In times of an ever increasing amount of data and a growing diversity of data types in different application contexts, there is a strong need for large-scale and ﬂexible indexing and search techniques. Metric access methods (MAMs) provide this flexibility, because they only assume that the dissimilarity between two data objects is modeled by a distance metric. Furthermore, scalable solutions can be built with the help of distributed MAMs.
Both IF4MI and RS4MI, which are presented in this thesis, represent metric access methods. IF4MI belongs to the group of centralized MAMs. It is based on an inverted file and thus offers a hybrid access method providing text retrieval capabilities in addition to content-based search in arbitrary metric spaces. In opposition to IF4MI, RS4MI is a distributed MAM based on resource description and selection techniques. Here, data objects are physically distributed. However, RS4MI is by no means restricted to a certain type of distributed information retrieval system. Various application ﬁelds for the resource description and selection techniques are possible, for example in the context of visual analytics. Due to the metric space assumption, possible application fields go far beyond content-based image retrieval applications which provide the example scenario here.
The main objective of this dissertation is to provide theoretical explanations and empirical evidence for the causes and consequences of technostress. The results of this dissertation posit that the IT usage context matters. This means that users perceive technostress when using IT for work and for private purposes; but the causes and consequences differ for both contexts. In the case of using IT for work, technological characteristics and techno-stressors cause employees to feel exhausted at the end of their work day, feel dissatisfied with their job, and develop intentions to quit their job. In the case of IT usage for private purposes, social stressors are identified as new sort of stressor influencing psychological and behavioral strain even more strongly than techno-stressors. Although users of a stressful IT become dissatisfied with its usage and develop intentions to stop using it, the dissertation finds that switching to and using one or more alternatives can be even more stressful. In this context, this dissertation also emphasizes the influence of additional variables, such as user personality on technology characteristics, stressors and strain, concluding that the perception of stressors and strain varies among individuals.
Bearing these conclusions in mind, IT can be seen as a double-edged sword: using IT can be a source of fun, but potentially also a source of stress to others and to ourselves.
The National Educational Panel Study (NEPS) set up a panel cohort of students starting in grade 5 and grade 9.
To realize the corresponding samples of students NEPS applied a complex stratified multi-stage cluster sampling approach.
To allow for generalizations from the sample to the universe especially aspects of complex sample designs have to be considered and are reflected by design weights.
When applying multi-stage sampling approaches unit nonresponse, that is, units refuse to participate, may occur on each stage where decisions towards participation are made.
To correct for potential bias induced by refusals of schools and students the derived design weights need to be adjusted.
Since participation decisions differ in many ways, for example by stage (school- or student-level), time (in the forerun of or during the panel) or reasons (school-level: workload or participation in other studies, student-level: not interested in the study, resentment to testing), design weights need to be carefully adjusted to reflect the participation decisions made on each stage properly.
Participation decisions on the school level take information from sampling and the school recruitment process into account and are modeled using binary probit models with random intercept considering the federal-state-specific recruitment.
Schools participating are subsampled providing access to students in grade 5 and 9.
Subsampling within schools provides a sample of two classes if at least three are present, otherwise all classes are selected.
In creating design weights this subsampling needs again to be incorporated in the weights. The students decision process on the next stage has to be accounted for in providing unit nonresponse adjusted weights.
These decision processes take clustering at the school level as well as information on the initial sample, that is, respondents and nonrespondents, into account.
The resulting net sample forms the panel cohorts of students in grade 5 and 9.
Based on the panel cohorts each student can again decide whether to participate or not for each successive wave. Providing additional information obtained in a parental interview with one parent this multi-informant perspective makes consideration of an additional participation decision necessary. Since participation decisions of a student and a parent are unlikely independent they should be modeled appropriately using bivariate models. To again account for a cluster structure these models are extended with a random intercept on the school level.
All these aspects of complex sample and survey designs as well as the different participation decisions involved need to be considered in weighting adjustments.
The results point at typical characteristics influencing participation decisions of schools, students and parents.
