158 Angewandte Psychologie
Filtern
Sprache
- Englisch (5) (entfernen)
Schlagworte
- Negotiation (1)
- Verhandlungsführung (1)
Institut
- Fakultät Wirtschaftswissenschaften (2)
- Fakultät Bildung (1)
- Fakultät Kulturwissenschaften (1)
- Fakultät Nachhaltigkeit (1)
- Institut für Bildung für Nachhaltige Entwicklung und Psychologie (IBP) (1)
- Institut für Kultur und Ästhetik Digitaler Medien (ICAM) (1)
- Institut für Psychologie (IFP) (1)
- Institut für Wirtschaftsinformatik (IIS) (1)
The present doctoral dissertations seeks to shed theoretical and empirical light on how complexity and different approaches to manage it affect perceptions, behaviors, and outcomes in integrative negotiations. Chapter 1 summarizes the following chapters, describes their individual contribution to the present thesis, and outlines avenues for future research. In Chapter 2, a theoretical model comprising of task- and context-based determinants of complexity in negotiations is developed. In Chapter 3, the effects of the number of issues (high vs. low) as one essential determinant of complexity on parties' trade-off behavior and joint outcomes are investigated in a series of four experiments. Furthermore, negotiators' cognitive categorizing of issues (i.e., their mental-accounting approach) is examined as the underlying psychological mechanism. Results reveal that more issues lead to a higher risk of scattering the integrative potential between cognitive categories (i.e., mental accounts), reducing trade-off quality and joint outcomes. In Chapter 4, the generalizability of the detrimental effect of the number of issues on joint outcomes is tested across varying numbers of issues in a meta-analysis. Moreover, boundary conditions for the effect are investigated. Results confirm the generalizability of the number-of-issues effect, but no relevant boundary conditions are identified. In Chapter 5, the effects of different mental-accounting approaches on negotiators' judgment accuracy, trade-off behaviors, and negotiation outcomes are examined in a series of five experiments. Results demonstrate that categorizing a moderate number of issues into each mental account leads to a higher judgment accuracy, trade-off quality, and joint outcomes, but only if negotiators manage to pool the integrative potential within these accounts. Finally, Chapter 6 takes a broader perspective on different integrative strategies in negotiations (i.e., expanding the pie, logrolling, solving underlying interests), thereby laying the groundwork for future research.
This dissertation presents an analysis of the relations to self and technology that emerge from and in the use of self-tracking technologies. The ethnographical study, combined with the Grounded Theory approach and a media analysis, demonstrates the complex intertwining or duality of control and care towards oneself that emerge or become possible in and through the application of ST technologies. ST devices assist in strengthening one's health and well-being in a playful way, building and maintaining a positive self-feeling, self-image and agency, and discovering unknown abilities and potentials within oneself. The ST technologies used provide orientation through complexity-reducing visualizations, highlighting patterns, and trend progression. They challenge through self-overload, dissatisfaction when not achieving goals, self-deception and distraction, narcissism and even loss of control - internally through compulsion to control as well as externally through loss of data otection and exploitation of private data by third parties, as well as handing over responsibility (in the form of decisions) to technology (algorithms) instead of self-responsibility. These two seemingly opposed yet concurrently existing self-relations reflect the dynamic between today's demands for self-responsibility (in health and performance terms) and the need for self-care and guidance for the many relevant, sometimes daily, decisions. They balance possibly existing tensions and ambiguities between the modes of self-relations that at first glance seem to be opposed and yet ultimately are jointly oriented towards the same goal, namely to master one's life (life maintenance) and to be in balance. The self-relations described in this thesis are supported, reinforced, or enabled by ST technology (and practice). Three different roles that ST technology can take in self-care and self-control were elaborated: technology as a means, a counterpart, and a promise. In relation to technology, another dialectic is visible, which shows the apparent contrast between its conception as a tool and means to achieve something and the approach to technology as an intimate counterpart (partner, nanny, coach) and a promise of salvation. The relationship with technology seems to intensify in and through the ST experience and takes on or is assigned a partner-like role by the users. Finally, the results indicate that the concept of (self-)optimization, contrary to its etymological meaning of a logic of increase, can also be understood differently, namely balancing. In this context, optimization does not necessarily mean the fastest, the highest, the strongest, but something that is achievable and satisfactory for the self - within the framework of the given and the desired. At the same time, the optimization understood as harmonizing and balancing in self-tracking becomes a lifelong task that, in principle, can never be completed because with the addition of new vital areas in life and throughout a lifetime also the individually understood and conceived balance often shifts.
