Baltic Journal of Economic Studies  

61

Vol. 3, No. 5, 2017

Corresponding author:
1 Department of Management, National Metallurgical Academy of Ukraine.
E-mail: vishnevskaya@i.ua 
2 Department of Management, National Metallurgical Academy of Ukraine.
E-mail: 13managua@gmail.com 
3 Department of Management, National Metallurgical Academy of Ukraine.
E-mail: kaut_olga@ukr.net 

DOI: http://dx.doi.org/10.30525/2256-0742/2017-3-5-61-69

DEVELOPMENT OF METHODOLOGY  
FOR THE CALCULATION OF THE PROJECT INNOVATION 

INDICATOR AND ITS CRITERIA COMPONENTS
Mariya Vishnevskaya1, Dmytro Kozenkov2, Olha Kaut3

National Metallurgical Academy of Ukraine, Ukraine

Abstract. Two main components of the problem studied in the article are revealed. At the practical level, the provision of 
the convenient tools allowing a comprehensive evaluation the proposed innovative project in terms of its possibilities 
for inclusion in the portfolio or development program, and on the level of science – the need for improvement and 
complementing the existing methodology of assessment of innovative projects attractiveness in the context of their 
properties and a specific set of components. The research is scientifically applied since the problem solution involves 
the science-based development of a set of techniques, allowing the practical use of knowledge gained from large 
information arrays at the initialization stage. The purpose of the study is the formation of an integrated indicator of 
the project innovation, with a substantive justification of the calculation method, as a tool for the evaluation and 
selection of projects to be included in the portfolio of projects and programs. The theoretical and methodological basis 
of the research is the conceptual provisions and scientific developments of experts on project management issues, 
published in monographs, periodicals, materials of scientific and practical conferences on the topic of research. The 
tasks were solved using the general scientific and special methods, mathematical modelling methods based on the 
system approach. Results. A balanced system of parametric single indicators of innovation is presented  – the risks, 
personnel, quality, innovation, resources, and performers, which allows getting a comprehensive idea of any project 
already in the initial stages. The choice of a risk tolerance as a key criterion of the “risks” element and the reference 
characteristics is substantiated, in relation to which it can be argued that the potential project holds promise. A tool 
for calculating the risk tolerance based on the use of matrices and vector analysis is proposed. Based on the fuzzy sets 
theory, a calculation of the “personnel” component is suggested on the basis of the analysis of the conformity factor 
of execution of potential project operations to the required competencies of the project manager. The suggested 
technology assessment as a part of a comprehensive indicator of the project innovation, unlike other technologies, 
considers the compliance of the project product characteristics with the consumers’ requirements and the options 
of a specific project. On the basis of ideal and matrix modelling, a mathematical model is obtained for determining 
the prospects of realizing consumer’s expectations regarding the project product. Practical implications. As a result of 
applying the suggested indicator of innovation, it is possible to obtain the information on the degree of the project 
innovativeness and risk tolerance, if the available resources are sufficient, whether the set of competencies of a project 
manager is in compliance with the project works, and whether the project product meets the requirements of the 
consumers. It is found that the innovation of projects can be considered as necessary and sufficient information for 
making optimal decisions. Simplicity, ease of use, efficiency, measurability, adaptability of innovation indicator extend 
its effectiveness in the field of the project management and provide the organization with a new tool for making 
appropriate management decisions. Application of the integrated project innovation indicator complements the 
classical methods of analysis of options, increases the effectiveness of the application of project management tools, 
especially at the stage of project selection to portfolio or program. The results of the research can be used for further 
development of scientific and methodological foundations to form a balanced system of indicators of innovative 
projects. The results implementation is a methodological and organizational basis for creating effective systems and 
technologies for managing the project programs and portfolios.

Key words: project innovation indicator, personnel assessment, quality assessment, risk assessment, project 
selection criteria

JEL Classification: H43, О22, M59, M110, D81, C65



Baltic Journal of Economic Studies  

62

Vol. 3, No. 5, 2017

1. Introduction
In management and project management, a special 

attention is given to the problem of effective mechanisms 
of selecting the projects to portfolios or development 
programs. After all, despite the heterogeneous nature 
of projects, the choice of a variety of possible options 
is not accidental. At the same time, the requirements 
for portfolios (programs) are reflected in managerial 
decisions as to which of the projects should be involved 
in a portfolio or a program.

Thus, the selection and optimal use of innovative 
projects are one of the key aspects of creating an effective 
management system. This requires the appropriate 
methodological and organisational support.

The problem solved by the authors in the study is 
relevant and significant for the project management. In 
fact, the challenging issues of determining the content 
of programs and the composition of portfolios require 
the creation of appropriate quality mechanisms for 
effective selection of projects. Under conditions of 
the inconstancy of the business environment, the 
availability of numerous risks, limited time, financial, 
and material resources, tools for selecting the projects 
on the basis of an integrated indicator, suggested in the 
study, becomes especially important.

