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 -