TY - JOUR AU - Hoppe, Julia Amelie AU - Tuisku, Outi AU - Johansson-Pajala, Rose-Marie AU - Pekkarinen, Satu AU - Hennala, Lea AU - Gustafsson, Christine AU - Melkas, Helinä AU - Thommes, Kirsten ID - 34295 JF - Computers in Human Behavior Reports KW - Artificial Intelligence KW - Cognitive Neuroscience KW - Computer Science Applications KW - Human-Computer Interaction KW - Applied Psychology KW - Neuroscience (miscellaneous) SN - 2451-9588 TI - When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty ER - TY - JOUR AU - Hoppe, Julia A. AU - Tuisku, Outi AU - Johansson-Pajala, Rose-Marie AU - Pekkarinen, Satu AU - Hennala, Lea AU - Gustafsson, Christine AU - Melkas, Helinä AU - Thommes, Kirsten ID - 44636 JF - Computers in Human Behavior Reports KW - Artificial Intelligence KW - Cognitive Neuroscience KW - Computer Science Applications KW - Human-Computer Interaction KW - Applied Psychology KW - Neuroscience (miscellaneous) SN - 2451-9588 TI - When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty VL - 9 ER - TY - JOUR AU - Hoppe, Julia Amelie AU - Melkas, Helinä AU - Pekkarinen, Satu AU - Tuisku, Outi AU - Hennala, Lea AU - Johansson-Pajala, Rose-Marie AU - Gustafsson, Christine AU - Thommes, Kirsten ID - 32273 JF - International Journal of Human–Computer Interaction KW - Computer Science Applications KW - Human-Computer Interaction KW - Human Factors and Ergonomics SN - 1044-7318 TI - Perception of Society’s Trust in Care Robots by Public Opinion Leaders ER - TY - GEN AU - Koch, Reinald AU - Holtmann, Svea AU - Giese, Henning ID - 35799 SN - 1556-5068 TI - Losses Never Sleep - The Effect of Tax Loss Offset on Stock Market Returns during Economic Crises VL - 269 ER - TY - CHAP AU - Betz, Stefan ED - Betz, Stefan ID - 49748 SN - 978-3-339-12536-1 T2 - Aktuelle Fragestellungen zu Produktion, Controlling und Logistik TI - Lagerkapazitätsdimensionierung als betriebswirtschaftliches Entscheidungsproblem ER - TY - CHAP AU - Betz, Stefan ED - Betz, Stefan ID - 49743 SN - 978-3-339-12536-1 T2 - Aktuelle Fragestellungen zu Produktion, Controlling und Logistik TI - Strategische Erfolgsplanung für Produktinnovationen ER - TY - GEN AU - Giese, Henning AU - Koch, Reinald AU - Gamm, Markus ID - 49875 TI - Tax Avoidance and Vertical Interlocks within Multinational Enterprises ER - TY - GEN AB - Information is one of the most important ingredients for decision-making. While the neoclassical assumption of perfect information is surely an important conceptual benchmark for discussing efficient allocations, it is obviously far from describing a rational choice under real conditions. In reality, optimal choices should be considered choices under imperfect information. Thus, decision-makers' information problem can be solved by two strategies. Either they collect an optimal set of information to make an optimal allocation choice under this imperfect information set or they can apply heuristic reasoning. In this paper, we suggest a formal model framework for the example of a simple consumer decision for the allocation of differentiated goods to explore information acquisition strategies in such a simple standard choice situation. Using the model variation under perfect information as a benchmark, we answer the following questions. First and most importantly, under imperfect information, can a heuristic rule substitute information acquisition as an optimal choice? Second, what is the role of risk aversion in the information acquisition process? Finally, we explore the differences to the benchmark, both ex ante the first purchase decision and ex post when repeated purchases and consumption allows for experiences with the choices made. AU - Burs, Carina AU - Gries, Thomas ID - 49308 KW - information economics KW - imperfect information KW - Bayesian learning KW - risk KW - heuristics KW - differentiated products TI - Decision-making under Imperfect Information with Bayesian Learning or Heuristic Rules VL - No. 149 ER - TY - JOUR AU - Feng, Yuanhua AU - Gries, Thomas AU - Letmathe, Sebastian AU - Schulz, Dominik ID - 33666 IS - 1 JF - The R Journal KW - Statistics