%0 Conference Paper %1 ls_leimeister %A Schöbel, Sofia %A Barev, Torben Jan %A Janson, Andreas %A Hupfeld, Felix %A Leimeister, Jan Marco %B Hawaii International Conference on System Sciences (HICSS) %D 2020 %K Best_Worst_Scaling Digital_Nudging Nudging Privacy_Nudges User_Preferences itegpub pub_aja pub_fhu pub_jml pub_nudger pub_ssc pub_tba %T Understanding User Preferences of Digital Privacy Nudges – A Best-Worst Scaling Approach %U http://pubs.wi-kassel.de/wp-content/uploads/2020/03/JML_769.pdf %X 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.

@inproceedings{ls_leimeister, abstract = {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.}, added-at = {2019-09-17T12:59:46.000+0200}, author = {Schöbel, Sofia and Barev, Torben Jan and Janson, Andreas and Hupfeld, Felix and Leimeister, Jan Marco}, biburl = {https://www.bibsonomy.org/bibtex/262e8e8672878bbc68de7a14c4c6f7a2d/ls_leimeister}, booktitle = {Hawaii International Conference on System Sciences (HICSS)}, eventdate = {January 2020}, eventtitle = {Hawaii International Conference on System Sciences (HICSS)}, interhash = {d2ba9153179fd1b591c188d35c5e56e5}, intrahash = {62e8e8672878bbc68de7a14c4c6f7a2d}, keywords = {Best_Worst_Scaling Digital_Nudging Nudging Privacy_Nudges User_Preferences itegpub pub_aja pub_fhu pub_jml pub_nudger pub_ssc pub_tba}, timestamp = {2021-07-14T13:12:05.000+0200}, title = {Understanding User Preferences of Digital Privacy Nudges – A Best-Worst Scaling Approach}, url = {http://pubs.wi-kassel.de/wp-content/uploads/2020/03/JML_769.pdf}, venue = {Maui, Hawaii, USA.}, year = 2020 }