The rapid diffusion of generative AI into academic workflows has created a structural tension in scholarly publishing. On one side, AI offers genuine productivity gains — literature synthesis, writing assistance, code generation. On the other hand, it introduces systemic risks that journals were never designed to manage, such as synthetic data, ghostwritten manuscripts, fabricated citations, and automated reviews that mimic but do not replicate critical scholarly judgment. Information Systems (IS), as a discipline that studies technology and organizations, occupies a uniquely reflexive position in this debate — IS scholars are simultaneously subject to these pressures and among the best-placed to theorize and respond to them.
Medicine is defined by uncertainty. Physicians routinely face clinical scenarios where their immediate knowledge falls short, and they turn to colleagues, databases, or literature to fill the gap. Generative AI (GenAI) introduces a novel advisory source to this workflow, combinin…
Abstract Asynchronous online discussions (AODs) are central to graduate online education, yet online students' social presence perceptions decrease over time, and learners with weaker peer-interaction experience the sharpest declines (Castellanos-Reyes, Richardson, & Maeda, 2024;…
Artificial intelligence (AI) has become a major focus of organizational strategy, public debate, and policy concern, even as its capabilities and risks remain uncertain. As a general-purpose technology, AI spans industries, labor markets, and regulatory domains, making its meanin…
Generative and agentic AI is rapidly reshaping how knowledge workers think, learn, and produce — lifting productivity substantially, with the largest gains concentrated among novices and lower-skilled workers (Brynjolfsson et al., 2025). Yet the same dynamic raises a deeper quest…
Diagnostic AI models for breast imaging increasingly achieve strong predictive performance, yet clinical adoption remains limited when systems are perceived as opaque. This challenge, known as algorithmic aversion, is especially critical in oncology workflows where clinicians mus…
Hierarchical component modelling (HCM) has become an indispensable analytical strategy in information systems (IS) research for representing multi-componential phenomena, such as technology readiness, organisational capability, user experience, and digital transformation. Despite…