This paper introduces a systematic technology trend monitoring (TTM) methodology based on an analysis of bibliometric data. Among the key premises for developing a methodology are: (1) the increasing number of data sources addressing different phases of the STI development, and thus requiring a more holistic and integrated analysis; (2) the need for more customized clustering approaches particularly for the purpose of identifying trends; and (3) augmenting the policy impact of trends through gathering future-oriented intelligence on emerging developments and potential disruptive changes. Thus, the TTM methodology developed combines and jointly analyzes different datasets to gain intelligence to cover different phases of the technological evolution starting from the 'emergence' of a technology towards 'supporting' and 'solution' applications and more 'practical' business and market-oriented uses. Furthermore, the study presents a new algorithm for data clustering in order to overcome the weaknesses of readily available clusterization tools for the purpose of identifying technology trends. The present study places the TTM activities into a wider policy context to make use of the outcomes for the purpose of Science, Technology and Innovation policy formulation, and R&D strategy making processes. The methodology developed is demonstrated in the domain of ''semantic technologies''.

Purpose -The aim of this paper is to identify ways for improvement of the foresight evaluation framework on the basis of analysis and systematisation of accumulated experience in the field of project management.Design/methodology/approach -The paper is based on a detailed literature review devoted to an evaluation of foresight and traditional projects. The approaches to project evaluation in the field of project management were investigated, and the main steps of traditional project evaluation process were determined. The most commonly applied steps of foresight evaluation were identified by the analysis of recent foresight evaluation projects. The comparison of evaluation frameworks for foresight projects and traditional projects allows to provide recommendations for foresight evaluation framework improvement.Findings -The paper identifies several lessons for foresight evaluation from project management. The elements which can enrich foresight evaluation framework are the following: the development of an evaluation model; the extensive use of quantitative methods; the elaboration of evaluation scales; the inclusion of economic indicators into evaluation; and the provision of more openness and transparency for evaluation results.Originality/value -Given the importance of foresight evaluation procedures and the lack of a commonly applied methodological approach, the value of this paper consists in identifying a foresight evaluation framework and enriching it with elements of project management.

No abstract

Technology foresight is mainly conducted by applying a combination of qualitative and quantitative methods. An evidence-based approach implies covering a wide range of information sources, as well as the active application of quantitative methods for processing. Therefore, it is very important to select the right sources of data, extract core information from them, and interpret the results correctly. In theoretical works devoted to identifying technology trends, the most widely used information sources are scientific publications and patents. There are also authors who propose relying on additional sources of data (media, conferences, business-related resources, and others). However, the issue of applicability and comparison of core and extra sources of information for monitoring technology trends has not received sufficient coverage in the literature. In connection with this, the purpose of this paper is to conduct a comparative analysis of the results of technology monitoring by using various information sources (scientific publications, patents, media, foresight-projects, conferences, international projects, dissertations, and presentations). The proposed approach is tested on the area of green energy and the results are described and analyzed. Possible factors that can affect the results of data processing are considered and discussed in order to more efficiently use the comparative analysis of quantitative and qualitative procedures for identifying, correcting, and updating global technology trends on a regular basis. JEL Classification: O31, O32, O33, O38.

No abstract