Data Mining for Internationalization

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Author(s): Luciana Dalla Valle (University of Milan, Italy)
Copyright: 2009
Pages: 7
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch067

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Abstract

The term “internationalization” refers to the process of international expansion of firms realized through different mechanisms such as export, strategic alliances and foreign direct investments. The process of internationalization has recently received increasing attention mainly because it is at the very heart of the globalization phenomenon. Through internationalization firms strive to improve their profitability, coming across new opportunities but also facing new risks. Research in this field mainly focuses on the determinants of a firms’ performance, in order to identify the best entry mode for a foreign market, the most promising locations and the international factors that explain an international firms’ performance. In this way, scholars try to identify the best combination of firms’ resources and location in order to maximize profit and control for risks (for a review of the studies on the impact of internationalization on performance see Contractor et al., 2003). The opportunity to use large databases on firms’ international expansion has raised the interesting question concerning the main data mining tools that can be applied in order to define the best possible internationalization strategies. The aim of this paper is to discuss the most important statistical techniques that have been implemented to show the relationship among firm performance and its determinants. These methods belong to the family of multivariate statistical methods and can be grouped into Regression Models and Causal Models. The former are more common and easy to interpret, but they can only describe direct relationships among variables; the latter have been used less frequently, but their complexity allows us to identify important causal structures, that otherwise would be hidden.

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