## 5 The IDEMATEL Algorithm
Based on this analysis, the recommendations are to choose a tool that excels in core features for academic research, ensures high data quality and security standards, and offers cost-effective solutions with good value. These results have practical implications for institutions and researchers selecting GenAI tools. Decision-makers should prioritize tools that demonstrate strong performance in functionality, data quality, and cost-effectiveness. While ease of use and integration are important for adoption, they are secondary to the tool's ability to deliver reliable, secure, and relevant research outputs. For example, an institution with strict data privacy requirements should weigh \"Data Quality and Security\" more heavily, possibly even above cost considerations. Conversely, for smaller research groups with limited funding, \"Cost and Value\" may take precedence, provided minimum standards for functionality and security are met. Ultimately, the IDEMATEL framework provides a structured approach to balancing these priorities and understanding the trade-offs involved in tool selection. Compared to traditional decision-making methods such as AHP or classic DEMATEL, IDEMATEL offers enhanced insights by explicitly capturing both direct and indirect influences among criteria. The close alignment of IDEMATEL results with those of the Revised DEMATEL (Lee) confirms the robustness of the method, while the causal analysis adds interpretive depth. Unlike simple ranking methods, IDEMATEL's causality diagram helps decision-makers visualize not just which criteria matter most, but how improving one area (such as functionality) can have cascading positive effects on others (such as reputation and user community). Table 10 presents a quantitative comparison of five prominent GenAI tools used in academic research, evaluated according to the IDEMATEL criteria. For the \"Cost and Value\" criterion, actual pricing information is provided. For all other criteria, the tools are rated on a 1-5 scale, where higher values indicate better performance or suitability. Please note that the scale allows for decimal values (e.g., 4.3 or 4.7) to reflect nuanced differences in performance among the tools. \begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Criterion** & **ChatGPT (OpenAI)** & **Perplexity** & **Scite.ai (Scite (New York, NY, USA)**)** & **Gemini (Google DeepMind)** & **Claude (Anthropic)** \\ \hline \multirow{3}{*}{Functionality and Objectives} & 4.5 (Conversational AI for text/code generation, no citations) & 4.8 (Real-time citations + file analysis) & 4.8 (Real-time citations + file context analysis) & 5 (Smart citation context analysis) & 4.7 (Multimodal AI for reasoning across text, images, audio) & 4.6 (Data analysis, and ethical automation) \\ \hline \multirow{3}{*}{Cost and Value} & \(\$20/month for Plus (GPT-4/ 4.5), free basic tier & \(\$20/month\) (Pro plan), free limited use & \(\$10/\$50/month\) (Institutional rates, access needed) & Free tier + enterprise pricing & \multirow{3}{*}{Custom pricing} \\ \hline \multirow{3}{*}{Data Quality and Security} & 4 (Pre-2023 data, no source verification, security respected) & 4.9 (Links to real sources, source citations) & 5 (1.2B+ verified citations, very strong on safety) & 4.5 (Multimodal accuracy) & 4.7 (Content modernation) \\ \hline \multirow{3}{*}{Ease of Use and Accessibility} & 4.5 (Web, app, and API interfaces, user-friendly design) & 4.8 (Browser extensions, simple interface) & 4.6 (intuitive KRAS) & 4.7 (Integrated KRAS) & 4.5 (Web, API, and enterprise solutions, ecosystem) & 4.5 (Web, API, and enterprise solutions, Multilingual support) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Quantitative comparison of GenAI tools. In order to reduce the complexity of the criteria relations, a threshold value is set to filter out negligible effects. The threshold calculated for **T** matrix with the arithmetic mean methods is \(\mu=0.599\).