Corporate Venture Capital and Startup Outcomes: The Roles of Investment Timing and Multiple Corporate Investors

ABSTRACT

The effects of corporate venture capital (CVC) investments on ventures’ revenues and innovation-related outcomes depend on the characteristics of the investors and on the dynamics of the investment process. Recently, venture financing literature has highlighted the importance of investment timing as a driver for investee ventures development and success. Building on the literatures on complementary assets and relative absorptive capacity, we explore how the timing of CVC investments affects ventures’ revenues and R&D intensity. Using a dataset of Norwegian ventures in knowledge-intensive industries, we find evidence for a differential effect of CVC investments when comparing a venture’s early- and late-stage, showing that investments received in late-stage increase ventures’ revenues, but decrease ventures’ R&D intensity. Further, we find that syndication with multiple CVC investors amplifies this effect. This study contributes to the understanding of the CVC-venture relationship and the impact on venture’s post-CVC outcomes.

KEYWORDS:

• Corporate venture capital
• investment timing
• relatedness
• co-investment
• venture performance

JEL:

• G24
• L26
• M13
• G24
• D25

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The Norwegian government, as data owner, stipulates that no one outside the approved research team is allowed to gain access to the sensitive register data that we use in this study, due to privacy concerns. In our case, we had access to private firm ownership, accounting details, and the personal data of employees. We therefore provide all the information necessary for others to replicate the study, including summary and distributional statistics, without breaking this stipulation in any way.

Notes

¹ Analysing accounting data in the second stage, we experience an attrition of observations because some firms failed to report data due to bankruptcy or similar legal procedures. T-tests reveal no significant differences between the full sample (1,646 observations) and the restricted sample (1,280 observations).

² Whereas R&D intensity is often measured as R&D expenditure by sales, prior studies on CVC investments and venture outcomes have shown that using total assets as a denominator to normalise R&D intensity is a more accurate measure, due to the volatile nature of ventures’ sales (Paik and Woo 2017). Other studies on early stage ventures have similarly standardised R&D expenditure by total assets (Kor 2006).

³ We are addressing the exposure to prior CVC econometrically as we are only estimating firm fixed effects models, which ensure that we are addressing the within-variation of a specific firm.

⁴ Effect size in % = (exp(β)-1) *100), in our case ((exp (1.04)-1) *100) = 183%. In TNOK: exp (5.468) *183%.

⁵ To compare industry relatedness, we build an index based on the education of people employed in each industry, which is similar to prior approaches that rely on the occupation of people employed in each industry (Sakhartov and Folta 2014) and labour flows between industries (Neffke and Henning 2013; Neffke, Otto, and Weyh 2017). We compare the relatedness between all CVC investor pairs in a syndicate and generate the mean relatedness. We split this value at the median to compare low and high relatedness syndicates. A detailed calculation is available on request.