Collective Consumer Behavior

There are still places available in the seminar. If interested, please write an email to manuel.mariani@business.uzh.ch by October 2, 2024 with your application (see procedure below).

Objective:

Why do most products, individuals and businesses remain little known, but a minority of them achieves runaway popularity and financial success? Why are some products and services adopted widely (e.g., Instagram, Tiktok, WeChat), but others failed spectacularly (e.g., Google glasses, Google+)? Which are key social factors to consider when designing a marketing intervention aimed at triggering the large-scale adoption of a new product or behaviour? What is the role of algorithms in all of that? To study these and more fascinating questions, the students will learn how to model the adoption of new products and behaviours in terms of the individual-level choices made by many consumers. Such choices are not independent, but shaped by the social networks that connect the consumers. As a result, the collective dynamics generated by many consumers' choices lead to counterintuitive properties often overlooked by standard econometric and machine-learning models, with critical implications for marketing and social-change policies.

To understand collective consumer behavior and its marketing implications, the students will learn agent-based modelling and social network analytics techniques. The seminar aims to provide the students with a researcher-like experience: Each student will study in depth one research paper, attempt to replicate one of its figures, and deliver a final presentation on it. The papers cover four themes: Success inequalities; Social contagion in new product diffusion; Influencer marketing; Algorithmic feedback.

The seminar includes initial lectures that will provide the students with the theoretical elements of agent-based modelling and network science needed to understand and replicate the paper; hands-on tutorials in Python on agent-based simulations; a lecture on how to prepare a research presentation; intermediate supervision sessions. The seminar is particularly suited to: Business students who wish to deepen their understanding of the social forces driving consumer behavior; students who aim to strengthen their Python skills with a simple research project; students who are unsure whether to enroll for a PhD program in the future, as they could learn by doing how the research process looks like.

Learning outcome:

The main objective is to offer the students theoretical knowledge and practical coding tools to understand the collective behaviors of consumers.  

On the theoretical side, the students will learn how to describe the spreading of new products and behaviors in terms of individual choices; analyze in which conditions certain social network structures and interventions may facilitate or prevent the large-scale adoption of new products and behaviors; and develop critical thinking about the main factors to consider when designing policies for social change.

On the practical side, the students will strengthen their coding skills by learning how to code simple agent-based models in Python and replicating one result from a published paper. They will also improve their presentation skills through a dedicated lecture and the preparation for their final presentation.

Contact:
manuel.mariani@business.uzh.ch

Type:
Seminar

Target audience:
MA students with R or Python programming knowledge and familiarity with basic probability theory. No prior knowledge of social network methods or social dynamics modeling is required. Assigned to “Wahlpflichtbereich BWL 4”.

Frequency:
Each Fall semester

AP(ECTS)-points:
3

Language:

English

Course material:
TBA

Prerequisites:

Students can choose to program in either R or Python. Therefore, we recommend the following courses:
A non-technical introduction to R (or equivalent knowledge)
OR
A non-technical introduction to Python (or equivalent knowledge).

Recommended prior knowledge:
Good programming knowledge in R or Python. Basic knowledge of probability theory.

Grading:

Presentation and Coding (70%)
In-Class Participation (30%)

Dates and Location:

Location: BIN-0-B.11

Syllabus: CCB 2024 Syllabus (PDF, 210 KB)

Registration:
The number of participants is limited to 8. To apply for this seminar, please send an email to manuel.mariani@business.uzh.ch until 25.09.2024. In your application, please include your CV, transcript, and a short letter of motivation.

Note:
This information supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In case of doubt, the official information at the VVZ is valid.