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Personally meaningful tourist experiences foster subjective mental wellbeing. Modern, human-centred technologies such as gamified technology have been recognised as a promising means to support tourists in their co-creation of meaningful tourist experiences. However, a deeper understanding and conceptualisation of tourists' engagement with gamified technologies in the tourist experience has remained absent so far. This study draws on positive psychology as the guiding theoretical lens to conceptualise and explore tourists' underlying motives for engaging with gamified technology, as well as the gratifications thereof for the tourist experience. In doing so, this thesis identifies how tourists generate meaning through interacting with gamified technology in the tourist experience, thereby fostering the co-creation of meaningful tourist experiences and contributing to subjective mental wellbeing. Being among the first studies to link the concepts of positive psychology, gamified technology, and tourist experiences, the results of this thesis provide rich findings on the underlying motives for tourists to engage with gamified technology during vacation, as well as the gratifications of gamified technology for the creation of meaning in the tourist experience. Using the theoretical lens of positive psychology and achievement motivation theory as the main theoretical underpinning, this study is positioned at the intersection of social psychology, human-computer interaction, and tourism as the field of application. Conceptually, this thesis provides an in-depth understanding of tourists’ engagement with gamified technology, including the socio-psychological motivators for engagement and the outcomes thereof for the tourist experience.
The wide accessibility of the Internet and web-based programs enable an increased volume of online interventions for mental health treatment. In contrast to traditional face-to-face therapy, online treatment has the potential to overcome some of the barriers such as improved geographical accessibility, individual time planning, and reduced costs. The availability of clients' treatment data fuels research to analyze the collected data to obtain a better understanding of the relationship among symptoms in mental disorders and derive outcome and symptom predictions. This research leads to predictive models that can be integrated into the online treatment process to assist clinicians and clients. This dissertation discusses different aspects of the development of predictive modeling in online treatment: Categorization of predictive models, data analyses for predictive purposes, and model evaluation. Specifically, the categorization of predictive models and barriers against the uptake of mental health treatment are discussed in the first part of this dissertation. Data analysis and predictive modeling are emphasized in the second part by presenting methods for inference and prediction of mood as well as the prediction of treatment outcome and costs. Prediction of future and current mood can be beneficial in many aspects. Inference of users' mood levels based on unobtrusive measures or diary data can provide crucial information for intervention scheduling. Prediction of future mood can be used to assess clients' response to the treatment and expected treatment outcome. Prediction of the expected treatment costs and outcomes for different treatment types allows simultaneous optimization of these objectives and to increase the cost-effectiveness of the treatment. In the third part, a systematic predictive model evaluation incorporating simulation analyses is demonstrated and a method for model parameter estimation for computationally limited devices is presented. This dissertation aims to overcome the current challenges of predictive model development and its use in online treatment. The development of predictive models for varies data collected in online treatment is demonstrated and how these models can be applied in practice. The derived results contribute to computer science and mental health research with client individual data analysis, the development ofpredictive models, and their statistical evaluation.
This doctoral thesis contains four empirical studies analysing the personal accountability of prime ministers and the electoral presidentialisation of parliamentary elections in European democracies. It develops the concept of presidentialised prime ministerial accountability as a behavioural element in the chain of accountability in parliamentary systems. The ongoing presidentialisation of parliamentary elections, driven by changes in mass communication and erosion of societal cleavages, that fosters an increasing influence of prime ministers' and other leading candidates' personalities on vote choices, has called performance voting – and the resulting accountability mechanism of electoral punishment and reward of governing parties – into question. This thesis analyses whether performance voting can be extended to the personal level of parliamentary governments and asks whether voters hold prime ministers personally accountable for the performance of their government. Furthermore, it explores how voters change their opinion of prime ministers and how differences in party system stability and media freedom between Western and Central Eastern Europe contribute to higher electoral presidentialization in Central Eastern European parliamentary elections. This thesis relies on several national data sources: the "British Election Study", the "German Longitudinal Election Study" and other German election surveys, the "Danish Election Study", as well as, data from the "Forschungsgruppe Wahlen". In addition, it utilises cross-national data from the "Comparative Study of Electoral Systems".
