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Montenegrin Journal of Economics

Volume 12, Number 4 December 2016

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Print edition ISSN 1800-5845 Web edition ISSN 1800-6698

COBISS.CG-ID 9275920

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Veselin Draskovic, University of Montenegro, Maritime Faculty of Kotor, Montenegro Co-Editors Dalia Streimikiene, Vilnius University, Kaunas Faculty of Humanities, Lithuania Yochanan Shachmurove The City College of the City University of New York, Department of Economics and Business, USA Radislav Jovovic University Mediterranean, Faculty of Business Studies, Montenegro Advisory Board

Harry M. Markowitz, Nobel Laureate Rady School of Management at the University of California, USA Oliver E. Williamson, Nobel Laureate University of California, Berkeley, USA Lloyd Blenman University of North Carolina-Charlotte, President at Midwest Finance Education Foundation, USA Victor Polterovich Central Economics and Mathematics Institute, Russian Academy of Science and Moscow School of Economics / Lomonosov's Mos-kow State University, Russia Yochanan Shachmurove The City College of the City University of New York, Department of Economics and Business, USA Associate Editor and Journal Administrator

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Serguei Aivazian, Central Economics and Mathematics Institute of the Russian Academy of Sciences / Lomonosov's Moscow State University, Russia Jaroslav Belás, Tomas Bata University in Zlín, Fakulty of management and economics, Department of Enterprise Economics, Czech Republic István Benczes, Corvinus University of Budapest, Faculty of Economics, Hungary Bolesław Borkowski, SGGW Warsaw, Faculty of Applied Informatics and Mathematics, Department of Econometrics and Statistics, Poland Laszlo Csaba, Central European University, Department of International Relations and European Studies, Budapest / Budapest University of Eco-nomic Sciences and Public Administration, Hungary Fan Gang, Graduate School of Chinese Academy of Social Sciences (CASS) / China's National Economic Research Institute (NERI), China Yuriy Gavrilec, Central Economics and Mathematics Institute of the Russian Academy of Sciences, Russia Wei Ge, Bucknell University, Department of Economics, Lewisburg, USA Balazs Hamori, Corvinus University of Budapest, Hungary Boris Popesko, Tomas Bata University in Zlín, Fakulty of Management and Economics, Czech Republic Yu Hsing, Southeastern Louisiana University, College of Business, Hammond, LA, USA Wen-jen Hsieh, University Road, Tainan / Art Center National Cheng Kung University, Taiwan Svetlana Kirdina, Institute of Economics Russian Academy of Sciences, Russia George Kleiner, Central Economics and Mathematics Institute of the Russian Academy of Sciences, Russia Siu Lee Jasmine Lam, Nanyang Technological University, Singapore

Montenegrin Journal of Economics, Vol. 12, No. 3 (December 2016)

Valeriy Makarov, Central Economics and Mathematics Institute of the Russian Academy of Sciences/ Lomonosov's Moscow State University / New Economic School, Russia Ludmila Malyaretz, Kharkiv National University of Economics, Faculty of Economic Informatics, Ukraine Vladimir Matveenko, National Research University Higher School of Economics, St. Petersburg, Russia Alojzy Nowak, University of Warsaw, Faculty of Management, Poland Yuriy Osipov, Lomonosov's Moscow State University, Faculty of Economics, Russia Jiancai Pi, School of Business, Nanjing University, China Evgeniy Popov, Institute of Economics, Urals Branch of Russian Academy of Sciences, Ekaterinburg, Russia Uriel Spiegel, Bar-Ilan University, Faculty of Social Sciences, Ramat-Gan, Israel Zhan Toshchenko, Russian State University for the Humanities, Russian Academy of Sciences, Moscow, Russia Merih Uctum, The Graduate Center City University of New York, USA João Paulo Vieito, Polytechnic Institute of Viana do Castelo, Portugal Milos Vulanovic, City University of Hong Kong Eric Wdoviak, Farmingdale State College, New York, USA Bagrat Yerznkyan, Central Economics and Mathematics Institute, Russian Academy of Science / State University of Management Moscow, Russia Regional Editorial Board

Sanja Bauk, University of Montenegro, Faculty of Maritime Studies Kotor, Montenegro Mimo Draskovic - Secretary of Editorial Boards, University of Montenegro, Maritime Faculty of Kotor, Montenegro Gordan Druzic, Croatian Academy of Sciences and Arts, Zagreb, Croatia Miomir Jaksic, University of Belgrade, Faculty of Economics, Serbia Borut Jereb, University of Maribor, Faculty of Logistics Celje, Slovenia Slobodan Lakic, University of Montenegro, Faculty of Economics Podgorica, Montenegro Andjelko Lojpur, University of Montenegro, Faculty of Economics Podgorica, Montenegro Tihomir Lukovic, University of Dubrovnik , Department of Economics and Business Economics, Croatia Ljubomir Madzar, Institute of strategic studies and development „Petar Karić“ of the Alfa University in Novi Beograd, Serbia Joze Mencinger, University of Ljubljana, Law School, Slovenia Janez Prašnikar, University of Ljubljana, Faculty of Economics, Institute for South-East Europe, Slovenia Ivan Ribnikar, University of Ljubljana, Faculty of Economics, Slovenia Guste Santini, University of Zagreb, Croatia Ivo Speranda, University of Dubrovnik , Department of Economics and Business Economics, Croatia


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Četiri puta godišnje. ISSN 1800-5845 = Montenegrin Journal of Economics COBISS.CG-ID 9275920

9 7 7 1 8 0 0 5 8 4 0 0 7

ISSN 1800-5845


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C O N T E N T S Evaluation of the Impact upon Consumer Attitudes of the Format and the Assortment in Retail Internationalisation Process Elze Rudiene, Irena Macerinskiene, and Jogaila Macerinskas ........................................

7 Company’s Value Creation Via Customer Satisfaction and Environmental Sustainability Influence Dalia Streimikiene, Aida Navikaite, and Vytis Varanavicius ..............................................

19 Modeling and Visualization of Quadratic Assignment Problems on the Example of Plant Location Slavomir Vukmirovic, Drago Pupavac, and Jusuf Sehanovic ...........................................

29 The Importance of Tourism Websites to Tourism Services And Assurance Of Their Competitiveness Vilija Bitė Fominienė .............................................................................................................

41 Investment in Entrepreneurship: Evidence from Russia Olga V. Kiseleva, and Iuliia S. Pinkovetskaia ....................………………………………..............

53

Change of National Intellectual Capital in EU Countries Irena Mačerinskienė, Jogaila Mačerinskas, and Rasa Aleknavičiūtė ..............................

65

Quantitative Scaling of Social Innovations Zhoomart K. Omonov, and Anna Yu. Veretennikov ............................................................

77

Ecologically-Conscious Consumer Purchases in Lithuania Vilma Tamuliene, Egle Kazlauskiene, and Lina Pileliene ..................................................

87

The Strategic Directions of Innovative Economy Development in Russian Agribusiness Vladimir I. Trukhachev, Vitaliy Z. Mazloev, Igor Y. Sklyarov, Yuliya M. Sklyarova, Elena N. Kalugina, and Anna V. Volkogonova ....................................................................

97 Impact of Organization Culture on l-Learning Edmundas Jasinskas, Biruta Svagzdiene, and Arturas Simanavicius ..............................

113

Travel Service - an Innovative Solution if Exist Biruta Svagzdiene, Edmundas Jasinskas, and Arturas Simanavicius ..............................

121

Economic Analysis of Industrial Development: a Case of Russian Coal Industry

Evgeny Lisin, Wadim Strielkowski, and Evgeniya Krivokora ............................................. 129


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Expanded Concept of Human Capital as Intangible Resource at Macro Level Žaneta Karazijienė, and Artūras Jurgelevičius ..................................................................... Author Guidelines .................................................................................................................

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Elze Rudiene, Irena Macerinskiene, and Jogaila Macerinskas/ Montenegrin Journal of Economics,

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Evaluation of the Impact upon Consumer Attitudes of the Format and the Assortment in Retail Internationalisation Process

ELZE RUDIENE1, IRENA MACERINSKIENE2, and JOGAILA MACERINSKAS3

1 Dr., Institute of Economics, Vilnius, Lithuania e-mail: [email protected] 2 Professor, Mykolas Romeris University, Institute of Economics, Vilnius, Lithuania e-mail: [email protected]

3 Associate Professor, Mykolas Romeris University, Institutte of Finance, Vilnius, Lithuania e-mail: [email protected]

A R T I C L E I N F O A B S T R A C T

Received June 09, 2016 Received in revised from August 02, 2016 Accepted November 25, 2016 Available online December 15, 2016

Previous experience has proved that the range of goods of any newly opened shopping centre is an important element of the retail internationalisation. An assortment of a trade object is an indication of satisfaction of consumers with the trading centre. Rarely retail companies directly inquire the opinion of the consumers before entering the market; they tend to prefer relying upon their interna-tional experience, or the results of an analysis of the trade market of the country or the region. The present paper examines and deals with the attitudes of the consumers, identifies and presents the composite parts of the elements, and provides their evaluation methodology. The study conducted and constituting the basis for the present paper proved that consumers have developed their attitude towards the assortment of the goods at the IKEA trade centre. The object of the present study is the assortment of goods at a retail centre. The purpose of the present study and the paper is an as-sessment of the impact of the assortment of goods upon the atti-tudes of consumers. A problem identified for the purpose of the present paper was the fact that the impact of assortment of goods upon the attitudes of consumers in the process of retail internation-alisation has not been studied neither from theoretical nor practical viewpoint, and no practical methodological manuals have been drawn up to assess the impact concerned.

JEL classification:

M20 DOI:

10.14254/1800-5845/2016.12-4.1 Keywords:

Format, retail internationalization, consumers, retailers

INTRODUCTION

Based on the definitions of an assortment of goods proposed and used by Sullivan, Adcock (2002), McGoldrick (2002), Pajuodis, Pranulis,Urbonavičius, Virvilaite (2011), Urbonavicius, Dikcius, Navickaite, (2011), Virvilaite, Dailydiene (2012) - a range of goods is a collection of goods compiled at trade objects and intended for meeting the needs of consumers. In general, it is only a general concept that does not disclose its content. On the one hand, retail trade companies dedicate specific attention to the formation of their range of merchandise, the as-


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sortment being one of the most important factors of the success in a different country; on the other hand, however, building of the assortment is a commercial secret that cannot be directly accessed or studied. The assortment of the goods is one of the most important factors and conditions of competitors, and may surprise consumers in the positive, or negative direction. When studying the process of standardisation of the assortment at retail trade companies in some foreign states Swoboda, Elsner (2013) classified the process as a peripheral factors in a marketing programme. That is related to the variability of the factor. There is a direct depend-ence between the consumer and the range of goods. In case the consumer fails to find the re-quired item, it is probable that the consumer will not return to the place again. Having not exam-ined the local market and the attitudes of the consumers towards the assortment, retail com-panies later are forced to correct the mistakes made, which is related to additional expenses. Having studied the performance of Carrefour in Japan Dupuis, Choi and Larke (2006), noted that the Company had serious difficulties regarding the policy of the assortment of the goods, because the Japanese consumer missed a wider range of French merchandise. And conversely, when entering the American market a retail trade company Tesco performed a comprehensive survey of consumers, and developed a new type of a store – fresh and easy – offering the as-sortment of goods most acceptable to consumers. The examples only confirm that subject to the prevailing circumstances individual retail companies may have different approaches to-wards the consumer as the most important participant of the retail trade internationalisation process. When entering a foreign country a trade company plans to open a trade centre with a specific assortment of goods that affect the consumers when developing their attitudes towards the assortment of the goods. Thus in order to avoid any mistakes in the formation of the as-sortment in a trade centre a retail company should carry out a survey of consumer attitudes towards the assortment of goods. Quite frequently, in case a company fails to do that it has to conduct such surveys shortly after the opening of the centre.

In order to evaluate the impact of the assortment of the goods upon the attitudes of con-sumers, it is important to disclose the constitute elements of the factor, i.e. which of its forms are most important for building the consumer attitudes. An opinion poll among consumers would produce the means of the impacts of the assortment of the goods upon each form of the consumers’ attitudes, where the only task thereafter would be to assess such means.

Authors of the present paper carried out interviews about the assortment of the goods at the IKEA trade centre among Lithuanian consumers immediately before its opening in Vilnius, and within one year after the opening.

The objective of the study is being achieved by addressing the following tasks:

Consumers have their opinions and attitudes towards the assortment of goods at a trade centre entering the local market;

Substantiation of the key elements of the impact of the assortment of the goods upon con-sumers’ attitudes;

Drawing up of a methodology for the evaluation of the impact of the assortment of goods upon consumers’ attitudes;

Consumer opinion survey carried out in Vilnius with a view to assessing the attitudes of Lith-uanian consumers towards the assortment of goods at the IKEA trade centre.

Methodology For the purpose of analysis of the format in internationalization process, re-tailers’ failure and success stories, and consumer expectations authors of the present paper used general research methods, i.e. analysis of the relevant scientific literature, comparative and systematic logical analysis.


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1. ASPECTS OF THE FORMATION OF CONSUMER ATTITUDES AND THEIR THEORETICAL RATIONALE

An attitude of the consumer is perceived as his developed opinion about a subject, a phe-nomenon, etc. The opinion or the attitude of a consumer develops being affected by a number of factors, and it is evolving. Some factors are common for the building and development of all the attitudes of consumers, other factors are derivative from the first, and third type factors are vital for building the consumers’ attitudes with regard to a specific subject or a phenomena. Since studying and examination of the actions is a separate and complex subject, further au-thors provide fragmented illustrations of the factors from each group. The group of common or general factors include such factors as the country's development level, its size, culture or de-mography. The factors assigned to the group affect the consumer's lifestyle, habits, wishes and abilities. The specific factors affecting the consumers’ attitude towards a retail company enter-ing the market, and the assortment of goods selected by it include the size of the company en-tering the market, the level of literacy of the consumers, presence (visiting) at the company of a specific selected trade object format in a foreign state, news about the coming trade company in the media, the information received from other consumers and other factors. The group of specific factors determines the development of each attitude of the consumer about the as-sortment of goods of the company entering the market. The synergy of such factors will consti-tute a basis for the formation of an individual attitude of each consumer regarding the compo-site elements of the assortment of the entering company, i.e. ranking the attributes in terms of their significance, as being most or less important, or completely unimportant. The range of such attributes may be very broad; however, a significant part of the attributes would overlap, which would permit compiling a set of most important attributes. The composite elements of format of the trade company entering the market can be readily identified both on the theoreti-cal, and the practical level. The practical way as discussed earlier would be a questionnaire opinion poll among consumers; the findings of such opinion poll would identify the most im-portant attributes. The theoretical analysis of research articles on the subject; the purpose of such analysis is to clearly identify consumer attitudes towards the most important attributes of the selected assortment of goods. The results produced by both methods should be similar.

