A visual scanning of potential disruptive signals for technology roadmapping: investigating keyword cluster, intensity, and relationship in futuristic data

ABSTRACT

Technology roadmapping of today’s era is necessarily based on comprehensive scanning of various signals with the disruptive potential in future paths of market, product, and technology. Previous attempts of data-driven technology roadmaps have mainly focused on data from such sources as patents and literatures. However, as these sources catalogue posteriori trends of evolution, roadmaps based on these data cannot be counted on to predict disruptions. In this regard, futuristic data in technology foresight websites may provide a better source. The objective of this research, in response, is to develop a support system for technology roadmapping that uses futuristic data. To this end, we suggest keyword-based visual scanning approach involving three keyword maps, used in succession: keyword cluster map, keyword intensity map, and keyword relationship map. Particularly, keyword intensity map is designed using weak signal theory which can help identify the visibility, diffusion, and interpretation of signals.

KEYWORDS:

• Technology roadmapping
• futuristic data
• weak signal
• visual analysis

Notes on contributors

Jieun Kim is a postdoctoral fellow in Data Science for Knowledge Creation Research Center at Seoul National University (SNU). She holds a BS and Ph.D. in Industrial Engineering from SNU. Her research interests are in technology planning and roadmapping, technology intelligence and visualisation.

Yongtae Park is a professor in the Department of Industrial Engineering at SNU. He holds a BS from SNU, and an MS and Ph.D. from the University of Wisconsin-Madison. His research topics cover various areas including technology innovation management and knowledge management.

Younjo Lee is a professor in the Department of Statistics at SNU. He has been recognised for his discovery of Hierarchical Generalised Linear Models and his research topics cover various areas including statistics, data mining, networks and communications.

Additional information

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MOE and MSIP) [grant number 2014R1A1A2054064 and 2011-0030814].