Technology has become a decisive factor for technology-intensive companies because of its significant influence on product development and process optimization. It is important to identify advantages or barriers of technologies, to compare them as well as to analyze the probability of being substituted. Therefore, scientific researchers and decision-makers in companies address their attention to technology intelligence, which is the sum of methods, processes, best practices, and tools used to identify business-sensitive information about technological developments or trends that can influence a company's competitive position.
The technology intelligence process strides across four levels: data, information, knowledge, and decisions. Data are symbols with no meaning. Information is data that has been given meaning by way of relational connection. Knowledge is the output of scouting, processing, and analyzing information. Decisions are made on the basis of knowledge [4]. Within the framework of technology intelligence, the main task is to procure accurate information about performances and developments of technologies, that is, to identify technology indicators. Technology indicators are those indexes or statistical data that allow direct characterization and evaluation of technologies throughout their whole lifecycles, for example, technological maturity, market segment, degree of innovation, or key player (country, company, …). Those technology indicators offer a direct view of technologies to decisionmakers.
People usually read documents one by one and collect the key information manually. But, the amount of information has dramatically increased in recent years. It is no longer possible to evaluate or characterize technologies by reading documents. Therefore, there is a demand for methods that support technology intelligence by systematically analyzing documents for the purpose of extraction of information relevant to technology indicators. One of the methods, which fulfils the requirements, is bilbiometrics.
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Chang, H., Gausemeier, J., Ihmels, S., Wenzelmann, C. (2008). Innovative Technology Management System with Bibliometrics in the Context of Technology Intelligence. In: Castillo, O., Xu, L., Ao, SI. (eds) Trends in Intelligent Systems and Computer Engineering. Lecture Notes in Electrical Engineering, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74935-8_25
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