Abstract
Dark patterns refer to tricks used in websites and apps to make you do things that you do not intend to do. This paper presents the board-game Dark Pattern, in which players install apps, draw dark patterns cards, and make choices about the sharing of personal data. To win the game, a player must share as little data as possible and play cards that punish other players. Two groups, the first with 56 students and the second with 45 students, played the game and then answered a survey with questions controlling their knowledge about the dark patterns types featured in the game. In addition, a further 50 students answered the same survey without playing the game. In this paper we present key findings about the dark patterns knowledge generated by playing the game. Then we present an exploratory analysis using Partial Least Square – Structural Equation modelling (PLS-SEM). We analysed whether dark patterns knowledge and risk perception, the likelihood of negative incidents due to data sharing, could predict the players behavioural intention to take proactive privacy steps. The two PLS-SEM models have a variance explained (R2) of 0.34 and 0.35 indicating that approximately 35% of the variance could be accounted for by the two variables included in the model. Taken together, the analyses indicated that playing the Dark Pattern game had a positive effect on behavioural intention to proactive privacy steps as a result of by playing the game.
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Tjostheim, I., Wales, C., Waterworth, J.A. (2024). Uncovering Dark Patterns - Learning Through Serious Gameplay About the Dangers of Sharing Data. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-031-45645-9_45
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