Viable communication systems
(2020)
Society has come to the point that it requires a "Great Transformation" towards sustainability to ensure the viability of the planet for a vital society. The energy transition plays a central role for this transformation. For transforming the patterns of energy production and use in an energy transition as part of the "Great Transformation", this process of change now needs to strengthen its focus on information, communication, and knowledge systems. Human society needs to establish a knowledge system that has the potential to create usable knowledge for sustainability solutions. This requires organizing a communication system that is sufficiently complex, interconnected, and, at the same time, efficient for integrating reflexive, open-ended, inter- and transdisciplinary learning, evaluation, and knowledge co-production processes across multiple levels. This cumulative dissertation contributes to research in this direction by applying a systemic sustainability perspective on the content and organization of communication in the field of research on sustainable energy and the operational level of municipal climate action as part of the energy transition. Regarding sustainability, this thesis uses strong sustainability and its principles as a frame for evaluating the content of communication. Regarding the systemic perspective, the thesis particularly relies on the following theories: (i) the human-environment system model by R. Scholz as an overarching framework regarding interactions between humans and nature, (ii) social systems theory by N. Luhmann to reflect the complexity of society, (iii) knowledge management to consider the human character of knowledge and a practice-oriented perspective, and (iv) management cybernetics, in particular, the Viable System Model by S. Beer as a framework to analyze and assess organizational structures. Furthermore, the thesis leverages the potential of text mining as a method to identify and visualize patterns in texts that reflect prevalent paradigms in communication. The thesis applies the above conceptual and methodological basis in three case studies. Case Study 1 investigates the measures proposed in 16 municipal climate action plans of regional centers in Lower Saxony, Germany. It uses a text mining approach in the form of an Summary interpretation network analysis. It analyzes how different societal subsystems are connected at the semantic level and to what extent sustainability principles can be recognized. Case Study 2 analyzes and reflects paradigms and discursive network structures in international scientific publications on sustainable energy. The study investigates 26533 abstracts published from 1990 to 2016 using a text mining approach, in particular topic modeling via latent Dirichlet allocation. Case Study 3 turns again to the cases of municipal climate action in Lower Saxony examined in Case Study 1. It examines the involvement of climate action managers of these cities in multilevel knowledge processes. Using design principles for knowledge systems, it evaluates to what extent knowledge is managed in this field across levels for supporting the energy transition and to what extent local innovation potential is leveraged or supported. The three case studies show that international research on sustainable energy and municipal climate action in Germany provide promising contributions to achieve a transformation towards sustainability but do not fully reflect the complexity of society and still support a growth paradigm, in contrast to a holistic sustainability paradigm. Further, the case studies show that research and local action are actively engaging with the diversity of energy technologies but are lagging in dealing with the socio-epistemic (communication) system, especially with regard to achieving cohesion. Using the example of German municipalities, Case Studies 1 and 3 highlight the challenges of achieving coherent local action for sustainability and bottom-up organizational learning due to incomplete or uncoordinated multilevel knowledge exchange.
Analysis of User Behavior
(2020)
Online behaviors analysis consists of extracting patterns from server-logs. The works presented here were carried out within the "mBook" project which aimed to develop indicators of the quantity and quality of the learning process of pupils from their usage of an eponymous electronic textbook for History. In this thesis, the research group investigates several models that adopt different points of view on the data. The studied methods are either well established in the field of pattern mining or transferred from other fields of machine learning and data mining. The authors improve the performance of archetypal analysis in large dimensions and apply it to unveil correlations between visibility time of particular objects in the e-textbook and pupils' motivation. They present next two models based on mixtures of Markov chains. The first extracts users' weekly browsing patterns. The second is designed to process essions at a fine resolution, which is sine qua non to reveal the significance of scrolling behaviors. The authors also propose a new paradigm for online behaviors analysis that interprets sessions as trajectories within the page-graph. In this respect, they establish a general framework for the study of similarity measures between spatio-temporal trajectories, for which the study of sessions is a particular case. Finally, they construct two centroid-based clustering methods using neural networks and thus lay the foundations for unsupervised behaviors analysis using neural networks.
