Abstract
In the past, the selection of content has been done manually. Nowadays, owing to the new generation of social recommender systems, the automated aggregation of content, based on social information from social networks, might become possible. Social ties provide information about the underlying structure of social networks. This information, integrated into a system, might affect a user's evaluation of a specific recommendation. However, there is no research about the integration of social ties and other determinants that could affect the value of a recommendation. We developed a research model and tested it in an online experiment using Facebook data for the use case of online news with 193 participants. The structural equation model results show that a strong tie relationship has positive influence on the value of a recommendation. The credibility of the recommending person and the recommendation's media source affect the value of a recommendation as well.
Dokumententyp: | Konferenzbeitrag (Paper) |
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Fakultät: | Betriebswirtschaft > Institut für Digitales Management und Neue Medien |
Themengebiete: | 300 Sozialwissenschaften > 330 Wirtschaft |
Sprache: | Englisch |
Dokumenten ID: | 104776 |
Datum der Veröffentlichung auf Open Access LMU: | 17. Jul. 2023, 09:19 |
Letzte Änderungen: | 17. Jul. 2023, 09:19 |