From signals to control: how core technologies shape the evolutionary trajectory of Korean railway systems

Purpose The Fourth Industrial Revolution has accelerated technological advancements across industries, necessitating that countries, research institutions and enterprises enhance their technological competitiveness. A key challenge in this process is the ability to predict promising technologies and integrate them into strategic decision-making. However, existing methods predominantly rely on expert-driven qualitative assessments, which can be subjective and inconsistent. This study aims to address these limitations by proposing a quantitative, data-driven framework for technology foresight and strategic development in Korea’s railway industry, with a specific focus on emerging digital and control systems. Design/methodology/approach This research integrates autoregressive integrated moving average (ARIMA) time-series forecasting and weighs social network analysis (SNA) to systematically identify emerging technological trends. Using 4,352 railway-related patents from the Korean Intellectual Property Office (KIPO) from 1990 to 2023, technology keywords were extracted through text mining using TF-IDF scores. Promising technologies were identified by analyzing their temporal growth patterns (including forecast confidence intervals) and network influence, enabling a data-driven approach to forecasting technological developments and informing strategic planning. Findings The analysis demonstrates that the synergistic use of ARIMA-based forecasting and SNA-driven influence assessment provides a robust and systematic methodology for identifying emerging technologies. The results highlight that core technologies related to “control,” “signal,” “sensor,” “device” and “speed” are poised for significant growth and hold central positions within the technology network. This quantitative approach enhances technology management by reducing reliance on subjective expert opinions and providing objective, data-driven insights. Practical implications This study offers a structured methodology for organizations to enhance technology foresight and strategic planning. By leveraging predictive analytics, policymakers and industry leaders can proactively identify high-potential technologies, optimize resource allocation and foster innovation in the railway sector, particularly in the transition toward automated and intelligent transportation systems. Originality/value This research contributes to the field of technology forecasting by introducing a reproducible, quantitative framework that combines time-series analysis with network theory. By justifying the methodological choices and demonstrating their synergy, this framework offers a novel and robust alternative to traditional methods for strategic decision-making and technology development, particularly in mature, high-tech industries like the railway sector.