There is a wide range of methodologies for policy evaluation and socio-economic impact assessment. A fundamental distinction can be made between micro and macro approaches. In contrast to micro models, which focus on the micro-unit, macro models are used to analyze aggregate variables. The ability of microsimulation models to capture interactions occurring at the micro-level makes them particularly suitable for modeling complex real-world phenomena. The inclusion of a behavioral component into microsimulation models provides a framework for assessing the behavioral effects of policy changes. The labor market is a primary area of interest for both economists and policy makers. The projection of labor-related variables is particularly important for assessing economic and social development needs, as it provides insight into the potential trajectory of these variables and can be used to design effective policy responses. As a result, the analysis of labor market behavior is a primary area of application for behavioral microsimulation models. Behavioral microsimulation models allow for the study of second-round effects, including changes in hours worked and participation rates resulting from policy reforms. It is important to note, however, that most microsimulation models do not consider the demand side of the labor market. The combination of micro and macro models offers a possible solution as it constitutes a promising way to integrate the strengths of both models. Of particular relevance is the combination of microsimulation models with general equilibrium models, especially computable general equilibrium (CGE) models. CGE models are classified as structural macroeconomic models, which are defined by their basis in economic theory. Another important category of macroeconomic models are time series models. This thesis examines the potential for linking micro and macro models. The different types of microsimulation models are presented, with special emphasis on discrete-time dynamic microsimulation models. The concept of behavioral microsimulation is introduced to demonstrate the integration of a behavioral element into microsimulation models. For this reason, the concept of utility is introduced and the random utility approach is described in detail. In addition, a brief overview of macro models is given with a focus on general equilibrium models and time series models. Various approaches for linking micro and macro models, which can either be categorized as sequential approaches or integrated approaches, are presented. Furthermore, the concept of link variables is introduced, which play a central role in combining both models. The focus is on the most complex sequential approach, i.e., the bi-directional linking of behavioral microsimulation models with general equilibrium macro models.

Within this thesis the hedging behaviour of airlines from 2005 to 2019 is analysed by using an unbalanced panel dataset consisting of a total of 78 airlines from 39 countries. The focus of the analysis is on financial and operational hedging as well as the influence of both on CO2 emissions and the development of emitted CO2 emissions. For the analysis Probit models with random effects and OLS models with fixed effects were used. The results regarding the relationship between leverage and financial hedging indicate a negative relationship between everage and financial fuel hedging and a non-linear convex relationship for highly leveraged airlines, which is contrary to the theory of financial distress. In addition, the study provides evidence that airlines using other types of derivatives, such as interest rate derivatives, engage in more fuel hedging. In terms of operational hedging, the analysis suggests that operating a diversified fleet is a complement to, rather than a substitute for, financial hedging. With regard to alliance membership, the results do not show that alliance membership is a substitute for financial hedging, as members of alliances are more likely to engage in hedging transactions and to a greater extent. The analysis shows that the relative CO2 emissions fall in the period under review, but this does not apply to the absolute amount. No general statement can be made about the influence of financial and operational hedging on CO2 emissions, as the results are mixed.

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 application of machine learning and deep learning methods to hydrological modelling has advanced significantly in recent years, offering alternatives to traditional conceptual and physically based approaches. Within the numerous algorithms, long short-term memory (LSTM) networks have proven themselves particularly useful for the task of streamflow modelling. This thesis provides a collection of publications that investigate the capabilities, limitations and interpretability of LSTM for the purpose of streamflow modelling and climate change impact assessment within the lowland Ems catchment in Northwest Germany. Within a comparative performance evaluation, LSTM and its predecessor, the recurrent neural network, demonstrate superior accuracy compared to the conceptual HBV model across various statistical performance metrics. However, a decline in performance was observed during low-flow conditions in certain sub-catchments. The evaluation of the flow duration curve revealed that the ML models more effectively capture the water balance, while HBV better represents streamflow dynamics. To enhance the interpretability of LSTM, six explainable artificial intelligence techniques were applied. These methods consistently identified seasonal patterns in the temporal relevance of hydroclimatic input data. In combination with an observed correlation between the internal LSTM states and catchment-scale soil moisture dynamics, the findings suggest that LSTM models are capable of implicitly learning the relevant hydrological processes. Following, the capabilities of LSTM to model climate change impact scenarios, particularly when they extend beyond historically observed climate conditions, are addressed. An ensemble of climate change projections is provided as hydroclimatic input to evaluate the performance of LSTMs and conceptual models. While all models reveal heterogeneous alterations in streamflow under future climate conditions, significant differences emerge based on the model type. Results provide evidence that LSTMs, in combination with the temperature-based Haude formula for estimating potential evaporation, work inadequately under altered climatic regimes, raising concerns about their applicability in long-term projections. The study also indicates the potential need to incorporate physical constraints into LSTM architectures to ensure model robustness and hydrological plausibility beyond the historical training range. Collectively, this thesis contributes important insights into the applicability and interpretability of LSTM models in streamflow modelling. Despite the presence of a physically realistic representation of soil moisture dynamics of the Ems catchment, no robust change signals for streamflow under climate change can be derived. Those results underscore the potential of LSTM model approaches for accurate streamflow simulation, however, they require us to always critically question LSTM results, particularly when they are applied outside the training range.

