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- Fachbereich Wirtschaftswissenschaften (40) (remove)
Häufig bremsen geringe IT-Ressourcen, fehlende Softwareschnittstellen oder eine veraltete und komplex gewachsene Systemlandschaft die Automatisierung von Geschäftsprozessen. Robotic Process Automation (RPA) ist eine vielversprechende Methode, um Geschäftsprozesse oberflächenbasiert und ohne größere Systemeingriffe zu automatisieren und Medienbrüche abzubauen. Die Auswahl der passenden Prozesse ist dabei für den Erfolg von RPA-Projekten entscheidend. Der vorliegende Beitrag liefert dafür Selektionskriterien, die aus einer qualitativen Inhaltanalyse von elf Interviews mit RPA-Experten aus dem Versicherungsumfeld resultieren. Das Ergebnis umfasst eine gewichtetet Liste von sieben Dimensionen und 51 Prozesskriterien, welche die Automatisierung mit Softwarerobotern begünstigen bzw. deren Nichterfüllung eine Umsetzung erschweren oder sogar verhindern. Die drei wichtigsten Kriterien zur Auswahl von Geschäftsprozessen für die Automatisierung mittels RPA umfassen die Entlastung der an dem Prozess mitwirkenden Mitarbeiter (Arbeitnehmerüberlastung), die Ausführbarkeit des Prozesses mittels Regeln (Regelbasierte Prozessteuerung) sowie ein positiver Kosten-Nutzen-Vergleich. Praktiker können diese Kriterien verwenden, um eine systematische Auswahl von RPA-relevanten Prozessen vorzunehmen. Aus wissenschaftlicher Perspektive stellen die Ergebnisse eine Grundlage zur Erklärung des Erfolgs und Misserfolgs von RPA-Projekten dar.
Bitcoin is a cryptocurrency and is considered a high-risk asset
class whose price changes are difficult to predict. Current research focusses
on daily price movements with a limited number of predictors. The paper at
hand aims at identifying measurable indicators for Bitcoin price movement s
and the development of a suitable forecasting model for hourly changes. The
paper provides three research contributions. First, a set of significant
indicators for predicting the Bitcoin price is identified. Second, the results of
a trained Long Short-term Memory (LSTM) neural network that predicts
price changes on an hourly basis is presented and compared with other
algorithms. Third, the results foster discussions of the applicability of neural
nets for stock price predictions. In total, 47 input features for a period of
over 10 months could be retrieved to train a neural net that predicts the
Bitcoin price movements with an error rate of 3.52 %.
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.
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.
Die Entwicklungen der Rechtsinformatik und des Informationsrechts zeigen, dass diese Disziplinen aktuell vor der Herausforderung stehen, eine interdisziplinäre Zusammenarbeit zwischen ihnen und anderen Disziplinen zu etablieren. Unterschiedliche Publikationskulturen erschweren die Erreichung dieses Ziels. Forschungsportale stellen themenspezifische, internetbasierte Verzeichnisse dar, die bereits vorhandene Informationen strukturiert zugänglich machen. Sie können die Beziehungen zwischen den Disziplinen fördern, indem sie bereits erzielte Arbeitsergebnisse disziplinenübergreifend bekannt machen und dadurch dazu beitragen, Synergiepotenziale und mögliche Kooperationspartner zu identifizieren.
Leveraging Social Network Data for Analytical CRM Strategies - The Introduction of Social BI.
(2012)
With a steady increase of regulatory requirements for business processes, automation support of compliance management is a field garnering increasing attention in Information Systems research. Several approaches have been developed to support compliance checking of process models. One major challenge for such approaches is their ability to handle different modeling techniques and compliance rules in order to enable widespread adoption and application. Applying a structured literature search strategy, we reflect and discuss compliance-checking approaches in order to provide an insight into their generalizability and evaluation. The results imply that current approaches mainly focus on special modeling techniques and/or a restricted set of types of compliance rules. Most approaches abstain from real-world evaluation which raises the question of their practical applicability. Referring to the search results, we propose a roadmap for further research in model-based business process compliance checking.
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.