Biotic communities experienced significant changes in recent decades. Climate change, the overexploitation of natural resources and the immigration of invasive species are major drivers for this change and present unknown challenges for communities worldwide. To assess the impact of these drivers, standardised long-term studies are required, which are currently lacking for many species and ecosystems. Analysing environmental samples and the DNA of associated organisms using metabarcoding and high-throughput sequencing provides a cost-efficient and rapid way to generate the high-resolution biodiversity data which is so direly needed. In this thesis, I demonstrate the great potential of using samples from the German Environ- mental Specimen Bank (ESB), a long-term monitoring archive that has been collecting and cryogenically storing highly standardised environmental samples since 1985. Modern analytical methods enable retrospective long-term biodiversity monitoring using these samples. In the first chapter, I illustrate metabarcoding as a central method, discussing its strengths and drawbacks, how to avoid them, and new application approaches. This chapter provides the methodological basis for the following studies. In subsequent chapters, I present time series analyses of communities associated with these environmental samples. While for Chapter two the focus is on terrestrial arthropod communities, in Chapter three aquatic and terrestrial communities across the tree of life are analysed. A null model was developed for this survey for robust conclusions. The studies covered the last three decades and revealed substantial compositional changes across all ecosystems. These changes deviated significantly from the model, indicating that the changes are occurring faster than expected. Moreover, a trend toward homogenization in many terrestrial communities was uncovered. Climate change and the immigration of invasive species in combination with the loss of site-specific species are suspected to be the main drivers for this. In a follow-up study, changes of arthropod communities in German and South Korean terrestrial ecosystems were compared using ESB leaf samples from these two countries. Since both ESBs are harmonised in sample collection and processing, comparative analyses were applicable. This research covered the last decade and revealed substantial declines in species richness in Korea. Abiotic and biotic factors are discussed as potential drivers of these results. Finally, the possibility of assessing tree health by analysing changes in functional fungal groups using German ESB samples was investigated. The results indicate that increasing infestation of specific functional groups is a proxy for declining tree health, with further analyses planned. In this dissertation, I present the great potential of samples from long-term monitoring archives to conduct retrospective biodiversity trend analyses across the tree of life. As technologies evolve, these samples will help to understand past and predict future ecosystem changes.

Entrepreneurship is recognized as an important discipline to achieve sustainable development and to address sustainability goals without losing sight of economic aspects. However, entrepreneurship rates are rather low in many industrialized countries with high income levels. Research clearly shows that there is a gap in the entrepreneurial process between intentions and subsequent actions. This means that not everyone with entrepreneurial ambitions also follows through and implements actions. This gap also exists for aspects of sustainability. As a result, there is a need to better understand the traditional and sustainability-focused entrepreneurial process in order to increase corresponding actions. This dissertation offers such a comprehensive perspective and sheds light on individual and contextual predictors for traditional and sustainability-focused behavior of entrepreneurs and self-employed across four studies. The first three studies focus on individual predictors. By providing a systematic literature review with 107 articles, Chapter 2 highlights the ambivalent role of religion for the entrepreneurial process. Relying on the theory of planned behavior (TPB) as theoretical basis, religion can have positive effects on entrepreneurial attitudes and behavioral control, but also negative consequences for other aspects of behavioral control and subjective norms due to religious restrictions. The quantitative empirical study in Chapter 3 similarly relies on the TPB and sheds light on individual perceptual factors influencing the sustainability-related intention-action gap in entrepreneurship. Using data from the 2021 Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) including 22,008 early-stage entrepreneurs from 44 countries worldwide, the results support our theoretical reasoning that sustainability-focused intentions are positively related to social entrepreneurial actions. In addition, it is demonstrated that positive perceptual moderators such as self-efficacy and knowing other entrepreneurs as role models strengthen this relationship while a negative perception such as fear of failure restricts social actions in early-stage entrepreneurship. The next quantitative empirical study in Chapter 4 examines the behavioral consequences of well-being at a sample of 6,955 German self-employed during COVID-19. This chapter builds on two complementary behavioral perspectives to predict how reductions in financial and non-financial well-being relate to investments in venture development. In this regard, reductions in financial well-being are positively related to time investments, supporting the performance feedback perspective in terms of higher search efforts under negative performance. In contrast, reductions in non-financial well-being are negatively related to time and monetary investments, yielding support for the broadening-and-build perspective indicating that negative psychological experiences narrow the thought-action repertoire and hinder resource deployment. The insights across these first three studies about individual predictors indicate that many different, subjective beliefs, perceptions and emotional states can influence the entrepreneurial process making entrepreneurship and self-employment highly individualized disciplines. The last quantitative empirical study provides an explorative view on a large number of contextual predictors for social and ecological considerations in entrepreneurial actions. Combining GEM data from 2021 on country level with further information from the World Bank and the OECD, a machine learning approach is employed on a sample of 84 countries worldwide. The results suggest that governmental and regulatory as well as cultural factors are relevant to predict social and ecological considerations. Moreover, market-related aspects are shown to be relevant predictors, especially socio-economic factors for social considerations and economic factors for ecological considerations. Overall, the four studies in this dissertation highlight the complexity of the entrepreneurial process being determined by many different individual and contextual factors. Due to the multitude of potential predictors, this dissertation can only give an initial overview of a selection of factors with many more aspects and interdependencies still to be examined by future research.

