Wirtschaftswissenschaften
Refine
Year of publication
Document Type
- Article (1108)
- Monograph/Edited Volume (701)
- Doctoral Thesis (404)
- Working Paper (109)
- Postprint (100)
- Review (72)
- Part of a Book (19)
- Master's Thesis (17)
- Other (13)
- Report (8)
Keywords
- Entrepreneurship (13)
- Germany (10)
- Verwaltung (9)
- entrepreneurship (9)
- Delphi study (8)
- Ethik (8)
- Korruption (8)
- Evaluation (7)
- Innovation (7)
- experiment (7)
Institute
- Wirtschaftswissenschaften (2571)
- Extern (31)
- Center for Economic Policy Analysis (CEPA) (11)
- Sozialwissenschaften (3)
- Zentrum für Australienforschung (2)
- Fachgruppe Betriebswirtschaftslehre (1)
- Institut für Informatik und Computational Science (1)
- Institut für Philosophie (1)
- Institut für Physik und Astronomie (1)
- Kommunalwissenschaftliches Institut (1)
Executive education (EE) has been an established means for management education. However, due to the ever-changing business environment, progress in education technology, and new competitors, EE has been continuously evolving and can be expected to further change. Employing a three-stage international Delphi study, we identify a plausible scenario for the further development of EE over the next decade. The results suggest major changes for management training. The panel expects major shifts in teaching methods and curricula construction. Business schools are expected to increase content customization, to adapt delivery formats, and to enhance coverage of topical issues to better respond to leaders' needs.
Researchers have shown that structuring issues and organizing an agenda before a negotiation lead to improved negotiation performance. By using issue analysis, negotiators become aware of their own and their opponents' preferences on negotiation issues and are able to use this knowledge to optimize their degree of success. Following research on asymmetrical preferences in negotiations, we introduce a new approach for issue analysis that considers the identification of one-sided preferences, specifically a 0-preference for issues from one party. We conducted an experimental study to test if this type of preference for an issue (chance issue) yields strategic potential for a negotiator. We also examined whether the identification of these chance issues could be particularly relevant for a low-power party in negotiations with a power imbalance, to overcome the lower scope of action due to the weaker negotiating position. The results indicate initial verification that no preference at all for one issue could lead to higher individual performance and noneconomic outcomes. Joint performance was positively affected by 0-preference, even in unbalanced power situations.
Climate change entails an intensification of extreme weather events that can potentially trigger socioeconomic and energy system disruptions. As we approach 1 degrees C of global warming we should start learning from historical extremes and explicitly incorporate such events in integrated climate-economy and energy systems models.
We collect a network dataset of tenured economics faculty in Austria, Germany and Switzerland. We rank the 100 institutions included with a minimum violation ranking. This ranking is positively and significantly correlated with the Times Higher Education ranking of economics institutions. According to the network ranking, individuals on average go down about 23 ranks from their doctoral institution to their employing institution. While the share of females in our dataset is only 15%, we do not observe a significant gender hiring gap (a difference in rank changes between male and female faculty). We conduct a robustness check with the Handelsblatt and the Times Higher Education ranking. According to these rankings, individuals on average go down only about two ranks. We do not observe a significant gender hiring gap using these two rankings (although the dataset underlying this analysis is small and these estimates are likely to be noisy). Finally, we discuss the limitations of the network ranking in our context.
This article examines the effect of parental socialization and interest in politics on entering and staying in public service careers. We incorporate two related explanations, yet commonly used in different fields of literature, to explain public sector choice. First, following social learning theory, we hypothesize that parents serve as role models and thereby affect their children's sector choice. Additionally, we test the hypothesis that parental socialization leads to a longer stay in public sector jobs while assuming that it serves as a buffer against turnover. Second, following public service motivation process theory, we expect that 'interest in politics' is influenced by parental socialization and that this concept, in turn, leads to a public sector career. A representative set of longitudinal data from the Swiss household panel (1999-2014) was used to analyse these hypotheses (n = 2,933, N = 37,328). The results indicate that parental socialization serves as a stronger predictor of public sector choice than an interest in politics. Furthermore, people with parents working in the public sector tend to stay longer in their public sector jobs. Points for practitioners For practitioners, the results of this study are relevant as they highlight the limited usefulness of addressing job applicants' interest in politics in the recruitment process. Human resources managers who want to ensure a public-service-motivated workforce are therefore advised to focus on human resources activities that stimulate public service motivation after job entry. We also advise close interaction between universities and public organizations so that students develop a realistic picture of the government as a future employer and do not experience a 'reality shock' after job entry.
Accurately solving classification problems nowadays is likely to be the most relevant machine learning task. Binary classification separating two classes only is algorithmically simpler but has fewer potential applications as many real-world problems are multi-class. On the reverse, separating only a subset of classes simplifies the classification task. Even though existing multi-class machine learning algorithms are very flexible regarding the number of classes, they assume that the target set Y is fixed and cannot be restricted once the training is finished. On the other hand, existing state-of-the-art production environments are becoming increasingly interconnected with the advance of Industry 4.0 and related technologies such that additional information can simplify the respective classification problems. In light of this, the main aim of this thesis is to introduce dynamic classification that generalizes multi-class classification such that the target class set can be restricted arbitrarily to a non-empty class subset M of Y at any time between two consecutive predictions.
