@article{58150,
  author       = {{Schryen, Guido and Marrone, Mauricio and Yang, Jack}},
  journal      = {{Electronic Markets}},
  title        = {{{Exploring the Scope of Generative AI in Literature Review Development}}},
  year         = {{2025}},
}

@book{59182,
  editor       = {{Beverungen, Daniel and Lehrer, Christiane and Trier, Matthias}},
  isbn         = {{9783031801242}},
  issn         = {{2195-4968}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Transforming the Digitally Sustainable Enterprise}}},
  doi          = {{10.1007/978-3-031-80125-9}},
  year         = {{2025}},
}

@book{59287,
  editor       = {{Beverungen, Daniel and Lehrer, Christiane and Trier, Matthias}},
  isbn         = {{9783031801211}},
  issn         = {{2195-4968}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Solutions and Technologies for Responsible Digitalization}}},
  doi          = {{10.1007/978-3-031-80122-8}},
  year         = {{2025}},
}

@inproceedings{60534,
  author       = {{Vorbohle, Christian}},
  booktitle    = {{Proceedings of the Business Model Conference 2025}},
  location     = {{Oulu, Finland}},
  title        = {{{Toward Design Principles to Enhance Visual Inquiry Tools for Ecosystem Design}}},
  year         = {{2025}},
}

@article{60169,
  abstract     = {{<jats:title>Abstract</jats:title>
          <jats:p>Data ecosystems can generate valuable business opportunities, but research on their emergence within specific industries is limited. The cultural event industry is characterized by a multifaceted cultural landscape and a fragmented and heterogeneous market of cultural event platforms. The emerging German cultural data ecosystem, envisioned to share event data in a data space, could foster data-driven innovation and enhance value creation in the cultural event industry. Yet, following the ecosystem-as-structure view, the platforms’ willingness to participate in the cultural data ecosystem depends on whether their business model aligns with at least one of the focal value propositions of the cultural data ecosystem. In this paper, we develop a taxonomy of cultural event platform business models, and derive six archetypes. Additionally, we interview industry representatives of these archetypes to shed light on the benefits and obstacles when participating in the cultural data ecosystem, and to identify potential focal value propositions, corresponding actor roles, and activities. Our work contributes to the discussion on taxonomies of data-sharing business models and the emergence of data ecosystems in the cultural event industry.</jats:p>}},
  author       = {{Althaus, Maike and Vorbohle, Christian and Müller, Michelle and Kundisch, Dennis}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Setting the stage for a flourishing cultural data ecosystem: A spotlight on business models of cultural event platforms}}},
  doi          = {{10.1007/s12525-025-00790-y}},
  volume       = {{35}},
  year         = {{2025}},
}

@inproceedings{60549,
  author       = {{Müller, Michelle}},
  booktitle    = {{Proceedings of the 33rd European Conference on Information Systems (ECIS)}},
  location     = {{Amman, Jordan}},
  title        = {{{Guardians of Giving – An Empirical Analysis of the Relationship between Charitable Crowdfunding and Acquisitive Crime,” Proceedings of the 33rd European Conference on Information Systems (ECIS)}}},
  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}},
}

@article{60004,
  abstract     = {{Process mining has been established as a data-driven approach to analyze and improve business processes based on event data documented in event logs. A core assumption for meaningful analyses is that event data accurately represent the real-world execution of business processes in an organization. However, anecdotal evidence and recent case studies show that these aspects do not always align, and the business process management community is only beginning to investigate the mechanisms generating mismatches between process execution and event data. This study aims to identify the role of workarounds goal-directed deviations from standard processes performed by process participants to overcome obstacles– in this context. Through an inductive multiple case study of 13 workarounds in four organizations, three mismatch categories between event logs and real-world process execution related to workarounds are identified and explored. This study contributes to the literature by describing how workarounds can act as mechanisms that cause mismatches between process execution and event data, adding to the discussion on process drift and workaround mining. Furthermore, exploring the mismatch categories offers insights for practitioners and researchers on how to handle and interpret data quality issues in event data.}},
  author       = {{Bartelheimer, Christian and Löhr, Bernd and Reineke, Malte Fabian and Aßbrock, Agnes and Beverungen, Daniel}},
  issn         = {{2363-7005}},
  journal      = {{Business & Information Systems Engineering}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Workarounds as a Cause of Mismatches in Business Processes—Insights from a Multiple Case Study}}},
  doi          = {{10.1007/s12599-025-00943-5}},
  year         = {{2025}},
}

