Technology clustering based on evolutionary patterns: The case of information and communications technologies

• Published in: Technological forecasting & social change Vol. 78; no. 6; pp. 953 - 967
• Main Authors: Lee, Hyoung-joo, Lee, Sungjoo, Yoon, Byungun
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.07.2011; Elsevier Science Ltd
• Subjects: Cluster analysis; Clustering; Communications industry; Convergence; Decision Making; Evolution; Evolutionary patterns; Forecasting; Information and communications technology; Information Technology; Innovation; Innovations; Markov analysis; Mathematical models; Patent analysis; Studies; Technological change; Technology; Technology clustering; Telecommunications; Trend analysis; Values; Evolutionary patterns; Information and communications technology; Technology clustering; Patent analysis; Trend analysis
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2011.02.002

Technology trend analysis anticipates the direction and rate of technology changes, and thus supports strategic decision-making for innovation. As technological convergence and diversification are regarded as emerging trends, it is important to compare the growth patterns of various technologies in a particular industry to help understand the industry characteristics and analyse the technology innovation process. However, despite the potential value of this approach, conventional approaches have focused on individual technologies and paid little attention to synthesising and comparing multiple technologies. We therefore propose a new approach for clustering technologies based on their growth patterns. After technologies with similar patterns are identified, the underlying factors that lead to the patterns can be analysed. For that purpose, we analysed patent data using a Hidden Markov model, followed by clustering analysis, and tested the validity of the proposed approach by applying it to the ICT industry. Our approach provides insights into the basic nature of technologies in an industry, and facilitates the analysis and forecasting of their evolution. ►We propose an approach to clustering technologies based on their growth patterns. ►Patent data were analysed using a Hidden Markov model, followed by clustering. ►The validity of the proposed approach was verified by applying it to the ICT industry. ►The approach facilitates the analysis and forecasting of technology evolution.