Digital Platform for Modeling the Development of Regional Innovation Systems of Russian Federation
Corresponding email: zhanna.burlutskaya@spbpu.com
Published at : 29 Dec 2023
Volume : IJtech
Vol 14, No 8 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i8.6843
Bolsunovskaya, M.V., Kudryavtseva, T.Y., Rudskaya, I.A., Gintciak, A.M., Zhidkov, D.O., Fedyaevskaya, D.E., Burlutskaya, Z.V., 2023. Digital Platform for Modeling the Development of Regional Innovation Systems of Russian Federation. International Journal of Technology. Volume 14(8), pp. 1779-1789
| Marina V. Bolsunovskaya | Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia |
| Tatiana Yu. Kudryavtseva | Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia |
| Irina A. Rudskaya | Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia |
| Aleksei M. Gintciak | Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia |
| Denis O. Zhidkov | Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia |
| Darya E. Fedyaevskaya | Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia |
| Zhanna V. Burlutskaya | Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia |
The paper aims at the design of a digital tool for
analyzing the impact of scientific and technological progress on socioeconomic
problems and sustainable development of the region. The research focuses on the
consistent development of a digital platform for analyzing and visualizing
digital data on regional innovation development, as well as predicting the
sustainable development of regions based on the available regional
infrastructure of innovation systems and the Russian regions' cluster
structure. When designing the digital platform, we gave special
attention to ensuring efficient data collection, processing, and analysis
processes required for studying the socio-economic system. In the course of the
work, an automated process of working with data was developed. The
digital platform is being developed as a flexible tool for a wide range of
users, from research centers, investors, and private enterprises to individual
users interested in regional innovation development models. As part of the
work, the process of selecting technical tools for the software implementation
of the platform in terms of tasks and technical features of designing digital
platforms is presented. The result of the work is a prototype of the Russian
regional innovation system digital platform with the implemented functionality
of a personal account, a module of simulation experiments, and various
approaches to data analysis and visualization. The research is carried out as
part of a project to develop a digital model of the regional innovation system
of the Russian Federation as a driver of sustainable development.
Digital platform; Information systems; Regional innovation system; Sustainable development
In 2022, the Russian economy faced changes in the foreign
policy and epidemiological situation in the country and around the world that
adversely affected the development, including socioeconomic systems. As a
result, the Russian Federation ranked 47th in the Global Innovation Index in
2022, reflecting the ability of countries to innovate
and the degree of success in their implementation. (Global Innovation Index, 2022).
According to a study by the Higher School of Economics (HSE, 2022),
negative contributions to the index composition were made, among other things,
by the insufficient maturity of the conditions provided for the innovation
creation and distribution cycle, the weakness of the institutional
infrastructure, and the legislative framework under development. At the same
time, the creation of incentives and the necessary infrastructure, the
consolidation of the interests of stakeholders, the coordination of
interaction, and the creation of a regulatory framework (HSE, 2017) relates to
government management measures, as well as financial support for individual
innovative initiatives. However, before selecting management tools and methods,
it is necessary to monitor the current state of the socioeconomic system to
develop the most effective plan (Harwahyu et al., 2022). In addition, the control actions on the management object (the process of
creating and distributing innovations) are performed by the participants of the
process, which requires an open data source. It should provide a way to assess
the current status and predict the system state (Stryn and Rodionova, 2020). Currently, socioeconomic research conducted, including for the purpose of
making informed management decisions, is subject to digitalization trends (Zagloel et al.,
2021; Zvereva et al., 2019). The introduction of digital
platforms is common not only for solving the tasks of state statistical offices
and research centers but also for business tasks (Shastitko and Markova, 2020),
positively influencing profit increase (Cenamor, Parida, and Wincent, 2019), creating flexibility,
expanding the coverage (Sutherland and Jarrahi, 2018) of enterprises'
activities (Gutman
et al., 2022; Koroleva, Baggieri, and Nalwanga, 2020). The use of digital platforms
allows automating of resource management processes (Abd-Rahman
et
al.,
2021), storing and analyzing big data, as well as modeling (Tarasov et al.,
2022), and predicting socioeconomic processes (Baran et al.,
2021; Belov et al., 2021).
In order to monitor the
state of the socioeconomic system of regional innovation development, this
study aims to develop a digital platform as a tool to support informed
management decision-making for a wide range of users, including research
centers, investors, private enterprises, and individual users interested in
regional innovation development models. A special feature of the digital
platform is the improved functionality, which includes not only storage and
visualization of data on the index of innovative development of regions but
also a flexible tool for forecasting indicators of the innovation system by
manually adjusting parameters. Within the project, the following techniques
have been developed: data analysis to assess the resource efficiency of the
regional innovation system, analysis and identification of cluster
characteristics, and analysis of the relationships between the innovation
systems and regional development parameters (Rudskaya et al., 2022; Kudryavtseva
et al., 2021). Each of the techniques is a unique development
and contains many steps implemented in the platform's program code.
