*2.3. Data Analysis*

We analyzed the dataset based on the following information: general characteristic (number of papers per year, citations, the total and average number of download publications), keywords (most common keywords and co-occurrence keywords), and text mining (abstract). After we have downloaded and extracted the data, descriptive statistical analysis using Stata was applied to calculate the number of papers by countries mentioned in abstracts. A network graph that illustrated the connection among authors' keyword co-occurrence network was created by the VOSviewer (version 1.6.8, Center for Science and Technology, Leiden University, Leiden, the Netherlands). For analyzing the contents of the abstracts, exploratory factor analysis (EFA) was employed to identify and visualize the research domains that stem from all content of the abstracts; to highlight research topics or terms most commonly co-occurring with each other, we used the 0.4. Jaccard's similarity index. Latent Dirichlet Allocation (LDA) was used for classifying papers into corresponding topics [8–12]. Principal component analysis (PCA) was used to create the keyword map as the technique is able to reduce the number of variables, and thus, cluster them into more manageable groups [13]. EFA, LDA, and PCA were conducted using Stata. The analytical techniques for each data type are shown in Table 1.



<sup>1</sup> WOS: Web of Science.
