@article{65105, author = {{zur Heiden, Philipp and Halimeh, Haya and Hansmeier, Philipp and Vorbohle, Christian and Althaus, Maike and Beverungen, Daniel and Kundisch, Dennis and Müller, Oliver}}, journal = {{Communications of the Association for Information Systems}}, title = {{{Data Spaces for Heterogeneous Data Ecosystems – Findings from a Design Study in the Cultural Sector}}}, year = {{2026}}, } @article{64870, author = {{Meyer, Marcel and Zapata Gonzalez, David Ricardo and Kaltenpoth, Sascha Benjamin and Müller, Oliver}}, issn = {{2169-3536}}, journal = {{IEEE Access}}, pages = {{218141--218153}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Benchmarking Time Series Foundation Models for Short-Term Household Electricity Load Forecasting}}}, doi = {{10.1109/access.2025.3648056}}, volume = {{13}}, year = {{2025}}, } @inproceedings{60680, abstract = {{Classical machine learning techniques often struggle with overfitting and unreliable predictions when exposed to novel conditions. Introducing causality into the modelling process offers a promising way to mitigate these challenges by enhancing predictive robustness. However, constructing an initial causal graph manually using domain knowledge is time-consuming, particularly in complex time series with numerous variables. To address this, causal discovery algorithms can provide a preliminary causal structure that domain experts can refine. This study investigates causal feature selection with domain knowledge using a data center system as an example. We use simulated time-series data to compare different causal feature selection with traditional machine-learning feature selection methods. Our results show that predictions based on causal features are more robust compared to those derived from traditional methods. These findings underscore the potential of combining causal discovery algorithms with human expertise to improve machine learning applications.}}, author = {{Zapata Gonzalez, David Ricardo and Meyer, Marcel and Müller, Oliver}}, keywords = {{Causal Machine Learning, Causality in Time Series, Causal Discovery, Human-Machine Collaboration}}, location = {{Amman, Jordan}}, title = {{{Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems}}}, year = {{2025}}, } @inproceedings{61098, author = {{Kaltenpoth, Sascha Benjamin and Skolik, Alexander Marcus and Müller, Oliver and Beverungen, Daniel}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032028662}}, issn = {{0302-9743}}, location = {{Sevilla}}, publisher = {{Springer Nature Switzerland}}, title = {{{A Step Towards Cognitive Automation: Integrating LLM Agents with Process Rules}}}, doi = {{10.1007/978-3-032-02867-9_19}}, year = {{2025}}, } @article{61160, abstract = {{As a possible solution to the demographic change and the resulting knowledge loss due to retirements in the Energy sector, this study aimed to develop a generic pipeline to implement and evaluate proof-of-concepts (PoCs) for LLM-based assistance systems in new domains. Our pipeline contains an LLM-based data generation strategy based on documents, a retrieval-augmented generation (RAG) architecture utilizing prompting techniques on existing German LLMs, and an LLM-based automatic evaluation strategy. We leverage our pipeline to evaluate five LLMs using data from a German DSO. We found that the Llama3 and the Mistral model are appropriately aligned for the task. We plan to pilot the RAG architecture in the DSO's infrastructure for future research and continuously research improvements using the generated human demonstrations.}}, author = {{Kaltenpoth, Sascha Benjamin and Müller, Oliver}}, issn = {{2770-5331}}, journal = {{ACM SIGEnergy Energy Informatics Review}}, number = {{4}}, pages = {{16--22}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Don't Touch the Power Line - A Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity Distribution System}}}, doi = {{10.1145/3717413.3717415}}, volume = {{4}}, year = {{2025}}, } @inproceedings{63397, abstract = {{Decarbonizing industrial process heat is a crucial step in mitigating climate change. While Process Mining (PM) has gained traction in sustainability research—such as optimizing production scheduling to reduce energy use or accounting for carbon footprints—it has largely overlooked the challenges and opportunities related to thermal energy, accounting for 66% of total energy demand in industrial processes. At the same time, Heat Integration (HI) is an established engineering discipline focused on maximizing the efficiency of thermal energy systems. However, HI traditionally relies on static or incomplete data about energy demands, limiting its effectiveness and accuracy. In this paper, we propose a novel framework that combines PM and HI to enable data-driven, process- and product-centric modeling of industrial energy demands. By integrating