The Role of Social Networks in Startup Growth Under Uncertainty

Thesis Abstract

The rapid emergence and growth of startups in volatile environments necessitates an empirical examination of how social networks influence growth trajectories under uncertainty, with prior research often treating networks as a static resource rather than a dynamic, uncertain-context mechanism. This study aims to investigate how structural, relational, and embedded network characteristics shape startup growth under varying levels of environmental uncertainty, and to identify the boundary conditions under which network effects amplify or hinder expansion. Specific objectives are (1) to quantify the relationship between network breadth, strength of ties, and access to pivotal resources (capital, customers, mentors) and startup growth; (2) to assess how informational asymmetry and perceived uncertainty moderate these relationships; (3) to examine the mediating role of resource mobilization capacity (e.g., advisory support, alliance formation) in the link between networks and growth; (4) to compare emergent versus established ecosystems in moderating network effectiveness; and (5) to develop a parsimonious, empirically validated model linking social networks to growth outcomes under uncertainty. The study adopts a cross-sectional mixed-methods design, combining a large-scale survey with in-depth interviews to triangulate findings. The population comprises technology and service-sector startups in their first five years of operation across three metropolitan innovation ecosystems. A stratified random sample of 420 startups will be surveyed, with 380 usable responses (approximately 90% response rate anticipated) to ensure adequate power for regression analyses. Complementary semi-structured interviews will be conducted with 40 startup founders and 20 senior mentors or accelerator managers to capture contextual nuances and causal mechanisms. Data collection utilizes a structured questionnaire measuring network structure (degree centrality, betweenness, network diversity), network quality (trust, reciprocity, multiplex ties), perceived uncertainty (PESTEL-derived indicators), resource mobilization (advisory quality, alliance formation), and growth metrics (revenue growth, user adoption, new markets entered). Validity and reliability will be ensured through pre-testing, Cronbach’s alpha checks (target ? ? 0.70 for multi-item scales), and confirmatory factor analysis. The primary analysis will proceed in three steps (i) descriptive statistics and correlation analysis to characterize the sample; (ii) hierarchical multiple regression to test direct, moderated, and mediated effects of network variables on growth while controlling for firm age, founder experience, and industry; (iii) structural equation modeling (SEM) to assess the overall model fit and alternative pathways. Mediation will be tested using bootstrapped confidence intervals (5,000 resamples). The analysis will explicitly distinguish the impact of structural network properties from relational quality, and will incorporate interaction terms to capture the moderating role of perceived uncertainty. Theoretical grounding draws on social capital theory (Granovetter, 1985; Burt, 1992) and the dynamic capabilities framework (Teece, Pisano, and Shuen, 1997), with supplementary reference to the contagion and information-aggregation insights of uncertainty theory and the resource-based view of the firm. Expected findings indicate that diverse and dense networks with strong, trusted ties to diverse resource providers will positively influence growth outcomes, particularly under moderate uncertainty, while excessive network complexity or predominantly loose ties may impede rapid scaling in extreme uncertainty. The mediating role of resource mobilization is anticipated to be significant, such that networks facilitate faster access to capital, customers, and strategic partnerships, which in turn accelerates growth metrics. The study also expects ecosystem maturity to condition network effectiveness, with more established ecosystems enabling more efficient conversion of network resources into growth. The contribution to knowledge includes a nuanced, empirically verified model of how social networks drive startup growth under uncertainty, clarifying mechanisms and boundary conditions for network efficacy, and informing both entrepreneurship theory and practical ecosystem design. Policy and practitioner implications include guidance for startup founders on network-building strategies, and for accelerators and policymakers on fostering network environments that reduce information asymmetry and enhance resource access during uncertain markets. Limitations include cross-sectional design and potential self-report biases, with future work suggested to pursue longitudinal data to capture dynamic network evolution and growth trajectories.