This thesis examines how Europe sustains its leadership and competitiveness as a global center for foreign direct investment (FDI) and trade between 1991 and 2023. While EU membership historically functioned as the dominant determinant of inward FDI and trade integration, its relative influence has declined as new structural factors, based on trade dynamics and export-platform strategies, have emerged, together with the growing presence of Asian, especially Chinese, investors establishing production hubs in Central and Eastern Europe to serve the wider EU market. Lower trade costs within Europe have reinforced this shift, leading EU investors to focus on vertical FDI, while non-EU investors to adopt export-platform FDI patterns. Chinese investment has moved from infrastructure-focused projects to strategic-sector FDI, highlighting Europe’s exposure to evolving global industrial and geopolitical dynamics. Chapter 2 examines how traditional determinants of FDI, including EU membership, interact with emerging drivers, such as trade interdependence, export-platform strategies, and Asian influence, to shape investment patterns in Europe. It employs a gravity-based empirical framework augmented with newly developed indicators, comprising the Bilateral Trade Interdependence Index, the Export-Platform Indicator, and Belt and Road Initiative (BRI) participation, together with a functional integration approach, covering over 95% of European countries and their global partners from 2010 to 2023. The findings indicate that trade dependency with non-EU partners grew most rapidly, increasing by 55% between 2011 and 2023. Stronger bilateral trade interdependence is found to significantly predict higher FDI inflows. The BRI analysis and functional classification indicate a shift from infrastructure-focused Chinese investment to strategic sectors, including electric vehicles and semiconductors. Since 2018, export-platform strategies have expanded from Europe’s core economies into Central and Eastern Europe, forming emerging production hubs, and have subsequently moved toward the Western Balkans and Turkey, likely reflecting evolving EU regulations and broader supply-chain realignments. Chapter 3 expands the FDI analysis to cover a longer timeframe, from 1991 to 2017, focusing on the period when EU membership exerted a strong influence on FDI in Europe, transforming member countries from primarily cost-attractive destinations into global investment centers. Using an augmented gravity model covering 39 host and origin countries, the analysis finds that EU membership increased FDI inflows by 23%, with investments from core EU members expanding into new EU member states, while FDI from non-EU countries decreased. At the same time, EU membership may also be driven by trade, and EEA participation reflects non-FDI motivations. The chapter also highlights that EU accession strengthens both market-seeking (horizontal) and efficiency-seeking (vertical) FDI motives and applies methods to address negative and zero FDI values issues, ensuring robust estimation. The inclusion of lagged and lead variables shows that the EU integration process is phased over time, affecting FDI inflows with lags of up to 10–15 years after accession. Chapter 4 expands the range of FDI determinants by deriving trade cost indices as a proxy for connectivity and extending the geographic scope of the analysis. In addition to EU members, the sample includes the Western Balkans, Turkey, and new EU candidates and applicants (Moldova, Ukraine, and Georgia) over the period 2000 to 2020, covering approximately 80% of European FDI flows. Trade costs are calculated for each country in the sample with its trade partners, not only within and between European subregions but also with non-EU partners such as China, and are combined with measures of FDI restrictiveness. The results show that China remains among the EU’s top three trading partners in goods and that trade costs significantly influence FDI inflows in Europe. The analysis also highlights that declining trade costs between European countries have reduced market-seeking (horizontal) FDI, while non-European investors, especially China, increasingly pursue export-platform FDI to serve third-country markets. A sharp reduction in trade costs between the Western Balkans and the EU (-45%) and a smaller decline with China (-35%) illustrates how regional integration reduces the need for local horizontal FDI while reinforcing Europe’s role as a hub for global production. Chapter 5 shows that despite concerns about increasing outside influence, developed European countries remain the dominant source of FDI in the region. The chapter focuses on China’s role, examining FDI patterns across advanced EU members, new member states, and Western Balkan economies between 2000 and 2019, while distinguishing the effects of EU integration and BRI participation on FDI. Chinese influence has expanded primarily through the Belt and Road Initiative, particularly in accession and neighboring countries. Although BRI participation does not significantly increase FDI on its own, reflecting the dominant part of loan-financed infrastructure rather than private investment, it has strengthened physical and digital connectivity, laying the groundwork for future, longer-term FDI. The analysis also shows that intra-EU trade costs declined significantly after the 2004 and 2007 enlargements, while trade costs between the Western Balkans and China have fallen steadily since the launch of the BRI in 2013. As a result, Chinese influence is more pronounced in new EU member states and Western Balkan economies than in Western Europe. Over time, enhanced connectivity and supply-chain integration may support more diversified FDI inflows.

This thesis presents four contributions in the domains of schema/ontology alignment and query processing. First, we present a novel alignment approach, denoted as FiLiPo (Finding Linkage Points), to align the schema of RDF knowledge bases with the response schema of RESTful Web APIs. FiLiPo only requires knowledge about a knowledge base (e.g., class names) but no prior knowledge about the Web APIs’ data structure. It uses fifteen different string similarity metrics to find an alignment between the schema of a knowledge base and that of aWeb API. Next, a benchmark system named ETARA (Evaluation Toolkit for API and RDF Alignment) is introduced that was created with the goal to simulate RESTful Web APIs and is able to cover all important characteristics of Web APIs, i.e., latency, timeouts, rate limits and, furthermore, provides configurable response structures (e.g., JSON or XML). Additionally, it was designed to support researchers during the development of alignment systems. Afterward, the alignments determined by FiLiPo are used to create a hybrid and federated query processor named TunA (Tunable Query Optimizer forWeb APIs and User Preferences), which allows SPARQL queries combining knowledge bases and RESTful Web APIs and is tunable towards user preferences, i.e., coverage, reliability and execution time. The primary goal of TunA is to return a query result that satisfies the user’s preferences in terms of data quality, even when using unreliable data sources by performing a majority vote over multiple sources. Lastly, we present a federated query processor, denoted as ORAQL (Overlap and Reliability Aware Query Processing Layer), which uses overlap information to reduce the number of selected sources that are available in a federation. The goal is to reduce redundant data and, hence, improve the query execution speed. Therefore, ORAQL uses a profile feature that provides information about the overlap between all data sources of a federation. Furthermore, we extend the quality estimation of TunA to cover Triple Pattern Fragment interfaces to ensure a user-provided reliability goal.

Price indices play a vital role in economic measurement as they reflect price levels and measure price fluctuations. Price level measures are used with macroeconomic indicators to express them in real terms. These measures are also used to index wages, rents, and pensions. Furthermore, they are used as a reference for monetary policy conducted by central banks. Therefore, the provision of accurate price indices is one of the most important goals of National Statistical Institutes (NSIs), and numerous studies have been devoted to this goal. This cumulative dissertation also contributes to this goal. It contains four chapters, each of which represents a separate research. The first two studies are devoted to the treatment of seasonal products by using different price index methods. The first research is co-authored with Ken van Loon. The third research is dedicated to finding the most accurate method to make price predictions for missing products. The fourth research is focused on the treatment of products by using different price index methods when products’ quality characteristics are available.

Bilevel problems are optimization problems for which parts of the variables are constrained to be an optimal solution to another nested optimization problem. This structure renders bilevel problems particularly well-suited for modeling hierarchical decision-making processes. They are widely applicable in areas such as energy markets, transportation systems, security planning, and pricing. However, the hierarchical nature of these problems also makes them inherently challenging to solve, both in theory and in practice. In this thesis, we study different nonlinear problem settings for the nested optimization problem. First, we focus on nonlinear but convex bilevel problems with purely integer variables. We propose a solution algorithm that uses a branch-and-cut framework with tailored cutting planes. We prove correctness and finite termination of the method under suitable assumptions and put it into context of existing literature. Moreover, we provide an extensive numerical study to showcase the applicability of our method and we compare it to the state-of-the-art approach for a less general setting on suitable instances from the literature. Furthermore, we discuss challenges that arise when we try to generalize our approach to the mixed-integer setting. Next, we study mixed-integer bilevel problems for which the nested problem has a nonconvex and quadratic objective function, linear constraints, and continuous variables. We state and prove a complexity-theoretical hardness result for this problem class and develop a lower and upper bounding scheme to solve these problems. We prove correctness and finite termination of the proposed method under suitable assumptions and test its applicability in a numerical study. Finally, we consider bilevel problems with continuous variables, where the nested problem has a convex-quadratic objective function and linear constraints. We reformulate them as single-level optimization problems using necessary and sufficient optimality conditions for the nested problem. Then, we explore the family of so-called P-split reformulations for this single-level problem and test their applicability in a preliminary numerical study.

