Criteria Analysis for the Selection of a Generative Artificial Intelligence Tool for Academic Research Based on an Improved Group DEMATEL Method

Generative Artificial Intelligence (GenAI) tools are transforming academic research by significantly enhancing efficiency, accuracy, and productivity. However, selecting the most appropriate GenAI tool requires careful evaluation of multiple, interdependent criteria. This paper makes two main contributions. First, it introduces IDEMATEL, an improved decision-making method that advances beyond the traditional DEMATEL approach. Unlike DEMATEL, which can encounter technical limitations when analyzing complex relationships, IDEMATEL ensures robust and reliable results by guaranteeing the necessary mathematical conditions for analysis in all cases. This enhancement makes IDEMATEL more broadly applicable and dependable for evaluating interrelated criteria. Second, the paper demonstrates the practical value of IDEMATEL by applying it to the selection of GenAI tools for academic research. Using this method, a comprehensive set of criteria—including functionality, ease of use, cost, data security, and community support—is systematically analyzed. The results provide researchers and decision-makers with clearer insights into how these factors interact and influence the selection process. By leveraging IDEMATEL, stakeholders can make more informed and confident choices, ensuring that the selected GenAI tools best meet the diverse needs of academic research.