Exploring bimodal multi-level networks: Network structure and dynamics driving herding effects and growth in livestreaming

The majority of social network research in the IS field relies on assumptions that social networks are single-level unified entities, disregarding the potential bimodal network structure with the coexistence of centralized and decentralized networks and overlooking the essential role of adjacent nodes as decision-makers. By challenging these assumptions, we propose that both centralized and decentralized networks can coexist within multi-level bimodal platforms via various role-based subgroups with different types of decision-makers. Livestreaming platforms, exemplifying the coexistence of multi-level networks within a single digital ecosystem, have become increasingly popular in various industries. Drawing on social impact theory (SIT), we examine the role of information from host-audience centralized and host–host decentralized networks in shaping subscription increment and herding effects. Using a panel dataset in livestreaming, we find that bidirectional ties in host–host networks strengthen fan acquisition and herding effect in the host-audience decentralized network, with hosts of lower social status gaining more fans than those with higher status in bidirectional ties. Our study contributes to the understanding of social network structure, herding effect, SIT and livestreaming by problematizing assumptions and offering a contextual explanation of livestreaming. Moreover, our work provides practitioners with valuable insights into leveraging network effects for hosts' success in livestreaming.