The electric vehicle industry is one of the hottest technology sections where patents are essential for companies to survive and compete. The goal of my work is to present EV startups with higher market value locate at more central positions in the patent citation network and to prove forward patent citation has statistical significance to EV startups' market value, but backward patent citation does not. This study is conducted in four steps: data collection and process, network visualization, network description, and quadratic assignment procedures (QAP). Because this study contributes to practical use in the business field, to ease the use by investors from any backgrounds and industries, I chose social network analysis software NodeXL, which is an easy-to-use and widely adopted add-in to Microsoft Excel. The network visualization presents that, the top two most valued U.S.-based EV startups, Tesla and Atieva, stay at the most pivotal position in the patent citation network. QAP shows strong evidence to support that the number of forward citations positively relates to EV startups' market value but does not exhibit evidence to prove the statistical significance of backward citation. The results of my study are expected to shed some insights for investors to find investment targets.
Yi Qian (Mon,) studied this question.