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Neue Publikation im Journal Nature Energy

28. Februar 2024

 

Unsere Studie "Exploring the potential of non-residential solar to tackle energy injustice" wurde zur Publikation im Journal "Nature Energy" angenommen.

Autoren: M. Wussow, C. Zanocco, Z. Wang, R. Prabha, J. Flora, D. Neumann, A. Majumdar, R. Rajagopal 

Abstract:

Despite the observed disparities in U.S. residential solar deployment, there is limited insight into whether these disparities exist for the non-residential sector. We use DeepSolar, a comprehensive photovoltaic database constructed with satellite imagery, to assess solar deployment equity based on the U.S. Justice40’s disadvantaged community measure. We find that disadvantaged communities have less non-residential solar (-38%), however, this disparity is notably higher for residential solar (-67%). Across-state variations are consistent for residential solar (-81% to -49%), yet highly heterogeneous for non-residential solar (-66% to +34%). Using scenarios to explore the potential for microgrids powered by solar on building rooftops larger than 1,000 square meters, we estimate that 63% of disadvantaged communities could meet at least 20% of annual residential electricity demand. Our research argues for a new focus on non-residential solar as a way to strengthen resilience and accelerate local deployment of clean energy resources to promote energy justice.

Beitrag für die "ACM Conference On Computer-Supported Cooperative Work And Social Computing"

4. Dezember 2023

 

Unsere Studie "Which linguistic cues make people fall for fake news? A comparison of cognitive and affective processing" wurde zur Präsentation auf der "ACM Conference On Computer-Supported Cooperative Work And Social Computing" angenommen.

Autoren: Bernhard Lutz, Marc Adam, Stefan Feuerriegel, Nicolas Pröllochs, Dirk Neumann

Abstract:

Fake news on social media has large, negative implications for society. However, little is known about what linguistic cues make people fall for fake news and, hence, how to design effective countermeasures for social media. In this study, we seek to understand which linguistic cues make people fall for fake news. Linguistic cues (e.g., adverbs, personal pronouns, positive emotion words, negative emotion words) are important characteristics of any text and also affect how people process real vs. fake news. Specifically, we compare the role of linguistic cues across both cognitive processing (related to careful thinking) and affective processing (related to unconscious automatic evaluations). To this end, we performed a within-subject experiment where we collected neurophysiological measurements of 42 subjects while these read a sample of 40 real and fake news articles. During our experiment, we measured cognitive processing through eye fixations, and affective processing in situ through heart rate variability. We find that users engage more in cognitive processing for longer fake news articles, while affective processing is more pronounced for fake news written in analytic words. To the best of our knowledge, this is the first work studying the role of linguistic cues in fake news processing. Altogether, our findings have important implications for designing online platforms that encourage users to engage in careful thinking and thus prevent them from falling for fake news.

Ein Preprint der Studie ist verfügbar unter https://doi.org/10.48550/arXiv.2312.03751

Neue Publikation im European Journal of Operational Research

20. November 2023

 

Unsere Studie "Designing Electricity Distribution Networks: The Impact of Demand Coincidence" wurde zur Publikation beim "European Journal of Operational Research" angenommen.

Autoren: Gunther Gust, Alexander Schlueter, Stefan Feuerriegel, Ignacio Ubeda, Jonathan Lee, Dirk Neumann

Abstract:

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments. For this purpose, we develop a novel model for designing electricity distribution networks, called the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC). Our analysis is two-fold: (1) We apply our model to a large sample of real-world networks from a Swiss distribution network operator. We find that a high demand coincidence due to, for example, a large-scale uptake of electric vehicles, requires a substantial amount of new network line construction and increases average network cost by 84% in comparison to the status quo. (2) We use a set of synthetic networks to isolate the effect of specific network characteristics. Here, we show that high coincidence has a more detrimental effect on large networks and on networks with low geographic consumer densities, as present in, e. g., rural areas. We also show that expansion measures are robust to variations in the cost parameters. Our results demonstrate the necessity of designing policies and operational protocols that reduce demand coincidence. Moreover, our findings show that operators of distribution networks should consider the demand coincidence of new electricity uses and adapt investment budgets accordingly. Here, our solution algorithms can support in strategic and operational network design tasks.

Doktorandenstelle ab März 2024

Am Lehrstuhl für Wirtschaftsinformatik ist ab März 2024 eine Doktorandenstelle zu besetzen.

