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Institut für Betriebswirtschaftslehre Chair for Marketing and Market Research

Dr. Manuel Sebastian Mariani

Research Group Leader and Principal Investigator

Tel.: +41 44 634 2918

“The internet connects us like the neurons in a giant brain. And with such collective intelligence, what cannot we be capable of?”

Adapted from Stephen Hawking

Research Group Leader

Manuel is a Research Group Leader at the University Research Priority Program on Social Networks since April 2022. From November 2017 to March 2022, he was a Senior Research Associate. 


Manuel's research aims to contribute to the solution of managerial and societally-relevant problems related to the collective behaviors of interconnected subjects at different scales, ranging from interconnected consumers, to content creators, algorithms, and firms. His research approach focuses on the "micro-macro" mutual influence between the heterogeneous drivers of individuals’ behavior and the emergence of collective behaviors. His main research ambition is to develop theoretical models and practical tools to understand how interventions on social systems implemented today will affect the collective behaviors of firms, consumers, and algorithms tomorrow. Examples of interventions studied in his ongoing research involve seeding programs for social change; changes in ranking and recommender algorithms in digital platforms; social network interventions.



  • Social Dynamics: Understanding the spreading of products and behaviors in society Master
  • Machine Learning - A non-technical introduction with applications to Marketing Master

Publications (selected since 2019)

M. Cui, M. S. Mariani, M. Medo, Algorithmic bias amplification via temporal effects: The case of PageRank in evolving networks, Communications in Nonlinear Science and Numerical Simulation 104, 106029 (2022).

M. Medo, M. S. Mariani, L. Lü, The fragility of opinion formation in a complex world, Communications Physics, 4(1), 1-10 (2021).

J. Wang, S. Xu, M. S. Mariani, L. Lü, The local structure of citation networks uncovers expert-selected milestone papers, Journal of Informetrics 15: 101220 (2021).

S. Xu, L. Lü, M. S. Mariani, Modeling the dynamics of firms’ technological impact. Chinese Physics B 30, 120517 (2021).

M. Medo, M. S. Mariani, L. Lü, The simple regularities in the dynamics of online news impact, Journal of Computational Social Science 1–18 (2021).

M. S. Mariani, M. Palazzi, A. Solé-Ribalta, J. Borge-Holthoefer, C. J. Tessone, Absence of a resolution limit in in-block nestedness, Communications in Nonlinear Science and Numerical Simulation 94, 105545 (2021).

M. S. Mariani, L. Lü, Network-based ranking in social systems: three challenges, Journal of Physics: Complexity 1, 011001 (2020).

S. Xu, M. S. Mariani, L. Lü, M. Medo, Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data, Journal of Informetrics 14: 101005 (2020).

S. Xu, Q. Zhang, L. Lü, M. S. Mariani, Recommending investors for new startups through diffusion on tripartite networks, Information Sciences 515, 103-115 (2020).

M. S. Mariani, M. Medo, F. Lafond, Early identification of important patents: Design and validation of citation network metrics, Technological Forecasting and Social Change 146: 644-654 (2019).

H. Liao, M.-K. Liu, M. S. Mariani, M. Zhou, X. Wu, Temporal similarity metrics for latent network reconstruction: The role of time-lag decay, Information Sciences 489: 182–192 (2019).

M. Medo, A. Zeng, Y.-C. Zhang, M. S. Mariani, Optimal timescale of community detection in growing networks, New Journal of Physics 21: 093066 (2019).

M. S. Mariani, Z.-M. Ren, J. Bascompte, C. J. Tessone, Nestedness in complex networks: observation, emergence, and implications, Physics Reports 813: 1–90 (2019).

S. Zhang, M. Medo, L. Lü, M. S. Mariani, The long-term impact of ranking algorithms in growing networks, Information Sciences 488, 257–271 (2019).

F. Zhou, L. Lü, M. S. Mariani, Fast influencers in complex networks, Communications in Nonlinear Science and Numerical Simulation 74, 69–83 (2019).


Current SNF Project


How individual-level choices drive collective consumer behavior in social networks

Many managerial decisions aim to influence collective consumer behavior, e.g., to stimulate the adoption of a new service or to promote sustainable consumption.

Collective consumer behavior results from the aggregation of many interconnected individual-level choices, whose dynamics determines the success or failure of new products and their creators. There exists a rich literature on both the drivers of individual-level choices and the collective dynamics of adoption processes, but only few studies integrating both perspectives. Specifically, the consumer behavior literature mainly focuses on the individual level by identifying the main drivers of consumer choices via experiments and surveys (e.g., Miller et al. 2011), but the obtained individual-level results are usually not integrated into dynamic models of innovation growth. On the other hand, the innovation growth literature mainly focuses on the collective level, by modeling the aggregate market dynamics of an innovation (Peres et al. 2010, Muller et al. 2019). The models are usually not calibrated on empirical individual-level behavior, which can results in misleading aggregate predictions of new product success. Studies integrating empirical individual-level choices and collective behavior are rare, yet much needed to improve marketing interventions aimed at influencing collective behavior.

This project is the first systematic effort to connect empirical individual-level choices with the collective success of new products and their creators in social systems. It will focus on three interrelated stages along a product’s lifecycle: the product’s creation (Work Package 1, WP1), its early adoption (WP2), and its collective adoption by a social network of individuals (WP3). The project has three main goals: (1) understand how individual-level creative strategies predict and drive the long-term collective success of product creators (WP1); (2) understand the role played by different groups of early adopters in the collective success of a new product (WP2, e.g., Mariani et al 2020); (3) understand how to integrate individual-level behavioral heterogeneity and social network structure to predict the collective success of a diffusion process as well as improve seeding policies (WP3). For each of the three goals, we will not only advance theoretical understanding, but also derive managerial implications for seeding and influencer marketing policies (e.g., Haenlein et al. 2020, Goldenberg et al 2021). These ambitious goals will be achieved via a rare combination of traditional methods in consumer behavior (such as discrete-choice models and experiments) with social network and machine learning methods.

– Goldenberg, J., Lanz, A., Shapira, D., & Stahl, F. (2021). Influencer Marketing. Impact at JMR. []

– Haenlein, M., Anadol, E., Farnsworth, T., Hugo, H., Hunichen, J., & Welte, D. (2020). Navigating the New Era of Influencer Marketing: How to be Successful on Instagram, TikTok, & Co. California Management Review, 63(1), 5-25. []

– Mariani, M. S., Gimenez, Y., Brea, J., Minnoni, M., Algesheimer, R., & Tessone, C. J. (2020). The wisdom of the few: Predicting collective success from individual behavior. arXiv preprint arXiv:2001.04777. []

– Miller, K. M., Hofstetter, R., Krohmer, H., & Zhang, Z. J. (2011). How should consumers’ willingness to pay be measured? An empirical comparison of state-of-the-art approaches. Journal of marketing research, 48(1), 172-184. []

– Muller, E., & Peres, R. (2019). The effect of social networks structure on innovation performance: A review and directions for research. International Journal of Research in Marketing, 36(1), 3-19. [

– Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A critical review and research directions.International journal of research in marketing, 27(2), 91-106. []

Weiterführende Informationen

Research focus: Collective consumer behavior; Human-algorithm interactions; Economic complexity.

Methodological focus: Network science; Agent-based modeling; Choice modeling; Machine learning.​​​​​​​

Institute of Fundamental and Frontier Science, UESTC, Chengdu (China).
URPP Social Networks, UZH, Zurich (Switzerland).

University of Zurich
Department of Business Administration
Chair for Marketing and Market Research
Andreasstrasse 15
8050 Zurich
Phone: +41 44 634 2918