0000000000985620

AUTHOR

Alex Sclip

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Greenfield FDI attractiveness index: a machine learning approach

2022

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…

FDI determinantsArtificial intelligenceAutomated machine learningFDI indexSettore SECS-P/11 - ECONOMIA DEGLI INTERMEDIARI FINANZIARIForeign direct investment Artificial intelligence FDI determinants Attractiveness factors Automated machine learning FDI indexVDP::Samfunnsvitenskap: 200Business and International ManagementGeneral Business Management and AccountingForeign direct investmentVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Attractiveness factors
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