=Paper=
{{Paper
|id=Vol-2713/paper05
|storemode=property
|title=Scenario forecasting information transparency of subjects' under uncertainty and development of the knowledge economy
|pdfUrl=https://ceur-ws.org/Vol-2713/paper05.pdf
|volume=Vol-2713
|authors=Hanna Kucherova,Anastasiia Didenko,Olena Kravets,Yuliia Honcharenko,Aleksandr Uchitel
|dblpUrl=https://dblp.org/rec/conf/m3e2/KucherovaDKHU20
}}
==Scenario forecasting information transparency of subjects' under uncertainty and development of the knowledge economy==
https://ceur-ws.org/Vol-2713/paper05.pdf
81
Scenario forecasting information transparency of
subjects’ under uncertainty and development of the
knowledge economy
Hanna Kucherova1[0000-0002-8635-6758], Anastasiia Didenko1[0000-0003-1136-403X],
Olena Kravets1[0000-0002-2980-5238], Yuliia Honcharenko1[0000-0003-1567-8618] and
Aleksandr Uchitel2[0000-0002-9969-0149]
1 Classic Private University, 70-b Zhukovskogo Str., Zaporizhzhia, 69002, Ukraine
ekonom.kpu@gmail.com
2 State University of Economics and Technology,
5 Stepana Tilhy Str., Kryvyi Rih, 50006, Ukraine
Abstract. Topicality of modeling information transparency is determined by the
influence it has on the effectiveness of management decisions made by an
economic entity in the context of uncertainty and information asymmetry. It has
been found that information transparency is a poorly structured category which
acts as a qualitative characteristic of information and at certain levels forms an
additional spectrum of properties of the information that has been adequately
perceived or processed. As a result of structuring knowledge about the factor
environment, a fuzzy cognitive model of information transparency was
constructed in the form of a weighted digraph. Structural analysis and scenario
forecasting of optimal alternatives of the fuzzy cognitive model made it possible
to evaluate the classes of factors, identify their limited relations, establish the
centrality of the roles of information transparency and information and
communication security in the system built and evaluate their importance when
modeling the situation self-development. Information visibility, reliability and
availability have been found to have the strongest impact on the system. Taking
into account different initial weights of the key factors — information
transparency and information and communication security — the study
substantiates the strategic ways for economic entities to achieve their goals in the
context of uncertainty and information asymmetry, which allows us to use this
approach as a tool for strategic management in the information environment.
Keywords: information transparency, forecasting, fuzzy cognitive modeling,
digraph, factors, relations, strategic management.
1 Introduction
Information transparency is a possibility for any economic stakeholders to track the
chain of actions and stages of forming the information content [29] which is important
enough to make effective management decisions. On the one hand, information
___________________
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
82
transparency in required scope ensures that its provider can be held accountable for the
content and consequences of its disclosure, while on the other it builds trust and reliance
on a company and reduces alienation between interaction parties [15]. That is, systemic
information transparency determines positive expectations regarding company
development, builds up confidence and improves business climate, which is always
relevant in the context of competition, economic downturn, changes in socio-political
and socio-economic vectors of development.
From the point of view of content understanding, information transparency is
interpreted by authors [6] as a requirement, norm, standard or as a goal which, being
set, defines the specifics of achieving it. Thus, transparency as one of the 8 major
characteristics of the Good governance concept [26] means that decisions taken and
their implementation follow rules and regulations. It also means that information is
freely available and directly accessible to those who are affected by the decisions and
their enforcement. According to the concept, main attributes of information are the
scope of information provided and that it is provided in easily understandable forms
and media. Another approach can be found in the article [25] whose authors define
transparency as perceived quality of the information intentionally shared by its sender.
We believe that information transparency is a poorly structured category which
nevertheless is a qualitative characteristic of information and at certain levels forms an
additional spectrum of properties of the information which has been adequately
perceived or processed. Ambiguity of information transparency complicates drawing
its overall portrait for comprehensive research and identifying its optimal level for
different economic entities and their various purposes.
Furthermore, in economics poorly structured categories are generally studied by
means of soft modeling including dynamic cognitive modeling and cognitive mapping.
In our case, information transparency is the object of the research, while cognitive
modeling of how transparency and its constructs influence each other is its subject.
Scientific search is aimed at structuring the information transparency factor
environment, identification of strategic changes in its level and the levels of its factors
which result in accelerated and decelerated development of the system, extracted from
a specific eco-socio-economic environment during t period. Information types, degree
of completeness of information, the type of the company’s operations, its size,
organizational and legal form are decisive factors for characterizing its information
transparency.
The openness of the organization's activities and management decisions, disclosure
of information in a complete, timely, reliable and understandable form creates space
for the formation of transparency in business for the main groups of stakeholders:
employees, investors, consumers, suppliers, creditors, government and local
authorities, etc. [5; 12; 17; 19; 20].
Own approach to the definition of information transparency of business entities is
proposed in the research [34]. The authors of the study understand information
transparency as providing free access for all stakeholders to information about various
areas of the enterprise's activities, management status, ownership structure, and other
data that will be useful for making decisions on interaction with this entity. The authors
of the article consider information transparency in two aspects: external and internal.
83
The external aspect contributes to the formation of a positive image and increases the
competitiveness of the enterprise. The internal aspect forms trust within the
organization. The authors argue that information transparency is an important tool in
the process of preventing and combating economic crime.
Transparency is an integral part of public administration, which is considered in the
article [30]. Transparency is determined by the degree of citizens' awareness of
important manifestations of the material and procedural aspects of the activities of state
authorities and local self-government. The author of [9] notes a close relationship
between information transparency of companies and the corporate governance model.
A high level of information transparency contributes to the formation of a high-quality
model of corporate governance, increasing investment attractiveness. According to the
[11] information transparency is a conceptual basis for solving investment risk
problems. In the [15] it is noted that the availability of information and transparency
helps to strengthen the reliability of the company and reduce misunderstandings
between the organization and stakeholders.
Transparency as an openness that allows stakeholders to receive the necessary
information for making informed decisions considers in [32]. Availability, timeliness,
relevance, and quality are considered characteristics of transparency. At the same time,
the lack of transparency is the basis for the development of corruption, it negatively
affects economic growth in any industry. Increasing information transparency helps to
improve management transparency.
The author of [24] considers transparency as a function that has three variables. This
function consists of the owner of the information, the information, and the persons who
have access to the information. Transparency is seen as a way of regulation, democracy,
and efficiency. Possession of information facilitates control over the entity. At the same
time, transparency is not a category that must maximize. The optimal level of
transparency is between “highly desirable” and “necessary”.
In the article [33] considers two conceptual approaches to transparency. The first
approach looks at information transparency, which involves increasing the quantity and
quality of information available to stakeholders. The second approach looks at
accountability, which manifests itself in the strengthening constraints on employees to
hold them accountable for their actions. So, on the one hand, transparency is a means
of transmitting information and on the other, it is a tool that restrains the actions of
officials. The article substantiates that due to different types of transparency and their
economic effects, it is impossible to introduce a single quantitative indicator to measure
the level of transparency.
