This study examines the extent of which older internet users can identify dark patterns in e-commerce sites. The purpose of this is to understand which types of dark patterns that older people 55+ can identify to which degree after receiving adequate knowledge about the subject of deceptive design and specifically dark patterns, and also if there is any connection between the results and factors such as demographic differences and the communicative nature of each dark pattern. This study shows the type of dark patterns that was identified correctly the most by the respondents was Low-stock message, which we categorized as having linguistic communicative attributes. The least correctly identified type of dark patterns was Aesthetic manipulation with its graphic communicative nature. Over all the respondents were able to identify dark patterns correctly to an extent of 50 %, and no distinctive demographic patterns relating to the outcome were found other than that higher education seemed to equal higher results. Regarding how the communicative attributes of each dark pattern affected the respondents' results, we did find that dark patterns of linguistic type were easier for the respondents to identify, whereas graphical dark patterns were the hardest. To gather the data a web-based survey was used and distributed through Facebook which generated 28 respondents in total. The data was then analyzed using frequency tables and simple descriptive statistics, and some of them were later translated into diagrams to visualize the results in an effective way.
Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hb-28893 |
Date | January 2022 |
Creators | Stoica, Daniela, Johansson, Alexandra, Linder, Lina |
Publisher | Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT |
Source Sets | DiVA Archive at Upsalla University |
Language | Swedish |
Detected Language | English |
Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess |
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