Extending Hype Cycle Prediction by Applying Graph Network Analysis

One of the most commonly used technology forecasting methods in the industrial world is Gartner’s technology hype cycle (THC). The THC is a yearly forecast of technology maturity based on a comprehensive assessment of various pieces of information such as news, papers, and research budgets. However, annual updates pose a problem in that it is difficult to utilize the information for management decisions in a timely manner. To deal with this problem, a variety of methods for drawing THCs have been studied. We proposed an approach to draw THC based on an integrated analysis of various information sources, such as papers and patents, by applying graph network analysis. The effectiveness of our approach was demonstrated using display technology as an example. In this study, we confirmed that the same approach can be applied to quantum computer technology to predict technology maturity, demonstrating the versatility of our approach.