## 3 A New Seven-Phase Methodology for Systematic Literature Review
### Phase 6: Use of ChatGPT 3.5 Artificial Intelligence for Textual Analysis
Studies also explored the pandemic's impact on tourism and SDGs globally, analyzing corporate commitment to sustainability post-COVID and trends in agroforestry research [155; 156; 157; 158]. In 2024, [PERSON] et al. applied the HJ-Biplot to maritime inspections, while [PERSON] used it in bibliometric studies on keratomycosis [159; 160]. [PERSON] analyzed genetic families of _Juglans neotropica_, emphasizing its value in forest genetics [161]. [PERSON] examined gamification in education, revealing limited teacher adoption, and [PERSON] and [PERSON] enhanced time-series analysis with the SSA HJ-Biplot [162; 163]. Applications extended to environmental analysis, from Ecuador's water quality to the environmental impact of food products, offering critical insights for sustainability [164; 165]. The chart provided in Figure 10 represents an analysis of the different areas where the seminal HJ-Biplot article has been applied, distributing a total of 121 articles across various disciplines. The largest application is found in the field of **Health**, with 18 articles, representing approximately 14.9% of the total. This is followed by the areas of **Sustainability** with 14 articles (11.6%), **Environmental Sciences** with 15 articles (12.4%), and the seminal article and its extensions with 10 articles (8.3%). Additionally, other areas like **Management** (9 articles, 7.4%), **Education** (8 articles, 6.6%), and **Economics** (7 articles, 5.8%) also show a notable level of application for this technique. Other applications include more specific disciplines such as **Bibliometrics** (6 articles, 5.0%), **Psychology** (6 articles, 5.0%), **Agronomy** (5 articles, 4.1%), and **Oenology** (4 articles, 3.3%). The areas with the fewest publications include Criminology, Politics, Journalism, Data Networks, Neuromedicine, and Silviculture, each with fewer than 4 articles, representing a smaller percentage of the total. This analysis indicates a diversification in the applications of HJ-Biplot with a significant trend toward its use in health, sustainability, and environmental topics. Figure 10: Areas of application of the HJ-Biplot. Table 7 presents a summary of the different extensions of the HJ-Biplot technique over the years. It starts in 1986 with the original development of the HJ-Biplot in [12]. From 2015 onwards, the emergence of several extensions can be observed, such as the Dynamic Biplot[74] and the Bootstrap HJ-Biplot in [73], which were followed by other significant variants such as the Compositional HJ-Biplot[108], Clustering Disjoint HJ-Biplot[111], SSA HJ-Biplot[135], Sparse HJ-Biplot[72], Cenet HJ-Biplot[71], and finally the ESSA HJ-Biplot in [163]. Figure 11 shows a detailed timeline of the development of the HJ-Biplot technique, manually created, from its inception in 1986 by Galindo to its most recent extensions and applications in 2024. The top and bottom parts of the graph detail the various applications of the HJ-Biplot and its extensions in different areas of knowledge over time. Since its creation, the HJ-Biplot has found applications in areas such as **Environmental Sciences, Health, Oncology, Bibliometrics, Economics,** and **Management**. As the extensions of the technique are developed, an increase in the variety and specificity of application areas is observed. This graph highlights how the use of the HJ-Biplot has evolved to include a multidisciplinary approach, covering both traditional applications and innovative fields in emerging areas. Additionally, it not only highlights the chronological evolution of the HJ-Biplot and its extensions but also the diversification of its applications, demonstrating its continued relevance and expansion in academic and applied research over nearly four decades.