event logs and thermal energy data, our approach allows for a fine-grained analysis of heat demand patterns corresponding to specific process activities and product variants. We demonstrate the applicability and advantages of the framework by simulating a pharmaceutical manufacturing process and evaluating energy demands and heat recovery potentials. Our findings show that our PM-enabled HI framework provides more accurate and actionable insights into the temporal and product-specific variation of thermal energy demands. By capturing the causal relationships between process activities, product characteristics, and energy consumption, our approach enables improved analysis, planning, and optimization for heat recovery and process decarbonization. This integration of PM and HI expands the analytical tools for both disciplines and contributes to advancing the sustainable transformation of industrial processes.}}, author = {{Zapata Gonzalez, David Ricardo and Brennig, Katharina and Benkert, Kay and Schlosser, Florian and Müller, Oliver}}, booktitle = {{ACM SIGEnergy Energy Informatics Review}}, issn = {{2770-5331}}, number = {{3}}, pages = {{19--31}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling}}}, doi = {{10.1145/3777518.3777520}}, volume = {{5}}, year = {{2025}}, } @inproceedings{63400, abstract = {{Data centers (DCs) form the backbone of our growing digital economy, but their rising energy demands pose challenges to our environment. At the same time, reusing waste heat from DCs also represents an opportunity, for example, for more sustainable heating of residential buildings. Modeling and optimizing these coupled and dynamic systems of heat generation and reuse is complex. On the one hand, physical simulations can be used to model these systems, but they are time-consuming to develop and run. Machine learning (ML), on the other hand, allows efficient data-driven modeling, but conventional correlation-based approaches struggle with the prediction of interventions and out-of-distribution generalization. Recent advances in causal ML, which combine principles from causal inference with flexible ML methods, are a promising approach for more robust predictions. Due to their focus on modeling interventions and cause-and-effect relationships, it is difficult to evaluate causal ML approaches rigorously. To address this challenge, we built a testbed of a miniature DC with an integrated waste heat network, equipped with sensors and actuators. This testbed allows conducting controlled experiments and automatic collection of realistic data, which can then be used to benchmark conventional and causal ML methods. Our experimental results highlight the strengths and weaknesses of each modeling approach, providing valuable insights on how to appropriately apply different types of machine learning to optimize data center operations and enhance their sustainability.}}, author = {{Zapata Gonzalez, David Ricardo and Meyer, Marcel and Müller, Oliver}}, booktitle = {{ACM SIGEnergy Energy Informatics Review}}, issn = {{2770-5331}}, number = {{2}}, pages = {{4--10}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study}}}, doi = {{10.1145/3757892.3757893}}, volume = {{5}}, year = {{2025}}, } @inproceedings{63525, abstract = {{Recommender systems (RS) can support sustainable development by steering users toward more sustainable choices. Sustainability-aware explanations represent one avenue for contributing to this goal by foregrounding the environmental and social aspects of the recommended products or services. This paper advances the line of research on sustainability-aware explanations by integrating nudging mechanisms into their design and by evaluating their effectiveness through a randomized between-subjects online vignette experiment across two item domains (). Our findings offer actionable design guidelines for building RS that foster sustainability-aware decision making and enrich the empirical foundation for impact-oriented research on explanation in RS. }}, author = {{Halimeh, Haya and Müller, Oliver}}, location = {{Prague, Czech Republic}}, title = {{{Towards Greener Choices: Decision Information Nudging for Sustainability-Aware Recommender Explanations}}}, doi = {{10.1007/978-3-032-13342-7}}, year = {{2025}}, } @techreport{63026, author = {{Althaus, Maike and Beverungen, Daniel and Flath, Beate and Halimeh, Haya and Hansmeier, Philipp and zur Heiden, Philipp and Kundisch, Dennis and Müller, Michelle and Müller, Oliver and Oberthür, Simon and Vorbohle, Christian and Momen Pour Tafreshi, Maryam and Mauß, Sebastian and Mücke, Alina and Müller, Jörg and Peter, Malte and Schmitt-Chandon, Ariane and Sellerberg, Kerstin and Steinhäuser, Moritz}}, title = {{{Positionspapier Use Case 1: Vernetzte Kulturplattformen}}}, year = {{2025}}, } @article{53130, author = {{Stumpe, Miriam and Dieter, Peter