The use of decision support systems in the process of export market selection

In today's world, where global competition is intensifying and technology is transforming business practices, targeting the right markets has become more strategically important than ever for export-oriented companies. Especially for firms operating in high value-added sectors such as electrical-electronics, machinery, and automotive, international success is shaped not only by product quality but also by effective market selection and timely decision-making capabilities. At this point, decisions made during the export process must be based not solely on intuitive experience but also on data-driven and systematic analyses. Traditional decision support systems often fall short in highly variable and multidimensional export environments. The increasing variety and volume of data have created a need for more advanced analytical tools for decision-makers. In response to this need, artificial intelligence (AI)-based methods have begun to play a significant role in managing foreign trade decisions in recent years. In particular, machine learning-based algorithms are capable of simultaneously evaluating countries' economic, commercial, logistical, and sectoral indicators to generate strategic insights. This doctoral dissertation, titled "The Use of Decision Support Systems in the Process of Export Market Selection," aims to evaluate market selection in the electrical-electronics, machinery, and automotive sectors of a Turkish export firm using AI-supported data analytics methods. The study integrates various indicators such as economic data from countries, bilateral sectoral trade statistics of countries with Türkiye, trade facilitation metrics, and logistics performance indicators into the analysis. In this context, artificial intelligence-based clustering analysis techniques such as Self-Organizing Maps (SOM) and k-means clustering were employed to group countries with similar characteristics. The export potential of each cluster was then evaluated through TOPSIS method. These analyses not only helped to identify potential export markets but also allowed for a comparison between Türkiye's current export performance and the clustering model results, thereby assessing the accuracy of the decision support systems. The findings of the study aim to demonstrate the advantages that AI-supported systems offer to companies in making faster and more accurate export decisions. The results are expected to contribute both to academic literature and to practitioners developing export strategies.