Quantitative Technology Forecasting: a Review of Trend Extrapolation Methods
It addresses a gap in systematically reviewing empirical evidence on trend extrapolation techniques for technology forecasting, which is incremental as it consolidates existing literature.
This study conducted a systematic review of quantitative trend extrapolation methods in technology forecasting, identifying 25 relevant studies and finding that growth curves and time series methods remain popular, while newer hybrid models are emerging.
Quantitative technology forecasting uses quantitative methods to understand and project technological changes. It is a broad field encompassing many different techniques and has been applied to a vast range of technologies. A widely used approach in this field is trend extrapolation. Based on the publications available to us, there has been little or no attempt made to systematically review the empirical evidence on quantitative trend extrapolation techniques. This study attempts to close this gap by conducting a systematic review of technology forecasting literature addressing the application of quantitative trend extrapolation techniques. We identified 25 studies relevant to the objective of this research and classified the techniques used in the studies into different categories, among which growth curves and time series methods were shown to remain popular over the past decade, while newer methods, such as machine learning-based hybrid models, have emerged in recent years. As more effort and evidence are needed to determine if hybrid models are superior to traditional methods, we expect to see a growing trend in the development and application of hybrid models to technology forecasting.