@inproceedings{60958,
  abstract     = {{Large Language Models (LLMs) excel in understanding, generating, and processing human language, with growing adoption in process mining. Process mining relies on event logs that capture explicit process knowledge; however, knowledge-intensive processes (KIPs) in domains such as healthcare and product development depend on tacit knowledge, which is often absent from event logs. To bridge this gap, this study proposes a LLM-based framework for mobilizing tacit process knowledge and enriching event logs. A proof-of-concept is demonstrated using a KIP-specific LLM-driven conversational agent built on GPT-4o. The results indicate that LLMs can capture tacit process knowledge through targeted queries and systematically integrate it into event logs. This study presents a novel approach combining LLMs, knowledge management, and process mining, advancing the understanding and management of KIPs by enhancing knowledge accessibility and documentation.}},
  author       = {{Brennig, Katharina}},
  booktitle    = {{AMCIS 2025 Proceedings. 11.}},
  keywords     = {{Process Mining, Large Language Model, Knowledge Management, Knowledge-Intensive Process, Tacit Knowledge}},
  location     = {{Montréal}},
  title        = {{{Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes}}},
  year         = {{2025}},
}

@inproceedings{61309,
  abstract     = {{Service ecosystems reshape service innovation by enabling value co-creation among diverse actors. However, small and medium-sized enterprises and public organizations face significant challenges navigating and leveraging these ecosystems due to resource constraints, knowledge gaps, and partnership difficulties. While digital innovation hubs have been introduced as potential intermediaries to foster innovation, existing models primarily focus on individual solutions and networking rather than orchestrating service innovation. This study investigates the design of a digital service innovation hub as an orchestrating entity that facilitates service innovation within ecosystems. Under the design science research paradigm, we analyze the challenges faced by small and medium-sized enterprises and public organizations and derive design requirements for these hubs. Based on 17 expert interviews and focus group validations, we define the problem
space and provide a requirements catalog for designing digital service innovation hubs as a step towards providing holistic support for service innovation initiatives.}},
  author       = {{Schäfer, Jannika Marie and Liebschner, Jonas and Rajko, Polina and Cohnen, Henrik and Lugmair, Nina and Heinz, Daniel}},
  booktitle    = {{Proceedings of the 20th International Conference on Wirtschaftsinformatik (WI 2025)}},
  keywords     = {{service innovation, ecosystem, innovation hubs, SMEs, public sector}},
  location     = {{Münster, Germany}},
  publisher    = {{Association for Information Systems (AIS)}},
  title        = {{{Designing Digital Service Innovation Hubs: An Ecosystem Perspective on the Challenges and Requirements of SMEs and the Public Sector}}},
  year         = {{2025}},
}

@inproceedings{61368,
  author       = {{Stumpe, Miriam and Speckenmeyer, Philipp and Schryen, Guido and Kleinjohann, Lisa and Weskamp, Christoph}},
  booktitle    = {{Proceedings of the Thirty-first Americas Conference on Information Systems (AMCIS 2025)}},
  title        = {{{Planning a Swarm-Based Mobility System with Autonomous Vehicles for Sustainable and Flexible Transportation in Rural Areas}}},
  year         = {{2025}},
}

@inproceedings{61370,
  author       = {{Göbel, J. and Betke, H. and Boldt, J. and Tran, M. L. and Schryen, Guido}},
  booktitle    = {{Proceedings of the Thirty-first Americas Conference on Information Systems (AMCIS 2025)}},
  title        = {{{The Impact of Chatbot Familiarity and Frequency of Use on Human-Likeness}}},
  year         = {{2025}},
}

