Gilad Ravid

Technology assessment (TAS) plays an important role prior to decision making about investments in existing and emerging technologies. The vast amount of data on the web has obviated the perception of using web search engine technology to look for information. However, relying on web search engines in search for relevant information to support TAS processes, decision makers face an abundance of data but are unable to screen noise or find hidden knowledge. This paper proposes a model to build knowledge-added concept map about a specific technology and the development of an underlying knowledge-mapping tool. The proposed knowledge maps are constructed on the basis of a novel method of co-word analysis based on webometric web counts. The approach is demonstrated and validated for a spectrum of information technologies. Results show that the research model assessments are highly correlated with subjective expert (n=136) assessment (r > 0.91), with inter-rater reliability scores being high as well (ICC > 0.92).