Being (Not) Successful in Internationalisation After Receiving Export Support: Which Predictors Are Able to Forecast It and How Accurately?

This paper aims to outline which predictors are able to forecast being (not) successful in internationalisation after receiving export support and how accurately they can perform this task. Using data on export grant recipients from an Estonian export support programme, 15 theoretically motivated predictors grouped into four domains are used to forecast 24 different proxies of (non-)success with logistic regression and neural networks. The domains focus on firms’ general characteristics, earlier financial and export performance, and export-grant-specific characteristics. The highest areas under the curve exceed the 0.9 threshold, therefore indicating excellent predictive abilities, while more specific (non-)success proxies can be predicted less accurately than general ones. Predictors portraying firm size and export support size emerge as the best in the case of both methods, while in different neural networks, at least one predictor from each of the four domains is among the most important ones. These results lead to multiple practical implications concerning how to select firms into export grant programmes.