Education is a key factor that can contribute to the economic growth, supporting the social mobility and the living standard improvement. Both from the scientific point of view, as well as from the policy making process point of view, it is essential to know how individuals choose their educational path, in order to understand what is and can be the role of different educational routes in ensuring social mobility and improving standard of living. In this article we explore the factors that explain attitudes and decisions of individuals for vocational vs. general education in Romania. Our analysis is based on data from a national survey among adult Romanian population. Attitudes regarding the choice of vocational vs. general education are analysed by employing decision trees method in order to assess the extent to which vocational education is considered a valuable education path or an educational alternative for those with lower socio-economic background.
The telecommunication industry is growing every day, increasing its competitiveness. In almost all European countries, the market penetration of mobile network users exceeded 100% (for example in Croatia it is over 130%). Acquiring new users is virtually impossible because there are no new users. There are only users of rival companies who are exposed to numerous marketing campaigns carefully designed to try to win them. That’s why customer retention activity and churn prevention is a necessity. The purpose of this paper is to predict customers who are willing to migrate to another Romanian mobile telecommunications company and to determine the strongest factors of influence in the consumer’s decision to leave their current service provider for another provider. Migration behavior analysis is developed for customers with postpaid subscriptions. We applied the ROSE package for re-sampling and decision trees on the dataset to identify decision makers in the migration process. The combination of the two techniques in our study did not significantly improve the performance of the classifier measured by the AUC (Area Under the Curve). After balancing the sample, however, we obtain the optimal value of the AUC coefficient (0.724) for the second cluster, making the correct prediction of the churn phenomenon on the analyzed data set. The study is an addition of Churn Analysis in Romanian Telecommunications Company, M. M. Matei Maer and A. Dumitrache (2018), where ROSE and logistic regression was applied to the same dataset for the same purpose: balancing the sample and churn prediction, but the value of the AUC coefficient was really low, making it difficult to accurately predict the churn phenomenon. Therefore, another purpose of the current paper is to compare the performance of the two techniques used in combination with ROSE on the same set of data.
Innovation is essential for European competitiveness and provides key inputs for developing business models that are conducive for a more sustainable economy. Recent evidences show that businesses have increased the management attention and investments they’re dedicating to sustainability. This paper aims to identify the most important drivers supporting companies to develop innovation activities oriented towards making the business models more sustainable. We explore microdata from the 2016 Innobarometer “EU Business Innovation Trends” (Flash Eurobarometer 433), covering 14,112 companies from 30 countries. Using statistical classification methods, we identify the most important factors that are related to innovation activities that have potential to shape the efficiency of raw materials usage and the environmental protection. Special focus is given to companies’ investments in training, software development, research and development, company branding, design of products and services, organization or business process improvements and acquisition of machines, equipment, software or licenses. Also, our analysis highlights the skills that are needed the most by companies in order to support their innovation activities targeting sustainability. Our results are useful for better understanding the attention that is given to sustainability by innovative companies and which are the main factors that boost innovation dedicated to sustainability.