Suchergebnisse

Why do populist citizens oppose climate change? Thus far, data constraints limited the ability to test different theoretical mechanisms against each other. We argue that populist attitudes affect climate attitudes through two distinct channels, namely institutional trust and attitudes towards science. The former argument focuses on political institutions as the central actors in implementing climate policy. Individuals who distrust these institutions are more sceptical about climate change. The latter argument claims that populists deny climate change because they distrust the underlying climate science. According to this view, populists would view climate scientists as part of the self-serving elite that betrays the people. Utilising data from the Austrian National Election Study and structural equation modelling, we find strong support for the relationship of populism and climate attitudes via attitudes towards science and institutional trust. Populists systematically hold more negative attitudes towards science and political institutions, and consequently deny climate change.

AbstractCommunication scholars are increasingly concerned with interactions between humans and communicative agents. These agents, however, are considerably different from digital or social media: They are designed and perceived as life-like communication partners (i.e., as "communicative subjects"), which in turn poses distinct challenges for their empirical study. Hence, in this paper, we document, discuss, and evaluate potentials and pitfalls that typically arise for communication scholars when investigating simulated or non-simulated interactions between humans and chatbots, voice assistants, or social robots. In this paper, we focus on experiments (including pre-recorded stimuli, vignettes and the "Wizard of Oz"-technique) and field studies. Overall, this paper aims to provide guidance and support for communication scholars who want to empirically study human-machine communication. To this end, we not only compile potential challenges, but also recommend specific strategies and approaches. In addition, our reflections on current methodological challenges serve as a starting point for discussions in communication science on how meaning-making between humans and machines can be investigated in the best way possible, as illustrated in the concluding section.