## 5 The IDEMATEL Algorithm
The reputation of the GenAI tool and user reviews tend to be influenced by the tool's performance (functionality, value, data security, etc.). Positive experiences with GenAI tools lead to better reviews and word-of-mouth recommendations in academic communities. The credibility of GenAI tools is still evolving, with many researchers remaining skeptical about AI-generated content. Tools like Grammar, Trinka, and Paperpal have strong reputations, whereas newer tools like Jenni.ai and Research Rabbit are still gaining trust. The level of support and community around the tool is important, especially for troubleshooting, learning how to use advanced features, or collaborating on research. However, it is largely dependent on the performance and user base of the tool. GenAI adoption in research is influenced by user feedback, academic acceptance, and continuous improvements. Tools like Consensus and Research Rabbit evolve based on community engagement, making them more dependent than influential. The most important criteria (with higher importance scores) are \"Functionality and Objectives\" (\(\mathbf{r}^{+}=9.33\)), \"Data Quality and Security\" (\(\mathbf{r}^{+}=9.05\)), \"Cost and Value\" (\(\mathbf{r}^{+}=8.94\)), and \"Reputation and Reviews\" (\(\mathbf{r}^{+}=8.40\)). The least important criteria (with lower importance scores) are \"Scalability and Integration\" (\(\mathbf{r}^{+}=7.84\)), \"Ease of Use and Accessibility\" (\(\mathbf{r}^{+}=7.77\)), and \"Support and Community\" (\(\mathbf{r}^{+}=7.36\)). While community and reviews are important, they depend on other factors and should improve naturally if the tool performs well in other key areas. Academic researchers require tools that can generate accurate and contextually relevant content for papers, presentations, and reports; assist with data analysis (e.g., conducting statistical analysis, creating models); and aid in the literature review by summarizing articles, extracting key information, and identifying research gaps. For researchers, particularly those with limited funding (e.g., graduate students or researchers at small institutions), cost is a significant consideration. GenAI tools in academic research must comply with data privacy regulations and produce high-quality outputs. Tools that store or process research data should ensure that sensitive information is protected. While reputation and support are dependent factors, they are still essential for long-term success. The IDEMATEL analysis reveals not only which criteria are most important for GenAI tool selection in academic research, but also how these criteria influence one another. For instance, \"Functionality and Objectives\" emerges as both a highly influential and highly important criterion, underscoring that a GenAI tool must align closely with the specific research needs it is intended to address. This finding suggests that, regardless of other features, a tool lacking core research functionalities is unlikely to be adopted widely in academic settings. Similarly, the criterion \"Data Quality and Security\" is identified as a key driver. In academic research, where data integrity and privacy are paramount, tools that do not ensure robust data protection or output reliability may be unsuitable, even if they excel in other areas. The high importance of the criterion \"Cost and Value\" reflects the budget constraints typical in academia, highlighting the need for solutions that offer a strong balance between price and performance. Interestingly, \"Reputation and Reviews\" and \"Support and Community\" are found to be more dependent (effect) criteria. This indicates that user satisfaction and community engagement are largely shaped by the tool's core features, security, and value, rather than being independent decision factors themselves. Therefore, efforts to improve a tool's reputation or community support should focus first on enhancing its fundamental capabilities.