Der ökologische Zustand der Erde befindet sich infolge von Umweltverschmutzung, Abfallaufkommen und CO₂-bedingtem Klimawandel in einem kritischen Zustand. Mit rund 40 % trägt der Bau- und Gebäudesektor erheblich zu den globalen Treibhausgasemissionen bei. Holz gilt als klimafreundliche Alternative zu Beton und Stahl, bedarf jedoch ebenfalls einer nachhaltigen Nutzung. Die Kreislaufwirtschaft bietet mit der Wiederverwendung ein zukunftsweisendes Konzept: So sind etwa 45% des beim Rückbau von Gebäuden anfallenden Holzes potenziell als Rohstoff nutzbar. Dadurch werden alternative Rohstoffquellen erschlossen und das Abfallaufkommen reduziert. Trotz dieses Potenzials liegt der Zirkularitätsgrad der Weltwirtschaft derzeit nur bei 7,2 %. Vor diesem Hintergrund untersucht die Dissertation, welche Wettbewerbsstrategien und welche organisationalen Fähigkeiten die Entwicklung zirkulärer Geschäftsmodelle fördern. Der Fokus liegt auf der Holzindustrie der DACH-Region, die historisch durch forstwirtschaftliche Nachhaltigkeit geprägt ist, jedoch bislang überwiegend linearen Strukturen folgt. Die Arbeit kombiniert theoretische Fundierung, eine vierjährige Literaturrecherche, Experteninterviews sowie im Zentrum eine quantitative Unternehmensbefragung (n = 200). Daraus wurde eine aktivitätsorientierte Skala zur Bewertung der Zirkularität eines Geschäftsmodells entwickelt. Analysiert wurden drei Perspektiven: Fähigkeiten, Strategien und Stakeholder. Im Kontext der Fähigkeitsperspektive wurde ermittelt, dass die dynamischen Fähigkeiten positive Implikationen auf die Umsetzung von Zirkularität haben. Im Forschungsfeld der Strategieperspektive wurde deutlich, dass die Innovationsführerschaft positive Effekte auf die Umsetzung der Kreislaufwirtschaft besitzt. Zudem weisen sowohl die Innovationsführerschaft als auch die Qualitätsführerschaft einen positiven indirekten Effekt über die dynamischen Fähigkeiten auf die Entwicklung zirkulärer Geschäftsmodelle auf. Im Rahmen der Stakeholderperspektive wurde eruiert, dass der Stakeholder-Druck im Zusammenwirken mit einem grünen Unternehmensimage eine Katalysator-Wirkung besitzt. Der Einfluss der Interessengruppen führt dazu, dass die Unternehmen ein grünes Image in eine substanzielle Umsetzungsphase überführen. Darüber hinaus wurde ersichtlich, dass der Stakeholder-Druck als zentraler Veränderungsfaktor wirkt. Während die direkten Auswirkungen der dynamischen Fähigkeiten durch den Druck zurückgehen, nehmen die indirekten Effekte auf die Erreichung von Zirkularität zu. Abschließend werden Handlungsempfehlungen für Unternehmen sowie wissenschaftliche Implikationen und zukünftige Forschungsmöglichkeiten abgeleitet.

The gender wage gap in labor market outcomes has been intensively investigated for decades, yet it remains a relevant and innovative research topic in labor economics. Chapter 2 of this dissertation explores the pressing issue of gender wage disparity in Ethiopia. By applying various empirical methodologies and measures of occupational segregation, this chapter aims to analyze the role of female occupational segregation in explaining the gender wage gap across the pay distribution. The findings reveal a significant difference in monthly wages, with women consistently earning lower wages across the wage distribution. Importantly, the result indicates a negative association between female occupational segregation and the average earnings of both men and women. Furthermore, the estimation result shows that female occupational segregation partially explains the gender wage gap at the bottom of the wage distribution. I find that the magnitude of the gender wage gap in the private sector is higher than in the public sector. In Chapter 3, the Ethiopian Demography and Health Survey data are leveraged to explore the causal relationship between female labor force participation and domestic violence. Domestic violence against women is a pervasive public health concern, particularly in Africa, including Ethiopia, where a significant proportion of women endure various forms of domestic violence perpetrated by intimate partners. Economic empowerment of women through increased participation in the labor market can be one of the mechanisms for mitigating the risk of domestic violence. This study seeks to provide empirical evidence supporting this hypothesis. Using the employment rate of women at the community level as an instrumental variable, the finding suggests that employment significantly reduces the risk of domestic violence against women. More precisely, the result shows that women’s employment status significantly reduces domestic violence by about 15 percentage points. This finding is robust for different dimensions of domestic violence, such as physical, sexual, and emotional violence. By examining the employment outcomes of immigrants in the labor market, Chapter 4 extends the dissertation's inquiry to the dynamics of immigrant economic integration into the destination country. Drawing on data from the German Socio-Economic Panel, the chapter scrutinizes the employment gap between native-born individuals and two distinct groups of first-generation immigrants: refugees and other migrants. Through rigorous analysis, Chapter 4 aims to identify the factors contributing to disparities in employment outcomes among these groups. In this chapter, I aim to disentangle the heterogeneity characteristic of refugees and other immigrants in the labor market, thereby contributing to a deeper understanding of immigrant labor market integration in Germany. The results show that refugees and other migrants are less likely to find employment than comparable natives. The refugee-native employment gap is much wider than other migrant-native employment gap. Moreover, the findings vary by gender and migration categories. While other migrant men do not differ from native men in the probability of being employed, refugee women are the most disadvantaged group compared to other migrant women and native women in the probability of being employed. The study suggests that German language proficiency and permanent resident permits partially explain the lower employment probability of refugees in the German labor market. Chapter 5 (co-authored with Uwe Jirjahn) utilizes the same dataset to explore the immigrant-native trade union membership gap, focusing on the role of integration in the workplace and into society. The integration of immigrants into society and the workplace is vital not only to improve migrant's performance in the labor market but also to actively participate in institutions such as trade unions. In this study, we argue that the incomplete integration of immigrants into the workplace and society implies that immigrants are less likely to be union members than natives. Our findings show that first-generation immigrants are less likely to be trade union members than natives. Notably, the analysis shows that the immigrant-native gap in union membership depends on immigrants’ integration into the workplace and society. The gap is smaller for immigrants working in firms with a works council and having social contacts with Germans. Moreover, the results reveal that the immigrant-native union membership gap is decreasing in the year since arrival in Germany.

This thesis deals with consumption-investment allocation problems with Epstein-Zin recursive utility, building upon the dualization procedure introduced by [Matoussi and Xing, 2018]. While their work exclusively focuses on truly recursive utility, we extend their procedure to include time-additive utility using results from general convex analysis. The dual problem is expressed in terms of a backward stochastic differential equation (BSDE), for which existence and uniqueness results are established. In this regard, we close a gap left open in previous works, by extending results restricted to specific subsets of parameters to cover all parameter constellations within our duality setting. Using duality theory, we analyze the utility loss of an investor with recursive preferences, that is, her difference in utility between acting suboptimally in a given market, compared to her best possible (optimal) consumption-investment behaviour. In particular, we derive universal power utility bounds, presenting a novel and tractable approximation of the investors’ optimal utility and her welfare loss associated to specific investment-consumption choices. To address quantitative shortcomings of those power utility bounds, we additionally introduce one-sided variational bounds that offer a more effective approximation for recursive utilities. The theoretical value of our power utility bounds is demonstrated through their application in a new existence and uniqueness result for the BSDE characterizing the dual problem. Moreover, we propose two approximation approaches for consumption-investment optimization problems with Epstein-Zin recursive preferences. The first approach directly formalizes the classical concept of least favorable completion, providing an analytic approximation fully characterized by a system of ordinary differential equations. In the special case of power utility, this approach can be interpreted as a variation of the well-known Campbell-Shiller approximation, improving some of its qualitative shortcomings with respect to state dependence of the resulting approximate strategies. The second approach introduces a PDE-iteration scheme, by reinterpreting artificial completion as a dynamic game, where the investor and a dual opponent interact until reaching an equilibrium that corresponds to an approximate solution of the investors optimization problem. Despite the need for additional approximations within each iteration, this scheme is shown to be quantitatively and qualitatively accurate. Moreover, it is capable of approximating high dimensional optimization problems, essentially avoiding the curse of dimensionality and providing analytical results.

