On the basis of Belgorod region innovation activity analysis, positive relationship between shipped innovation items and domestic research and development costs was found. Semantic differential was used for multiple estimation of BelSU innovation activity and competiveness. BelSU integrated index showed high-enough innovation activity of this universityyesBelgorod State Universit

Balabanova, T. V.

Gulyaev, I. I.

Lomovtseva, O. A.

Pryadko, S. N.

Shlakaneva, V. A.

DSpace at Belgorod State University

The Social Sciences 10 (6): 1432-1436, 2015ISSN: 1818-5800© Medwell Journals, 2015Corresponding Author: Ivan I. Gulyaev, Belgorod State University, Pobedy St. 85, 308015 Belgorod, Russia1432Estimation of Innovative Activity of the UniversityIvan I. Gulyaev, Olga A. Lomovtseva, Svetlana N. Pryadko,Tatiana V. Balabanova and Victoria A. ShlakanevaBelgorod State University, Pobedy St. 85, 308015 Belgorod, RussiaAbstract: Higher education institutions act as one of the most important factors for the development of region.Russian traditional educational institutions should be transformed to meet the challenges of modern world. Onthe basis of Belgorod region innovation activity analysis, positive relationship between shipped innovationitems and domestic research and development costs was found. Semantic differential was used for multipleestimation of BelSU innovation activity and competiveness. BelSU integrated index showed high-enoughinnovation activity of this university.Key words: Innovations, inventive activity, higher education institutions, patent, semantic differential,Belgorod regionINTRODUCTION to another institutional form. Basis of the modern typePursuing sustainable market position, modern higher management, as well as results of innovation activity ofeducational institutions become members of complex higher education institutions in the real economy transferinnovation systems. In response to the growing needs of are in focus of following researchers: Lomovtseva (2013),high-tech businesses, universities create innovative Lomovtseva and Kochetkov (2010), Stevenson (1983),business environment that is able to provide high Ovsyannikova (2005), Baranova and Fadeykina (2015),productivity of the region and lead to a significant Kamarulzaman et  al.  (2013),  Tanha  et  al.  (2011),structural changes. Osgood et al. (1957), Osgood (1956) and Shin et al. (2012).Modernization of higher education in Russia involvestransformation of traditional higher education institutions MATERIALS AND METHODSto another institutional form: entrepreneurial, innovative,research. The study started with literature review and statisticalHowever, practice shows that this transformation data that provides the background for estimation ofmeets numerous difficulties which have environment innovation activity of higher educational institutions innature and country specifics of higher education in Belgorod region. The dynamics of main statisticalRussia. Foreign models of higher education institutions indicators (time series data) characterizing innovationwhich are successful in other countries are not activity of Belgorod region in general was analyzed bycompetitive in Russian reality. For the purpose of solving economic and mathematical methods and statisticalthis scientific issue, conceptual model of innovative methods. R  was used to measure of how close the datauniversity, which combines achievements of traditional are to the fitted trend line. Exponential smoothing washigher education, modern requirements and criteria of the used to smooth out irregularities caused by so-calledknowledge economy is required. atypical events.Interest of scientists in universities functioning Following methods were used to solve consideredparticularities as factor of economic competitiveness is issues: analysis and synthesis, grouping andcurrently rising. However, not all aspects of determined classification, system analysis, hierarchy analysis,issue received adequate attention. Items considered in monographic analysis, expert evaluation, induction andthis research require specific solutions which take into deduction, questionnaires, rating and ranking, logicalconsideration multilevel professional training and comparison, theoretical generalization, scientificresearch and scientific activity of university students and observation, economic and mathematical methods andparticipants. Most of papers considered this issue are statistical methods.written by Foreign scientists but Russian scientists and To estimate an innovation activity of higherpublic figures are also dedicated to aspects of educational institutions following structural-logic schemetransformation of traditional higher education institutions is proposed (Fig. 1).universities formation, their strategic development and2The Soc. Sci., 10 (6): 1432-1436, 20151433Fig. 1: Structural-logic scheme of higher educationalinstitutions innovation activityRESULTS AND DISCUSSIONTo characterize