Developing technologically innovative industrial infrastructural facilities for their better efficiency: case study of technology parks in Russia

Yulia Kokh

SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 3, 2023

Research article

DOI: https://doi.org/10.48554/SDEE.2023.3.4

Developing technologically innovative industrial infrastructural facilities for their

better efficiency: case study of technology parks in Russia

Yulia Kokh*

Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russian Federation

*

Corresponding author: sidorova.iulia2010@yandex.ru

Abstract

T

he article discusses an approach to forming a model for evaluating the efficiency of a typical

technologically innovative industrial infrastructural facility using a nonparametric modelling. The

study models and measures the efficiency of technologically innovative industrial infrastructure

(case study of technology parks in Russia) by using a data envelope analysis (DEA) method. Facilities

are identified as efficient or inefficient from the standpoint of the DEA methodology, and the evaluation

results are compared with the results obtained in the Technopark National Ranking. The article also

presents recommendations for making technologically innovative industrial infrastructural facilities

more efficient in accordance with the results of the modelling; it substantiates the mechanism of ensuring

the competitiveness of technologically innovative industrial infrastructural facilities of the same type,

based on the technical efficiency achieved by a facility, as a result of solving an optimization problem

using the shell data analysis method.

Keywords: technologically innovative industrial infrastructure, development of innovative infrastructure, techni-

cal efficiency, data envelope analysis

Citation: Kokh, Yu., 2023. Developing technologically innovative industrial infrastructural facilities for their

better efficiency: case study of technology parks in Russia. Sustainable Development and Engineering Economics

3, 4. https://doi.org/10.48554/SDEE.2023.3.4

This work is licensed under a CC BY-NC 4.0

49 Sustainable development of regional infrastructure

SUSTAINABLE DEVELOPMENT AND ENGINEERING ECONOMICS 3, 2023

Научная статья

УДК 005.5

DOI: https://doi.org/10.48554/SDEE.2023.3.4

Развитие Объектов Производственно-Технологической Инновационной

Инфраструктуры с Целью Повышения Эффективности Их

Функционирования на Примере Технологических Парков России

Юлия Кох*

Санкт-Петербургский политехнический университет Петра Великого, Санкт-Петербург,

Российская Федерация

*

Автор, ответственный за переписку: sidorova.iulia2010@yandex.ru

Аннотация

С

татья затрагивает вопросы разработки подхода к формированию модели оценки

эффективности типового объекта производственно-технологической инновационной

инфраструктуры посредством непараметрического моделирования. В рамках исследования

смоделирована и оценена эффективность производственно-технологической инновационной

инфраструктуры (на примере технологических парков России) посредством метода оболочечного

анализа данных (Data Envelopment Analysis, DEA), выявлены эффективные и неэффективные

объекты с точки зрения методологии DEA; результаты оценки сравнены с результатами,

полученными в рамках расчета Национального рейтинга технопарков. Автором предложены

методические рекомендации по развитию объектов производственно-технологической

инновационной инфраструктуры с целью повышения эффективности их функционирования с

учетом результатов моделирования; обоснован механизм обеспечения конкурентоспособности

объектов производственно-технологической инновационной инфраструктуры одного

вида, основанный на достижении объектом технической эффективности в рамках решения

оптимизационной задачи посредством метода оболочечного анализа данных.

Ключевые слова: производственно-технологическая инновационная инфраструктура, развитие

инновационной инфраструктуры, техническая эффективность, Data Envelopment Analysis, метод анализа

оболочки данных

Цитирование: Кох, Ю., 2023. Развитие Объектов Производственно-Технологической Инновационной Ин-

фраструктуры с Целью Повышения Эффективности Их Функционирования на Примере Технологических

Парков России. Sustainable Development and Engineering Economics 3, 4. https://doi.org/10.48554/SDEE.2023.3.4

Эта работа распространяется под лицензией CC BY-NC 4.0

Устойчивое развитие региональной инфраструктуры 50

Developing technologically innovative industrial infrastructural facilities for their better efficiency:

case study of technology parks in Russia

1. Introduction

The gradually forming dependence of economic development on institutional conditions and the

larger roles of investment and new technology in accelerating economic growth have led to an inno-

vative economy based on the formation and development of national and regional innovation systems

wherein various innovative infrastructural facilities are growing and spreading. According to Article 2 of

Federal Law No. 127-FZ of 23.08.1996 “On Science and State Scientific and Technical Policy”, innova-

tion infrastructure is a set of organizations that contribute to the implementation of innovative projects,

including through providing managerial, logistical, financial, information, personnel, consulting, and

organizational services.

Technological industrial infrastructure provides innovative production facilities with access to an

industrial environment and is a type of infrastructure for innovative activity (Khanchuk and Semke,

2016). Technological industrial infrastructure for innovative activity includes innovation and technology

centres, technoparks, innovation and industrial parks, technology clusters, technology and innovation

zones, centres for the collective use of technology, business incubators, engineering centres, cluster de-

velopment centres, special economic zones, science cities, advanced development territories, spinouts,

internal ventures, and innovative development zones (Chistyakova, 2018; Zemtsov, 2011).

Innovative infrastructural facilities, which are increasing in number and type, often have subtle

differences, which aggravates the problem of their systematization and the design of programmes for

their development. Moreover, in literature, in practice, and in the reports of regulatory authorities, au-

thors often highlight the insufficient efficiency of innovative infrastructure. No complete evaluation

of the efficiency of (most) types of production and technologically innovative infrastructure has been

presented since most facilities have been in operation for a relatively short time and are significantly

diverse; further, there are either no methods for evaluating their efficiency or these methods have signif-

icant shortcomings.

This study puts forward recommendations for the development of technologically innovative in-

dustrial infrastructural facilities (using an example of technoparks) in order to increase their efficiency.

