Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods

Peng-Hung Tsai, Daniel Berleant (), Richard S. Segall (), Hyacinthe Aboudja (), Venkata Jaipal Reddy Batthula (), Sheela Duggirala () and Michael Howell ()
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Peng-Hung Tsai: Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204 USA
Daniel Berleant: Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204 USA
Richard S. Segall: ��Department of Information Systems & Business Analytics, Arkansas State University, Jonesboro, AR 72467 USA
Hyacinthe Aboudja: ��Department of Computer Science, Oklahoma City, University Oklahoma City, OK 73106 USA
Venkata Jaipal Reddy Batthula: Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204 USA
Sheela Duggirala: Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204 USA
Michael Howell: Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204 USA

International Journal of Innovation and Technology Management (IJITM), 2023, vol. 20, issue 04, 1-39

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.

Keywords: Technological forecasting; trend extrapolation; technology evolution; systematic literature review (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219877023300021

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