The use of foresight to anticipate and prioritise innovation system failures: The case of machine learning in broadcasting in South Korea

This article reports on a study applying foresight methods to explore and anticipate innovation system failures in relation to a particular case sector, that of broadcasting in South Korea. Although previous studies of system failures have contributed to an in-depth understanding of innovation system as an analytical concept and provided the base of policy intervention, they have failed to capture different degrees of system failures and their changes in the process of sectoral transformation. Through the application of a sectoral innovation system foresight approach to the broadcasting sector in South Korea’s encounters with artificial intelligence (AI), a series of current and future priorities among nine system failures are identified. The shift of nine system failure priorities between current and five-year time points is captured: the highest priority of system failures moves from directionality failures to market structure failures. By applying a sectoral innovation system foresight approach, we advance theory on system failures and innovation systems. We show that the use of sectoral innovation system foresight approaches can productively be applied to the understanding of current and potential system failures.