between users’ attitudes towards privacy and their actual privacy disclosure behaviors. This study attempts to explain the phenomenon of privacy paradox in the mobile social media context from the privacy fatigue perspective. Based on the Elaboration Likelihood Model (ELM) and employing the method of Propensity Score Matching (PSM), this paper confirmed that privacy fatigue could directly explain the privacy paradox. Among the findings, cynicism turned the relationship between privacy concern and privacy protection behaviors from positive influence to negative influence, while emotional exhaustion would weaken the positive influence relationship between privacy concern and the intention to undertake privacy protection behaviors. In addition, the study also revealed the heterogeneous effects of individual characteristics and usage characteristics variables on how the privacy fatigue influences privacy paradox.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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