This dissertation explores an unintended consequence of using personalized recommendations, that is, recommendations that are targeted to an individual consumer (e.g., personalized music playlists). I conceptualize that using personalized recommender systems can impede consumers’ learning of their own preferences and tastes from product experiences. Therefore, using these systems can decrease preference clarity, which is defined as certainty about individuals’ own preferences.
For example, people may feel less certain about their own music preferences after listening to auto-generated personalized playlists. This reduced preference clarity, in turn, reduces consumer willingness to generate word-of-mouth (WOM) about their consumption experiences, such as their intent to talk about music they listened to with others, or to post social media content on their favorite musicians.
Eight studies, using correlational and experimental designs and conducted with consumers who actively use personalization services (in the fashion and music domains), support this theorization. I end with a discussion of the potential theoretical extensions of this novel finding, as well as its practical implications.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/dwmg-jz25 |
| Date | January 2023 |
| Creators | Lee, Byung Cheol |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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