## 3 A New Seven-Phase Methodology for Systematic Literature Review
### Phase 5: One-Way Canonical Biplot Analysis with the MultBiplot Software  
Based on the characterized matrix, an additional column with the publication year of each article and another column labeled \"group\" were added. In this latter column, four publication period groups were established, which were distributed as follows: _Group 1_  
Figure 8: Processed data matrix with the characterization factor. Figure 7: Lexical matrix obtained from the CFA with the IRAMUTEQ(Version 0.7 alpha 2) software. (1986-1999), Group 2 (2000-2007), Group 3 (2008-2015), and Group 4 (2016-2024). These groups are essential for performing the one-way Canonical Biplot analysis, as they allow segmenting publications according to the mentioned periods. The data were then loaded into the MultBiplot _version 18.0312_ software [16], developed at the University of Salamanca by the Applied Multivariate Analysis research group, to conduct the corresponding MANOVA-Biplot, which will allow us to analyze the variability between the groups and associations with the lexical variables in the different periods. As shown in Table 3, the result of the Canonical Biplot shows the main dimensions, their eigenvalues, and the variance explained by each of these dimensions. In this case, the first dimension has an eigenvalue of 16.07, explaining 48.39% of the total variance of the model. The second dimension has an eigenvalue of 13.04, explaining 31.85% of the variance, while the third dimension, with an eigenvalue of 10.27, explains the remaining 19.76% of the variance. Another important result is the _quality of the representation_ of group means on the _canonical axes_. Table 4 shows how the different publication periods are projected onto the two main axes of the analysis. We observe that Group 4 (2016-2024) is very well represented, with a value of 877 on Axis 1 and 104 on Axis 2, yielding a cumulative value of 981. This indicates that most of the variability of this group is captured in the first axis, with a fairly high representation also in the second axis, suggesting that this group has a strong presence in the analyzed dimensions. On the other hand, Group 2 (2000-2007) shows the lowest representation, with a value of 227 on Axis 1 and 251 on Axis 2, accumulating a total of 478. This low cumulative value indicates that this group is not well represented on the main axes of the Biplot, suggesting that the characteristics of this period do not align as clearly with the primary directions of variability in the data. In contrast, Group 1 (1986-1999) and Group 3 (2008-2015) show more balanced representations, with cumulative values of 827 and 975, respectively, highlighting that these groups have a good projection on at least one of the main axes, although each shows different focal points of representation with Group 1 better represented on Axis 1 and Group 3 on Axis 2. The Canonical Biplot graph in Figure 9 shows that Axis 1 explains 48.39% of the variance, and Axis 2 explains 31.85%, meaning that together they represent 80.24% of the variability in the data. The publication period groups are distributed according to techniques derived from the HJ-Biplot [12]. The period 2016-2024 is associated with more recent techniques, such as Cenet HJ-Biplot [71] and Sparse HJ-Biplot [72], indicating a diversification in the development of these techniques. In contrast, the period \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group No.** & **Period** & **Axis 1** & **Axis 2** & **Cumulative** \\ \hline
1 & 1986–1999 & 730 & 97 & 827 \\
2 & 2000–2007 & 227 & 251 & 478 \\
3 & 2008–2015 & 208 & 767 & 975 \\
4 & 2016–2024 & 877 & 104 & 981 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quality of representation of group means \(\times\) 1000. 1986-1999 reflects the beginning of the HJ-Biplot, primarily associated with its original version, while the period 2008-2015 is linked to techniques such as the Bootstrapping HJ-Biplot [73] and Dynamic Biplot [74], suggesting an interest in adding robustness and dynamism to the analysis.