@inproceedings{50437, abstract = {{The humanitarian crisis resulting from the Russian invasion of Ukraine has led to millions of displaced individuals across Europe. Addressing the evolving needs of these refugees is crucial for hosting countries and humanitarian organizations. This study leverages social media analytics to supplement traditional surveys, providing real-time insights into refugee needs by analyzing over two million messages from Telegram, a vital platform for Ukrainian refugees in Germany. We employ Natural Language Processing techniques, including language identification, sentiment analysis, and topic modeling, to identify well-defined topic clusters such as housing, financial and legal assistance, language courses, job market access, and medical needs. Our findings also reveal changes in topic occurrence and nature over time. To support practitioners, we introduce an interactive web-based dashboard for continuous analysis of refugee needs.}}, author = {{Reimann, Raphael and Caron, Matthew}}, booktitle = {{Wirtschaftsinformatik}}, location = {{Paderborn, Germany}}, title = {{{Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach}}}, 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}}, } @article{45299, abstract = {{Many applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system’s explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity.}}, author = {{Kucklick, Jan-Peter}}, issn = {{1246-0125}}, journal = {{Journal of Decision Systems}}, keywords = {{Explainable AI (XAI), machine learning, interpretability, real estate appraisal, framework, taxonomy}}, pages = {{1--41}}, publisher = {{Taylor & Francis}}, title = {{{HIEF: a holistic interpretability and explainability framework}}}, doi = {{10.1080/12460125.2023.2207268}}, year = {{2023}}, } @inproceedings{50459, abstract = {{Organizations employ process mining to discover, check, or enhance process models based on data from information systems to improve business processes. Even though process mining is increasingly relevant in academia and organizations, achieving process mining excellence and generating business value through its application is elusive. Maturity models can help to manage interdisciplinary teams in their efforts to plan, implement, and manage process mining in organizations. However, while numerous maturity models on business process management (BPM) are available, recent calls for process mining maturity models indicate a gap in the current knowledge base. We systematically design and develop a comprehensive process mining maturity model that consists of five factors comprising 23 elements, which organizations need to develop to apply process mining sustainably and successfully. We contribute to the knowledge base by the exaptation of existing BPM maturity models, and validate our model through its application to a real-world scenario.}}, author = {{Brock, Jonathan and Löhr, Bernd and Brennig, Katharina and Seger, Thilo and Bartelheimer, Christian and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{European Conference on Information Systems (ECIS)}}, title = {{{A Process Mining Maturity Model: Enabling Organizations to Assess and Improve their Process Mining Activities}}}, 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}}, } @inproceedings{27506, abstract = {{Explainability for machine learning gets more and more important in high-stakes decisions like real estate appraisal. While traditional hedonic house pricing models are fed with hard information based on housing attributes, recently also soft information has been incorporated to increase the predictive performance. This soft information can be extracted from image data by complex models like Convolutional Neural Networks (CNNs). However, these are intransparent which excludes their use for high-stakes financial decisions. To overcome this limitation, we examine if a two-stage modeling approach can provide explainability. We combine visual interpretability by Regression Activation Maps (RAM) for the CNN and a linear regression for the overall prediction. Our experiments are based on 62.000 family homes in Philadelphia and the results indicate that the CNN learns aspects related to vegetation and quality aspects of the house from exterior images, improving the predictive accuracy of real estate appraisal by up to 5.4%.}}, author = {{Kucklick, Jan-Peter}}, booktitle = {{55th Annual Hawaii International Conference on System Sciences (HICSS-55)}}, keywords = {{Explainable Artificial Intelligence (XAI), Regression Activation Maps, Real Estate Appraisal, Convolutional Block Attention Module, Computer Vision}}, location = {{Virtual}}, title = {{{Visual Interpretability of Image-based Real Estate Appraisal}}}, year = {{2022}}, } @inproceedings{29539, abstract = {{Explainable Artificial Intelligence (XAI) is currently an important topic for the application of Machine Learning (ML) in high-stakes decision scenarios. Related research focuses on evaluating ML algorithms in terms of interpretability. However, providing a human understandable explanation of an intelligent system does not only relate to the used ML algorithm. The data and features used also have a considerable impact on interpretability. In this paper, we develop a taxonomy for describing XAI systems based on aspects about the algorithm and data. The proposed taxonomy gives researchers and practitioners opportunities to describe and evaluate current XAI systems with respect to interpretability and guides the future development of this class of systems.}}, author = {{Kucklick, Jan-Peter}}, booktitle = {{Wirtschaftsinformatik 2022 Proceedings}}, keywords = {{Explainable Artificial Intelligence, XAI, Interpretability, Decision Support Systems, Taxonomy}}, location = {{Nürnberg (online)}}, title = {{{Towards a model- and data-focused taxonomy of XAI systems}}}, year = {{2022}}, } @article{32866, author = {{Shollo, Arisa and Hopf, Konstantin and Thiess, Tiemo and Müller, Oliver}}, issn = {{0963-8687}}, journal = {{The Journal of Strategic Information Systems}}, keywords = {{Information Systems and Management, Information Systems, Management Information Systems}}, number = {{3}}, publisher = {{Elsevier BV}}, title = {{{Shifting ML value creation mechanisms: A process model of ML value creation}}}, doi = {{10.1016/j.jsis.2022.101734}}, volume = {{31}}, year = {{2022}}, } @article{35620, abstract = {{Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.