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- ZeitschriftenartikelA Dark Side of Telework: A Social Comparison-Based Study from the Perspective of Office Workers(Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Maier, Christian; Laumer, Sven; Weitzel, TimTelework became a necessary work arrangement during the global COVID-19 pandemic. However, practical evidence even before the pandemic also suggests that telework can adversely affect teleworkers’ colleagues working in the office. Those regular office workers may experience negative emotions such as envy which, in turn, can impact work performance and turnover intention. In order to assess the adverse effects of telework on regular office workers, the study applies social comparison theory and suggests telework disparity as a new theoretical concept. From the perspective of regular office workers, perceived telework disparity is the extent to which they compare their office working situation with their colleagues’ teleworking situation and conclude that their teleworking colleagues are slightly better off than themselves. Based on social comparison theory, a model of how perceived disparity associated with telework causes negative emotions and adverse behaviors among regular office workers was developed. The data were collected in one organization with telework arrangements (N = 269). The results show that perceived telework disparity from the perspective of regular office workers increases their feelings of envy toward teleworkers and their job dissatisfaction, which is associated with higher turnover intentions and worse job performance. This study contributes to telework research by revealing a dark side of telework by conceptualizing telework disparity and its negative consequences for employees and organizations. For practice, the paper recommends making telework practices and policies as transparent as possible to realize the maximum benefits of telework.
- ZeitschriftenartikelDesign Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems(Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Landwehr, Julius Peter; Kühl, Niklas; Walk, Jannis; Gnädig, MarioWith the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature.
- ZeitschriftenartikelSemi-Supervised Discovery of DNN-Based Outcome Predictors from Scarcely-Labeled Process Logs(Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Folino, Francesco; Folino, Gianluigi; Guarascio, Massimo; Pontieri, LuigiPredicting the final outcome of an ongoing process instance is a key problem in many real-life contexts. This problem has been addressed mainly by discovering a prediction model by using traditional machine learning methods and, more recently, deep learning methods, exploiting the supervision coming from outcome-class labels associated with historical log traces. However, a supervised learning strategy is unsuitable for important application scenarios where the outcome labels are known only for a small fraction of log traces. In order to address these challenging scenarios, a semi-supervised learning approach is proposed here, which leverages a multi-target DNN model supporting both outcome prediction and the additional auxiliary task of next-activity prediction. The latter task helps the DNN model avoid spurious trace embeddings and overfitting behaviors. In extensive experimentation, this approach is shown to outperform both fully-supervised and semi-supervised discovery methods using similar DNN architectures across different real-life datasets and label-scarce settings.
- ZeitschriftenartikelAlgorithmic Management(Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Benlian, Alexander; Wiener, Martin; Cram, W. Alec; Krasnova, Hanna; Maedche, Alexander; Möhlmann, Mareike; Recker, Jan; Remus, Ulrich
- ZeitschriftenartikelDesign Principles for Shared Digital Twins in Distributed Systems(Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Haße, Hendrik; Valk, Hendrik; Möller, Frederik; Otto, BorisDigital Twins offer considerable potential for cross-company networks. Recent research primarily focuses on using Digital Twins within the limits of a single organization. However, Shared Digital Twins extend application boundaries to cross-company utilization through their ability to act as a hub to share data. This results in the need to consider additional design dimensions which help practitioners design Digital Twins tailored for inter-company use. The article addresses precisely that issue as it investigates how Shared Digital Twins should be designed to achieve business success. For this purpose, the article proposes a set of design principles for Shared Digital Twins stemming from a qualitative interview study with 18 industry experts. The interview study is the primary data source for formulating and evaluating the design principles.
- ZeitschriftenartikelBISE Student(Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Sunyaev, Ali; Weinhardt, Christof; Aalst, Wil; Hinz, Oliver
- ZeitschriftenartikelCall for Papers, Issue 5/2024(Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Grisold, Thomas; Janiesch, Christian; Röglinger, Maximilian; Wynn, Moe Thandar
- ZeitschriftenartikelMultiexperience(Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Gnewuch, Ulrich; Ruoff, Marcel; Peukert, Christian; Maedche, Alexander
- ZeitschriftenartikelOpposing Effects of Response Time in Human–Chatbot Interaction(Business & Information Systems Engineering: Vol. 64, No. 6, 2022) Gnewuch, Ulrich; Morana, Stefan; Adam, Marc T. P.; Maedche, AlexanderResearch has shown that employing social cues (e.g., name, human-like avatar) in chatbot design enhances users’ social presence perceptions and their chatbot usage intentions. However, the picture is less clear for the social cue of chatbot response time. While some researchers argue that instant responses make chatbots appear unhuman-like, others suggest that delayed responses are perceived less positively. Drawing on social response theory and expectancy violations theory, this study investigates whether users’ prior experience with chatbots clarifies the inconsistencies in the literature. In a lab experiment ( N = 202), participants interacted with a chatbot that responded either instantly or with a delay. The results reveal that a delayed response time has opposing effects on social presence and usage intentions and shed light on the differences between novice users and experienced users – that is, those who have not interacted with a chatbot before vs. those who have. This study contributes to information systems literature by identifying prior experience as a key moderating factor that shapes users’ social responses to chatbots and by reconciling inconsistencies in the literature regarding the role of chatbot response time. For practitioners, this study points out a drawback of the widely adopted “one-design-fits-all�? approach to chatbot design.