Thus, for economic entities information can be transparent, partially transparent or
nontransparent. The main parameters of information transparency management are
time, tactical, strategic and technological constrains on the flow of information for a
limited or unlimited number of stakeholders. Information flow in the research [6] is
characterized by the following parameter tuple:
<І, IP, IR, IM, IE>, (1)
information (I), information provider (IP), information receiver (IR), information
medium (IM), and information entity (IE).
84
It seems reasonable to add information constraints (such as on the scope of
information usage) to this set of defined parameters. It is also appropriate to take into
account the period with its inherent social and economic trends, which can ensure more
accurate and adequate interpretation of results. The authors [6] place emphasis on the
importance of subject affiliation when studying the risks of achieving information
transparency, which explains different quality of certain level of information
transparency for different stakeholders. Information receivers and media also differ in
terms of their goals, capabilities and time allocated to the operation, which should also
be taken into consideration when assessing the feasibility of increasing or reducing
information transparency in order to improve rather than aggravate the situation.
Transparency depends on information type (financial, non-financial, business, public,
private etc.) so basic transparency attributes will be further explained through specific
characteristics that determine the specialization of a particular type of information. By
way of example we can mention corporate information: corporate transparency is
defined as widespread availability of relevant, reliable information about periodic
performance, financial position, investment opportunities, governance, value, and risk
of publicly traded firms [3]. Also, taking into account subjective nature of information
(for example, information for external stakeholders) a company's external information
transparency determines the degree of completeness of information regarding its own
business activities, provided by each company to the market [31].
2 Materials
One of the main attributes of information transparency is visibility of information content
which in its turn is determined by general availability of information, accessibility of
information to third parties and degree of approval to disseminate information [29].
Information visibility is also differentiated according to its degree as visible, partially
visible and invisible. In the article [29] the authors argue that high information visibility
may result in attention span, produce a flood of information, overwhelming our
cognitive and interpretive capabilities, thus rendering the information meaningless or
confusing. Moreover, maximum visibility can mask formality of actions or results of the
decisions taken, which are presented in the information [29]. On the other hand,
insufficient disclosure of information may result in an attempt to compensate for it with
some other, forecasted, predicted, guessed, insider information, trustworthiness and
reliability of which may not be sufficient to make an effective management decision.
The authors of the article [15] point out that transparency arises under the condition of
certain balance between the degree of information disclosure and the degree of its
perception and understanding by users. Hence, we can make a conclusion that only
limited (up to a certain level) visibility ensures high level of transparency, provided that
it is the key and quality information that is disclosed, not its noises. In general, the degree
of information transparency determines the associated risk of its disclosure or
concealment. According to the [15], information visibility is determined by how
responsible its providers are, that is, how consciously they assess the advantages,
necessity and need to disclose certain amount and type f information. The attitude and
85
policy of an economic entity in terms of establishing the boundaries of information
transparency determine the degree of confidence it enjoys as an information provider
and medium. Information availability depends on how well information reflects actions
and decision-making stages in easily recognizable data forms through a reliable storage
of those data. In the article [29] it is interpreted as a degree of complexity associated
with the extraction and interpretation of information. It is determined by such attributes
as possibility for an unlimited number of people to be informed, insignificant amount of
time, finances and efforts necessary to find the relevant information, veracious
perception of information content. Information availability is ensured by its relevant
classifiers, systematizes etc. Ongoing development of information and communication
technologies has been improving the conditions, forms and methods of storing and
transmitting information as accurately as possible, which allows us to expand the
possibilities of managing information transparency and its constructs. In order to assess
the level of information and communication technologies development, different indices
are used, including ICT Development Index, Networked Readiness Index (surveys at the
state, business and community levels). Growing complexity of information and
communication technologies requires continuous improvement of existing skills on the
part of economic entities and drives the need for life-long learning. At the same time,
technological advances mean higher associated risks, which urge stakeholders to provide
the required level of information and communication security.
Adequate and realistic perception of information content by receivers, which is
crucial for effective decision making, is determined by cognitive limitations of
economic entities. Information overload may result in deliberate distancing from all
information sources. In addition, dubious quality of information, its shortage or poor
relevance cause wrong decisions. We cannot ignore the human factor, i.e. the difference
in perception of the same facts by different economic entities, which also contributes
to information asymmetry. In general, the above determines how effectively an
economic entity uses the information visible to it. Information society has been creating
new information services and changing approaches to delivering old ones by means of
increasing use of modern technologies. Therefore, in order to understand the state of
implementing information and communication technologies in society and evaluate the
effectiveness of new technologies introduction, there is an urgent need to develop
approaches to evaluate knowledge and skills of the service receivers, their social
communication skills and skills large-scale data processing [18]. When discussing
information transparency, it is absolutely necessary to take into account the role of
transparency as an economic entity's accountability. It is not the information itself that
is considered, but rather the potential disclosure of that information, which forces
stakeholders to “do the right thing”, so information transparency is often quite closely
linked to the problem of corruption. Hence, we think of information transparency as a
vital tool that helps reduce corruption [33]. We find the list of information transparency
factors, suggested by the experts, substantial, but offer to add information transparency
levels, degrees, and constraints that occur in a real-life environment when it transfers
from one state to another. This will help to substantiate the feasibility of achieving a
change in specific degrees or levels of factors, informational transparency, in order for
stakeholders to achieve their goals. As a result of structuring knowledge on factor
86
loading of information transparency we have identified the following main factors that
accelerate and decelerate the development of an economic entity system: the degree of
confidence in the medium and source of information, the degree of the economic entity
responsibility, the scope of information visibility, the degree of information
availability, the degree of information accessibility to third parties, approval to
disseminate information, the degree of information reliability, the level of information
and communication technologies development, cognitive limitations of the user, the
level of information use efficiency, the level of information and communication
security, the level of corruption reduction, risks of information disclosure or
concealment.