No research articles or papers examining the attitudes of consumers, their attributes, and the assortment of goods selected by the new entrant were found, mostly because such studies are the prerogative of the companies. Researchers are left with a single task – to monitor and evaluate the strategic changes in the assortment policy. There is no doubt that the trade object type selected by a trade company affect also other elements of the marketing strategy, such as the assortment policy, advertising and image. In fact all these elements are related to a brand which may be created by the trade company or a manufacturer. A trade company may have more than one trade mark, furthermore, it may have a different trade mark in each country. A brand of a store emphasises its exceptional position, and brands of corporations of retail trade contribute to the pubcations of the assortment of goods. Articles by a number of authors, such as Dick, Jain, Richardson (1997), Ailawadi, Keller (2004), Brakus, Schmitt, Zarantonello (2009), Wlodarczyk-Spiewak (2011) may be considered to be theoretical research into consumers' per-ception of trade marks of stores, or of retail merchandise, the experience and the process of its creation. The evolution of a brand from the manufacturer to corporation shows the essential changes in the course of the retail trade internationalisation. A brand from the viewpoint of re-tail trade was an object of research by Moore, Fernie, Burt (2000), Uusitalo (2001), Kent (2003), Swoboda, Haelsig, Morschett, Schramm-Klein (2007), Anselmsson, Johansson (2009), Esbjerg, Bech-Larsen (2009), Veljkovic, Bogetic, Stojkovic (2015) and others. The researchers studied the issue from several different aspect – consumers, retailers and markets. Johansson, Burt (2004) were researching purchasing of private and manufacturer's trade marks in Great Britain, Sweden and Italy. It is a complex process whereby some companies purchase trade marks created by manufacturers, while others create and produce them themselves. Harris and


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de Chernatory (2001) specifically noted the changes in the brands of corporations. Burghausen, Fan (2002), Burt and Sparks (2002) studied the corporation brand in the retail sector as a gen-eral object of studies. Martenson (2007), Tarnovskaja, Egl and Burt (2008) were concerned about the image of the corporate brand, and its impact upon the development of the market strategy. The area of interest of Collins-Dott, Lindle (2003) was the relation between the trade mark and the image; while Semejin, Van Riel, Ambrosini (2004) – were interested in the change of the attitude of the consumer towards a store's trade mark. Coskun, Fevrier (2008) and Ozsomer (2012), Dmitrovic, Vida (2010) were comparing the global brands with local trade marks. Lopez, Fan (2009) followed the development of a Spanish trade mark Zara into an in-ternational trade mark, and Tarnovskaya, de Chernatony (2011) were following the process of developing the IKEA brand to global level through culture.

Minhan, Hudleston and Bianchi (2012) carried out social studies among consumers in Melbourn inquiring their views about an American retail trade company Costco. The consumers were presented an assortment of goods with international, Australian brands and Costco Kirk-land private labels. A dialogue was arranged with consumers regarding the assortment of goods; as a result, the consumers recognised most of the goods labelled with private labels, and the assortment was supplemented with goods with Australian labels. Customers also found the prices of the goods acceptable. A conclusion may be drawn in summarizing the observations above that the aspects such the breadth and the depth of the range of the merchandise, its complexity and the price. The findings were further confirmed by Tesco consumer studies car-ried out in the USA. A survey of consumption and purchasing habits enabled the company enter-ing the market to present to the public a new trade object type, and compile and assortment fully meeting the needs of the local consumers.

When studying the purchasing habits in grocery stores of different types was studying the attributes of the breadth and width of the assortment, and the price attribute.

A summary of theoretical and practical studies of brands identified the attributes most im-portant for the impact of the assortment of goods upon the attitudes of consumers; they were the breadth, depth and complexity of the assortment, as well as the selection possibility with respect to price (see Fig. 1). Figure 1. Attributes of consumer attitudes affected by the assortment of goods of a trade object

Source: drawn up by authors.


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The expression of the breadth of the assortment requires that the assortment satisfy cus-tomers. An opinion survey company Nielsen (2015) specified the following assortment evalua-tion criteria: all in a single place, i.e. available all what is required; in a single place, broad as-sortment, adequate stock of inventories. Consumers consider that not not only the breadth of the range of the merchandise, but also its depth is important. What is characteristic of the re-tailers is their trend to standardize the layout of stores, as well as the assortment of the goods, and their display pattern. Each consumer may be affected by the layout of the range of goods in a different way. A consumer whose needs are limited to a minimum basket of basic goods, are little concerned about the width or depth of the range, essentially being interested in the price factor only. And quite on the contrary for a consumer for whom the price of the goods is not the most important factor, as he might be more interested in the width or depth of the assortment, as well as its complexity. It may also be concluded that the formation of the attitudes of con-sumers regarding the assortment to a large extent depends on the purchasing capacity of each consumer. Therefore, the policies of assortment of goods in underdeveloped states is very dif-ferent from the policies in the developing or developed countries. Furthermore, consumers in different countries differently understand the assortment of goods, and its peculiarities.

A study of opinions of consumers about attributes of the assortment at a trading centre would allow identifying the impact of each attribute of each assortment of goods, and thus de-rive the overall value of the factor. 2. PURPOSE AND TETHODOLOGY OF THE STUDY

The purpose of the present survey is to establish the impact of the assortment of goods at a furniture trade centre IKEA upon the consumers visiting the centre. The following tasks were defined and implemented for the purpose of attaining the objective:

1. To identify the level of the impact of specific factors upon consumers based on the at-tributes of the respondents, and the knowledge about the IKEA trade centre.

2. Determine the strength of the impact of the assortment of the goods at the IKEA trade centre upon consumers;

3. Determine the strength of the impact of the elements of the assortment of the goods at the IKEA trade centre upon consumers.

As one of the findings during the first study was that part of the respondents have never

been to IKEA trade centres in other countries, and the results of the second study indicated that some of the respondents have never visited the IKEA centre in Vilnius as a trade object format, the following hypothesis was offered (H1): The expectations of and the impact upon the con-sumers who have never visited the IKEA centre in Vilnius as a trade object format, will be less significant than those who have visited the centre.

The theoretical rationale of the study methodology is based on the attributes of the poten-tial impact of the assortment of the goods at the trade object upon consumers as set forth in Fig. 1. The quantitative customer interviewing method allowed identifying the strength of the impact of the factor of the trade object format and its elements. As part of the study authors of the paper examined a possible impact of the IKEA trade centre as a trade object goods assort-ment upon the attitudes of the consumers. The first study was carried out immediately before the opening of the IKEA trade centre, and the second study was completed one year after the opening of the centre. The questionnaire was prepared following the instructions on the prepa-ration of questionnaire, seeking to ensure that all the necessary questions have been an-swered, and the purpose of the interview has been achieved. Significant attention was dedicat-ed to the social factors of the respondents; such factors are indicative of the distribution of the


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respondents according to their attributes and the knowledge about the IKEA centre, and in-strumental in establishing whether the potential impact upon the consumers will be affected by their gender, age, education and the knowledge about the IKEA centre. The most important task is to determine the level of the possible impact upon the consumers of each element of each factor of the assortment of goods at IKEA.

In order to obtain statistically reliable study results able to reflect the characteristics of the universal essence it is necessary to select the appropriate respondents. The opinion survey included only persons older than 18. Although persons under 18 might be decision makers in relation to purchases, they are not purchasers at the IKEA trade centre. The respondents were selected by way of probability stratified sampling method, because the population is not homo-geneous, and can be broken down into strata.

Respondents from all towns and cities of Lithuania took part in the study. An identical study was carried out one year after the opening of the IKEA trade centre. The principal objective to be achieved within the framework of the repeated interview was to ensure the participation of more than half of the respondents who had taken part in the first survey. Such method of inter-viewing would allow a more accurate systematisation of the data, and the results will better reflect the possible impact and the evaluation.

As was earlier pointed out a factor of a trade object format is made up of four elements and five estimate sections. The breakdown may be expressed as Vnvį = (Vn/4)/5; where Vnvį – mean of the n factor with respect to the number of factors and the estimate sections; Vn – mean of the n factor; 4 – number of factors; 5 – number of estimate sections.

In this specific case the coefficient of the complex impact is:

Fk = (F1/4)/5 + (F2/4)5 + (F3/4)/5 + (F4/4)/5;

where Fk – coefficient of the complex impact of the group of factors of a trade object format;

F1, 2, 3, 4 – mean of the first, second, third and fourth element of the group of the trade object format;

4 – total elements;

5 – total estimate sections. The coefficient of the complex impact of the group of factors of a trade object format may

not be higher than 1, and that of each element is 0.25. A more comprehensive and/or accurate survey can be carried out by comparing the coefficient of the complex impact of the trade object format with the maximum possible coefficient. 3. RESULTS OF THE FIRST SURVEY AND THEIR ASSESSMENT The first survey of the yet-to-be customers of the IKEA trade centre in Lithuania was carried out immediately before the opening of the centre in August 2013.

Total 698 respondents participated in the survey, however, only 663 questionnaires were se-lected as suitable to be assessed for the purpose of the study. As many as 64 respondents did not mark the box with the gender, therefore it was not possible to provide accurate statistics on the number of the respondents by gender. It should be noted that about 30 % of the respond-ents within the survey had visited IKEA trade centres in other countries. Therefore, for the pur-pose of discussing the results, this group of respondents was specifically singled out. The de-mographic characteristics of the respondents within the survey were presented in Table 1.


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Table 1. Total respondents, break down by gender, previous visits at the IKEA trade centres, and their education

Quantity Part (%) in total respon-dents quantity

1. All respondents 663 100 1.1. Men 347 52,3 1.2. Women 253 38,2 1.3. Not specified 63 9,5 2. Respondens those had visited other countries the IKEA stores and those who had not visited other coun-tries the IKEA stores

663 100

2.1. Had visited the IKEA stores 195 29,4 2.2. Had not visited the IKEA stores 468 70,6 3. Respondents repartition by education 663 100 3.1. Secondary education 57 8,6 3.1.1. Men 33 5,0 3.1.2. Women 24 3,6 3.2. Vocational education 79 11,9 3.2.1. Men 42 6,3 3.2.2. Women 37 5,6 3.3. Not ended university education 37 5,6 3.3.1. Men 17 2,6 3.3.2. Women 20 3,0 3.4. University education 427 64,4 3.4.1. Men 255 38,5 3.4.2. Women 172 25,9 3.5. Not specified 63 9,5


Figure 2. Mean averages of the impact of the elements of the range of goods at the IKEA trade centre upon the decisions of consumers.

3,8

3,95

4,08

4,22

0 1 2 3 4 5

Depth of the assortment in the IKEAstore

Width of the assortment in the IKEAstore

Completeness of the range of goodsin the IKEA store

Possibilities of selection ofassortment with respect to the…


The range of education of the respondents is fairly wide. As evident from the results, about 2/3 of the respondents were persons of higher education.No statistically significant difference of the results in terms of the gender (p = 0.108) was established when analysing the replies to


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the a statement ‘Do you know when the IKEA trade centre will be opened in Vilnius’. 56.5 % of men respondents (N = 288) and 43.5 % women (N = 222) were aware of the forthcoming open-ing of the IKEA trade centre.

The respondents were asked to assess (on a scale of 5, when 5 is the highest value), the impact of a specific factor towards the views of the consumers towards the opening of the IKEA centre. The responses to the question as to which attributes of the IKEA trade centre are vital for the decision of consumers to visit the centre (when the interviewing was conducted before the opening of the centre) led to a conclusion that the most important attribute was the possibil-ities of selection of assortment with respect to the price (4.22 points, see Fig. 2).

High scores, and scores of the other three attributes. This shows that the important factors for the consumers are the complexity, depth and width of the assortment of the goods at the IKEA centre. It may be concluded in this respect that the expectations of the consumers for the assortment factor are fairly high.

4. RESULTS OF THE SECOND SURVEY AND THEIR ASSESSMENT The second survey of Lithuanian customers was carried out one year after the opening of

the IKEA trade centre in Vilnius. Total 925 respondents participated in the survey. Of the total respondents 67% (N = 620) had visited the IKEA centre in Vilnius, and 33 % (N = 305) of them had never visited the centre. Social-demographic characteristics of the survey participants From among the respondents who have indicated their date 520 were women (56.2% of the total respondents), and 369 men (39.9%). 36 respondents (4%) did not indicate their gender.

An analysis of the distribution of the respondents by profession and the region in which the respondent was interviewed showed that the largest part of the respondents were specialists (46%, N = 410). There were 192 respondents representing Vilnius, 57 respondents were from Kaunas, 23 and 22 from Šiauliai region and Utena region, respectively. The difference between the results of the survey among the respondents that have visited the IKEA centre in Vilnius, and those who had never visited the centre was statistically significant (See Table 2).

Table 2. Mean averages of the impact of the elements of the range of goods at the IKEA trade centre upon decisions of consumers.

Feature of goods assortment Had you visited the IKEA store

in Vilnius? Average Standart

deviation Prob. T

Width of the assortment Yes 3,97 1,047

0,000 No 3,03 1,925

Depth of the assortment Yes 3,88 1,017

0,000 No 3,03 1,881

Completeness of the range of goods Yes 4,08 ,984

0,000 No 3,09 1,929

Possibilities of choosing from the range of goods from the price view-

point

Yes 4,12 ,945

0,000 No 3,24 1,955



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The average of the impact of each element of the goods range at the IKEA trade centre up-on respondents who have visited the centre range from 4.12 to 3.88, and with respect to those who have never visited the centre the average was above 3, and the range was 3.24-2.35 points. The findings of the survey supported the hypothesis of authors. The mean of each ele-ment of the respondents that had visited the centre, or those who had never visited the centre differed at 0.85-0.92 points, and could be explained by the following possible motives of the respondents:

a visit of the respondents to the IKEA trade centre helped them to develop their final opinion about each element of the range of the goods at the centre;

the respondents that have never visited the IKEA trade centre were to a much larger extent affected by the public opinion about the negative aspects of the trade centre (distanced, high prices, disappointing discount system, etc.)

It may be assumed that a visit to the IKEA centre would modify and better the attitude of the consumers who have not previously visited the centre, about the width, depth, complexity of the assortment, and the selection options with respect to the price. The high scores assigned by visitors to the centre for the width, depth, complexity of the assortment, and the selection op-tions with respect to the price were positively affected by the abundance of the goods, their presentation and display, and the impressiveness of the very trading centre. The scores as-signed by visitors to the centre for the impact of the individual elements upon decisions of con-sumers within the framework of the first study were very different from the results of the first study. The assortment bread and width score was by as much as 0.08 and 0.02 higher, and only the score for the selection possibility with respect to the price was accordingly -0.1 lower, but was still the highest of all elements. Nevertheless, price is the most sensitive element of the assortment of goods. It may be concluded that the administration of the IKEA trade centre managed to guess and meet the expectations of Lithuanian consumers. This is undoubtedly the result of IKEA as a global trade company. 5.INTEGRATED EVALUATION OF THE RESULTS OF THE FIRST AND SECOND SURVEY

The coefficient of the complex impact of the IKEA centre on the attitudes of the consumers computed for the first and second survey according to the formula submitted in the methodolo-gy part of the present paper represented the scale of the impact (see Table 3). Table 3. Coefficient of the complex impact of the IKEA centre goods assortment on the attitudes of the consumers computed for the first and second survey Name of survey Coefficient of the complex impact Derogation from the maximum

Survey I 0.8095 0.1905

Survey II 0.8025 0.1975

of which:

earlier visitors to the centre 0.8385 0.1615

Never visited 0.6195 0.3805

Source: drawn up by authors


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The overall coefficient of the complex impact during the second survey became lower by a lower coefficient of the respondents who have never visited the centre. The coefficient of the respondents who have visited the centre of the second survey was by 0.029 higher than the coefficient obtained during the first survey. The maximum possible value of the coefficient is 1.00, therefore the complex impact coefficient for the first and the second survey was relatively high. This was to an extent determined not only by a significant global experience of IKEA, but also by the surveys of the Lithuanian market. The coefficients of the impact upon consumer attitudes show a potential of such coefficients to grow, provided the administration of the IKEA centre takes appropriate measures, i.e. apply discount cards for all goods, improve the working hours of the shopping centre, and others. The results of both surveys demonstrate that the range of goods selected by a commercial company entering the market does have an impact upon the attitudes of the consumer. CONCLUSION

The common features of the trade object format of all commercial entities are only the de-fined number of the forms of goods, while all other characteristics may be very different. Even the goods assortment of the same retail commercial company may be different in different countries, with an exception of the number of forms of the goods that may also differ.

National consumers in one way or another respond to an entering retail company, and the assortment of the goods the company selected to implement. Thus the consumers start to gradually build their attitudes towards the assortment of the goods the company chose to im-plement. A lot of factors affect the results; such factors are general, derivative from the general results and specific factors important for the formation of the attitudes of an individual subject or a user of a phenomenon.

