• International Journal of Technology (IJTech)
  • Vol 11, No 8 (2020)

Method for Evaluating the Digital Potential of a Backbone Innovative Active Industrial Cluster

Method for Evaluating the Digital Potential of a Backbone Innovative Active Industrial Cluster

Title: Method for Evaluating the Digital Potential of a Backbone Innovative Active Industrial Cluster
Larissa Tashenova, Aleksandr Babkin, Dinara Mamrayeva, Ivan Babkin

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Cite this article as:
Tashenova, L., Babkin, A., Mamrayeva, D., Babkin, I., 2020. Method for Evaluating the Digital Potential of a Backbone Innovative Active Industrial Cluster. International Journal of Technology. Volume 11(8), pp. 1499-1508

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Larissa Tashenova 1. Institute of Industrial Management, Economics and Trade, Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya Str., 29, St. Petersburg
Aleksandr 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
Dinara Mamrayeva Faculty of Economics, Marketing Department, Y.A. Buketov Karaganda University, Universitetskaya Str., 28, Karaganda 100028, Kazakhstan
Ivan 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
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Abstract
Method for Evaluating the Digital Potential of a Backbone Innovative Active Industrial Cluster

Issues of digitalizing industrial enterprises and clusters in the formation of Industry 4.0 garner considerable attention. Here, the discussion is about backbone innovative active industrial enterprises, whose appearance is thanks to the evolutionary development of integrated industrial structures, and which are able to actively create, introduce, and commercialize innovative products and to use all the advantages of industrial automation. The authors attempt to develop a method for assessing the digital potential of backbone innovative active industrial clusters. The method is developed based on existing methods for and approaches to evaluating the innovation potential of industrial clusters and the digital potential of industrial enterprises, and makes it possible to calculate a final integral assessment, which includes calculating for each of the seven sub-potentials the parameters identified by experts to be important. In order to confirm the validity of the obtained results, the calculation is carried out using three methods: arithmetic mean, harmonic mean, and weighted geometric mean. The suggested method was successfully tested on the example cluster “Development of information technologies, radio electronics, instrumentation, communication and info-telecommunication devices of St. Petersburg.“

Backbone innovative active industrial cluster; Digital economy; Digital potential; Evaluation method; Innovation

Introduction

The current era of economic development has seen digital technology and the internet significantly transform many manufacturing, logistical, and technological business processes, models and structures. Below is a brief review of publications on the research topic.

Several authors have examined the role of the development of information technology in forming so-called Enterprise 4.0, whose activities are based on the active use of digitalization tools (Moreira et al., 2018). Some works pay special attention to the issues of forming a regional innovation environment, where the questions of developing an ICT sphere play just as important of a role (Rodionov and Rudskaya, 2017). These authors note the importance of issues regarding management in the structure of a synergistic paradigm, which, in turn, are necessary for creating sustainable organizational and economic systems for managing the innovative and digital potential of industrial enterprises and clusters (Babkin et al., 2017).

Works addressing these issues as they relate to the service sector include, for example, research dedicated to the development of the tourism industry and tourism clusters looking at issues of developing a sharing economy in the structure of urban tourism (Mamrayeva and Tashenova, 2017). Prior research also points out the importance of using digital tools when forming effective marketing channels for promoting the goods and services of small and medium businesses (Taiminen and Karjaluoto, 2015; Rahmatin et al., 2018).

Certainly, the development of a worldwide digital economy has led to the creation of an array of information dedicated to various areas of science addressing related problems. For example, in one of his works Bataev analyzes the stages of making and developing a digital economy (Bataev, 2018). A report from the United Nations Conference on Trade and Development, presented in 2019 and dedicated to the main aspects of developing a digital economy, notes the opportunity countries have to use digital data and platforms (UNCTAD, 2019). The work “Industry 4.0: Building the Digital Enterprise” examines promising opportunities for building digital enterprises in conditions referred to as the Fourth Industrial Revolution (Industry 4.0) (Geisbauer et al., 2016). Similar scientific aspects are also reflected in other several scientific works (Bazzoun, 2019; Sutherland and Jarrahi, 2018).