3 Methods
The methodology of information transparency evaluation includes a wide scope of
modern methods, from social research to modeling; results of many surveys and
observations are studied using econometric tools. The results of critical analysis
presented in the article [15] show that there are different approaches to transparency
evaluation (market-based, analyst perception-based and accounting-based measures)
and researchers invoke different techniques including web content analysis, verbal
protocol study, principal component analysis etc. However, it is index approach that
has gained popularity in this area of research. In addition, information transparency is
defined as a component of a more general indicator, or examined in the context of
information type (financial, fiscal, political, economic information). The index method
was used in the study [33] to measure Information Transparency Index and the
Accountability Transparency Index constructed using a methodology similar to the one
used in Transparency International’s Corruption Perceptions Index. In the first stage,
the researchers selected indicators that determine the index for the maximum period,
and normalized them. The resulting index was calculated as a mean of the components
of its normalized indicators. The large-scale study [2] provides a transparency index for
194 countries. The indices comprise an aggregate transparency index with two sub-
components: economic/institutional transparency and political transparency. By means
of correlation and regression analysis using substantiated indicators, the authors
showed that information transparency is associated with better human and economic
development indicators, higher competition and lower corruption. Political
transparency has also been studied separately; the main indicator used over the years
has been the Freedom House Freedom of the Press index. Its advantage is extensive
coverage across countries combined with significant temporal coverage (going back to
1979) [33]. Economic transparency index was presented in the article [10] where it was
developed using regression estimation (based on the World Bank and IMF data). The
index measures the frequency with which governments update economic data that they
make available to the public as a condition of existence (or absence) of laws on freedom
of information. The authors took into account the quantity of data released by
governments, rather than their timeliness. As a result, they proved that information
transparency is directly proportional to effective governance. It is also worth
87
mentioning the Open Budget Index, prepared by the International Budget Partnership,
which is aimed at studying fiscal transparency [33]. In addition to these indicators,
transparency is studied as a component within larger frameworks. In particular, the
World Bank’s CPIA indicators have a component on “transparency in the public
sector”. Information transparency for businesses in terms of obtaining information
about government policies and regulations affecting business activities is considered in
the Global Competitiveness Report, while freedom of the press as a condition for
information transparency is represented in the CIRI Human Rights database [33].
One of the priority indicators of the state of information transparency in Ukraine is
the Transparency Index of Ukrainian companies' websites; the dynamics of this
indicator is shown in figure 1.
35
y = 1,431x - 2865,
R² = 0,803
30
y = 6E-70e0,080x
R² = 0,808
25
21,7
%
21,5
20 19,3
y = 0,038x3 - 229,6x2 + 46305x - 3E+08
16,9 17,1 R² = 0,845
15
14
10
2012 2013 2014 2015 2016 2017 2018 2019 2020
Fig. 1. Dynamics of the Transparency Index of Ukrainian companies' sites.
The results of the analysis of the dynamics and structure of the Transparency Index of
Ukrainian companies' websites show that the level of indicators is low however it grows
every year, in particular: in 2012 the level of the studied indicator is 14%, in 2013 –
16.9%, in 2014 – 17.1%, in 2015 – 21.5%, in 2016 – 19.3%, in 2017 – 21.7% [35]. The
study involved sites of companies that are large taxpayers and leaders of information
transparency of the previous period. Transparency companies are in the energy sector,
the agricultural sector, communications and transport, metallurgy, and alcohol
production. Companies belonging to the mining industry have the worst values. DTEK,
SCM, and ArcelorMittal Kryvyi Rih are stable leaders in information transparency.
According to the method of construction, the index is an integrated value of
indicators and such categories as content, reporting, navigation, accessibility. The
degree of disclosure of the indicator “content” of companies is gradually growing. So
it was 6.7% in 2013, 6.3% in 2014, 9% in 2015, 10.3% in 2016, 12.8% in 2017 [35]. In
the category of “reporting” companies were assigned 9% in 2012, 13% in 2013, 14%
in 2014, 17% in 2015, 12% in 2016, and 16% in 2017. In the reporting category,
companies were assigned 9% in 2012, 13% in 2013, 14% in 2014, 17% in 2015, 12%
in 2016, and 16% in 2017. Companies scored 3.5 out of 8 possible points in terms of
navigation and 6 out of 17 possible points in terms of accessibility. The analysis of
indicators also confirmed the importance of the impact of the financial condition of
88
both companies and sectors on the level of information transparency of companies. The
experts made the following conclusions about the state of information transparency of
companies: it is necessary to ensure systematic updating of information, use modern
data visualization technologies, increase accessibility, accelerate disclosure of
information on compliance with sustainable development and non-financial reporting,
to research information needs of society and the market. Thus, to increase the level of
information transparency of Ukrainian companies, the target benchmarks are high
availability, visibility of relevant information. Information is presented in an
understandable form that fully reveals the principles of sustainable development and
the level of corporate social responsibility. Gives information about correlation
between the industry indicators and the Information Transparency Index (table 1).
Table 1. The correlation coefficient between the industry indicators and the Information
Transparency Index.
Indicator Industry Agriculture Financial Trade Transportation Real
and and storage estate
insurance activiti
activities es
Current liquidity, % - 0,69 -0,98 0,74 -
Absolute liquidity, % 0,83 0,7 -0,64 0,81 0,99 -
Financial autonomy
-0,61 0,59 -0,96 - -0,68 -0,72
ratio, %
Return on assets, % 0,87 0,78 - 0,7 0,63 -
Return on current assets
0,84 0,78 - 0,7 0,63 0,6
,%
Net margin, % 0,83 0,77 0,51 0,84 0,63 0,53
Return on total assets,
- 0,77 -0,65 - 0,54 0,55
%
Total asset turnover 0,91 - -0,58 - 0,77 0,58
Working capital
- - -0,58 - 0,78 -
turnover
Receivables turnover
0,8 - -0,55 - 0,75 -
ratio
According to table 1, there is a direct close relationship between the level of information
transparency and indicators of the financial condition of industrial and agricultural
complexes. There is also a direct but average density relationship between the studied
index and the transport and storage sector. Also, the feedback of the average density is
shown between the indexes of information transparency and indexes of the financial
condition of the financial and insurance sector. The trade sector has a direct close
relationship with the indicators of information transparency, however, not for the entire
studied financial index, only for absolute liquidity, return on assets and working capital,
net margin. Medium-density direct connection with information transparency is the
return on assets and return on current assets, net margin, total asset turnover, there is
89
inverse relationship with financial autonomy ratio of the real estate sector. Thus, there
is both a qualitative and quantitative link between information transparency and
sectoral indicators of the national economy, which determines the feasibility of further
research in this area.
The research [13] offers to use a modified Delphi technique in order to study
information transparency. This technique implies having rapid reviews from
stakeholders, namely researchers, policy makers, industry and health care providers.
However, the results of this approach are rather subjective as respondents often change
their opinion under the influence of different factors.
The paper [8] proposes four reference models which form a baseline for transparency
requirements in information systems. The models cover actors involved in the process
of ensuring transparency and information circulation among them, transparency
meaningfulness, transparency usefulness for a specific audience, and information
quality in transparency. The reference models are further used to create TranspLan, a
modeling language for capturing and analyzing information transparency requirements
among stakeholders [7]. Thus, a broad range of methodological tools gives researchers
quantitative measures of information transparency, the impact it makes and gets.
However, each approach or method has inherent risks when used in practice due to
assumptions, limitations that are not achievable in real life. In addition, there is no
general system of forming information transparency as an economic category since it
is impossible to formalize this poorly structured category without considerable impact
of subjectivity and assumptions. Considering the importance of the cognitive method
when studying poorly structured categories, we propose to analyze information
transparency using the methodology of cognitive modeling and impulse processing [17;
23]. The proposed approach is based on structuring knowledge about the object of
study, building a cognitive model of object development in the form of a digraph,
scenario modeling of the dynamics in the state of the system when it is stable and
changing, justification of the option that can help to achieve the goals of the research.
