Setting the stage for a flourishing cultural data ecosystem: A spotlight on business models of cultural event platforms - Althaus, Maike; Vorbohle, Christian; Müller, Michelle; Kundisch, Dennis (Electronic Markets)
Abstract 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.

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) - Müller, Michelle (Proceedings of the 33rd European Conference on Information Systems (ECIS))

Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems - Zapata Gonzalez, David Ricardo; Meyer, Marcel; Müller, Oliver
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.
Keywords: Causal Machine Learning; Causality in Time Series; Causal Discovery; Human-Machine Collaboration

Workarounds as a Cause of Mismatches in Business Processes—Insights from a Multiple Case Study - Bartelheimer, Christian; Löhr, Bernd; Reineke, Malte Fabian; Aßbrock, Agnes; Beverungen, Daniel (Business & Information Systems Engineering)
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.

Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes - Brennig, Katharina (AMCIS 2025 Proceedings. 11.)
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.
Keywords: Process Mining; Large Language Model; Knowledge Management; Knowledge-Intensive Process; Tacit Knowledge

Blockchain‑based reputation systemsfor business‑to‑business services: designing a reputation mechanism to reduce information asymmetry in professional consulting - Hemmrich, Simon; Nissen, Volker; Beverungen, Daniel; Pauls, Josias Daniel Miño (Information Systems and e-Business Management)
Reputation systems to rate companies’ performances remain largely unexplored inresearch and are scarcely used in business-to-business (B2B) practice. Such systemsare essential for businesses seeking trustworthy partners, as they help reduce infor-mation asymmetry, lower buyers’ transaction risks, and allow high-quality serviceproviders to justify premium pricing. Unlike traditional review-based systems in thebusiness-to-consumer (B2C) context, we propose a B2B reputation mechanism inwhich buyers commit to a rating payment before a transaction. Once the buyer final-izes the rating, this payment is executed and recorded on a blockchain as an immu-table, secure ledger. Our system mimics natural trust-building mechanisms with rat-ings that are (1) monetary-based, (2) stake-based, (3) non-aggregated, (4) involvecounter-ratings, (5) selectively sellable, (6) individually comparable, (7) stored ona blockchain, (8) and monitored by a third instance. This system provides a novelapproach to fostering trust in B2B transactions by reducing information asymme-try and transaction risk. We illustrate the mechanism’s application in the consultingsector. Our analysis has identified 23 institutional trust and distrust dimensions thatpromote establishing institutional trust through the proposed mechanism. Qualita-tive interviews suggest that, while complex and challenging to apply, this mecha-nism can foster trust in B2B transactions. Given the low maturity in the applicationdomain—rating professional business services with business reputation systems—and solution domain—using monetary stakes for ratings, this system stands as apotential invention.
Keywords: Information asymmetry; Trust; Reputation system; Monetary rating; Blockchain; Consulting

A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets - Hemmrich, Simon
Keywords: Reputation Systems; Rating systems; monetary ratings; incentive mechanism; systems theory; Market coordination; advanced review system

DFG fördert Forschungsprojekt - klare Wissensstrukturen in der Wirtschaftsinformatik - Kundisch, Dennis

Technostress and interventions to prevent it - Nastjuk, IIja; Pfaff, Theresa; Tarafdar, Monideepa; Trang, Simon (Research Handbook on Psychosocial Conditions at Work)

A two-level approach for multi-objective flexible job shop scheduling and energy procurement - Burmeister, Sascha Christian; Guericke, Daniela; Schryen, Guido (Cleaner Energy Systems)

Exploring the Scope of Generative AI in Literature Review Development - Schryen, Guido; Marrone, Mauricio; Yang, Jack (Electronic Markets)

Speedup and efficiency of computational parallelization: A unifying approach and asymptotic analysis - Schryen, Guido (Journal of Parallel and Distributed Computing)

A new mathematical formulation for the simultaneous optimization of charging infrastructure and vehicle schedules for electric bus systems - Stumpe, Miriam (Transportation Research Procedia)

Digitale Plattformen und Ökosysteme im B2B-Bereich - Fallstudien, Ansätze, Technologien und Tools
Das Herausgeberwerk präsentiert aktuelle Forschungsergebnisse und praktische Erkenntnisse aus dem Bereich von digitalen Plattformen und Ökosystemen im Business-to-Business-Kontext. Dabei liegt der Schwerpunkt auf empirischen und konzeptionellen Beiträgen. Neben Grundlagen, Enablern und Fallstudien werden ebenso mögliche Vorgehensweisen zur Entwicklung von Plattformen behandelt. Praktikerinnen und Praktiker aus den Bereichen Management, Strategische Planung und Business Development erhalten Impulse, um Digitale Plattformen und Ökosysteme erfolgreich voranzutreiben und so Potenziale innerhalb ihres Unternehmens zu realisieren. Forschende, Lehrende und Studierende aus den Bereichen Digitale Plattformen und Ökosysteme aus dem Business-to-Business-Kontext dienen die Beiträge als Anregung für intensive Diskussionen.

Designing taxi ridesharing systems with shared pick-up and drop-off locations: Insights from a computational study - Stumpe, Miriam; Dieter, Peter; Schryen, Guido; Müller, Oliver; Beverungen, Daniel (Transportation Research Part A: Policy and Practice)

Leveraging Business Modeling Tools For Ecosystemic Business Model Design - Vorbohle, Christian; Kundisch, Dennis (Journal of Business Models)

Anwendung von System Dynamics zur Geschäftsmodellinnovation in einem B2B-Ökosystem - Ksouri-Gerwien, Christoph; Vorbohle, Christian (Digitale Plattformen und Ökosysteme im B2B-Bereich)

An Integrated Fuzzy Analytic Network Process and Fuzzy Regression Method for Bitcoin Price Prediction - Amiri, Arman; Tavana, Madjid; Arman, Hosein (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

Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing - Mahmoodi, Ehsan; Fathi, Masood; Tavana, Madjid; Ghobakhloo, Morteza; Ng, Amos H.C. (Journal of Manufacturing Systems)
Keywords: Industrial and Manufacturing Engineering; Hardware and Architecture; Software; Control and Systems Engineering

A systematic review of the soft computing methods shaping the future of the metaverse - Tavana, Madjid; Sorooshian, Shahryar (Applied Soft Computing)
Keywords: Software