The objectives of the study were to develop a methodological approach to building a model for eval-

uating the efficiency of an technologically innovative industrial infrastructural facility; test the model

for evaluating the efficiency of technologically innovative industrial infrastructural facilities by using

the example of technology parks in Russia; identify efficient and inefficient technoparks by using the

proposed methodology; verify the adequacy of the model for application; and offer methodological rec-

ommendations for the development of technologically innovative industrial infrastructural facilities in

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order to increase their efficiency.

2. Literature review

An innovation system imposes favourable conditions for economic growth, increasing the compet-

itiveness of enterprises. Among other things, the interaction of players results in the innovative develop-

ment of regions. Considering modern integration, efficient ways of operating innovation systems should

be searched for in order to develop these systems (Solomatina and Slavnetskova, 2017).

Today, the competitiveness of a region’s economy depends not only on innovation but also on

organizational changes that contribute to commercial results – as well as on market techniques imple-

menting and promoting innovation, which often justifies the formation of technologically innovative

industrial infrastructure in a region (Akhmetshin et al., 2017; Rodionov et al., 2019).

Infrastructure develops as part of an evolutionary process, responding to the transformations and

advances of economic systems. The model of infrastructural support in a regional economic system is

in direct relationship with structural changes in economic systems, with the vector of infrastructural

support being chosen when creating an environment that ensures competitiveness (Kalenskaya, 2015).

51 Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4

Kokh, Yu.

Innovation policy tools should be selected to address the specific problems and goals of an innova-

tion system, as well as the peculiarities of administrative structures. The result of an investment policy

depends on how well innovation policy tools are defined, adapted, and correlated with the goals and

problems of the innovation system (Borrás and Edquist, 2013).

The researchers note that there is currently no comprehensive system for monitoring and evaluat-

ing innovation infrastructure in Russia. Moreover, there is a problem of inconsistency of the actual activ-

ities infrastructural innovation facilities are engaged in and the stated goals of creating an environment

that stimulates innovation (Barinova et al., 2014).

Among the main scientific approaches to assessing the efficiency of technologically innovative

industrial infrastructure, the following are worth highlighting:

- evaluating efficiency in a component analysis based on available indicators (assessing the contri-

bution of each component to the total variance) (Latkin and Kharchenkova, 2019);

- using benchmarking technologies (Rodionov et al., 2012);

- evaluating the infrastructure by measuring innovation, with a set of evaluation criteria being

selected for a specific research task. Such criteria can be economic efficiency, scalability, compatibility

with infrastructure, problem-solving in specific industries, compatibility with regulatory requirements,

degree of novelty, etc. (Bhattacharyya et al., 2017);

- making evaluations based on determining the attractiveness of innovative infrastructural facili-

ties for potential residents and investors (Tkachenko and Meteleva, 2019);

- evaluating the impact of the infrastructure on levels of entrepreneurship/startup activity (Au-

dretsch et al., 2015); and

- studying the functional dependencies of innovation production and innovation activity of the

region (Acs et al., 2002).

Researchers outside Russia have noted that the development of technologically innovative in-

dustrial infrastructural facilities should be based on forming development strategies, defining the tech-

nological areas of specialization of the region, building the infrastructure (Yim, 2014), and improving

communications within the facilities (Blasini, 2020).

Strategies for the development of innovative infrastructural facilities are influenced by the digital

transformation of regions (Polyakov and Stepanova, 2020), comprehensive assessment of the level of

economic security of the region given the innovative component (Zaytsev et al., 2021) evaluation of the

structural capital of the innovative infrastructural facilities (Babkin et al., 2022).

In most scientific approaches to evaluating the efficiency of innovative infrastructure, the infra-

structure is analysed as a subsystem of the regional innovation system. The basic principles of evaluation

rely on the availability of information as well as an integrated approach (based on the analysis of a group

of indicators/evaluation areas).

The comprehensiveness of an evaluation, which is often represented by multifactorial/multi-cri-

teria models, is preconditioned by the variety of activities of the infrastructural facility and by the ver-

satility of types of infrastructural facilities. Thus, the researchers propose to evaluate the efficiency of

a regional innovation system (in the context of the dynamics of innovative development indicators) by

considering the development indicators of the innovative infrastructure; we suggest that the efficiency

of infrastructural facilities themselves be evaluated in various areas of activity, with the attractiveness of

the facilities for investors being taken into account.

Given the specifics of technologically innovative industrial infrastructural facilities described

above, it seems that existing approaches are not sufficiently uniform in assessing their efficiency, nor can

Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4 52

Developing technologically innovative industrial infrastructural facilities for their better efficiency:

case study of technology parks in Russia

they adequately assess shortcomings in the operation of these facilities. In this study, we are suggesting

that innovative infrastructural facilities be evaluated in the context of their technical efficiency.

3. Materials and methods

In the literature, authors mainly distinguish technical from allocative efficiency. According to T.C.

Koopmans (1951), a manufacturer achieves technical efficiency if it is technologically impossible to

increase any output and/or to reduce any input without simultaneously reducing other outputs and/or

increasing other inputs.

Technical efficiency characterizes the ability of a decision-making unit (DMU) to efficiently use

available resources. It is always aimed at minimizing resource costs or maximizing outputs with the

available resources.

Facilities within a technologically innovative industrial infrastructure are typically characterized

by similar structures, modes of operation, and management. For our research, it seems appropriate to

study the technical efficiency of innovative infrastructural facilities, since the totality of facilities of the

same type is represented by a homogeneous sample that functions with limited resources and is charac-

terized by approximately the same indicators responsible for the output.

It is proposed to measure the efficiency of technologically innovative industrial infrastructural

facilities using a data envelope analysis (DEA) method. This method has proven itself in measuring the

efficiency of homogeneous facilities.