2. The analysis of recent scientific researches 
and articulation of the problem

Nowadays, there is a large number of approaches to 
projects selection, both on the basis of qualitative and 
quantitative indicators (Ohara, 2010), as well as by 
various integral and multi-criteria indicators, like in the 
study by Boock and Chau (2013, pp. 76-86). Where a 
transparent, easy-to-use for all project stakeholders, 
selection model based on a system of evaluation and 
cost criteria is represented. At the same time, the 
researches by Chang and Ishii (2013, pp. 935-948), as 
well as the study by Kuo, Chang and Chen (2013, p. 
335-349) suggest to use the hybrid MCDM model  – 
a multicriteria decision making model based on the 
combination of DEMATEL technology with ANP 
and VICOR methods. Katayev in his study (2014, pp. 
55-63) considers the integration of critical chain, critical 
path, and simulation methods with the use of a matrix 
model of project management, however with regard to 
labour resources only. Within the research (Morozov 
and Osetrin, 2009), the approaches to the formation of 
a projects portfolio are based on integrated indicators in 
the field of safety. An interesting “prism” model of the 
filtering process for new projects, taking into account 
the influence of the criteria for selecting the necessary 
projects for a particular portfolio is given by Zachko, Rak 
Y. and Rak T. (2008, pp. 54-61). The authors  – Rach, 
Koliada, and Antonian (2009, pp. 90-101) – suggested 
a method of evaluation for selecting the projects for a 
portfolio based on the analytical and hierarchical model. 

The subject of the study was later researched by Koliada 
(2010), who represented an effective tool for selecting 
the projects for a portfolio based on the concept of 
strategic unity. At the same time, sensitivity of integral 
index to changes in the incoming indices values. The 
authors Semko and Oleinikova (2010) suggest the 
analysis of indicators to form a projects portfolio, taking 
into account the synergism effect.

In the conditions of information overload and huge 
supply in the project market, it becomes increasingly 
difficult to form projects portfolios and programs from 
a variety of possible options. In connection with the 
adoption and implementation of an innovative strategy 
for the development of social and economic relations, 
this trend will be retained. As the number of projects 
grows, the development of an effective project selection 
tool will definitely remain relevant in the coming years.

In view of the above, it is suggested to use the tool 
for solving the identified problems based on the use 
of integrated comprehensive indicators of the project 
innovation. In contrast to the available indicators, it 
allows making a comprehensive assessment of risk 
tolerance, the effect of innovations, the degree of 
compliance of managing personnel’s competencies 
with the work, and the project product quality with the 
consumers’ expectations at the initial stages. Therefore, it 
will make it possible to reduce the number of redundant 
projects and simplify the procedure for rejecting the 
projects that do not meet the requirements.

3. Goal and objectives of the study
The purpose of the study is the formation of an 

integrated indicator of the project innovation, with a 
substantive justification of the calculation method, as a 
tool for the assessment and selection of the projects to 
be included in the projects portfolio and programs.

To achieve this goal, the following tasks shall be 
addressed:
– to design and define the methodology for calculating 
the integrated indicator of the project’s innovation and 
the balanced system of its criteria components;
– to present a methodology for calculating the criteria 
component – “risks”;
– to describe the method of calculation of the criteria 
component – “managing personnel”;
– to determine the methodology for calculating the 
criteria component – “quality”.

4. Materials and methods of research  
of the project assessment system

The problem under study includes two main 
components. At the practical level, the provision 
of the convenient tools allowing a comprehensive 
evaluation the proposed innovative project in terms 
of its possibilities for inclusion in the portfolio or 
development program. At the level of science, there is 



Baltic Journal of Economic Studies  

63

Vol. 3, No. 5, 2017
a need for improving and complementing the existing 
methodology of assessment of innovative projects 
attractiveness in the context of their properties and a 
specific set of components. The research is scientifically 
applied since the problem solution involves the 
development of a set of techniques, allowing the practical 
use of knowledge gained from large information arrays 
at the initialization stage.

One of the problem’s solutions has been suggested 
in the study (Malyi, Antonenko, Mazurkevych, 2008), 
where the innovation of the project is regarded as the 
amount of information necessary and sufficient to 
obtain the optimal solution in the course of the project 
implementation. According to the source (Kutsekon, 
2009), the resulting effect of any innovative activity is 
the production of a competitive product, strengthening 
of the position of an enterprise in the market and of the 
financial state of a company. With such an approach, 
choosing the best variant of the innovative project to be 
included in a portfolio involves getting great results at 
minimum cost.