This dissertation evaluated the efficacy of three different internet-based interventions that can be regarded as indirect interventions to reduce depression since they primarily targeted risk factors for depression. For this purpose three registered randomized controlled trials were conducted. In addition to assessing the efficacy of the interventions regarding the primary outcomes, the efficacy to reduce depression and further secondary outcomes was studied. In Study I (N=200) the efficacy of an internet-based stress management intervention (iSMI), which was adapted and tailored to career starting teachers, was compared to a waitlist control group (WLG). The participants of the intervention group (IG) reported significant reductions on the primary outcome perceived stress at post-intervention (T2) and three month follow-up (3-MFU). Furthermore, it was shown that the intervention indirectly also reduced depression at T2 and 3-MFU. The effects were sustained at an extended 6-MFU. Besides efficacy, the feasibility to complement the iSMI with a newly developed internet-based classroom management training was shown. Moreover, mediation analyses corroborated the role of problem- and emotion-focused coping skills in the intervention's effect on stress and the indirect effect of the intervention on depression through stress. Study II (N=262) demonstrated the efficacy of an internet- and app-based gratitude intervention on the reduction of primary assessed repetitive negative thoughts at T2 and 3-MFU, as compared to a WLG. The participants of the IG also reported significantly reduced depressive symptoms at T2, and 3-MFU, with significant clinically meaningful effects. The effects were sustained at an extended 6-MFU. Besides efficacy, mediation analyses showed that repetitive negative thinking mediated the gratitude intervention's effect on depression. Finally, Study III N(=351) showed that an internet-based intervention, tackling worries at the beginning of the COVID-19 pandemic, was effective as compared to an active mental health advice group. At T2, two weeks after randomization, the IG reported significantly reduced levels on the primary outcome worry as compared to controls. Participants of the IG also reported significantly reduced levels of depression at T2, with significant clinically meaningful reductions. The extended follow-ups in the IG indicated that the improvements from baseline were sustained until the 2-MFU and the 6-MFU. In a mediation analysis, worry was shown to mediate the intervention's effect on depression. Across all three studies a reliable deterioration of depression was occasionally observed. In summary, the studies in this dissertation demonstrated the efficacy of various indirect interventions focusing on rather common psychological problems to indirectly reduce depressive symptoms. The extent to which depression severity could be reduced is comparable to reductions found within participants with comparable baseline depression severity, in internet-based interventions directly addressing depressive symptoms. Indirect interventions are suggested to increase the uptake of interventions that reduce depressive symptoms, since they might be perceived as less stigmatizing and might broaden the range of interventions to choose from.
Mental health is an important factor in an individuals' life. Online-based interventions have been developed for the treatment of various mental disorders. During these interventions, a large amount of patient-specific data is gathered that can be utilized to increase treatment outcomes by informing decision-making processes of psychotherapists, experts in the field, and patients. The articles included in this dissertation focus on the analysis of such data collected in digital psychological treatments by using machine learning approaches. This dissertation utilizes various machine learning methods such as Bayesian models, regularization techniques, or decision trees to predict different psychological factors, such as mood or self-esteem, dropout of patients, or treatment outcomes and costs. These models are evaluated using a variety of performance metrics, for example, receiver operating characteristics curve, root mean square error, or specialized performance metrics for Bayesian inference. These types of analyses can support decision- making for psychologists and patients, which can, in turn, lead to better recommendations and subsequently to increased outcomes for patients and simultaneously more insight about the interplay between psychological factors. The analysis of user journey data has not yet been fully examined in the field of psychological research. A process for this endeavor is developed and a technical implementation is provided for the research community. The application of machine learning in this context is still in its infancy. Thus, another contribution is the exploration and application of machine learning techniques for the revelation of correlations between psychological factors or characteristics and treatment outcomes as well as their prediction. Additionally, economic factors are predicted to develop a process for treatment type recommendations. This approach can be utilized for finding the optimal treatment type for patients on an individual level considering predicted treatment outcomes and costs. By evaluating the predictive accuracy of multiple machine learning techniques based on various performance metrics, the importance of considering heterogeneity among patients' behavior and affect is highlighted in some articles. Furthermore, the potential of machine learning-based decision support systems in clinical practice has been examined from a psychotherapists' point of view.
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.