The internationalization started to be seen as an opportunity for many companies, this is one of the most crucial growth strategies for companies. Internationalization can be defined as a corporative strategy for growing through foreign markets. It can enhance the product lifetime and improve productivity and business efficiency. However, there is no general model for a successful international company. Therefore, the success of an internationalization procedure must be estimated based on different variables addressing the status, strategy, and market characteristics of the company. In this paper, we try to build a model for evaluating the internationalization success of a company based on existing past data.

 

We used machine learning techniques to evaluate the internationalization success of companies. Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Support Vector Machines (SVM) is known as the powerful machine learning tool for classification, the illustration of this algorithm can be seen in Figure 1. Since a lot of economic problems could be solved by SVM then the results are trustworthy, we try to conduct the model by using the algorithms. The algorithm uses past data of previous companies which have faced an internationalization process. Table 1 presents the accuracy and running time results of this research. The results are very encouraging. We found that SVM achieved 96.87% accuracy rate with linear kernel and 80% data training. It means SVM can be a useful tool to evaluate the internationalization success of companies.