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ABSTRACT
In this article, a multi-indicator method for assessing the financial efficiency of clusters, including the results of
research conducted in cotton-textile clusters, as well as proposals and recommendations for improving the
assessment of financial efficiency, has been developed.
KEYWORDS
Financial efficiency, multi-indicator evaluation, cotton-textile clusters, economic efficiency, evaluation methods,
evaluation methodology, evaluation criteria, product evaluation.
INTRODUCTION
Regions where food production provides a significant
share of the gross regional product should formulate a
regional development strategy, focusing on the quality
of local competitive advantages of regional food
systems. Such a policy is aimed at ensuring the
competitiveness of the regional economy. One of its
possible forms is the development of economic
integration based on the creation of agro-industrial
clusters.
In accordance with the Decree of the President of the
Republic of Uzbekistan dated December 12, 2023 No.
PF-205 "On additional measures for the further
development of free market relations in agriculture",
from January 1, 2024, the introduction of international
Research Article
A
MULTI-INDICATOR
METHOD
OF
EVALUATING
FINANCIAL
PERFORMANCE OF CLUSTERS
Submission Date:
January 01, 2024,
Accepted Date:
January 06, 2024,
Published Date:
January 11, 2024
Crossref doi:
https://doi.org/10.37547/ijmef/Volume04Issue01-05
Ochilov Ilhom Sayitkulovich
Associate Professor, TSUE, Independent Researcher (Doctor Of Science), Uzbekistan
Journal
Website:
https://theusajournals.
com/index.php/ijmef
Copyright:
Original
content from this work
may be used under the
terms of the creative
commons
attributes
4.0 licence.
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standards of financial reporting in clusters, which
attracted prestigious international audit organizations
to clusters In this case, the tasks of conducting an
independent external audit of their performance and
financial indicators were determined [1].
We believe that when evaluating cotton-textile
clusters, it is necessary to pay attention to the
following important features of their activity:
quick return on investment; high mobility of
production (the possibility of changing the product
range); dominance of small and medium-sized
businesses in the network; low organization of
entrepreneurship; the high importance of geographic
location for the success of a textile enterprise; weak
innovative component in products; prospects of
redirecting production to technical textiles; extensive
use of industrial branding; provision of qualified
personnel; rapid obsolescence of production facilities;
high resource intensity of the used technologies; low
rate of export of finished products; weak connections
between enterprises and scientific institutions; lack of
infrastructure elements to support industrial business
(business incubators, technology parks, etc.); high
dependence of textile workers on imported raw
materials.
These characteristics of the textile industry should be
taken into account when developing methods for
evaluating the efficiency of clusters. Focusing on these
characteristics, during the analysis, we can monitor the
progress of the cluster development and direct the
efforts of the participants in the right direction. In our
opinion, the approach to evaluating the effectiveness
of the textile cluster should meet the following
conceptual requirements:
1) methodological approach should be based on
quantitative indicators, while not excluding qualitative
assessment (the scope of expert assessment should be
limited);
2) open information of indicators;
3) differentiation of indicators according to their value;
4) grouping of indicators according to the main aspects
of activity;
5) taking into account the specific characteristics of the
cotton-textile industry;
6) careful selection of experts to increase the
objectivity of the overall assessment of efficiency.
Literary analysis. If we follow the research of scientists
from foreign countries, the methods of evaluating the
financial efficiency of clusters are presented in the
works
of
I.Ferova,
F.Shutilov,
N.Klepikova,
O.Nesmachnikh, E.Patrusheva [2-10]. In particular, the
method based on the private effect of the cluster (the
method of evaluating the synergistic effect and its
types; the method of evaluating the effect of reducing
transaction costs and infrastructure synergy; the
method of evaluating the effect of innovation
diffusion); methods of evaluating the economic
efficiency of the cluster as investment projects (net
present value indicator method; real options method);
indicator methods of cluster efficiency assessment
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(method of main indicators of cluster activity; method
of multi-indicator approach); methods based on the
assessment of the competitiveness of the cluster
(assessment of various aspects of the competitiveness
of the cluster, in which the position in the market,
technological leadership and the ability to renew;
assessment of the factors of the current and strategic
competitiveness of the cluster).
In our opinion, it is extremely important to take into
account the specific characteristics of one or another
industry when creating economic clusters. The above
methods,
taking
into
account
the
specific
characteristics of the network, provide opportunities
for comparison on the directions of formation of
competitive advantages, what should be paid special
attention to when evaluating cluster efficiency, and
individual evaluation criteria.
Research methodology. Systematic approach, analysis
and synthesis, comparison, categorization, grouping,
absolute and relative quantitative methods of
statistical and financial analysis were used in the
research process.