and Schryen, Guido and Müller, Oliver and Beverungen, Daniel}}, journal = {{Transportation Research Part A: Policy and Practice}}, title = {{{Designing taxi ridesharing systems with shared pick-up and drop-off locations: Insights from a computational study}}}, year = {{2024}}, } @inproceedings{55096, author = {{Bösch, Kevin and Müller, Oliver and Weinmann, Markus}}, booktitle = {{Proceedings of the Symposium on Statistical Challenges in Electronic Commerce Research}}, title = {{{Not your Average Digital Nudge: Heterogeneous Effects of Personalized Nudges with CausalML}}}, year = {{2024}}, } @article{55500, author = {{Gitzel, Ralf and Hoffmann, Martin and zur Heiden, Philipp and Skolik, Alexander Marcus and Kaltenpoth, Sascha Benjamin and Müller, Oliver and Kanak, Cansu and Kandiah, Kajan and Stroh, Max-Ferdinand and Boos, Wolfgang and Zajadatz, Maurizio and Suriyah, Michael and Leibfried, Thomas and Singhal, Dhruv Suresh and Bürger, Moritz and Hunting, Dennis and Rehmer, Alexander and Boyaci, Aydin}}, issn = {{2169-3536}}, journal = {{IEEE Access}}, pages = {{1--1}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Towards Cognitive Assistance and Prognosis Systems in Power Distribution Grids – Open Issues, Suitable Technologies, and Implementation Concepts}}}, doi = {{10.1109/access.2024.3437195}}, year = {{2024}}, } @inproceedings{56945, abstract = {{Adopting Large language models (LLMs) in organizations potentially revolutionizes our lives and work. However, they can generate off-topic, discriminating, or harmful content. This AI alignment problem often stems from misspecifications during the LLM adoption, unnoticed by the principal due to the LLM’s black-box nature. While various research disciplines investigated AI alignment, they neither address the information asymmetries between organizational adopters and black-box LLM agents nor consider organizational AI adoption processes. Therefore, we propose LLM ATLAS (LLM Agency Theory-Led Alignment Strategy) a conceptual framework grounded in agency (contract) theory, to mitigate alignment problems during organizational LLM adoption. We conduct a conceptual literature analysis using the organizational LLM adoption phases and the agency theory as concepts. Our approach results in (1) providing an extended literature analysis process specific to AI alignment methods during organizational LLM adoption and (2) providing a first LLM alignment problem-solutionspace.}}, author = {{Kaltenpoth, Sascha Benjamin and Müller, Oliver}}, booktitle = {{Wirtschaftsinformatik 2024 Proceedings}}, title = {{{Getting in Contract with Large Language Models - An Agency Theory Perspective On Large Language Model Alignment}}}, year = {{2024}}, } @inproceedings{37312, abstract = {{Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.}}, author = {{Leffrang, Dirk and Bösch, Kevin and Müller, Oliver}}, booktitle = {{Hawaii International Conference on System Sciences}}, keywords = {{Algorithm aversion, Time series, Decision making, Advice taking, Forecasting}}, title = {{{Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}}}, year = {{2023}}, } @inproceedings{50431, abstract = {{Recommender systems now span the entire customer journey. Amid the multitude of diversified experi- ences, immersing in cultural events has become a key aspect of tourism. Cultural events, however, suffer from fleeting lifecycles, evade exact replication, and invariably lie in the future. In addition, their low standardization makes harnessing historical data regarding event content or past patron evaluations intricate. The distinctive traits of events thereby compound the challenge of the cold-start dilemma in event recommenders. Content-based recommendations stand as a viable avenue to alleviate this issue, functioning even in scenarios where item-user information is scarce. Still, the effectiveness of content- based recommendations often hinges on the quality of the data representation they build upon. In this study, we explore an array of cutting-edge uni- and multimodal vision and language foundation models (VL-FMs) for this purpose. Next, we derive content-based recommendations through a straightforward clustering approach that groups akin events together, and evaluate the efficacy of the models through a series of online user experiments across three dimensions: similarity-based evaluation, comparison-based evaluation, and clustering assignment evaluation. Our experiments generated four major findings. First, we found that all VL-FMs consistently outperformed a naive baseline of recommending randomly drawn events. Second, unimodal text-based embeddings were surprisingly on par or in some cases even superior to multimodal embeddings. Third, multimodal embeddings yielded arguably more fine-grained and diverse clusters in comparison to their unimodal counterparts. Finally, we could confirm that cross event interest is indeed reliant on the perceived similarity of events, resonating with the notion of similarity in content-based recommendations. All in all, we believe that leveraging the potential of contemporary FMs for content-based event recommendations would help address the cold-start problem and propel this field of research forward in new and exciting ways.