Convergence is an important keyword that explains the propensity and nature of current development in the technology innovation field. Such innovation has occurred through convergences in various industries, from the computer, and semiconductor arenas to mobile telecommunication and healthcare. The possibilities for generating future technological innovations through convergence are infinite. Thus, firms face the challenge of having to constantly develop new technology through convergence in order to hold a dominant position.
In this context, firms can be dominant with a broad and systematic understanding of convergence trends and opportunities. A primary concern of both academic and industrial sectors is accurately identifying and forecasting technological convergences in a time of rapid change on the global level. Of the numerous methods that have been applied to meet these demands, patent analysis is the most commonly applied.
Research on technology convergence using patent information has been performed from various perspectives: the purpose, methodology, or object of analysis. However, in spite of their invaluable implications, the previous researchers had several limitations. First, many studies have focused on the convergence among technologies rather than on the convergence among technology and objectives in other fields. Second, previous works have emphasized past trends instead of forecasting based on quantitative data analysis. They have also focused on technology pairs, but convergence is likely to happen within more than three technologies simultaneously. Finally, most studies have either failed to seriously consider the logical issues regarding the selection of specific patent databases used in convergence studies or have merely neglected this aspect.
Therefore, this research proposes a systematic research framework to identify and forecast the technology convergence using the patent data. For this purpose, three modules are suggested. First, selection of the patent DB module is proposed. This module functions as a guideline for selecting the patent DB according to the study’s objectives. Second, the technology convergence module is proposed. This module focuses on identifying and forecasting the status of technology-technology convergence. Of particular interest are how to forecast the technology convergence more easily and accurately and how to visualize the results of such convergence more efficiently. Neural network analysis was applied to improve forecasting performance, and a design structure matrix was used to show the relationships among technologies. Finally, the technology-humanities convergence module is proposed. This module deals with issues with regard to identifying and forecasting the convergence of technology and humanities. Using keywords related to human needs, converging trends and areas of technology-humanities convergence were identified and forecasted through the neural network algorithm proposed in module two.
This will help more researchers and practitioners use the research framework more accurately, easily and efficiently. I believe this research can facilitate and encourage those who are taking charge of new technology developments and strategic policies to support the technology convergence.