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International Journal of Economics and Financial

Issues

ISSN: 2146-4138

available at http: www.econjournals.com

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

Globalization

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: nikalvsk@yandex.ru

1

ABSTRACT

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

1. INTRODUCTION

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

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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. THEORETICAL ASPECTS OF

INNOVATION PROJECTS RISKS

MANAGEMENT

2.1. Innovation Projects Risks Management

Methodology

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

methods.

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:

74

a.

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

risk;

• 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

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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. RESULTS

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

stages.

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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