Analysis and results. If we come to specific algorithms
for the development of the methodology for
evaluating the efficiency of cotton-textile clusters, first
of all, it is necessary to determine the specific
characteristics of economic clusters in the cotton-
textile industry. The cotton-textile industry of our
country has certainly maintained a positive trend in
recent years.
According to research, with the introduction of the
cluster method, the average yield increased by 4.9
centners compared to the lands outside the cluster. In
2020, the average yield of raw cotton was 2.89 t/ha,
which is 0.53 t/ha compared to outside the cluster, and
compared to 2018 0.77 t/ha is higher. But compared to
other cotton-exporting countries like Uzbekistan, this
yield is still very low, such as 3.1 t/ha in the USA, 4.4 t/ha
in Egypt, 5.3 t/ha in Turkey, 5.6 t/ha in Brazil and In
China it is equal to 5.8 t/h.
We believe that in the financial evaluation of cotton-
textile clusters, it is necessary to pay attention to the
following important features of their activity:
quick return on investment; high mobility of
production (the possibility of changing the product
range); dominance of small and medium-sized
businesses in the network; low organization of
entrepreneurship; the high importance of geographic
location for the success of a textile enterprise; weak
innovative component in products; prospects of
redirecting production to technical textiles; extensive
use of industrial branding; provision of qualified
personnel; rapid obsolescence of production facilities;
high resource intensity of the used technologies; low
rate of export of finished products; weak connections
between enterprises and scientific institutions; lack of
infrastructure elements to support industrial business
(business incubators, technology parks, etc.); high
dependence of textile workers on imported raw
materials.
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Our methodology is based on a multi-indicator
approach, and evaluation should be carried out in 11
main areas (Table 1). In this case, we get the indicators
that represent the specific characteristics of the
cotton-textile industry, which gives us the opportunity
to make a comprehensive assessment of the cluster's
activity.
Table 1
Criteria for assessing the efficiency of the cotton-textile cluster
№
Criteria name
Criterion content
Criterio
n
weight
Grade
1.
Estimating the composition of cluster members
1.1.
The share of cluster
participants in the
total
number
of
network
representatives
The ratio of the number of textile
enterprises in the cluster to the total
number of textile enterprises in the
republic
1-3
1-5
1.2.
Dynamics of share of
new enterprises
The growth rate of the number of new
companies in the cluster
1-3
1-5
1.3.
Non-production
organizations
share
The ratio of the number of non-
production enterprises in the cluster to
the total number of participants
1-3
1-5
1.4.
Dynamics of share of
foreign
(joint)
companies
Growth rate of the number of foreign
companies in the cluster
1-3
1-5
1.5.
Share
of
small
business
and
entrepreneurship
The ratio of the number of small
businesses and entrepreneurial entities
to the total number of participants
1-3
1-5
1.6.
Share of research
institutes
The ratio of the number of research
institutes to the total number of
participants
1-3
1-5
1.7.
Territorial
concentration
of
participants
If the main participants are located in
the same region (territorially close), 1
point is given, otherwise 0 points.
1-3
1-5
1.8.
Industry
concentration
of
participants
A score of 1 is given if all participants are
engaged only in the textile industry, and
0 otherwise
1-3
1-5
1.9.
Experience of market
participants
A score of 1 is given if most companies
have sufficient experience, and 0
otherwise
1-3
1-5
2.
The position of the cluster in the market
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2.1.
Cluster share in the
domestic market
The ratio of the volume of cluster
production to the total volume of supply
in the domestic market
1-3
1-5
2.2.
Dynamics of share in
the global supply of
textile products
The growth rate of the share of cluster
products in the world market
1-3
1-5
2.3.
Market dynamics
The growth rate of the number of
foreign markets mastered by the cluster
1-3
1-5
2.4.
Profitability of
geographical location
of the cluster
A score of 1 is given if the location of the
cluster is useful for the development of
its activities, otherwise a score of 0 is
given.
1-3
1-5
3.
Production potential of the cluster
3.1.
Load production
capacity
Using current cluster capabilities
1-3
1-5
3.2.
Depreciation of Fixed
Assets (AF)
The level of wear and tear of the
cluster's main tools
1-3
1-5
3.3.
Price level
Production and selling costs per unit of
product
1-3
1-5
3.4.
Labor productivity
The level of labor productivity in the
main (resolver) enterprises of the
cluster
1-3
1-5
3.5.
AF refresh rate
The share of updated funds in the total
structure of AF
1-3
1-5
3.6.
The degree of import
substitution of textile
raw materials
Share of local raw materials used in the
production of finished products of the
cluster
1-3
1-5
3.7.
