Unlocking the multiple interactions: exploring the interplay of start-ups, venture capitalists, and limited partners

Abstract

This dissertation investigates the evolving dynamics among three interdependent actors in the venture capital market—startups, venture capitalists/general partners (VCs/GPs), and limited partners (LPs). While prior research has predominantly focused on dyadic relationships (e.g., startup-VC or LP-GP), this dissertation adopts a multi-actor perspective to examine how multiple interactions with external partners jointly shape the behaviors of startups, VCs/GPs, and LPs. The first study, from the perspectives of startups, explores how startups’ heterogeneous interorganizational networks, including investment, syndication, and co-patenting ties, influence their ability to secure external funding. Employing a novel heterogeneous graph neural network (HGNN), the study demonstrates the necessity to consider heterogeneous networks in shaping investment outcomes. The second study turns to the perspectives of LPs and examines how dual information asymmetries—with GPs (agency risk) and with startups (screening risk)—jointly affect their decisions to bypass intermediaries and engage in direct investments. The findings suggest that these asymmetries act in concert, and their effects are contingent on market conditions. The third study centers on GPs’ strategic responses to disintermediation by LPs. Drawing on resource dependence theory, it shows that GPs primarily adopt adaptation strategies—such as improving deal quality—to retain LP support, and that these responses are moderated by their bargaining power. Collectively, these studies contribute to research on interorganizational networks, information asymmetry, and resource dependence by illuminating how how multiple interactions with external partners jointly shape the behaviors of startups, VCs/GPs, and LPs.

Date of Award	15 Nov 2025
Original language	English
Awarding Institution	University of Nottingham
Supervisor	Jin Chen (Supervisor) & Qingxin Meng (Supervisor)