“Will my flight be cancelled?” “Will I win the eBay auction?” These consumption and product acquisition situations would trigger the experience of . . . suspense. Suspense is defined as the overall anticipatory arousal associated with the hope and/or fear felt by a consumer assessing the likelihood of occurrence of an important and imminent consumption or acquisition event. If one views a potential outcome as causing pleasure (an approach appraisal), hope will be felt, while if one views a potential outcome as causing pain (an avoidance appraisal), fear will be felt. Other variables expected to indirectly impact suspense are frequency of probability change, degree of probability change and anticipation time. The conceptual model in this dissertation also proposes that people have an attitude toward the anticipation period and identifies four resolution emotions, satisfaction, disappointment, relief, and anguish, which may occur once the outcome is known. Further, attitude toward anticipation period and the resolution emotions are expected to affect attitude toward overall experience. Three studies were conducted. The objective of Studies 1 and 2 was to develop scales yielding reliable scores of hope, fear, and suspense. Fifty words related to hope, fear, and suspense were generated. In Study 1, 553 participants rated the words on the evaluative and activity dimensions using 18 semantic differential scale items. O-technique factor analysis was used to analyze the data in Study 1. In Study 2, 354 participants read one of three suspenseful stories, then indicated their hope, fear, and suspense. Exploratory and confirmatory factor analyses were used in Study 2. Study 3 consisted of an experiment in which 241 participants read a suspenseful house-buying scenario, then indicated their hope, fear, and suspense. Structural equation modeling was used to analyze the data in Study 3. Results supported the conceptualization of suspense: both hope and fear had a positive effect on suspense. Additionally, approach appraisal had a positive effect on hope, and avoidance appraisal had a positive effect on fear. The moderating effect of frequency of probability change was not supported. However, frequency of probability change did have a positive effect on both hope and fear.

Ratingsysteme sind ein zentraler Bestandteil der Kreditrisikomodellierung. Neben der Bonitätsbeurteilung auf der Ebene der Kreditnehmer und der Risikoquantifizierung auf der Ebene der Ratingklassen spielt dabei die Bildung der Ratingklassen eine wesentliche Rolle. Die Literatur zur Ratingklassenbildung setzt auf modellfreie, in gewisser Weise willkürliche Optimierungsverfahren. Ein Ziel der vorliegenden Arbeit ist es, stattdessen ein parametrisches statistisches Modell zur Bildung der Ratingklassen einzuführen. Ein geeignetes Modell ist im Bereich der Bruchpunktschätzung zu finden. Dieses Modell und die in der mathematischen Literatur vorgeschlagenen Parameter- und Intervallschätzer werden in der vorliegenden Arbeit dargestellt und gründlich diskutiert. Dabei wird Wert auf eine anwendungsnahe und anschauliche Formulierung der mathematisch-statistischen Sachverhalte gelegt. Anschließend wird die Methodik der Bruchpunktschätzung auf einen konkreten Datensatz angewendet und mit verschiedenen anderen Kriterien zur Ratingklassenbildung verglichen. Hier erweist sich die Bruchpunktschätzung als vorteilhaft. Aufbauend auf der empirischen Untersuchung wird abschließend weiterer Forschungsbedarf abgeleitet. Dazu werden insbesondere Konzepte für den Mehrklassenfall und für abhängige Daten entworfen.:1. Einleitung 2. Ratingsystem 3. Bruchpunktschätzung 4. Anwendung 5. Zusammenfassung und Ausblick / Rating systems are a key component of credit risk modeling. In addition to scoring at borrowers’ level and risk quantification at the level of rating classes, the formation of the rating classes plays a fundamental role. The literature on rating classification uses in a way arbitrary optimization methods. Therefore, one aim of this contribution is to introduce a parametric statistical model to form the rating classes. A suitable model can be found in the area of split-point estimation. This model and the proposed parameter and interval estimators are presented and thoroughly discussed. Here, emphasis is placed on an application-oriented and intuitive formulation of the mathematical and statistical issues. Subsequently, the methodology of split-point estimation is applied to a specific data set and compared with several other criteria for rating classification. Here, split-point estimation proves to be advantageous. Finally, further research questions are derived on the basis of the empirical study. In particular, concepts for the case of more than two classes and for dependent data are sketched.:1. Einleitung 2. Ratingsystem 3. Bruchpunktschätzung 4. Anwendung 5. Zusammenfassung und Ausblick