This task is solved by a combination of two algorithmic approaches. First, classifier calibration, which transforms predictions into posterior probability estimates that are intended to be well calibrated. The analysis provided focuses on monotonic calibration and in particular corrects wrong statements that appeared in the literature. It also reveals that bin-based evaluation metrics, which became popular in recent years, are unjustified and should not be used at all. Next, the validity of Platt scaling, which is the most relevant parametric calibration approach, is analyzed in depth. In particular, its optimality for classifier predictions distributed according to four different families of probability distributions as well its equivalence with Beta calibration up to a sigmoidal preprocessing are proven. For non-monotonic calibration, extended variants on kernel density estimation and the ensemble method EKDE are introduced. Finally, the calibration techniques are evaluated using a simulation study with complete information as well as on a selection of 46 real-world data sets.
Building on this, classifier calibration is applied as part of decomposition-based classification that aims to reduce multi-class problems to simpler (usually binary) prediction tasks. For the involved fusing step performed at prediction time, a new approach based on evidence theory is presented that uses classifier calibration to model mass functions. This allows the analysis of decomposition-based classification against a strictly formal background and to prove closed-form equations for the overall combinations. Furthermore, the same formalism leads to a consistent integration of dynamic class information, yielding a theoretically justified and computationally tractable dynamic classification model. The insights gained from this modeling are combined with pairwise coupling, which is one of the most relevant reduction-based classification approaches, such that all individual predictions are combined with a weight. This not only generalizes existing works on pairwise coupling but also enables the integration of dynamic class information.
Lastly, a thorough empirical study is performed that compares all newly introduced approaches to existing state-of-the-art techniques. For this, evaluation metrics for dynamic classification are introduced that depend on corresponding sampling strategies. Thereafter, these are applied during a three-part evaluation. First, support vector machines and random forests are applied on 26 data sets from the UCI Machine Learning Repository. Second, two state-of-the-art deep neural networks are evaluated on five benchmark data sets from a relatively recent reference work. Here, computationally feasible strategies to apply the presented algorithms in combination with large-scale models are particularly relevant because a naive application is computationally intractable. Finally, reference data from a real-world process allowing the inclusion of dynamic class information are collected and evaluated. The results show that in combination with support vector machines and random forests, pairwise coupling approaches yield the best results, while in combination with deep neural networks, differences between the different approaches are mostly small to negligible. Most importantly, all results empirically confirm that dynamic classification succeeds in improving the respective prediction accuracies. Therefore, it is crucial to pass dynamic class information in respective applications, which requires an appropriate digital infrastructure.
IMPACT German municipalities have prepared performance budgets for over 10 years. The incorporation of performance information into the budget is, however, still work in progress. Local politicians perceive the usability of non-financial information in the budget as low and do not use such information intensively for budget composition or other purposes. German municipal budgets are usually voluminous because of their highly detailed structure and the large amount of displayed performance data which rarely informs about outcomes. Such information does not meet the needs of councillors, for example in their struggles with political opponents. Some options for improving the usability of budgetary information are presented.
In this paper we examine the relationship between the default risk of banks and sovereigns, i.e. the 'doom-loop'. Specifically, we try to assess the effectiveness of the implementation of the new recovery and resolution framework in the European Union. We use a panel with daily data on European banks and sovereigns ranging from 2012 to 2016 in order to test the effects of the Bank Recovery and Resolution Directive on the two-way feedback process. We find that there was a pronounced feedback loop between banks and sovereigns from 2012 to 2014. However, after the implementation of the European Banking Union, in 2015/2016, the magnitude of the doom-loop decreased and the spillovers became not statistically significant. Furthermore, our results suggest that the implementation of the new resolution framework is a suitable candidate to explain this finding. Overall, the results are robust across several specifications.
Equity crowdfunding
(2021)
In this study, we explore the development of equity crowdfunding (ECF) over the next 5 to 10 years by conducting an international Delphi study. Our results indicate that the ECF market is expected to grow significantly. However, it is unlikely to disrupt other forms of financing and will not cover all SME financing needs. ECF will remain a funding technique for SMEs and small investors; it is unlikely to attract large corporations or institutional investors. Platforms will impose stricter requirements for capital raisers, expand their services, and innovate their business models. National governments will probably partly liberalize the ECF market.
Teaching and learning as well as administrative processes are still experiencing intensive changes with the rise of artificial intelligence (AI) technologies and its diverse application opportunities in the context of higher education. Therewith, the scientific interest in the topic in general, but also specific focal points rose as well. However, there is no structured overview on AI in teaching and administration processes in higher education institutions that allows to identify major research topics and trends, and concretizing peculiarities and develops recommendations for further action. To overcome this gap, this study seeks to systematize the current scientific discourse on AI in teaching and administration in higher education institutions. This study identified an (1) imbalance in research on AI in educational and administrative contexts, (2) an imbalance in disciplines and lack of interdisciplinary research, (3) inequalities in cross-national research activities, as well as (4) neglected research topics and paths. In this way, a comparative analysis between AI usage in administration and teaching and learning processes, a systematization of the state of research, an identification of research gaps as well as further research path on AI in higher education institutions are contributed to research.