By its essence, economic indicators are resultant, other 
groups’ indicators are indicative, they help to identify 
the causes of problems and prevent the consequences. 
There are a number of general rules for using alternative 
projects selection criteria; however, each enterprise has 
its own system of priorities. For example, the project’s 
compliance with the chosen strategy, completion time, 
social significance, market potential of the product, 
impact on the company’s image, level of risk, etc.

As the choice of any set of projects in the future 
provides a balance or imbalance of the entire portfolio or 
program, the creation of an effective system of projects 
assessment is of great importance and involves the use 
of a number of indicators. Therefore, it seems advisable 
to use the project innovation indicator, which is formed 
on the basis of a comprehensive system of indicators 
(criteria).

5. Calculation of the integrated  
project innovation indicator  
and its criterion components

Integrated (group) factorial indicator of the project 
innovation (w) is defined as the sum of the products of 
parametric single (in some cases – expert) assessments 
(di) and weight coefficients of the indicators being 
analysed (gi) using the formula:

w d gi i
i

n

=
=
∑ * ,
1

                                (1)

where di is the value of i indicator of the assessment;
gi is the weight coefficient of i indicator;
p is the number of assessment indicators.
Based on the results of previous studies, the 

components of the index of innovativeness of the 
project include the following criteria: risks, managing 
personnel, quality, innovation, resources and performers 

(subcontractors). In the limited volume of this work, 
consider in detail the methodology for calculating the 
first three of the six specified components.

World experience and business practices show 
the business entities management’s awareness of the 
importance of taking into account the risks as the 
elements of management strategies for a capital increase 
in the total value of the enterprise (Arseniev, Davydova, 
2017). This, according to Martin and William (2000), 
is an assessment of the performance and criterion 
of prospects for the economic growth and owners’ 
welfare. So, let us consider the first criteria component 
of creating the indicator of project innovation – “Risks”.

Quantitative assessment of potential risks is an 
integral part of the project selection stage. It provides 
a determination of the probability of risks occurrence 
and the possible impact of their consequences on the 
project. In this case, the measures chosen on the basis 
of risk assessment can be aimed at the risk mitigation 
directly, the elimination of risk factors or at the control 
of the economic consequences (Fig. 1).

In practice, risk assessments are often based on 
a simplified model  – several or one main indicator, 
representing the most important characteristics 
inherent in this project.

As a key criterion, we suggest a choice of project 
tolerance for possible risks. In particular, the 
risk tolerance (ability to withstand the business 
destabilization processes due to certain characteristics) 
will be assessed using matrices and vector analysis.

Suppose we have m projects described by n categories 
of risk. Then each of considered projects m can be 
interpreted as a point of a n-dimensional risk space 
with coordinates equal to the value of n risk categories 
for the selected project. Below are the acceptable risk 
assessments (value Xij where i is the index of the project, 
and j is the index of the project risk category).

Projects
Risk Assessments

Risk 1 Risk 2 Risk 3 ….. Risk j …. Risk N
Project 1 X11 X12 X13 … X1j … X1n
Project 2 X21 X22 X23 … X2j … X2n
Project 3 X31 X32 X33 … X3j … X3n

…… … …. …. … … … …
Project i Xi1 Xi2 Xi3 … Xij … Xin

…… … … … … … … …
Project m Xm1 Xm2 Xm3 … Xmj … Xmn

Assessments of the risk category may be heterogeneous 
due to the fact that the risk factors are manifested 
differently in projects of different types. Therefore, 
in order to eliminate the perversion in the further 
analysis that may be caused by this, it is necessary to 
carry out a preliminary procedure for standardizing risk 
assessments. This procedure involves the replacement 
of assessments of Xij, with Zij assessments, calculated 
using the formula:



Baltic Journal of Economic Studies  

64

Vol. 3, No. 5, 2017

Z
X X

ij
ij j

j

=
−

σ
,                                             (2)

where

X
m

Xj ij
i

n

=
=
∑

1

1
,                                            (3)

and

σ j ij j
i

n

m
X X= −











=
∑

1 2
1

1
2

( ) ,                                    (4)

where j = 1,2,3 ..., n; i = 1,2,3 ..., m; Xij are the values of 
the risk category j for the project i; X j  is the arithmetic 
mean of the risk category j; σ j  is the standard deviation 
of the risk category j; Zij is the standardized value of the 
risk category j for the project i.

