@inbook{57651,
  author       = {{Kempkes, Jens Peter and Kreuzhage, Katharina and Kundisch, Dennis and Seutter, Janina and Weskamp, Christoph}},
  booktitle    = {{Theatercontrolling}},
  isbn         = {{9783658449834}},
  pages        = {{121 -- 129}},
  publisher    = {{Springer Fachmedien Wiesbaden}},
  title        = {{{Digitale Transformation im Theater}}},
  doi          = {{10.1007/978-3-658-44984-1_10}},
  year         = {{2024}},
}

@article{47424,
  author       = {{Burmeister, Sascha Christian and Guericke, Daniela and Schryen, Guido}},
  journal      = {{Flexible Services and Manufacturing Journal}},
  title        = {{{A Memetic NSGA-II for the Multi-Objective Flexible Job Shop Scheduling Problem with Real-time Energy Tariffs}}},
  year         = {{2024}},
}

@article{35852,
  author       = {{Neumann, Jürgen and Gutt, Dominik and Kundisch, Dennis}},
  journal      = {{MIS Quarterly}},
  number       = {{4}},
  pages        = {{1709--1726}},
  title        = {{{Reviewing from a Distance: Uncovering Asymmetric Moderations of Spatial and Temporal Distances Between Sentiment Negativity and Rating}}},
  volume       = {{47}},
  year         = {{2023}},
}

@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{50121,
  abstract     = {{Many researchers and practitioners see artificial intelligence as a game changer compared to classical statistical models. However, some software providers engage in “AI washing”, relabeling solutions that use simple statistical models as AI systems. By contrast, research on algorithm aversion unsystematically varied the labels for advisors and treated labels such as "artificial intelligence" and "statistical model" synonymously. This study investigates the effect of individual labels on users' actual advice utilization behavior. Through two incentivized online within-subjects experiments on regression tasks, we find that labeling human advisors with labels that suggest higher expertise leads to an increase in advice-taking, even though the content of the advice remains the same. In contrast, our results do not suggest such an expert effect for advice-taking from algorithms, despite differences in self-reported perception. These findings challenge the effectiveness of framing intelligent systems as AI-based systems and have important implications for both research and practice.}},
  author       = {{Leffrang, Dirk}},
  booktitle    = {{International Conference on Information Systems}},
  keywords     = {{Artificial Intelligence, Algorithm Appreciation, Framing, Advice-taking, Expertise}},
  location     = {{Hyderabad, India}},
  number       = {{10}},
  title        = {{{AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization}}},
  year         = {{2023}},
}

@inproceedings{50118,
  abstract     = {{Despite the widespread use of machine learning algorithms, their effectiveness is limited by a phenomenon known as algorithm aversion. Recent research concluded that unobserved variables can cause algorithm aversion. However, the impact of an unobserved variable on algorithm aversion remains unclear. Previous studies focused on situations where humans had more variables available than algorithms. We extend this research by conducting an online experiment with 94 participants, systematically varying the number of observable variables to the advisor and the advisor type. Surprisingly, our results did not confirm that an unobserved variable had a negative effect on advice-taking. Instead, we found a positive impact in an algorithm appreciation scenario. This study provides new insights into the paradoxical behavior in which people weigh advice more despite having fewer variables, as they correct for the advisor's errors. Practitioners should consider this behavior when designing algorithms and account for user correction behavior.}},
  author       = {{Leffrang, Dirk}},
  booktitle    = {{Wirtschaftsinformatik Conference}},
  keywords     = {{Algorithm aversion, Data, Decision-making, Advice-taking, Human-Computer Interaction}},
  location     = {{Paderborn}},
  number       = {{19}},
  title        = {{{The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect of Unobserved Variables on Advice Utilization}}},
  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{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}},
}

@misc{48335,
  author       = {{Knorr, Lukas and Jungeilges, André and Pfeifer, Florian and Burmeister, Sascha Christian and Meschede, Henning}},
  publisher    = {{4. Aachener Ofenbau- und Thermoprozess-Kolloquium}},
  title        = {{{Regenerative Energien für einen effizienten Betrieb von Presshärtelinien}}},
  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{51770,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Workarounds are goal‐driven deviations from the standard operating procedures performed to overcome obstacles constraining day‐to‐day work. Despite starting as temporary fixes, they can become established across an organisation and trigger the innovation of processes and IT artefacts that can resolve misfits permanently. Although prior research has elicited antecedents and types of workarounds, it is not known how workarounds diffuse in an organisation and, thereby, innovating co‐workers' activities, IT artefacts, and organisational structures. The results of our multiple two‐year case study provide unique empirical insights into the diffusion of workarounds and how they can act as generative mechanisms for bottom‐up process innovation.</jats:p>}},
  author       = {{Bartelheimer, Christian and Wolf, Verena and Beverungen, Daniel}},
  issn         = {{1350-1917}},
  journal      = {{Information Systems Journal}},
  keywords     = {{Computer Networks and Communications, Information Systems, Software}},
  number       = {{5}},
  pages        = {{1085--1150}},
  publisher    = {{Wiley}},
  title        = {{{Workarounds as generative mechanisms for bottom‐up process innovation—Insights from a multiple case study}}},
  doi          = {{10.1111/isj.12435}},
  volume       = {{33}},
  year         = {{2023}},
}