In bilevel optimization, some of the variables of an optimization problem have to be an optimal solution to another nested optimization problem. This specific structure renders bilevel optimization a powerful tool for modeling hierarchical decision-making processes, which arise in various real-world applications such as in critical infrastructure defense, transportation, or energy. Due to their nested structure, however, bilevel problems are also inherently hard to solve—both in theory and in practice. Further challenges arise if, e.g., bilevel problems under uncertainty are considered. In this dissertation, we address different types of uncertainties in bilevel optimization using techniques from robust optimization. We study mixed-integer linear bilevel problems with lower-level objective uncertainty, which we tackle using the notion of Gamma-robustness. We present two exact branch-and-cut approaches to solve these Gamma-robust bilevel problems, along with cuts tailored to the important class of monotone interdiction problems. Given the overall hardness of the considered problems, we additionally propose heuristic approaches for mixed-integer, linear, and Gamma-robust bilevel problems. The latter rely on solving a linear number of deterministic bilevel problems so that no problem-specific tailoring is required. We assess the performance of both the exact and the heuristic approaches through extensive computational studies. In addition, we study the problem of determining optimal tolls in a traffic network in which the network users hedge against uncertain travel costs in a robust way. The overall toll-setting problem can be seen as a single-leader multi-follower problem with multiple robustified followers. We model this setting as a mathematical problem with equilibrium constraints, for which we present a mixed-integer, nonlinear, and nonconvex reformulation that can be tackled using state-of-the-art general-purpose solvers. We further illustrate the impact of considering robustified followers on the toll-setting policies through a case study. Finally, we highlight that the sources of uncertainty in bilevel optimization are much richer compared to single-level optimization. To this end, we study two aspects related to so-called decision uncertainty. First, we propose a strictly robust approach in which the follower hedges against erroneous observations of the leader's decision. Second, we consider an exemplary bilevel problem with a continuous but nonconvex lower level in which algorithmic necessities prevent the follower from making a globally optimal decision in an exact sense. The example illustrates that even very small deviations in the follower's decision may lead to arbitrarily large discrepancies between exact and computationally obtained bilevel solutions.

In machine learning, classification is the task of predicting a label for each point within a data set. When the class of each point in the labeled subset is already known, this information is used to recognize patterns and make predictions about the points in the remainder of the set, referred to as the unlabeled set. This scenario falls in the field of supervised learning. However, the number of labeled points can be restricted, because, e.g., it is expensive to obtain this information. Besides, this subset may be biased, such as in the case of self-selection in a survey. Consequently, the classification performance for unlabeled points may be limited. To improve the reliability of the results, semi-supervised learning tackles the setting of labeled and unlabeled data. Moreover, in many cases, additional information about the size of each class can be available from undisclosed sources. This cumulative thesis presents different studies to combine this external cardinality constraint information within three important algorithms for binary classification in the supervised context: support vector machines (SVM), classification trees, and random forests. From a mathematical point of view, we focus on mixed-integer programming (MIP) models for semi-supervised approaches that consider a cardinality constraint for each class for each algorithm. Furthermore, since the proposed MIP models are computationally challenging, we also present techniques that simplify the process of solving these problems. In the SVM setting, we introduce a re-clustering method and further computational techniques to reduce the computational cost. In the context of classification trees, we provide correct values for certain bounds that play a crucial role for the solver performance. For the random forest model, we develop preprocessing techniques and an intuitive branching rule to reduce the solution time. For all three methods, our numerical results show that our approaches have better statistical performances for biased samples than the standard approach.

This thesis consists of four highly related chapters examining China’s rise in the aluminium industry. The first chapter addresses the conditions that allowed China, which first entered the market in the 1950s, to rise to world leadership in aluminium production. Although China was a latecomer, its re-entry into the market after the oil crises in the 1970s was a success and led to its ascent as the world’s largest aluminium producer by 2001. With an estimated production of 40.4 million tonnes in 2022, China represented almost 60% of the global output. Chapter 1 examines the factors underlying this success, such as the decline of international aluminium cartels, the introduction of innovative technology, the US granting China the MFN tariff status, Chinese-specific factors, and supportive government policies. Chapter 2 develops a mathematical model to analyze firms’ decisions in the short term. It examines how an incumbent with outdated technology and a new entrant with access to a new type of technology make strategic decisions, including the incumbent’s decision whether to deter entry, the production choice of firms, the optimal technology adoption rate of the newcomer, and cartel formation. Chapter 3 focuses on the adoption of new technology by firms upon market entry in four scenarios: firstly, a free market Cournot competition; secondly, a situation in which the government determines technology adoption rates; thirdly, a scenario in which the government controls both technology and production; and finally, a scenario where the government dictates technology adoption rates, production levels, and also the number of market participants. Chapter 4 applies the Spencer and Brander (1983) framework to examine strategic industrial policy. The model assumes that there are two exporting firms in two different countries that sell a product to a third country. We examine how the domestic firm is influenced by government intervention, such as the provision of a fixed-cost subsidy to improve its competitiveness relative to the foreign company. Chapter 4 initially investigates a scenario where only one government offers a fixed-cost subsidy, followed by an analysis of the case when both governments simultaneously provide financial help. Taken together, these chapters provide a comprehensive analysis of the strategic, technological, and political factors contributing to China’s leadership in the global aluminium industry. Chapter 1: The Rise of China as a Latecomer in the Global Aluminium Industry This chapter examines China’s remarkable transformation into a global leader in the aluminium industry, a sector in which the country accounted for approximately 58.9% of worldwide production in 2022. We examine how China, a latecomer to the aluminium industry that started off with labor-intensive technology in 1953, grew into the largest aluminium producer with some of the most advanced smelters in the world. This analysis identifies and discusses several opportunities that Chinese aluminium producers took advantage of. The first set of opportunities happened during the 1970s oil crises, which softened international competition and allowed China to acquire innovative smelting technology from Japan. The second set of opportunities started at about the same time when China opened its economy in 1978. The substantial demand for aluminium in China is influenced by both external and internal factors. Externally, the US granted China’s MFN tariff status in 1980 and China entered the World Trade Organization (WTO) in 2001. Both events contributed to a surge in Chinese aluminium consumption. Internally, China’s investment-led growth model boosted further its aluminium demand. Additional factors specific to China, such as low labor costs and the abundance of coal as an energy source, offer Chinese firms competitive advantages against international players. Furthermore, another window of opportunity is due to Chinese governmental policies, including phasing out old technology, providing subsidies, and gradually opening the economy to enhance domestic competition before expanding globally. By describing these elements, the study provides insights into the dynamic interplay of external circumstances and internal strategies that contributed to the success of the Chinese aluminium industry. Chapter 2: Technological Change and Strategic Choices for Incumbent and New Entrant This chapter introduces an oligopoly model that includes two actors: an incumbent and a potential entrant, that compete in the same market. We assume that two participants are located in different parts of the market: the incumbent is situated in area 1, whereas the potential entrant may venture into the other region, area 2. The incumbent exists in stage zero, where it can decide whether to deter the newcomer’s entry. A new type of technology exists in period one, when the newcomer may enter the market. In the short term, the incumbent is trapped with the outdated technology, while the new entrant may choose to partially or completely adopt the latest technology. Our results suggest the following: Firstly, the incumbent only tries to deter the new entrant if a condition for entry cost is met. Secondly, the new entrant is only interested in forming a cartel with the incumbent if a function of the ratio of the variable to new technology’s fixed-cost parameters is sufficiently high. Thirdly, if the newcomer asks to form a cartel, the incumbent will always accept this request. Finally, we can obtain the optimal new technology adoption rate for the newcomer. Chapter 3: Technological Adoption and Welfare in Cournot Oligopoly This study examines the difference between the optimal technology adoption rates chosen by firms in a homogeneous Cournot oligopoly and that preferred by a benevolent government upon firms’ market entry. To address the question of whether the technology choices of firms and government are similar, we analyze several different scenarios, which differ in the extent of government intervention in the market. Our results suggest a relationship between the number of firms in the market and the impact of government intervention on technology adoption rates. Especially in situations with a low number of firms that are interested in entering the market, greater government influence tends to lead to higher technology adoption rates of firms. Conversely, in scenarios with a higher number of firms and a government that lacks control over the number of market players, the technology adoption rate of firms will be highest when the government plays no role. Chapter 4: International Technological Innovation and Industrial Strategies Supporting domestic firms when they first enter the market may be seen as a favorable policy choice by governments around the world thanks to their ability to enhance the competitive advantage of domestic firms in non-cooperative competition against foreign enterprises (infant industry protection argument). This advantage may allow domestic firms to increase their market share and generate higher profits, thereby improving domestic welfare. This chapter utilizes the Spencer and Brander (1983) framework as a theoretical foundation to elucidate the effects of fixed-cost subsidies on firms’ production levels, technological innovations, and social welfare. The analysis examines two firms in different countries, each producing a homogeneous product that is sold in a third, separate country. We first examine the Cournot-Nash equilibrium in the absence of government intervention, followed by analyzing a scenario where just one government provides a financial subsidy for its domestic firm, and finally, we consider a situation where both governments simultaneously provide financial assistance for their respective firms. Our results suggest that governments aim to maximize social welfare by providing fixed-cost subsidies to their respective firms, finding themselves in a Chicken game scenario. Regarding technology innovation, subsidies lead to an increased technological adoption rate for recipient firms, regardless of whether one or both firms in a market receive support, compared to the situation without subsidies. The technology adoption rate of the recipient firm is higher than of its rival when only the recipient firm benefits from the fixed-cost subsidy. The lowest technology adoption rate of a firm occurs when the firm does not receive a fixed-cost subsidy, but its competitor does. Furthermore, global welfare will benefit the most in case when both exporting countries grant fixed-cost subsidies, and this welfare level is higher when only one country subsidizes than when no subsidies are provided by any country.

