University of Mannheim

About: University of Mannheim is a education organization based out in Mannheim, Germany. It is known for research contribution in the topics: Context (language use) & Politics. The organization has 4448 authors who have published 12918 publications receiving 446557 citations. The organization is also known as: Uni Mannheim & UMA.

Informational efficiency of credit default swap and stock markets: The impact of credit rating announcements

Abstract: This paper analyzes the response of stock and credit default swap (CDS) markets to rating announcements made by the three major rating agencies during the period 2000–2002. Applying event study methodology, we examine whether and how strongly these markets respond to rating announcements in terms of abnormal returns and adjusted CDS spread changes. First, we find that both markets not only anticipate rating downgrades, but also reviews for downgrade by all three agencies. Second, a combined analysis of different rating events within and across agencies reveals that reviews for downgrade by Standard & Poor’s and Moody’s exhibit the largest impact on both markets. Third, the magnitude of abnormal performance in both markets is influenced by the level of the old rating, previous rating events and, only in the CDS market, by the pre-event average rating level of all agencies.

Recovery from job stress: The stressor-detachment model as an integrative framework

Abstract: Summary This paper reviews empirical evidence on psychological detachment from work during nonwork time. Psychological detachment as a core recovery experience refers to refraining from job-related activities and thoughts during nonwork time; it implies to mentally disengage from one's job while being away from work. Using the stressor-detachment model as an organizing framework, we describe findings from between-person and within-person studies, relying on cross-sectional, longitudinal, and daily-diary designs. Overall, research shows that job stressors, particularly workload, predict low levels of psychological detachment. A lack of detachment in turn predicts high strain levels and poor individual well-being (e.g., burnout and lower life satisfaction). Psychological detachment seems to be both a mediator and a moderator in the relationship between job stressors on the one hand and strain and poor well-being on the other hand. We propose possible extensions of the stressor-detachment model by suggesting moderator variables grounded in the transactional stress model. We further discuss avenues for future research and offer practical implications. Copyright © 2014 John Wiley & Sons, Ltd.

Open Innovation: Past Research, Current Debates, and Future Directions

Abstract: Executive Overview The concept of open innovation has recently gained widespread attention. It is particularly relevant now because many firms are required to implement open innovation, despite the difficulties associated with managing these activities. After providing a definition of open innovation delimiting it from open source, an overview of prior research is given, which identifies the following important topics of earlier open innovation research: technology transactions, user innovation, business models, and innovation markets. In light of current controversial debates about the value of the open innovation framework, we evaluate the literature and assess whether open innovation is a sustainable trend rather than a management fashion. On this basis, we present a conceptual framework that provides the foundation for discussing critical open innovation processes and their implications for managing open innovation at the organizational, project, and individual level. Thus, we assess the multilevel de...

Dimensions of Contact as Predictors of Intergroup Anxiety, Perceived Out-Group Variability, and Out-Group Attitude: An Integrative Model

Abstract: This study tested an integrative model of how dimensions of contact (quantitative, qualitative, and intergroup) are related to intergroup anxiety, perceived out-group variability, and out group attitude. Data were collected in a field study of minority (Hindu) and majority (Muslim) religious groups in Bangladesh. Path analysis revealed that dimensions of contact were significant predictors of all three criterion variables, although different dimensions emerged as predictors in each case, and there were some interactions with subjects' religious group. AU three dimensions of contact were associated with intergroup anxiety, but whereas quantitative contact had a significant impact on perceived out-group variability, qualitative contact was associated with out-group attitude. The model highlights the central role of intergroup anxiety as associated with dimensions of contact and as a predictor of perceived out-group variability and out-group attitude.

Automatic analysis of malware behavior using machine learning

Abstract: Malicious software - so called malware - poses a major threat to the security of computer systems. The amount and diversity of its variants render classic security defenses ineffective, such that millions of hosts in the Internet are infected with malware in the form of computer viruses, Internet worms and Trojan horses. While obfuscation and polymorphism employed by malware largely impede detection at file level, the dynamic analysis of malware binaries during run-time provides an instrument for characterizing and defending against the threat of malicious software. In this article, we propose a framework for the automatic analysis of malware behavior using machine learning. The framework allows for automatically identifying novel classes of malware with similar behavior (clustering) and assigning unknown malware to these discovered classes (classification). Based on both, clustering and classification, we propose an incremental approach for behavior-based analysis, capable of processing the behavior of thousands of malware binaries on a daily basis. The incremental analysis significantly reduces the run-time overhead of current analysis methods, while providing accurate discovery and discrimination of novel malware variants.