Spatial microsimulation is an important tool for integrating geographical information into the evaluation of public policies and the analysis of social phenomena in urban regions. These models simulate the behavior and interaction between units of the region, such as individuals, households or firms, under specific conditions that may or not involve projections over time. This requires a representative base data set for their respective units. In this thesis, we focus on the geo-referencing step of the population in the construction of this data set, where we define the location of the individuals so that the allocation obtained is representative in relation to the population of the region. To do this, we consider the assignment of households to dwellings with specific coordinates by solving a maximum weight matching problem where side constraints are included so that the allocation obtained satisfies statistical structures intrinsic to the considered region. The model of this problem represents each feasible assignment of household to dwelling as a binary variable, which results in billions of variables for medium-sized municipalities such as the city of Trier, Germany. Therefore, standard solvers for mixed-integer linear optimization are not able to solve it due to their high time and memory consumption. Hence, we develop two approaches capable of producing high-quality allocations using a reasonable amount of computational resources, one based on specific decomposition algorithms, and the other characterized by the application of an approximation algorithm in the framework of Lagrangian relaxation of the side constraints. We theoretically explore the allocations obtained by both approaches and perform an extensive computational study using synthetic data sets and real-world data sets associated with the city of Trier. The results show that the developed methods are able to obtain near-optimal solutions using significantly less memory and time than the solver Gurobi, which enables them to tackle significantly larger instances, with approximately 100 000 households and dwellings. Furthermore, the allocations obtained for the real-world data sets correspond to a realistic population distribution, which strengthens the practical applicability of our methods.

Extracellular enzymes in microbial communities play a central role in nutrient cycling and the degradation of (pollutant) substances in various natural and anthropogenic systems. Bound in aquatic biofilms, sludge aggregates, or even unbound at their interfaces, they are of great importance for both the environment and human health. In particular, in wastewater treatment plants and inland waters, hydrolytic activities influence the wide-reaching efficiency of nutrient removal and self-purification, thus contributing significantly to overall water quality. The main goal of this dissertation project was to investigate the factors that influence enzymatic activity and the health of microbial communities in activated sludge and river systems, particularly in relation to anthropogenic influences and natural environmental conditions. The aim was to contribute to a better understanding of the sensitivity of our freshwater ecosystems and to support the long-term preservation of water quality and ecological stability. The development and optimization of appropriate methods, as well as their testing and applicability, were the focal points. For this purpose, a fluorometric microplate assay was developed and adapted to determine both extracellular enzyme activities (EEAs) in activated sludge samples and in intact biofilms. Its suitability for field studies was subsequently tested. Inhibition and activity of selected hydrolases under different conditions were investigated to better understand the mechanisms and potential environmental risks posed by anthropogenic influences and seasonal fluctuations of hydrochemical and climatic parameters. The first phase of the doctoral thesis involved studies on the inhibition of alkaline phosphatase in activated sludge by oxyanions. Using the fluorometric microplate assay, the inhibitory effect was sensitively detected over a pH range of 7.0 to 8.5. IC50- and IC20-concentrations were calculated from modeled dose-response functions. It was found that vanadate and