Mining technology intelligence for policy and strategy development

Foresight

ISSN: 1463-6689

Article publication date: 3 June 2014

472

Citation

Daim, T. (2014), "Mining technology intelligence for policy and strategy development", Foresight, Vol. 16 No. 3. https://doi.org/10.1108/FS-11-2013-0064

Publisher

:

Emerald Group Publishing Limited


Mining technology intelligence for policy and strategy development

Article Type: Editorial From: foresight, Volume 16, Issue 3

Tugrul Daim is a Professor and PhD Program Director at Department of Engineering and Technology Management, Portland State University, Institute for Sustainable Solutions, Portland, Oregon, United States.

The first issue we developed out of the papers presented at the recent Portland International Conference on Management of Engineering and Technology (PICMET) in Vancouver, Canada, focused on the challenges created by the emerging technologies. The papers in that issue presented a review of advances in methods and their applications. This second issue continues to provide different methods and their applications targeting to address similar challenges. The amount of research in this has been growing. The fact that we came up with two issues instead of one is a good indicator of that. The common theme in all papers in this issue is the mining of technology intelligence metrics which include patents, keywords and research and development spending. Authors mine different databases to acquire these metrics and use them to explore policy and strategy options.

There are also five papers in this issue. The first two focus on policies and policy tools targeting emerging technologies. The third paper introduces use of social networking at a country level and links the first two papers to last two papers which also use social networking analysis at different levels.

The first two papers are more focused on technology management at the policy level. Choi et al. provide an insight into the fourth Korean technology foresight project. The paper provides a good framework to those planning to initiate a similar effort. The list of technological trends is also a good reference. The authors use Delphi, keyword search and relationship analyses. Lehtovaara et al. evaluate the relationship between the technological, market and political environment in the wind power industry and their impact on market diffusion. The paper utilizes patent analysis and case study method. Their findings indicate that policies play a critical role.

The last three papers focus on technology dynamics at different levels including country, company and technology. Huang and Shih use social network analysis to explore international technology diffusion. Their approach is unique and provides a new metric to explore technology flows among countries while identifying different roles played by different types of countries. The paper also provides a linkage between the policy-oriented papers in the first half of this issue with those in the second half and using social network analysis at different levels. On the other hand Chen and Pham also use social networking to identify technology flows or knowledge flows as they describe it among companies. Their focus in this paper is dye-sensitized solar cells. Huang and Shih use economic data while Chen and Pham use patents. Another paper by Kwon et al. explores the convergence of bio, nano and information technologies using social network and patent analyses and demonstrates that social network analysis when applied with patent analysis provides critical insight into technology dynamics.

Two papers from the prior issue are the link to this issue. The paper by Preschitschek et al. used patent analysis to study industry convergence while Paananen and Makinen used news media analysis to study technology adoption. Both papers provide examples of mining different types of data for technology intelligence.

We hope that you will enjoy both issues.

Tugrul Daim

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