Besides that the results stress the need to account for sample design and the nature of decision processes involved resulting in the actual participation.
The dissertation investigates the impact of a non-risk-weighted leverage ratio on the stability of financial institutions. We calculate leverage ratios (LR) and estimate probabilities of default (PD) and find a significant positive relationship between LR and PD. This might be explained by the fact that higher leverage ratios increase the cost of capital which in turn also increases interest rates that banks require for their loans. In fact, we find a significant positive relationship between LR and net interest margins. Following Stiglitz and Weiss (1981) increasing loan rates might attract borrowers who are more likely to default. This suggests that the potential introduction of a LR might lead to a destabilization of the banking sector since credit worthiness of borrowers might be reduced.
Previous research indicates that emotional intelligence (EI) is a personal resource for employees who work with people. Emotionally intelligent people have repeatedly been found to perform better on the job and to be less likely to experience burnout than people with low emotional abilities. However, the mechanisms that underlie such well-established relations are largely unknown. Furthermore, testing the supposed potential of EI in the context of personnel selection has been widely neglected in previous studies. To address some of the existing gaps in this research, I wanted to examine whether EI would have the potential to operate as a personal resource for people working in and applying for jobs that are associated with emotional demands.
The present dissertation consists of five parts. Chapter 1 provides an introduction to the construct of emotional intelligence and explains the relevance of EI in the context of working with people. At the end of Chapter 1, I present an overview of my empirical research on EI by presenting the central research questions that are revisited in the subsequent chapters.
Chapter 2 concerns the processing of emotional signals as a function of EI. Working with people is characterized by frequent and sometimes even conflict-laden interactions with clients. Previous research has indicated that EI is positively related to the quality of social interactions (e.g., Lopes et al., 2004). As emotions convey information about inner states, thoughts, and intentions (see Keltner & Haidt, 1999), the accurate appraisal of others’ emotions may help employees to act successfully in their social interactions with clients. For this reason, I examined the relation between EI and nonverbal dominance, which can be considered to be an adaptive strategy of emotion appraisal. As expected, emotionally intelligent people, especially those high in the ability to understand emotions, relied more strongly on the nonverbal part of emotion-relevant information when appraising others’ emotional states than people low on EI.
In Chapter 3, I take up existing gaps in research on the indirect effects of EI on work-related outcomes. Understanding the relation between teacher EI and student misconduct as an indicator of poor job performance for teachers was the goal of the research that is presented in this chapter. The results showed that teachers’ self-perceived EI was negatively related to student misconduct and that this relation was mediated by teachers’ tendency to attend to student needs. Furthermore, I investigated processes that may underlie the relation between teachers’ perceived abilities to appraise emotions and burnout. Results showed that both an intrapersonal (i.e., proactive coping) and an interpersonal process (i.e., attending to student needs) were mediators of the relation between the self-perceived ability to appraise one’s own emotions and burnout as well as between the self-perceived ability to appraise others’ emotions and burnout.
Chapter 4 concerns the relevance of EI in the context of personnel selection. Although a large number of studies have pointed to the potential of EI in personnel selection, research in real-life selection contexts has been scarce so far. Thus, the aim of the study that is presented in this chapter was to examine whether the EI of people who applied for the job of a flight attendant would predict aptitude ratings. There was a trend toward a positive direct effect of applicants’ ability to perceive emotions on the aptitude ratings. Furthermore, applicants’ abilities to understand and regulate emotions exerted indirect effects on the aptitude ratings through observer ratings on their job-relevant competencies.
An integration of and a conclusion about my research on EI in the context of working with people is shown in Chapter 5. The main research findings are summarized by providing answers to the central research questions of my dissertation. In the overall discussion, I take up the processes that seem to underlie the effects of EI on job performance and burnout and elaborate on the transferability of these processes to other professions. Furthermore, I point to the uniqueness of the EI facet emotion understanding and discuss possible maladaptive effects of an exaggerated EI. Following the general limitations and suggestions for future research, practical implications with regard to EI training and personnel selection are illustrated and an overall conclusion is drawn.