Differential equations yield solutions that necessarily contain a certain amount of regularity and are based on local interactions. There are various natural phenomena that are not well described by local models. An important class of models that describe long-range interactions are the so-called nonlocal models, which are the subject of this work. The nonlocal operators considered here are integral operators with a finite range of interaction and the resulting models can be applied to anomalous diffusion, mechanics and multiscale problems. While the range of applications is vast, the applicability of nonlocal models can face problems such as the high computational and algorithmic complexity of fundamental tasks. One of them is the assembly of finite element discretizations of truncated, nonlocal operators. The first contribution of this thesis is therefore an openly accessible, documented Python code which allows to compute finite element approximations for nonlocal convection-diffusion problems with truncated interaction horizon. Another difficulty in the solution of nonlocal problems is that the discrete systems may be ill-conditioned which complicates the application of iterative solvers. Thus, the second contribution of this work is the construction and study of a domain decomposition type solver that is inspired by substructuring methods for differential equations. The numerical results are based on the abstract framework of nonlocal subdivisions which is introduced here and which can serve as a guideline for general nonlocal domain decomposition methods.

Sozialunternehmen haben mindestens zwei Ziele: die Erfüllung ihrer sozialen bzw. ökologischen Mission und finanzielle Ziele. Zwischen diesen Zielen können Spannungen entstehen. Wenn sie sich in diesem Spannungsfeld wiederholt zugunsten der finanziellen Ziele entscheiden, kommt es zum Mission Drift. Die Priorisierung der finanziellen Ziele überlagert dabei die soziale Mission. Auch wenn das Phänomen in der Praxis mehrfach beobachtet und in Einzelfallanalysen beschrieben wurde, gibt es bislang wenig Forschung zu Mission Drift. Der Fokus der vorliegenden Arbeit liegt darauf, diese Forschungslücke zu schließen und eigene Erkenntnisse für die Auslöser und Treiber des Mission Drifts von Sozialunternehmen zu ermitteln. Ein Augenmerk liegt auf den verhaltensökonomischen Theorien und der Mixed-Gamble-Logik. Dieser Logik zufolge liegt bei Entscheidungen immer eine Gleichzeitigkeit von Gewinnen und Verlusten vor, sodass Entscheidungsträger die Furcht vor Verlusten gegenüber der Aussicht auf Gewinne abwägen müssen. Das Modell wird genutzt, um eine neue theoretische Betrachtungsweise auf die Abwägung zwischen sozialen und finanziellen Zielen bzw. Mission Drift zu erhalten. Mit einem Conjoint Experiment werden Daten über das Entscheidungsverhalten von Sozialunternehmern generiert. Im Zentrum steht die Abwägung zwischen sozialen und finanziellen Zielen in verschiedenen Szenarien (Krisen- und Wachstumssituationen). Mithilfe einer eigens erstellten Stichprobe von 1.222 Sozialunternehmen aus Deutschland, Österreich und der Schweiz wurden 187 Teilnehmende für die Studie gewonnen. Die Ergebnisse dieser Arbeit zeigen, dass eine Krisensituation Auslöser für Mission Drift von Sozialunternehmen sein kann, weil in diesem Szenario den finanziellen Zielen die größte Bedeutung zugemessen wird. Für eine Wachstumssituation konnten hingegen keine solche Belege gefunden werden. Hinzu kommen weitere Einflussfaktoren, welche die finanzielle Orientierung verstärken können, nämlich die Gründeridentitäten der Sozialunternehmer, eine hohe Innovativität der Unternehmen und bestimmte Stakeholder. Die Arbeit schließt mit einer ausführlichen Diskussion der Ergebnisse. Es werden Empfehlungen gegeben, wie Sozialunternehmen ihren Zielen bestmöglich treu bleiben können. Außerdem werden die Limitationen der Studie und Wege für zukünftige Forschung im Bereich Mission Drift aufgezeigt.

Some of the largest firms in the DACH region (Germany, Austria, Switzerland) are (partially) owned by a foundation and/or a family office, such as Aldi, Bosch, or Rolex. Despite their growing importance, prior research neglected to analyze the impact of these intermediaries on the firms they own. This dissertation closes this research gap by contributing to a deeper understanding of two increasingly used family firm succession vehicles, through four empirical quantitative studies. The first study focuses on the heterogeneity in foundation-owned firms (FOFs) by applying a descriptive analysis to a sample of 169 German FOFs. The results indicate that the family as a central stakeholder in a family foundation fosters governance that promotes performance and growth. The second study examines the firm growth of 204 FOFs compared to matched non-FOFs from the DACH region. The findings suggest that FOFs grow significantly less in terms of sales but not with regard to employees. In addition, it seems that this negative effect is stronger for the upper than for the middle or lower quantiles of the growth distribution. Study three adopts an agency perspective and investigates the acquisition behavior within the group of 164 FOFs. The results reveal that firms with charitable foundations as owners are more likely to undertake acquisitions and acquire targets that are geographically and culturally more distant than firms with a family foundation as owner. At the same time, they favor target companies from the same or related industries. Finally, the fourth study scrutinizes the capital structure of firms owned by single family-offices (SFOs). Drawing on a hand-collected sample of 173 SFO-owned firms in the DACH region, the results show that SFO-owned firms display a higher long-term debt ratio than family-owned firms, indicating that SFO-owned firms follow trade-off theory, similar to private equity-owned firms. Additional analyses show that this effect is stronger for SFOs that sold their original family firm. In conclusion, the outcomes of this dissertation furnish valuable research contributions and offer practical insights for families navigating such intermediaries or succession vehicles in the long term.