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.

Small and medium-sized enterprises (SMEs) and mid-sized companies are vital contributors to the global economy, driving employment growth, fostering innovation, and enhancing international competiveness. However, in the aftermath of the Great Financial Crisis (GFC) and the collapse of the large finance company CIT Group, which provided 60% loans to US middle-market firms, banks reduced their lending activities. Thus, it became challenging for firms to obtain long-term loans. The financing gap has increased further due to high interest rates, the COVID-19 pandemic, the unstable situation in the real estate market as well as higher costs due to the adoption of digital infrastructure and sustainability goals. Therefore, the search for alternative financing solutions outside bank lending and public markets became unavoidable for SMEs and mid-sized companies. Private debt funds entered the market, and, since the GFC, they have played a crucial role in offering alternative financing for firms globally. Private debt fund managers raise capital commitments through closed-end funds (like private equity) and make senior loans (like banks) directly to, mostly, middlemarket firms. The private debt market has experienced rapid growth in recent decades. The private debt funds assets under management (AuM) increased by 380% from 2008 to 2022, reaching $1.5 trillion AuM in 2022 . The high growth of private debt shows great interest from investors in this alternative asset class and lucrative investment opportunities. Despite its substantial and growing size, the private debt market is relatively understudied. This dissertation introduces private debt as an important alternative financing source, provides an overview of private debt strategies, seniority, and structure, discusses the legal considerations concerning private debt, and briefly compares the two most mature private debt markets: Europe and the U.S. Moreover, it assesses the size of the European private debt market and compares its development in different European regions. Furthermore, it examines in detail the business model of private debt funds based on a survey of 191 European and U.S. private debt managers with private debt assets under management of over $390 billion. Finally, it delves deeper into the relationship between private debt and private equity funds and their role in buyouts. To sum up, this dissertation provides a basis and inspiration for future research to expand upon and dive deeper into the world of private debt funds, their business model, and their impact on portfolio companies and the economy as a whole.

Mit Fokus auf historischen Liedern aus Deutschland, Frankreich, England, Irland, den USA, Österreich, den Niederlanden, Slowenien, Polen, Italien, Neuseeland und der Schweiz zeichnet dieses Buch ein breites und lebendiges Panorama der Frühen Neuzeit. Die Sammlung spannt den Bogen von klassischen Lerninhalten bis hin zu aktuellen Forschungsperspektiven: Reformation, Amerikanische und Französische Revolution, Höfische Kultur, Kriminalität, Seefahrt, militärische und diplomatische Konflikte sind ebenso Thema wie die Geschlechterordnung, globale Migration oder historische Identitäts- und Fremdheitsvorstellungen. Eine systematische Verschlagwortung erleichtert den Zugriff und erlaubt vielfältige Kombinationen für die akademische und schulische Lehre. Jedes der 101 Lieder wird mit Informationen zum historischen Kontext, zur Überlieferung und zu online verfügbaren Vertonungen präsentiert. Hinzu kommen Aufgabenstellungen und Anregungen für die Diskussion im Kurs oder Seminar. Auch das lange und mitunter ambivalente Nachleben neuzeitlicher Lieder im 19. und 20. Jahrhundert wird beleuchtet. Darüber hinaus bietet der Band methodische Hinweise und Anregungen zur eigenständigen Recherche und Analyse historischer Lieder, beispielsweise in Seminar- und Abschlussarbeiten.