Research literature and papers do not directly study the impact of the assortment of the goods upon the attitudes of the customers. The same is true regarding the composite parts of the attitude, i.e. elements. Such surveys may be conducted by retail companies, however, the results remain as commercial secret and the results are not made public.

The analysis carried out within the framework of the present paper of research literature on stores, retail corporations, its development and evolution, the attitude of consumers towards individual elements of the factor, the problems related to selection and implementation of the range of the goods by retail companies in individual regions of the world and countries identi-fied four most important elements constituting a basis for the formation of the consumer atti-tudes towards the range of goods.

The theoretical rationale of the study methodology is based on the attributes of the poten-tial impact of the range of the goods upon consumers. The study used the quantitative consum-er inquiry method, and referred to the methodology for the computation of the coefficient of the complex impact of the range of the goods upon the attitudes of consumers.

The first survey of the yet-to-be customers of the IKEA trade centre in Lithuania was carried out immediately before the opening of the centre in August 2013. Total 663 questionnaires were recognised to be suitable for research purposes. The study conducted and constituting the basis for the present paper proved that consumers do have their opinions regarding the range of goods of the IKEA trade centre. Consumers assigned 4.22 points of 5 possible to the attrib-ute of the possibility of choice of assortment with respect to the price. 4.08 points were as-signed for the complexity of assortment, 3.95 points were assigned for the width, and 3.80 points – for the depth of the assortment.

Total 925 respondents participated in the second survey conducted one year after the opening of the IKEA trade centre, of which 67% had earlier visited the IKEA centre,and the av-


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erages of the impact of the assortment of goods upon the attitudes of the consumers were sig-nificantly higher than those which had never visited the centre. The findings of the survey sup-ported the hypothesis of authors. The highest score – 4.12 – was assigned to the possibility to select the assortment with respect to the price. The results of the second survey proved that there should be two surveys to assess the effect of the assortment; one survey should be con-ducted before the opening of the centre, and the second must be at least six months after the opening of the centre. The first survey demonstrates the attitude of the consumers in the pro-cess of its formation, and the second survey shows the attitude already fully formed.

The coefficient of the complex impact upon the respondents who have visited the IKEA cen-tre the second survey is slightly higher than the identical coefficient obtained with the frame-work of the first survey. The high coefficient of the complex impact shows that by its trade ob-ject format the retail trade company IKEA essentially met the expectations of the customers.

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‘

Company’s Value Creation Via Customer Satisfaction and Environmental Sustainability Influence

DALIA STREIMIKIENE1, AIDA NAVIKAITE2, and VYTIS VARANAVICIUS3

1 Prof. dr. (HP), Mykolas Romeris University, Ateities str. 20, 08303 Vilnius, Lithuania, [email protected] 2 Kaunas University of Technology, K.Donelaicio st.73, LT-44309 Kaunas, Lithuania, [email protected] 3 Lithuanian Energy Institute, Breslaujos str. 3, 44403 Kaunas, Lithuania, [email protected]


Received September 05, 2016 Received in revised from September 25, 2016 Accepted December 05, 2016 Available online 15 December 2016

It is significant for organizational institutions to pay more atten-tion to the needs of their client base because today’s competitive business environment challenges companies. In addition to this, people invest in the expectation that when they sell, the value of each investment will have grown by a sufficient amount above its cost to compensate them for the risk they took. Therefore it is a strong argument for value creation. Hence, this current article is initiated on the value creation via two constructs: customer satis-faction and environmental sustainability. It could be noted that the present study is extensive in terms of displayed past models and other useful academic examples. It also should be noted that value of the company depends on the short term and long term performance. So, the decisions for managing the value drivers have to be made considering that they should bring long and short term benefit. Regarding this notion, it also puts forward an important background for future theory and practice investiga-tions. So, the problem of the study is: How to create firm‘s value via customer satisfaction and environmental sustainability? While, the aim of the study is: to emphasize customer satisfaction and environmental sustainability to its’ elements that could be incorporated in order to create firm‘s value. The objectives of the study are: 1) to analyze the value drivers and investigate the constraints and uncertainties of valuation, 2)to examine basic requirements for valuations, 3) to analyze environmental sus-tainability, 4) to investigate the concept of customer satisfaction incorporating contemporary conceptual models.

JEL classification:

Q56, Q57, O350 DOI:

10.14254/1800-5845/2016.12-4.2 Keywords:

value, environmental sustainability, customer satisfaction.

INTRODUCTION

Investigations of released academic papers regarding company’s value creation in pioneer-ing ways especially via two constructs: customer satisfaction and environmental sustainability have revealed that this field is highly unexplored. It is significant for organizational institutions to pay more attention to the needs of their client base because today’s competitive business environment challenges companies. In addition to this, people invest in the expectation that when they sell, the value of each investment will have grown by a sufficient amount above its cost to compensate them for the risk they took. Therefore, it is a strong argument for value cre-


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ation. It also should be noted that value of the company depends on the short term and long term performance. So, the decisions for managing the value drivers have to be made consider-ing that they should bring long and short term benefit.

The novelty of the study: this study proves that the firm‘s value creation can be reached via two elements: customer satisfaction and environmental sustainability. Customer satisfaction effects market share, while environmental sustainability has a positive impact on competitive advantage, so it is recommended to incorporate the idea of customer satisfaction and environ-mental sustainability management in firm‘s value creation. Obviously, the present study can be and is offered to be extrapolated in both aspects: theory and practice. It is also believed that the study paper would be relevant for the advancement of firm‘s strategy creation since customer satisfaction and environmental sustainability is the significant factor in the business field.

The object of this study is: firm‘s value via customer satisfaction and environmental sus-tainability.

The problem of the study is: How to create firm‘s value via customer satisfaction and envi-ronmental sustainability?

The aim of the study is: to emphasize customer satisfaction and environmental sustainabil-ity to its’ elements that could be incorporated in order to create firm‘s value.

The objectives of the study are:

To analyze the value drivers and investigate the constraints and uncertainties of valuation To examine basic requirements for valuations. To analyze environmental sustainability. To investigate the concept of customer satisfaction incorporating contemporary conceptual

models.

The methods of the study are:

Logical and comparative analysis of literature; Synthesis and deduction; Graphical methods. 1. VALUATION APPROACH DEFINITION

According to Koller, Goedhart, Wessels (2010), “value is the defining dimensions of meas-urement in a market economy. People invest in the expectation that when they sell, the value of each investment will have grown by a sufficient amount above its cost to compensate them for the risk they took” (p.3). The main measure of the value of the assets, it does not matter if it is share, bond or bank account, is the value which is created to its shareholders.

Valuation is the process to define the value of the chosen object. Financial valuations could be done to evaluate the value of options, bonds, stocks, investments, patents, all in all for eve-rything that could have value. Knowing the value is the key point in making the decision, does not matter if it is investing or accepting the investment. The main aim of the valuation is to de-fine the real value and to evaluate the potential gain. According to Damodaran (2006), “[…] we buy financial assets for the cash flow we expect to receive from them. Consequently, perception of value have to be backed up by reality, which implies that the price we pay for any asset should reflect the cash flows it is expected to generate. […] Asset price cannot be justified by merely using the argument that there will be other investor around who will pay the higher price in the future.” (p. 1)


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1.1 Value Drivers Value of the company depends on the short term and long term performance. So, the deci-

sions for managing the value drivers have to be made considering that they should bring long and short term benefit, or at least not to damage long term health with short term performance (Koller et al, 2010). The importance of value drivers for separate company depends on its fun-damentals, strategy, intangible and tangible assets, the employees of the company, manage-ment, objectives, company’s historical assets and debts. It also depends on the external con-straints that are macroeconomic situation which can make the high influence on company’s performance and growth. Market situation is also one of the constraints that is important for current and future perspective of value improvements, where highly important are the features of customers and competitors. 1.2 Constraints and Uncertainties of Valuation

Valuation has methodology, ways and rules that have to be used when the company is val-ued. The methods of valuation have the inputs that are complex. Selection of the figures for the valuation inputs is the art that is influenced by many factors. Selection process and selected inputs depends on factors such as the current economic trends, future perspective, company’s announcements and reports. Also people who are making the analysis or are related with them could be called as the factor that makes influence to the valuation.

One of the parts of valuation is the selection of the correct inputs. These days the variety of reachable information is wide and the decision which information is relevant for one or other input becomes the main constrain of the valuation, however, at the same time, it brings much more opportunities and possibilities. Still, in some cases some information for inputs could be missing or could be no correct which make the difficulty for identifying and understanding the possible uncertainties and biases.

One of the sources where biases occur is the analyst who is responsible for valuation. This is one of the points that have to be carefully selected before starting the valuation (Damodaran, 2002). For instance, the analyst may be the employee of the company that is valuated and the valuation result has the influence to his/her salary or wellness. In the best case of valuation the analyst relation with a valuated company should be neutral to decrease the possibility of biases, though person, who is not related with valuating object, may already have the primary opinion, understanding or experience, which could distort the result.

According to Damodaran (2006), as well the biases exist after valuation is made. It occurs because after finishing the valuation analyst begins to evaluate if his/her analysis is made cor-rectly. He/She starts to compare the valuation to other analysts’, compare her/his result with the market price, or analyze values and methods of similar valuations. He/She starts to consid-er if the method of valuation was chosen well, check which method and inputs are used by oth-er people. All in all the analyst starts to doubt if the made analysis are correct and begin to make changes that influence the increase of possibility of biases.

However, even if biases are mitigated and reduced to minimum the uncertainty make the big influence to the valuation. The expectations of company’s performance, growth or the way it operates may change. Also the macroeconomic situation may shift into the unpredictable way that would switch the forecasts in totally different way. As an example for this kind of uncertain-ties, the current disaster in Japan in 2011.03.11 can be used. According to Schlesinger, (2011), “Economic and market impact the massive earthquake in Japan will impact the world’s third largest economy. The most direct impact is likely to be seen in Japanese exporters, like Honda, Toyota and Sony. Additionally, Japanese insurance companies are likely to see large losses as a result of the claims associated with the earthquake.” So, analysts who were valuat-


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ing the companies that are impacted by that earthquake took totally different inputs, than they would be taking after this disaster. The situation comparing it before and after earthquake and the valuation result those are made before and after should be different. However, some valua-tions and decisions were made before this disaster and they cannot be turned back. As it is seen from this example the valuation method and the inputs could change in few days or even in few seconds, influencing the valuation result significantly. 2. BASIC REQUIREMENTS FOR VALUATION

According to Meitner (2006), for good valuation of the company, the certain requirements have to be maintained:

The valuation must be oriented into the future. The benefits that will be earned in the future are those that have to be taken in to valuation. The past benefits are not important for the valuation. To sum up, importance is not what was earned or had value in the past but what is going to be earned or have value in the future.

Everything should be taken into the account what could affect the utility of the company. Valuation is not only implementation of financial figures. It is important to take into the ac-count everything what affect company’s performance, future gain, and also value.

Possible uncertainties, that the company could face, have to be taken into the account of valuation. The possible future chances, possibilities, as well as dangers have to be evaluat-ed and incorporated to the valuation process. The valuation result has to reflect these up-side potentials and downside risks.

The valuation has to have the investor orientation. It is said that the valuation should be

adapted to the certain situation. This opinion disagrees with Damodaran (2006) approach, however it depends what is the aim of the valuation. Considering taking this requirement into the account of valuation, it is important to understand if valuation aim is to support one or other related party of the valued object. If valuation is made to get the real value, this requirement should not be taken into the account. 3. ENVIRONMENTAL SUSTAINABILITY

According to Hofer et. al. (2012), companies are using environmental activities to obtain competitive advantage against competitors which increases the value of the company. The managers are analyzing the environment actions of their competitors, because understanding the environmental sustainability actions have the same importance as following competitors’ financial situation, marketing and development actions. In most of the cases the size of the company becomes a disadvantage for environmental activities. The larger companies become not flexible and slower, which is the big disadvantage against competitors and to maintain company’s value growth. The growing companies should put a lot of effort to decrease the bu-reaucracies and allow quick implementations for environmental initiatives.

To hold a position of industry leader in environmental activities is not easy, the company has to recognize and counter the strategic moves of their competitors. Hofer et. al. (2012) un-derlines that: “Industry leaders consistently have inferior environmental performance, which puts them at a strategic disadvantage. Closing this performance gap is vital, especially consider-ing the growing emphasis put on the environment. Recognizing the importance of environmen-tal management and following these simple recommendations allows managers to improve their competitive standing, value and the environment at the same time.”

Taking into consideration the practical implementation of sustainable activities in order to reach the competitive advantage and increase company’s value it is chosen to take carpet in-


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dustry example. In 2005 Rusinko et al. did the research in U.S. carpet industry which generates 12 billion of annual revenue. The goal of the research was to develop and empirically test a model to predict which environmentally sustainable practices lead to what competitive out-comes and value growth. The carpet industry was chosen because this is the industry that has been under increasing pressure to reduce the environmental impacts of their products and their processes for over 10 years. The initiative developed by U.S. Green Building Council influence the creation of new market drivers for sustainable practices and the carpet industry had con-siderable experience in combining sustainable practices and products with competitive suc-cess. The sustainable practice of U.S. carpet industry and the outcome of created competitive advantage which increased companies’ value is shown below (Figure 1). Figure 1. Sustainable practices and competitive outcomes

Source: Rusinko et al. (2005) The companies in U.S. industry started to use environmental sustainability practices such

as nature friendly recycling, sustainable product design, employees training & education, re-source reduction and sustainable process design. These actions increased the competitive ad-vantage of industry players by decreasing their costs and lead time, increasing quality and per-formance, also the sustainable actions are the reason for increased sales and revenue.

Berns et al. (2009) developed a research on sustainability. The authors proved that these days the business is influenced by sustainability but not vice versa. Companies’ managers de-fine sustainability differently, and are focused on environmental impacts, others on social or personal, or even looking from economics sight. Adding up, the forces that are having the great-est impact on companies — are government legislation, consumer concerns and employee in-terest in sustainability. And the potential impact of sustainability efforts are:

A stronger brand and greater pricing power Greater operational efficiencies, more efficient use of resources, supply chain optimization,

lower costs and taxes Enhanced ability to attract, retain and motivate employees, greater employee productivity Improved customer loyalty, lower rate of staff turnover. Enhanced ability to enter new markets, more potential sources of revenue Lower market, balance-sheet and operational risks Lower cost of capital, greater access to capital, financing and insurance

Paying attention on company’s sustainability by environmental responsibility actions in-creases the effect on internal synergy (Fernandez-Guadano, 2015; Vveinhatdt, Andriukaitiene,


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2015; Sueldo, Streimikiene, 2016). Both environmental activities and synergy are increasing the value of the company. The scheme bellow shows the value creation matrix in the company which is influenced by potential impact of sustainability efforts. Figure 2. Value creation

Source: Berns et al. (2009)

The chart above is divided into three value creation levels. The first one include pricing power, cost saving and employee recruitment and engagement, the second one include market share and new market entry possibilities, and the third one is covering the risk premiums and cost of capital. The mentioned value creation levels are making influence by improving margin, increasing revenue. This leads to increased profit, better free cash flow, higher valuation multi-plier. And finally everything leads to a total increased shareholders return. Hence, to outperform other market players the firm has to incorporate value-creation strategy through delivering su-perior value for customers. 4. THE CONCEPT OF CUSTOMER SATISFACTION

After academic analysis regarding customer satisfaction it has been revealed that this par-ticular concept has been examined extensively. Some authors like Oliver (1997) and Zeithalm and Bitner (2000) state that customer satisfaction is the cllient’s emotional reaction whether his or her expectations has been fulfilled or not. Moreover, Gilbert-Jamison(2005) customer satisfaction only create a customer base that likes your products or services. Angelova and Ze-kiri (2011) convince that customer satisfaction majorly depends on these factors: “friendly em-ployees, courteous employees, knowledgeable employees, helpful employees, accuracy of bill-ing, billing timeliness, competitive pricing, service quality, good value, billing clarity and quick service” (p.234).

