INNOVATION-INVESTMENT POTENTІAL OF KHARKIV REGION (UKRAINE) AS A FACTOR OF THE REGIONS TOURIST ATTRACTIVENESS [310364]

INNOVATION-INVESTMENT POTENTІAL OF KHARKIV REGION (UKRAINE) AS A FACTOR OF THE REGION’S TOURIST ATTRACTIVENESS

L. M. Nemets*, K. V. Mezentsev*, V. A. Peresadko*, N. V. Guseva*, K. Yu. Segida*, Yu. I. Kandyba*, L. V. Kliuchko*, Cezar MORAR**

* V.N. [anonimizat]

**[anonimizat]. The key goals and objectives of development towards a "smart economy", as well as basic factors of becoming "intelligent (smart) economy" are given.The authors analyze innovation and investment potential of Kharkiv region as a [anonimizat]’s formation as a «smart-region." The place of Kharkiv region’s [anonimizat].To achieve the goals of the work ranking in all regions of Ukraine on selected statistical indicators was conducted. Formation factors as well as their influence on the use of innovation and investment potential of Kharkiv region have been analyzed. [anonimizat]. [anonimizat], to identify problems and outline prospects for further use of the region’s [anonimizat].

Key words: [anonimizat], [anonimizat], [anonimizat] a «smart-region" and Kharkiv as a «smart-city», defined as a modern, social city, a [anonimizat], tourist city (SMART = Social, Modern, Art, Research, Tourism). Moreover, the emergence of "smart economy" and the availability of people with jobs is one of the six strategic objectives for the city development by 2020, which includes three operational objectives:

innovative active entrepreneurial city (Kharkiv – Ukraine's [anonimizat] – active entrepreneurial city);

information – creative city (Kharkiv – a [anonimizat] – a [anonimizat] –a creative center of the European level);

scientific and educational city of knowledge economy (Development strategy of Kharkiv 2020).

Thus, the formation of Kharkiv region as an "intelligent region" with "smart economy" implies, among other objectives, a [anonimizat] a tourist center in Eastern Europe. A mandatory condition of the region’s [anonimizat], [anonimizat], which is impossible without investment. In economics intellectual products (innovation developments) as scientific results of human activities are of primary importance. Innovation is a [anonimizat] a whole (Fedotova, 2015).

Among the factors of “smart (intelligent) economic establishment we find innovation and research activities, development of science and education, expansion of IT-technologies, as well as intensive use of intellectual potential. Not least in the importance is investment in human capital, growth of highly technological section in the social production sphere, the added value increase in the composition of output created by intellectual component. The changing nature of human labor in favor of creative and intellectual activities is also a factor of such an economy where tourism and recreational activities are the priority areas.

The regions characterized by the existence of innovative economic structures and activities benefits from the existence of the appropriate regional development policies, that support and stimulate the economic growth based on innovation, rather than assisting the regions in decline (e.g. the old industrial regions) (Benedek, 2004; Cocean, 2005). A new perspective explains that the innovative regions through the regional context, as different propulsive industries support a complex regional system, considering also the delay of the regional innovation comparing with the historic expansion of industry from a specific area (Quatraro, 2008). Creativity is the main driving force in regional economic growth, concentrated especially in situations where the society generates and apply new ideas, information and technology (Strom, Nelson, 2009). The new regionalism rhetoric supports the idea that the regions have to be coupled strategically with the global economy through critical links (Yeung, 2009). The measures aimed at growing and diversifying the economic activities, at stimulating the investment in the private and public sector, contributing to reducing unemployment and leading to an improvement overall living standards, support sustainable actions that converge with the areas of competence of region, generate a dynamic and sustainable growth based also on the decentralization of the decision-making process (from the central or governmental level to the regional communities) and on the partnership between all actors involved in the overall regional development process (Benedek, 2004).

Methodology

PLEASE INCLUDE ABOUT ½ PAGE OF METHODOLOGY (INDICATORS, METHODS USED; DATA SOURCES ETC)AND FEW LINES ABOUT THE CITY OF KHARKIV (GENERAL ISSUES).

