Private markets investment volume and valuations exceed the level of the dot-com bubble (PwC and CBInsights, 2019). The available amount of capital surges as investors announce new multi-billion dollar funds (Kruppa, 2019). Even large, institutional funds in the Silicon Valley, who are used to investing in later stages, move upstream to invest in fledgling firms to achieve higher ownership and returns (Clark, 2019a). Despite the high private market liquidity, standing out from the crowd is critical and has become more difficult to achieve, even for innovative entrepreneurs (Planko et al., 2017). Curiously, venture capitalists who expect the latest technology and innovation from new ventures, did not themselves significantly innovate in their approach, including methods of evaluating ventures (Kupor, 2019). Few investors came up with new, differentiated investment strategies, one such example being data-driven investing (Pitchbook, 2018). Although venture capitalists seek to invest in firms which benefit substantially from the notion that “data is the new oil”, few practice to leverage data for their investment process (Parkins, 2017; Dance et al., 2018; Arroyo et al., 2019; Gompers et al., 2020). Instead, the overwhelming majority adheres to the motto “picking investments is an art, not science” and relies primarily on its networks as the most valuable resource (Bell, 2014; Huang and Pearce, 2015; Gompers et al., 2020). Venture capitalists’ focus on their social networks could not only negatively affect investment decisions and returns, but also promote group-think and stifle the progression of their investment thesis (Wuebker et al., 2015). Reviewing the previous works on entrepreneurship, venture evaluation, and venture capital revealed a significant gap in the literature. While investors and entrepreneurs depend heavily on their social networks, these networks play an insignificant role in venture evaluation. The existing frameworks are inadequate to accurately assess early-stage ventures and thus a rethink of methodology is needed to better capture the networked nature of today’s ventures (Miloud et al., 2012; Dusatkova and Zinecker, 2016). This thesis suggests a new perspective for early-stage venture evaluation, with particular focus on formalising the ventures’ social networks. Contributions made by this thesis are fourfold and relevant to entrepreneurs, investors, and academic theorists. Firstly, existing theories that explain venture fundraising success are expanded by adding a social network perspective. Secondly, this research provides a comprehensive overview of stakeholders’ roles and their constellation in social networks around the entrepreneurs and their ventures. Thirdly, for entrepreneurs, different modes of leveraging their social networks for critical business functions are identified. Lastly, an evaluation tool for venture capitalists to the investability of early-stage ventures is developed. In summary, results provide new insights into entrepreneurial strategies for leveraging social networks to enhance operations, differentiate from competitors, send positive signals to investors, and ultimately improve the venture’s assessment by the private market.
venture capital, entrepreneurship, early-stage ventures, new business ventures, startups, evaluation, valuation, data-driven investing, social capital, social networks, social network analysis, models
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