GreedyBoost: An Accurate, Efficient and Flexible Ensemble Method for B2B Recommendations. - Weipeng Zhang; Tobias Enders; Dongsheng Li (HICSS, 2017)

Exploring the Value of Data - A Research Agenda. - Tobias Enders (IESS, 2018)

Capturing Value from Data: Exploring Factors Influencing Revenue Model Design for Data-Driven Services. - Tobias Enders; Ronny Schüritz; Wiebke Frey (Wirtschaftsinformatik, 2019)

Priorisierung von Digitalisierungsprojekten entlang der gesamten kundenorientierten Prozesskette im Maschinenbau. - Thomas Pschybilla; Manuela Hofmann; Tobias Enders; Michael Vössing (HMD Prax. Wirtsch., 2019)

Capturing the Value: How to Charge for Smart Services. - Tobias Enders; Ronny Schüritz (Smart Service Management, 2020)

How to Implement an Open Data Strategy? Analyzing Organizational Change Processes to Enable Value Creation by Revealing Data. - Tobias Enders; Carina Benz; Ronny Schüritz; Pamela Lujan (ECIS, 2020)

Knowing What to Share: Selective Revealing in Open Data. - Tobias Enders; Clemens Wolff; Gerhard Satzger (ECIS, 2020)

Igniting the Spark: Overcoming Organizational Change Resistance to Advance Innovation Adoption - The Case of Data-Driven Services. - Tobias Enders; Dominik Martin; Garish Gagan Sehgal; Ronny Schüritz (IESS, 2020)

Designing Chemical Emergency Response Systems Based on Open Data. - Tobias Enders; Niklas Kühl 001; Jannis Walk; Marc Muff (HICSS, 2021)

Opening Pandora's Box? Guiding Organizations Through Selective Open Data Revealing. - Tobias Enders; Clemens Wolff; Laura Kienzle (PACIS, 2021)

Untangling the Open Data Value Paradox: How Organizations Benefit from Revealing Data. - Tobias Enders; Carina Benz; Gerhard Satzger (Wirtschaftsinformatik, 2021)

Why Should I Share? Exploring Benefits of Open Data for Private Sector Organizations. - Tobias Enders; Gerhard Satzger; Marcel Fassnacht; Clemens Wolff (PACIS, 2022)

Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems. - Tobias Enders; James Harrison; Marco Pavone 001; Maximilian Schiffer (L4DC, 2023)

Global rewards in multi-agent deep reinforcement learning for autonomous mobility on demand systems. - Heiko Hoppe; Tobias Enders; Quentin Cappart; Maximilian Schiffer (L4DC, 2024)

Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts. - Tobias Enders; James Harrison; Maximilian Schiffer (CoRR, 2024)

Multi-Agent Soft Actor-Critic with Global Loss for Autonomous Mobility-on-Demand Fleet Control. - Zeno Woywood; Jasper I. Wiltfang; Julius Luy; Tobias Enders; Maximilian Schiffer (CoRR, 2024)