Existing institutions no longer appear to be sufficiently capable to deal with the complexity and uncertainty associated with the wicked problem of sustainability. Achieving the required sustainability transformation will thus require purposeful reform of existing institutional frameworks. However, existing research on the governance of sustainability of sustainability transformations has strongly focused on innovation and the more "creative" aspects of these processes, blinding our view to the fact that they go hand with the failure, decline or dismantling of institutions that are no longer considered functional or desirable. This doctoral dissertation thus seeks to better understand how institutional failure and decline can contribute productively to sustainability transformations and how such dynamics in institutional arrangements can serve to restructure existing institutional systems. A systematic review of the conceptual literature served to provide a concise synthesis of the research on "failure" and "decline" in the institutional literature, providing important first insights into their potentially productive functions. This was followed up by an archetype analysis of the productive functions of failure and decline, drawing on a wide range of literatures. This research identified five archetypical pathways: (1) crises triggering institutional adaptations toward sustainability, (2) systematic learning from failure and breakdown, (3) the purposeful destabilisation of unsustainable institutions, (4) making a virtue of inevitable decline, and (5) active and reflective decision making in the face of decline instead of leaving it to chance. Empirical case studies looking at the German energy transition and efforts to phase out coal in the Powering Past Coal Alliance served to provide more insights on (a) how to effectively harness "windows of opportunity" for change, and (b) the governance mechanisms used by governments to actively remove institutions. Results indicate that the lock-in of existing technologies, regulations and practices can throw up important obstacles for sustainability transformations. The intentional or unintentional destabilisation of the status quo may thus be required to enable healthy renewal within a system. This process required active and reflective management to avoid the irreversible loss of desirable institutional elements. Instruments such as "sunset clauses" and "experimental legislation" may serve as important tools to learn through "trial and error", whilst limiting the possible damage done by failure. Focusing on the subject of scale, this analysis finds that the level at which failure occurs is likely to determine the degree of change that can be achieved. Failures at the policy-level are most likely to merely lead to changes to the tools and instruments used by policy makers. This research thus suggests that failures on the polity- and political level may be required to achieve transformative changes to existing power structures, belief-systems and paradigms. Finally, this research briefly touches on the role of actor and agency in the governance of sustainabilitytransformations through failure and decline. It finds that actors may play an important role in causing a system or one of its elements to fail and in shaping the way events are come to be perceived.
To improve the properties of thermochemical heat storage materials, salt mixtures were evaluated for their heat storage capacity and cycle stability as part of the innovation incubator project "Thermochemical battery" of the Leuphana university Lüneburg. Based on naturally occurring compound minerals, 16 sulfates, 18 chlorides and 5 chloride multi-mixtures, 18 bromides and 5 intermixtures between sulfates, chlorides and bromides were synthesized either from liquid solution or by dry mixing for TGA/DSC screening before continuing the heat storage evaluation with five different measurement setups at a laboratory scale. The TGA/DSC analysis served as a screening process to reduce the number of testing materials for the upscaled experiments. The evaluation process consisted of a three-cycle dehydration/hydration measurement at Tmax=100°C and Tmax=200°C. In case of the bromide samples a measurement of hydration conditions with Tmax=110°C and a water flow at e=18.68mbar, were added to the procedure to detect the maximum water uptake temperature. Also, a single dehydration to a temperature of Tmax=500°C was implemented to observe melting behavior and to easier calculate the samples’ stages of hydration from the remaining anhydrous mass. Materials which showed high energy storage density and improved cycle stability during this first evaluation were cleared for multi-cycle measurements of 10 to 25 dehydration and hydration cycles at Tmax = 100 to 120°C and the evaluations at m=20 to 100g scale. An estimate for the specific heat capacities at different temperatures of the materials which passed the initial stage was calculated from the TGA/DSC results as well. The laboratory scale measurement setup went through five stages of refining, which led to reducing the intended maximum sample mass from m=100g to m=20g. A switch from supplied liquid water to water vapor as the used reactant was also implemented in exchange for improved dehydration conditions. Introducing a vacuum