Evaluation of cluster efficiency measurement tool

Abstract

This study aims at verifying the validity of cluster efficiency measurement tool. In previous study benchmarking was employed (Tvaronavičienė et al., 2015) IntroductionAccording to Karaev et al (2007), clusters are recognizable as an important instrument for improving SMEs productivity, innovativeness and overall competitiveness through overcoming their size limitations. Although there are many various studies conducted in different countries but a common understanding of the cluster concept has not been generally accepted yet. One of the most prominent authorities in the field is M. Porter (1990), who claims that national clusters are formed by firms and industries linked through vertical (buyer/supplier) and/or horizontal (common customers, technology, etc.) relationships with the main players located in a single nation/state. Later this definition was supplemented by Porter (1998), who added institutions (formal organizations) such as universities. The ability for a country to form an industrial cluster can be related to its international competitive advantage. Reduced input costs of the manufacturers, development of common suppliers, training of professional labor and a technical knowledge spillover effect can be achieved through the formation of clustering (Hsu, 2014). The effectiveness of a cluster is supposed to be increased by facilitating the transmission of knowledge and the development of institutions, which can be achieved through geographical proximity. Another important feature that is stressed by Porter (1998) is encouraging of innovation through enhanced division of labor among companies with physical proximity among numerous competing producers.Questions related to performance of clusters are widely discussed in scientific literature: researchers discuss such aspects as measuring of innovations (Rezk at al. 2015), approaches and methods of cluster analysis (Tvaronavičienė at al. 2015a;2015b), technology transfer processes and driving forces (Tvaronavičienė, Černevičiūtė 2015;Ignatavičius et al.); composition and governance specifics (Branten, Purju 2015;Fuschi, Tvaronavičienė 2016;Bistrova et al. 2014;Lace et al. 2015; Mentelet al. 2016;Raudeliūnienė et al. 2016); participation of start-ups (Laužikas et al. 2015;Tvaronavičienė 2016). Hence, spectrum of questions related to clustering phenomena is wide; efficiency of functioning issues, as it was mentioned above, is still under discussion.This study aims at verifying the validity of cluster efficiency measurement tool. In previous study benchmarking was employed to compare the performance of participating clusters which would enable to improve their results by getting the information about their strengths and weaknesses over other clusters. You can find the "The Cluster Efficiency Study through Benchmarking" in journal Enterpreneurship and Sustainability Issues.The mentioned study was carried out in order to compare the most successful, in a certain extent, clusters in Lithuania. Benchmarking approach was employed as the most precise technique...

Economy of the Gothic period

“…Literature analysis reveals that clusters and cluster organizations are sector sensitive. The previous studies (Razminienė, Tvaronavičienė & Zemlickienė, 2016;Tvaronavičienė, Razminienė & Piccinetti, 2015;Tvaronavičienė, 2017) showed that every cluster differs depending on its sector. Therefore, involvement in circular economy should be measured according to the industry or type of activity for it is impossible to apply a universal model.…”

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“…Although activities do not reveal definitive figures, they show more personal opinion than numbers in processes. Hence, processes must get more prominence in counting the cluster efficiency (Razminienė et al 2016). …”

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