Discussion and results

The research aims at analyzing the innovation and investment potential of Kharkiv region as a factor of its tourist attraction, to identify the features of its formation and use in terms of Kharkiv region’s emergence as a «smart-region." To achieve this goal we set the following objectives: to determine the place of innovation and investment potential of Kharkiv region in national terms, to identify the factors of its formation in the region, to determine the territorial characteristics of innovation and investment potential of the region and its role in improving the image and tourist attractiveness.

The place of innovation and investment potential of Kharkiv region in the national context. From the standpoint of social geography innovation and investment potential of the region can be defined as the opportunities of regional socio-economic system to conduct innovative activity based on the resource component formed by scientific, intellectual, human, financial, technical and technological resources. The formation of the region’s investment potential is ensured by innovation development and achievement of competitive advantages in the region. Kharkiv region is characterized by all these components.

Kharkiv region occupies one of the leading places in Ukraine in terms of innovation and investment activity. Thus, regional differences in 19 indicators of investment and 19 indicators of innovation in 2015 have been analyzed. The analysis showed that:

in 2015 Kharkiv region was the leader in the country by three indicators (the number of companies that have sold innovative products abroad, in units; the number of industrial enterprises that implemented innovations, in units; the number of implemented new technological processes at the industrial enterprises; in units);

by five indicators (utilized capital investments from the state budget, UAH thousnds and % of total volume; utilized capital investments at the expense of local budgets,% of the total volume; the number of patents for inventions (national applicants, without non-classified ), in units; sales of innovative products from Ukraine, thousands UAH.) – occupied 2 nd place in the country;

by six indicators (utilized capital investments at the expense of local budgets thousands UAH.; number of industrial enterprises engaged in innovative activities, in units; number of industrial companies that have sold innovative products, in units; the volume of innovation products sold, thousands UAH; number of acquired new technologies (technical achievements) in Ukraine and abroad, in units; the number of transferred new technologies (technical achievements) in Ukraine and abroad, in units) – 3rd place in the country;

by one indicator (total expenditure on innovation activity, thousands UAH.) – 4th place in the country;

by seven indicators (direct foreign investments, mln. USA dollars and % from total volume; utilized capital investments, mln. UAH and % from total volume; number of implemented technological processes items per 1 industrial company that implemented innovations, units per 1 company; number of introduced innovative products at industrial enterprises; in units; the volume of utilized innovation products per 1 industrial company that implemented innovative products, thousands USD per 1 company – 5 th place in the country;

by six indicators– 6-th place in the country;

by two indicators – 8th place in the country;

by two indicators– 10-th place in the country;

by one indicator – 12th place in the country;

by one indicator– 13th- place in the country;

by two indicators– 14th place in the country

by one indicator– 15th place in the country;

by one indicator– 16th place in the country.

To determine the integral place of Kharkiv region by all indexes of innovative investment activities we have ranked all regions of Ukraine on selected statistical indicators (19 indicators of investment and 19 indicators of innovation activity). This process involved the following steps:

1. Rationing of simple indicators, bringing them to a common denominator.

2. Linear scaling of normalized parameters, i.e. parameters transformation so that their numerical values ​​– indexes were in the range of 0-1. To do this, extreme (lowest and highest) values were determined by each indicator by the formula (Niemets, Barkova & Niemets, 2009, p. 31):

(1)

where I j –index j of the normalized index of the sample, j = 1, 2, 3 …. N (N – number of indicators in the sample, in this case N = 38);

Xi, j – the current value of the j-normalized index, i – 1, 2, 3 … M (M – the number of objects characterized by j-index, in this case objects are regions of Ukraine and M = 25) ;

Xmin, j, Xmax, j – the smallest and largest value of j-normalized indicator in the number of observations.

According to formula (1), the object with the largest value of a certain indicator has index equal to 1, and the lowest – 0, the indices of all other objects have numeric values in the range of 0-1.

