Greenfield FDI attractiveness index: a machine learning approach
Competitiveness Review
; 32(7):85-108, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1806790
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.
Business And Economics--International Commerce; Foreign direct investment; Artificial intelligence; FDI determinants; Attractiveness factors; Automated machine learning; FDI index; Machine learning; Bibliometrics; Infrastructure; Sustainable development; Natural resources; Pandemics; Foreign investment; Behavioral economics; Tariffs; Gross Domestic Product--GDP; Coronaviruses; Financial services; Globalization; Efficiency; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Competitiveness Review
Year:
2022
Document Type:
Article
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