Dr. Radu Tanase

Tel.: +41 44 634 2918
radu.tanase@business.uzh.ch

Radu is Senior Research Associate at the Chair of Marketing for Social Impact. He holds a PhD in Economics from the University of Zurich and a master’s degree in Statistics from ETH Zurich. In his research, Radu studies how the interplay of individual and social factors impacts our decisions and tries to understand how this could be leveraged to help people make positive changes. Current topics include the impact of habits, values and social norms on (sustainable) decisions, consumer willingness to offset environmental damage and the effects of transparency on purchase behavior and customer-firm relationships. He uses a mix of empirical methods, agent-based simulations, controlled experiments and, since recently, large-scale field experiments.

Radu manages (and teaches within) the “Marketing Analytics” program, whose objective is to teach students with a non-technical background the fundamental principles behind statistics, machine learning and computer programming so that they are well prepared for modern business research. Radu was supported by UZH in the development of the program through the open_innovation grant.

Beyond academia, Radu has hands-on industry experience as a data scientist. In 2015, he co-founded Lionstep, a tech-centric recruiting company, serving as Chief Data Scientist and now as Data Science Advisor.

Peer reviewed articles 

• Schoenenberger, L., Schmid, A., Tanase, R., Beck, M., & Schwaninger, M. (2021). Structural analysis of system dynamics models. Simulation Modelling Practice and Theory, 110, 102333.  Link
• Tanase, R., Tessone, C. J., & Algesheimer, R. (2018). Identification of influencers through the wisdom of crowds. PloS one, 13(7), e0200109. Link
• Schoenenberger, L., & Tanase, R. (2017). Controlling Complex Policy Problems: A Multimethodological Approach Using System Dynamics and Network Controllability. Journal of Simulation, 1-9.  Link

Advanced working papers

• Tanase, R., Mariani S.M., Algesheimer, R. Integrating Behavioral Experimental Findings into Dynamical Models to Inform Social Change Interventions. Under review. Working paper available here 
• Rother K., Tanase, R., Natter M., Shen, L., Algesheimer, R. Voluntary Carbon Offsetting in Online Retailing: A Revealed-Preferences Perspective. Under review. 
• Crestini G., Giuffredi-Kähr A., Tanase, R., Natter M., Quelle T. Does price transparency benefit or harm online retailers? A retailer and customer perspective. To be re-submitted. 
• Costa, D., Tanase, R., Algesheimer, R. The Habitual Self: Counter-Intentional Habits as Main Barrier for Sustainable Food Choices. To be re-submitted. 
• Costa, D., Tanase, R., Algesheimer, R. A Longitudinal Analysis Using Twitter Data to Investigate the Effect of COVID-19 on Human Values and Derive Implications for Improving Crisis Management. Working paper. 
• Feng, M., Mariani SM., Algesheimer, R., Tanase, R. Seeding early adopters only works in homophilous networks. Evidence from an agent based model calibrated with choice experiments. Working paper. 
• Hitz., E., Lazzaro, L., Mariani SM., Algesheimer, R., Tanase, R. Dynamics of Social Influence in AI vs. Human Networks: An Exploration with ChatGPT. Working paper. 

Early-stage work

• Giuffredi-Kähr A., Pimper, M., Blas Riesgo, S., Merian, S., Natter M., Tanase, R. Transparency in Online Shopping: The Impact of Displaying Historical After-Sales Performance Metrics on Return Rates and Purchase Likelihood. On-going project.
• Zhang, H., Lazzaro, L., Furrer, NA., Cammelli F., Mariani SM., Addoah, T., Algesheimer, R., Garrett, RD., Tanase., R. Seeding interventions to increase participation in voluntary environmental programs. Evidence from a randomised controlled trial. On-going project. 
• Lazzaro, L., Furrer, NA., Cammelli F., Mariani SM., Algesheimer, R., Tanase., R. From local knowledge to network insights: leveraging key informants for strategic network interventions. On-going project. 
• Lazzaro, L., Mariani SM., Algesheimer, R., Tanase, R. Diffusion processes as the observation units: A framework for experimental investigation. On-going project.
• Feng, M., Mariani SM., Algesheimer, R., Tanase, R. Estimating individual level social influence effects from observed purchases. On-going project.

