Learning Through Gameplay About the Dangers of Sharing Data and Intention to Protect Personal Data

Dark Pattern is an analogue board game, based on Dark Pattern designs in websites and applications, which was developed in order to increase awareness of some of the risks and hazards consumers may face when installing apps and subsequently sharing, often unwittingly, their personal data. Players who avoid sharing personal data, while forcing others to do so, are more likely to win, than those that download apps within gameplay without a clear strategy to protect themselves. In a Worldcist-2023 paper we presented an analysis of 102 players of two groups, where group one played a 60-min session of the game, while group two played for 75 min. Based on our observation and analyses, we decided to increase the playtime to 90 min and run new play-sessions. Our exploratory analysis is based on Partial Least Square – Structural Equation modelling (PLS-SEM) followed by binary logistic regression analysis. The PLS-SEM analyses investigate the variables dark patterns knowledge and perception of harm and the perception of the likelihood of data sharing leading to a negative outcome or event and whether these were associated with players intention to take proactive steps to protect their personal data. The PLS-SEM models showed a variance explained (R2) of 0.20, which indicates that approximately 20% of the variance is attributed to the variables included in our model. Using binary logistic analysis, we identified one dark pattern trick and two factors that predict the intention to proactively protect one’s privacy. These include the deceptive design trick named “Friend Spam,” the harm caused by “Trick Questions,” and the perceived likelihood of “Privacy Zuckering.“ The perceived likelihood of ‘Privacy Zuckering’ arises from users not reading the fine print or overlooking the terms they agree to when accepting the terms of use.