• International Journal of Technology (IJTech)
  • Vol 12, No 7 (2021)

A Structural Functional Model for Managing the Digital Potential of a Strategic Innovatively Active Industrial Cluster

A Structural Functional Model for Managing the Digital Potential of a Strategic Innovatively Active Industrial Cluster

Title: A Structural Functional Model for Managing the Digital Potential of a Strategic Innovatively Active Industrial Cluster
Aleksander Babkin, Larissa Tashenova, Dinara Mamrayeva, Tamara Andreeva

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Cite this article as:
Babkin, A., Tashenova, L., Mamrayeva, D., Andreeva, T., 2021. A Structural Functional Model for Managing the Digital Potential of a Strategic Innovatively Active Industrial Cluster. International Journal of Technology. Volume 12(7), pp. 1359-1368

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Aleksander Babkin Institute of Industrial Management, Economics and Trade, Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya Str., 29, St. Petersburg 195
Larissa Tashenova Faculty of Economics, Marketing Department, Y.A. Buketov Karaganda University, Universitetskaya Str., 28, Karaganda 100028, Kazakhstan
Dinara Mamrayeva Faculty of Economics, Marketing Department, Y.A. Buketov Karaganda University, Universitetskaya Str., 28, Karaganda 100028, Kazakhstan
Tamara Andreeva Institute of Economics and Organization of Industrial Production of the Siberian Branch of the Russian Academy of Sciences, pr. Ak. Lavrentieva, 17, Novosibirsk 630000, Russia
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Abstract
A Structural Functional Model for Managing the Digital Potential of a Strategic Innovatively Active Industrial Cluster

The age of digital transformation produced by industrial enterprises shifting to new forms of production and actively using versatile information and communications technologies has made issues with the management of digital potential especially vital. These trends have prompted the formation of integrated intellectual structures, called strategic innovatively active industrial clusters, whose primary unique feature is that they operate on a single digital platform. The purpose of this study is the construction of a structural-functional model for managing the digital potential of a strategic innovatively active industrial cluster to use for creating a methodology to assess this potential. The research methods include analysis and synthesis as well as graphic interpretation and content analysis. The main results of this study are as follows: a structural-functional model has been developed for use in managing the digital potential of a strategic innovatively active industrial cluster that harmoniously reflects the roles of all the subsystems forming the cluster, and a methodology has also been developed based on the created model to help measure the digital potential of a cluster, using one such cluster in the city of St. Petersburg, Russia as an example. The novelty of this study is in its development of a model that fully and comprehensively reflects the special features of managing the digital potential of newly integrated structures such as strategic innovatively active industrial clusters. In addition, the practical significance of the obtained results is that they can be used to calculate the real current values of the digital potential of these clusters and assist in the making of relevant management decisions for operational and strategic planning.

Digital potential; Industrial cluster; Management model; Methodology to assess the digital potential; Single digital platform; Strategic innovatively active industrial cluster

Introduction

    Active digitalization processes have prompted the formation and development of a new type integrated structure: the strategic innovatively active industrial cluster (SIAIC) (Tashenova and Babkin, 2017). The primaryunique feature of this kind of cluster is that it operates on a single digital platform that has been actively introduced for the effective coordination of the activities of all participants in the cluster, including those of industrial, educational, financial, marketing and other enterprises. It should be noted that a distinguishing feature of these clusters is that the participants do not need to be geographically coordinated as connectivity is ensured via the digital platform (Babkin et al., 2020). Separately studying the innovative activity of industrial clusters and the enterprises that form them is significant as understanding the levels of this activity and the degree to which the results of intellectual labor can be commercialized by industrial clusters (including by those of a new type) makes it possible to determine their evolution from a proto-cluster to an SIAIC (Afanasieva et al., 2018; Aleksandrova and Sokolitsyn, 2019; Babkin and Tashenova, 2019; Asaturova and Kochman, 2020)

    Each year, the analytics company Clarivate publishes an analytical report called the Top 100 Global Innovators Report. This report contains the rankings of the leading innovative companies (including those in large industrial integrated structures, such as clusters, holdings, corporations and others.) based on the various innovative products that they have developed and introduced. In 2016, the following countries were the innovation leaders based on the number of innovative enterprises located within them: the United States (39%), Japan (34%) and France (10%). The United States has a large share of innovators due to the concentration of large industrial enterprises and clusters in the country, such as California’s life science clusters (Panetta, 2021). In 2021, the leader was again the United States (42%) followed by Japan (29%), while third place was shared by Taiwan (5%) and South Korea (5%) and the fourth place was taken by China (4%). Switzerland, Germany and France were the followers, each having 3% respectively. Many of the companies in Table 1 are big industrial consortiums. They are also members of regional and national innovatively active industrial clusters. It is important to note that in recent years, there have been a variety of works related and directly dedicated to the key development aspects of integrated industrial structures, namely clusters. It is common for the authors in this field to highlight the links between the level of a region’s economic growth and the development of the industrial clusters and agglomerations within it (Chen et al., 2018) as well as those between the specifics of innovative development projects and the scale of the industrial production in clusters with a variety of levels of innovative activity (Delgado, 2020). Studies have also considered the links between the social status of a cluster and the establishment of new enterprises, the growth of mono-cities (Ivanova et al., 2018; Luo et al., 2020) and university and regional innovative development (Koroleva and Kuratova, 2020; Rodionov and Velichenkova, 2020). These researchers have also assessed the externalities and internalities that allow enterprises in clusters to create and use innovations (Martínez-Cháfer et al., 2021) and transfer technologies (Soares et al., 2020), and they have analyzed the degree to which clusters have affected the productivity levels of the enterprises that are their members (Stichhauerova et al., 2020).