• Tanase, R. An interactive e-learning environment for teaching Marketing Analytics, Deep Dive on Self-Study Environments and AI @UZH, Zurich, Switzerland, 2024.
• Tanase, R. Interactive eLearning with custom extensions: R and Python programming, OOtalks@UZH, Zurich, Switzerland, 2024.
• Zhang, H., Lazzaro, L., Furrer, NA., Cammelli, F., Mariani, SM., Addoah, T., Algesheimer, R., Garrett, RD., Tanase, R. Seeding interventions to increase participation in voluntary environmental programs. Evidence from a randomised controlled trial, Sunbelt, Edinburgh, UK, 2024.
• Tanase, R., Algesheimer, R., Mariani, S.M. Unlocking the micro-macro link: Integrating Behavioral Experimental Findings into Dynamical Models to Inform Social Change Interventions Sunbelt, Edinburgh, UK, 2024.
• Feng, M., Mariani, SM., Algesheimer, R., Tanase, R. Seeding early adopters only works in homophilous networks. Evidence from an agent based model calibrated with choice experiments, Sunbelt, Edinburgh, UK, 2024.
• Lazzaro, L., Mariani, SM., Algesheimer, R., Tanase, R. Diffusion processes as the observation units: A framework for experimental investigation, Sunbelt, Edinburgh, UK, 2024.
• Rother K., Tanase, R., Natter, M., Shen, L., Algesheimer, R. Voluntary Carbon Offsetting in Online Retailing: A Revealed-Preferences Perspective, ARCS, Los Angeles, USA, 2024. 
• Tanase, R. Marketing Analytics as Inspiration for People Analytics. People Insights @Roche, 2024. 
• Tanase, R. How do seeding strategies impact treatment selection effects and potential effectiveness of sustainability interventions? ETH Zurich Research Seminar, 2024.
• Tanase, R., Mariani SM., Algesheimer, R. When and how individual behavioral models benefit seeding policies, EMAC, Odense, Denmark, 2023. 
• Tanase, R., Mariani SM., Algesheimer, R. Optimising Seeding Strategies By Incorporating Empirical Evidence Into Influence Maximisation Models, Networks, online, 2021. 
• Tanase, R., Mariani SM., Algesheimer, R. Optimising Seeding Strategies By Incorporating Empirical Evidence Into Influence Maximisation Models, ISMS Marketing Science, online, 2021. 
• Tanase, R., Mariani SM., Yang Z., Algesheimer, R. Will it spread? The role of consumer susceptibility in the diffusion process, Netsci, online, 2020. 
• Tanase, R., Mariani SM., Yang Z., Algesheimer, R. Will it spread? The role of consumer susceptibility in the diffusion process, ISMS Marketing Science, online, 2020. 
• Schoenenberger, L., Tanase, R (2016). Controlling complex policy problems: a multi- methodological approach using system dynamics and network controllability, CCS Conference on Complex Systems, Amsterdam, Netherlands
• Tanase, R., Tessone, C.J., Algesheimer, R. The Influence Potential. A New Approach to Identify Influential Individuals From Time-varying Social Interactions, Netsci-X, Wroclaw, Poland, 2016. 
• Tanase, R., Tessone, C.J., Algesheimer, R. Identifying Influential Individuals From Time- varying Social Interactions, Netsci, Seoul, South Korea, 2016. 
• Tanase, R., Tessone, C.J., Algesheimer, R. Who do we follow? A new approach to identify influential individuals from time-varying social interactions, CCS, Phoenix, USA, 2015. 

Note: Presenter in bold

Current courses (2024 & 2025)

• Prototyping data science products (Master level, view course) 
• Introduction to programming for Marketing Analytics (R or Python) (Master level, view course)
• R/Python – A non-technical overview of big data techniques, team work and interactive visualization (Master level,view course)​​​​​​​

​​​​​​​Past courses​​​​​​​

• HS23 Introduction to R for Marketing Analytics (MA seminar, E-learning format)
• FS20-24 R – A non technical introduction to big data techniques, team work and interactive visualization with applications to marketing. (MA course)
• HS19-23 Python – A non technical introduction with applications to Marketing (MA course)
• HS20 Marketing for sustainable consumption (MA seminar) 
• HS17 Social Media Marketing (MA seminar)
• HS15-HS16 Internet and Social Media Marketing (BA seminar) 
• HS15 Network Analytics (MA course)
• FS14-FS15 Marketing and Social Networks II (MA seminar) 
• HS13-HS14 Marketing and Social Networks (BA seminar) 
• FS14 Advanced Modeling Techniques (MA seminar)