🚀Ongoing work

• Unpacking GenAI use: Archetypes and their effects on performance: Observational panel study deriving a typology for GenAI adoption archetypes and linking it to performance while testing training as a moderator
• A Large-Scale Quasi-Experimental Study across Firms: study examining the business impact of AI augmentation at the individual, team, and organizational levels
• Who the User Is Matters: Experimental evidence on user diversity, performance, and trust in AI: Testing how user diversity shapes performance, trust, and reliance on AI across task types

🧠 Topics of Interest

• Trust in AI and Human-AI Interaction: Investigating how user diversity, confidence, explainability, and AI likability shape trust, reliance calibration, and oversight in human–AI collaboration
• Productivity Gains Across Levels: Examining the causal and cross-level effects of AI assistance on productivity from individual task performance to team throughput and organisational outcomes
• Cognitive and Behavioural Mechanisms: Exploring the role of cognitive flexibility, overconfidence, and behavioural adaptation as mediators of effective Human-AI Interaction
• Task- and Context-Dependence of AI ROI: Understanding how task characteristics (routine vs. uncertain, high- vs. low-stakes) and contextual moderators (uncertainty, stakes, baseline expertise) influence the net value of AI augmentation