For instance, Aldlaigan & Buttle (2005) say that customer satisfaction is the concept which displays the needs that should be obtained in order to get the positive judgment towards the product or service from the consumer. Taking into consideration the idea that satisfying cus-tomer is the profitable action (Fornell and Rust, 1997), the author Cochran (2003) says that satisfying customer is the action that company should do during the daily activities in order to stay in business.

For instance, the study of Kim and Lee (2013) validated a comprehensive model which could be stated as a mixture of two models and one of them is American Customer Satisfaction Index. The current model initiated on the analysis of factors that affect customer satisfaction. To be more precise, the study was aimed to examine the mobile environment. So, the designed research model was utilized as follows.


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Figure 3. Research model

Source: Kim and Lee (2013)

Moreover, Al-Wugayan & Pleshko (2011) used path analysis to examine the relationship among constructs. The model was proposed consisted of satisfaction, loyalty and market share in the banking sector of Kuwait (Figure 4). Sampling frame was comprised of bank 327 clients from 36 banks. The study firstly revealed the existence of positive relationship between cus-tomer satisfaction and loyalty, and secondly, it was supported a significant positive association between loyalty and market share.

Figure 4. Model of Satisfaction, Loyalty and Market Share

Source:A. Al-Wugayan,L.P. Pleshko, (2011, p. 6)

What is interesting, the study below is devoted for O'Sullivan & McCallig (2012). The au-thors integrated the Ohlson model and also borrowed data from American Customer Satisfac-tion Index. But the most important thing they revealed, that customer satisfaction positively moderates the earnings and therefore, it could be assumed, that customer satisfaction has an influence on company’s augmentation of financial performance, also on firm’s value (Figure 5).

Information Quality

Perceived Usefulness

Self-efficacy Customer

Satisfaction

Service Quality

Perceived Ease of Use


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Figure 5. The relationships between customer satisfaction, earnings and firm value

Source: D. O'Sullivan, J. McCallig (2012, p. 831)

After a brief analysis of empirical studies it could be concluded that the inquiry of customer satisfaction bears on industry sector, type of the venture and scholars’ distinguishing variables, which have an impact on overall perception of this object.

The profound analysis of academic papers has proved that this particular element is men-tioned in a plethora of works. Majorly, the authors describe customer satisfaction as an emo-tional reaction which highly depends on the fact whether the customer’s needs and expecta-tions are realized or not. The advantages of this notion are distinguished these ones: profit en-hancement, market share and firm’s value augmentation, possibility to predict client’s behavior. CONCLUSION

Knowing the value is the key point in making the decision, does not matter if it is investing or accepting the investment. The main aim of the valuation is to define the real value and to evaluate the potential gain. According to the authors value of the company depends on the short term and long term performance. So, the decisions for managing the value drivers have to be made considering that they should bring long and short term benefit, or at least not to dam-age long term health with short term performance.

In order to reach a good valuation of the company, the certain requirements have to be maintained: the valuation must be oriented into the future, everything should be taken into the account what could affect the utility of the company, possible uncertainties, that the company could face, have to be taken into the account of valuation, the valuation has to have the inves-tor orientation. It is said that the valuation should be adapted to the certain situation.

Companies are using environmental activities to obtain competitive advantage against competitors. The managers are analyzing the environment actions of their competitors, because understanding the sustainable environment actions have the same importance as following competitors’ financial situation, marketing and development actions. The potential impact of sustainability efforts are: stronger brand and greater pricing power, greater operational efficien-cies, more efficient use of resources, improved customer loyalty, enhanced ability to enter new markets, more potential sources of revenue.


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After academic analysis regarding customer satisfaction it has been revealed that this par-ticular concept has been examined extensively. Majorly, the authors describe customer satisfac-tion as an emotional reaction which highly depends on the fact whether the customer’s needs and expectations are realized or not. The advantages of this notion are distinguished these ones: profit enhancement, market share and firm’s value augmentation, possibility to predict client’s behavior. REFERENCES Aldlaigan, A., Buttle, F. (2005), “Beyond satisfaction: customer attachment to retail banks”, In-

ternational Journal of Bank Marketing, Vol. 23, No. 4, pp. 349-59. Al-Wugayan, A., Pleshko, L., P. (2011), “Study of Satisfaction, Loyalty, Market Share in Kuwait

banks offering mutual fund services”, Journal of International Business Research, Vol. 10, No. 2.

Angelova, B., Zekiri, J.(2011), “Measuring Customer Satisfaction with Service Quality Using American Customer Satisfaction Model (ACSI Model)”, International Journal of Academic Research in Business & Social Sciences, Vol. 1, No. 3, pp. 232-258.

Berns, M., Townend, A., Khayat, Z., Balagopal, B., Reeves, M., Hopkins M. S., Kruschwitz (2009), “Sustainability and Competitive Advantage”, MIT SLoan Management Review, Vol. 51, No. 1, pp. 20-26.

Cochran, C. (2003), Customer Satisfaction. Tools, Techniques, and Formulas for success, Par-ton Press, USA.

Damodaran, A. (2002), Investment valuation: tools and techniques for determining the value of any Assets, John Wiley & Sons, Inc.

Damodaran, A. (2006), Damodaran on Valuation: Security analysis for investment and corpo-rate finance, John Wiley & Sons, Inc.

Fornell, C., Rust, R.T, Anderson, E.W., (1997), “Customer satisfaction, productivity and profita-bility: differences between goods and services”, Marketing Science, No. 16, pp. 129-145.

Fernandez-Guadano, J., (2015), “Employee-Owned Firms: a Business Model of Social Economy with Potential”, Transformation in Business and Economics, Vol. 14, NO. 2 (35), pp. 191-204.

Gilbert-Jamison, T. (2005), The Six Principles of Service Excellence, Author House, USA. Hofer, C., Cantor, D. E. Dai, J. (2012), “The competitive determinants of a firm’s environmental

management activities: Evidence from US manufacturing industries”, Journal of Operations Management, No. 30, pp. 9-84.

Kim, J.,Y, Lee,H.,S. (2013), “Key Factors Influencing Customer Satisfaction in Korea's Mobile Service Sector”, Journal of Internet Banking & Commerce, Vol. 18, Issue 3, pp. 1-13.

Koller, T., Goedhart M., Wessels D. (2010), Valuation: Measuring and Managing the Value of Companies. Corporate valuation, McKinsey & Company, John Wiley & Sons, Inc.

Meitner, M. (2006), “The market approach to comparable company valuation”, Vol. 35, ZEW Economic Studies

Oliver, L.R. (1997), Satisfaction: A Behavioral Perspective on the Consumer, McGraw-Hill, New York.

O'Sullivan, D., McCallig J., (2012), “Customer satisfaction, earnings and firm value”, European Journal of Marketing, Vol. 46, No. 6, pp. 827-843.

Rusinko, C.A., Pastore, C., Pierce, J., Fleming, R., Frosten, S., Christoffersen, S. (2005), Sustain-ability as a Source of Competitive Advantage, NTC Project: SO3-PH01. Available at: http://www.ntcresearch.org/pdf-rpts/AnRp05/S03-PH01-A5.pdf

Schlesinger, J. (2011), “Japan Earthquake: Economic and Market Impact", Available at: http://moneywatch.bnet.com/economic-news/blog/financial-decoder/japan-earthquake-economic-and-market-impact/3390/


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Sueldo, M., Streimikiene, D. (2016), “Organizational Rituals as Tools of Organizational Culture Creation and Transformation: A Communicative Approach”, Transformation in Business and Economics, Vol. 15, No. 2 (38), pp. 89-111.

Vveinhardt, J., Andriukaitiene, R. (2015), “Determination of the Level of Management Culture and Social Responsibility in a Regional Organisation of Local Self-Government”, Transfor-mation in Business and Economics, Vol. 14, NO 2 (35), pp. 204-224.

Zeithaml, V., A., Bitner, M., J. (2000), Services marketing: Integrating customer focus across the firm, Irwin/McGraw-Hill, Boston.

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‘

Modeling and Visualization of Quadratic Assignment Problems on the Example of Plant Location

SLAVOMIR VUKMIROVIC 1, DRAGO PUPAVAC 2,

and JUSUF SEHANOVIC 3

1 University of Rijeka, Faculty of Economics Rijeka, Rijeka, Croatia 2 Polytechnic of Rijeka, Rijeka, Croatia, e-mail: [email protected] 3 Juraj Dobrila University of Pula, Department of Economics and Tourism «Dr. Mijo Mirkovic», Pula, Croatia


Received August 02, 2016 Received in revised from September 05, 2016 Accepted December 01, 2016 Available online 15 December 2016

The object of our research is the visualization of use and func-tioning of modelling programming languages used in solving complex quadratic assignment problems. In accordance with our research object, we have established a hypothesis statement that the entire process of creating a model for solving the quad-ratic assignment problem can be visualized using modelling programming languages in Excel interface. This means that pro-gramming languages can be easily used by economists in quanti-tative modelling and solving of complex, optimisation problems the easy way.

JEL classification: C61; M21.

DOI: 10.14254/1800-5845/2016.12-4.3

Keywords:

Quadratic assignment problem (QAP), plant location, visualisation, model, programming

INTRODUCTION

One of the most important strategic decisions made by many companies is where to locate their operations. The international aspect of these decisions is a consequence of the globaliza-tion process. Managers throughout the world are using different methods for solving location problems because location greatly affects both fixed and variable costs (Streimikiene, Mikalauskiene and Barakauskaite-Jakubauskiene, 2011; Kaplikski and Tupenaite, 2011; Ciegis, Dilius and Mikalauskiene, 2015). Location may determine up to 10% of the total cost of an industrial firm (Heizer and Render, 2004). Once management is committed to a specific lo-cation, many costs are firmly in place and difficult to reduce. Consequently, scientific approach to determine an optimal plant location can make the difference between success and failure. The location decision often depends on the type of business. For industrial location decisions, the strategy is usually minimizing costs. Accordingly, in this work we discuss the possibility of determining the optimal allocation of a set of n plants to a set of n locations on the global mar-ket. The objective is to select the optimal combination of pairs of plant location assignments with the goal of minimizing distances and flows (material, spare parts, semi finished products, and components) between the plants. Such problems can be solved using a mathematical


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Slavomir Vukmirovic, Drago Pupavac, and Jusuf Sehanovic / Montenegrin Journal of Economics,

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model for the problem of quadratic assignment (QAP – Quadratic Assignment Problem). The QAP formulation requires an equal number of distribution centres and locations. If the number of locations is greater than the number of distribution centres, a fictitious location (dummy vari-able) should be created and a zero flow between each of these and distribution centres must be assigned. If the number of distribution centres is greater than the number of locations, problem cannot be solved.

After the introduction part, in the second part of the paper we describe and discuss the quadratic assignment problem of plant location through the example of Volkswagen Group lo-gistics networks. In the third part of the paper, an illustrative example of QAP visualization is given, while in the forth part we discuss a process of creating a mathematical model to solve a quadratic assignment problem through the example of plat location problem. In the fifth part we describe and elaborate a process of model-based visualization to solve a QAP of plant location using synergy between LINGO (programming language) to object-oriented programming lan-guage (VBA for Excel). In the final part of the paper, we synthesize our research findings. 1. DESCRIPTION AND SIGNIFICANCE OF QUADRATIC ASSIGNMENT FOR PLANT LOCATION PROBLEM

The question “Where should we locate?” is more prominent in the minds of executives than it has ever been. Over the past three decades, business activities have become increasingly mobile. Many activities in the value chain, however, are tied neither to customers nor to re-sources but instead are mobile. They can be placed in any of numerous locations and moved as circumstances change. Mobile activities gravitate toward the location that allows them to be performed the most effectively at the lowest total cost. Today improvements in information, communication, and logistics technology allow firms to serve many markets from a distance, spread discrete activities around the globe, and coordinate them in a global system. Thus, managers must increasingly decide not only which countries to serve but also where to locate each activity in the value chain (Porter and Rivkin, 2012). The logistics networks of the Volkswagen Group, one of the world’s leading companies, is a typical example of the global val-ue chain (cf. Map 1).

Map 1: Global logistics networks of the Volkswagen Group

Source: Heizer and Render, 2004, p. 311.


31

In the Map 1, we see that VW Mexico ships unassembled vehicle sets and spare parts to VW Nigeria, and components such as engines, and shock absorbers etc. to VW Brazil, while concurrently receiving spare parts and components from Germany. The range of location op-tions such production has expanded dramatically as many countries have stabilized their mac-roeconomic policies, opened their markets, improved their infrastructure, strengthened their economic institutions, and upgraded the skills of their workforces. Countries that used to attract activities only on the basis of natural resources or cheap labour can now vie for activities that rely on more skill and involve more-complex manufacturing or services. Linear programming is the most commonly used method of solving such problems.

Two following aspects should be considered when defining spatial logistics chain configura-tion: 1) Level of centralization or geographic concentration of functions, and 2) Level of coordi-nation of dispersed activities. A producer can either have all its business activities placed in a specific location wherefrom goods or services are being supplied onto the whole world market, or can have its plants all over the world from which goods or services are being supplied onto each individual local market. Concentrated production means scale effects, use of highly spe-cialized knowledge and skills, and possibility of controlling the performance all over the world. Dispersive configuration of logistics chain allows for more attentive monitoring of market devel-opments, greater ability of adapting to market demands and consumer needs. Dispersion of activity in logistics chain ensures greater strategic flexibility, and allows producers to provide resources from more convenient locations, decrease stock, have less warehouses, and offer faster delivery. Accordingly, the following three major production models within global logistics chains can be identified:

I. Centralized production model – Production activity is concentrated in a single or maxi-mum two locations covering the entire world market. This model has positive impact on econo-mies which are likely to achieve significant scale effects;

II. Interrelated, dispersed production plants – Production, plants and assembly plants are placed at interrelated, centrally coordinated multiple, different locations. This is the most fa-vourable model for economies where the best solution seems to be to have individual compo-nents manufactured in different countries, or to purchase individual components from multiple, different countries. In such a model, plants where finished products are assembled are usually located near large markets, but components are purchased from widely dispersed locations.

III. Decentralized, dispersed production – This model is characterized by a larger number of locations all over the world but only partly interrelated. This is often seen in economies which do not exhibit the scale effect. This model is also suitable for economies characterized by signifi-cant differences between factor cost and technology from one market to another. Many factors (wage levels, skills availability, utility rates, taxes, subsides, shipping costs and reliability, local productivity, supervision costs, etc) affect the profitability of operating in a certain locale. Locat-ing factors are complex, interrelated and dynamic. Locating an activity in one country often has ripple effects on activities elsewhere. Because many managers are still trying to find the appro-priate way to improve processes for making location decisions. Four major methods are used for solving location problems: the factor-rating method, locational break-even analysis, the cen-ter-of-gravity method, and the transportation model (Heizer and Render, 2004).

2. ILLUSTRATIVE EXAMPLE OF VISUALIZATION OF QAP MODEL Quadratic Assignment Problem (QAP), as the name suggests, is a typical assignment prob-

lem where the goal function is a quadratic function or the second-order polynomial, which means that it includes the product of the two variables (Burkard et al. 1998). The assumption is that a company wants to minimize material flows between its plants by determining the optimal combination of pairs of plant-location assignments. Flows of materials moving between facto-


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ries and distances between locations where plants can be allocated have been defined. Scheme 1 shows distances between locations, and scheme 2 reveals flows of materials be-tween plants. Flows of materials have been expressed in average number of containers being transported per day.

