Refine
Year of publication
Document Type
- Article (105)
- Conference Proceeding (42)
- Book (32)
- Part of a Book (14)
- Working Paper (2)
- Doctoral Thesis (1)
Language
- English (196) (remove)
Has Fulltext
- no (196) (remove)
Keywords
- Active learning (1)
- Bank-issued Warrants (1)
- Business Models (1)
- Business Process Intelligence (1)
- Case Study (1)
- Challenges (1)
- Clinical decision support systems (1)
- Consensus (1)
- Cross-platform (1)
- Deep learning (1)
- Discourse ethics (1)
- Disposition Effect (1)
- Evaluation (1)
- Explainability (1)
- Feature selection (1)
- Finland (1)
- Gamification (1)
- Germany (1)
- Guidelines (1)
- IT Products (1)
Institute
- Fachbereich Wirtschaftswissenschaften (196) (remove)
In the past decade, many IS researchers focused on researching the phenomenon of Big Data. At the same time, the relevance of data protection gets more attention than ever before. In particular, since the enactment of the European General Data Protection Regulation in May 2018 Information Systems research should provide answers for protecting personal data. The article at hand presents a structuring framework for Big Data research outcome and the consideration of data protection. IS Researchers might use the framework in order to structure Big Data literature and to identify research gaps that should be addressed in the future.
Books Reviewed - European Democratization since 1800 edited by J. Garrard, V. Tolz and R. White
(2000)
Given the strong increase in regulatory requirements for business processes the management of business process compliance becomes a more and more regarded field in IS research. Several methods have been developed to support compliance checking of conceptual models. However, their focus on distinct modeling languages and mostly linear (i.e., predecessor-successor related) compliance rules may hinder widespread adoption and application in practice. Furthermore, hardly any of them has been evaluated in a real-world setting. We address this issue by applying a generic pattern matching approach for conceptual models to business process compliance checking in the financial sector. It consists of a model query language, a search algorithm and a corresponding modelling tool prototype. It is (1) applicable for all graph-based conceptual modeling languages and (2) for different kinds of compliance rules. Furthermore, based on an applicability check, we (3) evaluate the approach in a financial industry project setting against its relevance for decision support of audit and compliance management tasks.
We analyze the trading behavior of individual investors in option-like securities, namely bankissued warrants, and thus expand the growing literature of investors behavior to a new kind of securities. A unique data set from a large German discount broker gives us the opportunity to analyze the trading behavior of 1,454 investors, making 89,958 transactions in 6,724 warrants on 397 underlyings. In different logit regression, we make use of the facts that investors can speculate on rising and falling prices of the underlying with call and put warrants and that we also have information about the stock portfolios of the investors. We report several facts about the trading behavior of individual investors in warrants that are consistent with the literature on the behavior of individual investors in the stock market. The warrant investors buy calls and sell puts if the price of the underlying has decreased over the past trading days and they sell calls and buy puts if the price of the underlying has increased. That means, the investors follow negative feedback trading strategies in all four trading categories observed. In addition, we find strong evidence for the disposition effect for call as well as put warrants, which is reversed in December. The trading behavior is also influenced if the underlying reaches some exceptionally prices, e.g. highs, lows or the strike price. We show that hedging, as one natural candidate to buy puts, does not play an important role in the market for bank-issued warrants.
Determinants of earnings forecast error, earnings forecast revision and earnings forecast accuracy
(2012)
Earnings forecasts are ubiquitous in today’s financial markets. They are essential indicators of future firm performance and a starting point for firm valuation. Extremely inaccurate and overoptimistic forecasts during the most recent financial crisis have raised serious doubts regarding the reliability of such forecasts. This thesis therefore investigates new determinants of forecast errors and accuracy. In addition, new determinants of forecast revisions are examined. More specifically, the thesis answers the following questions: 1) How do analyst incentives lead to forecast errors? 2) How do changes in analyst incentives lead to forecast revisions?, and 3) What factors drive differences in forecast accuracy?
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.
Enterprise SOA Roadmap
(2008)
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.