In order to make an appropriate management decision 
regarding the selection of a project, it is necessary to 
select its reference characteristic, regarding which it 
can be argued that this project holds promise. Also, the 
project with a minimal risk can be chosen as a reference. 
Then Z0j is a standardized value of the risk category j for 
the project 0 will be defined as the minimum among all 
values for this category of risk:
Z Zj

i
ij0 = min .                                               (5)

The distance between individual projects and the 
reference project in the area of standardized risk 
assessments will be determined as follows:

C Z Z i mi ij j
j

n

0 0
2

1

1
2

1 2 3= −








 =

=
∑( ) ( , , ,... ) .           (6)

Upon the calculation of the distances between all 
the projects and the reference project in space, a vector 
of distances is obtained, which can be represented as 
follows:

C

C

C

C

C

i

m

=



























10

20

0

0

.                                         (7)

The resulting distances are the initial values for 
calculating the risk tolerance index Di  for each i-th 
project:
D

C
C

i
i= −1 0
0

,                                                 (8)

C C S0 0 02= + ,                                               (9)

Fig. 1. Bidder projects risk monitoring system

Acceptance 

Mitigation 

Transfer 

Quantitative and qualitative analysis of possible risk events under 
the project 

Project risk tolerance analysis 

 

Choice of management methods  

Preventive measures 

 
 
 

M
O

N
I

T
O

R
I

N
G

 O
F

 R
I

S
K

S
 A

N
 P

O
S

S
I

B
L

E
 

E
C

O
N

O
M

I
C

 C
O

N
S

E
Q

U
E

N
C

E
S

 O
F

 B
I

D
D

E
R

S
 

P
R

O
J

E
C

T
S

  
 

T e c h n i c a l  a n d  e c o n o m i c  
i n d i c a t o r s  o f  t h e  c o m p a n y  

Pr
oj

ec
t 

M
an

ag
er

 

Pr
of

es
si

on
al

 
ex

pe
rt

s 

Pe
rf

or
m

er
s 

R
is

k-
m

an
ag

er
s 

E x t e r n a l  
i n f o r m a t i o n  

Justification of methods of 
managing economic consequences 

Assessment of forecast risk coefficients 

Forecasting of economic consequences of 
project risks in micro-logistic system 

 



Baltic Journal of Economic Studies  

65

Vol. 3, No. 5, 2017

C
m

Ci
i

m

0 0
1

1
=

=
∑ ,                                               (10)

S
m

C Ci
i

m

0 0 0
2

1

1
21

= −










=
∑( ) ,                                       (11)

where i = 1,2,3 ..., m; Di is the risk tolerance index; C0 is 
the vector of distances of i project; C0  is the arithmetic 
mean of the vector of distances of i project; Ci0 is the 
distance between individual projects and the reference 
project; S0 is the standard deviation of the vector of 
distances of i project; m is the number of projects to be 
assessed.

The closer the value of the risk tolerance indicator 
of the project to 1, the more promising the project. 
The limit value at the stage of making a decision on 
the selection of projects by the risk indicator is the 
arithmetic mean of the risk tolerance level:

D
m

Di
i

m

=
=
∑

1

1
.                                           (12)

Thus, with an assessment by different risk categories 
for each potential project, using taxonomic analysis of 
the set of estimates, it is possible to divide the projects 
into 2 subsets – the promising ones and those showing 
no promise.

Consider the following criteria component of creating 
the project innovation indicator  – “personnel”. In 
particular, using the theory of fuzzy sets, an analysis of 
the coefficient of compliance of the potential project 
operations performance with the required competence 
of the project manager is suggested.

Suppose that X = {x1, x2, x3} is a set of project 
operations, Y = {y1, y2, y3} is a set of functions performed 
due to a certain competence, Z = {z1, z2, ..., zm} - is the set 
of competencies of the project manager required for the 
project implementation.

ФR:: X×Y [0,1] is the membership function of the 
fuzzy binary relation R. 

For all x∈X and all y∈Y function FR (x, y) is the 
degree of significance of a certain function execution 
(functional significance) for the performance of the 
relevant work when selecting the key competencies of 
the project.

Relation R in matrix form:

y1 y2 … yp

x1 ΦR x y1 1,( ) ΦR x y1 2,( ) … ΦR px y1,( )
R x= 2 ΦR x y2 1,( ) ΦR x y2 2,( ) … ΦR px y2,( )

… … … … …
… … … … …
… … … … …

xn ΦR nx y, 1( ) ΦR nx y, 2( ) … ΦR n px y,( )

Suppose that π: Y×Z→ [0,1] is a membership function 
of the fuzzy binary relation S. 