The share of the
cluster AF in the total
size of the network
AF
The share of the cluster AF in the total
size of the network AF
1-3
1-5
4.
Evaluation of the cluster product
4.1.
Product quality
Expert assessment of consumer
products based on quality,
environmental friendliness, aesthetics,
etc.
1-3
1-5
4.2.
Share of synthetic
products
The share of products with added
synthetic materials in the total product
of the cluster
1-3
1-5
4.3.
Competitiveness of
products in the world
market
Expert assessment of the demand for
cluster products in the world market
1-3
1-5
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4.4.
Brand presence
1 if the cluster uses one brand for selling
products, 0 otherwise
1-3
1-5
5.
Evaluating the effectiveness of cluster management
5.1.
Have a strategy
1 is set if an activity development
strategy has been developed for the
cluster, 0 otherwise
1-3
1-5
5.2.
Existence of
coordinating div
If a single div for coordinating the
activities of the participants is formed in
the cluster, 1 is set, otherwise, 0.
1-3
1-5
5.3.
The level of
development of
cooperative relations
If the organizational structure of the
cluster contributes to its development, 1
is assigned, otherwise 0
1-3
1-5
6.
Assessment of infrastructure provision of the cluster
6.1.
Availability of
industrial business
support
infrastructure
1 if the cluster interacts with industrial
parks, 0 otherwise
1-3
1-5
6.2.
Availability of
innovative
infrastructure
1 if the cluster interacts with technology
parks, business incubators, engineering
centers, venture funds, etc., otherwise 0
1-3
1-5
7.
Evaluation of the financial component
7.1.
Profitability of cluster
activity
Profitability of sales
1-3
1-5
7.2.
Investment
profitability
The ratio of profit to invested capital
1-3
1-5
7.3.
Dynamics of
transaction costs
The degree of reduction of transaction
costs for the organizations included in
the cluster is described
1-3
1-5
7.4.
Indicator of financial
stability of the cluster
The main (solvent) in the cluster is
evaluated on the example of azo
1-3
1-5
8.
Evaluation of the innovative component of the cluster
8.1.
Dynamics of
investments directed
to research and
experimental design
(IDRE) developments
Growth rate of investments directed to
IDRE developments
1-3
1-5
8.2.
Commercialization of
innovations
The number of successfully
implemented developments in the total
volume of annual IDRE works
1-3
1-5
8.3.
Number of new
cluster products
The number of new (improved)
products created by the cluster
1-3
1-5
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8.4.
Share of high-tech
products
The ratio of the volume of high-tech
products to the total volume of
products
1-3
1-5
8.5.
Innovative awarding
per unit of cluster
employees
The ratio of the number of awardees for
innovation to the total number of
employees of the cluster
1-3
1-5
9.
Investment evaluation
9.1.
NPV
Determining the profitability of
investing in a cluster project
1-3
1-5
9.2.
Return period
Calculation of the average return period
of invested funds
1-3
1-5
9.3.
The dynamics of the
volume of foreign
investments
Growth rate of foreign investment in the
cluster
1-3
1-5
10.
Evaluation of personnel policy
10.1.
Salary level
The ratio of the average wage in the
cluster to the specified minimum wage
1-3
1-5
10.2.
The percentage of
production
employees in the
cluster
Share of employees in the total number
of employees in the cluster
1-3
1-5
10.3.
Share of personnel
with higher education
Share of highly educated personnel in the
cluster
1-3
1-5
10.4.
Share of highly
productive jobs
The share of high-performance jobs in the
total composition of cluster employees
1-3
1-5
11.
Evaluation of efficiency from the perspective of the state
11.1.
Budget efficiency of
the cluster
It is determined on the basis of the
difference between tax and non-tax
budget receipts and the cluster support
costs of the competent authorities.
1-3
1-5
11.2.
Share of the cluster in
the gross regional
product (GRP).
The ratio of the volume of output
produced by the cluster to the Gross
Regional Product
1-3
1-5
11.3.
Population
employment in
cluster enterprises
Sum of jobs in all cluster members
1-3
1-5
11.4.
Regional investments
The share of investments received by
cluster participants in the total volume of
regional investments
1-3
1-5
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Now we will explain the stages of implementation of
the cluster efficiency evaluation methodology created
by us on the conditional "Researcher" example.
1. The object of analysis is defined (that is, a cotton-
textile cluster with a certain range of participants).
2. Determination of selection criteria and selection of
experts based on it (respondents' experience in the
studied field is taken into account, if necessary, rating
coefficients are determined for different experts).
3. Preparation of form for expert evaluation and
development of final form for analysis.