Striving for sustainable development by combating climate change and creating a more social world is one of the most pressing issues of our time. Growing legal requirements and customer expectations require also Mittelstand firms to address sustainability issues such as climate change. This dissertation contributes to a better understanding of sustainability in the Mittelstand context by examining different Mittelstand actors and the three dimensions of sustainability - social, economic, and environmental sustainability - in four quantitative studies. The first two studies focus on the social relevance and economic performance of hidden champions, a niche market leading subgroup of Mittelstand firms. At the regional level, the impact of 1,645 hidden champions located in Germany on various dimensions of regional development is examined. A higher concentration of hidden champions has a positive effect on regional employment, median income, and patents. At the firm level, analyses of a panel dataset of 4,677 German manufacturing firms, including 617 hidden champions, show that the latter have a higher return on assets than other Mittelstand firms. The following two chapters deal with environmental strategies and thus contribute to the exploration of the environmental dimension of sustainability. First, the consideration of climate aspects in investment decisions is compared using survey data from 468 European venture capital and private equity investors. While private equity firms respond to external stakeholders and portfolio performance and pursue an active ownership strategy, venture capital firms are motivated by product differentiation and make impact investments. Finally, based on survey data from 443 medium-sized manufacturing firms in Germany, 54% of which are family-owned, the impact of stakeholder pressures on their decarbonization strategies is analyzed. A distinction is made between symbolic (compensation of CO₂-emissions) and substantive decarbonization strategies (reduction of CO₂-emissions). Stakeholder pressures lead to a proactive pursuit of decarbonization strategies, with internal and external stakeholders varying in their influence on symbolic and substantial decarbonization strategies, and the relationship influenced by family ownership.

This thesis comprises of four research papers on the economics of education and industrial relations, which contribute to the field of empirical economic research. All of the corresponding papers focus on analysing how much time individuals spend on specific activities. The allocation of available time resources is a decision that individuals make throughout their lifetime. In this thesis, we consider individuals at different stages of their lives - students at school, university students, and dependent employees at the workplace. Part I includes two research studies on student's behaviour in secondary and tertiary education. Chapter 2 explores whether students who are relatively younger or older within the school year exhibit differential time allocation. Building on previous findings showing that relatively younger students perform worse in school, the study shows that relatively younger students are aware of their poor performance in school and feel more strain as a result. Nevertheless, there are no clear differences to be found in terms of time spent on homework, while relatively younger students spend more time watching television and less time on sports activities. Thus, the results suggest that the lower learning outcomes are not associated with different time allocations between school-related activities and non-school-related activities. Chapter 3 analyses how individual ability and labour market prospects affect study behaviour. The theoretical modelling predicts that both determinants increase study effort. The empirical investigation is based on cross-sectional data from the National Educational Panel Study (NEPS) and includes thousands of students in Germany. The analyses show that more gifted students exhibit lower subjective effort levels and invest less time in self-study. In contrast, very good labour market prospects lead to more effort exerted by the student, both qualitatively and quantitatively. The potential endogeneity problem is taken into account by using regional unemployment data as an instrumental variable. Part II includes two labour economic studies on determinants of overtime. Both studies belong to the field of industrial relations, as they focus on union membership on the one hand and the interplay of works councils and collective bargaining coverage on the other. Chapter 4 shows that union members work less overtime than non-members do. The econometric approach takes the problem of unobserved heterogeneity into account; but provides no evidence that this issue affects the results. Different channels that could lead to this relationship are analysed by examining relevant subgroups separately. For example, this effect of union membership can also be observed in establishments with works councils and for workers who are very likely to be covered by collective bargaining agreements. The study concludes that the observed effect is due to the fact that union membership can protect workers from corresponding increased working time demands by employers. Chapter 5 builds on previous studies showing a negative effect of works councils on overtime. In addition to co-determination by works councils at the firm level, collective bargaining coverage is an important factor in the German industrial relations system. Corresponding data was not available in the SOEP for quite some time. Therefore, the study uses recent SOEP data, which also contains information on collective bargaining coverage. A cross-sectional analysis is conducted to examine the effects of works councils in establishments with and without collective bargaining coverage. Similar to studies analysing other outcome variables, the results show that the effect of works councils exists only for employees covered by a collective bargaining agreement.

Even though proper research on Cauchy transforms has been done, there are still a lot of open questions. For example, in the case of representation theorems, i.e. the question when a function can be represented as a Cauchy transform, there is 'still no completely satisfactory answer' ([9], p. 84). There are characterizations for measures on the circle as presented in the monograph [7] and for general compactly supported measures on the complex plane as presented in [27]. However, there seems to exist no systematic treatise of the Cauchy transform as an operator on $L_p$ spaces and weighted $L_p$ spaces on the real axis. This is the point where this thesis draws on and we are interested in developing several characterizations for the representability of a function by Cauchy transforms of $L_p$ functions. Moreover, we will attack the issue of integrability of Cauchy transforms of functions and measures, a topic which is only partly explored (see [43]). We will develop different approaches involving Fourier transforms and potential theory and investigate into sufficient conditions and characterizations. For our purposes, we shall need some notation and the concept of Hardy spaces which will be part of the preliminary Chapter 1. Moreover, we introduce Fourier transforms and their complex analogue, namely Fourier-Laplace transforms. This will be of extraordinary usage due to the close connection of Cauchy and Fourier(-Laplace) transforms. In the second chapter we shall begin our research with a discussion of the Cauchy transformation on the classical (unweighted) $L_p$ spaces. Therefore, we start with the boundary behavior of Cauchy transforms including an adapted version of the Sokhotski-Plemelj formula. This result will turn out helpful for the determination of the image of the Cauchy transformation under $L_p(\R)$ for $p\in(1,\infty).$ The cases $p=1$ and $p=\infty$ are playing special roles here which justifies a treatise in separate sections. For $p=1$ we will involve the real Hardy space $H_{1}(\R)$ whereas the case $p=\infty$ shall be attacked by an approach incorporating intersections of Hardy spaces and certain subspaces of $L_{\infty}(\R).$ The third chapter prepares ourselves for the study of the Cauchy transformation on subspaces of $L_{p}(\R).$ We shall give a short overview of the basic facts about Cauchy transforms of measures and then proceed to Cauchy transforms of functions with support in a closed set $X\subset\R.$ Our goal is to build up the main theory on which we can fall back in the subsequent chapters. The fourth chapter deals with Cauchy transforms of functions and measures supported by an unbounded interval which is not the entire real axis. For convenience we restrict ourselves to the interval $[0,\infty).$ Bringing once again the Fourier-Laplace transform into play, we deduce complex characterizations for the Cauchy transforms of functions in $L_{2}(0,\infty).$ Moreover, we analyze the behavior of Cauchy transform on several half-planes and shall use these results for a fairly general geometric characterization. In the second section of this chapter, we focus on Cauchy transforms of measures with support in $[0,\infty).$ In this context, we shall derive a reconstruction formula for these Cauchy transforms holding under pretty general conditions as well as results on the behaviur on the left half-plane. We close this chapter by rather technical real-type conditions and characterizations for Cauchy transforms of functions in $L_p(0,\infty)$ basing on an approach in [82]. The most common case of Cauchy transforms, those of compactly supported functions or measures, is the subject of Chapter 5. After complex and geometric characterizations originating from similar ideas as in the fourth chapter, we adapt a functional-analytic approach in [27] to special measures, namely those with densities to a given complex measure $\mu.$ The chapter is closed with a study of the Cauchy transformation on weighted $L_p$ spaces. Here, we choose an ansatz through the finite Hilbert transform on $(-1,1).$ The sixth chapter is devoted to the issue of integrability of Cauchy transforms. Since this topic has no comprehensive treatise in literature yet, we start with an introduction of weighted Bergman spaces and general results on the interaction of the Cauchy transformation in these spaces. Afterwards, we combine the theory of Zen spaces with Cauchy transforms by using once again their connection with Fourier transforms. Here, we shall encounter general Paley-Wiener theorems of the recent past. Lastly, we attack the issue of integrability of Cauchy transforms by means of potential theory. Therefore, we derive a Fourier integral formula for the logarithmic energy in one and multiple dimensions and give applications to Fourier and hence Cauchy transforms. Two appendices are annexed to this thesis. The first one covers important definitions and results from measure theory with a special focus on complex measures. The second appendix contains Cauchy transforms of frequently used measures and functions with detailed calculations.

