M. (Martina) Pocchiari MSc

Rotterdam School of Management (RSM)
Erasmus University Rotterdam
ERIM PhD Candidate
Field: Marketing
Affiliated since 2017

Martina Pocchiari is a Doctoral Candidate in Empirical Quantitative Marketing at the Rotterdam School of Management, Erasmus University.

Her research interests lie in the areas of brand community engagement and dynamics, information consumption and more broadly behavioral economics. 

Martina holds a BSc degree in Business and Economics (2015, cum laude) from the University of Bologna. She also completed her MSc degree in Marketing Management at Rotterdam School of Management (2016, cum laude). During her master, Martina chose a specialization track in digital marketing and marketing analytics, and was a member of the Honours Programme cohort 2015-2016. Before starting her PhD in 2017, Martina was a marketing research consultant at Pointlogic, a Nielsen company.

PhD Track Co-occurring Activities, Inherent Community Characteristics and Brand Community Participation

Brand communities have been defined in the marketing literature as specialized, non-geographically bound communities, based on a structured set of social relationships among admirers of a brand. These communities are a fundamental part of a brand’s social construction and its legacy, and entail numerous benefits for its organizers and members. Thanks to technological advancements, people can join an increasing number of brand communities. This increased availability, however, poses a decision problem. Individuals have to decide on the number of community events and activities to attend, if any. This research explores the dynamics of individual participation to brand community activities. In particular, it explores how participation varies with different community characteristics; for example, the type of organizer and the past participation behaviour of the attendees. This research contributes to the literature in the marketing field about brand communities, individual participation to brand activities and consumer versus brand generated content.

Brand community, brand community participation, hierarchical Bayesian mixed models
Time frame
2017 -


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