pump for evaporating the water limited the influence of outside heat sources during hydration and in-situ dehydration was enabled as to not disturb the state the samples were settling in between measurements. Baseline calculation from blanc measurements with glass powder and attempts to calculate the specific heat capacity cp of the tested materials by 6 applying the Joule-Lenz-law to the measurement apparatus was another step of method development. The evaluation process of the laboratory scale tests at the final setting consisted of 1 to 5 cycle measurements of in-situ dehydration and hydrations with applied vacuum for t=30 minutes at p~30mbar. Upscaling the sample mass to m=20g allowed for a close observation of different material behaviors. Agglomeration, melting and dissolving of the m=10mg samples during the TGA/DSC analysis can be deducted from the recorded measurement curves and the state of the sample after measurement. However, at laboratory scale the visible volume changes, observed sample consistency after agglomeration and an automatic removal of molten and dissolved sample mass during the measurement allowed for a better characterization and understanding of the magnitude of the actual changes. This was done for the first time, particularly for mixed salts. Of the original number of 62 samples, 4 mixtures which passed the initial TGA/DSC screening namely {2MgCl2+ KCl}, {2MgCl2+CaCl2}, {5SrBr2+8CaCl2} and {2ZnCl2 + CaCl2} were chosen for further evaluation. The multi-cycle TGA/DSC measurements of {2MgCl2+ KCl}, {2MgCl2+CaCl2} and {5SrBr2+8CaCl2} showed an improved cycle stability for all three materials over the untreated educts. Of the four materials {2ZnCl2 + CaCl2} displayed the strongest deliquescence during hydration in the upscaled experimental setup. {2MgCl2+CaCl2} proved to be the most stable material regarding the heat storage density. The {MgCl2} content of the mixture is likely to partially or completely react to {Mg(OH)Cl} at temperatures of T>110°C, which however does not impede the heat storage density. {5SrBr2+8CaCl2} displayed a low melting point in hydrated state, causing a fast material loss. This makes it an undesirable storage material. A lower heating rate may still help to avoid an early melting. The {2MgCl2+KCl} mixture was the most temperature stable of the mixtures showing no melting or dissolving behavior. A reaction of the {MgCl2} component of the mixture to {Mg(OH)Cl} was not observed within the applied temperature range of T=25 to 200°C.
Panic disorder is a common anxiety disorder, which is associated with high subjective burden as well as a high cost for the health economy. According to the National Treatment Guideline S3, cognitive behavior therapy is recommended as the most effective psychological treatment. However, many people in need do not have access to cognitive behavior therapy. Internet-based interventions have proven to be an effective way to provide access to evidence-based treatment to those affected. For anxiety disorders, such as panic disorder and agoraphobia, a good effectiveness of internet-based interventions has been proven in numerous international studies. However, the internet has changed over the last few years: mobile technologies have considerable potential to further improve the adherence and effectiveness of internet-based interventions. Against this background, the authors developed the hybrid online training "GET.ON Panic". In this training, an app has been integrated into a browser-based online training. The app consists of a mobile diary for self-monitoring as well as a mobile exposure-guide that supports participants in self-exposure exercises in their everyday lives.In an initial exploratory feasibility study, qualitative interview data and quantitative measurements were collected in a pre-post design of 10 participants. Usage, user friendliness, user satisfaction and acceptance of the app were generally considered high. The use of interoceptive exposure exercises and daily summaries of anxiety and mood were the most widely performed and rated the best, while in vivo exposure exercises and the monitoring of acute panic symptoms were found to be difficult.In the efficacy study, 92 participants with mild to moderate panic symptoms were randomized into two parallel groups. After eight weeks, the intervention group showed a significant improvement in the severity of panic symptoms compared to the waiting control group. Using the intention-to-treat approach, a covariance analysis with baseline values as a covariate yielded a mean effect of Cohen's d=0.66 in reducing the panic symptoms in favor of the intervention group. This effect increased to d=0.89 after three months and stayed at d=0.81 at the 6-month measurement point. Response and remission rates were also significantly higher in the intervention group. This positive effect was also shown for secondary outcomes such as depressive symptoms and quality of life. A correlation between app usage and clinical outcomes could not be found. This work was the first to demonstrate that a hybrid online training based on cognitive behavior therapy is effective in reducing panic symptoms as well as panic disorder. In addition, this work contributes to a deeper understanding of the potential of mobile technologies in the field of e-mental health.