tungstate caused strong inhibitory effects, while molybdate moderately inhibited the enzyme. An increasing pH led to a reduction in the inhibitory effect of tungstate and molybdate. The inhibition effects of vanadate were not significantly affected by the pH. In municipal wastewater, the concentrations of such metal ions are usually low, but industrial wastewater may have pollutant loads that can significantly impact the removal of phosphorus-containing compounds, and thus the efficiency of treatment plants. In the second phase, an attempt was made to further adapt the developed methodology to investigate EEA and kinetics in intact freshwater biofilms. Four different types of bead materials (lava, glass, sintered quartz, and ceramics) fitting into a 96-well microplate were tested as carriers for biofilms on both the laboratory and field scale. The analysis included a total of seven hydrolases as representatives of key nutrient cycles such as phosphorus, carbon, and nitrogen: phosphatases, glucosidases, peptidases (two different types), and sulfatase. Experiments with increasing substrate concentrations led to classical kinetic profiles according to the Michaelis-Menten mechanism. This allowed for the prediction of the biofilm enzymes’ response to different substrate concentrations. Parameters such as Vmax and Km could be derived from the modeled curves. Ceramic beads are particularly suitable for long-term studies due to their high stability, while sintered quartz beads should be preferred for the use in stagnant media (material loss under turbulent conditions). Lava and glass beads, on the other and, proved suboptimal for uniform biofilm development due to their surface properties. The potential use of this fast and sensitive test for ecotoxicological or even human-toxicological studies was demonstrated by the effects of caffeine on the activity of PDE. The result of this part of the research represents a powerful tool for assessing environmental pollution and monitoring water quality. The high application potential was clearly highlighted in the final phase of the project. The goal here was to deepen the understanding of interactions between seasonal factors, anthropogenic influences, and biofilm processes in rivers by investigating EEA and biofilm parameters such as biomass and relating them to hydrochemical and climatic factors. Ceramic beads were exposed both upstream and downstream of a wastewater treatment plant discharge and sampled over a period of seven months. EEAs and biomass varied depending on the season and location, with higher microbial activity observed upstream in winter. Winter conditions led to the dilution of most nutrients as well as in an increse of dissolved oxygen. Nutrient concentrations analyzed downstream were significantly higher in the summer. Accumulation of nutrient or pollutants during the summer months cannot be excluded, which may have led to a general reduction in enzyme activities. Potential causes could be inhibitory effects on the enzymes, or a reduced enzyme activity due to a sufficiently high nutrient supply. In general, the sampling site upstream showed a more pronounced seasonal dynamics, with a significant proportion of the variance in biological parameters (activity and biomass) attributable to seasonal factors. A secondary component, likely reflecting the impact of the treatment plant discharge, explained another portion of the data variance. Regardless of the season, high correlations between biological parameters were observed upstream, while downstream the data were more decorrelated. This could be because the biofilms, under chronic stress, respond less dynamically to seasonal fluctuations. This dissertation illustrates that in addition to anthropogenic stress factors, seasonal fluctuations of hydrochemical and climatic parameters should also be considered in "stress downstream the pipe" studies. The selected methods are recommended for explaining and considering the data variance, as they highlight the complex interplay between microbial enzymatic activity, environmental factors, and pollutants in the activated sludge of wastewater treatment plants and also in aquatic systems. The novel bead assay could pave the way for the future standardization of effect-oriented studies on intact aquatic biofilms.