Traditional workflow management systems support process participants in fulfilling business tasks through guidance along a predefined workflow model. Flexibility has gained a lot of attention in recent decades through a shift from mass production to customization. Various approaches to workflow flexibility exist that either require extensive knowledge acquisition and modelling effort or an active intervention during execution and re-modelling of deviating behaviour. The pursuit of flexibility by deviation is to compensate both of these disadvantages through allowing alternative unforeseen execution paths at run time without demanding the process participant to adapt the workflow model. However, the implementation of this approach has been little researched so far. This work proposes a novel approach to flexibility by deviation. The approach aims at supporting process participants during the execution of a workflow through suggesting work items based on predefined strategies or experiential knowledge even in case of deviations. The developed concepts combine two renowned methods from the field of artificial intelligence - constraint satisfaction problem solving with process-oriented case-based reasoning. This mainly consists of a constraint-based workflow engine in combination with a case-based deviation management. The declarative representation of workflows through constraints allows for implicit flexibility and a simple possibility to restore consistency in case of deviations. Furthermore, the combined model, integrating procedural with declarative structures through a transformation function, increases the capabilities for flexibility. For an adequate handling of deviations the methodology of case-based reasoning fits perfectly, through its approach that similar problems have similar solutions. Thus, previous made experiences are transferred to currently regarded problems, under the assumption that a similar deviation has been handled successfully in the past. Necessary foundations from the field of workflow management with a focus on flexibility are presented first. As formal foundation, a constraint-based workflow model was developed that allows for a declarative specification of foremost sequential dependencies of tasks. Procedural and declarative models can be combined in the approach, as a transformation function was specified that converts procedural workflow models to declarative constraints. One main component of the approach is the constraint-based workflow engine that utilizes this declarative model as input for a constraint solving algorithm. This algorithm computes the worklist, which is proposed to the process participant during workflow execution. With predefined deviation handling strategies that determine how the constraint model is modified in order to restore consistency, the support is continuous even in case of deviations. The second major component of the proposed approach constitutes the case-based deviation management, which aims at improving the support of process participants on the basis of experiential knowledge. For the retrieve phase, a sophisticated similarity measure was developed that integrates specific characteristics of deviating workflows and combines several sequence similarity measures. Two alternative methods for the reuse phase were developed, a null adaptation and a generative adaptation. The null adaptation simply proposes tasks from the most similar workflow as work items, whereas the generative adaptation modifies the constraint-based workflow model based on the most similar workflow in order to re-enable the constraint-based workflow engine to suggest work items. The experimental evaluation of the approach consisted of a simulation of several types of process participants in the exemplary domain of deficiency management in construction. The results showed high utility values and a promising potential for an investigation of the transfer on other domains and the applicability in practice, which is part of future work. Concluding, the contributions are summarized and research perspectives are pointed out.

Survey data can be viewed as incomplete or partially missing from a variety of perspectives and there are different ways of dealing with this kind of data in the prediction and the estimation of economic quantities. In this thesis, we present two selected research contexts in which the prediction or estimation of economic quantities is examined under incomplete survey data. These contexts are first the investigation of composite estimators in the German Microcensus (Chapters 3 and 4) and second extensions of multivariate Fay-Herriot (MFH) models (Chapters 5 and 6), which are applied to small area problems. Composite estimators are estimation methods that take into account the sample overlap in rotating panel surveys such as the German Microcensus in order to stabilise the estimation of the statistics of interest (e.g. employment statistics). Due to the partial sample overlaps, information from previous samples is only available for some of the respondents, so the data are partially missing. MFH models are model-based estimation methods that work with aggregated survey data in order to obtain more precise estimation results for small area problems compared to classical estimation methods. In these models, several variables of interest are modelled simultaneously. The survey estimates of these variables, which are used as input in the MFH models, are often partially missing. If the domains of interest are not explicitly accounted for in a sampling design, the sizes of the samples allocated to them can, by chance, be small. As a result, it can happen that either no estimates can be calculated at all or that the estimated values are not published by statistical offices because their variances are too large.

This thesis focuses on the issues in price measurement and consists of three chapters. Due to outdated weighting information, a Laspeyres-based consumer price index (CPI) is prone to accumulating upward bias. Therefore, chapter 1 introduces and examines simple and transparent revision approaches that retrospectively address the source of the bias. They provide a consistent long-run time series of the CPI and require no additional information. Furthermore, a coherent decomposition of the bias into the contributions of individual product groups is developed. In a case study, the approaches are applied to a Laspeyres-based CPI. The empirical results confirm the theoretical predictions. The proposed revision approaches are adoptable not only to most national CPIs but also to other price-level measures such as the producer price index or the import and export price indices. Chapter 2 is dedicated to the measurement of import and export price indices. Such indices are complicated by the impact of exchange rates. These indices are usually also compiled by some Laspeyres type index. Therefore, substitution bias is an issue. The terms of trade (ratio of export and import price index) are therefore also likely to be distorted. The underlying substitution bias accumulates over time. The present article applies a simple and transparent retroactive correction approach that addresses the source of the substitution bias and produces meaningful long-run time series of import and export price levels and, therefore, of the terms of trade. Furthermore, an empirical case study is conducted that demonstrates the efficacy and versatility of the correction approach. Chapter 3 leaves the field of index revision and studies another issue in price measurement, namely, the economic evaluation of digital products in monetary terms that have zero market prices. This chapter explores different methods of economic valuation and pricing of free digital products and proposes an alternative way to calculate the economic value and a shadow price of free digital products: the Usage Cost Model (UCM). The goal of the chapter is, first of all, to formulate a theoretical framework and incorporate an alternative measure of the value of free digital products. However, an empirical application is also made to show the work of the theoretical model. Some conclusions on applicability are drawn at the end of the chapter.

Let K be a compact subset of the complex plane. Then the family of polynomials P is dense in A(K), the space of all continuous functions on K that are holomorphic on the interior of K, endowed with the uniform norm, if and only if the complement of K is connected. This is the statement of Mergelyan's celebrated theorem. There are, however, situations where not all polynomials are required to approximate every f ϵ A(K) but where there are strict subspaces of P that are still dense in A(K). If, for example, K is a singleton, then the subspace of all constant polynomials is dense in A(K). On the other hand, if 0 is an interior point of K, then no strict subspace of P can be dense in A(K). In between these extreme cases, the situation is much more complicated. It turns out that it is mostly determined by the geometry of K and its location in the complex plane which subspaces of P are dense in A(K). In Chapter 1, we give an overview of the known results. Our first main theorem, which we will give in Chapter 3, deals with the case where the origin is not an interior point of K. We will show that if K is a compact set with connected complement and if 0 is not an interior point of K, then any subspace Q ⊂ P which contains the constant functions and all but finitely many monomials is dense in A(K). There is a close connection between lacunary approximation and the theory of universality. At the end of Chapter 3, we will illustrate this connection by applying the above result to prove the existence of certain universal power series. To be specific, if K is a compact set with connected complement, if 0 is a boundary point of K and if A_0(K) denotes the subspace of A(K) of those functions that satisfy f(0) = 0, then there exists an A_0(K)-universal formal power series s, where A_0(K)-universal means that the family of partial sums of s forms a dense subset of A_0(K). In addition, we will show that no formal power series is simultaneously universal for all such K. The condition on the subspace Q in the main result of Chapter 3 is quite restrictive, but this should not be too surprising: The result applies to the largest possible class of compact sets. In Chapter 4, we impose a further restriction on the compact sets under consideration, and this will allow us to weaken the condition on the subspace Q. The result that we are going to give is similar to one of those presented in the first chapter, namely the one due to Anderson. In his article “Müntz-Szasz type approximation and the angular growth of lacunary integral functions”, he gives a criterion for a subspace Q of P to be dense in A(K) where K is entirely contained in some closed sector with vertex at the origin. We will consider compact sets with connected complement that are -- with the possible exception of the origin -- entirely contained in some open sector with vertex at the origin. What we are going to show is that if K\{0} is contained in an open sector of opening angle 2α and if Λ is some subset of the nonnegative integers, then the span of {z → z^λ : λ ϵ Λ} is dense in A(K) whenever 0 ϵ Λ and some Müntz-type condition is satisfied. Conversely, we will show that if a similar condition is not satisfied, then we can always find a compact set K with connected complement such that K\{0} is contained in some open sector of opening angle 2α and such that the span of {z → z^λ : λ ϵ Λ} fails to be dense in A(K).