For all y∈Y and all z∈Z πs (y, z) is the degree of 
membership or the degree of compatibility of the key 
competency with functional significance. Then in the 
matrix form, this relation is as follows: 

z1 z2 … zm

y1 πs y z1 1,( ) πs y z1 2,( ) … πs y zm1,( )
S y= 2 πs y z2 1,( ) πs y z2 2,( ) … πs y zm2,( )

… … … … …
… … … … …
… … … … …

yp πs y zp, 1( ) πs y zp, 2( ) … πs y zp m,( )

The relation T: x × Z  [0,1] is obtained, the elements 
of which are defined by the following membership 
function:

µ
π

Ai i

R i
y

R
y

x z
x y s y z

x y
,

, ,

,
( ) =

( ) ⋅ ( )

( )

∑

∑

Φ

Φ
 

for all x X y Y z Z∈ ∈ ∈, , .                  (13)
The relation T  in the matrix form:

z1 z2 … zm

x1 µA x z1 1 1,( ) µA x z2 1 2,( ) … µAm mx z1,( )
T x= 2 µA x z1 2 1,( ) µA x z2 2 2,( ) … µAm mx z2,( )

… … … … …
… … … … …
… … … … …

xn µA nx z1 1,( ) µA nx z2 2,( ) … µAm n mx z,( )

The sum ΦR
y

x y,( )∑  is equal to the degree of a fuzzy 
subset of functional significance y, indicating the level 
x, which is used in the study to assess key competencies 
z, µAi ix z,( ) . The function zi can be interpreted as 
a weighted degree of necessity of availability of a 
competence to perform the work x. For all x1 and x2 
of all z ∈ Z and all λ∈[0.1], this function meets the 
condition: 

µ λ λ µ µAi i i Ai i Ai ix z x z x z x z1 2 1 21, , min , , ,( ) + − ( )( )  ≥ ( ) ( ) 
µ λ λ µ µAi i i Ai i Ai ix z x z x z x z1 2 1 21, , min , , ,( ) + − ( )( )  ≥ ( ) ( )  , (14)

where λ is the size of the class interval, which will be 
calculated as follows: 

λ =
−z z
K

max min ,              (15)

where K is the number of classes, into which the 
variation of a sign should be split. 

For example, the number of competencies is 5-20 – the 
number of classes is 5, or the number of competencies is 
20-35 – the number of classes is 7.



Baltic Journal of Economic Studies  

66

Vol. 3, No. 5, 2017

At the same time, it should be noted that all μAi (x, zi) 
are convex. And hence their overlap is convex functions 
as well.

Let us determine the condition, by which the 
threshold for the distribution of project operations will 
be limited, taking into account the key competencies of 
the project manager 
l x z x z

i j x
Ai i Ai j< ( ) ( ) min max min , , ,, µ µ .        (16) 

Then
M x x x z x zi Ai

i j x
Ai i Ai j= ( ) ≥ ( ) ( ) { }µ µ µmin max min , , ,,  

for all x Mi∈ .                      (17)
In this way, we obtain a level set that describes 

operations, focused on a certain key competence.
Subsequently, if the project is classified as a low or 

inappropriate level of competence of a project manager, 
a decision is made to review the composition of the 
team – in particular, the selection of a project manager, 
experienced in the field of the project issues. In case the 
replacement is not possible, the decision to reject the 
project may be made. However, provided that analytical 
data on other components of the project innovation 
indicator are taken into account.

Let us turn to the next component of the suggested 
indicator  – “quality”. In a market economy, the level 
of production performance can be considered as a 
measure of satisfaction of consumers’ needs at the 
minimum cost. At the same time, the key characteristic 
that shapes the needs of consumers in a competitive 
environment is the quality of projects product or 
products. The assessment of the quality of carrying out 
the processes of the project activity and production 

is possible using various methods  – differential, 
generalization, comprehensive, instrumental, 
statistical, etc. Each of them has certain advantages 
and disadvantages.

The calculation of the “quality” indicator according 
to the ideal matrix modelling (Mazurkevich, 2009) 
is suggested on the basis of assessing the quality level 
of the project product in relation to the consumer’s 
requirements for the product.

This interconnection is illustrated in Fig. 2
Suppose, X x x xn= { }1 2, ,  is the set of project 

operations (the technological capabilities of the 
organisation used to obtain the product); 
Y y y yp= { }1 2, ,...   – a set of project product 

characteristics;
Z z z zm= { }1 2, ,...  – a set of consumer requirements for 

the product.
ΦR X Y: ,× → [ ]0 1   – a membership function of a 

fuzzy binary relation R (in matrix form  – ΦR x y( , ) ). 
For all x∈X and all y∈Y function FR (x, y) is the degree 
of significance of carrying out a specific operation to 
implement the relevant characteristic of the project 
product.

π:Y×Z→ [0,1] is the membership function of the fuzzy 
binary relation S. For all y ∈ Y and all z ∈ Z πs (y, z) is the 
degree of membership or the degree of compatibility of 
product characteristics with the consumer requirements 
for this product. Then in the matrix form this relation is 
as follows: πS y z,( ) .