Average indices of innovation and investment activity in the regions of Ukraine were calculated by formula (Niemets, Barkova & Niemets, 2009, p. 31):

(2)

All designations are as above.

Ranking of regions in Ukraine in terms of their innovation and investment activity in descending order of average index. As a result, we received the order in which the place (rating) of a region as to the development of innovative investment activity clearly defines its position among other regions. This procedure is done graphically on the Pareto charts (Niemets, Barkova & Niemets, 2009, p. 32-33):

* without temporarily occupied AR Crimea and the town of Sevastopol.

Fig.1. Pareto chart showing distribution of the regions of Ukraine as to the average index of innovation and investment activity in 2015

(Source: Own construction)

Thus, it is clear from Fig.1 that Kharkiv region occupies the second place in Ukraine as to the integral index of formation and use of innovation-investment potential, conceding only the city of Kyiv.

Factors of innovation and investment potential formation and use in Kharkiv region (based on factor analysis). The authors have analyzed the factors affecting the formation and use of innovation and investment potential of Kharkiv region based on the principal components method of varymax factor rotation. This allowed to calculate the factor load on each of the indicators. Considering available accessible statistical information for the study, we selected 87 indicators, reflecting the demographic, economic, social and environmental characteristics of the administrative units in Kharkiv region in 2015.They were linearly scaled, too. (Formula 1). Equation (1) is used to calculate the indicators of positive qualities or objects’ characteristics. For indicators of negative trends and the objects’ characteristics (such as mortality, the number of departures, pollutants emissions into the atmosphere from stationary sources, the number of victims of production accidents, etc.), the indexes are determined by the following formula (Niemets, Barkova & Niemets, 2009, p. 31):

(3)

Using inverse index values for "negative" indicators by formula (3), it is possible to correctly rank administrative units in the region according to all sample indicators of their innovation and investment potential. In other words, the administrative unit with higher "positive" and lower "negative" indicators of innovation and investment potential has higher priority ranking (Niemets, Barkova & Niemets, 2009, p. 31).

The first step of the factor analysis assumes that the number of factors is equal to the number of indicators. We calculated the variance of each factor to determine the optimum number of factors (Table 1).

Table 1

Absolute, relative and cumulative values of factors variance in formation and use of innovation-investment potential of Kharkiv region, 2015.

(Sours: Own construction as a result of factor analysis)

The optimal number of factors can be determined using 3 criteria:

– the Kaiser criterion: only factors with variance greater than 1 (in our case 10 factors) are selected;

– by a cumulative percentage: the factors that cumulatively cover approximately three-quarters of the initial information are selected as the determining ones, i.e. the cumulative percentage exceeds 75% (in our case 3 factors);

– the Cattell "scree" criterion: on the variances graph (scree plot) there is a place where the variance reduction slows at most from left to right. It is assumed that there is only a "factorial scree" to the right from this point.

– The variance graph (Fig. 2) shows 3-4 factors.

Fig.2.The variance graph ("scree") of formation and use of innovation-investment potential in Kharkiv region, 2015.

(Sours: Own construction as a result of factor analysis)

Thus, we assume that 4 factors have the biggest impact on the formation and use of innovation and investment potential in Kharkiv region.

As a result of varimax axes inversion we obtained a matrix of factor loadings (Table. 2).

Table 2

Factor loadings

(factor loadings (Varimax raw), extraction: principal components,

marked loadings are >0,7)

(Sours: Own construction as a result of factor analysis)