In common shape optimization routines, deformations of the computational mesh usually suffer from decrease of mesh quality or even destruction of the mesh. To mitigate this, we propose a theoretical framework using so-called pre-shape spaces. This gives an opportunity for a unified theory of shape optimization, and of problems related to parameterization and mesh quality. With this, we stay in the free-form approach of shape optimization, in contrast to parameterized approaches that limit possible shapes. The concept of pre-shape derivatives is defined, and according structure and calculus theorems are derived, which generalize classical shape optimization and its calculus. Tangential and normal directions are featured in pre-shape derivatives, in contrast to classical shape derivatives featuring only normal directions on shapes. Techniques from classical shape optimization and calculus are shown to carry over to this framework, and are collected in generality for future reference. A pre-shape parameterization tracking problem class for mesh quality is in- troduced, which is solvable by use of pre-shape derivatives. This class allows for non-uniform user prescribed adaptations of the shape and hold-all domain meshes. It acts as a regularizer for classical shape objectives. Existence of regularized solu- tions is guaranteed, and corresponding optimal pre-shapes are shown to correspond to optimal shapes of the original problem, which additionally achieve the user pre- scribed parameterization. We present shape gradient system modifications, which allow simultaneous nu- merical shape optimization with mesh quality improvement. Further, consistency of modified pre-shape gradient systems is established. The computational burden of our approach is limited, since additional solution of possibly larger (non-)linear systems for regularized shape gradients is not necessary. We implement and com- pare these pre-shape gradient regularization approaches for a 2D problem, which is prone to mesh degeneration. As our approach does not depend on the choice of forms to represent shape gradients, we employ and compare weak linear elasticity and weak quasilinear p-Laplacian pre-shape gradient representations. We also introduce a Quasi-Newton-ADM inspired algorithm for mesh quality, which guarantees sufficient adaption of meshes to user specification during the rou- tines. It is applicable in addition to simultaneous mesh regularization techniques. Unrelated to mesh regularization techniques, we consider shape optimization problems constrained by elliptic variational inequalities of the first kind, so-called obstacle-type problems. In general, standard necessary optimality conditions cannot be formulated in a straightforward manner for such semi-smooth shape optimization problems. Under appropriate assumptions, we prove existence and convergence of adjoints for smooth regularizations of the VI-constraint. Moreover, we derive shape derivatives for the regularized problem and prove convergence to a limit object. Based on this analysis, an efficient optimization algorithm is devised and tested numerically. All previous pre-shape regularization techniques are applied to a variational inequality constrained shape optimization problem, where we also create customized targets for increased mesh adaptation of changing embedded shapes and active set boundaries of the constraining variational inequality.

Zeitgleich mit stetig wachsenden gesellschaftlichen Herausforderungen haben im vergangenen Jahrzehnt Sozialunternehmen stark an Bedeutung gewonnen. Sozialunternehmen verfolgen das Ziel, mit unternehmerischen Mitteln gesellschaftliche Probleme zu lösen. Da der Fokus von Sozialunternehmen nicht hauptsächlich auf der eigenen Gewinnmaximierung liegt, haben sie oftmals Probleme, geeignete Unternehmensfinanzierungen zu erhalten und Wachstumspotenziale zu verwirklichen. Zur Erlangung eines tiefergehenden Verständnisses des Phänomens der Sozialunternehmen untersucht der erste Teil dieser Dissertation anhand von zwei Studien auf der Basis eines Experiments das Entscheidungsverhalten der Investoren von Sozialunternehmen. Kapitel 2 betrachtet daher das Entscheidungsverhalten von Impact-Investoren. Der von diesen Investoren verfolgte Investmentansatz „Impact Investing“ geht über eine reine Orientierung an Renditen hinaus. Anhand eines Experiments mit 179 Impact Investoren, die insgesamt 4.296 Investitionsentscheidungen getroffen haben, identifiziert eine Conjoint-Studie deren wichtigste Entscheidungskriterien bei der Auswahl der Sozialunternehmen. Kapitel 3 analysiert mit dem Fokus auf sozialen Inkubatoren eine weitere spezifische Gruppe von Unterstützern von Sozialunternehmen. Dieses Kapitel veranschaulicht auf der Basis des Experiments die Motive und Entscheidungskriterien der Inkubatoren bei der Auswahl von Sozialunternehmen sowie die von ihnen angebotenen Formen der nichtfinanziellen Unterstützung. Die Ergebnisse zeigen unter anderem, dass die Motive von sozialen Inkubatoren bei der Unterstützung von Sozialunternehmen unter anderem gesellschaftlicher, finanzieller oder reputationsbezogener Natur sind. Der zweite Teil erörtert auf der Basis von zwei quantitativ empirischen Studien, inwiefern die Registrierung von Markenrechten sich zur Messung sozialer Innovationen eignet und mit finanziellem und sozialem Wachstum von sozialen Startups in Verbindung steht. Kapitel 4 erörtert, inwiefern Markenregistrierungen zur Messung von sozialen Innovationen dienen können. Basierend auf einer Textanalyse der Webseiten von 925 Sozialunternehmen (> 35.000 Unterseiten) werden in einem ersten Schritt vier Dimensionen sozialer Innovationen (Innovations-, Impact-, Finanz- und Skalierbarkeitsdimension) ermittelt. Darauf aufbauend betrachtet dieses Kapitel, wie verschiedene Markencharakteristiken mit den Dimensionen sozialer Innovationen zusammenhängen. Die Ergebnisse zeigen, dass insbesondere die Anzahl an registrierten Marken als Indikator für soziale Innovationen (alle Dimensionen) dient. Weiterhin spielt die geografische Reichweite der registrierten Marken eine wichtige Rolle. Aufbauend auf den Ergebnissen von Kapitel 4 untersucht Kapitel 5 den Einfluss von Markenregistrierungen in frühen Unternehmensphasen auf die weitere Entwicklung der hybriden Ergebnisse von sozialen Startups. Im Detail argumentiert Kapitel 5, dass sowohl die Registrierung von Marken an sich als auch deren verschiedene Charakteristiken unterschiedlich mit den sozialen und ökonomischen Ergebnissen von sozialen Startups in Verbindung stehen. Anhand eines Datensatzes von 485 Sozialunternehmen zeigen die Analysen aus Kapitel 5, dass soziale Startups mit einer registrierten Marke ein vergleichsweise höheres Mitarbeiterwachstum aufweisen und einen größeren gesellschaftlichen Beitrag leisten. Die Ergebnisse dieser Dissertation weiten die Forschung im Social Entrepreneurship-Bereich weiter aus und bieten zahlreiche Implikationen für die Praxis. Während Kapitel 2 und 3 das Verständnis über die Eigenschaften von nichtfinanziellen und finanziellen Unterstützungsorganisationen von Sozialunternehmen vergrößern, schaffen Kapitel 4 und 5 ein größeres Verständnis über die Bedeutung von Markenanmeldungen für Sozialunternehmen.