innovation activity of highereducational institutions dynamics of innovation activityof Belgorod region was analyzed as primary data.Inventive activity level and patenting of inventionsrecognizes innovation and scientific and technologicalpotential of region. Figure 2 displays the dynamics ofamount patent applications increment from 2006-2013,(2006 comparable level) in absolute terms and per 10,000inhabitants of Belgorod region.In 2011 and 2013, the amount of applications per10 000 inhabitants of region pierces the level of 2006.Total amount of invention patent applications fromBelgorod  region  applicants  per  year  in  period  from2007-2013 not once increased the level of 2006.Consequently, the inventive activity per inhabitant growsfaster than in absolute figures.Reliability of trend line that describes the number ofapplications from Belgorod region is 0.79 (Fig. 3). In 2010,this trend line attained a minimum and at the time ofwriting tends to increase. As seen from analysis of research and developmentorganizations in Belgorod region in period from 2006-2013their amount decreases (over the period under review itdeclined by 28.5%), the number of higher educationalinstitutions that had a research and development activityalso reduced (by 25%) (Fig. 4).Analysis the research and development personnelstructure revealed that the total amount of personnelreduces (by 4.4% in period from 2006-2013), however,analysis the structure of research and developmentpersonnel revealed that share of researchers in personneltends to grow and in researched period increased from54.1-70.3% (Fig. 5).Fig. 2: The amount of invention patent applications fromBelgorod region applicants per year in period from2006-2013Fig. 3: The  amount  of  invention  patent  applicationsfrom Belgorod region applicants in period from2006-2013Fig. 4: The amount of research and developmentorganizations in Belgorod region from 2006-2013Fig. 5: Structure of research and development personnelfrom 2006-2013Belgorod region innovation costs dynamics can bedescribed by a linear trend line (R  = 0.93). Linear trend is2increasing, it indicates the increase the domesticinnovation costs in Belgorod region in future (Fig. 6).St = yt 1+(1 ) St 10< 1t 3a - - a - a £ £The Soc. Sci., 10 (6): 1432-1436, 20151434Fig. 6: Domestic innovation costs in Belgorod region from2006-2013The figure shows the volume of shipped innovativegoods, works and services. Demand for goods, works andservices is affected by so-called atypical events that iswhy for data dynamics analysis and forecasting theinfluence of these factors was neutralized by exponentialsmoothing method.To smooth out irregularities caused by so-calledatypical events exponential smoothing was used. Thesmoothing scheme starts with setting St (smoothed observation) to y  (original observation). Subscript referstto time period. Smoothed observation for period is:(1)Where, " the smoothing constant. The results of aseries of exponential smoothing “Volume of shippedinnovative goods, works and services” if " = 0.4 areshown in Table 1 and Fig. 7.Quantitative estimation of relationship betweeninternal research and development costs and volume ofshipped innovative goods, works and services (smootheddata) in Belgorod region was calculated by correlationcoefficient. For researched period (2006-2013) correlationcoefficient is 0.69 which indicates the positive relationshipbetween the studied variables. To evaluate innovative activity of university isreasonable to apply semantic differential method. Thismethod was developed by American psychologist C.Osgood in 1952 for semantic spaces (Osgood et al., 1957;Osgood, 1956) (individual and group). However thismethod could be applied not only as experimentalpsychology methods.Principle of method consists in selection of severalcharacteristics of item. It could be done for innovativeactivity of higher educational institution estimation,ascomponent of its competitiveness.Semantic space coordinates are rate scales. Onopposite poles of the coordinate system are oppositecriteria. This method is useful for estimation of  innovativeFig. 7: Volume of shipped innovative goods, works andservices from 2006-2013 (actual and smootheddata)Table 1: Volume of shipped innovative goods, works and services inBelgorod region from 2006-2013 (original and smoothedobservations)Volume of shipped innovative goods, worksand services, mln. rub.