Social Similarity and Structural Positioning in Venture Capital Open Access

Abstract

Venture capital is an idiosyncratic asset class. The seemingly endless number of different deal types and players involved makes it difficult to unpack firm performance. The literature underscores many tensions that firms in this space must confront when grappling with internal constraints. Many of these revolve around the role of knowledge similarity on performance. I focus on the level of knowledge matching between a venture capital deal team and its startup investment. I measure profitability outcomes among all firms in the dataset and find that at the later stages of a startup's development, similarity in knowledge matching is less important than working with an investor with the skills needed to help the startup grow. I also examine the importance of network structure, which is related to arguments about knowledge similarity. One is about existing information capabilities, and the second is about the flow of that information within the organization. My findings indicate that a high number of structural holes and a high network range, or in other words, a wider distance of knowledge spanned in the network, are helpful for performance. The combined effect of high structural holes and high levels of knowledge matching is what produces favorable funding outcomes, suggesting that venture capital firms and startups should think carefully about how to partner with one another given each other’s organizational structures. The knowledge held within an organization's employees is one of its biggest assets and should not be ignored. Given the ultimate goal of working better together to foster growth, firms can manage their resources, whether knowledge or network structure, for stronger competitive performance and growth. 

Table of Contents

1 The Role of Human Capital

1.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

1.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.2.1 Empirical Setting and Data Description . . . . . . . . . . . . . . . . . 10

1.2.2 Measures: Dependent Variables . . . . . . . . . . . . . . . . . . . . . 10

1.2.3 Measures: Independent Variables . . . . . . . . . . . . . . . . . . . . 11

1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2 The Importance of Structural Positioning

2.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2.1 Empirical Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2.2 Measures: Dependent Variables . . . . . . . . . . . . . . . . . . . . . 24

2.2.3 Network Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.2.4 Modeling Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.4 Conclusion

About this Master's Thesis

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School	Laney Graduate School
Department	Business
Degree	M.S.
Submission	Master's Thesis
Language	English
Research Field	Business Administration, Management Business Administration, Banking Business Administration, Entrepreneurship
Keyword	Structural holes Knowledge Brokerage Ventural capital Firm performance
Committee Chair / Thesis Advisor	Kocak, Ozgecan, Emory University
Committee Members	Lewis, Demetrius, UC Riverside

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