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Forecasting Innovation Performance via Deep Learning Algorithm : A Case of Korean Manufacturing Industry  

Hwang, Jeong-jae (건국대학교 기술경영학과)
Kim, Jae Young (건국대학교 기술경영학과)
Park, Jaemin (건국대학교 기술경영학과)
Publication Information
Journal of Korea Technology Innovation Society / v.21, no.2, 2018 , pp. 818-837 More about this Journal
Abstract
Technological innovation has inherent difficulties, largely due to the uncertainties of technology. Thus, the forecasting methodology to reduce the risk of uncertainty in the innovation process has been presented both in quantitative and qualitative fields. On the other hand, big data and artificial intelligence have attracted great interest recently, and deep learning, which is one of the algorithms of AlphaGo, is showing excellent performance. In this study, deep learning methodology was applied to the prediction of innovation performance. To make the prediction model, we used KIS 2016 data. The input factors were importance of information source and innovation objectives and the output factor was innovation performance index, which was calculated for this study. As a result of the analysis, it can be confirmed that the accuracy of prediction is improved compared with the previous studies. As learning progressed, the degree of freedom of prediction also improved.
Keywords
Innovation Performance; Big Data; Korea Innovatino Survey; Deep Learning; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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