Fakultät Wirtschafts- und Sozialwissenschaften

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Die Fakultät vereint Forschung und moderne Lehre nach internationalen Standards. Das Hohenheimer Modell verzahnt dabei betriebs- und volkswirtschaftliche, sozial- und rechtswissenschaftliche Aspekte.

Homepage: https://wiso.uni-hohenheim.de/

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Anger: The misunderstood and mismanaged workplace emotion
(2025) Umbra, Robin; Fasbender, Ulrike
Anger is a familiar yet often misunderstood and mismanaged emotion in organizational settings, commonly viewed as a purely negative force to be mitigated. This dissertation challenges such reductive perspectives by proposing a comprehensive reconceptualization of workplace anger as a potentially constructive emotion. It argues that, when properly understood and managed, anger can enhance employee productivity and well-being. Through a systematic examination of the antecedents, characteristics, and outcomes of anger in the workplace, this work introduces new theoretical frameworks and empirically validated strategies for harnessing anger’s positive potential. The dissertation is structured into three main sections that collectively advance the understanding of workplace anger. Chapter 1 establishes a foundational understanding by developing and validating a new State-Trait Anger Scale tailored for organizational research. This scale addresses limitations in existing measures by incorporating advanced psychometric techniques and a cross-cultural lens, revealing that workplace anger is influenced by both individual traits and situational factors, with significant variations across cultural contexts. A meta-analytic review follows, synthesizing the antecedents, concomitants, and consequences of workplace anger. The findings indicate that anger often arises from perceived negative workplace events and blame appraisals, which can primarily lead to destabilizing reactions. Chapter 2 advances the theoretical framework by introducing a novel perspective that links workplace anger to morality and perceived moral discrepancies. Through a series of empirical studies—including experience sampling, vignette experiments, and egocentric network analysis—this research demonstrates that anger frequently emerges from perceived transgressions of moral expectations in workplace interactions. The dissertation presents the Interaction Discrepancy Model, an innovative theoretical framework that integrates cognitive, social, and moral dimensions to better understand the dynamics of anger. This model elucidates how anger, a latent, morally and hedonically non-valanced construct, can motivate change-oriented behaviors aimed at rectifying moral discrepancies. Chapter 3 builds on these theoretical insights by developing practical strategies for constructive anger management in organizations. The research contrasts traditional mitigation-oriented strategies—such as suppression/rumination, avoidance, diffusion, and seeking social support—with constructive, approach-oriented strategies like confrontation and assertion. It shows that when anger is channeled appropriately through these constructive strategies, it can enhance both individual productivity and well-being. The empirical evidence further supports these findings, demonstrating that change-oriented strategies for managing anger are more effective in achieving work-related goals and maintaining well-being than mitigation-oriented approaches. This dissertation makes significant contributions to the fields of organizational psychology and organizational behavior by reconceptualizing workplace anger as a complex construct with both constructive and destructive potential. It introduces an empirically robust anger measurement tool that enhances research precision by addressing gaps in existing scales and incorporating advanced psychometric techniques. It also provides a meta-analytic overview of anger dynamics, offering a comprehensive synthesis of the antecedents, concomitants, and outcomes of anger in workplace settings. Furthermore, the dissertation offers theoretical advancements in the study of anger and emotions more broadly, integrating cognitive, social, and moral dimensions to provide a deeper understanding of emotional dynamics in organizational contexts. Additionally, it presents evidence-based strategies for practitioners to harness anger’s constructive potential, demonstrating how appropriate management of anger can lead to enhanced productivity and well-being. By challenging the conventional view of anger, this research opens new avenues for theory, practice, and future research, suggesting that anger, when understood and managed appropriately, can be a positive force in organizations.
Augmented reality marketing and consumer-brand relationships: how closeness drives brand love
(2024) Rauschnabel, Philipp A.; Hüttl‐Maack, Verena; Ahuvia, Aaron C.; Schein, Katrin E.
Marketers use augmented reality (AR) to place virtual brand-related information into a consumer's physical context. Grounded in the literature on AR, brand love, metaphor theory, and closeness as interpreted by the neural theory of language, the authors theorize that branded AR content can reduce the perceived physical, spatial distance between a consumer and a brand. This perceived closeness subsequently drives the closeness of the emotional relationship in the form of brand love. Two empirical studies validate this framework. Study 1 shows that using an AR app (vs. non-AR) increases the perceived physical closeness of the brand, which in turn drives brand love (i.e., relationship closeness). Study 2 replicates this finding in a pre-/post-use design. Here, high levels of local presence (i.e., the extent to which consumers perceive a brand as actually being present in their physical environment) drive perceived physical closeness, which leads to brand love. We also find that AR's power to generate brand love increases when the consumer is already familiar with the brand. We discuss managerial implications for AR marketing today and in a metaverse future in which AR content might be prevalent in consumers' everyday perceptions of the real world.
Comparing cars with apples? Identifying the appropriate benchmark countries for relative ecological pollution rankings and international learning
(2021) Hartmann, Dominik; Ferraz, Diogo; Bezerra, Mayra; Pyka, Andreas; Pinheiro, Flávio L.
