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This dissertation investigates corporate acquisition decisions that represent important corporate development activities for family and non-family firms. The main research objective of this dissertation is to generate insights into the subjective decision-making behavior of corporate decision-makers from family and non-family firms and their weighting of M&A decision-criteria during the early pre-acquisition target screening and selection process. The main methodology chosen for the investigation of M&A decision-making preferences and the weighting of M&A decision criteria is a choice-based conjoint analysis. The overall sample of this dissertation consists of 304 decision-makers from 264 private and public family and non-family firms from mainly Germany and the DACH-region. In the first empirical part of the dissertation, the relative importance of strategic, organizational and financial M&A decision-criteria for corporate acquirers in acquisition target screening is investigated. In addition, the author uses a cluster analysis to explore whether distinct decision-making patterns exist in acquisition target screening. In the second empirical part, the dissertation explores whether there are differences in investment preferences in acquisition target screening between family and non-family firms and within the group of family firms. With regards to the heterogeneity of family firms, the dissertation generated insights into how family-firm specific characteristics like family management, the generational stage of the firm and non-economic goals such as transgenerational control intention influences the weighting of different M&A decision criteria in acquisition target screening. The dissertation contributes to strategic management research, in specific to M&A literature, and to family business research. The results of this dissertation generate insights into the weighting of M&A decision-making criteria and facilitate a better understanding of corporate M&A decisions in family and non-family firms. The findings show that decision-making preferences (hence the weighting of M&A decision criteria) are influenced by characteristics of the individual decision-maker, the firm and the environment in which the firm operates.
In the modeling context, non-linearities and uncertainty go hand in hand. In fact, the utility function's curvature determines the degree of risk-aversion. This concept is exploited in the first article of this thesis, which incorporates uncertainty into a small-scale DSGE model. More specifically, this is done by a second-order approximation, while carrying out the derivation in great detail and carefully discussing the more formal aspects. Moreover, the consequences of this method are discussed when calibrating the equilibrium condition. The second article of the thesis considers the essential model part of the first paper and focuses on the (forward-looking) data needed to meet the model's requirements. A large number of uncertainty measures are utilized to explain a possible approximation bias. The last article keeps to the same topic but uses statistical distributions instead of actual data. In addition, theoretical (model) and calibrated (data) parameters are used to produce more general statements. In this way, several relationships are revealed with regard to a biased interpretation of this class of models. In this dissertation, the respective approaches are explained in full detail and also how they build on each other.
In summary, the question remains whether the exact interpretation of model equations should play a role in macroeconomics. If we answer this positively, this work shows to what extent the practical use can lead to biased results.
Internet interventions have gained popularity and the idea is to use them to increase the availability of psychological treatment. Research suggests that internet interventions are effective for a number of psychological disorders with effect sizes comparable to those found in face-to-face treatment. However, when provided as an add-on to treatment as usual, internet interventions do not seem to provide additional benefit. Furthermore, adherence and dropout rates vary greatly between studies, limiting the generalizability of the findings. This underlines the need to further investigate differences between internet interventions, participating patients, and their usage of interventions. A stronger focus on the processes of change seems necessary to better understand the varying findings regarding outcome, adherence and dropout in internet interventions. Thus, the aim of this dissertation was to investigate change processes in internet interventions and the factors that impact treatment response. This could help to identify important variables that should be considered in research on internet interventions as well as in clinical settings that make use of internet interventions.
Study I (Chapter 5) investigated early change patterns in participants of an internet intervention targeting depression. Data from 409 participants were analyzed using Growth Mixture Modeling. Specifically a piecewise model was applied to model change from screening to registration (pretreatment) and early change (registration to week four of treatment). Three early change patterns were identified; two were characterized by improvement and one by deterioration. The patterns were predictive of treatment outcome. The results therefore indicated that early change should be closely monitored in internet interventions, as early change may be an important indicator of treatment outcome.
Study II (Chapter 6) picked up on the idea of analyzing change patterns in internet interventions and extended it by using the Muthen-Roy model to identify change-dropout patterns. A sligthly bigger sample of the dataset from Study I was analyzed (N = 483). Four change-dropout patterns emerged; high risk of dropout was associated with rapid improvement and deterioration. These findings indicate that clinicians should consider how dropout may depend on patient characteristics as well as symptom change, as dropout is associated with both deterioration and a good enough dosage of treatment.