Die Verfügbarkeit von ortho-Phosphat (o-PO₄) trägt wesentlich zur Eutrophierung von Fließgewässern bei und gefährdet damit das Erreichen des „guten ökologischen Zustands“ gemäß der EU-Wasserrahmenrichtlinie. Da die kommunalen Kläranlagen zentrale Eintragsquellen darstellen, gewinnt die Reduktion von o-PO₄ an dieser Stelle an Bedeutung. Neben der chemischen Phosphorelimination bietet insbesondere die vierte Reinigungsstufe, primär zur Entfernung von Mikroschadstoffen konzipiert, einen Synergieeffekt mit potenziellen Phosphorentfernungsraten von bis zu 85 %. Zur Bewertung des Einflusses einer solchen Reinigungsstufe wurde ein Modell für ausgewählte saarländische Oberflächenwasserkörper (OWK) entwickelt, das den Trockenwetterfall als eutrophierungsrelevantes Szenario abbildet. Ein zentraler Bestandteil ist ein neu erarbeiteter Retentionsansatz, der biochemische und physikalische Prozesse wie Adsorption, Sedimentation und biologische Assimilation berücksichtigt. Auf Basis der Differenz zwischen emissionsseitig bilanziertem und gemessenem o-PO₄-Gehalt wurden für jeden OWK Verminderungsraten je Fließmeter abgeleitet und schließlich eine Gleichung zur Abschätzung der Retention in Abhängigkeit der Einzugsgebietsgröße formuliert. Die Validierung zeigt hinreichende Modellgenauigkeit, wenngleich negative Frachtdifferenzen in einigen Gewässern auf zusätzliche, nicht eindeutig quantifizierbare Einträge – etwa aus Landwirtschaft oder Kanalverlusten – hindeuten. Die Szenarienanalyse belegt, dass eine vierte Reinigungsstufe grundsätzlich zur Reduktion von o-PO₄ an den Messstellen beiträgt. Eine Unterschreitung des geltenden Orientierungswertes wird jedoch nur erreicht, wenn sämtliche Kläranlagen eines OWK nachgerüstet werden – und auch dann nur in einigen Fällen. Damit stellt die vierte Reinigungsstufe allein keine ausreichende Alternative zu den Maßnahmen des 3. Bewirtschaftungsplans des Saarlandes dar, kann jedoch als ergänzende Strategie zur Verringerung der Phosphoreinträge dienen.

Measuring the economic activity of a country requires high-quality data of businesses. In the case of Germany, this is not only required at national level, but also at federal state level and for different economic sectors. Important sources for high-quality business data are the business register and, among others, also 14 business surveys which are conducted by the Federal Statistical Office of Germany. However, the quality requirements of the Federal Statistical Office are in contrast to the interests of the businesses themselves. For them, answering to a survey's questionnaire is an additional cost factor, also known as response burden. A high response burden should be avoided, since it can have a negative impact on the quality of the businesses' responses to the surveys. Therefore, sample coordination can be used as a method to control the distribution of response burden while securing high-quality data. When applying already existing business survey coordination systems, developed by different statistical institutes, legal and administrative standards of German official statistics have to be taken into account. These standards consider different sampling fractions, rotation fractions, periodicity, and stratification of the aforementioned 14 business surveys. Therefore, the aim of this doctoral thesis is to check the existing business survey coordination systems for their applicability in the context of German official statistics and, if necessary, to modify them accordingly. These modifications include the introduction of individual burden indicators which aim to take the individual perception of response burden into account. For this purpose, several synthetic data sets have been created to test the application of the modified versions of the different business survey coordination systems through Monte Carlo simulation studies. These data sets include a large panel data set, reflecting the landscape of businesses in Rhineland-Palatinate and three smaller, synthetic data sets. The latter have been created with the help of the R package BuSuCo which has been developed within the scope of this thesis. The above mentioned simulation studies are evaluated based on different measures for estimation quality as well as for the concentration and distribution of response burden.

