Organizational Behavior & Human Resource Management
Mission Statement
The research interests of this interdisciplinary section cover the areas of Human Resource Management and Organizational Behavior. Specific fields of interest include leadership; teamwork; new team-based forms of organization; the roles of hierarchy, power, status and influence; emotions in organizations; personnel diagnostics (e.g., applicant selection, employee assessment); career management and career adjustments; assessing and promoting work performance in different contexts; personality and intelligence in careers; and the scientist-practitioner gap in Human Resource Management and Organizational Behavior.
The section aims to advance the research areas of Human Resource Management and Organizational Behavior at JLU and strengthen JLU’s national and international visibility in these fields to make JLU even more attractive to young researchers. The section strives to involve both senior and early-career researchers in all academic disciplines concerned with the human being in an organizational context and/or organizational structures.
Section Head
The OB & HRM Section is lead by the following team: Dr. Katja Wehrle, Dr. Sascha Etgen, Dr. Katerina (Aikaterini Eleni) & Dr. Marco C. Ziegler.
Notes:
Responsible for this website is Marco C. Ziegler.
Budgetholder: Section Head
Current Events
Announcements WiSe 2025/26:
Prof. Malte Friese (Universität des Saarlandes),13.05.26, 14:00. Title: Estimating the Climate Change Mitigation Potential of Behavior Change Interventions. Further information follows.
Abstract:
Behavioral interventions are an important means of addressing climate change. Interventions that are more effective as indicated by a standardized effect size are presumed to have greater potential to contribute to climate change mitigation. In this Perspective, we argue that current intervention design and reporting practices do not provide robust enough evidence to inform public policy recommendations regarding the most promising behavioral interventions for addressing climate change. First, we explain why interventions should be evaluated based on estimated reductions in greenhouse gas emissions, rather than on standardized effect sizes. Second, we present a conceptual framework illustrating that, to estimate an intervention’s overall climate change mitigation potential, researchers must consider its effect, persistence over time, and scalability to large, diverse populations and contexts. We illustrate how interactions among these components affect an intervention’s overall climate change mitigation potential. Failure to consider one or more of these components impairs researchers’ ability to identify the most promising behavioral interventions. Finally, we discuss the implications for public policy and derive recommendations for designing and reporting of future behavioral interventions. These recommendations will allow public policy recommendations to be based on stronger evidence.
Short Bio:
Malte Friese is a Professor of Social Psychology at Saarland University. He studied and conducted research at several universities in Germany and abroad, including Heidelberg, Basel, Brisbane, and Amsterdam. His research interests include self-regulation, sexuality and romantic relationships, sustainability, and meta-science.
Friday, 16. January 2026, 10:00 am (s.t.) Prof. Christian Dormann (Johannes Gutenberg-Universität Mainz & Adelaide University) mit dem Titel “On an Unavoidable Academic–Practitioner Divide: The Three-Body Problem, Sequential Moderation, and Other Longitudinal Creatures“ Room: Seminargebäude II, 103.
Abstract:There are situations in which causal evidence leaves little room for doubt: increasing one variable (e.g., social support) genuinely improves another (e.g., health). Yet, paradoxically, the very same evidence may compel researchers to advise practitioners, clients, or decision-makers to reduce that beneficial factor. Such recommendations appear irrational unless one takes the temporal structure of causal processes seriously. In longitudinal research using panel data, diary studies, and intensive longitudinal designs, these paradoxes give rise to what I refer to as longitudinal creatures. They emerge when dynamic systems are analysed appropriately, but they remain largely invisible under static approaches such as multilevel models or other time-aggregated analyses. Even widely used dynamic models, including cross-lagged panel models, tend to blur a critical distinction: they conflate the data-generating causal effects with the way those effects unfold over discrete observation intervals. As a consequence, regression-based results may point in directions that differ, sometimes even in sign, from the underlying causal dynamics. Continuous-time models disentangle causal effects from their temporal manifestation. By doing so, they reveal a class of longitudinal creatures in which true causal effects coexist with discrete-time associations that lead to counterintuitive, and sometimes contradictory, practical recommendations.Continuous-time models disentangle causal effects from their temporal manifestation. By doing so, they reveal a class of longitudinal creatures in which true causal effects coexist with discrete-time associations that lead to counterintuitive, and sometimes contradictory, practical recommendations.In this talk, I provide a brief introduction to continuous-time cross-lagged panel models and present several such longitudinal creatures, including the three-body problem, sequential moderation, and hidden centrality. Together, they illustrate an often unavoidable divide between academic causal understanding and the guidance researchers ultimately provide to practitioners.