Bewerberinnen und Bewerber (m/w/d) sollten sich für Datenbanken und Datenmanagementplattformen interessieren. Die Stelle ist eine Vollzeitstelle (100% TVL E13) und wird über ein EU-Projekt ab März 2024 finanziert. Es wird erwartet, dass der / die Promovierende während der Projektlaufzeit von 3-4 Jahren eine groß angelegte Plattform für das Datenmanagement implementiert. Der/die Promovierende soll sich außerdem mit Projektpartnern in ganz Europa, von den Kanarischen Inseln bis Griechenland, koordinieren und zusammenarbeiten.

Der Lehrstuhl für Wirtschaftsinformatik kann auf eine Vielzahl hochrangiger Veröffentlichungen in A+ und A-Journalen im Bereich Wirtschaftsinformatik und Operations Research verweisen. Frühere Promovierende haben nach Abschluss der Promotion renommierte Positionen an der University of Oxford, der ETH Zürich, der Universität Münster, der Universität Würzburg, der LMU München, der Erasmus Universität Rotterdam und in der Industrie angenommen.

Geeignete Bewerberinnen und Bewerber haben idealerweise einen technischen Hintergrund, wie Informatik, Mathematik oder Physik. Bewerber sollten gerne analytische Aufgaben lösen, kombiniert mit mittleren bis guten Programmierkenntnissen (z.B. C++, Python).

Interessierte werden gebeten, einen Lebenslauf mit aktuellen Noten an applications@is.uni-freiburg.de zu senden. Bei Fragen steht der Lehrstuhl für Wirtschaftsinformatik unter der oben genannten Adresse zur Verfügung und freut sich auf vielversprechende Bewerbungen.

Die Universität Freiburg verfolgt eine Gleichstellungspolitik bei der Besetzung von Promotionsstellen. Wir fordern daher qualifizierte Frauen besonders auf, sich zu bewerben. Bei gleicher Eignung werden behinderte Bewerberinnen und Bewerber bevorzugt.

Beitrag zur Conference on Information Systems and Technology

15. August 2023,

 

Unsere Studie "Modeling Interrelated Option Markets using Variational Autoencoders" wurde zur Präsentation an der "Conference on Information Systems and Technology" angenommen.

Autoren: Antal RatkuDirk Neumann

 

 

Neue Publikation in Decision Support Systems

27. Juni 2023

 

Unsere Studie "Utilizing the omnipresent: Incorporating digital documents into predictive process monitoring using deep neural networks" wurde zur Publikation beim Journal "Decision Support Systems" angenommen.

Autoren: Sergej Levich, Bernhard Lutz, Dirk Neumann

Abstract:

Predictive process monitoring (PPM) allows companies to improve the efficiency of their business processes by predicting aspects such as the process outcome, the next event, or the time until the next event. So far, existing studies have mainly focused on developing novel predictive models while using features solely from event logs. In this study, we aim to go beyond log data and increase the focus of PPM research towards external context information. To this end, we consider digital documents as they are omnipresent in many business processes and their inclusion can often be justified by a business rationale. However, incorporating digital documents into PPM models poses considerable challenges as they present unstructured data that can contain visual and textual cues of future process behavior, while manual feature extraction is generally not feasible. Therefore, we propose an approach that processes digital documents based on automated visual and textual feature extraction methods. Furthermore, we design a tailored integration module which transforms the extracted features from multiple document pages into a fixed-size representation that subsequently serves as input for the predictive models. Our evaluation, based on a real-world dataset of insurance claims from a mid-sized German insurance company, featuring 5,131 process instances with 32,058 events and 39,242 document pages, shows that incorporating digital documents improves the performance by significant margins in predicting the damage type, the next event, and the time until the next event. Finally, we analyze how digital documents contribute to the model’s predictions in terms of Shapley additive explanations.

Neue Publikation im European Journal of Information Systems

6. Juni 2023

 

Unsere Studie "Affective Information Processing of Fake News: Evidence from NeuroIS" wurde zur Publikation beim European Journal of Information Systems angenommen.

Autoren: Bernhard Lutz, Marc Adam, Stefan Feuerriegel, Nicolas Pröllochs, Dirk Neumann

Abstract:

Fake news undermines individuals' ability to make informed decisions. However, the theoretical understanding of how users assess online news as real or fake has thus far remained incomplete. In particular, previous research cannot explain why users fall for fake news inadvertently and despite careful thinking. In this work, we study the role of affect when users assess online news as real or fake. We employ NeuroIS measurements as a complementary approach beyond self-reports, which allows us to capture affective responses in situ, i.e., directly at the moment they occur. We draw upon cognitive dissonance theory, which suggests that users experiencing affective responses avoid unpleasant information to reduce psychological discomfort. In our NeuroIS experiment, we measured affective responses based on electrocardiography and eye tracking. We find that lower heart rate variability and shorter mean fixation duration are associated with greater perceived fakeness and a higher probability of incorrect assessments, thus providing evidence of affective information processing. These findings imply that users may fall for fake news automatically and without even noticing. This has direct implications for information systems (IS) research and practice as effective countermeasures against fake news must account for affective information processing.