However, the results of previous studies have proved [14] that the level of the values
of influencing factors plays a crucial role in substantiating an adequate and high-quality
forecast scenario for achieving the required level of information transparency. To solve
this problem, we propose to apply the methodology of fuzzy cognitive modeling.
The process of fuzzy cognitive modeling is presented as a sequence of steps. At the
first stage, the purpose of modeling and target indicators are determined. In the second
stage, there is a substantiation of a fuzzy cognitive map (causal graph) by forming a set
of factors of the subject area of research. Factors are divided into four categories: target,
which form the purpose of modeling, controlled – these are those that can be directly
influenced, intermediate – to describe the subject area, observed – externalities. Factors
are selected according to the level of significance and set for each level scale [1; 28]:
=< , >, (2)
~
where С = {с1, с2, …, сn} – set of factors, R – fuzzy causal relations on the set С, =
( ( , )/( , )) – fuzzy set of edges, , ∈ ; ( , ) – the degree of
~
belonging of the edge ( , ) to fuzzy set of oriented edges R [1; 28].
90
~
Elements rij R (i, j = 1, …, n) characterize the direction and degree of intensity
(weight) of the impact between the factors ci and cj [1; 28]:
rij = r (ci, cj), (3)
~
where r – indicator of intensity of influence (characteristic function of the relation R ),
which may have any value in the interval [–1, 1] with considering:
rij = 0, if the value ci does not depend on cj (no influence) [1; 28];
0 < rij < 1 if the influence ci to cj is positive (increasing the value of the factor-cause
ci leads to an increase in the value of the factor-consequence cj) [1; 28];
–1 < rij < 1 if the influence ci to cj is negative (increasing the value of ci leads to an
decrease in the value of cj) [1; 28].
The next step is to determine the initial state of the factors and the influence of external
factors [1; 28].
In the fourth stage, an adjacency matrix is formed = ×
based on
reasonable characteristics of the type of relationship between factors [1; 28].
Suppose a signed digraph with adjacency matrix А determines information
transparency and its factor environment. The vertices of the digraph are represented by
the set u1, u2, …, un. Every vertex ui has the value of vi(t) at discrete moments of time
t = 0, 1, 2, …. The value of vi(t + 1) is determined by the value of vi(t) and information
on whether other vertices uj, adjacent with ui, have increased or decreased their values
at moment t. The change of pj(t), set by the difference of vj(t) – vj(t – 1), is called a pulse
if t > 0. The initial condition should be specified when t = 0. We introduce the following
notation:
1,if edge is positive,
( , ) = −1,if edge is negaive, , (4)
0,if edge is missing.
Analysis of the statistical characteristics of the model, determination of the balance of
the system, consonance and dissonance of influence are carried out at the next stage [1;
28]. Detect the indirect interactions of factors on each other in the system, for this
purpose convert the initial matrix of the intensity of interactions C into a transitively
closed matrix Z, the elements of which are pairs ( , ,), where characterizes the
power of positive influence, – the strength of the negative influence of the i-th factor
on the j-th [1; 28].
Based on the Z matrix system characteristics of the fuzzy cognitive map can be
calculated (see table 2).
The consonance indicator expresses the degree of confidence in the sign and the
strength of the impact (the higher the consonance, the more convincing the expert's
opinion) [1; 28]):
pij – the number of positive influences of the i-th factor on the j-th factor;
nij – the number of negative influences of the i-th factor on the j-th factor;
kij – consonance of the influence of one factor on another;
91
dij – dissonance of the influence of one factor on another.
Table 2. System characteristics of fuzzy cognitive map [27].
Сharacteristics Dissonance of Consonance of influence
influence
p ij nij
Influence of the i-th factor on the j-th d ij 1 k ij k ij
p ij nij
1 n 1 n
Influence of the i-th factor on the system Di d ij Ki k ij
n j 1 n j 1
1 n 1 n
Influence of the system on the j-th factor Dj d ij Kj k ij
n i 1 n i 1
At this stage, dynamic modeling is performed based on the use of a pulse process
according to the following formula [1; 28]:
( + 1) = ( ( ) + ( + 1) + ( + 1) + ∑ ( , ( ))), (5)
where vi(t) – the value of factor сi at moment t; vi(t + 1) – the value of factor сi at moment
(t + 1); qi(t + 1) – external influence on сi at moment (t + 1); oi(t + 1) – controlling
influence on сi at moment (t + 1); rij = r(сi, сj) – intensity of influence between factors
ei and ej; pj (t) – change the value of cj at moment t; Т – operation Т-norms (the product
is used); S – operation S-norms (Lukasevich’s S-norm is used).
At the final stage, form a basic set of alternative strategies for system development
and justification of priority strategies for achieving targets.
Modern information technologies provide researchers with a possibility to simplify
cognition procedure. In particular, to achieve the goal of this research we can use some
software products, such as Decision Explorer Application, aimed at providing the user
with illustrative cognitive maps which can be further analyzed to better understand the
issue under study, links between its factors. Another specialized software product is
FCMapper which helps to analyze, model fuzzy cognitive maps, explore behavior of
the system and interaction between its factors. FCMapper calculates structural
characteristics of the system based on the created digraph, and provides a variety of
scenarios. The logistic squashing function is used for standard scenario calculation. It
can be written as f(x)=1/(1+e–x) [4]. The results obtained for each scenario are compared
with the initial one, where the output weights for each vertex are set at the same level,
that is, the situation self-development is modeled. The final development of the system
in the form of a cognitive model in the absence of external influences can be reduced
to a fixed-point attractor, a boundary cycle of repeating binary vectors, or to chaotic or
aperiodic attractors [21].
92
4 Results
As a result of structuring knowledge on factor environment of information transparency
we have identified a set of vertices and their cause-effect relations; together they
determine the corresponding cognitive model in the form of a weighted digraph (see
fig. 2).
Fig. 2. Cognitive model of information transparency [14].
For the constructed digraph, based on the situation simulation, the following vertices
have been found to have the greatest influence on target vertex 1: 8, 9, 11, 12, 14.
However, not all of these vertices can be affected in terms of goals and opportunities
of economic entities. The following figure shows the results of studying a simulation
of the vertex parameter dynamics in the context of system interaction without external
influence for t = 12, where t is the study period, and the initial values of the vertex
parameters vi= 0 (see fig. 3).
The diagram shows the dynamics of the influence those vertices 4, 9, 11, 12 have on
the target vertex (1). The diagram shows variable values of vertex 1, but we can see
that factors 12 and 4 tend to have a negative influence; factor 11 has a positive
influence, while factor 9 does not have a clearly defined tendency during the period
under study. Let us now examine the effect of changes in transparency level on vertices
2, 3, 7 and 13 (see fig. 4).
We can see a positive dynamics of the influence that target vertex 1 had on vertices
2, 3, 7 and 13 throughout the period under study. Having analyzed structural
characteristics of the cognitive map using FCMapper, we arrived at the following
results. One vertex functions as a Transmitter, it is vertex 9, the level of technology
development. Other vertices are Ordinary, there are no Receiver vertices in the model.
