*2.4. Data Analysis*

The extracted data were sorted by Macro in Microsoft Excel to calculate the indexes. The connection among countries by sharing co-authorships' data (we applied full counting for papers sharing by more than one country), networks of co-occurrence authors' keywords, and clusters of topic groups were visualized by VOSviewer (version 1.6.11, Center for Science and Technology, Leiden University, the Netherlands). The cluster topics of QO were then identified from the frequency of keywords and named by expert opinions.

The exploratory factor analysis (EFA) and Jaccard's similarity index were performed using STATA software version 15.0. This index was defined as the magnitude of the intersection divided by the magnitude of the union of two sets of co-occurring terms; thus, multi-dimensional scaling could be used to adjust a point for a topic category, the distance between items and color presented the partnership of certain key terms. To measure the likelihood of research trends (e.g., emerging research domains and landscapes), we utilized exploratory factor analysis (EFA), which allows us to test the variance in the domains and landscape appearing from the abstract's contents. The summary of the technique used for analyzing is described in Table A1.
