Scientometric analysis and text-mining have been applied to scientific and technological trend-tracking and related scientific performance evaluations for several years in China. Since 2012, NSL-CAS provides CTI (competitive technical intelligence) services based on metrics for supporting R&D decision-making. NSL helps technology-based firms improve their innovation capabilities via CTI, for technology novelty review, selection of innovation paths, product development evaluation, competitor monitoring, identification of potential R&D partners, and support for industrial technology and development strategizing. Scientometric methods have established many indicators for technology analysis that can be applied individually or in combinations. Composite indexes are another useful option. For CTI services, we choose or customize layer or level indexes schemas for different purposes. For supporting industrial technological strategy decision-making and innovation path identification, scientometric indicators can be used for R&D trend analysis. Specifically, in meso-technology analysis, bibliometrics and patent analysis indicators can be combined in accord with different subjects or stages of an emerging technology, whose characteristics can then be reflected by these mixed indicators. Scientometric indicators can profile the framework for research subjects, and patent metrics can describe the technology development trends. In micro-technology analysis, technology trends analysis is used for new technological product development in planning strategy for technology-based firms, and bibliometric indicators can identify directions of related scientific subjects and research directions. In fact, when a client expresses a CTI need, they request the meso- and micro-, and even macro-technology analysis. So when we execute a CTI service, we run an iteration and loop analysis through bibliometric and patent metrics. We focus theme tracing or subject analysis by tech-mining and co-wording. For macro analysis, such as competition from institutions or countries and regions, we pay close attention to the combination of scientometric and patent indicators and appropriate schemas for CTI services.
Author(s): X. Liu , Y. Sun, H. Xu, P. Jia, S. Wang, L. Dong, X. Chen
Organization(s): National Science Library, Chinese Academy of Sciences
Source: Anticipating Future Innovation Pathways Through Large Data Analysis pp 321-339