Gilad Ravid

In today's world, organizations conduct technology assessment (TAS) prior to decision making about investments in existing, emerging, and hot technologies to avoid costly mistakes and survive in the hyper-competitive business environment. Relying on web search engines in looking for relevant information for TAS processes, decision makers face abundant unstructured information that limit their ability to assess technologies within a reasonable time frame. Thus the following question arises: how to extract valuable TAS knowledge from a diverse corpus of textual data on the web? To cope with this question, this paper presents a web-based model and tool for knowledge mapping. The proposed knowledge maps are constructed on the basis of a novel method of co-word analysis, based on webometric web counts and a temporal trend detection algorithm which employs the vector space model (VSM). 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), and with predictive validity value above 85%. Thus, it seems safe to assume that this work can probably be generalized to other domains. The model contribution is emphasized by the current growing attention to the big-data phenomenon.