The
study examines international experience in the development and use of digital
platforms for solving miscellaneous tasks. The issues of choosing platform
design tools, data processing, and visualization are also raised. The
research is carried out as part of a project to develop a digital model of the
regional innovation system of the Russian Federation as a driver of sustainable
development.
2.1. Analysis of the world experience in the development of
digital platforms
The first stage of the research involved an analysis
of international experience in the development and use of digital platforms in
terms of diverse research tasks and implementation tools (Liu et al.,
2022; Sutherland and Jarrahi, 2018; Evtyanova, 2017). In this
context, a digital platform is defined as a digital tool for handling digital
data, encompassing tasks from collection and storage to analysis and modeling
based on that data. The analysis of the
international experience in developing digital platforms is carried out in
order to study development trends and trends in the design of technical
specifications for the platform. Therefore, the comparison criteria were the
development methodologies and technologies, the scope of the application, and
the app concept. Table 1 presents the results of the analysis of the
international scientific community's experience in developing digital
platforms.
Table 1 Analysis of digital platforms
|
Title |
Technologies used |
Methodologies used |
Scope of application |
|
DIGICOR (Liu et al., 2022) |
Java Messaging Services,
Docker, Amazon Web Services, Java, ActiveMQ, AngularJS |
EDSOA (Event-driven
service-oriented architecture), Microservice infrastructure, FaaS (Function
as a Service) or IaaS (Infrastructure as a Service), OPC Unified Architecture |
The establishment of
cooperative arrangements between small and medium-sized enterprises |
|
Digital web platform for
supercomputer modeling of particle deposition on substrates (Tarasov et al., 2022) |
JavaScript, Vue.js, Node.js,
Express, Sequelize ORM, Quasar Framework, ParaViewWeb, Yaml. |
SSR (Server-side rendering),
SPA (Single page application) |
Process modeling |
|
LMDSS framework (Lean
Manufactory Decision Support System) (Abd-Rahman et al., 2021) |
MySQL, PHP, Kepware |
KBM (Knowledge-Based Modeling) |
Decision support system for
improving the manufacturing process |
|
Analytical platform for
socioeconomic studies (Belov et al., 2021) |
Kafka, Flume, Spark, HDFS,
lizardfs, PostgreSQL, Contour BI, Neo4j |
- |
Support for socioeconomic
oriented applications |
By analyzing
the obtained research results, we identified the general characteristics of the
selected solutions, which were graded as the digital platform development
trends. Web app concepts imply a client-server architecture. In this case, the
platform program code is divided into (1) the client side responsible for
processing the user interface, sending requests to the server, and receiving
and processing responses from it; (2) the server side responsible for
interacting with the platform database, performing resource-intensive data
processing operations and then sending them to the client side. The use of
technologies such as relational (PostgreSQL, MySQL) and non-relational (Neo4j)
databases indicates the importance of assessing the database requirements, the
amount of data, and the audience of the platform users. The use of the
JavaScript programming language and its frameworks (Angular.js, Vue.js) is
considered a certain standard in web interface design.
2.2. Digital platforms for the regional innovation system analysis
Further, we considered similar systems developed by
specialists in order to analyze regional innovation development. This stage
will allow adjusting the functional requirements and addressing options for
visualizing integrated data. The Russian Cluster Observatory is a scientific,
methodological, analytical, and consulting center in the field of regional,
innovation, industrial, and cluster policy (HSE, 2023). The primary platform's visualization tool is a
cartogram. This type of graph is visually intuitive for the user and allows
quick comparison of regions. The platform has a wide register of information
about each specific cluster. Despite the advantages presented, the digital
platform does not provide sufficient tools for analyzing detailed data.
Canadian cluster map (Cluster
Map, 2016) is a more advanced
digital platform in terms of data analytics. This portal provides records of
open industry clusters and regional business environments in Canada. The
platform includes a broad range of data about the region and clusters. However,
the set of subjects for which information is available is limited.
The European Cluster Collaboration Platform (European Cluster Collaboration Platform, 2023) contains information about numerous cluster enterprises around the world.
The portal includes a large number of different filters based on which the
businesses of interest can be found. Note that, as in the case of the Russian
Cluster Observatory, this platform does not provide a methodology for analyzing
complex indicators. Hence, digital platforms
are employed globally as fully functional tools for acquiring, processing, and
analyzing digital data, enabling the prediction of socioeconomic indicator
dynamics. Nevertheless, it is important to recognize and highlight both the
distinctive features and drawbacks of existing solutions.
