Article
Refine
Year of publication
Institute
- Fachbereich Wirtschaftswissenschaften (116) (remove)
Language
- English (116) (remove)
Document Type
- Article (116) (remove)
Keywords
- rebound-effect (2)
- sustainability (2)
- Bank-issued Warrants (1)
- Brands (1)
- Centrifugal twisting moment (1)
- Change (1)
- Charging station (1)
- Clinical decision support systems (1)
- Consensus (1)
- Discourse ethics (1)
Die Garantie im Kaufrecht
(1995)
Divided government is often thought of as causing legislative deadlock. I investigate the link between divided government and economic reforms using a novel data set on welfare reforms in US states between 1978 and 2010. Panel data regressions show that, under divided government, a US state is around 25% more likely to adopt a welfare reform than under unified government. Several robustness checks confirm this counter-intuitive finding. Case study evidence suggests an explanation based on policy competition between governor, senate, and house.
Does stiffer electoral competition reduce political shirking? For a micro-analysis of this question, I construct a new data set spanning the years 2005 to 2012 covering biographical and political information about German Members of Parliament (MPs), including their attendance rates in voting sessions. For the parliament elected in 2009, I show that indeed opposition party MPs who expect to face a close race in their district show significantly and relevantly lower absence rates in parliament beforehand. MPs of governing parties seem not to react significantly to electoral competition. These results are confirmed by an analysis of the parliament elected in 2005, by several robustness checks, and also by employing an instrumental variable strategy exploiting convenient peculiarities of the German electoral system. The study also shows how MPs elected via party lists react to different levels of electoral competition.
Purpose
In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.
Design/methodology/approach
In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.
Findings
Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.
Originality/value
For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.
Researching the field of business intelligence and analytics (BI & A) has a long tradition within information systems research. Thereby, in each decade the rapid development of technologies opened new room for investigation. Since the early 1950s, the collection and analysis of structured data were the focus of interest, followed by unstructured data since the early 1990s. The third wave of BI & A comprises unstructured and sensor data of mobile devices. The article at hand aims at drawing a comprehensive overview of the status quo in relevant BI & A research of the current decade, focusing on the third wave of BI & A. By this means, the paper’s contribution is fourfold. First, a systematically developed taxonomy for BI & A 3.0 research, containing seven dimensions and 40 characteristics, is presented. Second, the results of a structured literature review containing 75 full research papers are analyzed by applying the developed taxonomy. The analysis provides an overview on the status quo of BI & A 3.0. Third, the results foster discussions on the predicted and observed developments in BI & A research of the past decade. Fourth, research gaps of the third wave of BI & A research are disclosed and concluded in a research agenda.