Many developed countries, including Germany, face a steady rise in the share of individuals obtaining higher education. While rising education itself bears a series of advantages as extensively studied in previous literature, it is also conceptually linked to a higher likelihood of working in an occupation that does not match one’s formal qualifications. Previous studies have predominantly evaluated how demographic or job‐related aspects correlate with the likelihood of being educationally ﴾mis﴿matched. However, they have largely ignored institutional facets of the educational system or industrial organization. Moreover, little is known about how private wealth affects educational mismatch or whether job satisfaction is homogenously affected among individuals once such a mismatch occurs. The five projects collected in this thesis aim to answer these open questions in the literature for Germany, using data from the Socio‐Economic Panel and employing different time intervals between 1984 and 2022. Beginning with the educational system in early childhood, Chapter 2 evaluates the impact of school‐starting age on the likelihood of over‐ and undereducation. It exploits the exogenous variation in school‐entry rules across federal states and years in Germany with regression discontinuity designs. The results report a negative impact of school‐starting age on the likelihood of undereducation, but no systematic relationship with overeducation. Subsequently, Chapter 3 explores the variation in education costs by leveraging the quasi‐experimental setting induced by the time‐limited introduction of tuition fees in several German federal states between 2006 and 2014. The increase in education costs among treated graduates results in a significantly higher likelihood of overeducation, which endures even several years post‐graduation. Chapter 4 focuses on the industrial relations system and examines the correlation between trade union membership and the likelihood and extent of educational ﴾mis﴿match. The results reveal that trade union members report significantly less overeducation at both the intensive and extensive margin and also a higher likelihood of being matched compared to non‐members. Furthermore, the heterogeneity analysis provides evidence that this correlation is driven by improved bargaining power instead of informational advantages. Chapter 5 focuses on private wealth as a determinant of educational mismatch by investigating the impact of a wealth shock through inheritances, lottery winnings or gifts on the likelihood of over‐ and undereducation. Due to the diminishing marginal returns of wages with increasing windfall gains the likelihood of undereducation is expected to decrease, while that of overeducation is expected to increase. Empirically, these suppositions are supported for overeducation, as its likelihood increases significantly after the windfall gain. Further analyses reveal that this effect is driven by individuals switching occupations while increasing their leisure time, and it materializes only for medium to large windfall gains. Contrary to the previous chapters, Chapter 6 focuses on educational mismatch, more precisely on overeducation, as the independent variable. In particular, it investigates the correlation between overeducation and job satisfaction. The results align with the previously established negative correlation for private sector employees exclusively. In contrast, interaction and subsample analyses reveal a positive correlation for public sector employees. This link is driven by individuals with a high degree of altruistic motivation and family orientation.

The role of implicit motives for affective, cognitive and behavioral processes has been a focal part of psychological research for decades. Yet, the majority of research in this field has been concentrated on processes involving implicit motives in adulthood. The systematic investigation of developmental correlates of implicit motives remains largely uncharted. The studies cumulated in this thesis aim to add to the sparse research on implicit motives in childhood and adolescence. Specifically, the development of the implicit power motive in the transition of middle to late childhood as a function of parenting behavior (Chapter 4), the predictive value of the implicit achievement motive for objective swimming performance in children and adolescents (Chapter 5) and the role of motive congruence for successful goal realization in adolescent samples across two cultures (Chapter 6) were investigated. Results of Study 1 (Chapter 4) indicate a negative longitudinal association of authoritarian parenting with the implicit power motive in children that is moderated by children’s perception of psychologically controlling parenting. Study 2 (Chapter 5) extends existing research on the assumed positive association of the implicit achievement motive and sports performance and demonstrates the moderating role of competitive anxiety on this association. Finally, Study 3 (Chapter 6) illustrates a moderating effect of implicit motives on the association of goal commitment and successful goal realization in German and Zambian adolescents, however, this effect was only observed in the domain of power motivation. Findings from all three studies are discussed in the context of the significance of implicit motives for psychological research.

