Making Sense of Digital Innovations: The Role of the Material Artefact

Users’ perceptions of a material artefact hold important implications of how they make sense of a digital innovation, expressed in their technological frames about the innovation. Yet, research on sensemaking offers little insights on the role of the material artefact for shaping users’ technological frames. This paper proposes a 2x2 experiment to investigate how newness as a crucial aspect of the material artefact influences users’ frames. Based on theories of resonance, we assume that this effect is mediated by cognitive and emotional resonance. We manipulate the technical and design newness of smart speakers to investigate our research model. Our findings contribute to research on technology sensemaking by illuminating the role of the newness of the material artefact. For developers, our results indicate how users’ understanding can be shaped by embodying familiar and non-familiar cues in digital innovations

That’s Not Who I Am! Investigating the Role of Uniqueness and Belongingness for Designing Successful Personalized Recommendations

Although many firms rely on personalization to enhance the user experience of their digital service, their efforts might backfire if users feel misunderstood by the personalized offerings. So far, the psychological processes underlying the phenomenon of feeling misunderstood by personalization systems and potential means to alleviate this perception remain largely uninvestigated. Building on the psychological concepts of uniqueness and belongingness, we propose a framework to investigate how transparency impacts users’ feeling of being misunderstood by personalization systems. To test our research model, we conduct an online experiment using Spotify’s “Discover Weekly” playlist. The results show that considering not only users’ uniqueness but especially their belongingness is decisive to avoid misunderstanding. Further, we find that transparent explanations of the system’s inner workings elicit a feeling of control among users, which fosters the perception that both users’ uniqueness and belongingness are considered, resulting in less misunderstanding and continued usage

I Know How You Feel: An Investigation of Users’ Trust in Emotion-Based Personalization Systems

By leveraging recent advancements in affective computing, emotion-based personalization systems provide media offerings tailored to users’ emotions. Although emotionally intelligent systems increasingly attract attention both in research and in practice, research on users’ perceptions of emotion-based personalization is sparse. Building on the personalization-privacy trade-off, I propose a comprehensive framework to investigate how the use of affective technologies for personalization impacts users’ trust mediated by their perceived benefits and threats. To test the research model, I conduct an experiment with a fictitious music personalization system and analyze the results using partial least squares structural equation modeling. The results show that emotion-based personalization positively impacts users’ trust via perceived emotional support but simultaneously hampers trust via privacy concerns and emotional creepiness

Look What I’m Interested in! Toward a Better Understanding of How Personalization and Self-Reference Drive News Sharing

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