• International Journal of Technology (IJTech)
  • Vol 11, No 4 (2020)

Developing Methods to Assess and Monitor Cluster Structures: The Case of Digital Clusters

Tatiana Kudryavtseva, Natalia Kulagina, Alexandra Lysenko, Mohammed Ali Berawi, Angi Skhvediani

Corresponding email: shvediani_ae@spbstu.ru


Cite this article as:
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

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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
Email to Corresponding Author

Abstract
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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

Introduction

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.

Conclusion

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.

Acknowledgement

    This research was supported by the Academic Excellence Project 5-100 proposed by Peter the Great St. Petersburg Polytechnic University.

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