Indicators: 1-2 – number of EDRPOU subjects (in units per 1000 population); 3 – number of residents; 4 – population density, persons per sq km; 5 – urban population, people; 6 – number of rural persons; 7-8 – fertility (persons per 1000 population, ‰; 9-10 – mortality (people per 1000 population, ‰); 11-12 – natural increase (decrease) of population (persons per 1000 population, ‰); 13-14 – a number of arrivals (person and per 1000 population, ‰); 15-16 – number of departures (persons and per 1000 population, ‰); 17-18 – net migration (persons and per 1000 population, ‰); 19 – the need of employers for workers to fill vacant jobs (vacant positions) persons; 20 – number of registered unemployed persons; 21 – load per one vacancy (vacant position), people 22 – employment of registered unemployed persons; 23 – the average number of staff, thsd. 24 – average monthly nominal salary of staff, hr.; 25 – passenger road transport, mln. pass. km; 26 – road passenger transport thousand..; pass 27 – kindergartens, units; 28 – children in kindergartens, people; 29 – general education establishments,units; 30 – children in secondary schools, individuals; 31 – number of pensioners who receive pension in the Pension Fund, people; 32 – the number of disable pensioners who receive pension in the Pension Fund, people; 33 – grants for reimbursement for housing and communal services, thousand hryvnas .; 34 – housing, thous. M² of total area; 35 – urban housing, thous. M² of total area; 36 – provision of housing, the average per capita square meters of total area; 37 – housing commissioning m² of total area; 38 – retail trade turnover, including restaurant management, mln. UAH .; 39 – emissions of pollutants into the atmosphere from stationary sources, ths. tons; 40 – emissions of pollutants into the atmosphere from stationary sources of pollution per capita, kg; 41 – the existence of waste, t; 42 – waste, t; 43 – land area, km2; 44 – the proportion of urban population,%; 45 – the proportion of rural population,%; 46 – number of towns ,units; 47 – number of settlements, units; 48 – number of villages, units; 49 – number of female population, persons; 50 – number of male population, persons; 51 – men for 1000 women, persons; 52 – the proportion of the population aged 0-14%; 53 – the proportion of the population aged 15-64,%; 54 – the proportion of the population aged over 65,%; 55 – the population aged 0-14 years, people; 56 – the number of people aged 15-64, persons; 57 – the population aged over 65, people; 58 – number of universities of I-II levels of accreditation, units; 59 – number of universities of III-IV levels of accreditation, units; 60 – the number of victims of industrial accidents, people; 61 – number of deaths from injuries related to production entities; 62 – number of pensioners who receive pension in the Pension Fund, per 1000 population; 63 – number of disabled people who are registered in the Pension Fund, people; 64 – number of disabled people who are registered in the Pension Fund, per 1000 population; 65 – number of detected crimes, units; 66 – transportation of goods by road, ths. tons; 67 – freight road transport mln. ton; 68 – profitability of agricultural production in the agricultural enterprises (total),%; 69 – profitability of agricultural production in the agricultural enterprises (crop),%; 70 – agricultural land, thous. Ha; 71 – the commissioning of housing per 1000 population m2; 72 – number of museums, units; 73 – number of clubs, units; 74 – number of library, units; 75 – number of movies demonstrators, units; 76 – children's health and recreation facilities working in summer, units; 77 – use of fresh water, mln. m3; 78 – wastewater discharge,mln. m3; 79 – hotels and similar accommodation units; 80 – specialized accommodation facilities, units; 81 – retail trade turnover per capita, USD.; 82 – wholesale trade turnover, mln. UAH .; 83 – number of enterprises, units; 84 – vlume of products (goods and services) per capita, USD .; 85 – the volume of products (goods and services), thous. USD .; 86 – income before tax, UAH thou. .; 87 – profitability of operating companies (the ratio of financial result from operating activities to the expenses for operating activities),%.

