Uppsats
Mitigating Single-Page Dark Patterns in Subscription Cancellation Interfaces
Kandidat-uppsats
Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)
Publicerad: 2026
Språk: Engelska
Nyckelord
klicka för att sökaSammanfattning
Dark patterns are interface designs that are created with the intention to manipulate users into making decisions that primarily benefit service providers rather than the users themselves. This study focuses on dark patterns within subscription cancellation interfaces, where manipulative design strategies such as Preselection, False Hierarchy, Hidden Information and Confirmshaming are commonly used to discourage users from cancelling subscriptions. Using a Design Science methodology, mitigation strategies were derived from existing literature and applied through a GPT-5.5 Instant LLM-assisted redesign approach that transformed manipulative interfaces into more transparent and user-friendly interface versions. A user study was then conducted where participants compared original subscription cancellation interfaces with redesigned non-dark versions to evaluate user perceptions and responses. The study showed that participants generally preferred the redesigned interfaces because they were perceived as clearer, more balanced, easier to understand, and less emotionally manipulative. The results suggest that participants perceived the redesigned interfaces as more transparent and less manipulative than the original interfaces. These findings indicate that the mitigation strategies and GPT-5.5 Instant-assisted redesign approach used in this study may support future research on transparent interface design.
Information
- Författare
- Angelkovik, André, Tamilore Oloyede, Fredrick
- Lärosäte / institution
- Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)
- Publiceringsdatum
- 2026
- Uppsatstyp
- Kandidat-uppsats
- Språk
- Engelska
Utforska vidare
Liknande uppsatser
Uppsatser med liknande ämnen och nyckelord.
Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)
Lindberg, Leia, Hugosson, Ester
Publicerad: 2026
Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)
Lorensson, Linus, Muhtadee, Faiyaz
Publicerad: 2026
Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)
Kozak, Ivanna
Publicerad: 2026
Kandidat-uppsats, Högskolan i Skövde/Institutionen för informationsteknologi
Dargren, Calle
Publicerad: 2026
Kandidat-uppsats, Jönköping University/Tekniska Högskolan
Rönnqvist, Emilia, Skoogh, Lovisa
Publicerad: 2026
Kandidat-uppsats, Högskolan i Gävle/Avdelningen för datavetenskap och samhällsbyggnad
Vambe, Vimbainaishe
Publicerad: 2026