The Impact of Foreign Venture Capital Intervention on Venture Capital Innovation of Startup Entrepreneurs Using Propensity Score Matching Model

Abstract

The research expects to give full play to the role of venture capital in corporate innovation and enhance the development capability of enterprises. Based on Propensity Score Matching (PSM) model, the characteristics of venture capital and startup enterprises are analyzed, and the innovation of venture capital is discussed. Next, the PSM model is used to analyze the innovation of venture capital intervention in enterprises from risk probability intervention, probability evaluation, matching equilibrium validity test, matching results analysis, different venture capital, and different background risks. The results show that the difference of standardized mean is close to 0, which accords with the equilibrium test. The significant impact of venture capital intervention on the Number of Invention Patent Applications (NIPA) and Number of Utility Model Patent Applications (NUMPA) is 0.1 and 0.01, respectively. Venture capital intervention has a significantly positive impact on NIPA and NUMPA but has no significant positive impact on Number of Design Patent Applications (NDPA). The impact of joint venture capital intervention on the NIPA, NUMPA, and NDPA is 0.0874, 0.0635, and 0.1213, respectively. Hence, the intervention of joint venture capital can greatly promote the increase of Number of Patent Applications (NPA), especially, NIPA, and NUMPA. Compared with private venture capital, joint venture capital plays a greater role in promoting the growth of NPA and NIPA. Compared with private venture capital and foreign venture capital, national venture capital has a stronger innovation orientation and a longer investment cycle, which can greatly improve innovation performance, such as NIPA, while private venture capital and foreign venture capital have a less significant impact on enterprise innovation performance. The results demonstrate that the foreign capital sharing assessment based on the PSM model can be a good predictor of the performance of startups. It is hoped that the research results can provide a reference for the development of startups.