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Unternehmerische Entscheidungen sollten auf einer angemessene Informationsgrundlage basieren. Diese rechtliche Aufforderung stellt Geschäftsleiter in der Praxis vor erhebliche Herausforderungen, die insbesondere die Erstellung von Prognosen betreffen. Um hier einen Unterstützungsbeitrag zu leisten, wird eine Heuristik vorgestellt, die das Treffen prognosebezogener Informationsentscheidungen anleitet. Die Heuristik übernimmt aus der Multiattributiven Nutzentheorie die Empfehlung, Informationsentscheidungen als Gesamtheit aus Zielfunktion und Bewertungsfunktion zu modellieren. In der Zielfunktion werden die Ziele der Informationsentscheidungen von den allgemeinen Unternehmenszielen wie etwa dem Gewinn gelöst. Statt dessen wird auf die Bewirkung besonderer Qualitätseigenschaften von Informationen abgestellt; das sind Vollständigkeit, Genauigkeit und Zuverlässigkeit. Daraus ergeben sich die beiden grundlegenden Ziele von Informationsentscheidungen: „Maximierung der Informationsqualität“ und „Minimierung der Informationskosten“. Auf dieser Grundlage besteht für die Bewertungsfunktion die Herausforderung darin, die positiven Nutzenwirkungen aus einer Vergrößerung der Informationsqualität abzuwägen gegen die negativen Nutzenwirkungen, die aus einer damit verbundenen Vergrößerung der Informationskosten resultieren. Um diese Herausforderung zu bewältigen, wird ein iteratives Verfahren aus sequentiell zu treffenden und in diesem Sinne inkrementellen Informationsentscheidungen vorgeschlagen. Wertvoll ist diese Heuristik einerseits in entscheidungstheoretischer Hinsicht, weil sie vorliegende methodische Überlegungen zum Treffen von Informationsentscheidungen erweitert. Vor allem aber besitzt die Heuristik einen praktischen Wert für Geschäftsleiter, da ihre Anwendung bessere Voraussetzungen dafür schafft, der rechtlichen Pflicht zur ordentlichen und gewissenhaften Geschäftsführung zu genügen.
Virtual reality in the hotel industry: assessing the acceptance of immersive hotel presentation
(2019)
In the hotel industry, it is crucial to reduce the inherent information asymmetry with regard to the goods offered. This asymmetry can be minimised through the use of smartphone-based virtual reality applications (SBVRs), which allow virtual simulation of real experiences and thus enable more efficient information retrieval. The aim of the study is to determine for the first time the user acceptance of these immersive hotel presentations for assessing the performance of a travel accommodation. For this purpose, the Technology Acceptance Model (TAM) was used to explain the acceptance behaviour for this new technology. A virtual reality application was specially developed, in which the participants could explore a hotel virtually. A total of 569 participants took part in the study. The structural equation model and the hypotheses were tested using a Partial Least Squares (PLS) analysis. The results illustrate that the immersive product experience leads to more efficient information gathering. The perceived usefulness significantly affects the attitude towards using the technology as well as the intention to use it. In contrast to the traditional TAM, the perceived ease of use of SBVRs has no effect on the perceived usefulness or attitude towards using the technology.
Social robots not only work with humans in collaborative workspaces – we meet them in shopping malls and even more personal settings like health and care. Does this imply they should become more human, able to interpret and adequately respond to human emotions? Do we want them to help elderly persons? Do we want them to support us when we are old ourselves? Do we want them to just clean and keep things orderly – or would we accept them helping us to go to the toilet, or even feed us if we suffer from Parkinson’s disease?
The answers to these questions differ from person to person. They depend on cultural background, personal experiences – but probably most of all on the robot in question. This book covers the phenomenon of social robots from the historic roots to today’s best practices and future perspectives. To achieve this, we used a hands-on, interdisciplinary approach, incorporating findings from computer scientists, engineers, designers, psychologists, doctors, nurses, historians and many more. The book also covers a vast spectrum of applications, from collaborative industrial work over education to sales. Especially for developments with a high societal impact like robots in health and care settings, the authors discuss not only technology, design and usage but also ethical aspects.
Thus this book creates both a compendium and a guideline, helping to navigate the design space for future developments in social robotics.
Robots and automata are key elements of every vision and forecast of life in the near and distant future. However, robots and automata also have a long history, which reaches back into antiquity. Today most historians think that one of the key roles of robots and automata was to amaze or even terrify the audience: They were designed to express something mythical, magical, and not explainable. Moreover, the visions of robots and their envisioned fields of application reflect the different societies. Therefore, this short history of robotics and (especially) anthropomorphic automata aims to give an overview of several historical periods and their perspective on the topic. In a second step, this work aims to encourage readers to reflect on the recent discussion about fields of application as well as the role of robotics today and in the future.
Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques
(2020)
This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance.
The three lines of defense model (TLoD) aims to provide a simple and effective way to improve coordination and enhance communications on risk management and control by clarifying the essential roles and duties of different governance functions. Without effective coordination of these governance functions, work can be duplicated or key risks may be missed or misjudged. To address these challenges, professional standards recommend that the chief audit executive (CAE) coordinates activities with other internal and external governance stakeholders (assurance providers). We consider survey responses from 415 CAEs from Austria, Germany, and Switzerland to analyze determinants that help to implement the TLoD without any challenges and to explore the extent of (coordination) challenges between the internal audit function and the respective governance stakeholders. Our results show a great variance in the extent of coordination challenges dependent on different determinants and the respective governance stakeholder.