@inproceedings{33885,
  author       = {{Seutter, Janina}},
  location     = {{Copenhagen, Denmark}},
  title        = {{{Online Reviews in B2B Markets: A Qualitative Study on the Underlying Motives }}},
  year         = {{2022}},
}

@inproceedings{30916,
  author       = {{Seutter, Janina}},
  booktitle    = {{Proceedings of the 30th European Conference on Information Systems (ECIS)}},
  location     = {{Timișoara, Romania}},
  title        = {{{Online Reviews in B2B Markets: A Qualitative Study of Underlying Motivations}}},
  year         = {{2022}},
}

@inproceedings{31062,
  author       = {{Poniatowski, Martin}},
  booktitle    = {{Proceedings of the 28th Americas Conference on Information Systems (AMCIS)}},
  location     = {{Minneapolis, USA}},
  title        = {{{How the Display of the Transaction Count Affects the Purchase Intention}}},
  year         = {{2022}},
}

@inproceedings{30939,
  author       = {{Vorbohle, Christian and Kundisch, Dennis}},
  booktitle    = {{Proceedings of the 30th European Conference on Information Systems (ECIS)}},
  location     = {{Timișoara, Romania}},
  title        = {{{Overcoming Silos: A Review of Business Model Modeling Languages for Business Ecosystems}}},
  year         = {{2022}},
}

@inproceedings{30734,
  author       = {{Althaus, Maike and Poniatowski, Martin and Kundisch, Dennis}},
  location     = {{Madrid, Spain}},
  title        = {{{Tackling Crises Together? - An Econometric Analysis of Charitable Crowdfunding During the COVID-19 Pandemic}}},
  year         = {{2022}},
}

@inproceedings{30212,
  author       = {{Vorbohle, Christian and Kundisch, Dennis}},
  title        = {{{Key Properties of Sustainable Business Ecosystem Relationships}}},
  year         = {{2022}},
}

@article{33250,
  author       = {{Szopinski, Daniel and Massa, Lorenzo and John, Thomas and Kundisch, Dennis and Tucci, Christopher}},
  journal      = {{Communications of the Association for Information Systems}},
  pages        = {{774--841}},
  title        = {{{Modeling Business Models: A cross-disciplinary Analysis of Business Model Modeling Languages and Directions for Future Research}}},
  volume       = {{51}},
  year         = {{2022}},
}

@article{23415,
  author       = {{Sperling, Martina and Schryen, Guido}},
  journal      = {{European Journal of Operational Research (EJOR)}},
  number       = {{2}},
  pages        = {{690 -- 705}},
  title        = {{{Decision Support for Disaster Relief: Coordinating Spontaneous Volunteers}}},
  volume       = {{299}},
  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{23566,
  author       = {{Kundisch, Dennis and Muntermann, J. and Oberländer, A. M. and Rau, D. and Röglinger, M. and Schoormann, T. and Szopinski, Daniel}},
  journal      = {{Business & Information Systems Engineering}},
  number       = {{4}},
  pages        = {{421--439}},
  title        = {{{An update for taxonomy designers: Methodological guidance from information systems research}}},
  volume       = {{64}},
  year         = {{2022}},
}

@inproceedings{33884,
  author       = {{Laux, Florian and Kundisch, Dennis}},
  location     = {{Copenhagen, Denmark}},
  title        = {{{Judgment or Choice? An Experimental Comparison of Evaluation Approaches for External Crowdvoting}}},
  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}},
}

@article{35740,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.</jats:p>}},
  author       = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}},
  number       = {{1}},
  pages        = {{375--396}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}},
  doi          = {{10.1007/s12525-022-00530-6}},
  volume       = {{32}},
  year         = {{2022}},
}

@inproceedings{34317,
  author       = {{Arslan, Kader and Trier, Matthias}},
  booktitle    = {{Proceedings of the 33rd Australasian Conference on Information Systems (ACIS 2022)}},
  keywords     = {{Social media, Social media marketing process, Social media strategy, Social media management, Guidelines}},
  location     = {{Melbourne, Australia}},
  title        = {{{Towards a Process Model for Social Media Marketing}}},
  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}},
}

@article{53238,
  author       = {{Tavana, Madjid and Khalili Nasr, Arash and Mina, Hassan and Michnik, Jerzy}},
  issn         = {{0038-0121}},
  journal      = {{Socio-Economic Planning Sciences}},
  keywords     = {{Management Science and Operations Research, Statistics, Probability and Uncertainty, Strategy and Management, Economics and Econometrics, Geography, Planning and Development}},
  publisher    = {{Elsevier BV}},
  title        = {{{A private sustainable partner selection model for green public-private partnerships and regional economic development}}},
  doi          = {{10.1016/j.seps.2021.101189}},
  volume       = {{83}},
  year         = {{2022}},
}

@article{53240,
  author       = {{Tavana, Madjid and Azadmanesh, Abdolreza and Nasr, Arash Khalili and Mina, Hassan}},
  issn         = {{1368-3500}},
  journal      = {{Current Issues in Tourism}},
  keywords     = {{Tourism, Leisure and Hospitality Management, Geography, Planning and Development}},
  number       = {{22}},
  pages        = {{3709--3734}},
  publisher    = {{Informa UK Limited}},
  title        = {{{A multicriteria-optimization model for cultural heritage renovation projects and public-private partnerships in the hospitality industry}}},
  doi          = {{10.1080/13683500.2021.2015299}},
  volume       = {{25}},
  year         = {{2022}},
}