The Effect of Dark Patterns and User Knowledge on User Experience and Decision-Making
Bibliographic record
Abstract
Dark patterns, aka deceptive designs, have become prevalent in the online environment. In this paper, we examined how dark patterns and knowledge of them impact user experience, decision-making, and vendor reputation using the purchase of a subscription plan on a hypothetical streaming website as proof of concept. We conducted a between-subjects study to examine the effect of two common dark patterns (confirmshaming and trick-question) compared against a control condition. Overall, users perceived both patterns as manipulative. However, this negative perception did not negatively impact the website’s perceived ease of use, trustworthiness and credibility. We found that users without knowledge of dark patterns were more likely to be persuaded by confirmshaming when making purchase decisions. In the confirmshaming condition, 68% of those without knowledge of dark patterns chose the expensive plan intended by the vendor over the cheap plan. The reverse is the case among those with knowledge of dark patterns: only 35% of them chose the expensive plan. This finding indicates that once users become aware of being manipulated, they are likely to go against the promoted choice, as 40% of knowledgeable users in the trick-question condition edited their initial choice, compared with 11% and 6% in the confirmshaming and control conditions, respectively. The findings highlight the need to raise awareness about dark patterns so that unsuspecting users are less likely to make decisions that are not in their best interest.
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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 | 0.008 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".