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Small- and medium-sized technology startups are vital drivers of technological innovation and economic growth. However, due to the high risks, uncertainties, and funding shortages they face in their early stages, venture capital has become a crucial asset for their development. Nonetheless, simply providing funding is not sufficient to ensuring success. Scientifically evaluating the performance of venture capital is essential for the efficient utilization of funds. The purpose of venture capital performance evaluation is to establish a systematic set of indicators to comprehensively assess small- and medium-sized technology startups from both financial and non-financial dimensions, measuring the actual effectiveness of investments. This helps investors identify high-potential enterprises and optimize investment decisions while enabling startups to identify management deficiencies and improve operational efficiency. Additionally, performance evaluations provide valuable references for governments and policymakers, allowing them to design more effective support policies and foster the healthy development of the technology entrepreneurship ecosystem. The significance lies in achieving the rational allocation of capital, technology, and management resources through scientific performance evaluation, reducing investment risks and enhancing the core competitiveness of enterprises.
Furthermore, the evaluation results can serve as a foundation for future investments, creating a virtuous cycle that promotes the sustainable development of small- and medium-sized technology startups. Ultimately, this contributes to national innovation capacity and economic restructuring, playing a strategically important role in building an innovation-driven economy. The research on venture capital performance evaluation has evolved over time, focusing on more scientific and systematic evaluation methods. In the early stages, Fu and Tang (2005) proposed a theoretical model for transforming risk costs into expected utility returns and developed a performance evaluation system based on risk agency efficiency; they also analyzed how different principal-agent relationships influenced venture capital performance. Subsequently, Yu and Fu (2006) viewed the venture capital process as a sustainable system and introduced a project screening model based on the industry life cycle theory; they also used the AHP (Analytic Hierarchy Process) method to quantitatively evaluate project performance and returns under different exit strategies. In the 2010s, research became more detailed and practical. Wang and Wang (2010) examined the factors influencing entrepreneurial venture capital performance in the Guanzhong high-tech industrial belt and identified entrepreneurs’ comprehensive qualities as the most crucial evaluation indicators; they called for increased government support and the rational allocation of venture capital. A year later, Wang (2011) used questionnaire surveys and the AHP method to construct a performance evaluation indicator system for Guanzhong’s high-tech industrial belt and proposed policy recommendations to further enhance regional venture capital performance. In the same year, Wang and Wang (2011) applied the management entropy theory to develop a performance evaluation system for high-tech enterprises and demonstrated that this method helped enterprises identify deficiencies, improve management levels, and optimize resource utilization. As research progressed, E. Li and Zhou (2013) focused on small- and medium-sized technology startups. They constructed a performance evaluation system based on financial and non-financial dimensions and, through membership degree, correlation, and differentiation analysis, selected 23 evaluation indicators to provide a basis for scientific investment decisions.