## 3 The Selection of DEMATEL Method for GenAI Tools Criteria Analysis  
The aim of this section is to show the reasons for selecting the DEMATEL method for criteria analysis of GenAI tools. Subjective weighting methods in multi-criteria decision-making (MCDM) are essential for determining the relative importance of criteria based on decision-makers' preferences. Popular methods are the Analytic Hierarchy Process (AHP) [31, 32] Analytic Network Process (ANP) [33], Decision-Making Trial and Evaluation Laboratory (DEMATEL) [5], and the Best Worst Method (BWM) [34]. A summary of the criteria weighting methods is presented in [35]. DEMATEL considers interdependence and causal relationships, offering both short-term and long-term improvement perspectives when it is integrated with AHP [36]. The paper [37] compares and proposes hybrid models combining AHP, BWM, MAC-BETH, and DEA for multi-criteria decision-making. The paper [38] compares the results of AHP, limited AHP, and BWM, finding that BWM's results are comparable to standard AHP while limited AHP is generally inferior. The choice of method and number of comparisons significantly influences the resulting priority vector [38]. A comparison of AHP and BWM is presented in the paper [39]. The strategy of the paper [40] is to compare ANP with DEMATEL-ANP for the selection of a location. A comparison of subjective weighting methods--AHP, ANP, DEMATEL and BWM--for core mechanism, features, and strengths and weaknesses is presented in Table 2.  
The selection of DEMATEL (Decision-Making Trial and Evaluation Laboratory) method over other multi-criteria weighting methods like AHP, ANP and BWM is determined in principle by the causal relationship analysis, visualization of complex systems, and flexibility of application. DEMATEL excels in analyzing causal relationships between criteria, helping to identify which factors have the most influence on others. This method generates visual representations (cause-effect diagrams) that clearly illustrate the relation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Core Mechanism** & \multicolumn{2}{c}{**Features**} & \multicolumn{2}{c}{**Strengths**} & \multicolumn{2}{c}{**Weaknesses**} \\ \hline \multirow{4}{*}{AHP} & Hierarchical pairwise & - & Handles qualitative/quantitative & & & - & Exponential growth of \\  & comparisons using & criteria & & & & \\  & Saaty’s 1–9 scale to & - & Widely validated & & & - & Rank reversal \\  & derive weights. & - & Robust consistency & & & - & - \\  & & checks & & & & - & Can be subjective \\ \hline \multirow{4}{*}{ANP} & Extends AHP to & - & Models complex & & & - & Computationally intensive \\  & account for & - & interrelations & - & Models complex & - & Requires expertise \\  & interdependencies & - & Flexible for & & dependencies & & - \\  & among criteria using & - & networked systems & & & - & Complex to implement and interpret \\  & feedback loops. & & & & & - & \\  & & & & & & - \\ \hline \multirow{4}{*}{DEMATEL} & Builds causal & - & Identifies & & - & Visualizes complex & - & Subjective causality assessments \\  & relationships & cause–effect & - & relationships & - & Limited to \\  & between criteria & - & Visualizes system & - & Can identify key & & - \\  & using influence & - & dynamics & & drivers & & - \\  & matrices. & & & & & - & Can be difficult to \\  & & & & & & & - \\ \hline \multirow{4}{*}{BWM} & Compares all criteria & - & Requires fewer & - & More consistent & - & Sensitive to outlier judgments \\  & against predefined & - & comparisons & & than AHP & - & Limited flexibility \\  & “best” and “worst” & - & Ensures high & - & Requires fewer & & for adding new criteria \\  & anchors. & & consistency & & & AHP & - & Can be more \\  & & & & & & abstract for \\  & & & & & & & decision-makers \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison of a set of subjective weighting multi-criteria methods from core mechanism, features, and strengths and weaknesses.