Predicting an ICT business process innovation as a digital transformation with machine learning techniques

Abstract

As digital transformation accelerates worldwide, ICT innovation capabilities are attracting attention as the core competitiveness of companies. However, ICT technologies evolve faster than other technologies, and it is difficult to predict their performance due to high uncertainty in terms of innovation characteristics. This study uses machine learning methods to predict ICT business process innovation performance and derives variables that most importantly affect performance prediction. This study used data from the 2020 Korea Innovation Survey. The main result was that the random forest model accurately predicted the ICT business process innovation performance. Among the four explanatory variables, information sources were the most critical factor in predicting innovation performance. This study provides several implications. First, it contributed to the research of technology forecasting by presenting a machine learning model that can accurately predict ICT innovation performance. Second, this study suggests that managers should actively utilise external information to engage in ICT innovation.

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