Based on the factor loadings values we have found the content of factors affecting the formation and use of innovation and investment potential in Kharkiv region (it includes the indicator with the largest load, that is closest to the module unit). If the indicator’s factor load is less than 0.7 for each of the 4 factors ( "threshold" value), this figure is not included in the analysis because it is statistically negligible (Mezentsev, 2004, p. 35). In the analysis of simulation results, it has been found out that 21 of the 87 variables have a load factor lower than the threshold, so they are not taken into account. Given the proportion of variance, we consider the first two factors the most important (1 st factor accounts for 51.4% of the total variance of the output data, the 2nd – 9.1%). Interpretation of factor analysis made it possible to identify the following hypothetical factors:

socio-economic (51 index (Table. 2);

2) settlement (surface area, km2; the number of villages, units; men per 1.000 women, people; agricultural land, thous. ha; the number of clubs, units);

3) housing and commercial (number of settlements, units; commissioning of housing per 1000 population, m2; the volume of products (goods and services) per one person, UAH)

4) socio-demographic (mortality, ‰; natural increase (decrease) of population, ‰; the proportion of the population aged 15-64,%; the proportion of the population aged over 65,%).

The last line in the table 2 shows the intensity of the factors’ influence. Thus, the numerical values of ~ 0.5733 ~ 0.0845 ~ 0.039 ~ 0.0498 correspond to 57.3%, 8.5%, 3.9% and 5.0% of the total variance.

According to the results of the factor analysis we calculated factor values ​​ in the context of towns and districts of Kharkiv region, i.e. the weight factor (Table. 3). A weight factor is a measure of administrative territorial units’ contribution in the region to each factor. The matrix of factor weights is calculated by multiplying the output data matrix by a matrix of factor loadings. They are treated as relative evaluation of certain factors expression in each administrative-territorial unit and serve as the basis for their grouping.

If the factor weights value is around 0, the impact of this factor corresponds to the impact for the whole region, if it is higher (especially more than 1) – the impact of this factor is significantly larger, and if it is lower (less than 1) , it is significantly smaller than in the region on the whole (Mezentsev, 2004, p. 35).

Table 3

Factor weight expression in formation and use of innovation and investment potential as to the administrative-territorial units in Kharkiv region

(factor scores, rotation: Varimax raw, extraction: principal components)

(Sours: Own construction as a result of factor analysis)

Fig. 3-6 show the Pareto charts for each of the identified key factors

Fig. 3. Ranking of towns and districts of Kharkiv region as to the values of a social and economic factor, 2015 (Sours: Own construction)

Fig.4. Ranking of towns and districts of Kharkiv region as to the values of a settlement factor, 2015 (Sours: Own construction)

Fig.5. Ranking of towns and districts of Kharkiv region as to the values of a housing and commercial factor, 2015 (Sours: Own construction)

Fig.6.Ranking of towns and districts of Kharkiv region as to the values of a social-demographic factor, 2015 (Sours: Own construction)

The value of operating factors are significantly differentiated in the region as the map schemes show (Fig. 7-10). Here, we do not see streamlined spatial structures.

Fig. 7.The value of a social and economic factor in the context of towns and districts of Kharkiv region, 2015 (Sours: Own construction)

.Fig.8. The value of a settlement factor in terms of towns and districts of Kharkiv region, 2015 (Sours: Own construction)

Fig. 9. Value of residential and commercial factors in the context of towns and districts of Kharkiv region, 2015 (Sours: Own construction)

Fig. 10. The value of a socio-demographic factor in the context of towns and districts of Kharkiv region, 2015 (Sours: Own construction)

The strongest effect of an inverse nature socio-economic factor is observed in the city of Kharkiv and Kharkiv region (Fig. 3, 7), due to the largest concentration of of social and economic activity objects in the regional center.

Big values of this factor are also characteristic of districts located around Kharkiv (Volchansky, Zmievskiy, Derhachivskiy) and the areas where towns of regional subordination are located (Lozova, Izyum). Peripheral areas and towns with poor socio-economic indicators are not investment attractive and innovation active.

A settlement factor is also inverse by its function and most strongly manifested in the towns of regional subordination and highly urbanized areas (Fig. 4, 8), as the highest innovation and investment potential is found in the towns and areas with predominant urban population (Izyum, Lozova, Pervomaysk, Kupiansk, Chuguyiv, Lyubotin, Kharkiv, Pechenezhsky, Dergachi districts, etc.) (Niemets, Segida & Guseva, 2015).

