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Networks and supply-side user participation on two-sided platforms

PhD ceremony:L. (Lijun) Wang, MScWhen:January 22, 2026 Start:11:00Supervisors:prof. dr. T.H.A. (Tammo) Bijmolt, M. (Marvin) Hanisch, PhDCo-supervisor:dr. Q. DongWhere:Academy building UGFaculty:Economics and Business
Networks and supply-side user participation on two-sided platforms

Supply-side users on two-sided platforms are fundamentally important for platform operations, as they provide the services or products required by demand-side users. Without a stable base of supply-side users, two-sided platforms cannot sustain their development or effectively compete with market rivals. This dissertation focuses on supply-side users’ participation, encompassing both individual performance and attrition. The central research question is how platforms can leverage two types of networks—macro-level and micro-level—to improve supply-side users’ performance and reduce their risk of attrition. Using a dataset from an online freelancing platform, the analysis shows that while both same-sided and cross-sided network size independently enhance individual performance, their combined effect is substitutive, producing diminishing returns when both expand simultaneously. Furthermore, our research reveals that supply-side users who are more embedded in peer networks are significantly less likely to leave the platform. The attrition-reducing benefits of peer embeddedness are further amplified when supply-side users maintain diverse and stable relationships with demand-side users. Overall, these findings challenge conventional platform management strategies that pursue simultaneous expansion of both user groups without accounting for their interactive effects, suggesting that a balanced, strategic allocation of resources is imperative for optimal supply-side user performance and two-sided platform development. Furthermore, our conclusions deepen the understanding of the social ties and relational context of two-sided platforms by comprehensively considering different user groups and identifying the network interplay both within and between these groups.

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