Look what I'm interested in! Toward a better understanding of how personalization and self-reference drive news sharing

Abstract

Along with the continuing shift from traditional to digital news consumption, many news consumers share news via social networks and messaging services. Hence, news providers benefit from an increase in user involvement and a growing awareness of their news offerings. Although personalizing digital news offerings has become common practice, we know little about how personalization affects news sharing. Building on the stimulus-organism-response model, we propose a comprehensive framework to investigate how personalization and self-referential cues impact users' sharing intention mediated by their cognitive and affective reactions. To test our research model, we conduct an experiment with a fictitious news application and analyze the results using partial least squares structural equation modeling. The results reveal that personalization and self-reference impact users' perceived preference fit and perceived enjoyment, which in turn drive news sharing. The findings have important implications for researchers and news providers.