Sustainable landscape development is the main goal of decision makers worldwide. Achieving this goal in the long term leads to achieving social, economic and environmental sustainability. Remote sensing has been playing an essential role in monitoring remote areas. This study has employed part of the role of remote sensing in supporting the direction of decision makers towards sustainable landscape development. The study has focused on some of the main elements affecting sustainable environment: land uses, specifically agricultural land uses, water quality, forests, and water hazards such as floods. Three research programs were undertaken to investigate the role of Terrasar-x imagery, as a source of remote sensing data, in monitoring the environment and achieving the previous stated elements. The investigation was intended to investigate the effectiveness of TSX imagery in identifying the cropping pattern of selected study areas by employing a pixel-based supervised maximum likelihood classifier, as published in Paper I, assessment of the efficiency of using TSX imagery in determining land use and the flood risk maps by applying an object-based decision tree classifier as published in Paper II, and determination of the potential of inferential statistics tests such as the two samples Z-test and multivariate analysis, for example Factor Analysis, for identifying the kind of forest canopy, based on the backscattering coefficient of TSX imagery of forest plots, as presented in Paper III. Papers I and II covered two pilot areas in the Lower Saxonian Elbe Valley Biosphere Reserve “das Biosphärenreservat "Niedersächsische Elbtalaue" around Walmsburger Werder and Wehninger Werder. Paper III focused on the Fuhrberger Feld water protection area near Hanover in Germany. The inputs for this research were mainly SAR Imagery and the ground truth data collected from field surveys, in addition to databases, geo-databases and maps. The study presented in Paper I used two filters to decrease speckle noise namely De-Grandi as multi-temporal speckle filter, and Lee as an adaptive filter. A multi-temporal classification method was used to identify the different crops using a pixel-based maximum likelihood classifier. The classification accuracy was assessed based on the external user accuracy for each crop, the external producer accuracy for each crop, the Kappa index and the external total accuracy for the entire classification. Three cropping pattern maps were produced namely the cropping pattern map of Wehninger Werder in 2011 and the cropping pattern maps of Walmsburger Werder in 2010 and in 2011. The study showed that image filtering was essential for enhancing the accuracy of crop classification. The multi-temporal filter De-Grandi enhanced the producer accuracy by about 10% compared to the Lee filter. Furthermore, gathering and utilizing large ground truth data greatly enhanced the accuracy of the classification. The research verified that using sequence images covering the growing season usually improved the classification results. The results exposed the effect of the polarization and demonstrate that the majority of the classifications produced according to the crop calendar had higher total producer accuracy than using all acquisitions. The study demonstrated undertaken in Paper II applied the decision tree object-based classifier in determining the major land uses and the inundation extent areas in 2011 and 2013 using the Lee-filtered imagery. Based on the maps produced for the land uses and inundation areas, the hazard areas due to the floods in 2011 and 2013 were identified. The study illustrated that 95% of the inundated area was classified correctly, that 90% of vegetated lands were accurately determined, and around 80% of the forest and the residential areas were correctly recognized. The research undertaken in Paper III statistically analyzed the backscattering coefficient of the Lee-filtered TSX in some forest plots by the Factor Analysis and two sample Z-test. The study showed that Factor analysis tools succeeded in differentiating between the coniferous forest and the deciduous forest and mixed forest, but failed to discriminate between the deciduous and the mixed forest. On one hand, only one factor was extracted for each sample plot of the coniferous forest with approximately equal loadings during the whole acquisition period from March 2008 to January 2009. On the other hand, two factors were extracted for each deciduous or mixed forest sample plot, where one factor had high loadings during the leaf-on period from May to October, and the other one had high loadings during the leaf-off period from November to April. Furthermore, the research revealed that the two sample Z-test enabled not only differentiation between the deciduous and the mixed forest against the coniferous forest, but also discrimination between deciduous forest and the mixed forest. Statistically significant differences were observed between the mean backscatter values of the HH-polarized acquisitions for the deciduous forest and the mixed forest during the leaf-off period, but no statistically significant difference was found during the leaf-on period. Moreover, plot samples for the deciduous forest had slightly higher mean backscattering coefficients than those for the mixed forest during the leaf-off period.