@article{61377,
  author       = {{Schneider, Martin and Hemsen, Paul and Kundisch, Dennis}},
  journal      = {{management revue - Socio-Economic Studies, Special Issue “Digital Transformation of Work”.}},
  publisher    = {{Nomos Verlag}},
  title        = {{{Who are the Actively Participating Crowdworkers? A Qualitative Comparative Analysis of a German Text Creation PlatformSocio-Economic Studies, }}},
  year         = {{2025}},
}

@inproceedings{61381,
  author       = {{Grieger, Nicole and Okumus, Hasan and Burdorf, Sven and Kundisch, Dennis}},
  booktitle    = {{Proceedings of the MCIS 2025}},
  title        = {{{Traffic Scenario Detection – A Comparison of CNN-RNN and Transformer-based Architectures in the Context of Autonomous Driving}}},
  year         = {{2025}},
}

@inproceedings{61380,
  author       = {{Althaus, Maike}},
  booktitle    = {{Proceedings of the MCIS 2025}},
  title        = {{{Tech, Trash and Theft – Exploring the Impact of Food Waste Apps on Local Shoplifting in Grocery Stores and Supermarkets}}},
  year         = {{2025}},
}

@article{61366,
  author       = {{Paré, Guy and Wagner, Gerit and Tate, Mary and Schryen, Guido and Templier, Mathieu}},
  issn         = {{0960-085X}},
  journal      = {{European Journal of Information Systems}},
  pages        = {{1--25}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Theorising Forward: Positioning Deductive Elaboration in the Information Systems Research Repertoire}}},
  doi          = {{10.1080/0960085x.2025.2550403}},
  year         = {{2025}},
}

@inproceedings{61375,
  author       = {{Reineke, Malte Fabian and Löhr, Bernd and Aßbrock, Agnes and Bartelheimer, Christian and Beverungen, Daniel}},
  booktitle    = {{International Conference on Business Process Management 2025}},
  isbn         = {{9783032028662}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{From Temporary Fixes to Informed Decisions—Design Echelons for Evaluating Workarounds}}},
  doi          = {{10.1007/978-3-032-02867-9_32}},
  year         = {{2025}},
}

@phdthesis{61916,
  abstract     = {{Diese Dissertation untersucht Optimierungsverfahren für die nachhaltige Gestaltung von Energiesystemen mit Schwerpunkt auf dem Elektrizitätssektor im Kontext der Energiewende. Aufbauend auf Methoden des Operations Research werden Planungs- und Steuerungsprobleme in den Bereichen Stromverteilnetze und energiebewusste Produktionsplanung adressiert. Die Arbeit umfasst fünf Beiträge: (P1) entwickelt ein lineares Multi-Commodity-Flow-Modell für die kostenoptimale Erweiterung großskaliger Verteilnetze unter Berücksichtigung von Resilienzszenarien und analysiert den Zusammenhang zwischen Modellparametern und Rechenzeiten; (P2) und (P3) befassen sich mit der mehrzielorientierten flexible Job Shop Scheduling-Optimierung unter Echtzeit-Strompreisen, wobei (P3) zusätzlich CO2-Emissionen als Zielgröße integriert; (P4) erweitert dieses Szenario um simultane Energiebeschaffungsentscheidungen aus Netz, erneuerbaren Quellen und Speichersystemen unter Unsicherheit mittels Rolling-Horizon-Ansatz; (P5) vergleicht unterschiedliche Klassen von Many-Objective Evolutionary Algorithms (dominanz-, indikatoren- und dekompositionsbasiert) im Hinblick auf Konvergenz, Diversität und Vollständigkeit der Paretofront. Die entwickelten Modelle und Algorithmen – darunter memetische Varianten von NSGA-II, NSGA-III, θ-DEA und HypE – werden durch umfassende Rechenexperimente evaluiert. Die Ergebnisse liefern praxisrelevante Handlungsempfehlungen für Netzbetreiber, produzierende Unternehmen und politische Entscheidungsträger und leisten einen Beitrag zur effizienten, zuverlässigen und emissionsarmen Energieversorgung der Zukunft.}},
  author       = {{Burmeister, Sascha Christian}},
  publisher    = {{LibreCat University}},
  title        = {{{Optimization Techniques for Sustainable Energy System Design}}},
  doi          = {{10.17619/UNIPB/1-2403}},
  year         = {{2025}},
}