@article{46492,
  author       = {{Seutter, Janina and Kutzner, K. and Stadtländer, M. and Kundisch, Dennis and Knackstedt,  R.}},
  journal      = {{Electronic Markets}},
  number       = {{1}},
  pages        = {{47}},
  title        = {{{“Sorry, Too Much Information”. Designing Online Review Systems that Support Information Search and Processing}}},
  volume       = {{33}},
  year         = {{2023}},
}

@article{46040,
  author       = {{Seutter, Janina and Müller, Michelle and Kundisch, Dennis}},
  journal      = {{Wirtschaftsinformatik & Management}},
  number       = {{4}},
  pages        = {{282--289}},
  title        = {{{Verdrängen Smart-Product-Service-Systeme die Interaktion in Online-Communities? – Empirische Evidenz aus einer Rezept-Community}}},
  volume       = {{15}},
  year         = {{2023}},
}

@article{53220,
  author       = {{Tavana, Madjid and Khalili Nasr, Arash and Ahmadabadi, Alireza Barati and Amiri, Alireza Shamekhi and Mina, Hassan}},
  issn         = {{2542-6605}},
  journal      = {{Internet of Things}},
  keywords     = {{Management of Technology and Innovation, Artificial Intelligence, Computer Science Applications, Hardware and Architecture, Engineering (miscellaneous), Information Systems, Computer Science (miscellaneous), Software}},
  publisher    = {{Elsevier BV}},
  title        = {{{An interval multi-criteria decision-making model for evaluating blockchain-IoT technology in supply chain networks}}},
  doi          = {{10.1016/j.iot.2023.100786}},
  volume       = {{22}},
  year         = {{2023}},
}

@article{53218,
  author       = {{Tavana, Madjid and Soltanifar, Mehdi and Santos-Arteaga, Francisco J. and Sharafi, Hamid}},
  issn         = {{0957-4174}},
  journal      = {{Expert Systems with Applications}},
  keywords     = {{Artificial Intelligence, Computer Science Applications, General Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Analytic hierarchy process and data envelopment analysis: A match made in heaven}}},
  doi          = {{10.1016/j.eswa.2023.119902}},
  volume       = {{223}},
  year         = {{2023}},
}

@article{53219,
  author       = {{Tavana, Madjid and Hajipour, Vahid and Alaghebandha, Mohammad and Di Caprio, Debora}},
  issn         = {{2666-8270}},
  journal      = {{Machine Learning with Applications}},
  keywords     = {{General Medicine}},
  publisher    = {{Elsevier BV}},
  title        = {{{A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems}}},
  doi          = {{10.1016/j.mlwa.2022.100445}},
  volume       = {{11}},
  year         = {{2023}},
}

@article{53217,
  author       = {{Tavana, Madjid and Mina, Hassan and Santos-Arteaga, Francisco J.}},
  issn         = {{0148-2963}},
  journal      = {{Journal of Business Research}},
  keywords     = {{Marketing}},
  publisher    = {{Elsevier BV}},
  title        = {{{A general Best-Worst method considering interdependency with application to innovation and technology assessment at NASA}}},
  doi          = {{10.1016/j.jbusres.2022.08.036}},
  volume       = {{154}},
  year         = {{2023}},
}

@article{53216,
  author       = {{Tavana, Madjid and Soltanifar, Mehdi and Santos-Arteaga, Francisco J.}},
  issn         = {{0254-5330}},
  journal      = {{Annals of Operations Research}},
  keywords     = {{Management Science and Operations Research, General Decision Sciences}},
  number       = {{2}},
  pages        = {{879--907}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Analytical hierarchy process: revolution and evolution}}},
  doi          = {{10.1007/s10479-021-04432-2}},
  volume       = {{326}},
  year         = {{2023}},
}