DEA models have been successfully used in the scientific environment to assess the efficiency of

a regional innovation system (Zemtsov and Kotsemir, 2019; Rudskaya, 2017), innovation activity as a

whole (Feng et al., 2021), individual technologies in the production of equipment (Jie et al., 2012), and

the efficiency of environmental innovation (Yang et al., 2022) and to measure the stimulating effect of

tax incentives on the innovative activity of enterprises (He, 2021).

The DEA methodology defines the “efficiency of operation” of facilities in terms of the efficiency

of converting input parameters into output ones. DEA is a nonparametric evaluation method and can be

used to measure the technical efficiency of facilities. Nonparametric evaluation models are characterized

by the fact that their structure is determined by actual data, while the nature and number of parameters

can be flexible. The DEA method is distribution-free, that is, it can be used independently of the nature

of the data distribution. In the DEA methodology, the results of the determined efficiency coefficients

do not depend on the nature of the data distribution; it is suitable for calculating the desired coefficients

without suggestions about distributions, but the results will correspond to those obtained through stan-

dard multidimensional analysis.

The DEA method is also chosen due to the need to measure the efficiency of facilities by compar-

ing a significant number of indicators that can be expressed in various units of measurement. In addition,

DEA allows one not to test hypotheses about the relationships of the indicators, since the parameters can

be selected by the researcher based on the measurement goals and the specifics of the facilities.

Given the specifics of the objects being evaluated, a number of indicators must be included in the

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evaluation that are difficult to directly relate to resources or outputs. In particular, DEA can take into ac-

count variables that are external to the facility (such parameters are difficult to manage in the short term).

In our case, such specific variables may be the areal characteristics of the property complex of the inno-

vative infrastructural facility (land plots/buildings/premises) or the capacity of energy supply facilities.

The basis of the DEA method is the construction of the efficiency boundary, which is an analogue

of the production function (Alimkhanova and Mitsel, 2019). The production function determines the

maximum output of goods that can be produced from a given number of input parameters (resources)

when using a technology. The production boundary, or the efficiency boundary, is determined in the case

that several types of products are made. In this case, the facilities that show the maximum output from a

53 Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4

Kokh, Yu.

fixed number of input factors will be recognized as efficient: their points in the input–output space will

be at the efficiency boundary. The points at the efficiency boundary will correspond to the facilities that

function inefficiently in terms of converting input parameters into output. At the same time, the degree

of inefficiency of facilities will vary depending on how distant the point is from the efficiency boundary.

Use of the DEA method is suitable for determining a efficiency limit that is not known in practice.

Again, the DEA methodology defines the efficiency of operating facilities in terms of the efficiency

of input parameters converted into output. There are quite a few DEA models. They are usually catego-

rized according to the following criteria (Lissitsa and Babiéceva, 2003):

- type of production function (partially linear, partially nonlinear or partially linear-logarithmic – a

partially linear function is considered in research studies as a simplified normal case);

- orientation of the model (focused on resources or outputs, or a model without orientation); and

- returns to scale (constant or variable).

Researchers choose the model independently, taking into account the objectives of evaluating

efficiency, the type of data analysed, the number of parameters responsible for inputs and outputs, the

limitations of the model, returns to scale, etc.

To analyse the efficiency of technology parks, we choose the basic DEA model (i.e. CCR, standing

for the first letters of the creators’ names: Charnes, Cooper, Rhodes). This model measures the efficien-

cy of the DMU by combining input and output parameters into scalar input and output indicators. This

model corresponds to a partially linear view of the production function.

The model also assumes the presence of constant returns to scale (CRS), so the values of the output

variables change proportionally in accordance with the magnitude of technical efficiency. The input pa-

rameters remain unchanged. For the purposes of our study, it is assumed that it is necessary to set perma-

nent returns to scale, which implies a potentially infinite growth of indicators responsible for the output.

Thus, DEA efficiency is the ratio of the sum of weighted output indicators to the sum of weighted

input indicators (Formula 1).

∑ weighted output indicators

Efficiency = . (1)

∑ weighted input indicators

The advantage of the DEA method is that there is no need to set weights in advance, since the

weights will be determined automatically as the linear programming problem is solved to maximize the

ratio of outputs to inputs.

A distinctive feature of the DEA model is that the result of the evaluation is relative rather than

absolute efficiency; the result indicates the efficiency of a DMU in relation to other DMUs named in the

sample to be evaluated. For the purposes of our research study, this feature is an advantage, as we want

to compare the efficiency of facilities of the same type. The DEA method allows one to determine the

most efficient infrastructural facilities among facilities of the same type, with a measure of inefficiency

being determined for all the rest.

Given that the final set of indicators for evaluating various types of technologically innovative

industrial infrastructure may differ slightly based on the specifics of facilities (for example, the input

indicator for technology parks may be floor area, while the property complex of SEZs and industrial

parks is represented mainly by undeveloped land plots), further building of the model for measuring the

efficiency of infrastructural facilities will be proposed by the authors using an example of technology

parks (technoparks) in Russia.

The next step in building the DEA model is to determine a set of input and output indicators and

collect data on them. We use data from statistical reports on Russian technoparks (with due regard to

Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4 54

Developing technologically innovative industrial infrastructural facilities for their better efficiency:

case study of technology parks in Russia

the rankings by the Association of Clusters, Technoparks and SEZs of Russia) as data for the analysis.

Thus, the DMU whose efficiency is evaluated is a Russian technopark. The indicators for evaluating

technoparks are chosen given the specifics of their operation, the features of the property complex, and

the need to evaluate the infrastructural facility together with its management company.

Technoparks as innovative infrastructural facilities are engaged in activities aimed at creating

favourable conditions for residents to carry out scientific, technical, and innovative activities. A tech-

nopark houses and ensures the development of innovative companies that are its residents. The property

complex of a technopark is represented by a complex of real estate objects with premises for various

purposes (production, offices, administrative spaces, etc.).

