Climb or Jump – Status-Based Seeding in User-Generated Content Networks

(co-authored with Jacob Goldenberg, Daniel Shapira, and Florian Stahl)

This paper addresses optimal seeding policies in user-generated content networks by challenging the role of influencers. On such platforms, the content is generated and offered by individuals, small groups, and firms that are interested in self-promotion. Using data from SoundCloud, the world’s leading user-generated content network in the music domain, we study creators of music who seek to increase the exposure of their content by building and increasing their follower base through directing promotional actions to other users of the networking platform. Focusing on the network status of both creator and seeding targets, we find that, in particular, unknown creators of music do not benefit from seeding high-status users. In fact, it appears that unknown creators should ignore predominant seeding policies and slowly “climb” across status levels of seeding targets rather than attempt to “jump” towards those with the highest status. Our research extends the existing seeding literature by introducing the concept of risk to dissemination dynamics in online communications. We show that unknown creators of music do not seed specific status levels but rather choose a portfolio of seeding targets while solving risk versus return trade-offs. We discuss various managerial implications for information dissemination and optimal seeding in user-generated content networks.

More details on Dr. Andreas Lanz can be found at: https://quantmarketing.bwl.uni-mannheim.de/de/researchgroup/andreas_lanz/