The growth of Internet of Things (IoT) systems is driven by their potential to improve efficiency, enhance decision-making, and create new business opportunities across various domains. In this paper, the main selection problems in IoT-type systems, criteria used in multi-criteria evaluation, and multi-criteria methods used for solving IoT selection problems are identified. Then, a Hybrid Group Multi-Criteria Approach for solving selection problems in IoT-type systems is proposed. The approach contains the Best Worst Method (BWM) weighting method, multi-criteria Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Complex Proportional Assessment Method (COPRAS), and a method that combines the solutions obtained using the four considered multi-criteria methods to obtain a single solution. The SAW, TOPSIS, VIKOR, and COPRAS methods were analyzed in relation to their advantages, disadvantages, inputs, outputs, measurement scale, type of normalization, aggregation method, parameters, complexity of implementation, and interactivity. An application of the Hybrid Group Multi-Criteria Approach for IoT platform selection and a comparison between the SAW, TOPSIS, VIKOR, and COPRAS solutions and the solution of the proposed approach is realized. A Spearman correlation analysis is presented.

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.