The cluster analysis algorithm for industries and aspects
of human life in a country is applied in all platforms considered, which
characterizes this method as the commonly used one in such platforms. It is
also worth noting that none of the commercial platforms described above allows
for predicting the development of the system with respect to changing the
parameters of one of the systems. In addition, a significant drawback of all
the presented systems is that they are built on the basis of predefined models
and weights, which, in most cases, were chosen not empirically but according to
the expert method. In practice, this
will mean that when the external environmental conditions (economic, political,
and others) change, it will be impossible to predict the dynamics of the
indicators of the innovation system (Jonny and Toshio, 2021). It is this problem that is planned to be solved by
developing a simulator for modeling scenarios of system development by manually
configuring parameters.
3.1. Selection
of tools and technical implementation
The
choice of development tools is based on the requirements for the platform
functionality and the specifics of its use.
It is necessary to develop two sides of the application: the server one,
which is responsible for interacting with the database and performing data
analytics algorithms demanding computing power, and the client one, which is
responsible for displaying the results and a user-friendly interface (Liu et al., 2022; Shastitko and
Markova, 2020; Constantinides, Henfridsson, and Parker, 2018).
TypeScript
was chosen as the programming language for the server and client sides. This
solution provided the platform's code base consistency. A huge number of
libraries have been developed for JavaScript, the programming language into
which TypeScript is compiled, which simplifies the development process and
provides the necessary functionality of the digital platform. The platform's
code base consistency allows developers to work successfully on both the client
and server sides at the same time. In the case of the discussed above DIGIOR
platform (Liu
et al., 2022), which used Java for the application's
server-side and JavaScript for the client side, separate commands for each side
of the platform would be required. TypeScript also facilitates code reading and
managing by providing a description of types for the JavaScript language.
Since
the digital platform implies the availability of a large amount of analytical
information, data visualization is arranged in the form of various graphs. For
this purpose, the Chart.js library is used, which simplifies the process of
creating graphs in the web interface. The web interface is created by using the
React library. This solution is superior to analogs in that the interface
written with AngularJS (another library for client interface development used
in the DIGIOR platform (Liu et al., 2022)) is difficult to
design and maintain. At the same time, Vue.js, used in a Digital web platform
for supercomputer deposition modeling (Tarasov et al., 2022), is
a fairly new technology, which is why frequent updates can lead to strong
obsolescence of the code or stop its correct operation altogether. Initially,
the SSR (server-side-rendering) concept was applied in the client-side design,
which is why the decision was taken to use the Next.js library, providing tools
for the technology mentioned above. However, later we decided to abandon this
concept since it complicated the CI/CD process and required additional
manipulations to synchronize states and switch to pure React.
An
additional tool for implementing the server part of the platform is the Nest.js
framework, which provides a wide range of solutions for organizing
authorization, authentication, creating a REST API interface, and other
aspects. The architectural solutions provided by it are easily scalable and
well-supported. The technology allows for seamless transitions between
designing client and server sides without necessitating a change in the
programming language. JWT tokens are used to limit access to a registered
user's personal account. Unregistered users can view the available statistics
by region on the platform's home page and on the page of a separate region.
Only registered users have access to the simulator and data download. Some
functions of the personal account are limited by the role system. In addition
to the user, the admin role has been added, which allows access to the
database.
3.2. Data
processing and visualization
Since
the platform under development has a huge number of Russian regional innovation
system indicators, an important task was to conveniently present these data in the
client interface. For these purposes, it was decided to organize the indicators
in the form of various graphs, as shown in Figure 1(a) and (b) and Figure 2,
which made it convenient to conduct a comparative analysis in terms of time and
different regions.
A
comprehensive visualization of the main indicators available on the platform
home page is implemented as a cartogram. This way, the user gets the
opportunity to compare the dynamics of regional innovation development with an
emphasis on territorial affiliation.
In
order to ensure the consistency of data provided, it was decided to use a
minimum set of color solutions since the cartogram is presented exclusively in
green. The shade saturation is determined by the key indicator value. The
general process of data handling when using a digital platform includes 4
stages (Figure 3):
1. Data input. The platform
administrators select the information from open government sources of
statistics necessary for calculating indicators. Then, the initial data
processing is carried out, and they are uploaded to the platform through the
personal account. The client-side initial data is processed on the server,
taking into account the methods used, and then stored in the database. After
the initial data processing, some indicators can already be used for graphical
visualization on the platform pages;
2. Analytical data processing. The
uniqueness of this platform lies in the use of methods for calculating regional
innovation development indicators (Rudskaya et al., 2022;
Kudryavtseva et al., 2021; Kryzhko et al., 2020);
3. Operation. At this stage of the
platform's operation, the user gets the opportunity to perform the necessary
data samples and conduct simulation experiments through the developed
simulator;
4. Data visualization. The
processed data is presented through various graphs and tables, enabling users
to independently assess information pertaining to regional innovation
development. Often, the same data visualization is duplicated in both graph and
table formats, enhancing the ease of perception for new users.
