Dr. Manuel Sebastian Mariani

Tel.: +41 44 634 29 18
manuel.mariani@business.uzh.ch

Since April 2022, Manuel is a research group leader at the Department of Business Administration of the University of Zurich (affiliated with the University Research Priority Program on Social Networks until December 2024). Prior to this, he was a senior research associate at the same department, and held a position as Research Associate Professor in complexity science at the University of Electronic Science and Technology of China (UESTC) in Chengdu.

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.

Fei Wang
Maria Poiaganova
Mingwei Wang (collaborator)

• Social Dynamics: Understanding the spreading of products and behaviors in society

• Machine Learning - A non-technical introduction with applications to Marketing

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.

References
– Goldenberg, J., Lanz, A., Shapira, D., & Stahl, F. (2021). Influencer Marketing. Impact at JMR. [https://www.ama.org/2022/02/16/the-research-behind-influencer-marketing/]

– 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. [https://journals.sagepub.com/doi/full/10.1177/0008125620958166]

– 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. [https://arxiv.org/abs/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. [https://journals.sagepub.com/doi/abs/10.1509/jmkr.48.1.172]

– 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. [https://www.sciencedirect.com/science/article/pii/S0167811618300284

– 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. [https://www.sciencedirect.com/science/article/pii/S0167811610000236]