It is understood among research and policy makers that addressing unsustainable individual consumption patterns is key for the vision of sustainable development. Education for Sustainable Consumption (ESC) is attributed a pivotal role for this purpose, aiming to improve the capacity of individuals to connect to and act upon knowledge, values and skills in order to respond successfully and purposefully to the demands of sustainable consumption. Yet despite political, scientific, and educational efforts and increasing awareness in the general population, little has been achieved to substantially change behavioral patterns so far. As part of the explanation for this shortcoming, it has been argued that current ESC practices have neglected the personal dimension of sustainable consumption, especially the affective-motivational processes underlying unsustainable consumption patterns. Against this background, this cumulative thesis is guided by the question how personal competencies for sustainable consumption can be defined, observed, and developed within educational settings. Special attention is given to mindfulness practices, describing the practice of cultivating a deliberate, unbiased and openhearted awareness of perceptible experience in the present moment. Drawing upon an explorative, qualitative research methodology, the thesis looks at three different mindfulness-based interventions aiming to stimulate competencies for sustainable consumption, reaching out to a total number of 321 participants (employees and university students). In this thesis, the author suggests to define personal competencies for sustainable consumption as abilities, proficiencies, or skills related to inner states and processes that can be considered necessary or sufficient to engage with sustainable consumption (SC). These include ethics, self-awareness, emotional resilience, selfcare, access to and cultivation of personal resources, access to and cultivation of ethical qualities, and mindsets for sustainability. The thesis holds that the observation of personal competencies benefits from a combination of different methodological and methodical angles. When working with self-reports as empirical data, a pluralistic qualitative methods approach can help overcoming shortcomings that are specifically related to individual methods while increasing the self-reflexivity of the research. Moreover, it is suggested to let learners analyze their own personal statements in groups, applying scientific methods. The products of the group analyses represent data based on an inter-subjectively shared perspective of learners that goes beyond self-estimation of personal competencies. In terms of developing personal competencies for SC, it can be concluded that mindfulness practice alone is not sufficient to build personal competencies for SC. While it can stimulate generic personal competencies, individuals do not necessarily apply these competencies within the domain of their consumption. Nevertheless, mindfulness practice can play an important role in ESC, insofar as it lays the inner foundation to engage with sustainability-related issues. More precisely, it allows learners to experience the relevance of their inner states and processes and the influence they have on actual behaviors, leading to a level of selfawareness that would not be accessible solely through discursive-intellectual means. Furthermore, participants experience mindfulness practice as a way to develop ethical qualities and access psychological resources, entailing stronger emotional resilience and improved well-being. In order to unleash its full potential for stimulating personal competencies for SC, however, the findings of the thesis suggest that mindfulness practice should be (a) complemented with methodically controlled self-inquiry and (b) related to a specific behavioral change. In this vein, self-inquiry-based and self-experience-based learning – two pedagogical approaches developed during the period of research for this thesis – turned out to be promising pedagogies for educational settings striving to stimulate the development of personal competencies for SC.