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This dissertation analyses external appointees and successions on boards and consists of three papers which are all empirical in nature. It provides insights into the present literature from a meta-perspective, enlarges the understanding of external successions to German executive bank boards and extends the rare number of studies on the internal supervisory bodies of bank institutions. The first paper highlights the existing literature: conducting a literature search process, the paper aggregates 102 empirical results from 28 journal articles and working papers published between 1990 and 2017. The meta-analysis focuses on how researchers address the build-in issue that outsiders are not randomly assigned to firms. The results reveal that the relationship of outside successions and performance varies significantly with the methodological characteristics of the original studies. The following two papers concentrate on successions in banking institutions. More specifically, the second study examines the appointments of executive directors external to the bank and the consequences of that appointment on bank performance. The study addresses in particular alternative explanations, i.e. outside selection and/or joint endogeneity, while examining external executive appointments and their consequences on bank performance. The second empirical paper lend significant support to the view that some outsiders are better predisposed to helping the bank turn around poor performance and that the selected proxies of managerial ability, which are based on the historical return on assets and risk-return efficiency measured at outsiders' former banks, are able to identify such good outsiders. Finally, the third paper considers the link between the executive and the supervisory board. The study points to the conclusion that newly appointed executives to the supervisory board differ from their non-appointed counterparts with a particular set of experiences. The study provides evidence for the view that the pre-appointment financial situation, measured by several proxies of bank risk and performance, has significant influence on the decision to appoint such an experienced member to the supervisory board. This dissertation is framed by an introduction and concluding chapter where the author reflects on the research questions of her empirical studies, summarizes the results and identifies some possibilities for future research.
Sustainability transitions research proposes fundamental changes of societal systems' organisation to overcome persistent societal challenges, such as climate change or biodiversity loss, and allowing systems to become more sustainable. This thesis adresses an underlying tension in sustainability transitions research: between transitions as an open-ended process of fundamental change and the normative direction of this change: sustainability. In doing so, three themes are in the focus of the research: individual agency, normativity and transdisciplinary collaboration. Thereby, the thesis aims to strengthen process-oriented and potentially transformative approaches to sustainability transition research, in contrast to primarily descriptive-analitical approaches. Transition management as a recent and salient example of transdisciplinary transition research is chosen to provide research framework and application context. Based on conceptual-theoretic, empirical case study and reflexive work, three main results are contributed: First, a psychologically enriched understanding of individual and sustainability related agency in conceptual and empirical understandings of transition management is developed. This builds on two perspectives: a psychologically enriched capability approach as well as the analysis of social effects (social learning, empowerment and social capital development) of transition management to capture sustainability oriented agency increases. As second main result, normative considerations, namely sustainability, are included into transition management on conceptual and empirical levels. Therein, substantive, procedural and intentional aspects of sustainability are combined: Substantive aspects are covered by proposing capabilities, behavioral freedoms to live a valuable life, as normative yardsticks to measure developments. Procedural aspects include a detailed understanding of facilitating a learning journey towards making sustainability meaningful in the local transition management cases and setting up experiments for its realiziation. Intentional aspects are addressed by linking social effects of transition management to awareness, motivations and feelings of responsibility towards sustainability. As a third main result, the transdisciplinary collaboration in transition management of creating an arena as an interactive learning space is conceptualized and explored, as well as the roles of the researchers therein. Key issues of this learning space, the community arena, are drawn out and ideal-type roles and activities of researchers in addressing these issues are proposed and empirically analysed. As synthesis of thesis results, ten principles of sustainability transition management are proposed.
Online marketing, especially Paid Search Advertising, has become one of the most important paid media channels for companies to sell their products and services online. Despite being under intensive examination by a number of researchers for several years, this topic still offers interesting opportunities to contribute to the community, particularly because of its large economic impact and practical relevance as well as the detailed and widely unfiltered view of consumer behavior that such marketing offers. To provide answers to some of the important questions from advertisers in this context, the author present four papers in his thesis, in which he extends previous works on optimization topics such as click and conversion prediction. He applies and extends methods from other fields of research to specific problems in Paid Search. After a short introduction, the dissertation starts with a paper in which the authors illustrates a new method that helps advertisers to predict conversion probabilities in Paid Search using sparse keyword-level data. They address one of the central problems in Paid search advertising, which is optimizing own investments in this channel by placing bids in keyword auctions. In many cases, evaluations and decisions are made with extremely sparse data, although anecdotal evidence suggests that online marketing is a typical "Big Data" topic. In the developed algorithm presented in this paper, the authors use information such as the average time that users spend on the advertiser's website and bounce rates for every given keyword. This previously unused data set is shared between all keywords and used as prior knowledge in the proposed model. A modified version of this algorithm is now the core prediction engine in a productive Paid Search Bid Optimization System that calculates and places millions of bids every day for some of the most recognized retailers and service providers in the German market. Next, the author illustrates the development of a non-reactive experimental method for A/B testing of Paid Search Advertising activities. In that paper, the authors provide an answer to the question of whether and under what circumstances it makes economic sense for brand owners to pay for Paid Search ads for their own brand keywords in Google AdWords auctions. Finally, the author presents two consecutive papers with the same theoretical foundation in which he applies Bayesian methods to evaluate the impact of specific text features in Paid Search Advertisements.
The emission of anthropogenic trace substances into the aquatic environment continuously poses challenges to water suppliers. The contamination of raw waters with organic trace substances requires complex water treatment processes to secure drinking water quality. The routine monitoring of these raw waters as well as the behavior and fate of organic trace substances during different treatment processes is of great interest to recognize and counter potential dangers at an early stage. Non-target screening using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) allows the detection of thousands of compounds within a single run and covers known as well as unknown substances. Compared to the established analytical techniques, this is a decisive advantage for the monitoring of raw and process waters during water treatment. While the analytical technique LC-HRMS has undergone significant developments in recent years, the algorithms for data processing reveal clear weaknesses. This dissertation therefore deals with reliable processing strategies for LC-HRMS data. The first part of this work seeks to highlight the problematics of false positive and false negative findings. Based on repeated measurements, various strategies of data processing were assessed with regard to the repeatability of the results. To ensure that real peaks were barely or not removed by the filtering procedure, samples were spiked with isotope-labeled standards. The results emphasize that the processing of sample triplicates results in sufficient repeatability and that the signal fluctuation across the triplicates emerged as a powerful filtering criteria. The number of false positives and false negatives could be significantly reduced by the developed strategies which consequently improve the validity of the data. The second part of this thesis addresses the development of processing strategies particularly aimed at assessing water treatment processes. The detected signals were tracked across the treatment process and classified based on their fold changes. A more reliable signal classification was achieved by implementing a recursive integration approach. Special integration algorithms allow a reliable signal classification even though the signal to be compared was below the intensity threshold. Different combinations of replicates of process influents and effluents were processed for evaluating the repeatability. The good repeatability was indicated by the results of both the plausibility checks and the ozonation process (ozonation of pretreated river water) and thus points to high reliability. The applicability of the developed strategies to real world applications is demonstrated in the last part of this work. Besides the prioritization of the generated results, the main focus was the identification of recognized compounds. The developed strategies clearly improve the validity of the underlying data. The combination of LC-HRMS analysis with reliable processing strategies opens up multiple possibilities for a more comprehensive monitoring of water resources and for the assessment of water treatment processes. The processing strategies and validation concepts may be easily transferred to other research fields.