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Institute
- Wirtschaftswissenschaften (856) (remove)
Algorithmic management
(2022)
Algorithmic management
(2022)
The organisation of legislative chambers and the consequences of parliamentary procedures have been among the most prominent research questions in legislative studies. Even though democratic elections not only lead to the formation of a government but also result in an opposition, the literature has mostly neglected oppositions and their role in legislative chambers. This paper proposes to fill this gap by looking at the legislative organisation from the perspective of opposition players. The paper focuses on the potential influence of opposition players in the policy-making process and presents data on more than 50 legislative chambers. The paper shows considerable variance of the formal power granted to opposition players. Furthermore, the degree of institutionalisation of opposition rights is connected to electoral systems and not necessarily correlated with other institutional characteristics such as regime type or the size of legislative chambers.
This article merges theoretical literature on non-controlling minority shareholdings (NCMS) in a coherent model to study the effects of NCMS on competition and collusion. The model encompasses both the case of a common owner holding shares of rival firms as well as the case of cross ownership among rivals. We find that by softening competition, NCMS weaken the sustainability of collusion under a greater variety of situations than was indicated by earlier literature. Such effects exist, in particular, in the presence of an effective competition authority.
Volatile supply and sales markets, coupled with increasing product individualization and complex production processes, present significant challenges for manufacturing companies. These must navigate and adapt to ever-shifting external and internal factors while ensuring robustness against process variabilities and unforeseen events. This has a pronounced impact on production control, which serves as the operational intersection between production planning and the shop- floor resources, and necessitates the capability to manage intricate process interdependencies effectively. Considering the increasing dynamics and product diversification, alongside the need to maintain constant production performances, the implementation of innovative control strategies becomes crucial.
In recent years, the integration of Industry 4.0 technologies and machine learning methods has gained prominence in addressing emerging challenges in production applications. Within this context, this cumulative thesis analyzes deep learning based production systems based on five publications. Particular attention is paid to the applications of deep reinforcement learning, aiming to explore its potential in dynamic control contexts. Analysis reveal that deep reinforcement learning excels in various applications, especially in dynamic production control tasks. Its efficacy can be attributed to its interactive learning and real-time operational model. However, despite its evident utility, there are notable structural, organizational, and algorithmic gaps in the prevailing research. A predominant portion of deep reinforcement learning based approaches is limited to specific job shop scenarios and often overlooks the potential synergies in combined resources. Furthermore, it highlights the rare implementation of multi-agent systems and semi-heterarchical systems in practical settings. A notable gap remains in the integration of deep reinforcement learning into a hyper-heuristic.
To bridge these research gaps, this thesis introduces a deep reinforcement learning based hyper- heuristic for the control of modular production systems, developed in accordance with the design science research methodology. Implemented within a semi-heterarchical multi-agent framework, this approach achieves a threefold reduction in control and optimisation complexity while ensuring high scalability, adaptability, and robustness of the system. In comparative benchmarks, this control methodology outperforms rule-based heuristics, reducing throughput times and tardiness, and effectively incorporates customer and order-centric metrics. The control artifact facilitates a rapid scenario generation, motivating for further research efforts and bridging the gap to real-world applications. The overarching goal is to foster a synergy between theoretical insights and practical solutions, thereby enriching scientific discourse and addressing current industrial challenges.
States, in their conflicts with militant groups embedded in civilian populations, often resort to policies of collective punishment to erode civilian support for the militants. We attempt to evaluate the efficacy of such policies in the context of the Gaza Strip, where Israel's blockade and military interventions, purportedly intended to erode support for Hamas, have inflicted hardship on the civilian population.
We combine Palestinian public opinion data, Palestinian labor force surveys, and Palestinian fatalities data, to understand the relationship between exposure to Israeli policies and Palestinian support for militant factions.
Our baseline strategy is a difference-in-differences specification that compares the gap in public opinion between the Gaza Strip and the West Bank during periods of intense punishment with the gap during periods when punishment is eased. Consistent with previous research, we find that Palestinian fatalities are associated with Palestinian support for more militant political factions. The effect is short-lived, however, dissipating after merely one quarter.
Moreover, the blockade of Gaza itself appears to be only weakly associated with support for militant factions. Overall, we find little evidence to suggest that Israeli security policies toward the Gaza Strip have any substantial lasting effect on Gazan support for militant factions, neither deterring nor provoking them relative to their West Bank counterparts.
Our findings therefore call into question the logic of Israel's continued security policies toward Gaza, while prompting a wider re-examination of the efficacy of deterrence strategies in other asymmetric conflicts.
One for all, all for one
(2022)
We propose a conceptual model of acceptance of contact tracing apps based on the privacy calculus perspective. Moving beyond the duality of personal benefits and privacy risks, we theorize that users hold social considerations (i.e., social benefits and risks) that underlie their acceptance decisions. To test our propositions, we chose the context of COVID-19 contact tracing apps and conducted a qualitative pre-study and longitudinal quantitative main study with 589 participants from Germany and Switzerland. Our findings confirm the prominence of individual privacy calculus in explaining intention to use and actual behavior. While privacy risks are a significant determinant of intention to use, social risks (operationalized as fear of mass surveillance) have a notably stronger impact. Our mediation analysis suggests that social risks represent the underlying mechanism behind the observed negative link between individual privacy risks and contact tracing apps' acceptance. Furthermore, we find a substantial intention–behavior gap.
Advancing digitalization is changing society and has far-reaching effects on people and companies. Fundamental to these changes are the new technological possibilities for processing data on an ever-increasing scale and for various purposes. The availability of large and high-quality data sets, especially those based on personal data, is crucial. They are used either to improve the productivity, quality, and individuality of products and services or to develop new types of services. Today, user behavior is tracked more actively and comprehensively than ever despite increasing legal requirements for protecting personal data worldwide. That increasingly raises ethical, moral, and social questions, which have moved to the forefront of the political debate, not least due to popular cases of data misuse. Given this discourse and the legal requirements, today's data management must fulfill three conditions: Legality or legal conformity of use and ethical legitimacy. Thirdly, the use of data should add value from a business perspective. Within the framework of these conditions, this cumulative dissertation pursues four research objectives with a focus on gaining a better understanding of
(1) the challenges of implementing privacy laws,
(2) the factors that influence customers' willingness to share personal data,
(3) the role of data protection for digital entrepreneurship, and
(4) the interdisciplinary scientific significance, its development, and its interrelationships.
This study investigates the effect of different anticonsumption constructs on consumer wellbeing. The study assumes that people will only lower their level of consumption if doing so does not also lower personal wellbeing. More precisely, this research investigates how specific subtypes of sustainable anticonsumption (e.g., voluntary simplicity, collaborative consumption, and debt-free living) relate to different states of consumer's wellbeing (e.g., financial, psychosocial, and subjective wellbeing). This work also examines whether consumer empowerment can improve personal wellbeing and strengthen the anticonsumption wellbeing relationship. The results show that voluntarily foregoing consumption does not reduce wellbeing and consumer empowerment plays a significant role in supporting sustainable pathways to consumer wellbeing. This study reasons that empowerment improves consumer sovereignty, but may be detrimental for consumers heavily concerned about debt-free living. The present investigation concludes by proposing implications for public and consumer policymakers wishing to promote appropriate sustainable (anticonsumption) pathways to consumer wellbeing.