Investigating the Evolving Knowledge Structures in New Technology Development
The development of new technology has been identified as one of the key enablers to support business and economic growth in developed countries. For example, the United Kingdom (UK) has invested £968 Million into the creation of Catapult centres to provide ‘pull through’ of low Technology Readiness Level (TRL) research and science. While these Catapults have been instrumental in developing new technologies, the uptake of new technology within industry remains a considerable challenge.
One of the reasons for this is that of skills and competencies, and in particular, defining the new skills and competencies necessary to effectively apply and operate the new technology within the context of the business. Addressing this issue is non-trivial because the skills and competencies cannot be defined a priori and will evolve with the maturity of the technology. Therefore, there is a need to create methods that enable the elicitation and definition of skills and competencies that co-evolve with new technology development, and what are referred to herein as knowledge structures.
To meet this challenge, this paper reports the results from a dynamic co-word network analysis of the technical documentation from New Technology Development (NTD) programmes at the National Composites Centre (NCC). Through this analysis, emerging knowledge structures can be identified and monitored, and be used to inform industry on the skills & competencies required for a technology.
KeywordsKnowledge management Competency mapping Knowledge structures Graph theory Dynamic network analysis Co-word analysis
The work reported in this paper has been funded by the Engineering and Physical Sciences Research Council (EPSRC). Grant references EP/K014196/2, EP/R513556/1 & EP/R013179/1.
- 1.Catapult. https://catapult.org.uk. Accessed 20 Feb 2018
- 3.Hagberg, A.A., et al.: Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in Science Conference (SciPy2008). Pasadena, CA USA, August 2008, pp. 11–15 (2008)Google Scholar
- 6.Liu, Y., et al.: CHI 1994-2013: mapping two decades of intellectual progress through co-word analysis. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 3553–3562. ACM (2014)Google Scholar
- 7.Gopsill, J., et al.: The evolution of terminology within a large distributed engineering project. In: ICED (2015)Google Scholar
- 9.Jones, S., et al.: Subject lines as sensors: co-word analysis of e-mail to support the management of collaborative engineering work. In: ICED (2015)Google Scholar
- 10.Loper, E., et al.: NLTK: the natural language toolkit. In: Proceedings of the ACL-02 Workshop on ETMTNLP, Philadelphia, Pennsylvania, pp. 63–70. Association for Computational Linguistics (2002). https://doi.org/10.3115/1118108.1118117