Research Areas
Digital Markets, Platforms, and Data-Driven Commerce
I study decision-making and market outcomes in digital ecosystems—where incentives, information, and platform design shape behavior. This includes sharing-economy and mobility platforms as well as data-driven retail and operational decision problems (e.g., service design, fulfillment, and returns).
Selected Papers
- Masuch, Landwehr, Flath & Thiesse (2024) — The Faster, the Better? Short Delivery Times and Product Returns (Journal of Retailing)
- Hauser, Flath & Thiesse (2021) — Catch me if you scan: Data-driven Prescriptive Modeling for Smart Store Environments (European Journal of Operational Research)
- Teubner & Flath (2019) — Privacy in the Sharing Economy (Journal of the Association for Information Systems)
- Hauser, Günther, Flath & Thiesse (2019) — Towards Digital Transformation in Fashion Retailing (Business & Information Systems Engineering)
- Ströhle, Flath & Gärttner (2019) — Leveraging Customer Flexibility for Car-Sharing Fleet Optimization (Transportation Science)
- Flath, Friesike, Wirth & Thiesse (2017) — Copy, Transform, Combine: Exploring the Remix as a Form of Innovation (Journal of Information Technology)
- Teubner & Flath (2015) — The Economics of Multi-Hop Ride Sharing (Business & Information Systems Engineering)
Collaboration, Competition, and Human–Algorithm Interaction
This stream examines how individuals and teams collaborate and compete in high-frequency digital environments, and how algorithmic systems (e.g., matching, ranking, AI assistants) shape decision-making, learning, and performance.
Selected Papers
- Elbert, Landeck & Flath (2026) — From Engine-Optimal to Human-Optimal: Designing Behavior-Grounded AI Training Systems (ECIS 2026)
- Elbert, Klockmann, Kosse, von Schenk, Stein & Flath (2026) — Beyond Elo: Improving Matchmaking Using the Team Player Effect (IEEE Transactions on Games)
- Elbert et al. (2025) — What Drives Team Success? Large-Scale Evidence on the Team Player Effect (arXiv)
- Elbert, Stein & Flath (2025) — Beyond Play: Competitive Game Data as an Empirical Resource (ICIS 2025)
- Teubner, Flath, Weinhardt, van der Aalst & Hinz (2023) — Welcome to the Era of ChatGPT et al. (Business & Information Systems Engineering)
- Elbert, Stein & Flath (2023) — Process and Strategy Mining in Real-Time Strategy Games (Wirtschaftsinformatik Proceedings)
Energy Systems, Flexibility, and Market Design
I work on data-driven and optimization-based approaches to coordinate distributed energy resources under uncertainty—linking economic incentives, operational constraints, and real-world behavior.
Selected Papers
- Derzbach, Pham, Dhakal & Flath (2026) — Interpretable Prosumer Load Forecasting via Physics-Informed Kolmogorov-Arnold Networks (ACM e-Energy)
- Ameling, Derzbach, Gust & Flath (2026) — Risk and Reward: Evaluating Household Energy Storage for Optimizing Demand-Side Flexibility Under Dynamic Tariffs (Energy Informatics)
- Derzbach & Flath (2025) — Assessing Electricity Customers Using Procurement Cost Metrics (ACM e-Energy)
- Gärttner, Flath & Weinhardt (2017) — Portfolio and Contract Design for Demand Response Resources (European Journal of Operational Research)
- Schuller, Flath & Gottwalt (2015) — Quantifying Load Flexibility of Electric Vehicles (Applied Energy)
- Salah et al. (2015) — Impact of Electric Vehicles on Distribution Substations (Applied Energy)
- Flath et al. (2014) — Improving Electric Vehicle Charging Coordination Through Area Pricing (Transportation Science)
- Flath et al. (2012) — Cluster Analysis of Smart Metering Data (Business & Information Systems Engineering)
Methods for Decision Intelligence and Business Analytics
Across domains, I develop reusable analytics methods and toolchains—spanning machine learning, process analytics, and optimization—to support robust decision-making in business and other organizational settings.
Selected Papers
- Griebel et al. (2023) — Deep Learning-Enabled Segmentation of Ambiguous Bioimages with deepflash2 (Nature Communications)
- Greif, Stein & Flath (2023) — Information Value Analysis for Real-Time Silo Fill-Level Monitoring (INFORMS J. Applied Analytics)
- Oberdorf, Schaschek, Weinzierl, Stein, Matzner & Flath (2023) — Predictive End-to-End Enterprise Process Network Monitoring (Business & Information Systems Engineering)
- Stahl, Stein & Flath (2021) — Analytics Applications in Fashion Supply Chain Management (IEEE Transactions on Engineering Management)
- Greif, Stein & Flath (2020) — Peeking into the Void: Digital Twins for Construction Site Logistics (Computers in Industry)
- Segebarth et al. (2020) — On the Objectivity, Reliability, and Validity of Deep Learning Enabled Bioimage Analyses (eLife)
- Flath & Stein (2018) — Towards a Data Science Toolbox for Industrial Analytics Applications (Computers in Industry)