Das Thema des Erlebnisses steht bereits seit langem im Fokus von Anbietern von Dienstleistungen. Dies gilt insbesondere für den Tourismus, einer Branche, deren Produkte zu einem signifikanten Teil aus solchen bestehen. Entsprechend der Prominenz des Themas, vor allem in den Bereichen touristischer Produktentwicklung und Marketing, ist dieses bereits breit in der Forschung diskutiert worden. Trotz ausgiebiger Publikationsaktivitäten ist der tatsächliche Wissensstand in diesem Thema dennoch auffällig gering. Ein wichtiges Problem liegt darin begründet, dass die Terminologie im Bereich von Erlebnissen noch nicht allgemein akzeptiert und scharf abgegrenzt ist. So muss zwischen Erlebnissen und Erfahrungen unterschieden werden. Erstere treten während des Prozesses der Wahrnehmung einer touristischen Dienstleistung auf und bilden die Basis für Erfahrungen, welche prägend hinsichtlich der Wahrnehmung wirken und im Gesamtkontext der Reise betrachtet werden. Dieser Unterscheidung wird nicht nur in der englischsprachigen Literatur, in der beide Begriffe mit dem Begriff Experience beschrieben werden, sondern auch in der deutschsprachigen Literatur zumeist zu wenig Rechnung getragen, was dazu führt, dass häufig zu Erlebnissen publiziert wird, obwohl eigentlich Erfahrungen beschrieben werden. Problematisch ist dies vor allem, weil damit ein Phänomen untersucht wird, dessen Basis nahezu gänzlich unbekannt ist. Wichtige Fragen, welche zum Verständnis von Erlebnissen und damit auch von Erfahrungen bleiben unbeantwortet: 1) Welche Faktoren werden in der Genese von Erlebnissen wirksam? 2) Wie wirken diese zusammen? 3) Wie wird die Stärke eines Erlebnisses determiniert? 4) Wie werden Erlebnisse stark genug um den Konsum einer touristischen Dienstleistung zu prägen und damit gegebenenfalls zu Erfahrungen zu werden? In der vorliegenden Arbeit wurden diese Fragen beantwortet, womit ein erster Schritt in Richtung der Füllung einer für die Tourismuswissenschaft nicht unbedeutenden Forschungslücke gelungen ist. Um Erlebnisse, den Prozess der Genese dieser und deren Bewertung durch den Gast verstehen zu können, wurde ein triangulierter, zweistufiger Forschungsprozess ersonnen und in einem naturtouristischen Setting im Nationalpark Vorpommersche Boddenlandschaft zur Anwendung gebracht. Es handelt sich dabei um einen Mixed-Methods-Ansatz: 1) Induktive-qualitative Studie auf Basis der Grounded Theory a. Ziel: Identifikation von Wirkkomponenten und deren Zusammenspiel und Generierung eines Modells b. Methoden: Verdeckte Beobachtung und narrative Interviews c. Ergebnisse: Modelle der Genese punktueller Erlebnisse und prägender Erlebnisse 2) Deduktive-quantitative Studie a. Ziel: Überprüfung und Konkretisierung der in 1) generierten Modelle b. Methoden: Fragebogengestützte, quantitative Befragung und Auswertung mittels multivariater Verfahren c. Ergebnisse: Zusammenfassung der beiden Modelle zu einem finalen Modell der Erlebnis- und Erfahrungsgenese Das Ergebnis des Vorgehens ist ein empirisch erarbeitetes und validiertes, detailliertes Modell der Genese von Erlebnissen und der Bewertung dieser durch den Erlebenden in Bezug auf deren Fähigkeit zu Erfahrungen zu werden. Neben der Aufarbeitung und Konkretisierung dieses Prozesses konnte zusätzlich die in viele Richtungen diskutierte Bedeutung von Erwartungen und Produktzufriedenheit mit Blick auf die Bewertung von Erlebnissen geklärt werden. So konnte empirisch nachgewiesen werden, dass Erlebnisse, die auf Überraschungen, dem Unerwarteten, basierten besonders resistent gegenüber Störfaktoren waren und positive Erlebnisse zwar durchaus im Zusammenhang mit Produktzufriedenheit stehen aber sich vor allem durch eine zumindest temporär gesteigerte Lebenszufriedenheit manifestieren. Damit konnte das Hauptkriterium für die Bewertung von Erlebnissen mit Blick auf ihre Tauglichkeit zu Erfahrungen identifiziert werden. Für die weitere Forschung kann die vorliegende Arbeit mit dem finalen Modell der Erlebnis- und Erfahrungsgenese einen soliden Ausgangspunkt bilden. So bieten zahlreiche Faktoren im Modell die Möglichkeit zur weiteren Forschung. Auch sollten die Ergebnisse in weiteren touristischen Kontexten überprüft werden. Für die touristische Praxis kann die vorliegende Arbeit zahlreiche Hinweise geben. So bedeutet die Generierung von Erlebnissen im touristischen Kontext mehr als nur die Erfüllung von Erwartungen. Die widerstandsfähigsten Erlebnisse sind jene, die den Gast zu überraschen vermögen. Ein qualitativ hochwertiges, den Gast zufriedenstellendes Produkt ist dabei nicht mehr als ein Basisfaktor. Wirklich erfolgreich ist ein erlebnisbasierender Ansatz nur dann, wenn dieser es vermag die Lebenszufriedenheit des Gastes zu steigern.