Artificial intelligence (AI) is being increasingly integrated into enterprises to foster collaboration within humanmachine teams and assist employees with work-related tasks. However, introducing AI may negatively impact employees’ identifications with their jobs as AI is expected to fundamentally change workplaces and professions, feeding into individuals’ fears of being replaced. To broaden the understanding of the AI identity threat, the findings of this study reveal three central predictors for AI identity threat in the workplace: changes to work, loss of status position, and AI identity predicting AI identity threat in the workplace. This study enriches information systems literature by extending our understanding of collaboration with AI in the workplace to drive future research in this field. Researchers and practitioners understand the implications of employees’ identity when collaborating with AI and comprehend which factors are relevant when introducing AI in the workplace.

The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.

Information systems such as social media strongly influence public opinion formation. Additionally, communication on the internet is shaped by individuals and organisations with various aims. This environment has given rise to phenomena such as manipulated content, fake news, and social bots. To examine the influence of manipulated opinions, we draw on the spiral of silence theory and complex adaptive systems. We translate empirical evidence of individual behaviour into an agent-based model and show that the model results in the emergence of a consensus on the collective level. In contrast to most previous approaches, this model explicitly represents interactions as a network. The most central actor in the network determines the final consensus 60–70% of the time. We then use the model to examine the influence of manipulative actors such as social bots on public opinion formation. The results indicate that, in a highly polarised setting, depending on their network position and the overall network density, bot participation by as little as 2–4% of a communication network can be sufficient to tip over the opinion climate in two out of three cases. These findings demonstrate a mechanism by which bots could shape the norms adopted by social media users.