The current functioning of technoparks is inseparable from the activities of their management

company. In the past, we found that the management companies of technologically innovative industrial

infrastructural facilities show various economic results and are often loss-making.

Table 1 presents the data on the input and output indicators selected for evaluation.

Taking into account the fact that, objectively, there is a time interval between the formation of

inputs and outputs of technoparks, statistical reporting of 2017 and 2020, respectively, is used as data.

Because the specifics of the functioning of technoparks, the Association of Clusters, Technoparks and

SEZs of Russia justifies a time interval of 3 years, during which the outputs of a technological park are

formed, the authors also chose a time lag equal to 3 years.

Table 1. Input and output parameters of the model

Inputs (2017) Outputs (2020)

1. Number of residents (Residents), units 1. Total amount of tax and customs deductions

of residents, (Deductions to Budget), million

2. Floor area of premises, (Premises), thousand

rubles

square meters

2. Residents’ total revenue, (Revenue), million

3. Total investments by residents, (Investments),

rubles

million rubles

3. Number of intellectual property objects regis-

4. Residents’ R&D costs, (R&D Costs), million

tered by residents, (R&D Outputs), units

rubles

4. Total exports of products from technopark

5. Investments in technopark infrastructure,

residents, (Exports), million rubles

(Public Funds), million rubles

5. Average revenue of the management company,

6. Investments in technopark infrastructure,

(Revenue of MC), million rubles

(Non-Public Funds), million rubles

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Source: compiled by the authors.

The Open Source DEA analysis package was used for the calculations.1

4. Results and Discussion

Table 2 presents the results of the calculations for the selected output model.

Table 2. DEA results

DMU Name Efficiency Value Efficiency

SIGMA. Novosibirsk Technopark 0.116749673 No

High Technology Park, KhMAO-Yugra 0.159961673 No

High Technology Park in the Republic of Mordovia 0.187029155 No

Moscow Technopolis 0.230380186 No

Open Source DEA. URL: https://opensourcedea.org/

1

55 Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4

Kokh, Yu.

DMU Name Efficiency Value Efficiency

Scientific Technology Park of Novosibirsk Akademgorodok 0.243049062 No

Nanotechnology Center “Tekhnospark” 0.317588322 No

Sarov Technopark 0.332998344 No

Mosgormash Technopark 0.362845452 No

Center for Nanotechnology and Nanomaterial in the Republic of Mor- No

dovia 0.386907389

Lipetsk Technopark 0.513586331 No

Yakutia Technopark 0.670842295 No

West Siberian Innovation Center 0.793345412 No

High Technology Park “Zhigulevskaya Dolina” 0.997106825 No

Electropolis Industrial Technopark 1 Yes

Idea Innovation-Industrial Technopark 1 Yes

IKSEl Industrial Technopark 1 Yes

Polus Technopark 1 Yes

Mayak Technopark 1 Yes

Strogino Technopark 1 Yes

Kalibr Technopark 1 Yes

Elma Technopark 1 Yes

High Technology Park “IT Park” 1 Yes

Ankudinovka High Technology Park 1 Yes

St. Petersburg Technopark 1 Yes

Istok Technopark 1 Yes

Yablochkov Technopark 1 Yes

Kuzbass Technopark 1 Yes

Rameev High Technology Park 1 Yes

Kosmos-Neft-Gas Technopark 1 Yes

High Technology Park of Sverdlovsk Oblast 1 Yes

Idea-Yugo-Vostok Innovative Technology Park 1 Yes

Ulyanovsk Nanocenter (ULNANOTECH) 1 Yes

Podolie Technopark 1 Yes

Slava Technopark 1 Yes

Contact Technopark 1 Yes

Source: compiled by the authors

Thus, according to the results of the DEA, 22 technoparks are within the border of efficiency (i.e.

they are efficient; the value of their technical efficiency = 1) in the methodology we use. Thirteen tech-

nology parks do not seem to function efficiently. Figure 1 shows the remoteness of each from the border

of efficiency.

The West Siberian Innovation Center is closest to achieving technical efficiency (efficiency value

0.8). The developing technoparks with efficiency indicators of 0.5–0.8 are the Yakutia Technopark and

the Lipetsk Technopark. The remaining technoparks show relatively low levels of technical efficiency

(<0.5).

Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4 56

Developing technologically innovative industrial infrastructural facilities for their better efficiency:

case study of technology parks in Russia

Figure 1. Remoteness of technology parks from the efficiency boundary

Table 3 presents a comparison of the efficiency evaluation results of the Russian technoparks ob-

tained by the Association of Clusters, Technoparks and SEZs of Russia as part of the National Rankings

of Technoparks for 2019-2020 and the results obtained by the DEA method.

The technoparks whose efficiency evaluation results differ significantly are highlighted in the table.

Table 3. Comparing the evaluation of the efficiency of Russian technoparks

National Ranking of Russia Tech-

DEA

noparks 2019–2020

Technopark

Level (authors’

Efficiency, % Efficiency Level Efficiency Value

interpretation)

SIGMA. Novosibirsk Tech-

98.53% Moderately high 0.1 Weak

nopark

High Technology Park,

111.91% Highest 0.2 Weak

KhMAO-Yugra

High Technology Park in the

100.62% High 0.2 Weak

Republic of Mordovia

Moscow Technopolis 124.77% Highest 0.2 Weak

Scientific Technology Park of

99.77% Moderately high 0.2 Weak

Novosibirsk Akademgorodok

Nanotechnology Center “Tekh-

166.30% Highest 0.3 Weak

nospark”

Sarov Technopark 92.84% Moderately high 0.3 Weak

Mosgormash Technopark 82.48% Sufficient 0.4 Weak

Center for Nanotechnology

and Nanomaterial in the Re- 102.61% High 0.4 Weak

public of Mordovia

Lipetsk Technopark 78.85% Sufficient 0.5 Developing

Yakutia Technopark 92.55% Moderately high 0,7 Developing

West Siberian Innovation

90.28% Moderately high 0,8 Developed

Center

57 Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4

Kokh, Yu.

