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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorChatzimparmpas, A.
dc.contributor.authorPeperzak, Jelle
dc.date.accessioned2025-04-29T00:01:31Z
dc.date.available2025-04-29T00:01:31Z
dc.date.issued2025
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/48880
dc.description.abstractAs technology evolves, so do user gratifications, requiring media platforms to evolve their features accordingly or risk being replaced. While numerous studies have applied the Uses and Gratifications (U\&G) theory to identify gratifications on Over-The-Top (OTT) platforms such as Netflix and Disney+, none that we know of explored how the platforms' features facilitate these gratifications. This study proposes `key gratifications' by capturing thematic overlaps of various gratifications from related studies. We also identified three gratifications through exploratory interviews: convenient navigability, entertainment, and social enhancement. To examine relationships between reported gratifications and perceived recommendation quality, we developed Testflix, a tool for simulating OTT interfaces with customizable recommendation algorithms. Our results show that recommendation quality has a stronger relationship with convenient navigability than with entertainment and social enhancement. Additionally, we find that all these relationships are positive and linear. We discuss the study’s efficacy and limitations and offer implications for future research on OTT-related gratifications.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectThis study investigated how different types of recommender systems influenced the ability of OTT platform users to derive the various types of gratifications they seek through interaction with the platform.
dc.titleInvestigating the Relationship between Perceived Quality of Recommended Content and User Gratifications on OTT Platforms
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsOTT platforms, Uses & Gratifications, Recommender System
dc.subject.courseuuHuman-Computer Interaction
dc.thesis.id45365


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