Scheme 1. Matrix of distances Scheme 2. Matrix of flows between plants between locations

L11 L12 L130 10 5 L13

0 13 L230 L33

P11 P12 P130 42 90 P13

0 110 P230 P33

Scheme 1 indicates distances between locations, and scheme 2 reveals quantities of ma-terials being transported between plants. L means location, while P means plant. Each pair of indexes next to location mark means distance between the two locations. We can see in the scheme 1, for instance, that the distance L12 between the location 1 and location 2 is 10, while L13 indicates that the distance between the location 1 and location 3 is 5 etc. Data in the scheme 2 have been marked in the same way. In the Table 1, we see that N=3 (3x3 as-signment), which means that it is possible to make 9 combinations of pairs of plant location assignments from which we can select that with the minimum product of distances between locations and quantities of materials being transported between those. Table 1. Possible combinations of sumproducts of the distances and flows

Locations PlantsL1 10 42 P1L2 13 110 P2L3 5 90 P3

The initial model can be built in the same way as the model shown in the scheme 3, graph-ically indicating the model goal. It is necessary to calculate the optimal combination of assign-ments for each location Li and plant Pj, so that each location is assigned exactly one plant and each plant is assigned exactly one location with the goal of minimizing the transportation cost.

Scheme 3. Initial QAP model

L1 L2 P1 P2 Li, Pj Li, PjL3 P3 Li, Pj i=1,2,3

Scheme 4. QAP model solution

L1 L2 P1 P2 L1, P2 L2, P3L3 P3 L3, P1 i=1,2,3


33

The solution reveals the value of minimum cost calculated using a model for optimizing the assignment of plants to locations. The optimal solution in the scheme 4 indicates that the plant 1 has been assigned to the location 2, plant 2 has been assigned to the location 3, and plant 3 has been assigned to the location 1.

We have selected the optimal combination of products, and included those into the equa-tion, to determine the minimum cost, respecting the given limitations. The sum of included products gives the minimum result. We can see in the table that replacing any product that has been included into the equation with another product of values of pairs in the columns that has not been included into the equation is not likely to produce a more favourable result or lower value than the one computed. This has been expressed in terms of the following mathematical formula: L12*P13+L23*P12+L13*P23 = 10*90+13*42+5*110 = 1996.

The table shows optimal combination of products of distances and flows whose sum is the minimum cost. Distances and flows have been shown in the columns C and D. It is potentially possible to include each pair of products of the values in the columns when approaching the problem, so that a single value from one column can be multiplied with any other value from another column. This means that the number of limitations in quadratic programming would be equal to the second power of the number of rows as opposed to single dimensional limitations where the number of inequations equals the number of rows.

In the scheme 5 we have compared the result of optimal solution with a result which could have been obtained by a random, unintentional, or unplanned assignment where production plants, for instance, could be assigned to locations according to their ordinal numbers. In other words, the plant 1 could have been assigned to the location 1 etc. The comparison of results revealed difference between the costs that accounts for a decrease in cost achieved by using modelling languages and QAP optimization software.

Scheme 5. Comparison of results

L1, P2 L2, P3 L1, P1 L2, P2 Saving = R1-R2L3, P1 R1=1996 L3, P3 R2=2300 = 304

Optimal assignment of plants to locations can also be shown graphically (cf. scheme 6). Scheme 6a represents a random assignment giving a cost of 2300 units of money; Scheme 6b indicates optimal plant location assignment with the minimum cost of 1996 units of money. It follows that optimal plant location assignment ensures cost almost 15.2 % lower.

Scheme 6a Scheme 6b

P1

L1

P2

L2 L3

P3

P1 L1

P2

L2L3 P3

We are able to compute cost and define pairs to be assigned to each other by adding the products of distances and flows for all pairs of plant location assignments respecting the given limitations. The minimum cost formula can easily be checked by replacing any product multi-plied with 1 in the total sum of products with product multiplied with 0; product replacement will


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not produce more favourable results. Or, in other words, the value of minimum cost obtained in such a way will never be lower.

3. MATHEMATICAL MODEL OF QUADRATIC ASSIGNMENT PROBLEMS FOR PLANT LOCATIONS

The Quadratic Assignment Problem was originally introduced in 1957 by Koopmans and Beckman who were trying to model a plant location problem. The problem models the following real-life problem: There are a set of n plants and a set of n locations. For each pair of locations, a distance is specified and for each pair of plants a weight or flow is specified (e.g., the amount of supplies transported between the two plants). The problem is to assign all plants to different locations with the goal of minimizing the sum of the distances multiplied by the corresponding flows. Intuitively, the cost function encourages factories with high flows between each other to be placed close together (Burkard et al. 1998; Sunderesh and Heragu, 1997). We have demon-strated the mathematical model used to solve a QAP problem by an example of factory location assignment. Table 2 illustrates an example of problem of allocating factories to locations with the goal of minimizing the cost of material transportation between the factories.

Table 2. Distances between locations and unit transportation costs

Table 2a. Material flow cost Table 2b. Distances between locations

12

13

14

15

16

17

A B C D E

Distances betwen locations (D)R S 2 3 4 51 100 80 70 402 80 100 503 70 904 40

2

3

4

5

6

7

A B C D E

Material flow costs (M)R S 2 3 4 51 2 10 10 42 15 30 353 20 404 10

The goal is to allocate factories to the locations in an optimal way to achieve minimum product of cost for the quantities of materials being transported between the production plants and distances between their locations. In the example, distance between the location dj and location dk is marked as djk, and material being transported between the plant mi and plant ml along those distances is marked as mil Plant mi being assigned to location dj is marked as xij, given that xij = 1. This has been expressed in terms of a mathematical formula below (Burkard et al. 1998; Sunderesh and Heragu, 1997):

jkil

n

lklij

n

k

n

j

n

i

dmxx 1111

Min

The model reveals that there is a set of N factories and a set of N locations which are to produce N*N variables of type xij and N*N*N*N variables of type zijkl. The formula reveals that the size of model is exponentially increasing by accretion in the variable N. It follows that mod-els with greater values N require specialized programs with robust computing platforms. Smaller size QAP models can be converted into Integer Linear Programming so that the product xijxkl is being replaced with a single variable zijkl. In this case, the goal function can be expressed in terms of a mathematical formula below (Sunderesh and Heragu, 1997):

jkil

n

lijkl

n

k

n

j

n

i

dmz 1111

Min


35

If the matrices are symmetric, data can be written as shown in the tables 2ª and 2b. The goal function has been expressed in terms of a mathematical formula below:

1 1, 1 1

n n n n

ijkl il jki j j k k l

Min z m d

1 1, 1 1

n n n n

ijkl il kji j j k k l

z m d

We need to define limitations as well. Limitations exist to ensure that each factory is placed

onto exactly one location. In other words, each location can be assigned exactly one factory. These limitations can be defined in terms of the formulas (1) and (2) below. Each factory l can be assigned exactly one location k.

1

1 1,.., (1)n

lkl

x for k n

Each location k can be assigned exactly one factory l.

1

1 1,.., (2)n

lkk

x for l n

The variable x is binary and has been expressed in terms of a mathematical formula below:

0,1 , 1,.., (3)ijx for i j n

The following limitations apply as well. If the object l has been assigned to the location k,

than there should be a location j that is different from k (j#k) for each object i that is different from l (i#l), to which the object i can be assigned. This limitation has been expressed in terms of the following mathematical formula (4):

1, #

( ) # (4)n

lk ijlk lkijj j k

x z z for i l

If the object l has been assigned to the location k, than there should be an object i that is

different from l (i#k) for each location j that is different from k (j#k), to which location j can be assigned. This limitation has been expressed in terms of the following mathematical formula (5).

1, #

( ) # (5)n

lk ijlk lkiji i l

x z z for j k

4. VISUAL MODEL DESIGN OF THE QUADRATIC ASSIGNMENT PROBLEM FOR PLANT LOCATION PROBLEM

Mathematical models can be created using programming languages for mathematical modeling. In our example we are going to use LINGO’s modeling language. Modeling languages enable us to structure a problem in a mathematical form of indexes and bases. The main char-acteristic of a modelling language is the ability to group similar entities into sets. Once the enti-ties have been grouped as a set, they are represented by the characteristics of that set. In such a way, entity groups can be represented using a single algebraic formulation. LINGO software is a program designed to effectively solve problems in mathematical programming (Walkenbach, 2003). Scheme 7 shows how an optimal value is computed using LINGO software.


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Scheme 7. Computation of optimal solution using LINGO software

Scheme 8. Model of computation for binary variables and optimal combinations of plant loca-tion assignments

15

16

17

18

19

20

21

22

29

31

38

39

40

41

45

48

55

161

168

175

193

200

255

265

266267

268

269

B C D E F G H I J K L M N O P

Assignment combinations Z(i,j,k,l) i j l k Assignment combinationsi,j,k,l computed by 0 1 1 2 1 l,k computed byVisual Basic 0 1 1 2 2 Visual Basic

0 1 1 2 3 Binary variables X(l,k)Binary variables Z(i,j,k,l) 0 1 1 2 4 computed by Lingocomputed by Lingo 0 1 1 2 5 X(l,k) l k

0 1 1 3 1 0 1 10 1 1 3 2 1 1 20 1 1 4 4 1 2 40 1 1 5 1 1 3 10 1 2 2 3 1 4 31 1 2 2 4 0 4 40 1 2 2 5 0 4 51 1 2 3 1 0 5 10 1 2 3 5 1 5 51 1 2 4 31 1 2 5 51 2 4 3 11 2 4 4 31 2 4 5 51 3 1 4 31 3 1 5 51 4 3 5 50 4 5 5 5

Export (copying) of binary variablesX and Z from Lingo to Excel

ENDDATA

Ass

ign

men

t co

mb

inat

ion

s

Ass

ign

men

t co

mb

inat

ion

s

DATA:@OLE("cabana2.xls","Z") = Z@OLE("cabana2.xls","X") = X

Scheme 8 shows the model for computing values of binary variables and optimal assign-ment combinations. We have created a graphic representation of the QAP problem using a syn-ergy between LINGO’s modelling language to an object-oriented programming model, VBA for Excel created by the authors of this work. We have defined the cells in the Excel table in form of vectors for the variables X and Z with the command Define Name (Walkenbach, 2003). We see in the Scheme 8 that the variable X has been assigned the cell range MP21:M45, and variable Z has been assigned the cell range G16:G265. We are able to copy the values from the LINGO program into the Excel table with the command Object Linking and Embedding (OLE), as shown in the scheme (Schrage, 2003). The values of binary variables X and Z computed in the LINGO program have been copied into the Excel table in such a way. The cell range corresponds to the


37

number of possible combinations. The variable Z indicates possible combinations of the prod-ucts of values of cost of the material flows (Table 2a) and distances between locations (Table 2b). There are 250 such combinations. The variable X indicates possible combinations of as-signing factories to locations and there are 25 of those. For easy reference, the rows where binary values equal 0 have been hidden.

We can see in the scheme 8 that optimal combinations have been assigned value 1. Opti-mal combinations of plant location assignments have been defined in the cell range M21:O45 having a variable value of X(l,k) = 1. It can be seen in the table that optimal combinations of plant location assignments are ({1,2], {2,4}, {3,1}, {4,3} and {5,5}. Cell range G16:K265 con-tains optimal combinations of the values of products in the matrices of material cost and dis-tances between locations. Optimal combinations of plant location assignments have been de-fined by the binary variable Z(i,j,l,k) given that Z(i,j,l,k) = 1.

Scheme 8 is the visualization of limitations set up in our mathematical model for solving the quadratic assignment problem. Cell range M21:O45 reveals that each factory l has been assigned exactly one location k that corresponds to the limitation (1) in the mathematical mod-el, and each location k has been assigned exactly one factory l that corresponds to the limita-tion (5). Cell range G16:K265 reveals that for each combination of the value l (representing factory l) and value k (representing location k) there is exactly one value i (representing factory i), which is different from the value l and to which each location k being different from the value j can be assigned. In this way we are able to visualize the limitation (4) in the mathematical model. Similarly, we are able to visualize the limitation (5).

Scheme 9. Visual model for QAP solution in Excel interface

1

2

3

4

5

6

7

8

9

10

11

12

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17

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25

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A B C D E F G H I J K L M N O

Material flow costs (M)R S 2 3 4 5 (1,2)*(2,4) 1 2 100 2001 2 10 10 4 (1,3)*(1,2) 1 10 100 10002 15 30 35 (1,4)*(2,3) 1 10 80 8003 20 40 (1,5)*(2,5) 1 4 50 2004 10 (2,3)*(1,4) 1 15 70 1050

(2,4)*(3,4) 1 30 70 2100R - row index (2,5)*(4,5) 1 35 40 1400S - column indeks (3,4)*(1,3) 1 20 80 1600

(3,5)*(1,5) 1 40 40 1600Distances betwen locations (D) (4,5)*(3,5) 1 10 90 900R S 2 3 4 5 Total costs = 108501 100 80 70 402 80 100 50 Z(i,j,l,k) i j l k X(l.k) l k3 70 90 1 1 2 2 4 1 1 24 40 1 1 2 3 1 1 2 4

1 1 2 4 3 1 3 11 1 2 5 5 1 4 31 2 4 3 1 1 5 51 2 4 4 31 2 4 5 51 3 1 4 31 3 1 5 51 4 3 5 5

T(i,j,l,k) M(i,l) * D(j,k) +

3

Min

imal

cos

ts

D(k,j ) ) =

Op

tim

al a

ssig

nm

ent

+

Mat

eria

l flo

w c

osts

Dis

tan

ces

bet

wee

n lo

cati

ons

5 5

Z(i,j,l,k) * (

Optimal plants / locations assignment

2 43 1

M(i,l) *M(r,s)*D(r,s)

4

Plant Location1 2


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Scheme 9 shows the visual model for QAP solution in Excel interface. Combinations of plant location assignments have been sorted based on the values of binary variables followed by se-lecting only optimal combinations as shown in the scheme 9. For instance, the cell range M21:O45 in the scheme 8 contains optimal combinations sorted based on the binary variable values X(l,k). The cell range M16:M20 in the scheme 9 contains selected optimal combinations of factory l to location k assignments. In a similar way, we have sorted possible combinations and selected optimal combinations of values of the products of material cost M(i,l) and distanc-es between locations D(j,k).

Cell range G1:O13 contains minimum cost computed in the QAP model. The values of vari-able Z(i,j,l,k) equalling 1 indicate optimal combinations of plant location assignments. The col-umn G contains indexed cells in the matrices of material cost and distances between locations that correspond to optimal combinations in the cell range G16:K25.

For instance, combination of values in the range G16:K25 for the factories i (1) and l (2) in the first row indicates position of the first row and second column in the material cost matrix revealing that the unit value of cost is 2. We can also see in the first row of the table that com-bination of values for the locations j (2) and k (4) indicates position of the second row and forth column in the distance matrix which reveals that the distance is 100. Column T(i,j,l,k) in the cell range D3 contains the product of these two values equalling to 200. The remaining values of products of optimal combinations of material cost and distances between locations have been computed in the same way.

Cell range D14 contains the sum of products which corresponds to the optimal value of minimum cost equalling to 10850. The obtained value of total minimum cost equals the value computed using LINGO software.

CONCLUSION Graphic representation of how the input data, programs as processes and output data are

structured enables us to understand the entire process of creating models for solving the quad-ratic assignment problems using information technology, and how the mathematical model is working. This allows managers to use models flexibly regardless of their size (number of varia-bles), directions of flows of goods or other flows (one direction or multiple direction), or kinds of problems. For instance, the model illustrated in this paper can be used for completely different kinds of problems which are solved using quadratic assignment method. Examples of such problems include the minimizing of flows of people and documents between the offices in a building, or minimizing the flows of material between the installations in a plant and the like.