The impact of housing and commercial factor (inverse) is most strongly felt in the areas located around Kharkiv (Kharkiv, Zmievskiy, Derhachevskiy, Volchansky districts), i.e in its suburbs which , due to suburbian processes, are attractive for population relocation for permanent residence , building or buying a house, starting a business, renting premises, etc. (Fig. 5, 9).

A socio-demographic factor (inverse) is most evident in towns and districts with a relatively favorable demographic situation, high proportion of a core age group population (15-64 years), and at the same time, a small proportion of people aged 65+ which, on the one hand, provides a high labour resource potential of the territory, and, on the other hand, provides little demographic burden on working age people (Fig. 6, 10).

Grouping of towns and districts of Kharkiv region in terms of innovation and investment potential (based on cluster analysis). To analyze the territorial characteristics of innovation and investment potential of Kharkiv region we used a cluster analysis, which allowed to split the districts and towns into clusters,i.e. groups, which included administrative territorial units with similar territorial characteristics of innovation and investment potential.

Cluster analysis is one of hierarchical classification methods that implies division of the original collection of objects or parameters into clusters (groups, classes) in multidimensional space. The coordinates of this space are all statistical indicators included in the sample. The clustering criterion is the minimum distance in the parameters space as the distance factor is a key concept in the cluster analysis.

(Mezentsev, 2004). In this study, as the distance factor we used Euclidean distance, which is the geometric distance in the multidimensional space between any objects (in this case, between the districts and towns of the region) and is calculated as follows (Mezentsev, 2004, p. 34):

(4)

where Dij – the distance between the objects (or clusters centers);

Xj, Xi – coordinates of objects;

n – the number of coordinates (dimension of space).

There are many methods of cluster analysis. To group the administrative- territorial units in Kharkiv region according to a similar innovation and investment potential we have chosen the Ward method. First, when each object (town or district) is a separate cluster, the average values ​​of all indexes are calculated. Then, squares of Euclidean distances are calculated from individual indicators of each cluster to the cluster estimated average. These distances are summed up, and a new cluster combines the clusters with the smallest increase in the total sum of distances (Blizorukov, 2008, p. 41). Gradually, more and more administrative territorial units combine, aggregating clusters. With each step a greater number of different towns and areas are included into clusters. The last step combines all administrative territorial units into one cluster (Mezentsev, 2004, p. 35). Thus, the obtained clusters are groups of administrative – territorial units that have similar development features. Implementation of clustering can trace the formation of regional groups and towns and their reorganization in time, allowing to identify the most stable trends and their established groups. This is very important for the development of perspective programs of optimal areas development. The results of cluster analysis are visually represented as a dendrogram – a tree- diagram containing n levels, each of which corresponds to one step in the sequential process of clusters consolidation (Bureeva, 2007).

To group the towns and districts of Kharkiv region by similar innovation and investment potential we selected nine indicators characterizing innovation and investment: capital investments (total, mln. UAH.; per person, UAH per 1 person; the share of administrative-territorial unit in total volume,%), capital investments in housing construction (total, mln. UAH .; per person, UAH per 1 person; the share of the administrative-territorial unit in their total volume,%), innovation active enterprises (total number, units; the share of innovation active enterprises in total number of enterprises,%; the proportion of a given administrative-territorial unit in the total number of innovation active enterprises,%).

The resulting calculation data are visualized as a dendrogram (Fig. 11-12) and presented in a map scheme (Fig. 13).

Fig. 11. Tree diagram of Kharkiv region districts clustering in terms of innovation and investment activity in 2015 (Sours: Own construction by Kharkiv region in 2015; Cities and villages of Kharkiv region in 2015)

Based on the analysis of Fig. 13, it may be noted that Kharkiv region has a considerable territorial differentiation in formation and use of innovation and investment potential of its administrative units, which is primarily due to the specific socio-economic development.