KW - Probability and Uncertainty KW - Numerical Analysis KW - Statistics and Probability SN - 2073-4859 TI - The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series VL - 14 ER - TY - CONF AU - Arslan, Kader AU - Trier, Matthias ID - 34317 KW - Social media KW - Social media marketing process KW - Social media strategy KW - Social media management KW - Guidelines T2 - Proceedings of the 33rd Australasian Conference on Information Systems (ACIS 2022) TI - Towards a Process Model for Social Media Marketing ER - TY - CHAP AU - Hrnjadovic, Damir ED - Betz, Stefan ID - 50382 SN - 978-3-339-12536-1 T2 - Aktuelle Fragestellungen zu Produktion, Logistik und Controlling TI - Möglichkeiten und Grenzen der Messung, Kontrolle und Sicherung der Qualität logistischer Prozesse ER - TY - CHAP AU - Fiedler, Moritz ED - Betz, Stefan ID - 50384 SN - 978-3-339-12536-1 T2 - Aktuelle Fragestellungen zu Produktion, Logistik und Controlling TI - Implikationen einer wertorientierten Unternehmensführung für Standortplanungsentscheidungen ER - TY - CHAP AU - Osthoff, Lennart ED - Betz, Stefan ID - 50386 SN - 978-3-339-12536-1 T2 - Aktuelle Fragestellungen zu Produktion, Logistik und Controlling TI - Betriebliche Standortplanung international agierender Unternehmen ER - TY - CHAP AU - Opitz, Oliver ID - 50395 SN - 978-3-339-12536-1 T2 - Aktuelle Fragestellungen zu Produktion, Logistik und Controlling TI - Kategorisierungsmöglichkeiten für die Erzeugung von Dienstleistungen ER - TY - CHAP AU - Faupel, Christian ED - Betz, Stefan ID - 50405 SN - 978-3-339-12536-1 T2 - Aktuelle Fragestellungen zu Produktion, Logistik und Controlling TI - Entscheidungsunterstützung durch das Controlling mittels Reporting Design ER - TY - BOOK ED - Betz, Stefan ID - 37399 TI - Aktuelle Fragestellungen zu Produktion, Controlling und Logistik ER - TY - CONF AB - Existing process mining methods are primarily designed for processes that have reached a high degree of digitalization and standardization. In contrast, the literature has only begun to discuss how process mining can be applied to knowledge-intensive processes—such as product innovation processes—that involve creative activities, require organizational flexibility, depend on single actors’ decision autonomy, and target process-external goals such as customer satisfaction. Due to these differences, existing Process Mining methods cannot be applied out-of-the-box to analyze knowledge-intensive processes. In this paper, we employ Action Design Research (ADR) to design and evaluate a process mining approach for knowledge-intensive processes. More specifically, we draw on the two processes of product innovation and engineer-to-order in manufacturing contexts. We collected data from 27 interviews and conducted 49 workshops to evaluate our IT artifact at different stages in the ADR process. From a theoretical perspective, we contribute five design principles and a conceptual artifact that prescribe how process mining ought to be designed for knowledge-intensive processes in manufacturing. From a managerial perspective, we demonstrate how enacting these principles enables their application in practice. AU - Löhr, Bernd AU - Brennig, Katharina AU - Bartelheimer, Christian AU - Beverungen, Daniel AU - Müller, Oliver ID - 36912 SN - 978-3-031-16103-2 T2 - International Conference on Business Process Management TI - Process Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing ER - TY - JOUR AB - PurposeEnabled by increased (“big”) data stocks and advanced (“machine learning”) analyses, the concept of human resource analytics (HRA) is expected to systematically improve decisions in human resource management (HRM). Since so far empirical evidence on this is, however, lacking, the authors' study examines which combinations of data and analyses are employed and which combinations deliver on the promise of improved decision quality.Design/methodology/approachTheoretically, the paper employs a neo-configurational approach for founding and conceptualizing HRA. Methodically, based on a sample of German organizations, two varieties (crisp set and