## 2 Literature Review
### Correspondence Analysis (CA)  
Correspondence analysis (CA) is a data analysis method that expresses a two-dimensional or higher contingency table in a low-dimensional space to identify the combination of columns and rows [70]. Likert scales, commonly used to measure attitudes, opinions, or perceptions, generate categorical or ordinal data that can be challenging to analyze using traditional statistical methods.  
CA is designed specifically for categorical data, which are often encountered in surveys, questionnaires, and factor analysis. It is beneficial when the data involve numerous categories or levels [71]. CA addresses this challenge by transforming contingency tables into a low-dimensional space, enabling researchers to explore the relationships between rows (respondents) and columns (factors) [70]. This method computes the association between factors and highlights their relative importance by identifying the principal components that explain the most variance in the data. For example, in a survey assessing the benefits of technology in a given industry, CA can rank the factors based on their contributions to the overall variance, making it easier to determine the most influential factors in the dataset. This function is particularly valuable when dealing with complex  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Challenges** & **References** \\ \hline
1 & Complexity of AI algorithms & [20] \\
2 & Data quality issues & [46; 56] \\
3 & Lack of understanding of AI capabilities & [59] \\
4 & Ethical concerns & [9; 62] \\
5 & Data privacy and security concerns & [9; 20; 46; 59] \\
6 & Lack of standardization of integrated AI and DT in & [63; 64; 65] \\
7 & Lack of logistics and availability of IT infrastructure & [66; 67; 68] \\ \hline \end{tabular}
\end{table}
Table 4: Challenges of integrating AI-driven DTs.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Resource Implementation** & **References** \\ \hline
1 & Training and education & [5; 28; 45; 59] \\
2 & Access to case studies and best practices & [28; 45] \\
3 & Financial incentives or subsidies & [28; 52; 69] \\
4 & Technical support from vendors & [9; 28; 45] \\
5 & Resolving data privacy and security concerns & [45; 59] \\ \hline \end{tabular}
\end{table}
Table 5: Resource implementation of AI-based DTs.  
datasets where the relationships between factors and respondents are not immediately apparent [71; 72].  
Moreover, CA is especially effective as it accounts for the ordinal nature of Likert-scale data by focusing on relative proportions rather than assuming linear relationships. It evaluates the degree to which specific factors contribute to the overall patterns in the data and visualizes these relationships in a two-dimensional space. This visualization aids in understanding clusters of factors or respondents with similar patterns, allowing for the nuanced assessment and ranking of factors [70]. For instance, when evaluating challenges in implementing digital twins in construction, CA can reveal whether specific challenges (such as high costs or a lack of skilled professionals) are closely associated with particular respondent groups. By ranking factors based on their contributions to the principal dimensions of variance, CA provides a robust framework for prioritization and decision making. Its ability to handle categorical data and its interpretive power make it an essential tool for assessing survey-based Likert-scale data in various fields, including education, healthcare, and engineering [72; 73].