Behind the screens. Privacy and advertising preferences in VoD —the role of privacy concerns, persuasion knowledge, and experience
Bibliographic record
Abstract
This study explores stated preferences for privacy and advertising in the Video on Demand (VoD) context, focusing on Netflix subscribers in Poland. We investigate how privacy concerns, persuasion knowledge, and consumer experience affect these preferences. The study design involved a hypothetical regulatory scenario that mandated platforms to either guarantee minimal data usage or offer compensation for data sharing. Within a discrete choice experiment framework, study participants were presented with hypothetical scenarios and asked to choose between three types of subscription plans, varying in the extent of personal data sharing and ad support. Additionally, a treatment was introduced in which respondents interacted with a mock Netflix environment to enhance their recognition of data practices and increase familiarity with hypothetical outcomes through a simulated experience. Responses from 2087 participants were analyzed using hybrid choice modeling. The results reveal that users are sensitive to the disclosure of personal information in the context of VoD, yet they are open to accepting monetary compensation for a certain degree of sharing. Users with greater persuasion knowledge are more willing to exchange data for discounts, provided the plans do not include personalized ads. Conversely, users with higher privacy concerns prefer plans with minimal data sharing, even when discounts are offered. We observe direct effects of the treatment on both privacy valuation and advertising preferences, particularly regarding time, with the treatment group being significantly more sensitive to ad length. In addition, the treatment group exhibits reduced privacy concerns and no significant difference in persuasion knowledge. Our findings suggest that VoD providers could enhance user control over their data and emphasize transparency, aligning with the increasing reliance on data-driven business models. • Users are willing to accept monetary compensation for data sharing in VoD, but dislike personalized ads. • Users with higher privacy concerns prefer plans with minimal data sharing. • Users with greater persuasion knowledge are more willing to exchange data for discounts. • Experience with attention strategies using data increases privacy concerns yet has no effect on persuasion knowledge.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".