Case-Based Reasoning (CBR) is a symbolic Artificial Intelligence (AI) approach that has been successfully applied across various domains, including medical diagnosis, product configuration, and customer support, to solve problems based on experiential knowledge and analogy. A key aspect of CBR is its problem-solving procedure, where new solutions are created by referencing similar experiences, which makes CBR explainable and effective even with small amounts of data. However, one of the most significant challenges in CBR lies in defining and computing meaningful similarities between new and past problems, which heavily relies on domain-specific knowledge. This knowledge, typically only available through human experts, must be manually acquired, leading to what is commonly known as the knowledge-acquisition bottleneck. One way to mitigate the knowledge-acquisition bottleneck is through a hybrid approach that combines the symbolic reasoning strengths of CBR with the learning capabilities of Deep Learning (DL), a sub-symbolic AI method. DL, which utilizes deep neural networks, has gained immense popularity due to its ability to automatically learn from raw data to solve complex AI problems such as object detection, question answering, and machine translation. While DL minimizes manual knowledge acquisition by automatically training models from data, it comes with its own limitations, such as requiring large datasets, and being difficult to explain, often functioning as a "black box". By bringing together the symbolic nature of CBR and the data-driven learning abilities of DL, a neuro-symbolic, hybrid AI approach can potentially overcome the limitations of both methods, resulting in systems that are both explainable and capable of learning from data. The focus of this thesis is on integrating DL into the core task of similarity assessment within CBR, specifically in the domain of process management. Processes are fundamental to numerous industries and sectors, with process management techniques, particularly Business Process Management (BPM), being widely applied to optimize organizational workflows. Process-Oriented Case-Based Reasoning (POCBR) extends traditional CBR to handle procedural data, enabling applications such as adaptive manufacturing, where past processes are analyzed to find alternative solutions when problems arise. However, applying CBR to process management introduces additional complexity, as procedural cases are typically represented as semantically annotated graphs, increasing the knowledge-acquisition effort for both case modeling and similarity assessment. The key contributions of this thesis are as follows: It presents a method for preparing procedural cases, represented as semantic graphs, to be used as input for neural networks. Handling such complex, structured data represents a significant challenge, particularly given the scarcity of available process data in most organizations. To overcome the issue of data scarcity, the thesis proposes data augmentation techniques to artificially expand the process datasets, enabling more effective training of DL models. Moreover, it explores several deep learning architectures and training setups for learning similarity measures between procedural cases in POCBR applications. This includes the use of experience-based Hyperparameter Optimization (HPO) methods to fine-tune the deep learning models. Additionally, the thesis addresses the computational challenges posed by graph-based similarity assessments in CBR. The traditional method of determining similarity through subgraph isomorphism checks, which compare nodes and edges across graphs, is computationally expensive. To alleviate this issue, the hybrid approach seeks to use DL models to approximate these similarity calculations more efficiently, thus reducing the computational complexity involved in graph matching. The experimental evaluations of the corresponding contributions provide consistent results that indicate the benefits of using DL-based similarity measures and case retrieval methods in POCBR applications. The comparison with existing methods, e.g., based on subgraph isomorphism, shows several advantages but also some disadvantages of the compared methods. In summary, the methods and contributions outlined in this work enable more efficient and robust applications of hybrid CBR and DL in process management applications.