multi-value) of qualitative comparative analysis (QCA) are employed to identify combinations of data and analyses sufficient and necessary for HRA success.FindingsThe authors' study identifies existing configurations of data and analyses in HRM and uncovers which of these configurations cause improved decision quality. By evidencing that and which combinations of data and analyses conjuncturally cause decision quality, the authors' study provides a first confirmation of HRA success.Research limitations/implicationsMajor limitations refer to the cross-sectional and national sample and the usage of subjective measures. Major implications are the suitability of neo-configurational approaches for future research on HRA, while deeper conceptualizing and researching both the characteristics and outcomes of HRA constitutes a core future task.Originality/valueThe authors' paper employs an innovative theoretical-methodical approach to explain and analyze conditions that conjuncturally cause decision quality therewith offering much needed empirical evidence on HRA success. AU - Strohmeier, Stefan AU - Collet, Julian AU - Kabst, Rüdiger ID - 50463 IS - 3 JF - Baltic Journal of Management KW - Management of Technology and Innovation KW - Marketing KW - Organizational Behavior and Human Resource Management KW - Strategy and Management KW - Business and International Management SN - 1746-5265 TI - (How) do advanced data and analyses enable HR analytics success? A neo-configurational analysis VL - 17 ER - TY - JOUR AB - Psychologists claim that being treated kindly puts individuals in a positive emotional state: they then treat an unrelated third party more kindly. Numerous experiments document that subjects indeed ‘pay forward’ specific behavior. For example, they are less generous after having experienced stinginess. This, however, is not necessarily driven by emotions. Subjects may also imitate what they regard as socially adequate behavior. Here, I present an experiment in which imitation is not possible at the next opportunity to act with a stranger: after being given either a fun or an annoying job, subjects have to decide whether to be generous or not. I find that although subjects who are given the annoying job report more negative emotions than those with the fun job, they do not treat an unrelated third person more unkindly in terms of passing on less money. AU - Schnedler, Wendelin ID - 34473 JF - Games and Economic Behavior KW - Economics and Econometrics KW - Finance SN - 0899-8256 TI - The broken chain: Evidence against emotionally driven upstream indirect reciprocity VL - 136 ER - TY - CONF AB - In recent years, many cases of deep neural networks failing dramatically when faced with adversarial or real-world examples have been reported. Such failures, which are quite hard to detect, are often related to a generalization problem known as shortcut learning. Yet, with state-of-the-art transformer models now being ubiquitous in financial text mining, one cannot help but wonder how reliable the results conveyed in the ever-growing literature genuinely are. Against this background, we expose, in this work, how vulnerable contemporary financial text mining approaches are to shortcut learning. Focussing on the common learning task of financial sentiment classification, we assess, using two entity-based sampling strategies and our publicly-available dataset, the discrepancies between i.i.d. and o.o.d. performance estimates of four transformer models. Our results reveal that o.o.d. performance estimates are consistently weaker than those of their i.i.d. counterparts, with the error rate increasing by as much as 29.7%, thus, demonstrating how this issue can, when overlooked, lead to misleading evaluations. Moreover, we show how additional preprocessing steps, such as entity removal and vocabulary filtering, can help reduce the effects of shortcut learning by filtering out entity-related linguistic cues. AU - Caron, Matthew ID - 42631 T2 - 2022 IEEE International Conference on Big Data (Big Data) TI - Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift ER -