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

Abstract

This paper addresses seeding policies in user-generated content networks by challenging the role of influencers in a setting of unpaid endorsements. On such platforms, the content is generated by individuals and firms interested in self-promotion. Data from a worldwide leading music platform are used to study unknown creators of music who seek to increase exposure of their content by expanding their follower base through directing outbound activities to other users. We find that the responsiveness of seeding targets strongly declines with status difference; hence, unknown creators of music (the majority) do not generally benefit at all from seeding influencers. Instead, they should gradually build their status by targeting low-status users rather than attempt to “jump” by targeting high-status ones. This research extends the seeding literature by introducing the concept of risk to dissemination dynamics in online communications, showing that unknown creators of music do not seed specific status levels but rather choose a portfolio of seeding targets while solving a risk versus return trade-off. Various managerial implications for optimal seeding in user-generated content networks are discussed.

Keywords: user-generated content networks, influencer marketing, seeding, unpaid endorsements, online communications, social networking

JEL Classification: M31