@inproceedings{27507, abstract = {{Accurate real estate appraisal is essential in decision making processes of financial institutions, governments, and trending real estate platforms like Zillow. One of the most important factors of a property’s value is its location. However, creating accurate quantifications of location remains a challenge. While traditional approaches rely on Geographical Information Systems (GIS), recently unstructured data in form of images was incorporated in the appraisal process, but text data remains an untapped reservoir. Our study shows that using text data in form of geolocated Wikipedia articles can increase predictive performance over traditional GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to automatically extract geographically weighted vector representations for text is established and used alongside traditional structural housing features to make predictions and to uncover local patterns on sale price for real estate transactions between 2015 and 2020 in Allegheny County, Pennsylvania.}}, author = {{Heuwinkel, Tim and Kucklick, Jan-Peter and Müller, Oliver}}, booktitle = {{55th Annual Hawaii International Conference on System Sciences (HICSS-55)}}, keywords = {{Real Estate Appraisal, Text Regression, Natural Language Processing (NLP), Location Intelligence, Wikipedia}}, location = {{Virtual}}, title = {{{Using Geolocated Text to Quantify Location in Real Estate Appraisal}}}, 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{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{41486, abstract = {{Now accounting for more than 80% of a firm's worth, brands have become essential assets for modern organizations. However, methods and techniques for the monetary valuation of brands are still under-researched. Hence, the objective of this study is to evaluate the utility of explanatory statistical models and machine learning approaches for explaining and predicting brand value. Drawing upon the case of the most valuable English football brands during the 2016/17 to 2020/21 seasons, we demonstrate how to operationalize Aaker's (1991) theoretical brand equity framework to collect meaningful qualitative and quantitative feature sets. Our explanatory models can explain up to 77% of the variation in brand valuations across all clubs and seasons, while our predictive approach can predict out-of-sample observations with a mean absolute percentage error (MAPE) of 14%. Future research can build upon our results to develop domain-specific brand valuation methods while enabling managers to make better-informed investment decisions.}}, author = {{Caron, Matthew and Bartelheimer, Christian and Müller, Oliver}}, booktitle = {{Proceeding of the 28th Americas Conference on Information Systems (AMCIS)}}, location = {{Minneapolis, USA}}, title = {{{Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation}}}, 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}}, } @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}}, } @inproceedings{17140, author = {{Thiess, Tiemo and Müller, Oliver and Tonelli, Lorenzo}}, booktitle = {{International Conference on Wirtschaftsinformatik}}, title = {{{Design Principles for Explainable Sales Win-Propensity Prediction Systems}}}, doi = {{https://doi.org/10.30844/wi_2020_c8-thiess}}, year = {{2020}}, } @inproceedings{17095, abstract = {{In order to sustain their competitive advantage, data driven organizations must continue investing in business intelligence and analytics (BI&A) while mitigating inherent cost increases. Research shows that examining outlays by individual BI&A artifact (e.g. reports, analytics) is necessary, but introduction in practice is cumbersome and adoption is slow. BI&A service-oriented cost allocation (BIASOCA) represents an improvement to this situation. This approach enables to render the BI&A cost pool accountable and improves cost transparency, which leads to a higher BI&A penetration of economically viable applications in organizations. Against this background, this paper aims at designing and implementing BIASOCA in a medium-sized company. To record organizational impact and increase customer acceptance, this study is carried out as action design research (ADR). Our findings indicate improvements in BI&A management from working with consumers to locate cost savings and drivers. After invoicing, consumers’ BI&A awareness increased, releasing resources while also making a better understanding of BIASOCA necessary. We detail how to implement BIASOCA in a real-life setting and the challenges attendant in so doing. Our research contributes to theory and practice with a set of design principles highlighting, besides the accuracy of cost accounting, the importance of collaboration, model comprehensibility and strategic alignment.