Fake news undermines individuals’ ability to make informed decisions. However, the theoretical understanding of how users assess online news as real or fake has thus far remained incomplete. In particular, previous research cannot explain why users fall for fake news inadvertently and despite careful thinking. In this work, we study the role of affect when users assess online news as real or fake. We employ NeuroIS measurements as a complementary approach beyond self-reports, which allows us to capture affective responses in situ, i.e., directly in the moment they occur. We draw upon cognitive dissonance theory, which suggests that users experiencing affective responses avoid unpleasant information to reduce psychological discomfort. In our NeuroIS experiment, we measured affective responses based on electrocardiography and eye tracking. We find that lower heart rate variability and shorter mean fixation duration are associated with greater perceived fakeness and a higher probability of incorrect assessments, thus providing evidence of affective information processing. These findings imply that users may fall for fake news automatically and without even noticing. This has direct implications for information systems (IS) research and practice as effective countermeasures against fake news must account for affective information processing.

Urban mobility is currently undergoing three fundamental transformations with the sharing economy, electrification, and autonomous vehicles changing how people and goods move across cities. In this paper, we demonstrate the valuable contribution of decision support systems that combine data-driven analytics and simulation techniques in understanding complex systems such as urban transportation. Using the city of Berlin as a case study, we show that shared, autonomous electric vehicles can substantially reduce resource investments while keeping service levels stable. Our findings inform stakeholders on the trade-off between economic and sustainability-related considerations when fostering the transition to sustainable urban mobility.

In this paper, we analyze possible interferences that occur when developing information systems for cyberphysical systems – hybrids containing legacy technical components and added IT modules. In particular, the enhancement of electricity systems through IT components, resulting in smart grid and smart home solutions, is experiencing increasing adoption rates in advanced and developing countries alike. Due to the substantial interdependence with and reliance on often decades-old legacy infrastructure, these cyberphysical systems present particular opportunities and challenges for the associated information systems. We propose that one channel through which information systems improve cyberphysical systems is by enhancing the components of the legacy system with new features. Introducing the Design–Interference Model, we conceptualize IS design for cyberphysical systems and outline possible obstacles and interferences associated with it. Using a simulation experiment for a smart grid application as a showcase, we derive insights into how to address these challenges. We condense these results into actionable advice that, first, seeks to validate existing guidelines for design-oriented research, second, extends and focuses them on cyberphysical system applications and, third, provides guidance to researchers and practitioners in this increasingly relevant area of information system development.