This research aims to identify the common causal complexity for the Global Innovation Index (GII), which measures various dimensions of the innovation ecosystem by country. We take all these variables as antecedents and the GII score representing the innovation competence of each country as the outcome and employ GII dataset from 2016 to 2020 for analysis. Because fuzzy set/Qualitative Comparative Analysis (fsQCA) has advantages over conventional statistical analysis and is good at expressing different causal complexities for a problem, this study utilizes it as the research method for analysis. The findings identify a common causal combination with the highest consistency and coverage among all the causal combinations in each year. This causal combination can be used as a representative to interpret GII.

本研究旨在确定全球创新指数 (GII) 的常见因果复杂性，该指数按国家/地区衡量创新生态系统的各个方面。我们将所有这些变量作为前因，以代表各国创新能力的 GII 得分为结果，并采用 2016 年至 2020 年的 GII 数据集进行分析。由于模糊集/定性比较分析（fsQCA）相对于传统的统计分析具有优势，并且善于表达问题的不同因果复杂性，因此本研究将其作为分析的研究方法。调查结果确定了一个常见的因果组合，在每年的所有因果组合中具有最高的一致性和覆盖率。这种因果组合可以作为解释 GII 的代表。