Classification of the cognitive model vertices by these classes helps us better
understand the structure of the graph. Having analyzed the cognitive model using the
Decision Explorer Application, we identified two key elements of the cause-effect
relationship system (based on the calculation of vertices centrality which reflects the
93
strength of relationships between the vertices). The index that shows the proportion of
existing connections of potentially possible (density) is calculated to be 0.14. It gets
values within [0; 1]. The higher its value is, the more active interaction between the
vertices takes place. They are the information and communication security (12) and
information transparency (1). Using FCMapper software you can set the initial weights
of vertices and investigate their influence on other system indicators. To quantify the
system development dynamics, the influence of one vertex on the other is represented
by the following set of values: very strong (0.8; 1]; strong (0.6; 0.8]; medium (0.4; 0.6];
weak (0.2; 0.4]; and very weak (0; 0.2]. During the situation self-development
modeling (the total number of iterations is 60) the constructed model came to a stable
state, which was achieved due to feedbacks, with the maximum number of
iterations (51) for the degree of information accessibility to third parties (6) and
risks (14), the fewest iterations (25) were required for the availability (5) and
technology development (9) vertices. The calculations obtained indicate that the factor
with the greatest impact on the system is the scope of information visibility (4).
Information reliability (8) and approval to disseminate information (7) are found to be
of high influence. The degrees of confidence (2), responsibility (3) and information
availability (5) also showed significant influence. Then we analyzed the situation
development scenarios under the change of the key elements of the cause-effect
relationship system, namely the level of information and communication security (12)
and information transparency (1). For the first scenario the initial vertex value was set
at a low level (0.1), for the second scenario at a medium level (0.5) and for the third
one at a high level (0.9). When the value of vertex 12 is of low and medium level, the
most positive changes are observed for the following vertices: information
transparency (1), the degree of responsibility (3), the degree of information
accessibility to third parties (6), the level of corruption reduction (13) and risks (14).
The high value of vertex 12 has the most positive influence on the level of information
use efficiency (11). When the value of vertex 12 is of low and medium level, the most
negative changes are observed for such vertices as the degree of information
reliability (8) and the level of information use efficiency (11) The high value of
vertex 12 has the most negative effect on the vertices of information transparency (1),
the degree of responsibility (3), the degree of information accessibility to third
parties (6), and risks (14). Now, we can have a close look at the influence of information
transparency (1) on the cognitive model factors. In general, the level of information
transparency does not affect the system significantly, it is 0.32 in the context of the
system self-development scenario. At the same time, a decrease in the level of vertex 1
reduces the influence of such factors as the degree of responsibility (3) and the degree
of confidence (2) on the system. An increase in the level of vertex 1 results in the
growing influence of the corruption reduction factor (13), while low level of vertex 1
causes an increase in the level of corruption (13). A low level of vertex 1 slightly
increases such factors as risks (14) and information accessibility to third parties (6), the
value of the approval to disseminate information (7) increases when the level of
vertex 1 is higher.
94
60
Values of the vertex parameters, vi(t)
40
20
4
0
1 3 5 7 9 11 9
-20 11
-40 12
-60
-80
The study period, t
Fig. 3. The dynamics of the value of target vertex 1 under the influence of vertices 4, 9, 11, 12.
30
Values of the vertex parameters, vi(t)
25
20
15 2
10 3
7
5
13
0
1 3 5 7 9 11 13
-5
-10
The study period, t
Fig. 4. The dynamics of the influence of target vertex 1 on vertices 2, 3, 7, 13.
Considering that the determining factors are characterized by different initial levels, we
will investigate the created cognitive model, taking into account fuzzy logical
conclusions regarding their influence on information transparency.
For research, we represent the cognitive model as a fuzzy map. For this, in the
digraph, the vertices will be designated as linguistic variables [16]. The set of linguistic
variables is characterized by the following parameter tuple:
, (6)
95
where: Bi – linguistic variables, = 1,14, B1 – “information transparency”, B2 – “the
degree of confidence”, B3 – “the degree of responsibility”, B4 – “the scope of
information visibility”, B5 – “the degree of information availability”, B6 – “the degree
of information accessibility to third parties”, B7 – “approval to disseminate
information”, B8 – “the degree of information reliability”, B9 – “the level of information
and communication technologies development”, B10 – “cognitive limitations of the
user”, B11 – “the level of information use efficiency”, B12 – “the level of information
and communication security”, B13 – “the level of corruption reduction”, B14 – “risk of
information”. Т = {“low”, “below average”, “average”, “above average”, “high”};
Х={information space}; G – procedure for the formation of new terms using logical
connective “and”, “or”; М – semantic procedure for the formation of fuzzy variables X,
and the corresponding fuzzy set for the terms G(T) according to the translation rules
fuzzy connective “and”, “or”.
In this case, T is the terms of these input and output variables of the fuzzy model,
which are represented as fuzzy sets = , ( ) : ∈ , ( ) ∈ [0; 1]
, where – elements of the set X,
( ) – the membership function of a fuzzy set [16]:
low ∈ [0; 0,2)
⎧ [0,2; 0,37)
⎪below average ∈
( )= average ∈ [0,37; 0,63) (7)
⎨above average ∈ [0,63; 0,8)
⎪
⎩ high ∈ [0,8; 1]
We present a linguistic description of the values of the factors and their measured values
using the Harrington’s desirability functions. The scale value is confined to the closed
range of [0, 1]. Zero value corresponds to the worst measured factor state, and one
corresponds to the best measured factor value. For a static analysis of the situation, we
will calculate the consonance and dissonance of the cognitive map based on the
research in [27]. Consonance determines how consistent the presence of factors in the
system is. Dissonance determines how well-reasoned the influence of the system on
each of the factors. The following table 3 gives system indicators of cognitive model.
The highest values of the consonance of the influence of a factor on the system are
such indicators as the degree of confidence, the degree of information availability, the
degree of information accessibility to third parties, approval to disseminate information,
the level of corruption reduction. The digraph of the interaction of factors is shown
below in fig. 5.
The highest values of the consonance of the influence of system on the factor are
such indicators as the degree of information availability, the level of information and
communication technologies development, cognitive limitations of the user. The
digraph of the interaction of factors is shown below in fig. 6.
The highest values of the dissonance of the influence of system on the factor are
such indicators as the degree of confidence and approval to disseminate information.
Analysis of the dissonance of the influence of the system on the factor revealed the
need to increase the degree of confidence in the medium and source of information, and
96
the need to expand the boundaries of permission to disseminate information. All these
actions, in general, lead to a decrease in dissonance. The highest values of the
dissonance of the influence of factor on the system are such indicators as the level of
information and communication technologies development and risk of information.
Table 3. The main system indicators of the cognitive model of information transparency.
