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International Journal of Economics and Financial
ISSN: 2146-4138
available at http:
International Journal of Economics and Financial Issues, 2015, 5(Special Issue) 73-79.
Economics and Society in the Era of Technological Changes and Globalization
Risk Management of Innovation Projects in the Context of
Liudmila V. Nikolova1*, Juriy Ju Kuporov2, Dmitriy G. Rodionov3
Peter The Great St. Petersburg Polytechnic University, 29 Politekhnicheskaya Street, St. Petersburg, 195251, Russia, 2Peter The
Great St. Petersburg Polytechnic University, 29 Politekhnicheskaya Street, St. Petersburg, 195251, Russia, 3Peter The Great St.
Petersburg Polytechnic University, 29 Politekhnicheskaya Street, St. Petersburg, 195251, Russia. *Email:
Globalization is becoming increasingly important in the economy. It strongly influences the advanced technologies and innovation processes. The
present stage of economic development differs from the preceding one by the increased role and autonomy in the innovation processes management that
has led to the need for change in the approach to the application and development of innovation project risk management methods and techniques. The
existing methods and ways of assessing and managing innovation project risks do not allow obtaining the maximum effect from their implementation.
Therefore there was a need to develop new methods and techniques that would take into account the market conditions and the use of new financial
instruments and strategies. The article considers the application of the system optimization method when building a risk management model for
innovation projects in the context of globalization. Scientific novelty includes the development of a method and a model to calculate limiting values
of factors, which bring the target value of the corresponding criterion of the innovation project efficiency to critical limit at the solution to direct and
inverse problems. As a result, the authors have built a model of innovation project sustainability region in the context of globalization, using MATHCAT
software (computer algebra system from a class of computer-aided design, focused on preparation of interactive documents with computations and
visual tracking).
Keywords: Globalization, Innovation Project, Sustainability Model, Sustainability Method, Risk Factor, System Optimization
JEL Classifications: C61, C63, F62, O33, O32
Integrity and the cyclical development of the world community
allow considering the globalization, on the one hand, as a process,
and, on the other hand, as a system, which is at a certain stage
of development. Special features of the innovation processes in
the context of globalization are reflected in the following works
(Levén et al., 2014; Sirgy et al., 2004; Dreher, 2006; Tsai, 2007;
Nikolova et al., 2014; Nikolova et al., 2014; Tüzün et al., 2015;
Vambery and Mayer, 2012; Varma et al., 2007).
Risks management of investment projects is possible using
economic and mathematical models. An economic and
mathematical model is a powerful method of cognition of the
external world, as well as prediction and control. An economic
and mathematical model allows penetrating into the essence of
the studied phenomena and influence them (Marmier et al., 2013;
Marxt and Brunner, 2013; Mikkola, 2001; Moutinho et al., 2015;
Stubbs and Cocklin, 2008; Schumpeter, 1939; Short et al., 2012;
Borowiec, 2013; Buyanov et al., 2002; Grachova, 2001).
The economic and mathematical models, developed by Myers and
Pogam, namely “Longer” model of financial planning and the model
of the optimal allocation of company monetary assets (the problem
of capital rationing) are widely known in the scientific world. They
are used in the sensitivity analysis and scenario analysis methods.
Recently the models by Gracheva are becoming popular. These are
models for evaluation of project efficiency considering anti-risk
International Journal of Economics and Financial Issues | Vol 5 • Special Issue • 2015
Nikolova, et al.: Risk Management of Innovation Projects in the Context of Globalization
activities, the integrated risk-related cost optimization model, the
external and internal risk-related costs optimization model, which
are also used in the sensitivity analysis and scenario analysis
methods. The computational complexity inherent in probabilistic
simulation models of assessment, management, and optimization
proposed by Novokreschenov, which are based on the probabilistic
simulation method, has led to the fact that these models are rarely
used in solving of practical problems on investment. The above
noted mathematical economic models can be successfully used
for risk assessment and innovation projects management, though
neither of them considers the technique to calculate limiting values
of the factors affecting the efficiency of the innovation project.
