Understanding User Preferences of Digital Privacy Nudges – A Best-Worst Scaling Approach
| Autor: | Torben Jan Barev, Sofia Schöbel, Andreas Janson, Jan Marco Leimeister, Felix Hupfeld |
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| Rok vydání: | 2020 |
| Předmět: |
Information management
Nudge theory business.industry Computer science 05 social sciences Internet privacy computer science information management Nudging User Preferences Privacy Nudges Digital Nudging Best Worst Scaling 020207 software engineering 02 engineering and technology GeneralLiterature_MISCELLANEOUS Best–worst scaling 0202 electrical engineering electronic engineering information engineering Information system 0501 psychology and cognitive sciences Relevance (information retrieval) Meaning (existential) other research area business social sciences 050107 human factors |
| Zdroj: | HICSS |
| ISSN: | 2572-6862 |
| DOI: | 10.24251/hicss.2020.479 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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