A manager who is able to understand how a computing model for solving the quadratic as-signment problems is created and is working will know how to manage purchasing processes and use of equipment needed to implement quadratic assignment problem applications. In addition, it is important to know to decide hardware capacity with respect to exponential accre-tion in variables, after adding each new vector in a quadratic matrix. For instance, a student version of LINGO software will work at 8x8 matrix level; after that you will need to purchase a professional version of the software. Problems that occur in the real-life require matrices of larger size, which calls for an evaluation of maximum matrix size. In other words, it is important to evaluate the level of profitable investing in new hardware.

In this work, we have proven the hypothesis that methodological approach to visualization and systematic use of modelling programming languages in Excel interface enables us to visual-ize the entire process of creating models for solving the quadratic assignment problems. We have proven our hypothesis through an example of using LINGO’s modelling language to solve a problem of quadratic assignment of distribution centres to locations. We have compared the result obtained using the QAP method (10850) with the result computed by a random, uninten-


39

tional, or unplanned assignment (12000) where, for instance, distribution centres could have been assigned to locations based on their ordinal numbers. Our comparison revealed a de-crease in cost of more than 10%. LITERATURE Burkard, R.E., Cela, E., Pardalos. P.P. Pitsoulis L.S. (1998), “The Quadratic Assignment Prob-

lem”, Handbook of Combinatorial Optimization, Vol. 3, pp. 241-337.

Ciegis, R., Dilius, A., Mikalauskiene, A. (2015), “Evaluation of Economic Growth in Terms of Sus-tainability”, Transformation in Business and Economics, VOL. 14, NO. 1 (34), pp. 105-126.

Heizer, J., Render, B. (2004), Operations Management, 7th Issue, Prentice Hall, New Jersey.

Kaplikski, O., Tupenaite. A. (2011), “Review of the Multiple Criteria Decision Making Methods, Intelligent and Biometric Systems Applied in Modern Construction Economics”, Transfor-mation in Business and Economics, Vol. 10, No. 1, pp. 166-182.

Porter, M., E., Rivkin J., W. (2012), “Choosing the United States”, Harvard Business Review, Vol. 90, No. 3, pp. 81-88.

Schrage, L. (2003), Optimization Modeling with LINGO, Lindo Systems Inc. Streimikiene, D, Mikalauskiene, A., Barakauskaite-Jakubauskiene, N. (2011), “Sustainability

Assessment of Policy Scenarios”, Transformation in Business and Economics, Vol. 10, No. 2, pp. 168-165.

Sunderesh, S. Heragu, S. (1997), Facilities Design, PWS Publishing Company, International Thompson Publishing Inc.

Walkenbach, J. (2003), Excel 2003 Bible, Willey Publishing Inc., Indianapolist, Indiana.

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‘

The Importance of Tourism Websites to Tourism Services And Assurance Of Their Competitiveness

VILIJA BITĖ FOMINIENĖ1

1 Associate Professor, PhD., at the Department of Sports management, Economics and Sociology, Lithuanian Sports University, Kaunas, Lithuania, e-mail: [email protected]


Received Jun 03, 2016 Received in revised from August 21, 2016 Accepted October 22, 2016 Available online 15 December 2016

In the recent years, a considerable number of scientists have shown interest in the significance of innovations in tourism sector, have analyzed the impact of information technologies to tourism services and their competitiveness. Unfortunately, such studies in Eastern European countries, including Lithuania missing and it is also un-clear what the structure of the website features are crucial to tour-ism service customers and could determine the competitiveness of such a service. The aim of this research is to evaluate website fea-tures from the internet tourism service users’ point of view. A tour-ism website survey, analyzing the characteristics of electronic ser-vices revealed that for potential tourism service buyers the most important tourism website e-services are Interactivity and Function-ality of the page. In descending order of importance there are these features: website interaction, mailing list subscription, online reser-vation and payment, information safety and security. Least im-portant e-services for tourism website are Design and Information. These are the following (from least important): location features, website map, company information and website customization.

JEL classification: L83, D24, Z310, Z330

DOI: 10.14254/1800-5845/2016.12-4.4

Keywords:

Tourism sector, innovations, e-comerce, tourism service, competitiveness.

INTRODUCTION

In many countries of the world tourism is regarded as one of the most rapidly growing sec-tors of services, the significance of which is constantly increasing. Relying on the analysis of the World Tourism Organization, in 83% of countries tourism is among the five sectors providing income and in 38% of countries it is the major source of a foreign currency. This sector attracts 4.7% of all investments, creating about 260 million working places and 9% of world’s national product. The European Union is not an exception, here tourism industry produces more than 4% EU GDP with about 2 million functioning organizations. A considerable attention to tourism and its development is devoted in Lithuania, particularly, regarding SWOT analysis results for tour-ism sector which evidence that there are more opportunities for the development than weak-nesses and threats in Lithuanian tourism segment. All these factors would enable us to make some presumptions that tourism is widely spread phenomenon and essential factor determin-ing economic development while seeking competitive benefits both on regional and on global


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scale, aiming to retain, protect and strengthen competitive positions on the global market that has been becoming more and more competitive with every day (Fernando and Long, 2012).

Nevertheless, even with tourism developmental potential in Lithuania and alternating con-ception of quality standards for tourism as a service sector, the development of this sector is not sufficient in the country. Such a situation is partly impacted by strong competition in tour-ism, the object of which is the product produced by a tourism service and offered to customers. In most cases the competitiveness of such service depends on the strategy of such service preparation and implementation. This strategy could imply various factors – price, quality, varie-ty, demand and innovations (Beniušienė and Svirskytė, 2008). Development of competitive tourism services in Lithuania is focused on formation of public tourism infrastructure, improve-ment of tourism services quality, extending their variety, marketing services and innovations (Jasinskas et al., 2014; Barkauskienė and Barkauskas, 2012).

It is impossible to function in the market as a sole tourism service founder and provider, apart from this, the competitive strategies which functioned in the past are by far insufficient today. Application of new information and communication technologies in tourism business, the significance of innovations has been growing alongside and becoming the factor providing and supporting long term competitive advantages (Beniušienė and Svirskytė, 2008).

Information technologies which are usually related to the Internet development, are consid-ered the foundation in tourism business innovations in Lithuania (Vaškaitis and Armaitienė, 2014). However, the increasing numbers of tourism services providers on the Internet market force to look for the most efficient ways of attracting and retaining customers. Internet websites of appropriate quality, satisfying customers’ demands are of vital significance in tourism ser-vices selling process (Spremić and Strugar, 2008). These websites become not only as commu-nication mediators with customers but also impact the growth of competitiveness in tourism sector (Loiacono et al., 2007).

In the recent years, a considerable number of scientists have shown interest in the signifi-cance of innovations in tourism sector (Booyens and Rogerson, 2016; Vaškaitis and Armaitienė, 2014), have analyzed the impact of information technologies to tourism services and their competitiveness (Cohen and Olsen, 2013; Buhalis and O'Connor, 2005; Buhalis and Deimezi, 2004), researched characteristics of internet sites providing tourism services (Iliachenko, 2006). Unfortunately, such studies in Lithuania missing and it is also unclear what the structure of the website are crucial to tourism service customers and could determine the competitive-ness of such a service. The aim of this research is to evaluate website features from the inter-net tourism service users’ point of view.

1. LITERATURE REVIEW Information technologies (IT), are innovations which have operated upon the great changes

in tourism sector in the recent decades and contributed to competitiveness in tourism services. These technologies opened widely the doors for all subjects in tourism sector giving them op-portunities to present themselves on the Internet, offer their products and services, improve their activities, unite and increase the efficiency of inside business processes, communicate in a more constructive manner with all concerned subjects, including governmental institutions and potential clients (Frew, 2000; Buhalis and Deimezi, 2004; Brown and Kaewkitipong, 2009). However, the major focus in implementing and using new IT in tourism industry, is set on assur-ance of productive cooperation between service a provider and a customer (Sharma and Car-son, 2001).

A long ago as in the year 2002, 80000 of Australian population ordered tourism services via the Internet and half of these people surfed the Internet at work searching for information on tourism services (Murphy and Tan, 2003). Some years later, in 2005, about 90% of the USA


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population used IT when choosing travel destination and about 50% of developed countries population used IT for planning and choosing their holidays, relying on the information on the Internet (Buhalis and O'Connor, 2005). Information technologies impact Lithuanian people to a great extent too – in the year 2009 more than 40% of population searched information on trav-eling on the Internet, and these numbers have been growing with every year.

Current rapid development of IT and especially the Internet, contribute to the development of prompt “worn out” of tourism service (Benckendorff, 2006), producing new generation of tourists which are more competent and look for exceptional value for money (Buhalis, 2008). Tourists that tend to use the Internet for tourism services are usually people aged 26-55, having higher education (Morrisonn et al., 2001). What is more, it has been found that such customers are becoming more and more impatient, they do not tend to wait long before taking their deci-sion and their needs are becoming more and more sophisticated (Bojnec and Kribel, 2005).

These tendencies impact that electronic space has become the key to the successful pro-ceedings in modern competitive tourism sector (Buhalis, 2008). Visual, informative space - the Internet creates favorable conditions for tourism organizations to (Pan and Fesenmaier, 2000), thus, the internet website of every single tourism organization becomes essential in tourism services selling process (Spremić and Strugar, 2008).

Nevertheless, competitive benefits are not guaranteed while shifting a part of business to the Internet. The quality of the Internet website is vital, the quality and the standards, satisfying customers’ expectations in all possible ways. In this Internet age customers can choose from numerous almost similar websites, thus, the foremost significant factor is the quality assuring that a customer will not search for further alternatives. The increase in direct buying and the growth of service competitiveness could be impacted only by such websites of tourism compa-nies’ which manage to become the mediators of communication with customers and provide thorough information (Loiacono et al., 2007).

Scientific research of Internet services is rather scarce for the reason that the evaluation on the quality is vague due two several reasons – conditions of business surroundings and re-search methods applied (Davidavičienė and Tolvaišas, 2011). Conceptual discussions on what has to be evaluated are also frequent in this case. These discussions imply two tightly correlat-ed aspects – the perceived internet website quality and internet website creation quality (Guseva and Ruževičius, 2006). It should be pointed out that scientists distinguish quite differ-ent elements for website quality evaluation.

Some authors (Stockdale, 2006) assert that the major element of a website, helping to re-tain customers is website design – arrangement of texts, pictures, graphics, views using sound, movement and, in the future, even smell technologies. This is one of the major quality charac-teristics of a website, making customers to visit it again (Rosen and Purinton, 2004). This signif-icant marketing index does not only help create more business opportunities but also perfects a company’s image (Law and Leung, 2002). Other authors state that the major element of an internet website could be named its interactivity that is usually understood as website’s proper-ty to compose communication of various flows between website users and an organization per-sonnel, assuring information availability via the Internet and making deals via the Internet (Hapenciuc and Condratov, 2007).

Website information is another essential factor which has to be mentioned. The research performed together with Swedish tourism services users, which are greatly concerned on a def-inite tourism service availability on the information presented on the internet website and other related to it information, evidences this (Iliachenko, 2006).

Clear website navigation, information arrangement, easy to use, the content of information, help received in a real time span, reliability, safety assurance, design and easy search, etc.


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should also be mentioned as quality elements of an internet website (Davidavičienė and Tolvaišas, 2011).

Although websites of tourism organizations do not have any commercial significance, they could enlarge sales at least up to 33%, whether customers’ advice related to internet site is-sues will be taken into consideration (Law et al., 2009). They could also serve for presentation of new or differentiated tourism products on the market and for attraction of new customers (Buhalis and Michopoulou, 2011) who over the time period will become “loyal” to the same trade mark (Hashim and Murphy, 2007). It goes without saying that well planned and presented service system could boost the volume of sales, improve travel site “reputation” and competi-tiveness.

Meanwhile as IT applications have more than a few benefits in tourism, it was proven that it can also have several drawbacks. Research results show that a part of tourists does not buy tourism service via the Internet, as this manner of buying eliminates personal service, causes worries on safety issues, some customers lack IT using skills, are short of time or do not have technologies at a hand. (Hsu et al., 2004). Thus, it should be agreed that tourism organisations, seeking to satisfy customers demands, have to collect information on clients at any stage of service selling process - until and after they visit a website – as this would allow them to better realize customers’ behaviour, choices, problems (Buhalis and O‘Connor, 2005) and that means to react to the changes in customer’s opinions. Such analysis would help to improve the quality of Internet websites of tourism companies and consequently the efficiency of new services presentation to customers, contribute to making their final choice and with time going - increase the volume of sales and competitiveness of service. 2. RESEARCH MATERIALS AND METHODS

Quantitative research was applied for evaluation of significance of internet site properties to a customer in tourism service. The research analyzed tourism customers’ opinions on elec-tronic services - characteristic properties of internet websites, determining the process of tour-ism service on the Internet and competitiveness of tourism service.

The research instrument is questionnaire. The first part of the questionnaire is designated for establishment of social demographic features of the researched - gender, age, education, internet using skills. The second part is for measuring the significance of properties of tourism service websites. The respondents were given 20 statements on internet website elements, structured into 4 qualitative site factors and features:

F1 – Interactivity (Online order tracking, Product personalization, Customised product search, Online reservation and payment, The exystence of selective system, User-friendly inter-face, Information safety and security, Website interaction);

F2 – Design (Website map, Location features, Website customisation); F3 – Information (Company info, Product info, Tourism info, Direct links); F4 – Functionality (Chat/customer forum/Guestbook, Personal info storage, Mailing list

subscription, Browser efficiency, Multimedia).

Statements revealing the significance of website properties were given for assessment in 10 Likert-type degree, where - 1 is - insignificant and 10 - significantly important. The signifi-cance of factors distribution was evaluated up to social demographic factors.

The research respondents were selected using simple random selection method. Only such respondents were asked who indicated that they have travelled in the last 3 years and have used internet websites when buying a tourism service. 202 questionnaires were filed.

Women, aged 20-35 years old and having higher education constituted the greater part of the researched (Table 1).


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Table 1. Distribution of respondents in the total sample.

The demographic data Distribution of respondents (%, n)

Age Younger than 20 years old 12.4%, n=25

20-35 years old 43.1%, n=87

36-50 years old 24.3%, n=49

More than 50 years old 20.2%, n=41

Gender Male 35.1%, n=71

Female 64.9%, n=131

Educational Background Secondary Education 5.4%, n=11

Professional Education 26.7%, n=54

Incomplete Higher Education 16.3%, n=33

Higher Education 51.5%, n=104

The larger part of the respondents have been using the Internet longer than for five years

(85,1%, n=172) and more than a half (56,9%, n=115) indicated that they use the Internet on a regular basis i.e. more than 4 times per week.

The research was performed in October 2015 – February 2016.

The results obtained were processed with Excel, responses percentage and average were calculated. Percentage of responses frequency to questionnaire statements was calculated. Difference in responses frequency between groups was calculated with x² („chi” square) crite-ria. Their critical boundaries were established with significance level p<0,05. Calculations were processed with SPSS 17.0 program.

3. RESEARCH RESULTS AND ANALYSIS The research revealed that the primary objectives of respondents to use tourism organiza-

tions online site most are relevant to a specific and needed tourist information search (48.5%, n=98) and the willingness to acquire the desired service (49.1%, n=99). With no intent tourism organizations pages usually monitors only 2.4% of the respondents.

The analysis of essential features of e-service website revealed (Table 2) that the essential factors of the tourism website e-services for respondents who wish to purchase travel services online is the interactivity (M = 6.6 points) and the functionality (M = 6.6 points) of the page. For potential buyers of tourism product/service it is very important that the website gives an opportunity to contact directly with the organization (website interaction, M = 7.6) and directly, without time-consuming, purchase the item online (online reservation and payment, M = 7.4). Also, respondents will choose those websites that submit for consumer selective system (M = 7.2), which allows understandable and easily choose a service according to the desired criteria and which are able to convince the customer service in search of information security on web-


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site (M = 7.3). This opinion is also confirmed by others. It is argued that website features that improve the customer’s website safety and security should be of concern to a tourism organiza-tion, offering an online payment facility (Chu, 2001).