The authors developed a model enabling calculation of limiting
values of factors affecting the efficiency of the innovation project,
named a model of financial sustainability of innovation projects
risk factors management (hereinafter – the sustainability model).
The objective of the present model consists in the formation of a
sustainability region of innovation project. This model is created
using the method of sustainability analysis of innovation project
taking into account risk factors (hereinafter – sustainability
method), which was developed by the authors.
2.1. Innovation Projects Risks Management
To create a risk management model of innovation project of the
region it is necessary to emphasize the methodological, methodical
and operating principles, which coherently integrate the diverse
interests at both macro- and micro-levels in a single whole
(Buyanov et al., 2002).
1. Methodological principles, i.e., the most general principles
that define the conceptual provisions of investment, and most
importantly – independent of the specifics of the concerned
type of risk (even invariant with respect to the nature and
specific content of the target and value system). When forming
the investment methodology, the optimal control theory is
applied at studying of the systems, as well as system analysis
Methodological guidelines take into account contemporary
features of investing that allows justifying new approaches
to the creation of innovation risks management model in the
region. This principle is based on the following rules:
• The uniformity of the risks means that all participants of
business activities have the same perception of risk;
• The positiveness of the risks means that the integrated risk
factor is at least not more than the level of acceptability.
In an innovation project this principle is associated with
the “efficiency” principle;
• The objectivity of the risks means the necessity to perform
correct formation of the structure and characteristics of
the changing object when conducting its assessment;
• The correctness of the risks means that the assessment
shall be executed with the certain formal requirements:
Providing integral monotonicity, i.e., within a certain
range of indicators, increasing the intensity of activity
leads in increase of the risk, at that in the border areas
the uncertainty changes qualitatively;
b. Disproportionality, i.e., the increase in risk is not
directly proportional to the intensity of the activity
(within a given range of indicators variations);
c. Transitivity, i.e., if the first situation is less risky than
the second one, and the second situation is less risky
than the third one, this means that the first situation
is less risky than the third one;
d. Additivity, i.e., the risk is equal to the sum of
individual risks:
• The complexity of the risks means that together they
should form a closed system;
• Interdependence of risks means that in the event of some
risks other risks arise due to the interaction effects.
2. Methodological principles, i.e., principles directly associated
with the structure of the innovation project, its specificity,
implementation features, and specific situations. The
following rules underline this principle:
• Discordance of risks means that any new project has its
specific impact on the innovation project; the greater the
significance of a new project discordance the greater the
• Divergence of risks perception is due to availability of
risks at various implementation stages of the innovation
project that predetermines the divergence of interests
between project participants and their different attitude
to the possible damage;
• The agility of risks of the innovation project suggests
that the methodological support takes into account the
variability of risks;
• The consistency of the innovation project risks is
conditioned by the need that in case of the risks
occurrence, prevention processes must be coordinated
with other processes.
3. Operating principles, i.e., the principles relating to the
availability, reliability, and uniqueness of the information and
capability of its processing
• The ability to model innovation project risks means the
ability to describe the emerging risks by the model
• The ability to simplify the innovation project risks means
that in risk assessment we choose the method that is most
simple in the information and computational context.
This has resulted in a creation of the methodology for investigation
of innovation project risk analysis in the context of globalization.
The authors defined certain methodological, methodical and
operating principles, which are based on the rules applied in the
formation of model and risk management methods.
2.2. Justification of the Model of Sustainability of
Innovation Projects Risk Factors Management
The sustainability model is formed on the basis of the innovation
project sustainability analysis method developed by the authors.
This method takes into account risk factors and is a logical
extension of sensitivity and scenarios analysis.