Neue Publikation in Expert Systems With Applications

9. Mai 2023

 

Unsere Studie "Reliable route planning and time savings in real-world urban intermodal transportation networks: Evidence from Hamburg, Germany" wurde zur Publikation beim Journal Expert Systems With Applications angenommen.

Autoren: Matthias Ruß, Gunther Gust

Abstract:

Cities around the world suffer from congestion and pollution caused by road traffic. Intermodal transport, which integrates multiple modes in a single journey, is a promising concept for shifting commuters away from private cars and making urban traffic more sustainable. Urban intermodal transport requires reliable route planners—i. e., planners identifying routes that have a high probability of being fast—because reliability matters for commuters. Designing such reliable route planners for the comprehensive set of modes in modern, real-world urban transportation networks is complex. Challenges arise due to the uncertainties inherent in travel times, transfer connections, availabilities of shared vehicles, and the location of shared vehicles. In this paper, we overcome these challenges by providing a reliable route planner that includes the comprehensive set of modes present in real-world urban transportation networks, including cars, trains and buses, station-based bikesharing, free-floating shared e-scooters, as well as walking. As a second contribution, we provide an in-depth analysis of the potential travel-time savings that such a reliable planner generates. Thereby, we generate new knowledge regarding the effect of several parameters and route characteristics on travel time savings—such as the required level of on-time arrival probability, traffic congestion, trip complexity, e-scooter availability, route costs, and more. Our findings are generated based on a large-scale empirical case study in the city of Hamburg covering more than 320,000 travel connections between 730 locations. Reliable planning yields in our base scenario average travel time savings of 8.9% in comparison to a conventional planner. For single routes, these savings reach up to 37.1%. We furthermore find that time savings increase with the required level of on-time arrival probability and the level of traffic. Moreover, the savings increase with the number of rides included in a trip; however, trip distance has no clear effect. Constrained cost budgets of commuters can considerably prolong travel times, whereas the effect of e-scooter availability is comparatively small. For transportation research, these findings extend current knowledge on travel time savings enabled by reliable planning. For transportation practice, our methodology can provide the basis for implementations in routing information systems, such as Google Maps, as well as planning decision support systems for transportation authorities to evaluate interventions, such as investing in the reliability of public transit or adding buffer times to schedules.

Beitrag zum NeuroIS Retreat 2023

Unser Lehrstuhl wird in Kooperation mit dem Lehrstuhl für Public und Non-Profit Management (Prof. Lindenmeier) einen Beitrag beim NeuroIS Retreat 2023 in Wien vorstellen. Wir präsentieren ein Experimentdesign, welches Ansätze zur Reduktion von Algorithmus-Aversion im Kontext von identitätsbasierten Konsumgütern wie Mode evaluiert.

Titel: Exploring the Role of Post-Hoc Explanations in Mitigating Algorithm Aversion in Identity-Based Consumption: An Eye-Tracking Study

Autoren: Yannik Schlepper, Bernhard Lutz, Jörg Lindemeier, Dirk Neumann

Abstract: Customers have a general tendency to discount algorithmic over human recommendations, a phenomenon commonly known as "algorithm aversion." Within areas driven by identity-based consumption such as fashion, designing efficient recommender systems is particularly challenging due to highly individualistic preferences and tastes. In this study, we analyze algorithm aversion towards fashion recommender systems with regards to social and personal identity and post-hoc explanations of algorithmic recommendations. In line with self-categorization theory and theory of planned behavior, we hypothesize that, to minimize algorithm aversion, the post-hoc explanations of algorithmic recommendations need to target customers' salient identity. Accordingly, we propose a 3x3 between-subject experiment with eye-tracking, where participants are shown several pairs of algorithm- or human-based fashion recommendations. In the treatment groups, we either activate customers' social or personal identity, while the explanations of algorithmic recommendations emphasize the customers' mainstream or unique taste. Furthermore, we expect that consumers with activated social or personal identity are more likely to report a different preference than their preference measured by the first and total number of eye fixations. Thereby, we expect to extend information systems research on algorithm aversion and post-hoc explanations of algorithms towards identity-based consumption. In addition, our findings have practical implications for online retailers.