My research delves into the intricacies of organizational systems and processes at the intersection of management science, organization science, complexity, business informatics, and economics. To study and comprehend the complex dynamics within organizations, I use simulation-based techniques, specifically agent-based modeling and simulation. My research aims to uncover the impact of incentives, task allocation, collaboration, and autonomy on organizational performance, and to examine the relationships between individuals, teams, and organizations. I am also interested in exploring the interplay of social norms and other factors on organizational behavior. To gain a more comprehensive understanding of the phenomena under study, I employ method triangulation. On this homepage, you will find information about some of my research interests.
Designing organizations for (self-organized) bottom-up task allocation
This research project focuses on designing organizations for (self-organized) bottom-up task allocation, examining ways to create structures and processes that allow for employees to be involved in the task allocation process. This approach to organizational design emphasizes the empowerment of employees to take ownership of their work and make decisions about what tasks they will work on, rather than relying on top-down management to dictate assignments. By enabling bottom-up task allocation, organizations can foster greater innovation, adaptability, and employee engagement. The research likely examines the impact on employee motivation, productivity and overall performance by implementing bottom-up task allocation design, and explore the factors that are conducive for bottom-up task allocation to be successful, also it will look at the best practices, challenges and solutions for implementing bottom-up task allocation in an organization.
Validation and verification of agent-based models
I am interested in a multi-method research approach to validate and verify agent-based models, particularly their behavioral assumptions. The approach involves using both experimental research and simulation-based experiments to examine the behavior of the agents and the accuracy of the model. The experimental research will involve collecting data on the behavior of individuals or groups in a controlled environment, while simulation-based experiments will enable researchers to test the model under different scenarios and conditions. By using a multi-method research approach, researchers can triangulate data from different sources to gain a more complete understanding of the agents’ behavior and the model’s accuracy. The research will likely examine the impact of these models on various domains, such as economics, social sciences, and engineering, and explore the best practices and challenges in the validation and verification of agent-based models.
Agentization of (micro-) economic models
This research project aims to conduct a systematic analysis of the simplifying assumptions made in standard agency theory models, with a focus on assessing the extent to which these assumptions limit the derived contracts’ applicability in situations where they do not hold. Specifically, the project will focus on problems studied within the hidden-action framework and will aim to quantify the “costs” of these assumptions in terms of lost utility for the principal. The project aims to address limitations in the agency theory literature, which is suspected to fail in explaining empirical phenomena and to focus on problems of little substantive interest, which could be dangerous if problems in organizational settings are to be solved. The project was funded by the Jubiläumsfond of the Oesterreichische Nationalbank.
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.