Linguistic Consonance Consonance Dissonance Dissonance Influencing Influencing
variables of the of the of the of the of the of the
influence of influence of influence influence factor on system on
factor on system on of factor on of system the system the factor
the system the factor the system on the
factor
B1 0,867 0,621 0,133 0,379 0,150 -0,098
B2 0,908 0,532 0,092 0,468 0,029 0,013
B3 0,701 0,684 0,299 0,316 0,040 0,025
B4 0,887 0,625 0,113 0,375 -0,113 0,161
B5 0,908 1 0,092 0 -0,018 0,036
B6 0,908 0,823 0,092 0,177 -0,018 -0,052
B7 0,908 0,532 0,092 0,468 -0,018 0,045
B8 0,662 0,666 0,338 0,334 0,018 0,068
B9 0,486 1 0,514 0 0,071 0
B10 0,640 1 0,360 0 0,085 -0,036
B11 0,633 0,870 0,367 0,131 0,107 0,021
B12 0,622 0,803 0,378 0,197 -0,107 0,050
B13 0,908 0,641 0,092 0,359 0,052 -0,011
B14 0,582 0,823 0,418 0,177 -0,107 -0,052
the degree of approval to disseminate the degree of information
confidence + + information accessibility to third parties
+
+
the degree of + the scope of
responsibility information visibility
+
the level of + -
corruption reduction + the degree of
information
transparency information availability
Fig. 5. Digraph of consonance of the influence of factor on the system (slice level 0,7).
Let us give a characteristic to each factor of the system:
B1 – “information transparency” most strongly influences on the system among other
factors (0.15), which is confirmed by the high value of the consonance of the influence
97
of the factor on the system (0.867), but the system as a whole reduces the transparency
of information;
B2 – “the degree of confidence” influences on the system relatively weakly (0.029),
the system, in turn, does not influence on a factor considerably (0.013). The dissonance
of the system's influence on the factor is sufficiently high (0.468);
B3 – “the degree of responsibility” - for this indicator, the consonances of the
influence of the factor and the system are sufficiently high and have approximately
equal values. This factor provides the strengthening of the system. This is an indicator
of the prospect of strengthening the system due to the awareness of the need to disclose
a certain amount and type of information;
B4 – “the scope of information visibility” has significant negative influence on the
system. But the system reinforces this factor. The value of the consonances of the
influence of the factor on the system is quite high (0.887).
B5 – “the degree of information availability” has a high value of the indicators of the
consonances of the factor and the system, their values are approximately equal. It
indicates that this factor strengthens the system. The prospect of strengthening is
possible by expanding access to information. But the factor has a significant negative
impact on the system;
B6 – “the degree of information accessibility to third parties” weakens the system
generally, and so does it. The values of the consonance of the influence of the factor
and system are high;
B7 – “approval to disseminate information” influences negatively on the system. The
system does not influence significantly on a factor. The value of the consonance of
influence of the factor is high. The value of the dissonance of the system's influence on
the factor has a sufficiently high value in comparison with other vertices;
B8 – “the degree of information reliability” the system influences this factor
significantly and factor strengthens the system. The consonance of the factor and the
system are equivalent, that is, it is necessary to strengthen the degree of reliability of
the information;
B9 – “the level of information and communication technologies development” - the
system has no influence on this factor, but the dissonance of the influence of the factor
on the system is of the highest;
B10 – “cognitive limitations of the user” the system affects the factor strongly and
reduces the user's cognitive skills;
B11 – “the level of information use efficiency” has a significant impact on the system.
The ratio of the consonances of influence indicates unused opportunities to increase the
efficient use of information;
B12 – “the level of information and communication security” influences negatively
on the system. The ratio of the consonances of influence indicates unused opportunities
to increase the level of information and communication security
B13 – “the level of corruption reduction” the ratio of the consonances of influence
indicates unused opportunities to reducing corruption;
B14 – “risk of information” has a significant negative impact on the system. The
system diminishes the value of this factor. The dissonance of the influence of a factor
on the system is of sufficient importance (0.418).
98
the degree of information the degree of information
availability accessibility to third parties
-
+
the level of information and the level of information the level of information and
communication technologies use efficiency- + communication security
development -
+
-
cognitive limitations -
of the user risk of information
Fig. 6. Digraph of consonance of the influence of system on the factor (slice level 0,7).
A mathematical instrument of impulse processes is used to obtain a forecast of the
development of the situation when implementing various alternatives. It allows you to
predict the values of factors at discrete times. The “Igla” decision support system was
used for modeling [28]. The initial values of the factors and the expected range of the
initial values of the factors are determined by the following levels (see Table 4).
Table 4. Initial values of variables and expected area.
Variables Initial value Expected values
1. information transparency “low” “average”
2. the degree of confidence “low” “high”
3. the degree of responsibility “low”
4. the scope of information visibility “average”
5. the degree of information availability “average”
6. the degree of information accessibility to third
“average”
parties
7. approval to disseminate information “average”
8. the degree of information reliability “average”
9. the level of information and communication
“low”
technologies development
10. cognitive limitations of the user “average”
11. the level of information use efficiency “below average” -
12. the level of information and communication
“below average” -
security
13. the level of corruption reduction “low” “high”
14. risk of information “high” -
The program “Igla” has generated over 600 alternatives. The choice of alternatives
is carried out per the purpose of the study. Let's consider the selected alternatives in
more detail.
The diagram 7 shows the dynamics of the influence that alternatives 522, 537, 558,
567, 570 have on the target factor “information transparency”.
99
Fig. 7. The dynamics of the value of “information transparency” under the influence of
alternatives 522, 537, 558, 567, 570.
The diagram 8 shows the dynamics of the influence that alternatives 574, 525, 542, 575,
541 have on the target factor “information transparency”.
Fig. 8. The dynamics of the value of “information transparency” under the influence of
alternatives 574, 525, 542, 575, 541.
Since previous research by specialists has shown that a high level of information
transparency leads to an increase in the risk of misuse of information. Therefore, we
100
consider those alternatives in which the level of information transparency corresponds
to the average value. These are alternatives to 537 and 541.
The diagram 9 shows the dynamics of the influence that alternatives 537 and 541
have on the factor “the degree of confidence”.
Fig. 9. The dynamics of the value of “the degree of confidence” under the influence of
alternatives 537 and 541.
Thus, during the first 10 months, under the influence of both alternatives, the factor
“the degree of confidence” gradually increases to a maximum. But in the following
periods, under the influence of Alternative 537, the factor takes on a higher level than
under the influence of Alternative 541. The diagram 10 shows the dynamics of the
influence those alternatives 537 and 541 have on the factor “the level of corruption
reduction”.
Thus, under the influence of both alternatives, the factor “the level of corruption
reduction” gradually increases to a maximum. But under the influence of Alternative
537, the factor takes on a higher level than under the influence of Alternative 541.
Therefore, the best Alternative is 537, which meets the conditions. It was constructed
as follows: in the first step and for one month it is necessary “the scope of information
visibility” lower to “below average”, “the degree of information availability” increase
to “above average”, “the level of information use efficiency” to leave at the level of
“below average”. In general, it is necessary to increase the degree of information
reliability and take measures to increase the level of information accessibility, including
for third parties. Providing the required level of information reliability remains a
difficult issue due to the weak structuring of this category.
