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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.

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.

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.

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.

This paper investigates the effect of entrepreneurial orientation (EO) on small- and medium-sized enterprises' (SMEs) access to credit. Starting with the idea that SMEs' strategy-making process, structures and behaviour can favour credit access, the authors also explore the moderating role of bank lending technologies in shaping this relationship.

This study relies on a unique survey of Austrian and Italian SMEs which contains detailed information on access to credit, EO dimensions, relationship lending and firm-level characteristics. The authors perform stepwise logistic regressions to assess whether EO interacts with SME's access to finance, and how relationship lending enhances this relationship.

Proactiveness, autonomy and competitive aggressiveness are important constructs for improving access to bank financing. Those dimensions became more important when a relationship bank is involved, suggesting a role for relationship lending in overcoming SMEs' opaqueness. In addition, relationship lending is crucial for innovative SMEs in overcoming credit denial rates.

The small sample did not allow to analyse the effect of EO on discouraged borrowers. Furthermore, alternative measures of relationship lending (such as geographical proximity or the length of the relationship) and the share of credit granted by the relationship bank would have been interesting to further validate our results.

This study shows that EO dimensions and the type of lending technology are relevant for the financial success of SMEs. More precisely, the authors show that diversity within the banking system helps innovative, autonomous, proactive and competitive SMEs. These important pieces of soft information are injected into the final lending decision when a relationship bank is involved. The evidence suggests the need for SMEs to interact with local banks to fully exploit their EO posture.

To the authors' knowledge, this paper is the first attempt to analyse whether relationship lending can affect the EO–credit access relation.