Unnar Theódórsson

Unnar Theódórsson is a Postdoc at the Department of Organization, Copenhagen Business School (CBS), and holds a part-time Assistant Professor position (20% appointment) at the University of Iceland. His research examines how organizations approach human resources under conditions of uncertainty, complexity, and change, with particular focus on talent identification, nurturing, and retention as both management processes and social-political practices.

Dr. Theódórsson's primary research interests include:

• Talent Management - investigating what counts as talent, who decides, and on what basis within organizational contexts
• Human Resource Management - especially regulatory compliance challenges and technological innovation impacts
• Business Ethics - examining organizational nepotism and cronyism in Nordic banking sectors
• Qualitative and Mixed Methods - with strong expertise in ethnography and in-depth interviewing
• Career Development and Onboarding - particularly in public sector frontline roles through collaborations like Herlev Municipality
• Strategic HR and Risk Management - balancing organizational demands with individual employee aspirations

His recent research demonstrates a concentrated focus on banking sector talent management, analyzing how financial institutions navigate skilled talent shortages amidst regulatory pressures and technological disruption. Through methodologically rigorous qualitative and mixed methods approaches, he investigates talent identification frameworks, retention strategies, and the social-political dimensions of HR practices.

Dr. Theódórsson's collaborative work with researchers like Tom Gudlaugsson and Sigrun Gudmundsdottir has produced significant contributions to understanding Nordic banking HR practices, with particular attention to how organizations create sustainable work trajectories while maintaining regulatory compliance. His growing interest in technology's intersection with traditional HR functions suggests evolving research directions examining data-driven talent management practices.