@article{59335,
  abstract     = {{Technological advancements and evolving value orientations reshape future value creation and pose new requirements for service innovation. While a variety of disciplines are developing new approaches to drive service innovation, this is primarily done in isolation and generates only fragmented solutions. Sociological theory has proposed “boundary objects” as an effective umbrella for communication and cooperation among communities. Therefore, we introduce continuous value shaping (CVS) as a boundary object describing service innovation approaches along five principles. We reflect on this concept through the different disciplinary lenses of researchers in service marketing, information systems, service engineering, sociology of work, and innovation management. These perspectives highlight how the CVS principles already connect to discourses within the individual disciplines. However, the CVS concept will not only provide an umbrella to embrace existing activities in different academic disciplines. It also assists to identify research themes that will benefit from uniting the power of these disciplines, and it can serve as an integrating framework to conceptualize complex service innovation approaches. Thus, the CVS concept should guide both researchers and practitioners to develop and implement novel innovation and transformation efforts—in and across organizations.}},
  author       = {{Böhmann, Tilo and Roth, Angela and Satzger, Gerhard and Benz, Carina and Beverungen, Daniel and Boes, Andreas and Breidbach, Christoph and Gersch, Martin and Gudergan, Gerhard and Hogreve, Jens and Kurtz, Christian and Langes, Barbara and Leimeister, Jan Marco and Lewandowski, Tom and Meiren, Thomas and Nägele, Rainer and Paluch, Stefanie and Peters, Christoph and Poeppelbuss, Jens and Robra-Bissantz, Susanne and Schultz, Carsten and Schumann, Jan H. and Wirtz, Jochen and Wünderlich, Nancy V.}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Continuous value shaping (CVS), Service research, Service innovation, Digitalization, Sustainability, Interdisciplinary research}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Continuous value shaping: A boundary concept for innovating service innovation approaches}}},
  doi          = {{10.1007/s12525-025-00771-1}},
  volume       = {{35}},
  year         = {{2025}},
}

@article{61410,
  abstract     = {{Purpose: The purpose of this study is to identify, analyze, and explain the implications that could
arise for service settings if AI systems develop, or are perceived to develop, consciousness – the
ability to acknowledge their own existence and the capacity for positive or negative experiences.

Design/methodology/approach: This study proposes and explores four hypothetical scenarios in
which conscious AI in service could manifest. We contextualize our resulting typology in the
health service context and integrate extant literature on technology-enabled service, AI
consciousness, and AI ethics into the narrative.

Findings: This study provides a unique theoretical contribution to service research in the form of
a Type IV theory. It enables future service researchers to apprehend, explain, and predict how
functionally conscious AI in service might unfold.

Originality: An increasingly prolific public discourse acknowledges that conscious AI systems
may emerge. Against this backdrop, this study aims to systematically explore a question that is
perhaps the most critical and timely, but also inherently speculative, in relation to AI in service
research by introducing much-needed theory and terminology.

Practical implications: The ethical use of conscious AI in service could emerge as a distinct
competitive advantage in the future. Achieving this outcome involves speculative yet actionable
recommendations that include training, guiding, and controlling how humans engage with such
systems, developing appropriate wellbeing protocols for functionally conscious AI systems, and
establishing AI rights and governance frameworks.}},
  author       = {{Breidbach, Christoph and Lars-Erik, Casper Ferm and Maglio, Paul and Beverungen, Daniel and Wirtz, Jochen and Twigg, Alex}},
  journal      = {{Journal of Service Management}},
  keywords     = {{AI, AI consciousness, AI ethics, service systems}},
  publisher    = {{Emerald}},
  title        = {{{Conscious Artificial Intelligence in Service}}},
  year         = {{2025}},
}