Study III (Chapter 7) compared adherence and outcome in different participant groups and investigated the impact of adherence to treatment components on treatment outcome in an internet intervention targeting anxiety symptoms. 50 outpatient participants waiting for face- to-face treatment and 37 self-referred participants were compared regarding adherence to treatment components and outcome. In addition, outpatient participants were compared to a matched sample of outpatients, who had no access to the internet intervention during the waiting period. Adherence to treatment components was investigated as a predictor of treatment outcome. Results suggested that especially adherence may vary depending on participant group. Also using specific measures of adherence such as adherence to treatment components may be crucial to detect change mechanisms in internet interventions. Fostering adherence to treatment components in participants may increase the effectiveness of internet interventions.
Results of the three studies are discussed and general conclusions are drawn.
Implications for future research as well as their utility for clinical practice and decision- making are presented.
Auf der Grundlage einer Fragebogenstudie wurden unterschiedliche Elemente eines förderlichen Umgangs mit Gesundheitsinformationen betrachtet und ihre Zusammenhänge mit personspezifischen Merkmalen analysiert. Als zentrale Aspekte der Informationsprozesse wurden die drei Elemente Gesundheitsinformationskompetenz, Gesundheitsinteresse und gesundheitsspezifische Informationsgewohnheiten konzeptuell voneinander getrennt. Auf der Basis des bisherigen Forschungsstands wurde zunächst ein theoretisches Modell des Umgangs mit Gesundheitsinformationen entwickelt, das die Bedeutung der Kompetenz und des Interesses für gesundheitsbezogene Informationsgewohnheiten hervorhebt, individuelle Ausprägungen dieser drei Elemente mit soziodemografischen Faktoren, Persönlichkeitseigenschaften, Überzeugungen und dem Gesundheitszustand in Beziehung setzt sowie Verbindungen zu gesundheitsrelevanten Verhaltensweisen beschreibt. Dieses Modell wurde anschließend an einer Stichprobe von 352 Berufsschülerinnen und -schülern aus drei Berufsbereichen (Wirtschaft/Verwaltung, Technik und Gesundheit) empirisch überprüft. Über multiple Regressionsanalysen wurden bedeutsame Prädiktoren für die drei Hauptelemente Kompetenz, Interesse und Informationsgewohnheiten identifiziert, über logistische Regressionen und Korrelationen ihre Zusammenhänge mit dem Gesundheitsverhalten überprüft. Darüber hinaus wurden lineare Strukturgleichungsmodelle zur Vorhersage des Informationsverhaltens entwickelt. Die Ergebnisse bestätigen die konzeptionelle Trennung der drei Faktoren, die jeweils mit unterschiedlichen Prädiktoren verbunden waren. Auf der Basis der Befunde werden Ansatzpunkte für die weitere Forschung und die Förderung eines kompetenten Umgangs mit Gesundheitsinformationen diskutiert.
Bei Albert Dietz und Bernhard Grothe handelt es sich um zwei bedeutende Architekten im französisch-saarländischen Grenzgebiet. Sie gründeten 1952 eine Arbeitsgemeinschaft mit dem Ziel, auf gemeinschaftlicher Basis den nach dem Krieg entstandenen Bedarf an profanen und sakralen Wiederaufbau- und Neubaumaßnahmen in ihren Bauwerken möglichst effektiv reaslisieren zu können. Diese Arbeit befaßt sich ausschließlich mit den Sakralbauten, die deren künstlerische und architektonische Leistungen auf anschauliche Weise demonstrieren und belegen.
Global food security poses large challenges to a fast changing human society and has been a key topic for scientists, agriculturist, and policy makers in the 21st century. The United Nation predicts a total world population of 9.15 billion in 2050 and defines the provision of food security as the second major point in the UN Sustainable Development Goals. As the capacities of both, land and water resources, are finite and locally heavily overused, reducing agriculture’s environmental impact while meeting an increasing demand for food of a constantly growing population is one of the greatest challenges of our century. Therefore, a multifaceted solution is required, including approaches using geospatial data to optimize agricultural food production.
The availability of precise and up-to-date information on vegetation parameters is mandatory to fulfill the requirements of agricultural applications. Direct field measurements of such vegetation parameters are expensive and time-consuming. On the contrary, remote sensing offers a variety of techniques for a cost-effective and non-destructive retrieval of vegetation parameters. Although not widely used, hyperspectral thermal infrared (TIR) remote sensing has demonstrated being a valuable addition to existing remote sensing techniques for the retrieval of vegetation parameters.
