Developing Methods to Assess and Monitor Cluster Structures: The Case of Digital Clusters
Corresponding email: shvediani_ae@spbstu.ru
Published at : 16 Oct 2020
Volume : IJtech
Vol 11, No 4 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i4.4191
Kudryavtseva, T., Kulagina, N., Lysenko, A., Berawi, M.A., Skhvediani, A., 2020. Developing Methods to Assess and Monitor Cluster Structures: The Case of Digital Clusters. International Journal of Technology. Volume 11(4), pp. 667-676
| Tatiana Kudryavtseva | Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia |
| Natalia Kulagina | Bryansk State University of Engineering and Technology, Bryansk 241035, Russia |
| Alexandra Lysenko | Bryansk State University of Engineering and Technology, Bryansk 241035, Russia |
| Mohammed Ali Berawi | Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
| Angi Skhvediani | Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia |
The purpose of this work is to develop a methodology to
assess and monitor cluster structures. The authors’ proposed method assesses
the level of cluster structure development by considering cluster
transformation analysis in the information and communication sectors of the
regional economy, prerequisites for cluster formation, and the current level of
digital cluster development in the region. To evaluate the prerequisites of
digital economy cluster formation, an integral indicator is calculated and a
multi-parameter approach is used to evaluate cluster effectiveness. The
integral indicator includes 17 values calculated using the scorecard evaluation
method. To make conclusions about the stages of IT cluster development, the
authors provide the scale used to interpret integral indicator values. This
scale classifies cluster development using four levels: beginner, elementary,
intermediate, and advanced. A comparative analysis of IT cluster development in
the Kaluga and Bryansk regions of the Russia reveals that IT clusters in Kaluga
are at an advanced level of development due to its highly developed
infrastructure and work flow organization, while IT clusters in Bryansk are at
the beginner stage. This shows that Kaluga has a more effective industrial
policy for clusters. The proposed methodology allows researchers to compare
clusters from different regions and monitor their development.
Cluster; Innovation; IT cluster; Regional innovation system; Russia
The development of
cluster structures boosts regional and national economic processes, which has a
positive effect on the investment attractiveness and socio-economic potential
of the region and leads to the creation of new enterprises and jobs (Rudskaya
and Rodionov, 2017; Isaksen, 2018; Lehmann and Menter, 2018; Schepinin et al.,
2018).
In the 20th century, clusters began to be considered the most
important factor in regional development (Gutman
et al., 2017; Kozonogova et al., 2019). Regions with developed cluster
structures are more competitive; clusters are a foothold for successful
regional economies. The aggregation of enterprises and organizations into
cluster makes it possible to increase their effectiveness (Kudryavtseva et al., 2020). In addition,
clusterisation can provide higher localization economies from land and
infrastructure usage (Berawi, 2018; Berawi et al.,
2019).
In accordance with the Russian Federation’s two main development
programs—Digital Economy of the Russian Federation (Government of the Russian
Federation, 2017) and Economic Development and
Innovative Economy (Government of the Russian
Federation, 2014)—clusters (digital clusters in
particular) should become one of the main forms of economic activity to ensure
economic growth. Therefore, it is essential to analyze existing approaches to
cluster development evaluation and test them using a Russian digital cluster.
The paper aims to
develop a method for assessing and monitoring cluster effectiveness. To do
this, it is necessary to complete the following tasks:
- consider the existing definitions of “cluster” in the works of Russian and international researchers;
- study scientific approaches used to assess the effectiveness of cluster structures; and
- propose a method for assessing the effectiveness of cluster structures that considers cluster development analysis in the information and communication sectors of the region’s economy, prerequisites for cluster formation, and the current level of digital cluster development in the region.
The
“Literature review” section discusses various definitions of the term “cluster”
present in Russian and international scientific works, as well as existing
approaches for assessing cluster effectiveness. The “Data and method” section
describes the method for evaluating the effectiveness of digital clusters,
taking into account the analysis of prerequisites for cluster formation and the
current level of digital development in the regions. The “Results and
discussion” section discusses the test results for the authors’ method of
assessing the effectiveness of digital economy clusters in the Bryansk region.
The main findings of the work are summarized in the “Conclusions” section.
While the study revealed that there are many definitions of the term
“cluster”, approaches in the literature highlighted the same cluster
characteristics: geographical affiliation, integration of production processes,
relationship between enterprises, and benefits for the enterprises in the
cluster. These approaches to assessing cluster effectiveness can be divided
into the following groups: methods based on measuring individual effects,
methods based on cluster assessment through investment projects, parametric
methods, and methods based on assessing cluster competitiveness. Most available
methods and techniques for assessing cluster effectiveness are related to
industrial clusters and are therefore not applicable to digital clusters.
Based on the results obtained, it is possible to assess a cluster's
capacity for innovative activities in the field of digital products and the
prospects for achieving the main strategic goal of the cluster. The authors
proposed a method that classifies administrative districts by their stage of
digital development. This is a starting point for the digitalization strategy
in the region, as it enables researchers to achieve regional projects’ targets
in the field of digital economy.
This
research was supported by the Academic Excellence Project 5-100 proposed by
Peter the Great St. Petersburg Polytechnic University.
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