This study examines the manifestations of sexism, racism, and classism in the output of six text-to-image generative AI systems within the constructs of power, success, and beauty. A total of 180 images were generated using three prompts for each AI tool. Our analysis focused on detecting gender, racial, and class biases, as well as age discrimination. The findings reveal an underrepresentation of women and People of Color across the generated images. Additionally, the tendency to depict women in a sexualized manner was prominent. Data also indicated a bias towards younger depictions of women relative to men and People of Color relative to white individuals. The images overwhelmingly represented individuals as belonging to a higher socioeconomic class, pointing towards a systemic bias within AI systems towards privilege.

Organizations and even entire industries rethink their processes to align them with principles of circularity. Data is a key driver in the transformation toward a circular economy. Digitalization opens up a vast solution space of new design opportunities for this transformation but also leads to possible tensions between those opportunities. We seek to advance our understanding of the tensions and challenges faced regarding data in the circular economy. Therefore, we conducted 17 interviews with experts from the automotive industry – one of the world's leading industries directly affected by the ongoing transformation – to collect possible issues from a data viewpoint. Based on 274 distinct statements (codes), we employed a dialectical inquiry approach and derived four main tensions concerning the role of data. Our results create awareness of trade-offs and support decision-making, contribute to organizations’ transformation toward a circular economy, and help to foster sustainability.

Human actions in various areas are driving carbon emissions, which have a significant impact on climate change and hence humans worldwide. Amongst other, consumer activities including online shopping exacerbate that effect. One countermeasure is to offset the carbon emissions caused. However, knowledge about the driving factors that increase consumers’ willingness to offset emissions is scarce. Deriving hypotheses from literature with the theoretical lens of Value-Attitude-Behavior Hierarchy (VABH) and using structural equation modeling, this study shows that internal drivers (environmental values, mindfulness, and response efficacy) and external drivers (social influence) significantly impact offset willingness, consequently affecting offsetting behavior. To shed light on this aspect, we conducted an correlational study. 250 Participants made purchases on a fictitious online store to investigate the underlying drivers of pro-environmental behavior. We discuss the results and derive implications for research as well as practice.