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National Ranking of Russia Tech-

DEA

noparks 2019–2020

Technopark

Level (authors’

Efficiency, % Efficiency Level Efficiency Value

interpretation)

High Technology Park “Zhigu-

118.03% Highest 1 Efficient

levskaya Dolina”

Electropolis Industrial Tech-

90.54% Moderately high 1 Efficient

nopark

Idea Innovation-Industrial

110.73% Highest 1 Efficient

Technopark

IKSEl Industrial Technopark 57.90% Sufficient 1 Efficient

Polus Technopark 125.60% Highest 1 Efficient

Mayak Technopark 56.67% Sufficient 1 Efficient

Strogino Technopark 117.20% Highest 1 Efficient

Kalibr Technopark 111.48% Highest 1 Efficient

Elma Technopark 135.49% Highest 1 Efficient

High Technology Park “IT

107.23% High 1 Efficient

Park”

Ankudinovka High Technolo-

88.66% Sufficient 1 Efficient

gy Park

St. Petersburg Technopark 102.17% High 1 Efficient

Istok Technopark 82.02% Sufficient 1 Efficient

Yablochkov Technopark 75.12% Sufficient 1 Efficient

Kuzbass Technopark 82.21% Sufficient 1 Efficient

Rameev High Technology Park 96.64% Moderately high 1 Efficient

Kosmos-Neft-Gas Technopark 73.55% Sufficient 1 Efficient

High Technology Park of

138.66% Highest 1 Efficient

Sverdlovsk Oblast

Idea-Yugo-Vostok Innovative

52.78% Sufficient 1 Efficient

Technology Park

Ulyanovsk Nanocenter (UL-

104.74% High 1 Efficient

NANOTECH)

Podolie Technopark 64.80% Sufficient 1 Efficient

Slava Technopark 134.24% Highest 1 Efficient

Contact Technopark 78.12% Sufficient 1 Efficient

Source: compiled by the authors

Thus, significant differences in efficiency were revealed in relation to 18 technoparks out of 35.

The reliability of the model we propose for evaluating the efficiency of innovative infrastructural

facilities should be verified. Thus, it seems reasonable to confirm the significance of the factors selected

for the model and their impact on the outcome.

Researchers have identified several ways to justify the factors selected for DEA models (Nataraja

and Johnson, 2011). They include: the Pastor test, which can be used to assess the significance of the

input variables selected for the analysis by evaluating the model when they are excluded from it (Pastor

et al., 2002); regression models; principal component analysis (factor analysis); and bootstrap analysis.

To establish the significance of the selected variables, we built multiple regression models. Since

regression analysis is sensitive to the type of data distribution, we normalized the distribution of the

source data (by logarithm) and checked the normality of the distribution based on the analysis of de-

Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4 58

Developing technologically innovative industrial infrastructural facilities for their better efficiency:

case study of technology parks in Russia

scriptive statistics using SPSS.

The results obtained from the regression models show that all the independent variables selected

for the efficiency assessment model are significant for it. Therefore, the presented model is adequate

and applicable for taking measures to evaluate the efficiency of technologically innovative industrial

infrastructural facilities and to put forward recommendations for their more efficient operation and de-

velopment.

Researchers often consider the impact of innovative infrastructure on the competitiveness of a

region’s economy (Rodionov and Sedov, 2013). However, thanks to the many types of innovative infra-

structure now in use, there is competition for residents and investment between them. This is evident,

among other things, in the blurring of fundamental differences between technologically innovative in-

dustrial infrastructural facilities, which, in fact, grant their residents access to a production environment,

providing them with space and infrastructure.

The advantages of the DEA method include the ability to determine the target values of inputs

and outputs for each technopark whose efficiency is determined as insufficient and to allow the former

to achieve technical efficiency. Better efficiency of technologically innovative industrial infrastructural

facilities and their increased competitiveness (among facilities of the same type) can be ensured with

higher technical efficiency.

Projecting the point of an inefficient facility onto the efficiency boundary relies on the basic posi-

tion of the DEA methodology, according to which if there are DMUs that manufacture a certain quantity

of products from a limited number of factors, then an inefficient DMU can use the same number of fac-

tors of production to make the same quantity of product. Thus, the competitiveness of technologically in-

novative industrial infrastructural facilities can be formed if their technical efficiency is ensured among

infrastructural facilities of this type (Figure 2).

Focus on inputs or

Choose the DEA model for evaluation

outputs, etc.

Select the indicators for evaluation Consider the goals and specifics

that are responsible for inputs and of operation of this type of

outputs infrastructural facilities

Collect the statistics for the indicators Check the adequacy of the model

selected for this type of infrastructural by identifying the significance of

facilities the indicators

Identify the technical efficiency of the

facility among this type of

infrastructural facilities using the DEA

method

In case technical efficiency is not Make managerial decisions for

Check the achievement of technical

achieved, determine the target values achieving the level of the target

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efficiency

of the recommended indicators indicators

Figure 2. Forming the competitiveness of technologically innovative industrial infrastructural facilities

of a single type by ensuring technical efficiency

According to the analysis, it can be stated that the efficiency of innovative infrastructural facilities

can be influenced both by public authorities (which take part in funding the construction of engineer-

59 Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4

Kokh, Yu.

ing, transport, and other infrastructural facilities, grant incentives via legal regulation, etc.) and by the

innovative infrastructural facility itself through the management company, which ensures the attraction

of residents to the territory of the facility, management of the property complex, and the provision of

various kinds of services to residents aimed at improved the results of innovation and economic activity.

