Purpose This study aims to propose a comprehensive greenfield foreign direct investment (FDI) attractiveness index using exploratory factor analysis and automated machine learning (AML). We offer offer a robust empirical measurement of location-choice factors identified in the FDI literature through a novel method and provide a tool for assessing the countries' investment potential. Design/methodology/approach Based on five conceptual key sub-domains of FDI, We collected quantitative indicators in several databases with annual data ranging from 2006 to 2019. This study first run a factor analysis to identify the most important features. It then uses AML to assess the relative importance of each resultant factor and generate a calibrated index. AML computational algorithms minimize predictive errors, explore patterns in the data and make predictions in an empirically robust way. Findings Openness conditions and economic growth are the most relevant factors to attract FDI identified in the study. Luxembourg, Hong Kong, Singapore, Malta and Ireland are the top five countries with the highest overall greenfield attractiveness index. This study also presents specific indices for the three sectors: energy, financial services, information and communication technology (ICT) and electronics. Originality/value Existent indexes present deficiencies in conceptualization and measurement, lacking theoretical foundation, arbitrary selection of factors and use of limited linear models. This study's index is developed in a robust three-stage process. The use of AML configures an advantage compared to traditional linear and additive models, as it selects the best model considering the predictive capacity of many models simultaneously.

Alon, I., Bretas, V. P. G., Sclip, A., Paltrinieri, A., Greenfield FDI attractiveness index: a machine learning approach, <<COMPETITIVENESS REVIEW>>, 2022; 32 (7): 85-108. [doi:10.1108/cr-12-2021-0171] [https://hdl.handle.net/10807/237335]

Greenfield FDI attractiveness index: a machine learning approach

Paltrinieri, Andrea
Writing – Original Draft Preparation
2022

Abstract

Purpose This study aims to propose a comprehensive greenfield foreign direct investment (FDI) attractiveness index using exploratory factor analysis and automated machine learning (AML). We offer offer a robust empirical measurement of location-choice factors identified in the FDI literature through a novel method and provide a tool for assessing the countries' investment potential. Design/methodology/approach Based on five conceptual key sub-domains of FDI, We collected quantitative indicators in several databases with annual data ranging from 2006 to 2019. This study first run a factor analysis to identify the most important features. It then uses AML to assess the relative importance of each resultant factor and generate a calibrated index. AML computational algorithms minimize predictive errors, explore patterns in the data and make predictions in an empirically robust way. Findings Openness conditions and economic growth are the most relevant factors to attract FDI identified in the study. Luxembourg, Hong Kong, Singapore, Malta and Ireland are the top five countries with the highest overall greenfield attractiveness index. This study also presents specific indices for the three sectors: energy, financial services, information and communication technology (ICT) and electronics. Originality/value Existent indexes present deficiencies in conceptualization and measurement, lacking theoretical foundation, arbitrary selection of factors and use of limited linear models. This study's index is developed in a robust three-stage process. The use of AML configures an advantage compared to traditional linear and additive models, as it selects the best model considering the predictive capacity of many models simultaneously.
2022
Inglese
Alon, I., Bretas, V. P. G., Sclip, A., Paltrinieri, A., Greenfield FDI attractiveness index: a machine learning approach, <<COMPETITIVENESS REVIEW>>, 2022; 32 (7): 85-108. [doi:10.1108/cr-12-2021-0171] [https://hdl.handle.net/10807/237335]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/237335
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