Table 2. The most important features of the electronic services classification according to the factors.

Item Mean Std.Dev. F1 Interactivity 6.6 Online order tracking 4,4 1,5 Product personalization 5,3 1,9 Customised product search 6,2 2,1 Online reservation and payment 7,4 0,9 The exystence of selective system 7,2 1,6 User-friendly interface 7,0 1,3 Information safety and security 7,3 1,3 Website interaction 7,6 1,5 F2 Design 4.3 Website map 3,8 1,4 Location features 3,5 2,1 Website customisation 5,5 1,7 F3 Information 6.1 Company info 5,4 2,1 Product info 7,0 2,2 Tourism info 7,1 1,9 Direct links 4,6 2,0 F4 Functionality 6.6 Chat/customer forum/Guestbook 6,6 2,0 Personal info storage 6,3 1,5 Mailing list subscription 7,5 1,8 Browser efficiency 7,2 2,1 Multimedia 5,6 1,1

It is also revealed that for respondents are important mailing list subscription to receive personalized information about offers, campaigns, news, etc. (M = 7.5), and the ability to oper-ate the website safely and efficiently in any browser (Browser efficiency - M = 7.2). How im-portant website features the respondents also indicated the ability to get other relevant tourism information on the website (Tourism info - M = 7.1), and Product info (M = 7.0) - ability to gather in-depth information about tourism products/services.

As the less important website features for respondents were distinguished Location fea-tures (M = 3.5) - website's ability to show customer's current location within the website and Website map (M = 3.8) - access the website map, i.e., a hierarchical diagram that shows sche-matically the website's structure. These features attributed to the design factor, which is deter-mined as the least important issue for consumers. And even thought this study demonstrated the following results, many scientists (Wu et al., 2013, Chu, 2001) design factor distinguishes as particularly important to consumers and their decision. Design is a primary image which is visible to consumer and which may lead to its self-determination. It becomes important to have an attractive webpage. Therefore it must be carefully thought out and constantly updated ac-cording to the changing consumer’s needs. Such data mismatch can be explained by both con-


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sumers’ cultural and demographic differences or consumption experience differences (Guseva, 2008, Iliachenko, 2006).

Direct links to other websites providing relevant tourism information also are not so im-portant factor for potential tourism service buyers (M = 4.6).

A similar study was conducted in Sweden in 2005-2006 (Iliachenko, 2006). Results of 199 respondents showed that there is no considerable differences between the needs of the re-spondents in Lithuania and Sweden. For Swedish respondents, like Lithuanian, the most im-portant tourism website e-services are Interactivity-related and Information-related. As the most important features of these factors respondents from Sweden has identified the following: tour-ism information, product information, online reservation and payment, customized product search, website interaction and direct links. Least important e-services for tourism websites, as well as in this study, were Design and Functionality related.

The analysis of different factors importance to different users according to their socio-demographic characteristics has revealed certain differences.

Assessing site interactivity, i.e. site's ability to create a variety of flow communication be-tween site users and the organization's staff, ensuring the accessibility of online information and transaction execution on the Internet (Hapenciuc and Condratov, 2007), it is found that the site's interactivity is more important for older individuals - 50 years and older (M = 6.6), for per-sons with incomplete higher education (M = 6.8) and for women (M = 6.9) (Figure 1).

Figure 1. The distribution of website interactivity factor on age, educational background and gender aspects

4,7

5,8

6,8

4

0

1

2

3

4

5

6

7

8

Educational…

6,9

5,8

0

1

2

3

4

5

6

7

8

Female Male

Gender

Assessing functionality of the website, which is usually defined as a site feature that allows to facilitate access to the services, and its elements are related to site usage comfort, disclosed that it is more important for younger - up to 20 years of age (M = 7.7) and for persons between 20-35 (M = 7.4) and for those who have secondary and professional education (M = 7.5).


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Figure 2. The distribution of the importance of website technical factor on age and educational background aspects

Assessing website design, i.e. the initial image seen by users, in other words, it is what re-late to the appearance and design of the website, which is appealing and pleasing the customer : colors, funds, local ability, maps, revealed that design is more important for persons in 50 years of age and over, compared to other age groups (Figure 3).

Figure 3. The distribution of the importance of website design on age aspect

The analysis of separate features of Design factor revealed that there are no differences

compared respondents by educational groups and compared by gender. However, other authors presents opposite results. Cyr and Bonanni (2005) investigation revealed that women, com-pared with men, tend to place more emphasis on the importance of design, and Wu et al., 2013 argues that Design for younger consumers is important in making the decision to purchase online.


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Assessing website information, i.e. site ability to provide a consumer with relevant and time-ly information about services or other relevant to the customer’s tasks information, disclosed that website information is more important for women than for men (accordingly 6.8 and 5.4) and for persons with secondary education (M = 6.7) compared with other education groups.

CONCLUSION One of the ways to compete in the age of information technology is a website that focuses

on the role of the exchange of information between the two sides - the buyer and seller. Howev-er Tourism website not only represents the organization, but also gives users the ability to pur-chase tourism services. Electronic commerce success, based on trade in the internet space and therefore the competitiveness of tourist services due to various website elements. Analyzing them is usually covers the following main website e-service factors: Interactivity, Design, Infor-mation and Functionality.

The research revealed that the primary objectives to use tourism website for potential tour-ism service buyers in Lithuania most are relevant to a specific and needed tourist information search and the willingness to acquire the desired service. Tourism website survey, analyzing the characteristics of electronic services revealed that for potential tourism service buyers the most important tourism website e-services are Interactivity and Functionality. In descending order of importance there are these features: website interaction, mailing list subscription, online reser-vation and payment, information safety and security. Least important e-services for tourism website are Design and Information. These are the following (from the least important): location features, website map, company information and website customization.

The analysis of meaning for users of individual factors of the tourism organization websites on the social demographic aspect factors have been identified certain differences. It is found that the site's interactivity is more important for older individuals, for persons with incomplete higher education and for women. Website functionality is more important for younger tourism service buyers and for those who have secondary and professional education. Design is more important for persons in 50 years of age and over and website information is more important for women and for persons with secondary education. REFERENCES Barkauskienė, K., Barkauskas, V. (2012), „Kauno regiono gyventojų turizmo produktų

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Olga V. Kiseleva, and Iuliia S. Pinkovetskaia / Montenegrin Journal of Economics,

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‘

Investment in Entrepreneurship: Evidence from Russia1

OLGA V. KISELEVA1, and IULIIA S. PINKOVETSKAIA2 1 The study was financially supported by the Russian Humanitarian Scientific Foundation grant, project № 16-12-73002 2 Associate Professor of the Economic analysis and state management department, Ulyanovsk State University. 3 Associate Professor of the Economic analysis and state management department, Ulyanovsk State University. E-mail: [email protected]



The aim of the study was to analyze patterns, describe the amount of investment in small and medium entrepreneurship by types of entrepreneurial structures and types of economic activities. This was done by empirical testing of hypotheses about the presence of a differentiation value of investment and the possibility of describing the current distributions in regions of the Russian Federation with use of economic-mathematical models. When you build models by using the methodology of density functions of normal distribution. As input data we used official statistical indicators characterizing the set of small enterprises, medium enterprises and individual entrepreneurs in all regions of the Russian Federation. The compu-tational experiment was based on building two classes of models describing the volumes of investments accounted, respectively, to one entrepreneurial structure and one employee, and five main types of activities in which specialize small and medium enterprises, individual entrepreneurs. It is proved that the volume of investment, both in terms of entrepreneurial structure and one of its employees differ significantly depending on the number of employees of entre-preneurial structures, regions of our country. Also proved that there is significant differentiation in the levels of investment by kinds of economic activities. The developed model and the resulting patterns can be used to solve a wide range of tasks monitoring of business activities, design of development and forecasting this sector of the economy at Federal, regional and municipal levels of government.

JEL classification:

G11; G31; L26 DOI:

10.14254/1800-5845/2016.12-4.5 Keywords:

small enterprises, medium enterprises, individual entrepreneurs, investment, regions of the country, types of economic activity

INTRODUCTION

Development of subjects of small and medium entrepreneurship is based on efficient use of investment. That is why the justification of investment volumes seem to be an urgent prob-lem at the present stage of formation of the entrepreneurial sector of the national economy.

Small and medium entrepreneurship begin to create in the Russian Federation since the nineties years of the twentieth century. Before that in the Soviet Union dominated state-owned enterprises and organizations. In the nineties years, began the transformation of the economy associated with the emergence of private entrepreneurs. In addition, there was a significant


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imbalance with a predominance in the economy of large companies, associations and industrial complexes. They usually were not effective.

In the past time entrepreneurship has experienced significant growth. To date, the role of entrepreneurial structures (small and medium enterprises, individual entrepreneurs) in our country increased significantly. In this sector of the economy in 2010 worked more than 23 million people. The total number of entrepreneurial structures was about 4.5 million. The vol-ume of production and services has reached to 30.8 trillion rubles (Federal service of state, 2016).

One of the significant trends of contemporary development of world economy is the increas-ing role of small and medium businesses, as more mobile and flexible forms of organization of business life. This is evidenced by the increase in the share of small business in GDP of the country, growth in the number of employed in small business and entrepreneurship. Over the past 15 years the share of small and medium business in Russia's GDP has nearly doubled, however it is still not comparable with the indicator in developed countries. Thus, the share of small and medium business in the GDP of the European countries reached 58% in the U.S.A. – 50%. However, small and medium entrepreneurship in our country have not yet reached the level characteristic of developed countries that show, in particular, recently published research results (National report “Global entrepreneurship monitor”, 2016). In Russia it is only 21%. The dynamics of employment in the sphere of individual entrepreneurship over the last 15 years, unstable. Their share in total employment in Russia does not exceed 10%.

Despite the fact that the dynamics of investment of small enterprises in fixed capital is pos-itive, so the investment growth of small enterprises in fixed assets in 2012 year compared to the previous year increased by 20,8%, in 2013 year - 10,2%, in 2014 year - 15,6%, one of the major problems of entrepreneurship development are lack of own funds, high interest rates on loans, and difficulties in obtaining loans (Kiseleva et al., 2015).

The need for a substantial increase in the turnover of entrepreneurial structures was re-peatedly observed in the messages of the President to the Federal Assembly and Government decisions. At present, therefore, it is important to develop evidence-based recommendations for the further development of entrepreneurship in the country, increase of its role in the national economy. The increase flow of investments in the sphere of small business is an effective way of work. However, public financing and realization of target programs are not always able to provide a sufficient volume of investment resources, aided by the banking sector.

In this regard, among the urgent scientific problems extends the definition of the regulari-ties characterizing the investment needs for the development of entrepreneurship at the pre-sent stage. Common problems of investments into the fixed capital of enterprises and organiza-tions are considered in the works of many authors, among which are (Bystrov et al., 2008; Usowicz, 2011; Nurmukhametov, 2009; Erden, Holcombe, 2005; Voss, 2002). Some aspects of the investment policy of small and medium entrepreneurship represented in a number of mon-ographs and papers (Pichler, 2002; Domar, 1957; Van Horn, 2003; Gitman, Jonk, 1997; Skuras et al., 2008; Anson, 2003). Note that these problems are not adequately reflected in research-es of russioan economists, you can only specify works (Livshic et. al, 2000; Regional aspects of functioning and development of small entrepreneurship in Russia, 2010; Vilenskiy, 2011; Zlobin & Plakhova, 2008). The modeling of investment were considered in the monograph (Micek, 2011).

Criteria for small and medium enterprises was established in the Federal law “On the de-velopment of small and medium entrepreneurship in the Russian Federation” dated 24.07.07 year № 209-FZ (2007). The main criterion is the number of employees that for small enterpris-es should not exceed 100 persons, and for the average enterprise ranges from 101 to 250 people. Small and medium entrepreneurship in accordance with paragraph 1 of article 3 of the act also include individual entrepreneurs. Further, in this paper three types of business entities


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– small enterprises, medium enterprises and individual entrepreneurs are called the entrepre-neurial structure. Thus, entrepreneurship is seen quite widely, which is consistent with the con-cept presented in the work of P. Reynolds, N. Bosma, E. Autio, S. Hunt, N. De Bono, I. Servais, P. Lopez-Garcia, N. Chin (Reynolds et al., 2005). Note that entrepreneurial structures characterize by the same types of economic activities, they compete on the same markets, have largely the same production technology, work as risky activities. Individual entrepreneurs differ from small and medium enterprises on their organizational-legal form.

The paper discusses the results of research the authors devoted to the analysis and evalu-ation of the current volumes to date in investment of small and medium entrepreneurship, the empirical testing of hypotheses about the presence of a differentiation values of investment and the possibility of describing the existing regularities with the use of economic-mathematical models. As indicators of investment in fixed capital of entrepreneurial structures are consid-ered, the volume of investments in the calculation for one such structure and for one its worker.

1. METHODOLOGICAL APPROACH AND ALGORITHM In the present study, the authors have put forward the following hypotheses. Hypothesis 1.

The volume of investments, both in terms on one entrepreneurial structure and one of its em-ployees differ significantly depending on the number of employees (size) of entrepreneurial structure, regions of the country, and types of economic activities, on which specialize entre-preneurial structures. Hypothesis 2. As mathematical models that can be used to estimate the volumes of investment in entrepreneurial structures is appropriate to apply the functions densi-ty of normal distribution.

The aim of the study was to develop methodologies and tools of analysis volumes of in-vestment in entrepreneurial structures. Thus were solved the following tasks: proved methodi-cal approach and algorithm of the study, developed economic-mathematical models describing the volume of investments in 2010 year, made the analysis of the obtained models and formu-lated the regularities that characterize the achieved to date the level of investments.

The research methodology is based on the consideration of aggregates of entrepreneurial structures in all regions (republics, territories, areas) of the country. The need for this approach due to the following objective prerequisites. Entrepreneurial structure, acting as an independent economic entity, defines its goals and objectives, based on the specific situation, and leads a risky economic activity. In each of the regions of the Russian Federation the number of these entrepreneurial structures is very large. Description of individual enterprises (entrepreneurs) and their subsequent aggregation is a very complex and time-consuming process. Therefore it would be logical to build models for a set of entrepreneurial structures, combined by territorial and industrial characteristics.

The author's algorithm for estimating the levels of investment in entrepreneurial structures included the following steps:

formation of the information base, describing for each region of the country and type of economic activity the number of small enterprises, medium enterprises and individual en-trepreneurs, number of its employees and volume of investments in fixed capital;

determination of the volume of investments on the aggregations, respectively, small enter-prises, medium enterprises, individual entrepreneurs in every region on the types of activity;

the calculation of the amount of investment attributable to one entrepreneurial structure and one of its employees for each region of the country on the types of activity;

constructing functions density of normal distribution, approximating the values of invest-ment volumes accounted, respectively, for one small enterprise, medium enterprise and


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one individual entrepreneur, and the functions that describe the amount of investment per one of its employee (first class models);

constructing functions density of normal distribution, approximating the values of invest-ment volumes attributable to one entrepreneurial structure by major types of economic ac-tivity (second class model);

assessment of the quality constructed functions on the accepted criteria;

definition of laws of development entrepreneurial structures based on the developed func-tions.

2. DATA OF RESEARCH AND INSTRUMENTS

While constructing models as input data were used the indicators characterizing the sets of small enterprises, medium enterprises and individual entrepreneurs in all regions of the Rus-sian Federation. Considered data for 21 republics, 9 territories and 46 provinces of the country. To exclude double-counting not considered data on Autonomous districts and Autonomous re-gion. When modeling authors use the statistical data for 2010 year presented in the report on continuous monitoring activities of small and medium entrepreneurship made by Federal ser-vice of state statistics (2010).

