The influence of user knowledge and usage behaviour on decision-making and perceived reputation of streaming sites that use dark patterns
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
In this paper, we examined how dark patterns (confirmshaming and trick-question), user knowledge, number of services owned and usage frequency impact users' decision-making and service reputation using a subscription-based streaming website as proof-of-concept. Overall, users perceived both patterns as manipulative. However, this negative perception did not adversely impact the perceived trustworthiness and credibility of the website. While in the confirmshaming condition, 68% of those without knowledge of dark patterns selected the expensive plan promoted by the service over the cheap (standard) plan, the reverse is the case among those with knowledge, 35% of whom selected the expensive (premium) plan. This finding indicates that as users become knowledgeable about dark patterns, they are more likely to reject the service-promoted choice, as 40% of knowledgeable users in the trick-question condition edited their initial choice, compared with 10% and 6% in the confirmshaming and control conditions, respectively. Moreover, low-frequency and low-services users in the trick-question condition were most likely to fall for the expensive plan. However, high-frequency and high-services users in the confirmshaming condition were most likely to fall for the expensive plan. The findings highlight the need to raise awareness about dark patterns to prevent unsuspecting users from making financial decisions against their best interest.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it