 

Table 1 Five leading innovative companies worldwide

Company name

Country

Industrial sector

3M

USA

Chemistry and materials

ABB

Switzerland

Industrial systems

Abbott

USA

Pharmaceuticals

AGC

Japan

Chemistry and materials

Aisin Seiki

Japan

Automotive

 Note: compiled based on the report by the analytics company Clarivate, Top 100 Global Innovators 2021

 

    A number of publications have sought to analyze the efficiency of cluster innovation policies (Fotso, 2021) and the formation and development of regional and national innovation systems and the methods for assessing them ((Ksenofontova, 2015; Kulagina et al., 2019; Rudskaya et al., 2020; Gutiérrez et al., 2021). They have also studied [.8] [LT9] the interplay between cluster structures and the service sector, including the matters related to meeting the needs of the sharing economy (Mamrayeva and Tashenova, 2017; Mamrayeva and Tashenova, 2020; Zhao et al., 2021).

Given the ever-growing role of information and communications technology and its specific use by SIAICs, new research areas have opened up and been explored in papers discussing the use of artificial intelligence, the industrial internet of things, big dataand cyber-physical systems within smart manufacturing (Durana et al., 2021; Stornelli et al., 2021) as well as of the approaches to management that have resulted in new business models for the age of additive production (Bencsik, 2020; Patalas-Maliszewska and Topczak, 2021).

Some works have sought approaches for analyzing the level of economic security of a cluster (Polyanin et al., 2020), developing methods for assessing and monitoring cluster structures, including digital ones (Kudryavtseva et al., 2020; Lyukevich et al., 2020).

It is obvious that industrial clusters, including SIAICs, have been significantly affected by the COVID-19 pandemic, which has fundamentally changed the nature and quantity of the products being made and has altered the essence of production processes, logistics, investments and other cluster activities (Agus et al., 2021; Dai et al., 2021; Rodionov et al., 2021; Shevtsova et al., 2021), transferring them to the digital environment. Therefore, given the digital transformation of the economy that is currently taking place, the issues related to the management of the digital potential (DP) of industrial clusters, including of SIAICs, are relevant and important areas of study today. Accordingly, the purpose of this study is the creation of a structural-functional model built to help manage the DP of an SIAIC. Furthermore, the objectives of study are to identify the subsystems and ecosystems of the constructed model, determine the position of the organizational economic management mechanism and use the model to elucidate the DP in a case study of the following cluster: Developing Information Technology, Radio Electronics, Tool Engineering, Communications Means and Infotelecommunications of St. Petersburg.

    For the most part, the analysis shows that we still currently face an acute problem related to the management of the DP of a new type of industrial enterprise, namely the SIAIC, thus confirming the significance and relevance of the research presented in this paper. Thus, the authors consider an innovatively active industrial cluster as the object of this study, while the purpose of this study is the development a structural-functional model for managing the DP of an SIAIC to help devise a methodology for assessing the DP of the cluster.

Conclusion

The structural-functional DP management model of an SIAIC developed in this article is presented as the set of interrelated components that comprise it, indicating its scientific novelty. Most important is the digital platform that can be used to coordinate all activities among participants as well as provide an organizational economic mechanism for managing DP.

The model that was built was used to form the methodology for assessing the DP of the studied SIAIC, thus calculating the actual DP values of a genuine operational industrial cluster. This proves that the model and methodology are appropriate and applicable for the performance of practical calculations.

In their further research, the authors plan to study the special features of tool-formation and the scientific-methodological provision of DP management of SIAICs for the period of digital transformation of enterprises and clusters based on the concept of Industry 5.0 as a new trend for social and economic reforms.

The limitations of future research will be related to the study of the possibilities for replacing the sets of quantitative and qualitative data that can be included, taking into consideration of the changes caused by Industry 5.0, in the integrated assessment of DP in the context of the subpotentials identified in the present study and reflected in the created methodology.

Acknowledgement

This research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program Priority 2030 (Agreement 075-15-2021-1333, dated 30.09.2021).


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