}}, author = {{Halimeh, Haya and Freese, Florian and Müller, Oliver}}, booktitle = {{Workshop on Recommenders in Tourism, co-located with the 17th ACM Conference on Recommender Systems}}, title = {{{Event Recommendations through the Lens of Vision and Language Foundation Models}}}, year = {{2023}}, } @inproceedings{45270, abstract = {{Clinical depression is a serious mental disorder that poses challenges for both personal and public health. Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model’s interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies.}}, author = {{Halimeh, Haya and Caron, Matthew and Müller, Oliver}}, booktitle = {{Hawaii International Conference on System Sciences}}, keywords = {{Social Media and Healthcare Technology, early depression detection, liwc, mental health, transfer learning, transformer architectures}}, title = {{{Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features}}}, year = {{2023}}, } @inproceedings{37058, abstract = {{Digital technologies have made the line of visibility more transparent, enabling customers to get deeper insights into an organization’s core operations than ever before. This creates new challenges for organizations trying to consistently deliver high-quality customer experiences. In this paper we conduct an empirical analysis of customers’ preferences and their willingness-to-pay for different degrees of process transparency, using the example of digitally-enabled business-to-customer delivery services. Applying conjoint analysis, we quantify customers’ preferences and willingness-to-pay for different service attributes and levels. Our contributions are two-fold: For research, we provide empirical measurements of customers’ preferences and their willingness-to-pay for process transparency, suggesting that more is not always better. Additionally, we provide a blueprint of how conjoint analysis can be applied to study design decisions regarding changing an organization’s digital line of visibility. For practice, our findings enable service managers to make decisions about process transparency and establishing different levels of service quality. }}, author = {{Brennig, Katharina and Müller, Oliver}}, booktitle = {{Hawaii International Conference on System Sciences}}, keywords = {{Digital Services, Line of Visibility, Process Transparency, Customer Preferences, Conjoint Analysis}}, location = {{Lāhainā}}, title = {{{More Isn’t Always Better – Measuring Customers’ Preferences for Digital Process Transparency}}}, year = {{2023}}, } @inbook{50450, author = {{Brennig, Katharina and Benkert, Kay and Löhr, Bernd and Müller, Oliver}}, booktitle = {{Business Process Management Workshops}}, isbn = {{9783031509735}}, issn = {{1865-1348}}, title = {{{Text-Aware Predictive Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?}}}, doi = {{10.1007/978-3-031-50974-2_33}}, year = {{2023}}, } @techreport{47107, author = {{Beverungen, Daniel and zur Heiden, Philipp and Lehrer, Christiane and Trier, Matthias and Bartelheimer, Christian and Bradt, Tobias and Distel, Bettina and Drews, Paul and Ehmke, Jan Fabian and Fill, Hans-Georg and Flath, Christoph M. and Fridgen, Gilbert and Grisold, Thomas and Janiesch, Christian and Janson, Andreas and Krancher, Oliver and Krönung, Julia and Kundisch, Dennis and Márton, Attila and Mirbabaie, Milad and Morana, Stefan and Mueller, Benjamin and Müller, Oliver and Oberländer, Anna Maria and Peters, Christoph and Peukert, Christoph and Reuter-Oppermann, Melanie and Riehle, Dennis M. and Robra-Bissantz, Susanne and Röglinger, Maximilian and Rosenthal, Kristina and Schryen, Guido and Schütte, Reinhard and Strahringer, Susanne and Urbach, Nils and Wessel, Lauri and Zavolokina, Liudmila and Zschech, Patrick}}, pages = {{16}}, publisher = {{Department of Information Systems, Paderborn University}}, title = {{{Implementing Digital Responsibility through Information Systems Research: A Delphi Study of Objectives, Activities, and Challenges in IS Research}}}, year = {{2023}}, } @article{45112, author = {{Beverungen, Daniel and Kundisch, Dennis and Mirbabaie, Milad and Müller, Oliver and Schryen, Guido and Trang, Simon Thanh-Nam and Trier, Matthias}}, journal = {{Business & Information Systems Engineering}}, number = {{4}}, pages = {{463 -- 474}}, title = {{{Digital Responsibility – a Multilevel Framework for Responsible Digitalization}}}, doi = {{10.1007/s12599-023-00822-x}}, volume = {{65}}, year = {{2023}}, }