International Journal of Economics and Financial Issues | Vol 5 • Special Issue • 2015
Nikolova, et al.: Risk Management of Innovation Projects in the Context of Globalization
Innovation projects sensitivity analysis method is a singleobjective optimization problem, i.e., its implementation needs
the use of a single objective function – factor affecting the
efficiency of the innovation project. The authors propose to
consider further development of the sensitivity analysis method,
i.e., to move from a single-objective analysis to a multi-factor
analysis, using the analytical method of Pontryagin for solution
to variational problems with restrictions that are encountered at
control optimization in dynamic systems.
Analytical method, grounded by Pontryagin, is used to justify the
innovation project sustainability method under the uncertainty and
risk conditions. The investment sustainability estimation method
provides calculation of limiting values of factors affecting the
efficiency of the innovation project in solving both direct and
inverse problems.
The innovation project is a complex dynamic system. Its risk
management needs consideration of many risk factors.
In some cases risk factors can be reduced to a single risk and
thereby to revert to a known single-objective optimization method.
The simplest way of such reduction consists in the so-called
weighting the criteria. If ƒ1 (х),…, ƒn (х) are the objective functions
expressing the values of the used criteria, than for each of them
positive weighting factor λі is selected according to the influence
of this criterion on the investment efficiency. The criteria weighing
operation (of the objective functions) ƒ1 (х),…, ƒn (х) consists
in their replacement by just one criterion (objective function)
ƒ (х) = λ1.ƒ1 (х) +… + λn.ƒn (х) (Chernoruchcky, 2001).
However, for the risk management of innovation project such
reduction is practically impossible; therefore a vector (multicriteria) objective function is used in the course of optimization.
In this case, the admissible domain M can be changed in the
optimization process. Moreover, its targeted change is the main
essence of the optimization process for this class of problems.
Since the laws of possible changes in the admissible domain M are
usually set by system of models, the described approach to
optimization problems is called systemic. In systemic approach,
the changes that specify the admissible domain in the space of
those or other parameters occur as a result of the sequence of
solutions chosen from a discrete set of possible solutions; at that,
at the beginning of the optimization process this set itself is not
completely specified and is updated in the course of innovation
project development and implementation.
One of the peculiar formalized settings of system optimization
problems is a double-criteria analysis. Suppose that an appropriate
solution is uniquely determined by the choice of the values of
these criteria. In other words, the desired solution is searched
directly in the space К of optimization criteria, which we denote
as х1 and х2. Solving starts with the choice in a given space К of
a certain point А0 with coordinates a0, b0, as a desired solution
to the problem. Further, the initial restrictions F1(0) (x1,x2) ≥
0…, Fn(0) (x1,x2) ≥ 0, specifying the initial valid region Р0, are
constructed. The fact, whether point А0 belongs to the region Р0
is determined through the direct validation. In the first case we
can apply in principle conventional (classical) optimization
procedure either with respect to one of the criteria х1, х2, or their
certain combinations. However, at the systemic approach a totally
different stratagem usually is used as follows: In accordance with
the highest level model М,  which manages the choice of criteria,
the point А0 is excluded from the admissible domain Р0.
Then the restrictions, which are not valid at the point А0 are
separated (in this case, these are F3(0) и F4(0)). Turning to the
models М3 and М4, which form these restrictions, one or another
solutions, which change the appropriate restrictions in the right
direction (if such change is possible), are considered interactively.
Right is the direction, which reduces the absolute value of negative
disparities Fі(0) (а0, b0) (in this case F3(0) (а0, b0) и F4(0)(а0, b0)).
It should be borne in mind that in many cases, the restrictions Fі
are interrelated, so that changing one of them leads to change
a certain part of other restriction. Solutions selection management
to change the restrictions is determined by minimizing of some
penalty function g0 (а0, b0). The maximum absolute value of
negative disparities λiFi(0) (а0, b0) is chosen as such a function
(where λ1 – are some positive weighting coefficients). If there are
no disparities, than g0 (а0, b0) = 0 by definition.
