Researchers invested enormous efforts to understand and mitigate the concerns of users as technologies collect their private data. However, users often undermine \emph{other} people's privacy when, e.g., posting other people's photos online, granting mobile applications to access contacts, or using technologies that continuously sense the surrounding. Research to understand technology adoption and behaviors related to collecting and sharing data about non-users has been severely lacking. An essential step to progress in this direction is to identify and quantify factors that affect technology's use. Toward this goal, we propose and validate a psychometric scale to measure how much an individual values \emph{other} people's privacy. We theoretically grounded the appropriateness and relevance of the construct and empirically demonstrated the scale's internal consistency and validity. This scale will advance the field by enabling researchers to predict behaviors, design adaptive privacy-enhancing technologies, and develop interventions to raise awareness and mitigate privacy risks.
https://doi.org/10.1145/3544548.3581496
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)