Let us consider the methodological model for developing innovative infrastructural facilities by

making them more efficient (Figure 3).

Attract residents to innovative infrastructural facilities

Form positive experience in developing

Provide access to cutting-edge Increase the attractiveness of resident Increase information openness of

and implementing commercially

equipment and technology status innovative infrastructural facilities

effective innovative projects

Develop property complex and infrastructure

Equip the territory of the facility Create property objects for various

Build engineering-technological

Develop auxiliary types of purposes (technical, production, Attract private funds for

infrastructure on the territory of the with communal and transportation

innovative infrastructure administrative facilities, offices, building infrastructure

facility infrastructure, energy carriers warehouses)

Create and develop Use public-private partnership

Build social and service

modern Management companies receive mechanisms, life cycle contracts for Build residential premises on

infrastructure on the territory of the

telecommunication and state property in trust implementing big infrastructural the territory of the facility

facility

digital infrastructure projects

Increase investment levels of residents; increase investment in R&D

Apply a progressive scale of

Guarantees to the residents Contribute to the protection of

incentives and preferences Develop the financial Attract subsidies and grants

that deploy their production intellectual results

depending on investment innovative infrastructure

facilities on a long-term basis

levels

Public-funds Non-Public-funds

Residents Property complex Investment Investment in R&D investments in investments in

infrastructure infrastructure

Deductions to the

Residents’ revenue R&D results Export Revenue of the MC

budget

Analyse the outputs of the infrastructural facility

Figure 3. Methodological model for developing innovative infrastructural facilities and increasing

their efficiency with key outputs

The presented model reflects the previously established links between the variables and contrib-

utes to improved efficiency of infrastructural facilities by using their key inputs.

Let us take a closer look and put forward methodological recommendations to management bod-

Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4 60

Developing technologically innovative industrial infrastructural facilities for their better efficiency:

case study of technology parks in Russia

ies (management companies, authorized public authorities) for developing the main areas of innovative

infrastructural facilities.

Actions aimed at attracting residents to technologically innovative industrial infrastructural fa-

cilities.

1. Provide access to cutting-edge equipment and technology.

The attractiveness of an innovative infrastructural facility for residents is primarily ensured by

the possibility of using the expensive innovative devices, equipment, modern telecommunications, and

digital technology needed in research and development.

Thus, in order to attract residents, the management company of an innovative infrastructural facil-

ity should:

- provide residents with access to high-quality, advanced engineering and other technical infra-

structure necessary for the organization of the production process (on favourable terms);

- provide access to modern telecommunications and digital infrastructure;

- ensure stability of the residents’ business conditions; and

- develop different kinds of services rendered to residents and give them privileged conditions.

2. Form positive experience in the development and implementation of commercially efficient

innovative projects in order to attract innovative industrial companies as residents.

3. Increase the attractiveness of resident status by providing various kinds of incentives (tax, cus-

toms, property). It is possible to consider the issue of exemption from individual payments for a period

of 3 years or for the management company to provide residents with additional incentives, apart from

those defined by the legislation for the type of facility.

4. Increase the information openness of innovative infrastructural facilities.

It is necessary to create and develop geoinformation systems containing comprehensive informa-

tion about the design and functioning of innovative infrastructural facilities.

It also seems that attracting residents will be facilitated by holding various conferences, com-

petitions for non-residents, advertising, congress and exhibition activities, implementing educational

programmes, etc.

Actions aimed at developing the property complex and infrastructure of technologically innovative

industrial infrastructure.

1. Develop different types of related innovative infrastructure in facilities in order to provide res-

idents with a full range of services (engineering centres, spinouts, collective use centres, cluster de-

velopment centres, prototyping centres, business incubators, accelerators, etc.) and develop consult-

ing innovative infrastructure (organizations providing services on the problems of intellectual property,

standardization, licensing, etc.).

2. Provide the best engineering possible on the territory of the facility, including housing and com-

munal services, transport infrastructure, energy carriers, and real estate objects that can serve various

purposes (technical, industrial, and administrative buildings, offices, warehouses).

3. Build social and service infrastructure on the territory of the facility.

4. Build residential premises on the territory of the facility.

5. Use public–private partnership mechanisms and life cycle contracts for large infrastructure proj-

ects.

61 Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4

Kokh, Yu.

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6. Allot state property to management companies in trust.

7. Increase the percentage of used area in the facility and optimize maintenance costs of the prop-

erty complex.

8. Increase the profitability of public funds invested in the infrastructure of facilities by attracting

more investments per 1 ruble of public investments.

9. Ensure the output of products with a big share of value added by combining projects into tech-

nological chains.

10. Engage external investors in building infrastructure by increasing the attractiveness of work

with residents of infrastructural facilities (lower logistics costs when goods are received from manufac-

turers, no customs barriers in work with residents, etc.).

Investors can be large companies interested in acquiring high-quality innovative products from

the residents of facilities. In this case, investors will be sought by the management company, which can

work actively in the information field: present the residents’ products, search for long-term sales chan-

nels, and promote innovative products made at the facility.

Large residents can also take part in funding the building of infrastructure. According to the de-

cision of the management company, the costs they incur can be partially reimbursed by providing them

with additional incentives and services.

Actions aimed at increasing investment in industrial and technological innovative infrastructural

facilities on the part of residents for acquiring and creating fixed assets, for building and reconstruction

(expansion, modernization), etc.

1. Provide guarantees preserving the lease conditions of residents that deploy their production

facilities on a long-term basis at the facility.

2. Apply a progressive scale of incentives and preferences depending on investment level.

3. Develop the financial innovative infrastructure, including various foundations: venture, insur-

ance, public, investment foundations; attract leasing companies, banking and other credit organizations,

business angels, and other development institutions to finance projects.

Actions aimed at increasing investments in R&D.