This thesis examined the potential of TIR imaging spectroscopy as an important contribution to the growing need of food security. The main scientific question dealt with the extraction of vegetation parameters from imaging TIR spectroscopy. To this end, two studies impressively demonstrated the ability of extracting vegetation related parameters from leaf emissivity spectra: (i) the discrimination of eight plant species based on their emissivity spectra and (ii) the detection of drought stress in potato plants using temperature measures and emissivity spectra.
The datasets used in these studies were collected using the Telops Hyper-Cam LW, a novel imaging spectrometer. Since this FTIR spectrometer presents some particularities, special attention was paid on the development of dedicated experimental data acquisition setups and on data processing chains. The latter include data preprocessing and the development of algorithms for extracting precise surface temperatures, reproducible emissivity spectra and, in the end, vegetation parameters.
The spectrometer’s versatility allows the collection of airborne imaging spectroscopy datasets. Since the general availability of airborne TIR spectrometers is limited, the preprocessing and
data extraction methods are underexplored compared to reflective remote sensing. This counts especially for atmospheric correction (AC) and temperature and emissivity separation (TES) algorithms. Therefore, we implemented a powerful simulation environment for the development of preprocessing algorithms for airborne hyperspectral TIR image data. This simulation tool is designed in a modular way and includes the image data acquisition and processing chain from surface temperature and emissivity to the final at-sensor radiance data. It includes a series of available algorithms for TES, AC as well as combined AC and TES approaches. Using this simulator, one of the most promising algorithms for the preprocessing of airborne TIR data – ARTEMISS – was significantly optimized. The retrieval error of the atmospheric water vapor during the atmospheric characterization was reduced. As a result, this improvement in atmospheric characterization accuracy enhanced the subsequent retrieval of surface temperatures and surface emissivities intensely.
Although, the potential of hyperspectral TIR applications in ecology, agriculture, and biodiversity has been impressively demonstrated, a serious contribution to a global provision of food security requires the retrieval of vegetation related parameters with global coverage, high spatial resolution and at high revisit frequencies.
Emerging from the findings in this thesis, the spectral configuration of a spaceborne TIR spectrometer concept was developed. The sensors spectral configuration aims at the retrieval of precise land surface temperatures and land surface emissivity spectra. Complemented with additional characteristics, i.e. short revisit times and a high spatial resolution, this sensor potentially allows the retrieval of valuable vegetation parameters needed for agricultural optimizations. The technical feasibility of such a sensor concept underlines the potential contribution to the multifaceted solution required for achieving the challenging goal of guaranteeing global food security in a world of increasing population.
In conclusion, thermal remote sensing and more precisely hyperspectral thermal remote sensing has been presented as a valuable technique for a variety of applications contributing to the final goal of a global food security.
This dissertation deals with consistent estimates in household surveys. Household surveys are often drawn via cluster sampling, with households sampled at the first stage and persons selected at the second stage. The collected data provide information for estimation at both the person and the household level. However, consistent estimates are desirable in the sense that the estimated household-level totals should coincide with the estimated totals obtained at the person-level. Current practice in statistical offices is to use integrated weighting. In this approach consistent estimates are guaranteed by equal weights for all persons within a household and the household itself. However, due to the forced equality of weights, the individual patterns of persons are lost and the heterogeneity within households is not taken into account. In order to avoid the negative consequences of integrated weighting, we propose alternative weighting methods in the first part of this dissertation that ensure both consistent estimates and individual person weights within a household. The underlying idea is to limit the consistency conditions to variables that emerge in both the personal and household data sets. These common variables are included in the person- and household-level estimator as additional auxiliary variables. This achieves consistency more directly and only for the relevant variables, rather than indirectly by forcing equal weights on all persons within a household. Further decisive advantages of the proposed alternative weighting methods are that original individual rather than the constructed aggregated auxiliaries are utilized and that the variable selection process is more flexible because different auxiliary variables can be incorporated in the person-level estimator than in the household-level estimator.
In the second part of this dissertation, the variances of a person-level GREG estimator and an integrated estimator are compared in order to quantify the effects of the consistency requirements in the integrated weighting approach. One of the challenges is that the estimators to be compared are of different dimensions. The proposed solution is to decompose the variance of the integrated estimator into the variance of a reduced GREG estimator, whose underlying model is of the same dimensions as the person-level GREG estimator, and add a constructed term that captures the effects disregarded by the reduced model. Subsequently, further fields of application for the derived decomposition are proposed such as the variable selection process in the field of econometrics or survey statistics.