The relation T: X × Z → [0,1] is obtained, the elements 
of which are defined by the following membership 
function 

z z1 2, z z1 3, … z zm m−1,

x1 µA x z1 1 1,( ) ^ µA x z2 1 2,( ) µA x z1 1 1,( ) ^ µA x z2 1 3,( ) … µAm mx z1 1, −( ) ^ µAm mx z1,( )
W x= 2 µA x z1 2 1,( ) ^ µA x z2 2 2,( ) µA x z1 2 1,( ) ^ µA x z2 2 3,( ) … µAm mx z2 1, −( ) ^ µAm mx z2,( )

… … … … …
… … … … …
… … … … …

xn µA nx z1 1,( ) ^ µA nx z2 2,( ) µA nx z1 1,( ) ^ µA nx z2 3,( ) … µAm n mx z, −( )1 ^ µAm n mx z,( )

Fig. 2. Interconnection of quality with consumer requirements for the project product

 

Q
U

A
L

IT
Y

  

Organizational aspect –  
technological opportunities 

C
O

N
SU

M
E

R
S 

 
R

E
Q

U
IR

E
M

E
N

T
S 

Target aspect –  
the degree of reaching the goal 

Functional aspect –  
product properties 



Baltic Journal of Economic Studies  

67

Vol. 3, No. 5, 2017

µ
π

Ai i

R i
y

R
y

x z
x y s y z

x y
,

, ,

,
( ) =

( ) ⋅ ( )

( )

∑

∑

Φ

Φ
 

for all x X y Y z Z∈ ∈ ∈, , .         (18)
Then in the matrix form, this relation is as follows: 
µA x z,( ) .
Thus, 

 
µ

µ
A

A Åòàëîí

x z

x z

,

,
%

( )
( )

×∑
∑

100 ,               (19)

determines the organization’s ability to implement 
the expectations of consumers about this product, that 
is, determines the quality of the product for a particular 
consumer. 

µA Åòàëîíi x z,( )∑  It is calculated using the formula: 

µ
π

Ai i Åòàëîí

R Åòàëîí S i Åòàëîí
y

R Åòàë

x z
x y y z

x y
,

, ,

,
( ) =

( ) ⋅ ( )

( )
∑

∑Φ

Φ
îîí

y
∑

∑


















 

for all x X y Y z Z∈ ∈ ∈, , ,  (20)

µ
π

Ai i

R Åòàëîí S i
y

R Åòàëîí
y

x z
x y y z

x y
,

, ,

,
( )

( ) ⋅ ( )

( )















∑

∑

∑

Φ

Φ






















×∑ 100%  

for all x X y Y z Z∈ ∈ ∈, ,         (21)
The second relation is matching the needs:

µ
π

Ai i

R S i Åòàëîí
y

R
y

x z
x y y z

x y
,

, ,

,
( )

( ) ⋅ ( )

( )























∑
∑

∑
∑

Φ

Φ















×100%  

for all x X y Y z Z∈ ∈ ∈, ,     (22)
The suggested technology of quality assessment 

as a part of a comprehensive indicator of the project 
innovation, unlike other technologies, considers the 
compliance of the project product characteristics 
with the consumers’ requirements and the options 
of a particular project. Such information is extremely 
important for the manager at the stage of making a 
decision on the inclusion of a potential project in a 
projects portfolio or a program.

In the next steps, the project manager calculates the 
data of three other components of the integrated project 

 

 

 

 

 

 

 

 

  

 

 

 

 

 

 

 

 

 

  

rejection of the 
project 
implementation  

no 

yes 

Projects portfolio (programs) optimisation 

Creation of an optimised projects portfolio (program) and allocation of resources in the projects 

Bidders projects to be selected 

Determination of basic parameters of each bidder project  

Projects analysis for the 
selection to projects 
portfolio (program) 

based on the calculation  
w 

rejection of the 
project 
implementation 
 

no 

Calculation of the 
project efficiency 

Project inclusion into the projects portfolio or a program 

yes 

assessment of: 
- risks 
- management 
personnel 
- quality 
- innovations 
- resources 
- performers 

 
Fig. 3. The place of the integrated indicator of the project innovation in the algorithm  
of the projects portfolio or program creation



Baltic Journal of Economic Studies  

68

Vol. 3, No. 5, 2017
innovation indicator  – “resources”, “innovations” and 
“performers”. As a result, the matrix consisting of 
numbers from 0 to 1 is obtained. Since the value of the 
ideal matrix should be 0, then this is a priority option, 
while the matrix with the value of 1 is the inadmissible 
option. Further using the given formula (1), the project 
innovation indicator is directly defined.

Fig. 3 shows the place of the developed indicator of 
projects innovation in the general algorithm of the 
project portfolio (program) creation.