The goal of this work is to compare operators that are defined on probably varying Hilbert spaces. Distance concepts for operators as well as convergence concepts for such operators are explained and examined. For distance concepts we present three main notions. All have in common that they use space-linking operators that connect the spaces. At first, we look at unitary maps and compare the unitary orbits of the operators. Then, we consider isometric embeddings, which is based on a concept of Joachim Weidmann. Then we look at contractions but with more norm equations in comparison. The latter idea is based on a concept of Olaf Post called quasi-unitary equivalence. Our main result is that the unitary and isometric distances are equal provided the operators are both self-adjoint and have 0 in their essential spectra. In the third chapter, we focus specifically on the investigation of these distance terms for compact operators or operators in p-Schatten classes. In this case, the interpretation of the spectra as null sequences allows further distance investigation. Chapter four deals mainly with convergence terms of operators on varying Hilbert spaces. The analyses in this work deal exclusively with concepts of norm resolvent convergence. The main conclusion of the chapter is that the generalisation for norm resolvent convergence of Joachim Weidmann and the generalisation of Olaf Post, called quasi-unitary equivalence, are equivalent to each other. In addition, we specify error bounds and deal with the convergence speed of both concepts. Two important implications of these convergence notions are that the approximation is spectrally exact, i.e., the spectra converge suitably, and that the convergence is transferred to the functional calculus of the bounded functions vanishing at infinity.

In this dissertation, I analyze how large players in financial markets exert influence on smaller players and how this affects the decisions of the large ones. I focus on how the large players process information in an uncertain environment, form expectations and communicate these to smaller players through their actions. I examine these relationships empirically in the foreign exchange market and in the context of a game-theoretic model of an investment project. In Chapter 2, I investigate the relationship between the foreign exchange trading activity of large US-based market participants and the volatility of the nominal spot exchange rate. Using a novel dataset, I utilize the weekly growth rate of aggregate foreign currency positions of major market participants to proxy trading activity in the foreign exchange market. By estimating the heterogeneous autoregressive model of realized volatility (HAR-RV), I find evidence of a positive relationship between trading activity and volatility, which is mainly driven by unexpected changes in trading activity and is asymmetric for some of the currencies considered. My results contribute to the understanding of the drivers of exchange rate volatility and the role of large players in the flow of information in financial markets. In Chapters 3 and 4, I consider a sequential global game of an investment project to examine how a large creditor influences the decisions of small creditors with her lending decision. I pay particular attention to the timing of the large player’s decision, i.e. whether she makes her decision to roll over a credit before or after the small players. I show that she faces a trade-off between signaling to and learning from small creditors. By being a focal point for coordination, her actions have a substantial impact on the probability of coordination failure and the failure of the investment project. I investigate the sensitivity of the equilibrium by comparing settings with perfect and imperfect learning. The results highlight the importance of signaling and provide a new perspective on the idea of catalytic finance and the influence of a lender-of-last-resort in self-fulfilling debt crises.

Background: Large language models (LLMs) are increasingly used in mental health, showing promise in assessing disorders. However, concerns exist regarding their accuracy, reliability, and fairness. Societal biases and underrepresentation of certain populations may impact LLMs. Because LLMs are already used for clinical practice, including decision support, it is important to investigate potential biases to ensure a responsible use of LLMs. Anorexia nervosa (AN) and bulimia nervosa (BN) show a lifetime prevalence of 1%-2%, affecting more women than men. Among men, homosexual men face a higher risk of eating disorders (EDs) than heterosexual men. However, men are underrepresented in ED research, and studies on gender, sexual orientation, and their impact on AN and BN prevalence, symptoms, and treatment outcomes remain limited. Objectives: We aimed to estimate the presence and size of bias related to gender and sexual orientation produced by a common LLM as well as a smaller LLM specifically trained for mental health analyses, exemplified in the context of ED symptomatology and health-related quality of life (HRQoL) of patients with AN or BN. Methods: We extracted 30 case vignettes (22 AN and 8 BN) from scientific papers. We adapted each vignette to create 4 versions, describing a female versus male patient living with their female versus male partner (2 × 2 design), yielding 120 vignettes. We then fed each vignette into ChatGPT-4 and to “MentaLLaMA” based on the Large Language Model Meta AI (LLaMA) architecture thrice with the instruction to evaluate them by providing responses to 2 psychometric instruments, the RAND-36 questionnaire assessing HRQoL and the eating disorder examination questionnaire. With the resulting LLM-generated scores, we calculated multilevel models with a random intercept for gender and sexual orientation (accounting for within-vignette variance), nested in vignettes (accounting for between-vignette variance). Results: In ChatGPT-4, the multilevel model with 360 observations indicated a significant association with gender for the RAND-36 mental composite summary (conditional means: 12.8 for male and 15.1 for female cases; 95% CI of the effect –6.15 to -0.35; P=.04) but neither with sexual orientation (P=.71) nor with an interaction effect (P=.37). We found no indications for main effects of gender (conditional means: 5.65 for male and 5.61 for female cases; 95% CI –0.10 to 0.14; P=.88), sexual orientation (conditional means: 5.63 for heterosexual and 5.62 for homosexual cases; 95% CI –0.14 to 0.09; P=.67), or for an interaction effect (P=.61, 95% CI –0.11 to 0.19) for the eating disorder examination questionnaire overall score (conditional means 5.59-5.65 95% CIs 5.45 to 5.7). MentaLLaMA did not yield reliable results. Conclusions: LLM-generated mental HRQoL estimates for AN and BN case vignettes may be biased by gender, with male cases scoring lower despite no real-world evidence supporting this pattern. This highlights the risk of bias in generative artificial intelligence in the field of mental health. Understanding and mitigating biases related to gender and other factors, such as ethnicity, and socioeconomic status are crucial for responsible use in diagnostics and treatment recommendations.