Thema dieser Dissertation ist das deutsche Selbstbildnis im 17. Jahrhundert. Ziel der Arbeit war es, das deutsche Selbstbildnis als eigene Gattung zu etablieren. Hierzu wurden die Selbstbildnisse deutscher Maler des 17. Jahrhunderts ausgewählt, gilt doch diese Zeit noch immer als ‚totes Jahrhundert‘. Grundlage der Untersuchung war eine Sammlung von 148 Objekten, die einer grundlegenden Analyse unterzogen wurden. Das früheste Selbstbildnis in dieser Sammlung stammt von 1600, das späteste wurde um 1700 angefertigt. Künstler aus dem gesamten Alten Reich, ob aus Schlesien und Böhmen, Nord-oder Süddeutschland oder aus den österreichischen wie schweizerischen Landen sind hier vertreten. Die Selbstbildnisse stammen von Malern in der gesamten breite ihrer Karriere. So sind gleichermaßen Selbstbildnisse von Gesellen wie Meistern, von Hofmalern bis hin zu Freimeistern vertreten. Besonders wichtig war es, nicht nur Selbstbildnisse im Gemälde oder Kupferstich in die Untersuchung aufzunehmen, sondern auch Stammbucheinträge.
Die ausführliche Betrachtung und Gegenüberstellung der deutschen Selbstbildnisse mit denen ihrer europäischen Kollegen hat gezeigt, dass auch deutsche Maler den gängigen Darstellungstypen wie etwa dem virtuoso folgten. Aber die deutschen Maler imitierten nicht nur, sondern experimentierten und gingen mit ihren Vorbildern spielerisch um. Daneben folgten sie natürlich auch den Trends der Selbstinszenierung. Sie drückten in ihren Selbstbildnissen ihren Wunsch nach sozialer und gesellschaftlicher Emanzipation des gesamten Berufsstandes aus. So war das deutsche Selbstbildnis eigenständiger Ausdruck des Aufbruches deutscher Künstler in eine neue Zeit.
Competitive analysis is a well known method for analyzing online algorithms.
Two online optimization problems, the scheduling problems and the list accessing problems, are considered in the thesis of Yida Zhu in the respect of this method.
For both problems, several existing online and offline algorithms are studied. Their performances are compared with the performances of corresponding offline optimal algorithms.
In particular, the list accessing algorithm BIT is carefully reviewed.
The classical proof of its worst case performance get simplified by adapting the knowledge about the optimal offline algorithm.
With regard to average case analysis, a new closed formula is developed to determine the performance of BIT on specific class of instances.
All algorithm considered in this thesis are also implemented in Julia.
Their empirical performances are studied and compared with each other directly.
This doctoral thesis includes five studies that deal with the topics work, well-being, and family formation, as well as their interaction. The studies aim to find answers to the following questions: Do workers’ personality traits determine whether they sort into jobs with performance appraisals? Does job insecurity result in lower quality and quantity of sleep? Do public smoking bans affect subjective well-being by changing individuals’ use of leisure time? Can risk preferences help to explain non-traditional family forms? And finally, are differences in out-of-partnership birth rates between East and West Germany driven by cultural characteristics that have evolved in the two separate politico-economic systems? To answer these questions, the following chapters use basic economic subjects such as working conditions, income, and time use, but also employ a range of sociological and psychological concepts such as personality traits and satisfaction measures. Furthermore, all five studies use data from the German Socio-Economic Panel (SOEP), a representative longitudinal panel of private households in Germany, and apply state-of-the-art microeconometric methods. The findings of this doctoral thesis are important for individuals, employers, and policymakers. Workers and employers benefit from knowing the determinants of occupational sorting, as vacancies can be filled more accurately. Moreover, knowing which job-related problems lead to lower well-being and potentially higher sickness absence likely increases efficiency in the workplace. The research on smoking bans and family formation in chapters 4, 5, and 6 is particularly interesting for policymakers. The results on the effects of smoking bans on subjective well-being presented in chapter 4 suggest that the impacts of tobacco control policies could be weighed more carefully. Additionally, understanding why women are willing to take the risks associated with single motherhood can help to improve policies targeting single mothers.