The undisputed leader is the city of Kharkiv, as well as Kharkiv and Dergachi districts which are most attractive for investment and innovative activity, and in general, they have the most innovative and investment potential. Due to their favorable economic and geographical position (around Kharkiv), these areas are in many respects ahead of all other parts of the region as to the economic and social sphere development.

Fig. 12. Tree diagram of the towns clustering in Kharkiv region in terms of innovation and investment activity in 2015 (Sours: Own construction, by Kharkiv region in 2015; Cities and villages of Kharkiv region in 2015)

Fig. 13. Grouping of towns and districts of Kharkiv region by similar innovation and investment activities in 2015 (Sours: Own construction)

Implementation of clustering allowed to group the towns and districts of Kharkiv region by similar innovation and investment activity, to define the territorial distribution of innovation and investment potential of the region, which helps to identify problems and outline prospects for further use of its available resources. As concerns tourist attractiveness of certain areas of the region, we note the important socio-economic component of the potential, namely in the city of Kharkiv, Kharkiv and Dergachi districts where there are greater opportunities for development of tourism infrastructure. Analysis of settlement and socio-economic factors has shown that there is no obstacle for the development of tourism infrastructure in the region.

Conclusions

Present-day positioning of Kharkiv region and the city of Kharkiv as a SMART region defined as a modern, social city, a city of arts, research, tourist city, stipulates achievement of strategic development objectives, the key ones among them being creation of innovation active business, information and creative, scientific -educational city of ​​the knowledge economy, development of tourism and recreational activities in the region,transformation of Kharkiv into a tourist center in Eastern Europe. The main factors of becoming a smart economy are innovation and research activities, development of science and education, expanding the scope of IT-technologies and intensive use of intellectual potential, investment in human capital, increase in the share of high-tech sector in the structure of social production, growth of value added share in the output created by the intellectual component, changes in the nature of human labor for creative and intellectual activity, etc.

Innovation and investment potential of Kharkiv region can be considered as a region’s factor of tourist attraction. The authors have determined the features of its formation and use in terms of a «smart-region" as well as the place of innovation and investment potential of Kharkiv region in national terms, factors identifying its formation in the region and territorial characteristics. The results of all the regions of Ukraine’s ranking on selected statistical indicators of innovation and investment have shown that Kharkiv region occupies 2nd place in Ukraine by a combined indicator of formation and use of innovation and investment potential, second only to the city of Kyiv. Based on the factor analysis the formation and use of innovation and investment potential of Kharkiv region have been explained, factors affecting it

have been analysed, administrative units in the region have been ranked for all sample indexes. It has been found out that the formation and use of innovation and investment potential of Kharkiv region are mostly influenced by 4 factors: socio-economic, resettlement, housing, commercial, social and demographic. Factors values have also been calculated in terms of towns and districts of Kharkiv region.

Based on the cluster analysis towns and districts of Kharkiv region have been grouped according to indexes of innovation and investment potential. It has been found out that Kharkiv region is characterized by a considerable territorial differentiation in the formation and use of innovation and investment potential of its administrative units, which is primarily due to the specific socio-economic development. The undisputed leader is the city of Kharkiv and Kharkiv and Dergachi areas that have the highest investment attractiveness and innovative activity, and, in general, – the most innovative and investment potential. Due to their favorable economic and geographical situation (around Kharkiv), these areas by many indicators of economic and social development are ahead of all other areas of the region. Clustering allowed to group the towns and districts of Kharkiv region by the similarity of their innovation and investment activity, to define the peculiarities of the territorial distribution of innovation and investment potential in the region, which helps identify problems and outline prospects for further use of its available resources.

The economic potential of the region, which includes natural-resource, labour resources and innovation and investment potential is the basis for the territorial development,including its competitiveness, tourist attractiveness,etc. Moreover, to determine the optimal use of innovation and investment potential of the region as a tourist’s attractive factor it is necessary to study its natural resources, to determine its recreational opportunities. The above mentioned will optimize the use of innovation and investment potential of the territory to improve the level of tourist attraction. The authors are dealing with the given issues.

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