Andreas Janson
Thumbnail Image
Title
Dr.
Last Name
Janson
First name
Andreas
Email
andreas.janson@unisg.ch
Phone
+41 71 224 2592
Homepage
LinkedIn
Twitter
https://twitter.com/janson_andreas

Filters

3
3
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    The Dark Side of Privacy Nudging – An Experimental Study in the Context of a Digital Work Environment
    ( 2021)
    Barev, Torben Jan
    ;
    Schwede, Melanie
    ;
    Janson, Andreas
    Privacy nudging is a promising method to get users to disclose less personal information in digital work environments. In this work, we tested two digital privacy nudges: a social and a framing nudge. To empirically measure the effectiveness of the two privacy nudges, we developed an online experiment. We evaluated 223 data sets with the partial least squares structural equation modeling (PLS-SEM) and a two-factorial Analysis of Variance (ANOVA). The results indicate that privacy nudges negatively influence information disclosure behavior. The privacy framing nudge has a direct and indirect effect, while the privacy social nudge has only an indirect effect on the information disclosure behavior. Individuals exposed to the privacy nudges perceived the stimulus as threat. Importantly, both nudges spark negative feelings. With this research, we are contributing to the discussion of what drives privacy nudge effectiveness and influences information disclosure behavior in digital work environments.
    Type: conference paper
    Journal: Hawaii International Conference on System Sciences (HICSS)
    Volume: 54
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/110815
  • Publication
    Designing Effective Privacy Nudges in Digital Environments: A Design Science Research Approach
    (Springer, Cham - Vinton G. Cerf Award., 2020)
    Barev, Torben Jan
    ;
    Janson, Andreas
    ;
    Leimeister, Jan Marco
    When using digital technologies, various data traces are left behind for collection, storage and analysis. Innovative solutions for information systems are needed that mitigate privacy risks and foster information privacy. One mechanism to achieve this is using privacy nudges. Nudges are a concept from behavioral eco-nomics to influence individual’s decisions. However, many nudges show low or at least less effects than choice architects hope for and expect. Therefore, this de-sign science research (DSR) project focusses on developing evidence-based de-sign principles for privacy nudges to improve their effectiveness and pave the way for more privacy sensitive IT systems. In this context, we adopt a DSR ap-proach from Vaishnavi & Kuechler. From a theoretical perspective, we are con-tributing to the discussion of what drives privacy sensitive behavior. We extend generic nudge design models, making them applicable in the context of data dis-closure. For practitioners, we provide guidance on how to design and implement effective privacy nudges in the user interface of digital work systems.
    Type: conference paper
    Journal: International Conference on Design Science Research in Information Systems and Technology (DESRIST)
    URL: https://link.springer.com/chapter/10.1007/978-3-030-64823-7_37
    DOI: https://doi.org/10.1007/978-3-030-64823-7_37
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/112558
  • Publication
    Understanding User Preferences of Digital Privacy Nudges – A Best-Worst Scaling Approach
    ( 2020-01)
    Schöbel, Sofia
    ;
    Barev, Torben Jan
    ;
    Janson, Andreas
    ;
    Hupfeld, Felix
    ;
    Leimeister, Jan Marco
    Digital nudging in privacy has become more important to protect users of information systems while working with privacy-related data. Nudging is about altering a user’s behavior without forbidding any options. Several approaches exist to “nudge” users to change their behavior. Regarding the usage of digital privacy nudges, research still has to understand the meaning and relevance of individual nudges better. Therefore, this paper compares the preferences of users for different digital nudges. To achieve this goal, it presents the results of a so-called best-worst scaling. This study contributes to theory by providing a better understanding of user preferences regarding design variations of digital nudges. We support practitioners by giving implications on how to design digital nudges in terms of user preferences.
    Type: conference paper
    Journal: Hawaii International Conference on System Sciences (HICSS)
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/112508