Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods

Abstract

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 literature 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 the 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 the growth curves and time series methods were shown to remain popular over the past decade while the 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 a growing trend in the development and application of hybrid models to technology forecasting.

Evolutionary Phases in Emerging Quantum Technologies: General Theoretical and Managerial Implications for Driving Technological Evolution

“…In economics of innovation, logistic model (having a symmetric S-shape) is a valuable tool for visualizing the evolutionary trajectory of a technology over time ( [21], [73. S-shaped model is characterized by a gradual initial growth phase, followed by a period of rapid acceleration, and finally a phase of deceleration and saturation [78]- [79]. The logistic S-model, having a symmetry around the inflection point [31], makes easier to identify phases and technological pathway [38].…”

“…Logistic model for these characteristics of simplicity is used for a wide variety of social and natural phenomena, including technological diffusion, product lifecycles, the spread of infectious diseases, etc. [79]. In this study, we utilize patent data to trace the evolution of quantum technologies based on a logistic model.…”

Quantitative scenarios for cascading risks in AI, climate, synthetic bio, and financial markets by 2075

“…Notable exceptions to this situation are found within the technology forecasting literature which wholeheartedly embraces the use of quantitative indicators to extrapolate historical trends, particularly to forecast economic growth, R&D patterns, the maturity and impact of emerging technologies, sectoral industrial development, and more (Parraguez et al, 2020;Tsai et al, 2023). That tradition uses a multiplicity of methods, including trend extrapolation (Tsai et al, 2023), Delphi surveys (Beiderbeck et al, 2021), and technology roadmapping (Halal, 2013;Cho, 2018), and also a multitude of data sources, including expert interviews or questionnaires, patent data, research publication data, economic indicators, online platform data, and more (Feng et al, 2022). Scholars have also tried to model the risk of future natural hazards based on future exposure and vulnerability of infrastructure in addition to current context and historical hazard data (Cremen, Galasso and McCloskey, 2022).…”