2026-07-18T05:27:51Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/38622022-04-29T12:02:47Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

00925njm 22002777a 4500 dc Herrero Cosío, Álvaro author Corchado, Emilio author Jiménez Palmero, Alfredo author 2011-10 This interdisciplinary research project focuses on relevant applications of Knowledge Discovery and Artificial Neural Networks in order to identify and analyze levels of country, business and political risk. Its main goal is to help business decision-makers understand the dynamics within the emerging market countries in which they operate. Most of the neural models applied in this study are defined within the framework of unsupervised learning. They are based on Exploratory Projection Pursuit, Topology Preserving Maps and Curvilinear Component Analysis. Two interesting real data sets are analyzed to empirically probe the robustness of these models. The first case study describes information from a significant sample of Spanish multinational enterprises (MNEs). It analyses data pertaining to such aspects as decisions over the location of subsidiary enterprises in various regions across the world, the importance accorded to such decisions and the driving forces behind them. Through a projection-based analysis, this study reveals a range of different reasons underlying the internationalization strategies of Spanish MNEs and the different goals they pursue. It may be concluded that projection connectionist techniques are of immense assistance in the process of identifying the internationalization strategies of Spanish MNEs, their underlying motives and the goals they pursue. The second case study covers several risk categories that include task policy, security, and political stability among others, and it tracks the scores of different countries all over the world. Interesting conclusions are drawn from the application of several business intelligence solutions based on neural projection models, which support data analysis in the context of country and political risk analysis 0957-4174 http://hdl.handle.net/10259/3862 10.1016/j.eswa.2011.04.136 Neural visualization models Exploratory Projection Pursuit Unsupervised learning Country and political risk Business intelligence Knowledge extraction Unsupervised neural models for country and political risk analysis