Should Personalization Be Optional on Paid Streaming Platforms?: An Experiment on User Preferences for Personalization or Increased Data Privacy
Video
Team Information
Team Members
Bengusu Ozcan, Graduate of MA in Quantitative Methods in Social Sciences, Graduate School of Arts and Sciences, Columbia University
Faculty Advisor: Rachel Cummings, Assistant Professor, Industrial Engineering and Operations Research, Columbia University
Abstract
Users' willingness to pay for personal data privacy or sell their personal data to big internet platforms leveraging online advertising have been researched extensively. Despite not being researched as much, paid streaming platforms, such as Netflix or Spotify, also utilize personal data to enable their personalized services. Unlike receiving personalized ads from free internet services, users of these platforms pay for the service and also enable the company to use their personal data to receive personalized recommendations. However, there are not many studies comparing the value the user receives from personalization versus the value the platform extracts from users' data. We conducted an online experiment assuming that if people knew the extent to which their data is utilized by paid streaming platforms, their willingness to pay for these services may change, hence these platforms may consider personalization as an optional feature. Our results did not find strong evidence supporting this hypothesis. Our findings indicate that users may not reflect their true behavior in studies about online personal data preferences, which is addressed by acceptability gap or privacy paradox in the literature.
Team Lead Contact
Bengusu Ozcan: bo2297@columbia.edu