Die Nutzung von Cloud-Software und skalierten Web-Apps sowie Web-Services hat in den letzten Jahren extrem zugenommen, was zu einem Anstieg der Hochleistungs-Cloud-Rechenzentren führt. Neben der Verbesserung der Dienste spiegelt sich dies auch im weltweiten Stromverbrauch von Rechenzentren wider, der derzeit etwas mehr als 1% (entspricht etwa 200 TWh) beträgt. Prognosen sagen für die kommenden Jahre einen massiven Anstieg des Stromverbrauchs von Cloud-Rechenzentren voraus. Grundlage dieser Bewegung ist die Beschleunigung von Administration und Entwicklung, die unter anderem durch den Einsatz von Containern entsteht. Als Basis für Millionen von Web-Apps und -Services beschleunigen sie die Skalierung, Bereitstellung und Aktualisierung von Cloud-Diensten. In dieser Arbeit wird aufgezeigt, dass Container zusätzlich zu ihren vielen technischen Vorteilen Möglichkeiten zur Reduzierung des Energieverbrauchs von Cloud-Rechenzentren bieten, die aus einer ineffizienten Konfiguration von Containern sowie Container-Laufzeitumgebungen resultieren. Basierend auf einer Umfrage und einer Auswertung geeigneter Literatur werden in einem ersten Schritt wahrscheinliche Probleme beim Einsatz von Containern aufgedeckt. Weiterhin wird die Sensibilität von Administratoren und Entwicklern bezüglich des Energieverbrauchs von Container-Software ermittelt. Aufbauend auf den Ergebnissen der Umfrage und der Auswertung werden anhand von Standardszenarien im Containerumfeld die Komponenten des de facto Standards Docker untersucht. Anschließend wird ein Modell, bestehend aus Messmethodik, Empfehlungen für eine effiziente Konfiguration von Containern und Tools, beschrieben. Die Messmethodik sollte einfach anwendbar sein und gängige Technologien in Rechenzentren unterstützen. Darüber hinaus geben die Handlungsempfehlungen sowohl Entwicklern als auch Administratoren die Möglichkeit zu entscheiden, welche Komponenten von Docker im Sinne eines energieeffizienten Einsatzes und in Abhängigkeit vom Einsatzszenario der Container genutzt werden sollten und welche weggelassen werden könnten. Die resultierenden Container können im Sinne der Energieeffizienz auf Servern und gleichermaßen auf PCs und Embedded Systems (als Teil von IoT und Edge Cloud) eingesetzt werden und somit nicht nur dem zuvor beschriebenen Problem in der Cloud entgegenwirken. Die Arbeit beschäftigt sich zudem mit dem Verhalten von skalierten Webanwendungen. Gängige Orchestrierungswerkzeuge definieren statische Skalierungspunkte für Anwendungen, die in den meisten Fällen auf der CPU-Auslastung basieren. Es wird dargestellt, dass dabei weder die tatsächliche Erreichbarkeit noch der Stromverbrauch der Anwendungen berücksichtigt werden. Es wird der Autoscaler des Open-Source-Container-Orchestrierungswerkzeugs Kubernetes betrachtet, der um ein neu entwickeltes Werkzeug erweitert wird. Es wird deutlich, dass eine dynamische Anpassung der Skalierungspunkte durch eine Vorabauswertung gängiger Nutzungsszenarien sowie Informationen über deren Stromverbrauch und die Erreichbarkeit bei steigender Last erreicht werden kann. Schließlich folgt eine empirische Untersuchung des generierten Modells in Form von drei Simulationen, die die Auswirkungen auf den Energieverbrauch von Cloud-Rechenzentren darlegen sollen.

Surveys play a major role in studying social and behavioral phenomena that are difficult to observe. Survey data provide insights into the determinants and consequences of human behavior and social interactions. Many domains rely on high quality survey data for decision making and policy implementation including politics, health, business, and the social sciences. Given a certain research question in a specific context, finding the most appropriate survey design to ensure data quality and keep fieldwork costs low at the same time is a difficult task. The aim of examining survey research methodology is to provide the best evidence to estimate the costs and errors of different survey design options. The goal of this thesis is to support and optimize the accumulation and sustainable use of evidence in survey methodology in four steps: (1) Identifying the gaps in meta-analytic evidence in survey methodology by a systematic review of the existing evidence along the dimensions of a central framework in the field (2) Filling in these gaps with two meta-analyses in the field of survey methodology, one on response rates in psychological online surveys, the other on panel conditioning effects for sensitive items (3) Assessing the robustness and sufficiency of the results of the two meta-analyses (4) Proposing a publication format for the accumulation and dissemination of metaanalytic evidence

In vielen Branchen und vor allem in großen Unternehmen gehört eine Unterstützung von Geschäftsprozessen durch Workflow-Management-Systeme zum gelebten Alltag. Im Zentrum steht dabei die Steuerung kontrollflussorientierter Abläufe, während Prozesse mit einem Schwerpunkt auf Daten, Informationen und Wissen meist außen vor bleiben. Solche wissensintensive Prozesse (engl.: knowledge intensive processes) (KiPs) sind Untersuchungsgegenstand in vielen aktuellen Studien, welche ein derzeit aktives Forschungsgebiet formen. Im Vordergrund solcher KiPs steht dabei das durch die mitwirkenden Personen eingebrachte Wissen, welches in einem wesentlichen Maß die Prozessausführung beeinflusst, hierdurch jedoch die Bearbeitung komplexer und meist hoch volatiler Prozesse ermöglicht. Hierbei handelt es sich zumeist um entscheidungsintensive Prozesse, Prozesse zur Wissensakquisition oder Prozesse, die zu einer Vielzahl unterschiedlicher Prozessabläufe führen können. Im Rahmen dieser Arbeit wird ein Ansatz entwickelt und vorgestellt, der sich der Modellierung, Visualisierung und Ausführung wissensintensiver Prozesse unter Verwendung Semantischer Technologien widmet. Hierzu werden als die zentralen Anforderungen zur Ausführung von KiPs Flexibilität, Adaptivität und Zielorientierung definiert. Daran anknüpfend werden drei zentrale Grundprinzipien der Prozessmodellierung identifiziert, welche in der ersten Forschungsfrage aufgegriffen werden: „Können die drei Grundprinzipien in einem einheitlichen datenzentrierten, deklarativen, semantischen Ansatz (welcher mit ODD-BP bezeichnet wird) kombiniert werden und können damit die zentralen Anforderungen von KiPs erfüllt werden?” Die Grundlage für ODD-BP bildet ein Metamodell, welches als Sprachkonstrukt fungiert und die Definition der angestrebten Prozessmodelle erlaubt. Darauf aufbauend wird mit Hilfe von Inferenzierungsregeln ein Verfahren entwickelt, welches das Schlussfolgern von Prozesszuständen ermöglicht und somit eine klassische Workflow-Engine überflüssig macht. Zudem wird eine Methodik eingeführt, die für jede in einem Prozess mitwirkende Person eine maßgeschneiderte, adaptive Prozessvisualisierung ermöglicht, um neben dem Freiheitsgrad der Flexibilität auch eine fundierte Prozessunterstützung bei der Ausführung von KiPs leisten zu können. All dies erfolgt innerhalb einer einheitlichen Wissensbasis, die zum einen die Grundlage für eine vollständige semantische Prozessmodellierung bildet und zum anderen die Möglichkeit zur Integration von Expertenwissen eröffnet. Dieses Expertenwissen kann einen expliziten Beitrag bei der Ausführung wissensintensiver Prozesse leisten und somit die Kollaboration von Mensch und Maschine durch Technologien der symbolischen KI ermöglichen. Die zweite Forschungsfrage greift diesen Aspekt auf: „Kann in dem ODD-BP Ansatz ontologisches Wissen so integriert werden, dass dieses in einer Prozessausführung einen Beitrag leistet?” Das Metamodell sowie die entwickelten Methoden und Verfahren werden in einem prototypischen, generischen System realisiert, welches grundsätzlich für alle Anwendungsgebiete mit KiPs geeignet ist. Zur Validierung des ODD-BP Ansatzes erfolgt eine Ausrichtung auf den Anwendungsfall einer Notrufabfrage aus dem Leitstellenumfeld. Im Zuge der Evaluation wird gezeigt, wie dieser wissensintensive Ablauf von einer flexiblen, adaptiven und zielorientierten Prozessausführung profitiert. Darüber hinaus wird medizinisches Expertenwissen in den Prozessablauf integriert und es wird nachgewiesen, wie dieses zu verbesserten Prozessergebnissen beiträgt. Wissensintensive Prozesse stellen Unternehmen und Organisationen in allen Branchen und Anwendungsfällen derzeit vor große Herausforderungen und die Wissenschaft und Forschung widmet sich der Suche nach praxistauglichen Lösungen. Diese Arbeit präsentiert mit ODD-BP einen vielversprechenden Ansatz, indem die Möglichkeiten Semantischer Technologien dazu genutzt werden, eine eng verzahnte Zusammenarbeit zwischen Mensch und Maschine bei der Ausführung von KiPs zu ermöglichen. Die zur Evaluation fokussierte Notrufabfrage innerhalb von Leitstellen stellt zudem einen höchst relevanten Anwendungsfall dar, da in einem akuten Notfall in kürzester Zeit Entscheidungen getroffen werden müssen, um weitreichenden Schaden abwenden und Leben retten zu können. Durch die Berücksichtigung umfassender Datenmengen und das Ausnutzen verfügbaren Expertenwissens kann so eine schnelle Lagebewertung mit Hilfe der maschinellen Unterstützung erreicht und der Mensch beim Treffen von richtigen Entscheidungen unterstützt werden.