Fakultät Management und Technologie
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In past digital health interventions, an issue has been that participants drop out over time which is referred to as the "law of attrition" (Eysenbach, 2005). Based on this, the study proposes that though initially, participants respond to the intervention, there is a hypothesized second diminishing effect of an intervention. However, the study suggests that on top, there is a third effect. Independent of the individual notification or nudge, people could build the knowledge, skills and practice needed to independently engage in the behavior themselves (schraefel and Hekler, 2020). Using behavioral theory and inspired by prior animal computational models of behavior, the thesis proposes a dynamical computational model to allow for a separation of intervention and internalization. It is targeted towards the specific case of the HeartSteps intervention that could not explain a diminishing immediate effect of the intervention, second hypothesized effect, while a person’s overall steps remained constant, third effect (Klasnja et al., 2019). The study incorporates a habituation mechanism from learning theory that can account for the immediate diminishing effect. At the same time, a reinforcement mechanism allows participants to internalize the message and engage in behavior independently. The simulation shows the importance of a participant’s responsiveness to the intervention and a sufficient recovery period after each notification. To estimate the model, the study uses data from the HeartSteps intervention (Klasnja et al., 2019; Liao et al., 2020), a just-in-time adaptive intervention that sent two to five walking suggestions per day. The study runs a Bayesian estimation with Stan in R. Additional validation tests are needed to estimate the accuracy of the model for different individuals. It could however serve as a template for future just-intime adaptive interventions due to its generic structure. In addition, this model is of high practical relevance as its derived dynamics can be used to improve future walking suggestions and ultimately optimize notification-based digital health interventions.
In the study, predictive models for predicting therapy outcome are created using the dataset from E-COMPARED project, which belongs to the so-called type 3 models that use data from the intervention and preintervention phases to predict treatment outcomes, which can help to adapt intervention to maximize treatment. The predictive models aim to classify patients into two groups, improved and nonimproved. Since it is important to determine whether the models contribute to improvement of treatment, research questions that can contribute to the usage of type 3 models are established. The study focuses on the following three questions: (1) How accurately can the therapy outcome be predicted by various machine learning algorithms? Answering this question can let the people concerned obtain information about the reliability of contemporary predictive models. In addition, if the predictive power of the models is good, it is more likely to be used to assist therapists’ decisions. (2) Which kind of data is more important in predicting the therapy outcome? The answer to this question can show which dataset should be considered first to make better predictive models. Therefore, it can be helpful for researchers who want to make predictive models in the future and eventually help to facilitate personalized therapy. (3) What are the features with strong predictive power? The answer to this question can affect the people concerned, especially therapists. Therapists can use the most influential features revealed to adjust and improve future treatments.
Does the presentation of travel experience affect personal prestige of tourists? Prestige enhancement has been considered a motive for travel by tourism researchers for decades. Yet, the question whether representation of travel experience actually leads to personal prestige enhancement has been widely neglected so far. The study of prestige benefits of travel is a necessary endeavour to develop suitable methodological approaches toward the concept, in order to close critical knowledge gaps and enhance scientific understanding. The present thesis lays out the rationale and results of three research projects which shed light onto the relationship between touristic self-presentation and its effects on personal prestige evaluations of the social environment. The empirical studies conducted in the frame of this dissertation conclude in the following main findings:
Leisure travel is a useful means for people to self-express in a positive way, and material representations of travel are frequently displayed to others. Tourists make use of travel experience to self-present in a positive way by uploading photos on social media, collecting and displaying souvenirs, wearing jewellery and clothing from their last trip, or talking about their trips to others. They express positive self-messages about personal character traits, affiliation to social in-groups and proof of having travelled somewhere. The findings ascertain the utility of travel representations for positive self-expression, showing that travel experience is an effective vehicle for conspicuous consumption and self-expression as an antecedent for personal prestige enhancement.
Personal prestige is an element of social relations, and holds capacity to affect perceptions of social inclusion and social distinction, so it has to be conceptualised as a multidimensional construct. In a tourism context, personal prestige is reliably measurable along the four dimensions of hedonism, social inclusion, social distinction and prosperity. The herein developed Personal Prestige Inventory (PPI) is a valid, reliable and parsimonious measurement tool which substantially enhances methodological approaches toward empirical research into personal prestige.
The way in which people represent travel experience to others measurably affects how their personal prestige is evaluated by social others. Empirical evidence of a series of experimental studies provides support for the assumption that representation of travel experience has an effect on the social evaluation of tourists’ personal prestige. Experimental variance suggests small to moderate effects on personal prestige depending on the amount of leisure information given about a person, participation in tourism, and the destination and type of travel represented. This evidence is reasonable basis to conclude that whether and how people travel, and whether and how they share travel experience with others, does measurably affect social other’s evaluation of their personal prestige.
By providing qualitative evidence for positive self-presentation through leisure travel, and the subsequent development and experimental application of the Personal Prestige Inventory (PPI) in a tourism context, the present dissertation enhances scientific understanding of personal prestige in the context of leisure travel and provides useful methodological advancements for further research into the topic.