1. Assist in protecting the results of intellectual activity by co-financing residents’ costs of main-

taining patents, and help them protect R&D results outside the Russian Federation in countries chosen

by the right holder (legal assistance and co-financing of costs).

2. Develop venture financing mechanisms for promising innovative projects and production facil-

ities, including for the terms of co-financing of projects by the management company.

3. Attract subsidies (for reimbursing some R&D costs, some costs related to paying interest on

loans, etc.) and grants.

In short, the methodological model is aimed at developing innovative infrastructural facilities by

encouraging measures for the formation and use of the key inputs of these facilities. We refer to resi-

dents, the property complex of the facilities, investments in R&D, and investing public and non-public

funds in the engineering, transport, social, and other infrastructure of such facilities.

It seems that implementation of the proposed methodological model can provide innovative infra-

structural facilities with the inputs necessary to increase outputs.

Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4 62

Developing technologically innovative industrial infrastructural facilities for their better efficiency:

case study of technology parks in Russia

5. Conclusion

This study proves that its model is appropriate for investigating the technical efficiency of innova-

tive infrastructural facilities, as the totality of facilities of the same type is represented by a homogeneous

sample that functions with certain limited inputs and is characterized by similar indicators responsible

for the outputs.

According to the analysis, the efficiency of an innovative infrastructural facility is influenced both

by public authorities and by the facility through its management company.

The main results of the study are as follows:

1. The study proposes and justifies the choice of a model for analysing the efficiency of technolog-

ically innovative industrial infrastructural facilities. It substantiates a set of input and output indicators

for evaluating the efficiency of technology parks in Russia, given the specifics of their functioning as

well as the distinctions of their property complexes.

2. The study evaluates the efficiency of technology parks in Russia by using the DEA method. It

compares the efficiency of Russian technoparks as evaluated by the Association of Clusters, Technoparks

and SEZ of Russia in the National Rankings of Technoparks for 2019–2020 with the results obtained by

the DEA method. The reliability of the proposed model is confirmed with the significance of the selected

factors and the influence of each on the output, which is supported by the regression models we built.

3. The study puts forward recommendations for improving the competitiveness of technologically

innovative industrial infrastructural facilities and thus ensuring their technical efficiency among innova-

tive infrastructural facilities of the same type.

4. Based on the analysis and the relationships identified between the variables, we propose a meth-

odological model for developing innovative infrastructural facilities based on improving their efficiency

with key inputs. We point out the main measures aimed at attracting residents to facilities, developing

the property complex, and increasing the volume of investments made by residents and the volume of

investments in R&D.

Our methodological model for the development of innovative infrastructural facilities is aimed at

developing these facilities through measures for their formation and use of main inputs. The model re-

flects the relationships between the variables previously set in the study. The results of the study can be

used in the practical activities of the management bodies of innovative infrastructural facilities as well

as by specialized public authorities pursuing state policy in the formation and development of innovative

infrastructural facilities.

References

Acs, Z., Anselin, L., Varga, A., 2002. Patents and innovation counts as measures of regional production of new knowledge. Res. Policy 31(7), 1069–

1085. https://doi.org/10.1016/S0048-7333(01)00184-6

Akhmetshin, E., Barmuta, K., Yakovenko, Z., 2017. Advantages of cluster approach in managing the economy of the Russian Federation. Int. J. Appl.

Bus. Econ. Res. 15(23), 355–364.

Alimkhanova, A.N., Mitsel, A.A., 2019. Evaluating the efficiency of enterprises using the DEA method. TUSUR Reports 22(2), 104–108.

https://doi.org/10.21293/1818-0442-2019-22-2-104-108

Audretsch, D.B., Heger, D. & Veith, T., 2015. Infrastructure and entrepreneurship. Small Bus. Econ. 44, 219–230. https://doi.org/10.1007/s11187-014-9600-6

Babkin, A., Alekseeva, N., Tashenova, L., Karimov, D., 2022. Study and assessment of the structural capital of an innovation industrial cluster. Sustain.

Dev. Eng. Econ. 2, 4. https://doi.org/10.48554/SDEE.2022.2.4

Barinova, V.A., Maltseva A.A., Sorokina A.V., Eremkin V.A., 2014. Approaches to evaluating the efficiency of innovative infrastructural facilities in

Russia. Innovations 3(185), 42–51.

Bhattacharyya et al., 2017. Criteria to assess potential reverse innovations: opportunities for shared learning between high- and low-income countries.

Glob. Health. 8. https://doi.org/10.1186/s12992-016-0225-1

Blasini, B.S., Dang, R.J., Minshall, T., Mortara, L., 2020. The role of communicators in innovation clusters, in: Pfeffermann, N. (ed.) New Leadership

in Strategy and Communication. Springer, Cham. https://doi.org/10.1007/978-3-030-19681-3_9

Borrás, S., Edquist, C., 2013. The choice of innovation policy instruments. Technolog. Forecast. Soc. Change. 80(8) (October), 1513–1522.

https://doi.org/10.1016/j.techfore.2013.03.002

Chistyakova, O.V., 2018. Industrial and technological infrastructure of innovative entrepreneurship in resource-oriented regions Stud. Baikal State

Univ. 28(4), 682–693. https://doi.org/10.17150/2500- 2759.2018.28 (4).682-693

Feng, Y., Zhang, H., Chiu, Yh. et al., 2021. Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage

63 Sustain. Dev. Eng. Econ. 2023, 3, 4. https://doi.org/10.48554/SDEE.2023.3.4

Kokh, Yu.

meta-frontier dynamic network DEA model. Scientometrics 126, 3091–3129. https://doi.org/10.1007/s11192-020-03829-3

He, Y. 2021. Research on incentive effect of tax preference on enterprise innovation based on DEA model, in: Jain, L.C., Kountchev, R., Tai, Y. (eds.)

