Suchergebnisse

International audience ; The article takes place in the research area of the ongoing European project 'Cluster Development Med' (Horizon 2020) regarding the innovation and technology in the sustainable development field. Authors suggest a model, which allows to identify the most successful clusters and make comparisons between and within them. The analysis defines where they perform a positive or negative trend in the area examined. The model embraces three dimensions of cluster activity, so called, "Resources, Activities, Processes". They have been determined on the work of the Lithuanian association "Knowledge Economy Forum" , called "Cluster Analysis". Data for practical evaluation purposes were obtained through two stage process. First, an interview on site with the coordinators of the Lithuanian clusters for data collection was done through a questionnaire survey, developed in collaboration with the Science and Innovation Agency of the Lithuanian Government. Secondly, the data have been aggregated by applying one of multi-criteria methods, specifically, Simple Additive Weighting method. Finally, the multi-criteria analysis results were used to estimate the efficiency of the clusters. The results were compared through suggested benchmarking. Suggested model can be used for evaluation of performance of different clusters.

International audience The article takes place in the research area of the ongoing European project 'Cluster Development Med' (Horizon 2020) regarding the innovation and technology in the sustainable development field. Authors suggest a model, which allows to identify the most successful clusters and make comparisons between and within them. The analysis defines where they perform a positive or negative trend in the area examined. The model embraces three dimensions of cluster activity, so called, "Resources, Activities, Processes". They have been determined on the work of the Lithuanian association "Knowledge Economy Forum" , called "Cluster Analysis". Data for practical evaluation purposes were obtained through two stage process. First, an interview on site with the coordinators of the Lithuanian clusters for data collection was done through a questionnaire survey, developed in collaboration with the Science and Innovation Agency of the Lithuanian Government. Secondly, the data have been aggregated by applying one of multi-criteria methods, specifically, Simple Additive Weighting method. Finally, the multi-criteria analysis results were used to estimate the efficiency of the clusters. The results were compared through suggested benchmarking. Suggested model can be used for evaluation of performance of different clusters.