Background: As digital mental health delivery becomes increasingly prominent, a solid evidence base regarding its efficacy is needed. Objective: This study aims to synthesize evidence on the comparative efficacy of systemic psychotherapy interventions provided via digital versus face-to-face delivery modalities. Methods: We followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for searching PubMed, Embase, Cochrane CENTRAL, CINAHL, PsycINFO, and PSYNDEX and conducting a systematic review and meta-analysis. We included randomized controlled trials comparing mental, behavioral, and somatic outcomes of systemic psychotherapy interventions using self- and therapist-guided digital versus face-to-face delivery modalities. The risk of bias was assessed with the revised Cochrane Risk of Bias tool for randomized trials. Where appropriate, we calculated standardized mean differences and risk ratios. We calculated separate mean differences for nonaggregated analysis. Results: We screened 3633 references and included 12 articles reporting on 4 trials (N=754). Participants were youths with poor diabetic control, traumatic brain injuries, increased risk behavior likelihood, and parents of youths with anorexia nervosa. A total of 56 outcomes were identified. Two trials provided digital intervention delivery via videoconferencing: one via an interactive graphic interface and one via a web-based program. In total, 23% (14/60) of risk of bias judgments were high risk, 42% (25/60) were some concerns, and 35% (21/60) were low risk. Due to heterogeneity in the data, meta-analysis was deemed inappropriate for 96% (54/56) of outcomes, which were interpreted qualitatively instead. Nonaggregated analyses of mean differences and CIs between delivery modalities yielded mixed results, with superiority of the digital delivery modality for 18% (10/56) of outcomes, superiority of the face-to-face delivery modality for 5% (3/56) of outcomes, equivalence between delivery modalities for 2% (1/56) of outcomes, and neither superiority of one modality nor equivalence between modalities for 75% (42/56) of outcomes. Consequently, for most outcome measures, no indication of superiority or equivalence regarding the relative efficacy of either delivery modality can be made at this stage. We further meta-analytically compared digital versus face-to-face delivery modalities for attrition (risk ratio 1.03, 95% CI 0.52-2.03; P=.93) and number of sessions attended (standardized mean difference –0.11; 95% CI –1.13 to –0.91; P=.83), finding no significant differences between modalities, while CIs falling outside the range of the minimal important difference indicate that equivalence cannot be determined at this stage. Conclusions: Evidence on digital and face-to-face modalities for systemic psychotherapy interventions is largely heterogeneous, limiting conclusions regarding the differential efficacy of digital and face-to-face delivery. Nonaggregated and meta-analytic analyses did not indicate the superiority of either delivery condition. More research is needed to conclude if digital and face-to-face delivery modalities are generally equivalent or if—and in which contexts—one modality is superior to another.