Managing Organizational Cyber Security – The Distinct Role of Internalized Responsibility

Desirable user behavior is key to cyber security in organizations. However, a comprehensive overview on how to manage user behavior effectively, in order to support organizational cyber security, is missing. Building on extant research to identify central components of organizational cyber security management and on a qualitative analysis based on 20 semi-structured interviews with users and IT-Managers of a European university, we present an integrated model on this issue. We contribute to understanding the interrelations of namely user awareness, user IT-capabilities, organizational IT, user behavior, and especially internalized responsibility and relation to organizational cyber security

Opening the Black-Box of AI: Challenging Pattern Robustness and Improving Theorizing through Explainable AI Methods

Machine Learning (ML) algorithms, as approach to Artificial Intelligence (AI), show unprecedented analytical capabilities and tremendous potential for pattern detection in large data sets. Despite researchers showing great interest in these methodologies, ML remains largely underutilized, because the algorithms are a black-box, preventing the interpretation of learned models. Recent research on explainable artificial intelligence (XAI) sheds light on these models by allowing researchers to identify the main determinants of a prediction through post-hoc analyses. Thereby, XAI affords the opportunity to critically reflect on identified patterns, offering the opportunity to enhance decision making and theorizing based on these patterns. Based on two large and publicly available data sets, we show that different variables within the same data set can generate models with similar predictive accuracy. In exploring this issue, we develop guidelines and recommendations for the effective use of XAI in research and particularly for theorizing from identified patterns