A number of solutions R1 …, Rm, appear as a result of the
management, leading to a decrease in the value of penalty
function, which after the тth solution is denoted as gm (a0, b0).
Modifying restrictions, each of the taken solutions leads to a
corresponding change of the admissible domain. Consider two
such changes: The first one changes the restrictions F3(0), F2(0),
replacing them respectively by the restrictions F3(1), F2(1), while the
second one affects only one restriction F4(0), replacing it with the
restriction F4(1). Obtained admissible domain Р2 is restricted by the
lines F1(0), F2(1), F3(1), F4(1), while the corresponding values of the
penalty function are equal to g2 (а0,b0). Preliminary selection of the
final valid domain is impossible due to the fact that the sequence of
domains Р0, Р1 … may not be ordered by embedding. In addition,
the huge complexity when forming new restrictions does not allow
performing this work in advance, because this would require a lot
of extra work to change non-essential restrictions.
If g2 (а0, b0) ≠ 0, whereas there are no solutions resulting in a
further decrease in the value of penalty function, we return back
to the model М of the highest level, which controls the selection
of the desired solution А (а, b). Through a sequential series of
solutions D1, D2,…, Dk changing the initial solution to the problem
А0 (а0, b0) the latter is replaced by А1 (а1 b1),…, Ak (ak, bk) until
next point Ak (ak bk) is found in the admissible domain (k = 1).
Solutions on changes are selected from the feasible set of solutions
to minimize the penalty function. This process is close to the
classical optimization process, except for the fact that the steps
are chosen not arbitrarily, but in accordance with the admissible
(by the model М) solutions.
After the point Ак gets into the final admissible domain Рт,
an additional optimization procedure can be applied for any
combinations of хх and х2 criteria within this admissible domain.
International Journal of Economics and Financial Issues | Vol 5 • Special Issue • 2015
Nikolova, et al.: Risk Management of Innovation Projects in the Context of Globalization
This procedure differs from the classical one just by the fact that
the choice of optimization steps is not arbitrary but is controlled by
the model М of the highest level. If some restrictions, amenable to
further changes in the desired direction, interfere with the chosen
criterion in terms of its further improvement, the optimization
process can be continued by incorporating sequential solutions
on such changes.
The most important feature of system optimization, common for all
the approaches, in addition to multicriteriality and the possibility of
changing the admissible domain, is the interaction of the different
level models. In the current case this is an interaction of two
systems during the structural analysis: Risks system, consisting
of risk factors, and innovation project implementation system,
i.e., a model of М level.
An unambiguous solution to the problem through the choice of
the values of all optimization criteria cannot be used to justify an
innovation project risk management model, because there is no
uniqueness of solution to this problem. The space, in which the
solution is sought, in addition to the coordinates corresponding
to the optimization criteria, may have also other coordinates. In
this case the above-described optimization process becomes more
complicated due to the fact that the points А1 (а1, b1) are replaced
by hyperplanes – the areas of the sustainable investments. The
definition of the penalty function also complicates: It can be
determined, for example, as a distance from the hyperplane to the
next valid region in space with specified compressions (expansions)
along the axes corresponding to the optimization criteria – the
change agents of the limiting values in the sustainability model.
3.1. The Innovation Project Implementation Model
Uncertainty and risk are an objective reality of an innovation
project, an integral part affecting all its phases and implementation
Imagine a hypothetical model of an innovation project in the
form of a system formed from two interacting systems – risks
system, consisting of risk factors, and the system of conditionallyspecified implementation stages (Figure 1). The combination
of these systems represents a model for implementation of
real innovations. An innovation project is defined also as the
totality of the above noted systems and is a closed process – an
innovation system, which obeys the laws of the systems optimal
control theory.
Innovation system is a system, whose implementation is fraught
with risks in solving both current and long-term investment
objectives of innovation projects different in their scales. The
diversity of approaches to th …
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