3D Imaging Technologies—Multidimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, Vol.

iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.

236. Springer, Singapore. https://doi.org/10.1007/978-981-16-3180-1_9

Jie, D., Yong, L., Yanhong, W., 2012. Study on the evaluation of technological innovation efficiency of equipment manufacturing industry in China

based on improved DEA, in: Gaol, F. (ed.) Recent Progress in Data Engineering and Internet Technology. Lecture Notes in Electrical Engi-

neering, Vol. 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28798-5_2

Kalenskaya, N., 2015. The formation of the model of the infrastructural ensuring of regional innovative development. Procedia – Soc. Behav. Sci. 177,

203–208. https://doi.org/10.1016/j.sbspro.2015.02.386

Khanchuk, N.N., Semke, Yu.S., 2016. The role of innovation infrastructure in the regional innovation system. Bull. Trans-Baikal State Univ. 22(6),

124–133. https://doi.org/10.21209/2227-9245-2016-22-6-124-133

Koopmans, T.C., 1951. Analysis of production as an efficient combination of activities, in: T.C. Koopmans (ed.). Activity Analysis of Production and

Allocation. John Wiley & Sons, New York, pp. 33–37.

Latkin, A.P., Kharchenkova E.V., 2019. Innovative approach to evaluating the efficiency of territories with special economic status on the Far East.

Azimut of Sci. Res.: Econ. Manag. 8. No. 1(26), 381–384. https://doi.org/10.26140/anie-2019-0801-0091

Lissitsa, A., Babiéceva T., 2003. Data Envelope Analysis (DEA). Modern Methodology for Measuring the Efficiency of Production, Dis-

cussion Paper, No. 50, Institute of Agricultural Development in Central and Eastern Europe (IAMO), Halle (Saale). URL:

http://nbn-resolving.de/urn:nbn:de:gbv:3:2-23263

Nataraja, N.R., Johnson, A.L., 2011. Guidelines for using variable selection techniques in data envelopment analysis. Eur. J. Oper. Res. 215(3),

662–669. https://doi.org/10.1016/j.ejor.2011.06.045

Pastor, J., Ruiz, J., Sirvent, I., 2002. A statistical test for nested radial DEA models. Oper. Res. 50(4), 728–735. https://doi.org/10.1287/opre.50.4.728.2866

Polyakov, R., Stepanova, T., 2020. Innovation clusters in the digital economy, in: Ashmarina, S., Mesquita, A., Vochozka, M. (eds.) Digital Transfor-

mation of the Economy: Challenges, Trends and New Opportunities. Advances in Intelligent Systems and Computing, Vol. 908. Springer,

Cham. https://doi.org/10.1007/978-3-030-11367-4_19

Rodionov, D.G., Kichigin O.E., Silantieva T.N., 2019. Specifics in evaluating the competitiveness of an innovative regional cluster: institutional ap-

proach. Sci. Tech. Bull. SPbPU. Econ. Sci. 12(1), 43–58. https://doi.org/10.18721/JE.12104

Rodionov, D.G., Sedov, A.I., 2013. Innovative infrastructure as an element ensuring competitiveness of a region (case study: the Republic of Mordo-

via). Sci. Tech. Bull. St. Petersburg Polytech. Univ. Econ. Sci. 1–2(163), 95–102.

Rodionov, D.G., Tsypkin, Yu.V., Sinelnikova, S.S., 2012. Efficiency of technoparks and business incubators. Sci. Techn. Bull. St. Petersburg Polytech.

Uni. Econ. Sci. 4(151), 150–159.

Rudskaya, I.A., 2017. Evaluating the efficiency of the Russian regional innovation system by the phases of the innovation process. Bull. Volgograd

State Univ. Ser. 10, Innovative Activities 11(3), 23–34. https://doi.org/10.15688/jvolsu10.2017.3.4

Solomatina, N.A., Slavnetskova, L.V., 2017. The coordination center of cluster development as the basis for the functioning of the innovation system in the

time of integration. Bull. Saratov Univ. New Series. Econ. Manag. Law. 17(1), 61–68. https://doi.org/10.18500/1994-2540-2017-17-1-61-68

Tkachenko, I.N., Meteleva, M.A., 2019. Evaluating the quality of public management of infrastructure objects of investment activity at the meso-level

based on the attractiveness ranking of objects for potential investors. Bull. Kemerovo State Univ. Series: Pol., Sociolog. Econ. Sci. 4(1),

149–158. https://doi.org/10.21603/2500-3372-2019-4-1-149-158

Yang, J., Wu, J., Li, X., et al., 2022. Sustainability performance analysis of environment innovation systems using a two-stage network DEA model

with shared resources. Front. Eng. Manag. 9, 425–438. https://doi.org/10.1007/s42524-022-0205-5

Yim, D.S., 2014. Development of an innovation cluster in the region: experience of Gwanggyo Technovalley in Korea, in: Oh, DS., Phillips, F. (eds.)

Technopolis. Springer, London. https://doi.org/10.1007/978-1-4471-5508-9_24

Zaytsev, A., Sun, P.K., Elkina, O., Tarasova, T., Dmitriev, N., 2021. Economic security and innovative component of a region: a comprehensive assess-

ment. Sustain. Dev. Eng. Econ. 2, 4. https://doi.org/10.48554/SDEE.2021.2.4

Zemtsov, S., Kotsemir, M., 2019. An assessment of regional innovation system efficiency in Russia: the application of the DEA approach. Scientomet-

rics 120, 375–404. https://doi.org/10.1007/s11192-019-03130-y

Zemtsov, S.P., 2011. Modernization and Innovative Development of Russia: Economic and Geographical Approach. LAP LAMBERT Academic Pub-

lishing GmbH and Co Saarbrucken, Germany. https://doi.org/10.13140/RG.2.1.2944.6165

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