Predicting research projects’ output using machine learning for tailored projects management

Abstract

ABSTRACT With the increasing interest and investment in research and development (R&D), the need for more efficient research project management has grown. Accordingly, we built prediction models to classify research projects that were expected to show excellent research output. Specifically, we applied five machine learning techniques to build prediction models. In an empirical analysis of data on research projects funded by South Korea over the last five years (2014–2018), we found that the automated machine learning model (autoML), in which the machine builds the most suitable learning model, shows relatively greater and more robust performance than models based on other techniques. We also established that research funding and project type played the most important roles in predicting excellent research projects. This study is significant because it shows the need for a paradigm shift in building an evidence-based project management system by verifying the utility and applicability of a data-driven approach in R&D project management.