The developed methods for project selection based on 
the innovation indicator considers the project portfolio 
and the program as a system of ordered elements and 
allows for simplification of the screening procedure and 
the reduction of the number of “redundant” projects, 
and thus provides the organization with an entirely new 
tool for making appropriate management decisions on 
the issue of assessment and choices.

It can be argued that the use of a project innovation 
indicator will complement the classical project selection 
tools to project portfolios or development programs. 
However, at the same time, all the criteria must be 
defined and approved by senior management, the 
limitations should be established by the appropriate 
departments, all analytical project data shall be recorded 
in the relevant documents, potential conflicts between 
projects must be timely discovered and eliminated. The 
created projects portfolio/program in the future should 
be checked for sensibility and go through the process of 
optimization. For example, using the recurrence relation 
of Richard Bellman.

However, it should be noted that some of its 
components can be calculated on the initial stages 
using the expert method only, which slightly reduces 
the accuracy of results. This method of individual 
expert estimations is based on the expression of 
expert opinions independently of each other and 
on the use of these opinions as the final result of the 
expert evaluation. However, this disadvantage may be 
neutralized or minimized. In order to do this, in the 
course of processing the results of the expert survey, the 
expert opinions consistency analysis is required using a 
concordance coefficient. The confidence estimation of 
the expert survey results must also be carried out using 
a Delphi method.

7. Conclusions
To date, there are numerous approaches to the 

selection of projects both on the basis of qualitative 
and quantitative indicators. Methods for project 
financing analysis, scenario methods, optimization, 
cost-benefit method, and other methods can be used 
for the assessment and option selection. Summarizing 
the results, it is worth noting that the use of the project 
innovation indicator for this purpose is feasible as it has 
certain advantages. Among them are: simplicity, ease 

of use, performance, measurability, and adaptability. 
All of its components are available to the manager or 
the project manager at an early stage, and they allow 
getting a comprehensive overview of the project. The 
above complements the classical methods of alternative 
analysis, increases the efficiency of the application of 
the project management tools, especially at the stage of 
project selection to a portfolio or a program.

According to the tasks, the following conclusions are 
obtained:

1. An integrated factorial projects innovation 
indicator is created, which is defined as the sum of 
the results of parametric single estimates and weight 
coefficients of the factors under analysis. Based on the 
review conducted by the existing indicators of project 
selection and the results of previous studies, suggested 
the following composition of criteria for defining the 
projects innovation indicator: risks, personnel, quality, 
innovation, resources and performers. According to 
the authors, these components are optimal in terms 
of the completeness of the scope of potential projects 
analysis for the involvement in a particular program or 
projects portfolio. After all, as a result of the application 
of the suggested innovation indicator, it is possible to 
obtain information on the degree of innovation and 
risk tolerance of the project. And whether there are 
sufficient resources available, if the project manager’s 
set of competencies meets requirements of the project 
operations, and whether the project product is in 
compliance with requirements of customers. It is found 
that the innovation of projects can be considered as 
necessary and sufficient information for making optimal 
decisions. And since this value is non-dimensional, there 
is the possibility of comparing different projects to each 
other and making appropriate managerial decisions, 
based on a particular, measurable characteristic.

2. Risk tolerance is selected as the key criterion for the 
“risks” component and the reference characteristic, in 
relation to which it can be argued that this project holds 
promise. After all, the ability to withstand the current 
destabilization processes of a business environment due 
to particular characteristics is one of the most important 
qualities. A tool for calculating the risk tolerance based 
on the use of matrices and vector analysis is suggested. 
It is found that since the risk category estimates may be 
heterogeneous, in order to eliminate the perversion, 
it is necessary to carry out a procedure for their 
standardization. The limit value at the stage of making a 
decision on the selection of projects by the risk indicator 
is the arithmetic mean of the risk tolerance level. Thus, 
the closer the value of the indicator of the project risk 
tolerance to 1, the more promising the project will be 
considered.

3. The calculation of the “personnel” component is 
presented based on the analysis of the coefficient of 
compliance of potential project operations with the 
required competencies of the project manager, employing 



Baltic Journal of Economic Studies  

69

Vol. 3, No. 5, 2017
the theory of fuzzy sets. An algorithm for determining the 
conditions, limiting the threshold of the project works 
distribution, considering the key competencies of the 
project manager, is provided. An equation for describing 
the work focused on a certain key competence is obtained. 
The criterion for making a positive decision on the 
selection of projects by the “personnel” indicator will be 
the maximum correspondence of a set of competencies of 
the project manager to a set of certain project operations, 
or vice versa.