One of the most difficult tasks that economies face is how to generate economic growth without causing environmental damage. Research in economic complexity has provided new methods to reveal structural constraints and opportunities for green economic diversification and sophistication, as well as the effects of economic complexity on environmental pollution indicators. However, no research so far has compared the ecological efficiency of countries with similar productive structures and levels of economic complexity, and used this information to identify the best learning partners. This matters, because there are substantial differences in the environmental damage caused by the same product in different countries, and green diversification needs to be complemented by substantial efficiency improvements of existing products. In this article, we use data on 774 different types of exports, CO2 emissions, and the ecological footprint of 99 countries to create first a relative ecological pollution ranking (REPR). Then, we use methods from network science to reveal a benchmark network of the best learning partners based on country pairs with a large extent of export similarity, yet significant differences in pollution values. This is important because it helps to reveal adequate benchmark countries for efficiency improvements and sustainable production, considering that countries may specialize in substantially different types of economic activities. Finally, the article i) illustrates large efficiency improvements within current global output levels, ii) helps to identify countries that can best learn from each other, and iii) improves the information base in international negotiations for the sake of a cleaner global production system.
The double-edged dynamics of social comparisons: micro-level drivers of employees’ knowledge behaviors
(2025) Rinker, Laura; Fasbender, Ulrike
Amidst worldwide developments such as globalization, workforce aging, and the accelerating pace of advancements, organizations depend on effective knowledge flows to maintain competitive and enable innovation. Because interpersonal knowledge exchange is central to organizational knowledge management, organizations must gain an understanding of what drives individual knowledge behaviors. This cumulative dissertation offers a timely investigation of social comparisons as critical socio-cognitive underpinnings of such knowledge behaviors. The underlying research seeks to deepen the understanding of the micro-level drivers of knowledge behaviors by tracing them back to employees’ social comparison experiences. The first manuscript combines the identification-contrast model of social comparisons with informal workplace learning theorizing to examine the social-cognitive roots of workplace learning. Specifically, it considers how employees’ emotionally charged (un)favorability perceptions of their social comparisons guide their daily engagement in narrow and broad informal learning behaviors through reflection processes focusing on successes or failures. The hypothesized model is tested using a ten-day experience sampling study (NLevel 2 = 175 employees, NLevel 1 = 1,256 employee-day observations). Results demonstrate that the different types of reflection translate both favorable and unfavorable social comparison experiences into learning-oriented knowledge behaviors. The findings additionally stress the moderating influence of organizational support. The second manuscript joins social comparison and stress appraisal theories to investigate the ambivalent potential of upward comparisons as work stressors. Drawing from the challenge-hindrance stress framework, it probes a dual pathway model connecting upward social comparisons with different knowledge behaviors through an approach pathway (via challenge appraisal) and an avoidance pathway (via hindrance appraisal). The hypotheses are tested based on two experimental studies with employees (NStudy 1 = 206, NStudy 2 = 414). Finding no support for the approach pathway, the research identifies hindrance appraisals as a cognitive mechanism to explain how upward comparisons harm knowledge flows. However, these adverse effects are mitigated by an between the focal employee and the comparison target. The third manuscript integrates social comparison frameworks and affective events theory to examines the daily emotional complexities of social comparisons. It seeks to clarify how the multiple facets of daily social comparisons can lead to both facilitative and harmful behavioral reactions, probing the mediating effect of discrete social comparison-induced emotions. The findings from a ten-day experience sampling study (NLevel 2 = 155 employees, NLevel 1 = 960 employee-day observations) demonstrate that daily social comparisons are linked to knowledge behaviors via inspiration, envy, and sympathy. In addition, the results reveal the complementary effects of the two cardinal social comparison axes (i.e., horizontal and vertical). In conclusion, this dissertation establishes social comparisons as a multi-faceted socio-cognitive antecedent of employees’ knowledge behaviors, providing novel insights into cognitive and emotional underpinnings and multi-level boundary conditions. Offering a more holistic perspective of social comparisons and their impact on knowledge behaviors, this work opens avenues for scholars to develop a deeper understanding of the socio-cognitive roots of organizational behavior. Moreover, the findings equip practitioners with actionable insights to utilize social comparisons as micro-level drivers, instead of barriers, of knowledge flows.
Predictor preselection for mixed‐frequency dynamic factor models: a simulation study with an empirical application to GDP nowcasting
(2025) Franjic, Domenic; Schweikert, Karsten; Franjic, Domenic; Core Facility Hohenheim and Institute of Economics, University of Hohenheim, Stuttgart, Germany; Schweikert, Karsten; Core Facility Hohenheim and Institute of Economics, University of Hohenheim, Stuttgart, Germany
We investigate the performance of dynamic factor model nowcasting with preselected predictors in a mixed‐frequency setting. The predictors are selected via the elastic net as it is common in the targeted predictor literature. A simulation study and an application to empirical data are used to evaluate different strategies for variable selection, the influence of tuning parameters, and to determine the optimal way to handle mixed‐frequency data. We propose a novel cross‐validation approach that connects the preselection and nowcasting step. In general, we find that preselecting provides more accurate nowcasts compared with the benchmark dynamic factor model using all variables. Our newly proposed cross‐validation method outperforms the other specifications in most cases.

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