Daniel Keogh, Data Scientist, Quantum Metric, 10807 New Allegiance Dr, Ste 155, Colorado Springs, CO 80921, USA, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Daniel K.N. Johnson, Ph.D., Professor of Economics, Colorado College, Department of Economics and Business, 14 E Cache la Poudre Street, Colorado Springs, CO 80903, USA, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. 


PURPOSE: The existing literature on the success of startup enterprises is thorough in investigating individual factors, but relatively weak in testing those factors in combination. This research tests for interactive effects, i.e., complementarities, between those factors. METHODOLOGY: We use a Cox proportional hazard model to estimate longevity in startups, supplementing it with maximum likelihood estimation of two metrics of success (employment and revenue). In each model, we explicitly test for interactions between terms, thus advancing the literature. FINDINGS: Panel data analysis shows that financing strategy matters to startup success, especially when combined with specific human and social capital attributes of the founders. For example, angel investors and venture capital investors benefit differently from founders with industry experience; founders with higher educational achievement generate more revenue than their peers specifically when their startups collaborate in university partnerships. IMPLICATIONS FOR THEORY AND PRACTICE: The paper suggests specific ways in which entrepreneurs should think about financing options that are complementary with their founder attributes. Further, it suggests that the literature must be very thoughtful, not only about the indicators of success but about advice to policymakers, financiers and entrepreneurs because of the nuanced nonlinearities and interactions we demonstrate. ORIGINALITY AND VALUE: We contribute to the literature on startup financing with a large dataset, careful modelling of interactive complementarities of between inputs, correction of the potential sample selection bias in previous studies, and a suite of modelled outcomes (survival, employment, and revenue) which allow for nuanced results.

Keywords: startup,business survival, revenues, venture financing, human capital, competitive advantage, new ventures, firm performance