------------------------------------------------------------Years Original Smoothed2006 2052.7 2052.702007 13377.9 8847.822008 32978.9 23326.472009 10437.5 15593.092010 9391.6 11872.192011 15457.4 14023.322012 21683.4 18619.372013 21246.5 20195.65activity of university in all and provides the estimation byseveral criteria simultaneously. The result is “Graphicprofile innovation activity of university”.As in Osgood (1956, 1957)’s Method, here three basic1-7 rating scales (Table 2). Next stage of estimation ofinnovation activity of university by semantic differentialmethod is expert evaluation of university activity.Employees of BelSU was taken as experts. For thesepurposes  they  evaluate BelSU on rating scales fromTable 2. On analytical stage of collected data proceeding,the average estimations of innovation activity byevaluated criteria were received. It is essential to take intoconsideration significance level of each criterion to getmore reliable estimation.University innovation activity developmentcoefficient (K ) calculated by Eq. 2:inni iinniB KKK´= ååinn0.9 0.6 1.05K 2.550.45 0.2 0.35+ += =+ +The Soc. Sci., 10 (6): 1432-1436, 20151435Table 2: Estimation innovation activity of university rating scale (by semantic differential method)University rating----------------------------------------Semantic descripiion Parameters Increasing Decresing VariablesInventive activity and innovation patenting level High 3, 2, 1, 0 1, 2, 3 Far behind the necessary levelResearchers share in personnel structure High - 1, 2, 3 LowExisting strategy of university development Efficient (results tocompetitiveness level growth) - 1, 2, 3 Inefficient (changes required)Fig. 8: The results of university innovation activityestimation calculated by semantic differentialmethod (for BelSU)(1)Where:B = Criterion estimation by rating scaleiK = Significance level of criterioniBased on expert evaluations following significancelevels were determined: inventive activity and innovationpatenting level (0.45); researchers share in personnelstructure (0.2); existing strategy of universitydevelopment (0.35).To achieve the high level of innovation developmentand ability of university to become a regional growthpoint, inventive and invention patenting activity level isthe main competitive advantage and significant criteria ininnovation activity evaluation by semantic differentialmethod.Proposed methodological approach was used tocalculate the index of innovative activity development.Visualization of the result is shown in Fig. 8. Index ofuniversity innovation activity development:it is high enough. With this method we can evaluate andrank the university in terms of development of innovativeactivity. It is characteristic for the method of semanticdifferential to have of many advantages: it has highvisibility; you can quickly identify ways to improve theinnovative capacity and to calculate the final grade. Themain problem of using the semantic differential methodconcerning of selection of parameters and theirimportance and quality selection of experts.CONCLUSIONThe results of analysis of innovative activity in theregion indicates that universities must perform in additionto the traditional functions and created a transfer ofknowledge in the production and commercialization ofintellectual property. The estimation of innovativeuniversity will contribute to the creation of suchstructures  in  the  Russian  Federation  as  well  as  thespeedy transformation of traditional universities inentrepreneurship and innovation with achievements oftraditional and modern requirements for the educationalactivity. REFERENCESBaranova, I.V. and N.V. Fadeykina, 2015. The role ofsubjects of scientific and educational complex in thedevelopment of innovative economy. Siberian Schoolof Finance. Innovation and Investment, 1 (108): 82-91.Kamarulzaman,  A.B.,  A.H.  Harris,  S.M.  Zahid  andN.A.A. Aziz, 2013. Commercialisation of UniversityResearch: An Investigation of Researchers’Behaviour. Communications of IBIMA. http://www.ibimapublishing.com/journals/CIBIMA/2013/120942/120942.pdf.Lomovtseva, O.A. and S.V. Kochetkov, 2010. Set ofinstruments of measuring of economic efficiency ofthe innovative capacity. Belgorod State UniversityScientific Bulletin. History Political Sci. Econ.Information Technologies, 19-1 (90): 56-64.The Soc. Sci., 10 (6): 1432-1436, 20151436Lomovtseva,  O.A.,  2013.  Region  in  the  context  of Stevenson, H.H., 1983. A perspective onsocial development and the evolution of economic entrepreneurship. Harvard Business School Workingthought. Belgorod State University Scientific Paper.Bulletin. History Political Science Economics Shin, J. C., Lee, S. J., Kim, Y. (2012). Knowledge-basedInformation Technologies, 8-1 (151): 20-27. innovation and collaboration: A triple-helix approachOsgood, C.E., 1956. Method and Theory in Experimental in Saudi Arabia. Scientometrics, 90 (1): 311-326.Psychology. Oxford. Tanha D., A. Salamzadeh, Z. Allahian and Y. Salamzadeh,Osgood, C.E., G. Suci and P. Tannenbaum, 1957. The 2011. Commercialization of University Research andMeasurement of Meaning. University of Illinois Innovations in Iran: Obstacles and Solutions. J.Press. Knowledge Management, Economics and InformationOvsyannikova,   G.L.,   2005.   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Estimation of Innovative Activity of the University

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Estimation of Innovative Activity of the University

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