Impact of incubator support on startup survival in urban ecosystems: A field study

Thesis Abstract

This study investigates how incubator support influences startup survival within urban ecosystems, addressing the persistent challenge of firms failing within the first three years despite access to formal and informal support networks. The aim is to quantify the impact of incubation services on startup viability, with specific objectives to (1) assess the relative contribution of mentorship, access to finance, and entrepreneurial infrastructure provided by incubators; (2) examine the moderating role of sectoral focus (technology vs. non-technology startups) and firm age; (3) identify organizational learning and network-building processes as mediators of survival; and (4) compare survival rates across incubated and non-incubated ventures in five major metropolitan clusters. The study adopts a pragmatic mixed-methods design anchored in the Resource-Based View and the Institutional Theory of entrepreneurship to illuminate how incubator resources convert into sustainable venture performance. A stratified random sample of 320 startups was drawn from the urban ecosystem across five metropolitan areas, comprising 180 incubated ventures and 140 non-incubated controls, with stratification by industry, age, and founders’ demographics. Data collection combined structured surveys and in-depth interviews conducted between 2023 and 2025. Survey instruments measured incubator intensity (mentorship frequency, seed funding access, coworking facilities, and business development services), founder capabilities, firm-level inputs (human capital, capital intensity, and R&D exposure), and survival indicators (positive cash flow, profitability, and continuity beyond 24 months). The qualitative component employed semi-structured interviews with 40 founders and 20 incubator managers to explore mechanisms of value creation, knowledge transfer, and network effects. Validity procedures included pilot testing, content validity checks with domain experts, and triangulation across data sources. Reliability was ensured via Cronbach’s alpha for multi-item scales (? > 0.80 deemed acceptable). Data analysis integrated quantitative and qualitative procedures descriptive statistics and logit regression to estimate the probability of survival, with robust standard errors to address heteroskedasticity; interaction terms to test moderating effects of sector and firm age; mediation analysis using bootstrapped indirect effects to assess the roles of organizational learning and networks; and thematic analysis of interview transcripts to extract recurring patterns and validate quantitative findings. Key expected findings include (i) incubator-supported startups exhibit a statistically higher survival probability over 24–36 months compared to non-incubated peers, with odds ratios in the range of 1.8 to 2.4 after controlling for sector, age, and initial capital; (ii) mentorship quality and access to external finance emerge as the strongest direct drivers of survival, while shared infrastructure primarily enhances early-stage legitimacy and market access; (iii) sectoral differences reveal technology-intensive ventures benefit more from formalized mentorship and equity-linked funding, whereas non-technology startups gain more from market entry support and network-building; (iv) organizational learning and network-building partially mediate the incubator effect, explaining approximately 25–40% of the total effect on survival. The study contributes to knowledge by providing robust empirical evidence on the mechanisms through which incubators influence venture viability in urban contexts, refining the Resource-Based View with an emphasis on relational and institutional resources, and offering a comparative framework for evaluating incubator impact across city ecosystems. The practical implications suggest policy-makers and ecosystem builders should prioritize high-quality mentorship, accessible early-stage funding, and structured network opportunities, while tailoring programs to sector-specific needs and founder age. Limitations include potential self-selection bias and the challenge of isolating incubator effects from broader urban economic dynamics; future research is recommended to explore longitudinal trajectories beyond 36 months and to assess differential impacts of public versus private incubator models.