Reliability is defined as a feature that allows to characterize the content of
information on the presence of errors, distortions, biases, the degree of reflection of
101
reality, the combination of which directly affects management decisions and their
effectiveness. It influences the usefulness of financial statements. Its level is revealed
due to additional, clarifying characteristics, in particular: “completeness, neutrality,
discretion, the prevalence of essence over form and correct presentation” of
information. Reliability is determined using other clarifying information, which is
explained by the established relationship between information sources.
Fig. 10. The dynamics of the value of “the level of corruption reduction” under the influence of
alternatives 537 and 541.
Reliability characterizes the authenticity of facts, phenomena, processes in terms of
their subjectivity and variability of definition. It is determined according to the scale of
the decision-maker by using available information, which may be limited. For its
quantitative calculation, scientific methods and empirical research are used. The results
of these studies are convincing enough to make the user feel trust to them. The expert
verifies the information fact using expert research methods. The interpretation of the
results of these studies is influenced by the subjectivity and competence of the
evaluator.
Therefore, the results of assessing the reliability of information are taken into
account with the assumptions that underlie the applied methods of expert research.
First, they are regulated by regulatory and legislative acts for economic experts, both
state and non-state. Second, the expert himself accepts the assumptions to generate
conclusions.
Thus, the availability of information is a subjective-objective category. It is
determined not only by the sphere of financial and economic relations since it is closely
related to its reliability, persuasiveness of fact. But accessibility can only exist if there
are an object and subject of research. The subject is an expert, based on his knowledge,
102
skills, experience, abilities, considerations, as well as the information he has, makes
decisions on the degree of correspondence of the fact being investigated to reality.
This category is not normatively regulated, it cannot be measured quantitatively, and
expert methods are used to study it. Experts research this category in the context of a
certain area, which is regulated by the relevant regulatory legal acts, and the object of
which is the results of the financial and economic activities of the entity. But is it
possible to talk about one hundred percent reliability of the results of the entity's
activities presented in the financial statements. Since this reporting is made and
evaluated by specialists who are subjective, and the influence of the human factor,
opportunistic behavior in general, gives rise to the risk of errors, fraud, and the like.
Thus, we can say with a certain degree of probability that all information that has
not been fully verified, and is not generated and processed automatically, is partially
reliable.
The characteristic principle of reliability is manifested by establishing a minimum
deviation between the actual data and the results obtained. It determines the close
relationship of the studied category with the comparability of the data, and only together
with the usefulness and materiality, the reliability ensures the quality of information.
In [22] it was proved that the reliability is divided into three categories. The first,
internal, which reflects the generally accepted characteristics of knowledge about the
obtained facts. The second, relative, which indicates the compliance of the facts with
the requirements of the information user. The third, absolute, which determines the
level of similarity of the facts with the really possible. Thus, information is
characterized by the formal features of its construction, by the value for the person and
by the reality of the existence of the given facts.
The reliability is determined by three criteria. The first is the validity that is the
sources of information confirm the facts or phenomena under investigation. The next
one is the consistency that is a fact or phenomenon in its manifestation does not create
contradictions with other proven facts or phenomena. The third is the credibility,
according to which the sources of information are verified and correspond to realities,
and the information carrier is sufficiently protected. Therefore, the level of reliability
can be assessed by the results of the ratio of the number of facts that do not correspond
to reality to the total number of facts, by calculating the “probability of errors in the
transmission of information”. It is advisable to highlight the boundaries within which
the results are assigned a certain degree of reliability: full or partial, or establish the
level at which a fact or phenomenon is determined unreliable. In this case, it is
necessary to take into account the specification of information, the sources of its
formation, the method of analysis of results, users, the consequences of its use in
different scenarios. Thus, information can become, in one situation and for one user,
reliable and valuable, and in another situation and for another user – partially reliable,
or in general – unreliable, invaluable.
103
5 Conclusion
It involves structuring knowledge about the factor environment, identifying strategic
changes in the level of information transparency and the levels of influence of its
factors. Information transparency is defined as a poorly structured category which
nevertheless acts as a qualitative characteristic of information, a certain level of which
forms an additional spectrum of properties of information which has been adequately
perceived or processed. In the course of the study, a cognitive model of information
transparency was constructed in the form of a weighted digraph. The results of its
structural analysis revealed that the degree of transparency and information and
communication security have the most powerful influence on the state of the system.
The results showed that higher levels of information and communication security lead
to lower risks, lower degree of information accessibility to third parties and information
transparency in general. At the same time, only the high level of information and
communication security is associated with an increase in the degree of information
reliability, the level of visible information use efficiency and information transparency.
The analysis of the cognitive model factors which affect the level of information
transparency showed that its level is most significantly decreased by the growing level
of information and communication security, while it is most significantly increased by
the growing efficiency of visible information use, higher level of technological
development and reduced scope of information visibility. The results of this study allow
us to identify the strategic elements of managing information transparency as a tool for
economic entities to achieve their goals in the information environment.
At the same time, it is necessary to take into account the initial levels of factors that
influence information transparency. Because the researched categories are poorly
structured, the methodology of fuzzy cognitive modeling was used. The fuzziness of
the system is manifested in the level variation of factors and the relationships between
them, which allows simulating the scenarios of its development under conditions of
various levels of initial data.
For static analysis of the situation, the consonance and dissonance of the cognitive
model are calculated, it is determined how justified the presence of factors in the system
is and how well-argued the influence of the system on each of the factors is. Analysis
of the dissonance of the influence of the system on the factor revealed the need to
increase the degree of confidence in the medium and source of information, and the
need to expand the boundaries of permission to disseminate information. All these
actions, in general, lead to a decrease in dissonance. The highest values of the
dissonance of the influence of factor on the system are such indicators as the level of
information and communication technologies development and risk of information.
A mathematical instrument of impulse processes is used to obtain a forecast of the
development of the situation when implementing various alternatives. It allows you to
predict the values of factors at discrete times. The “Igla” decision support system was
used for modeling scenarios of the situation, more than 600 alternatives were analyzed,
in accordance with the purpose of research were chosen the best.
The best alternative is constructed as follows: in the first step and for one month it
is necessary the factor “the scope of information visibility” lower to “below average”,
104
“the degree of information availability” increase to “above average”, “the level of
information use efficiency” to leave at the level of “below average”. It is necessary to
increase the degree of information reliability and take measures to increase the level of
information accessibility, including for third parties. Providing the required level of
information reliability remains a difficult issue due to the weak structuring of this
category and the associated risks, which are the focus of further research.