Figure 3 The general process of data handling
3.3. User's personal account
The
user's personal account is designed to provide additional access to the
interactive and computing capabilities of the platform. Within the first prototype
of the user's personal account development, the following concept is assumed:
• The user's personal account contains one
additional page with the user's personal data;
• Access to an additional mode of working
with the platform is provided through the user's personal account;
• An additional mode of working with the
platform allows recalculating the regional innovation index and the region's
rating by changing the values of the calculation subjects.
It is worth noting that according to the
research, the results of which are presented in the previous papers of the
authors, the innovation index depends on the following indicators of regional
development: economic development dynamics, social development dynamics,
digitalization dynamics, activities of business entities, research centers,
universities, and public policy. Each of these factors is divided into many
more specific innovation development indicators.
Changing
the calculation subject value leads to a recalculation of both the innovation
index and each subject contribution. Therefore, in an additional mode,
visualization of data on the share contribution of each subject in graphical
and tabular form is added.
To analyze the contribution of
changes in each of the innovation development index components, the chain
substitution method of factor analysis is applied. Initially, the algorithm
refers to the data on the innovative development index, indicators of actors,
and conditions for a certain region for the year. The last parameters are set
by the user through the interface. Then, differences between the data entered
by the user and the values obtained from the database are found. Factor
analysis is used to assess the impact of changes in these parameters on the
increase or decrease in the index (Starovoytov et al., 2019; Kucherova et
al., 2014). The chain substitution method is implemented by the gradual replacement
of factors. Since the innovation development index of the region depends on 7
indicators, it is required to make 7 substitutions.
At
this stage of development, the possibility of registration and authorization is
implemented. The minimum required data
for registration includes the user name and email address. This list will be
expanded at the next stages of the project. The additional mode functionality
has already been implemented on the server side of the application and will be
further finalized in the user interface.
The
developed digital platform includes a wide range of analytical data on the
innovation development of the Russian regions. The home page of the platform
provides access to regional data, including the regional innovation index,
evaluation of the regional innovation system effectiveness, and information
about regional clusters. Each of the regions can be considered separately from
the others, providing detailed information about the region, such as employment
in various areas of the region, increase or decrease in employment, region's
technical efficiency, indicators of actors, and conditions affecting the
innovation index. The simulator allows for predicting regional innovation
development based on changes in actors and conditions of the region.
The
automated functional and integration tests were developed to enable the
addition of new functions without any changes to the already existing
application logic and monitor the correctness of the interaction between the
platform's code base and external modules, such as a database. The load testing
of the platform revealed the maximum possible throughput of the digital
platform, which coincided with the expected values.
At
the next stage of the digital platform development, it is planned to finalize
the simulator and automate analytical data processing techniques and data
loading modules. Thus, the digital platform does not consider statistics on the
most active regional enterprises that have a special impact on the
socioeconomic development of the region, as is done on the platforms of the
Russian Cluster Observatory (HSE, 2023), Canadian Cluster Map (Cluster Map, 2016),
and Cluster Collaboration Platform (European Cluster
Collaboration Platform, 2023). Such enterprises' register
arrangement, as in the Russian Cluster Observatory (HSE, 2023),
will increase the analytical capacity of the platform.
The research focuses on the consistent development
of a digital platform for analyzing and visualizing digital data on regional
innovation development, as well as predicting the sustainable development of
regions based on the available regional infrastructure of innovation systems
and the Russian regions' cluster structure. Within the current context, the
digital platform is being designed as a tool for studying the impact of
scientific and technological progress on socioeconomic problems and sustainable
regional development. The study provides an analysis of international
experience in the development and use of digital platforms for the analysis of
various socioeconomic systems. Based on the analyzed data and the project
features, functional requirements for the platform are being developed. The
process of selecting technical tools for the software implementation of the
platform in terms of tasks and technical features of designing digital
platforms is also provided. TypeScript was chosen as the main development
language as the most versatile and functional tool for the server and client
sides of the application. In the course of the research, an algorithm for
working with data within a digital platform has been given, taking into account
different user roles. The result of the work is a prototype of a digital
platform for the Russian regional innovation system, featuring implemented
functionalities such as a personal account, a module for simulation
experiments, and diverse approaches to data analysis and visualization. The
results encompass a detailed description of the technical aspects of platform
design and can be adapted for use in other digital solutions. The research is
carried out as part of a project to develop a digital model of the regional
innovation system of the Russian Federation as a driver of sustainable
development.
This work was supported by the Ministry of Science
and Higher Education of the Russian Federation under contract No.
075-03-2023-004 dated 13.01.2023.
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