Privacy‐Enhancing Factors and Consumer Concerns: The Moderating Effects of the General Data Protection Regulation
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
Abstract Privacy is a fundamental right, with humans often wanting to keep their information private. Technological advancements are now challenging this right by reducing our control and creating enhanced privacy risks. The General Data Protection Regulation (GDPR) is a law introduced to protect this right. The aim of this paper is to analyse how privacy‐enhancing factors can influence consumer privacy concerns and whether these have been affected by consumer beliefs relating to the GDPR. This paper examines the influence of four privacy‐enhancing factors (i.e. organizational trust, perceived personalization value, perceived consumer control and data transparency), which mostly have personality or attitude‐like traits, and the GDPR as a moderating variable. Data were collected from 1154 respondents residing in European Union countries. Results reveal that personalization value has a significant negative relationship with privacy concerns, while consumer control shares a significant positive relationship with privacy concerns. Organizational trust and data transparency did not have a significant effect on privacy concerns. The relationship between the privacy‐enhancing factors and privacy concerns was not moderated by consumer belief in the GDPR. Implications and recommendations are provided to indicate which privacy‐enhancing factors should be chosen to reduce privacy concerns and to highlight the role of the GDPR in moderating these relationships. Ultimately, the study's findings provide useful insights for firms operating online in Europe and marketers aiming to reduce their consumers’ privacy concerns.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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