Strategic Forecasting Methods for Micro and Nanotechnologies

Strategic forecasting is essential for decision-making in the knowledge-based economy, where rapid technological change demands structured anticipation. This paper critically reviews the main forecasting methods used for micro and nanotechnologies and evaluates their theoretical basis, strengths, and limitations. A systematic literature review covers quantitative approaches, including time-series extrapolation, S-curve diffusion models, bibliometric analysis, and patent-landscape analysis, as well as qualitative methods such as the Delphi technique, scenario planning, strategic roadmapping, and expert judgment. Hybrid and AI-enhanced frameworks are also examined. The review shows that quantitative methods are objective but largely retrospective, whereas qualitative methods are flexible but vulnerable to bias and limited validation. Hybrid approaches appear most promising, yet they remain unevenly implemented. The identified methodological gaps support the development of an integrated, adaptive forecasting framework for emerging technologies.