4. The calculation of “quality” component on the basis 
of ideal-matrix simulation is proposed. A mathematical 
model for determining the prospects of implementing 
consumers’ expectations regarding the project product 
is obtained. Therefore, the suggested technology 
of quality assessment as a part of a comprehensive 
project innovation indicator, unlike other technologies, 
considers the compliance of the project product 
characteristics with the consumers’ requirements and 
the options of a particular project. The best project will 
be the one with the maximum degree of significance 
of a particular work to implement the appropriate 
characteristics of the project product and the maximum 
degree of compatibility of product characteristics with 
the consumer requirements for this product.

The results of the research can be used for further 
development of scientific and methodological 
foundations to form a balanced system of indicators 
of innovative projects. The implementation of the 
results obtained represents a methodological and 
organizational basis for creating effective systems and 
technologies for managing the programs and project 
portfolios both at the level of individual enterprises and 
at the regional level.

The next step in this study may be the application 
of the suggested innovation indicator, as one of the 
components of the mathematical model for calculation 
of projects clusters, because the need to activate the 
innovation factor under conditions of significant 
regional inequality stimulates interest in the theory of 
innovative regional clusters. Thus, with the availability 
of a certain cluster of projects that may be included in 
the program, it is necessary to conduct the qualitative 
and quantitative selection of projects. At the same time, 
qualitative selection can be started by constructing a 
matrix of combined criteria and project distribution 
within this matrix. Such a matrix can be created for a 
combination of such criteria as: “the project innovation 
indicator – profit” or “the project innovation indicator – 
profitability”.

References:
Arsenyev, Ya. N., & Davydova, T. Ya. (2017). Problem business risk management. Topical questions of contemporary 
science, Aspekt Publishing, 41.
Boock, M. H., & Chau, M. (2013). The Use of Value Engineering in the Evaluation and Selection of Digitization 
Projects. Evidence Based Library and Information Practice, 2(3), 76-86.
Chang, Y. F., &  Ishii, H. (2013). Probing the Implementation of Project Management Office by Using DEMATEL 
with a Hybrid MCDM Model. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 25(6), 935-948.
Kataev, D. S. (2014). Matrichnoe upravleniе trudovyimi resursami promyishlennyih predpriyatiy. Upravlinnya 
proektami ta rozvitok virobnitstva, 1(49), 55-63.
Kolyada, O. P. (2010). Instrumentalnyi zasib vidboru proektiv u portfel vyshchoho navchalnoho zakladu v ramkakh 
kontseptsii stratehichnoi yednosti. Vostochno-Evropeiskyi zhurnal peredovykh tekhnolohyi,1/2 (43), 31. 
Kuo, Sh.-Y., Chang, K.-W., & Chen Sh.-Ch. (2013). Decision-making on Transport Policy: A Comparison between 
Scholars and Stakeholders. Journal of the Eastern Asia Society for Transportation Studies, 10, 335-349.
Kutsekon, L. O. (2009). Innovatsiyi yak obektivna neobhidnist zabezpechennya konkurentospromozhnosti 
ekonomiki. Strategiya innovatsiynogo rozvitku ekonomiki ta aktualni problemi menedzhment-biznes osviti, 191-194.
Maliy, V. V., Antonenko, S. V., & Mazurkevich, A. I. (2008). Riven innovatsiynosti proektu. Upravlinnya proektami 
ta rozvitok virobnitstva, 1(25), 9-12.
Martin, J., & William, P. (2000). Value Based Management. The Corporate Response to the Shareholder Revolution. 
Harvard Biasness School Press, 249. 
Mazurkevich, A. I. (2009). Model otsenki kachestva. Upravlinnya proektami stan ta perspektivi, 127-128.
Morozov, V. V., & Osetrin, K. M. (2009). Rozrobka modeli formuvannya portfelya budivelnih proektiv. Upravlinnya 
proektami stan ta perspektivi, 59-60.
Ohara, Sh. (2010). The Logical Context and Evolution of P2M Program Life Cycle Management: In Pursuit of 
Sustainable and Seamless Innovation in Global Competition. Journal of International Association of Project & 
Program Management, 5, 23-40.
Rach, V. A., Kolyada, O. P., & Antonyan, E. A. (2009). Metod invariantnyih pokazateley opisaniya strategiy razvitiya 
kak instrument formirovaniya portfelya proektov. Upravlinnya proektami ta rozvitok virobnitstva, 2(30), 90-101.
Semko, I. B., & Olenikova, T. Yu. (2010). Sinergetichniy efekt portfelya proektiv. Vostochno-Evropeiskyi zhurnal 
peredovykh tekhnolohyi,1/2 (43), 60-62.
Zachko, O. B., Rak, Yu. P., & Rak, T. Ye. (2008). Pidkhody do formuvannia portfelia proektiv udoskonalennia 
systemy bezpeky zhyttiediialnosti. Upravlinnya proektami ta rozvitok virobnitstva, 3(27), 54-61.