In recent years, research has tackled aspects of technological modeling of the manufacturing process, resource planning of a business based on the wide use of computer technology, cloud storage, and the Internet of Things (Mourtzis et al., 2015). Other scientists have paid special attention to the industrial Internet of Things and, as a whole, issues of digital transformation, while a certain emphasis has been placed on the barriers that can prevent effective informatization and creating Nature 5.0 (Berawi, 2020). Other work takes a broad look at promising areas for introducing and using blockchain technology, as well as problems on the road to the automatization and digitalization of manufacturing, including for industrial enterprises (Babkin et al., 2018; Kudryavtseva et al., 2020). It is important to note that the use of digital tools has been studied not only in the manufacturing activities of industrial enterprises, but also in logistics, marketing (Semenov et al., 2018; Karjaluoto et al., 2015), design (Davies, 2015; Demidenko et al., 2018), in reaching target indicators and key business goals (Nylén and Holmström, 2015), and in building effective digital business strategies able to function in the conditions of Industry 4.0 (Bharadwaj et al., 2013). The formation of integrated structures, including the industrial clusters presented here, is the result of many economic transformations and an understanding of the role of such kinds of associations from the management side of industrial enterprises (Novikov and Babkin, 2014). The concept of a cluster was first introduced by M. Porter, who defined the term “cluster” as a “group of geographically adjacent interactive companies and the organizations relating to them, acting in a specific field and complementing each other” (Porter, 1998).

In general, a “backbone innovative active industrial cluster” (BIAIC) is defined as follows: “This is a group of economic subjects of various areas of activity (research, engineering, manufacturing, and others), developing, possessing, and putting into practice globally competitive technology, based on which systematic inter-sectoral (or, at the very least, sectoral) development is ensured, which allows the goals given above to be achieved by using already existing and introduced digital information platforms, transitioning to new models and forms of conducting business and effectively using innovation projects not taken separately but combined in a way effective for the economy, including the sector, region and cluster” (Tashenova and Babkin, 2017; Babkin and Tashenova, 2019).

Taking into account the specifics of cluster activities of this kind (operating on digital platforms with active use of various digital tools), the issue of evaluating its digital potential becomes especially relevant. It is important to note that the digital potential of BIAIC should be understood as “the combined total of various sub-potentials (logistical, scientific, organizational and managerial, infrastructure, financial and economic, staff, and information and telecommunications), which should reflect two aspects of cluster activity: its possibility and capacity” (Babkin and Tashenova, 2020a).

At the same time, other authors of scientific articles have evaluated some criteria differently, or assessed the digital potential of individual industrial enterprises or industrial organizations, but not integrated structures such as the presented clusters (Kozlov and Teslya, 2019).

Some authors define digital potential as “the combined total of the data itself, software, and technical devices for its storage and processing and for the personnel using this data for management” (Popov and Semyachkov, 2017).

Several articles do not give a clear definition of the concept of “digital potential.” In a 2017 article, however, Kovalchuk and Alekseev write that they understand it as the “assessment monitoring of the indicators of a digital infrastructure and the general structural trends of geolocations” (Kovalchuk and Alekseev, 2017). Yet other researchers consider digital potential in terms of the most important component for the development of the world’s regions, particularly Europe (these authors also point out that Europe realizes only 12% of its digital potential) (Bughin et al., 2016).

The lack of a methodological base determines the need to create a complex quantitative methodology to assess the level of digital potential (DP) of BIAICs based on a selection of various methods, tools, techniques, procedures, and operations. In our earlier studies it was noted that we had not identified any works dedicated to and directly related to the method of assessing the DP of BIAICs; at the same time, a significant number of publications were discovered which were focused on determining the level of innovation potential of industrial enterprises (Babkin et al., 2013; Konkina et al., 2019).

        Our methodology for assessing the digital potential of a backbone innovative active industrial cluster is developed based on an understanding of the unique features of using a specific set of methods, combination of stages, and algorithms for evaluating digital potential. Thus, it is important to note the specifics of the research object in question: the digital potential, appearance, and measurement of which are complex and multi-faceted in nature. Consequently, based on the assessment of the DP of a backbone innovative active industrial cluster, the authors propose using an aggregate of seven sub-potentials, including 75 parameters (at the initial stage; and 32 parameters after conducting an expert evaluation and checking it according to the reliability criterion) (Babkin et al., 2019).

Conclusion

The method presented here allows for an evaluation of the current digital potential of a backbone innovative active industrial cluster; at the same time, it seems practically impossible and, in the authors’ opinion, incorrect to make assessments for the long term, since the digital component of the activity of any industrial enterprise, and all the more that of a complex integrated structure, is subject to frequent changes due to the development, constant improvement, and appearance of new digitalization tools, again underlining the need to reexamine the components of digital potential within specific time periods.

It is important to mention that the evidence on the current state of the digital potential of BIAICs basing their activities on digital platforms is, practically, one of the keys to forming the right set of management solutions.


Acknowledgement

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

Supplementary Material
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