4. Completing the cluster efficiency assessment form
by experts. In doing so, they are asked to consider a set
of evaluation criteria for 11 areas and assign a weight to
each of the indicators according to its importance in
order to evaluate the efficiency of the cluster. For this,
in column 3 of the form in Table 2, the approximate
weight of the indicator according to the three-point
system is defined as follows: 3 - the indicator is very
important for evaluation (has a high weight); 2 - the
indicator is of average importance for evaluation (has
an average weight); 1 - the indicator is of low
importance for evaluation (has a low weight).
5. Evaluation of cluster efficiency by experts. In this
case, the assessment is carried out on a five-point scale
(table 2, column 4) against the defined average values
of the criteria: 5 points (the highest score) - if it clearly
exceeds the average values in the industry specified for
the textile industry; 4 points - if it is slightly above the
average values in the network; 3 points indicate the
approximate equality of the network average and
cluster indicator; 2 points means that there are a
number of worse cluster indicators compared to the
network average; 1 point - the cluster shows a
significant lag in this indicator. At the same time, it
should be noted that only a two-point evaluation
system can be used for some evaluation criteria,
because it is difficult to evaluate the criteria perfectly,
but their use in the methodology is important, which is
important for the correct evaluation of the cluster
activity.
6. Collection of questionnaires filled out by the
researcher. The weight coefficients set by the experts
should be transferred from the three-point system to
the decimal point (parts of one) and the sum of the
weights attached to each of the 11 criteria should be
equal to 1.
7. Multiply the evaluations of each of the experts by the
weight coefficients given in fractions of one and
calculate the sum of points for each of the 11 criteria.
Obtaining the final score of all experts (each
completed questionnaire will have its own final score).
8. Calculation of the General (total) score of cluster
efficiency:
8.1. The average is calculated according to the
arithmetic formula as usual, if the rating coefficients
are not assigned to the experts.
8.2. If the rating coefficients for experts are set,
according to the weighted average arithmetic formula.
In this case, it is necessary to calculate not only the
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total evaluation, but also the total scores for 11 groups
of indicators (Table 2).
9. The results of the final form are used in the process
of strategic and tactical planning of cluster activities.
10. To re-evaluate the effectiveness of the cotton-
textile cluster at the end of the planning period to
determine the level of achievement of the goals.
Table 2
The final form of cluster performance evaluation
№
Indicator groups
Total score
1.
Estimating the composition of cluster members
5
2.
The position of the cluster in the market
5
3.
Production potential of the cluster
5
4.
Evaluation of the cluster product
5
5.
Evaluating the effectiveness of cluster management
5
6.
Assessment of infrastructure provision of the cluster
5
7.
Evaluation of the financial component
5
8.
Evaluation of the innovative component of the
cluster
5
9.
Investment evaluation
5
10.
Evaluation of personnel policy
5
11.
Evaluation of efficiency from the perspective of the
state
5
Total (total) score:
55
CONCLUSIONS
In conclusion, we can say that the methodology
proposed by us for evaluating cluster performance has
its own characteristics, which are important in
performance analysis. First, it reflects the indicators
that take into account the characteristics of the
studied network clusters, where the weights of the
criteria are placed on the basis of similar judgments
(based on the study of experts' opinions) and
analyzed, a systematic classification is formed and
conclusions are made easier. Secondly, the period of
application of the methodology is limited, because the
situation in the modern economy can change
dramatically in one direction or another (for example,
a ban on the import of foreign textile products as a
result of the tightening of sanctions policy), which
requires adjustments to the assessment procedure.
Taking
into
account
these
concluding
recommendations allows for a correct assessment of
the effectiveness of local textile clusters. Thus, in order
to increase the efficiency of the measures taken by the
state to support clusters, there is an increasing need to
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improve the methods of correct assessment of cluster
activity, taking into account the peculiarities of the
industry. In our opinion, the issue of evaluating the
effectiveness of cluster structures will not lose its
relevance in the near future. Due to limited financial
resources in modern conditions, there are serious
problems of choosing the most profitable investment
option. The proposed methodology for cluster
evaluation solves this problem to some extent.
In order to improve the assessment of the financial
efficiency of clusters, the following should be
implemented:
use of financial efficiency criteria and improvement of
assessment methodology;
establish the use of a complex indicator of financial
efficiency assessment;
introduction of international standards of financial
reporting in clusters;
establishing the practice of conducting an independent
external audit of their activity efficiency and financial
indicators by involving prestigious international
auditing organizations in clusters;
establishing assessment practices based on the
indicators of the "European Memorandum".
REFERENCES
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Decree of the President of the Republic of
Uzbekistan dated December 12, 2023 No. PF-
205 "On additional measures to further
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эффективности
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Ф.В.
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