An exploration to visualise the emerging trends of technology foresight based on an improved technique of co-word analysis and relevant literature data of WOS

ABSTRACT

To explore the possible research fronts and emerging trends of technology foresight, through integrating co-occurrence of keywords and burst terms detection into an improved technique of co-word analysis based on the relevant literature in Web of Science is proposed. Based on the new analytical technique, the links among hot keywords, burst terms are probed, and the core literature related to the emerging trend or interdisciplinary researches are identified. Obviously, the attempts of new bibliometric method in this paper could be a valuable supplement to the traditional co-word analysis; meanwhile, it maybe is much more helpful for those interdisciplinary researchers.

KEYWORDS:

• Technology foresight
• co-word analysis
• burst terms detection
• research front discovery
• association rules mining
• core literature recommending

Acknowledgements

I would like to thank the editors and the anonymous referees of TASM for their detailed comments and very helpful advice for this paper.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes on contributor

Munan Li is an associate professor in the School of Business Administration at the South China University of Technology. He is the depute dean of Guangdong Province Key Lab on innovation methods & DMS. He has the certification of MR-TRIZ level 3 and a professional trainer of TRIZ for SMEs. He holds a degree in automation engineering from the North China University of Technology, and an M.Sc. in the control theory & engineering, a PhD in computer science, and a post-doctoral degree in management science from South China University of Technology. His research interests include innovation management, technology strategy, industry clusters and information technology. His works are partly published in Scientometrics, Advances in Mechanical Engineering, Journal of systems engineering and electronics, etc.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 71673088], Fundamental Research Funds for the Central Universities [grant number 2015XZD15], the Soft Science Research Funds of Guangdong Province [grant number 2015A070704015] and the Innovation Methods Research Funds of Guangdong Province [grant number 2014A040402003].