}}, author = {{Kucklick, Jan-Peter and Müller, Oliver}}, issn = {{2158-656X}}, journal = {{ACM Transactions on Management Information Systems}}, keywords = {{Interpretability, Convolutional Neural Network, Accuracy-Interpretability Trade-Of, Real Estate Appraisal, Hedonic Pricing, Grad-Ram}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal}}}, doi = {{10.1145/3567430}}, year = {{2022}}, } @inproceedings{36912, abstract = {{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.}}, author = {{Löhr, Bernd and Brennig, Katharina and Bartelheimer, Christian and Beverungen, Daniel and Müller, Oliver}}, booktitle = {{International Conference on Business Process Management}}, isbn = {{978-3-031-16103-2}}, title = {{{Process Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing}}}, doi = {{10.1007/978-3-031-16103-2_18}}, year = {{2022}}, } @inproceedings{42631, abstract = {{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.}}, author = {{Caron, Matthew}}, booktitle = {{2022 IEEE International Conference on Big Data (Big Data)}}, location = {{Osaka, Japan}}, publisher = {{IEEE}}, title = {{{Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift}}}, doi = {{10.1109/bigdata55660.2022.10020933}}, year = {{2022}}, } @inproceedings{25113, abstract = {{Our world is more connected than ever before. Sadly, however, this highly connected world has made it easier to bully, insult, and propagate hate speech on the cyberspace. Even though researchers and companies alike have started investigating this real-world problem, the question remains as to why users are increasingly being exposed to hate and discrimination online. In fact, the noticeable and persistent increase in harmful language on social media platforms indicates that the situation is, actually, only getting worse. Hence, in this work, we show that contemporary ML methods can help tackle this challenge in an accurate and cost-effective manner. Our experiments demonstrate that a universal approach combining transfer learning methods and state-of-the-art Transformer architectures can trigger the efficient development of toxic language detection models. Consequently, with this universal approach, we provide platform providers with a simplistic approach capable of enabling the automated moderation of user-generated content, and as a result, hope to contribute to making the web a safer place.}}, author = {{Caron, Matthew and Bäumer, Frederik S. and Müller, Oliver}}, booktitle = {{55th Hawaii International Conference on System Sciences (HICSS)}}, location = {{Online}}, title = {{{Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models}}}, year = {{2022}}, } @inproceedings{21204, author = {{Kucklick, Jan-Peter and Müller, Oliver}}, booktitle = {{ The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data in Financial Services}}, title = {{{A Comparison of Multi-View Learning Strategies for Satellite Image-based Real Estate Appraisal}}}, year = {{2021}}, } @inproceedings{22514, author = {{Kucklick, Jan-Peter and Müller, Jennifer and Beverungen, Daniel and Müller, Oliver}}, booktitle = {{European Conference on Information Systems}}, location = {{Virtual}}, title = {{{Quantifying the Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning Approach}}}, year = {{2021}}, } @inbook{32868, author = {{Nagbøl, Per Rådberg and Müller, Oliver and Krancher, Oliver}}, booktitle = {{The Next Wave of Sociotechnical Design}}, isbn = {{9783030824044}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Designing a Risk Assessment Tool for Artificial Intelligence Systems}}}, doi = {{10.1007/978-3-030-82405-1_32}}, year = {{2021}}, } @inproceedings{26812, author = {{Leffrang, Dirk and Müller, Oliver}}, booktitle = {{IEEE Workshop on TRust and EXpertise in Visual Analytics}}, title = {{{Should I Follow this Model? The Effect of Uncertainty Visualization on the Acceptance of Time Series Forecasts}}}, doi = {{10.1109/TREX53765.2021.00009}}, year = {{2021}}, } @inproceedings{24547, abstract = {{Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time.}}, author = {{Müller, Oliver and Caron, Matthew and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen}}, booktitle = {{8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)}}, keywords = {{expected possession value, handball, tracking data, time series classification, deep learning}}, location = {{Online}}, title = {{{PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data}}}, year = {{2021}}, } @inproceedings{25029, abstract = {{In early 2021, the finance world was taken by storm by the dramatic price surge of the GameStop Corp. stock. This rise is being, at least in part, attributed to a group of Redditors belonging to the now-famous r/wallstreetbets (WSB) subreddit group. In this work, we set out to address if user activity on the WSB subreddit is associated with the trading volume of the GME stock. Leveraging a unique dataset containing more than 4.9 million WSB posts and comments, we assert that user activity is associated with the trading volume of the GameStop stock. We further show that posts have a significantly higher predictive power than comments and are especially helpful for predicting unusually high trading volume. Lastly, as recent events have shown, we believe that these findings have implications for retail and institutional investors, trading platforms, and policymakers, as these can have disruptive potential.}}, author = {{Caron, Matthew and Gulenko, Maryna and Müller, Oliver}}, booktitle = {{42nd International Conference on Information Systems (ICIS 2021)}}, keywords = {{Retail investors, GameStop, Social Networks, Reddit, WallStreetBets}}, location = {{Austin, Texas}}, title = {{{To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge of the GME Stock}}}, year = {{2021}}, } @inproceedings{17348, author = {{Kucklick, Jan-Peter and Müller, Oliver}}, booktitle = {{Symposium on Statistical Challenges in Electronic Commerce Research (SCECR)}}, title = {{{Location, location, location: Satellite image-based real-estate appraisal}}}, year = {{2020}}, }