I investigate the design, dynamics, and resilience of organizational systems at the intersection of management science, complexity, business informatics, and economics. Using method triangulation anchored by agent-based modeling and simulation (ABMS), my research explores how macro-level organizational behavior emerges from micro-level interactions. My work focuses on four core areas: how digital and disruptive technologies (like advanced AI) drive organizational resilience and spread via social contagion; how management accounting and social norms shape sustainable transitions in competitive environments; how structural factors like autonomy and incentives impact performance; and how to improve the reproducibility and methodological rigor of computational models in the social sciences. On this homepage, you will find information about my current research projects and publications.
Organizational resilience and digital technologies
It is well known that control systems must enable adaptability in volatile environments. I am interested in how organizations can be steered through crises using digital technologies.
Disruptive technologies and the emergence of control practices
The application of new, disruptive technologies in organizations (e.g., advanced AI) is not neutral but rather changes organizational realities and decision-making structures. In this context, I am particularly interested in exploring the innovation potential of disruptive technologies and how they spread across corporate networks. Specifically, building on my recent work, I will focus on the roles of peer-effects, technology-related interfaces, and social contagion in the adoption of innovative control systems.
Behavioral control and sustainability
I am interested in how management accounting shapes the sustainable transition. Specifically, I am interested in (1) the tension between cooperation and competition. How can control systems (comprising formal and informal mechanisms) foster sustainable equilibria in coopetition settings?, and (2) the interplay between formal incentive mechanisms and social norms in sustainable environments.
Reproducibility in the Social Sciences
My research in this area focuses on the reproducibility of agent-based models in social sciences, emphasizing the necessity for consistent and repeatable results across various studies. This involves a detailed examination of model construction, parameter settings, and data inputs to ensure transparency and replicability. The goal is to establish protocols and best practices for documenting and sharing models, facilitating effective peer review and collaboration. This endeavor is crucial in evaluating the reproducibility of models in fields like sociology, political science, and economics, and contributes to the discourse on the reliability of computational methods in social science research. By tackling these issues, the research aims to strengthen the credibility of agent-based models as a key tool for analyzing complex social dynamics.
Agent-based modeling and simulation for management and organization science
This research project applies agent-based modeling and simulation techniques to investigate questions related to management and organization science. Agent-based modeling is a computational approach that allows researchers to create a digital representation of a system or process, where individuals (called agents) interact with each other and their environment. By simulating these interactions, researchers can gain insights into how the system operates, evolves, and responds to different scenarios. In the context of management and organization science, this project seeks to use agent-based modeling to study various phenomena such as decision making, cooperation, competition, and emergence of structures in organizations. The goal is to contribute to a better understanding of the underlying dynamics and mechanisms of organizational behavior and to provide practical recommendations for managers and practitioners. The use of this computational approach enables to explore how the different agents interact, how they make decisions, and how they influence the overall performance of the organization. It is likely that the research uses different case studies or scenarios to test the model’s predictions and to validate its results.