References
1. Bakurova, A.V.: Samoorganizacija social'no-jekonomicheskih sistem: modeli i metody
(Self-organization of socio-economic systems: models and methods). KPU, Zaporizhzhia
(2010)
2. Bellver, A., Kaufmann, D.: Transparenting transparency: initial empirics and policy
applications. http://ssrn.com/abstract=808664 (2005). Accessed 25 Aug 2020
3. Bushman, R., Smith, A.: Transparency, financial accounting information, and corporate
governance. SSRN Electronic Journal 9, 65–87 (2003)
4. FCMappers. http://www.fcmappers.net (2020). Accessed 25 Aug 2020
5. Havrylenko, M., Shiyko, V., Liliana, L., Khvostina, I., Yashcheritsyna, N.: Economic and
mathematical modeling of industrial enterprise business model financial efficiency
estimation. E3S Web of Conferences 166, 13025 (2020).
doi:10.1051/e3sconf/202016613025
6. Hosseini, M., Shahri, A., Phalp, K., Ali, R.: Transparency as a Requirement. In: Proc. 21st
REFSQ Conference. Poster and Demo Track, Bournemouth University, pp. 222–223
http://ceur-ws.org/Vol-1342/03-Posters.pdf (2015). Accessed 17 Aug 2020
7. Hosseini, M., Shahri, A., Phalp, K.: A modelling language for transparency requirements
in business information systems. In: Proc. of the 28th international conference on advanced
information systems engineering (CAiSE), pp. 239–254 (2016)
8. Hosseini, M., Shahri, A., Phalp, K.: Four reference models for transparency requirements
in information systems. Requirements Engineering 23(2), 251–275 (2018).
doi:10.1007/s00766-017-0265-y
9. Iorhachova, M.I.: Informatsiina prozorist kompanii – skladnyk modeli korporatyvnoho
upravlinnia (Information transparency of the company is a component of the corporate
governance model). Ekonomika ta suspilstvo 17, 543–550 (2018)
10. Islam, R.: Does more transparency go along with better governance? Economics and
Politics 18(2), 121–167 (2006)
11. Kaminskyi, A.B.: Informatsiina prozorist u diialnosti ISI: teoretychne obgruntuvannia i
praktyka realizatsii (Information transparency in CII activities: theoretical justification and
implementation practice). Finansy Ukrainy 11, 60–70 (2010)
12. Khvostina, I., Havadzyn, N., Horal, L., Yurchenko, N.: Emergent Properties Manifestation
in the Risk Assessment of Oil and Gas Companies. CEUR Workshop Proceedings 2422,
157–168 (2019)
13. Knottnerus, J.: Promoting transparency of research and data needs much more attention.
Journal of Clinical Epidemiology 70, 1–3 (2016)
14. Kucherova, H., Didenko, A., Kravets O.: Modeling information transparency of economic
entities under uncertainty. Advances in Economics, Business and Management Research
99, 201–205 (2019). doi:10.2991/mdsmes-19.2019.38
105
15. Kundeliene, K., Leitoniene, S.: Business Information Transparency: Causes and Evaluation
Possibilities. Procedia – Social and Behavioral Sciences 213, 340–344 (2015).
doi:10.1016/j.sbspro.2015.11.548
16. Leonenkov, A.: Nechetkoe modelirovanie v srede MATLAB i fuzzy TECH (Fuzzy
modeling in MATLAB and fuzzy TECH). BHV-Peterburg, Russia (2005)
17. Los, V.O., Ocheretin, D.V.: Cognitive modeling of tourism development. Business Inform
10(477), 166–173 (2017)
18. Measuring the Information Society Report 2018.1. https://www.itu.int/en/ITU-
D/Statistics/Pages/publications/misr2018.aspx (2018). Accessed 17 Aug 2020
19. Oliinyk, V., Kozmenko O.: Optimization of investment portfolio management. Serbian
Journal of Management 14(2), 373–387 (2019). doi: 10.5937/sjm14-16806
20. Olijnyk, O.O.: Naprjamy pidvyshhennja prozorosti dijaljnosti orghanizacij (Areas of
increase of transparency of activity of organizations). Socialjno-trudovi vidnosyny: teorija
ta praktyka 1, 224–230 (2017)
21. Osoba, O., Kosko, B.: Fuzzy cognitive maps of public support for insurgency and terrorism.
Journal of Defense Modeling and Simulation: Applications, Methodology, Technology
14(1), 17–32 (2017)
22. Pierce, R.: Evaluating information: Validity, reliability, accuracy, triangulation. In:
Research Methods in Politics: A Practical Guide. Sage Publications (2008)
23. Roberts, F.: Discrete mathematical models, with applications to social, biological, and
environmental problems. Prentice-Hall, Englewood Cliffs (1976)
24. Schauer, F.: Transparency in Three Dimensions, University of Illinois Law Review 4,
1339–1357 (2011)
25. Schnackenberg, A., Tomlinson, E.: Organizational transparency: A new perspective on
managing trust in organization-stakeholder relationships. Journal of Management 42.7,
1784–1810 (2016)
26. Sheng, Y.K.: What is Good Governance? United Nations Economic and Social
Commission for Asia and the Pacific. United Nations Economic and Social Commission
for Asia and the Pacific. https://www.unescap.org/sites/default/files/good-governance.pdf
(2009). Accessed 17 Aug 2020
27. Silov, V.B.: Prinjatie strategicheskih reshenij v nechetkoj obstanovke (Making strategic
decisions in a fuzzy environment). INPRO-RES, Moscow (1995)
28. Sistema podderzhki prinjatija reshenij “Igla” (Decision support system “Igla”).
http://iipo.tu-bryansk.ru/quill/index.html (2020). Accessed 25 Aug 2020
29. Stohl, C., Stohl, M., Leonard, P.: Managing Opacity: Information Visibility and the Paradox
of Transparency in the Digital Age. International Journal of Communication 10, 123–137
(2016)
30. Tkach, I.V.: Pryntsypy prozorosti ta vidkrytosti yak nevidiemna skladova publichnoho
upravlinnia: teoretychnyi aspekt (The principles of transparency and openness as an integral
part of public administration: a theoretical dimension). Ynternauka 12(22), 25–32 (2016)
31. Vaccaro, A.: Privacy, security, and transparency: ICT-related ethical perspectives and
contrasts in contemporary firms. Social inclusion: Societal and organizational implications
for information systems 208, 245–258 (2006)
32. Vishwanath, T., Kaufmann, D.: Toward transparency: New approaches and their
application to financial markets. World Bank Research Observer 16, 41–57 (2001)
33. Williams, A.: A global index of information transparency and accountability. Journal of
Comparative Economics 43, 804–824 (2014). doi:10.1016/j.jce. 2014.10.004
34. Zachosova, N.V., Ghorjachkivsjka, I.V.: Informacijna prozoristj ta zabezpechennja
interfejsnoji bezpeky jak elementy poperedzhennja ta protydiji zlochynam u ekonomichnij
106
sferi na mizhnarodnomu rivni (Information transparency and provision of interface security
as elements of the prevention and counteraction to economic crime at the international
level). Naukovyj visnyk Uzhghorodsjkogho nacionaljnogho universytetu 8(1), 90–93
(2016)
35. Zinchenko, A., Reznik, N., Saprykina, M.K.: Indeks prozorosti sajtiv ukrayins`ky`x
kompanij 2017 (Transparency Index of Ukrainian companies' sites). Yuston, Kyiv (2018)