}}, author = {{Grytz, Raphael and Krohn-Grimberghe, Artus and Müller, Oliver}}, booktitle = {{European Conference on Information Systems}}, title = {{{Business Intelligence & Analytics Cost Accounting: An Action Design Research Approach}}}, year = {{2020}}, } @inproceedings{21563, abstract = {{Historically, the field of financial forecasting almost exclusively relied on so-called hard information – i.e., numerical data with well-defined and unambiguous meaning. Over the last few decades, however, researchers and practitioners alike have, following the advances in natural language understanding, started recognizing the benefits of integrating soft information into financial modelling. In line with the above, this paper examines whether contemporary attention-based sequence-to-sequence models, known as Transformers, can help improve stock return volatility prediction when applied to corporate annual reports. Using a publicly available benchmark dataset, we show, in an empirical analysis, that out-of-the-box Transformer models have the ability to outmatch current state-of-the-art results and, more importantly, that our proposed feature-based Transformer approach can outperform a robust numerical baseline. To the best of our knowledge, this is the first empirical study focusing on stock return volatility prediction (1) to ever experiment with state-of-the-art Transformer architectures and (2) to demonstrate that a model based solely on soft information can surpass its numerical counterpart. Furthermore, we show that by including an additional numerical feature into our best text-only model, we can push the performance of our model even further, suggesting that soft and hard information contain different predictive signals.}}, author = {{Caron, Matthew and Müller, Oliver}}, booktitle = {{2020 IEEE International Conference on Big Data (Big Data)}}, location = {{Online}}, pages = {{4383--4391}}, title = {{{Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility}}}, doi = {{10.1109/BigData50022.2020.9378134}}, year = {{2020}}, } @article{4682, author = {{Schmiedel, T. and Müller, Oliver and vom Brocke, J.}}, journal = {{Organizational Research Methods}}, keywords = {{online reviews, organizational culture, structural topic model, topic modeling, tutorial}}, pages = {{941----968 }}, title = {{{Topic Modeling as a Strategy of Inquiry in Organizational Research: A Tutorial With an Application Example on Organizational Culture}}}, doi = {{https://doi.org/10.1177/1094428118773858}}, year = {{2019}}, } @inbook{16251, author = {{Müller, Oliver}}, booktitle = {{The Art of Structuring}}, isbn = {{9783030062330}}, title = {{{Structuring Unstructured Data—Or: How Machine Learning Can Make You a Wine Sommelier}}}, doi = {{10.1007/978-3-030-06234-7_29}}, year = {{2019}}, } @inproceedings{17096, abstract = {{Augmented Reality (AR) technologies have evolved rapidly over the last years, particularly with regard to user interfaces, input devices, and cameras used in mobile devices for object and gesture recognition. While early AR systems relied on pre-defined trigger images or QR code markers, modern AR applications leverage machine learning techniques to identify objects in their physical environments. So far, only few empirical studies have investigated AR's potential for supporting learning and task assistance using such marker-less AR. In order to address this research gap, we implemented an AR application (app)with the aim to analyze the effectiveness of marker-less AR applied in a mundane setting which can be used for on-the-job training and more formal educational settings. The results of our laboratory experiment show that while participants working with AR needed significantly more time to fulfill the given task, the participants who were supported by AR learned significantly more.}}, author = {{Sommerauer, Peter and Müller, Oliver and Maxim, Leonard and Østman, Nils}}, booktitle = {{International Conference on Wirtschaftsinformatik}}, title = {{{The Effect of Marker-less Augmented Reality on Task and Learning Performance}}}, year = {{2019}}, } @article{4684, abstract = {{Recent years have seen the emergence of physical products that are digitally networked with other products and with information systems to enable complex business scenarios in manufacturing, mobility, or healthcare. These “smart products”, which enable the co-creation of “smart service” that is based on monitoring, optimization, remote control, and autonomous adaptation of products, profoundly transform service systems into what we call “smart service systems”. In a multi-method study that includes conceptual research and qualitative data from in-depth interviews, we conceptualize “smart service” and “smart service systems” based on using smart products as boundary objects that integrate service consumers’ and service providers’ resources and activities. Smart products allow both actors to retrieve and to analyze aggregated field evidence and to adapt service systems based on contextual data. We discuss the implications that the introduction of smart service systems have for foundational concepts of service science and conclude that smart service systems are characterized by technology-mediated, continuous, and routinized interactions.}}, author = {{Beverungen, Daniel and Müller, Oliver and Matzner, Martin and Mendling, Jan and vom Brocke, Jan}}, issn = {{14228890}}, journal = {{Electronic Markets}}, keywords = {{Boundary object, Internet of things, Service science, Smart products, Smart service}}, pages = {{7--18}}, publisher = {{SpringerNature}}, title = {{{Conceptualizing smart service systems}}}, doi = {{10.1007/s12525-017-0270-5}}, volume = {{29}}, year = {{2019}}, } @inproceedings{4676, author = {{Sommerauer, Peter and Müller, Oliver}}, booktitle = {{Hawaii International Conference on System Sciences}}, title = {{{Augmented Reality in Informal Learning Environments: Investigating Short-term and Long-term Effects}}}, doi = {{10.24251/HICSS.2018.176}}, year = {{2018}}, } @article{4679, author = {{Jaakonmäki, Roope and Simons, Alexander and Müller, Oliver and vom Brocke, Jan}}, issn = {{1741-0398}}, journal = {{Journal of Enterprise Information Management}}, number = {{5}}, pages = {{704----723}}, title = {{{ECM implementations in practice: objectives, processes, and technologies}}}, doi = {{10.1108/JEIM-11-2016-0187}}, year = {{2018}}, }