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please find the attached journal ,and summarize it.words:500include references if any pdf.pdf Unformatted Attachment Preview 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 73 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 75 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 ... Purchase answer to see full attachment