Entrepreneurial structures currently specialize on five main types of activities (The System of “Tax help”, 2016). The first type is manufacturing, specialized in the manufacture of food products, textile and garment products, as well as other similar production. The construction includes entrepreneurial structures, leading new construction, reconstruction, major and cur-rent repairs of buildings and constructions, including individual construction. The third type of activities - wholesale and retail trade, repair of motor vehicles, motorcycles, household goods and personal items. In the future this activity will be called in short - trade. Entrepreneurial structures of transport and communications include the enterprise in land, water, air types of transport and all types of enterprises of telecommunications and postal activity. The fifth type of activity involves not only a variety of real estate transactions, lease and provision of related ser-vices, and also scientific activities, as well as a range of support services. Table 1 shows data reflecting the total number of entrepreneurial structures included in the general population in conducting researches, investment volumes and volumes of their products.

Table 1: Whole characteristic of entrepreneurial structures

Small enterprises

Medium enterprises

Individual entrepreneurs

Entrepreneurial structures

Quantity, thousand

units 1644 26 2927 4597

Volume of investment, billion rubles

520,3 254,3 136,8 911,4

Turnover, trillion rubles

18,94 7,42 4,49 30,85

Table 2 presents indicators that describe the distribution of entrepreneurial structures and

volumes of investment by types of economic activity.


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Table 2: Distribution of entrepreneurial structures and volumes of investment

Quantity of entrepreneurial structures, thousand units

Volume of invest-ment, billion rubles

Trade 2185,4 145,3 Manufacturing 322,8 123,5 Construction 267,7 109,8

Transport and communications 430,6 67,7 Operations with real estate 666,6 231,4

Other 723,9 233,7 During the research were used methods of logical, economic-mathematical analysis, sta-

tistics. For the solution of tasks and information processing applied were used computer pro-grams “Statistica”, “Microsoft Excel”, “Mathcad”. Verification of the obtained functions was conducted according to Pearson (Kremer & Putko, 2002), Kolmogorov-Smirnov, Shapiro-Wilk (Vukolov, 2004). 3. FIRST CLASS OF THE DEVELOPED MODELS

The developed models, describing the values of investment volumes for all regions of the country accounted, respectively, for one small enterprise, medium enterprise and one individual entrepreneur discuss further (all the following formulas and tables given in the paper are get by the author): investments in fixed capital per one small enterprise ( 1x , billion rubles)

03,02

)45,0(

11

21

216,0

6,7)(

x

exy

(1)

investments in fixed capital per one medium enterprise ( 2x , billion rubles)

93,342

2)74,112(

291,5

5,331)( 22

x

exy (2)

investments in fixed capital per one individual entrepreneur ( 3x , billion rubles)

4

23

108,42

)058,0(

332022,0

69,1)(

x

exy

(3)

Models, reflecting the value of investments (in thousands rubles) in all regions of the coun-

try per one worker employed, respectively, in small enterprises, medium enterprises and for individual entrepreneurs is provided below:

investments in fixed capital per one employee of small enterprise ( 4x )


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58

9,2742

)56,47(

44

24

258,16

855)(

x

exy

(4)

investments in fixed capital per one employee of medium enterprise ( 5x )

3

25

1042,12

)22,93(

55278,37

2357)(

x

exy

(5)

investments in fixed capital per one employee, that work with individual entrepreneur ( 6x )

79,732

)56,22(

66

26

259,8

3,589)(

x

exy

(6)

The developed models allow to estimate the average on regions of the country the value of

investment accounted respectively for one small enterprise, medium enterprise and individual entrepreneur and one its worker. In addition, with the use of models can be determined the range of values of these indicators.

4. SECOND CLASS OF THE DEVELOPED MODELS The developed models, describing the value of investments (in thousands rubles) in all re-

gions of the country, attributable to one entrepreneurial structure by main economic activity are presented below: for entrepreneurial structures of the trade

08,7652

2)62,637(

266,27

5,1657)( 77

x

exy

(7)

for entrepreneurial structures of the manufacturing

31067,332

2)99,3658(

254,183

14,12257)( 88

x

exy

(8)

for entrepreneurial structures of the construction

31015,332

2)23,3459(

207,182

10725)( 99

x

exy

(9)

for entrepreneurial structures of the transport and communications

31039,32

2)47,14010(

219,58

4420)( 1010

x

exy

(10)

for entrepreneurial structures, specialize on the operations with real estate


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31073,332

2)53,30711(

265,183

67,8666)( 1111

x

exy

(11)

Models (7)-(11) allow us to estimate the average on main types of economic activity the

value of the investments attributable to one entrepreneurial structure, in the regions of the country. In addition, with the use of models can be defined the ranges of values investment for each of the activity types. 5. MODEL VALIDATION IN TERMS OF QUALITY

Logical and statistical analysis showed that all developed models are well approximate the original data over the entire range of their changes. Table 3 shows the estimated values of the main statistics for the three quality criteria. A comparison of these calculated values showed that the statistics for the Pearson criterion is less than the table value of the criterion equal to 4,61. Similarly, the values calculated from Kolmogorov-Smirnov test is less than table value of 0.15. Statistics Shapiro-Wilk is close to one. Thus, for all considered criteria, the developed models are of high quality and can be used to describe the studied regularities.

Table 3: The calculated values of statistics by the quality criteria

Number of function

The calculated values by the quality criteria

Kolmogorov-Smirnov Pearson Shapiro-Wilk

(1) 0,06 4,03 0,97 (2) 0,08 3,76 0,95 (3) 0,04 3,39 0,94 (4) 0,04 3,10 0,98 (5) 0,09 3,19 0,95 (6) 0,08 3,83 0,91 (7) 0,08 2,91 0,98 (8) 0,07 3,73 0,98 (9) 0,05 1,48 0,95

(10) 0,03 2,87 0,97 (11) 0,06 3,54 0,96

Since all the developed economic-mathematical models good approximate the original data and have a high quality of the accepted criteria, we can conclude that hypothesis 2 about the feasibility of using the functions of density normal distribution to estimate the volume of in-vestment in entrepreneurial structures found in the process of research confirmed. 6. ANALYSIS OF THE DEVELOPED MODELS

A feature of the functions of density normal distribution (Ventsel, 2001) is the fact that on them without complex calculations can be defined average values and ranges of variation of considered parameters. These intervals for most (68%) regions of the country computed from the average squared deviations of the indicators. When computing the bounds of the interval to the average value of the indicator respectively added and subtracted the indicated deviation.


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Average values and ranges of variation of investment in fixed capital, attributable to one en-trepreneurial structure and one employee in the data for 2010 year, presented in table 4. They are based on the developed models.

The figures shown in table 4, can be used in the solving task of monitoring the volume of investments by all levels of management and the regulation of entrepreneurship (federal, re-gional, municipal), identifying needs for financial resources when planning activities in credit organizations and funds, specialized on the help and support of entrepreneurship. The value of the investments per one employee with the growth of size of enterprises growing.

Table 4: Characteristics of investment in entrepreneurial structures

Entrepreneurial structures

On one entrepreneurial struc-ture, billion rubles

On one employee, thousand rubles

Average value Interval Average value

Interval

Small enterprises 0,45 0,29-0,61 47,56 30,98-64,14 Medium enterprises 11,74 5,83-17,65 93,22 55,44-131,00

Individual entrepreneurs 0,06 0,04-0,08 22,56 13,97-31,15

Necessary to know that the average number of workers employed by individual entrepre-neurs, as shown by earlier studies (Pinkovetskaia, 2012) is significantly below the average number of employees of small enterprises. Accordingly, the volumes of investment per worker on individual entrepreneurs have the least value for small enterprises this value is twice as big and for medium enterprises more than in four times. This trend is confirmed by the analysis of investment in large enterprises of our country. The average value of investment per employee of large enterprises (as shown by the calculations of the authors), is 160 thousand rubles per year, which is almost twice higher the corresponding indicators for medium enterprises.

The amount of investment accounted for as one entrepreneurial structure and per one em-ployee vary greatly in specific regions of the country, which is evident from the intervals of changes of these indicators. The relevant data may be used in the formation of projects and programs of entrepreneurship development, especially in regions where its level is not suffi-cient.

Average values and intervals of variation of investment in fixed capital per entrepreneurial structure of different industries according to the data for 2010 year are presented in table 5. They are based on the developed models, describing the investment in entrepreneurial struc-tures, specialized on five main types of economic activity.

Table 5: Characteristics of investment per entrepreneurial structure by main types of economic activity, thousand rubles

Type of activity Average value Interval Trade 63,62 35,96-91,28

Manufacturing 365,99 182,45-549,53 Construction 345,23 163,16-527,30

Transport and communications 140,47 82,28-198,66 Operations with real estate 307,53 123,88-491,18


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The investment volumes as shown by the data given in table 5 differ significantly according to the types of economic activity. The smallest investment is typical for trade enterprises, be-cause, in our opinion, the specifics of these are, as a rule, small shops. In addition, trade enter-prises have received the greatest development in previous years and, to date, their formation has basically ended. A small investment in entrepreneurial structures of transport and commu-nication can be explained by the weak development this type of activity in most regions of the country and the small size of the concerned enterprises. Entrepreneurial structures dedicated to three other types of economic activity are characterized by similar levels of investment.

The data in table 5, can be used to solve problems of monitoring, planning and forecasting volumes of investment. The most current provided feasibility studies for development of entre-preneurial structures, specialized on the types of activities that have not received sufficient development in specific regions and municipalities.

The indicators shown in tables 4 and 5, which characterizes the intervals of change of vol-ume investments allow us to conclude that the volume of investments per one entrepreneurial structure and one of its employees differ significantly depending on the number of employees (size) of entrepreneurial structure, by regions on the country and by types of economic activity, which specialize entrepreneurial structure. Thus, hypothesis 1 in the research process was con-firmed. CONCLUSIONS

The conducted researches allowed to draw the following conclusions:

proven ability to use density functions of the normal distribution as models for describing the volume of investments in fixed capital entrepreneurial structures in the regions of the country and on the main types of economic activity;

developed models approximate good the original data and are of high quality according to accepted criteria;

showed the presence of differentiation of the volumes of investment attributable to one entrepreneurial structure and one employee, in different regions of the country and by the types of activity;

defined average for the regions of the country values of the investment attributable to one entrepreneurial structure and to one worker, and the intervals of changes in these indica-tors, characterize the majority (68%) of the regions;

established that entrepreneurial structures characterize directly proportional relationship between their size and the amount of investment: the larger the enterprise, the more in-vestment accounted on one enterprise and also per one employee;

defined mean values and intervals of variation volumes of investment in entrepreneurial structures on the main types of economic activity;

shown that the relatively lower volume of investment characterize the entrepreneurial struc-tures, specialized on trade, transport and communications, and relatively greater for struc-tures in the manufacturing, construction and carrying out operations with real estate.

The hypotheses were confirmed during the research. Proposed methodical approach and

algorithm of modeling can be used to estimate the volume of investment in entrepreneurial structures in the regions of the Russian Federation and their municipalities.

The obtained results are of certain theoretical and practical significance, in particular, in further scientific researches of small and medium entrepreneurship, as well as justify the pro-posals for its functioning and proving the using of investment. They are of interest for credit, financial organizations and specialized on supporting the entrepreneurial structures funds. As


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well the achieved results can be wide used in education and getting another type of education employees of entrepreneurial structures.

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Irena Mačerinskienė, Jogaila Mačerinskas, Rasa Aleknavičiūtė / Montenegrin Journal of Economics,

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Change of National Intellectual Capital in EU Countries

IRENA MAČERINSKIENĖ1, JOGAILA MAČERINSKAS2, and RASA ALEKNAVIČIŪTĖ3

1 Professor dr. Mykolas Romeris University, Institute of Economics, Vilnius, Lithuania, e-mail: [email protected] 2 Assoc. Professor dr. Mykolas Romeris University, Institute of Finance, Vilnius, Lithuania 3 Doktorate student, Mykolas Romeris University, Institute of Economics, Vilnius, Lithuania



The importance of national intellectual capital is widely recognized, however it is difficult to define the value of these resources. In this paper the value of national intellectual capital in EU countries and its change is analysed. The aim of the analysis is to show how the level of national intellectual capital changes in EU countries and what the main sources of this change are. Firstly, the concept of national intellectual capital is described. Secondly, national intellec-tual capital measurement capabilities are analysed, and a model for measuring national intellectual capital is proposed. The proposed measurement model combines two indicators values aggregation functions: refined methods of factor scores and a non-refined meth-od (SAW). This model is applied to evaluate the level of national intellectual capital in EU countries during the period of 2002-2012. The findings have shown that the level of national intellectual capi-tal increased in all analysed EU countries except Finland. The growth was mainly influenced by the growth of human capital and structural capital, though the level of social capital has decreased in many countries during this period. The growth of national intellectu-al capital was lower in countries, which initially had higher levels of intellectual capital; and this finding shows that national intellectual capital converges between EU countries.

JEL classification:

O34, O30, F02 DOI:

10.14254/1800-5845/2016.12-4.6 Keywords:

national intellectual capital, human capital, social capital, structural capital, relational capital

INTRODUCTION

Innovative capabilities and national intellectual capital becomes main sources of competi-tiveness (Andriessen 2005, Taranenko 2013, Krušinskas, Bruneckienė, 2015). The concept of intellectual capital offer some alternatives to tangible resources, as they are not consumed when applied to value-creation processes (Bornemann 2012). Due to this national intellectual capital is the only resource which could sustain long term economic growth. Another feature of intellectual capital which highlights its importance for competitive advantage is its heterogenei-ty and immobility. National intellectual capital is considered to be bound to spatial environment and hardly transferred or changed. Malmberg and Maskell (2005) analyse influence of geo-


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graphical proximity to knowledge creation and diffusion and shows processes which helps to maintain intellectual capital level in specific territorial unit. Finding such capabilities which can differentiate country in the global economy and measurement of those capabilities is important tool for strategic management. Scientists highlight importance of different capabilities: internal technological innovation capabilities (Lahovnik, Breznik 2014), human capital (Gižienė, Simanavičienė, 2012; Tabellini, 2010), knowledge spillovers fostered by international relations (Keller, 2009), social capital (Agénor, Dinh, 2013; Hall, Lerner, 2010), institutions (Grootaert ir kt., 2008), advanced information communication technologies (Vu, 2011; Erumban, Das, 2016) etc. Though, complex complex national intellectual capital research is rare as most of research-es specialize on one component of national intellectual capital. The importance of the research of intellectual capital is widely recognized. Scientists are working on theoretical models of intel-lectual capital development (Edvinsson and Malone 1997; Malhotra 2000, 2003), improving the methodology for measuring intellectual capital (Andriessen and Stam, 2005; Hervas-Oliver and Dalmau-Porta, 2007; Lin and Edvinsson, 2011; Beskese et al., 2014; Bontis, 2004; Bu-račas et al., 2012) and showing the potential intellectual capital for creating the value added (Pulic 2004). However most of research efforts are given to investigate company level intellec-tual capital and separate national intellectual capital components. In this article national intel-lectual capital in EU countries is investigated as whole construct. The aim of this research is to show how the level of national intellectual capital changes in EU countries and what the main sources of this change are.

The objectives are as follows: 1) to present a conceptual framework of national intellectual capital, 2) to investigate approaches for measuring national intellectual capital and to propose a model for measuring national intellectual capital, 3) to analyse the level of national intellectu-al capital in EU countries and its change during the period of 2002-2012. The research meth-ods used include scientific literature analysis, which was used to investigate the concept of national intellectual and its measurement models. Expert survey was used to evaluate the im-portance of the components of national intellectual capital. The Kendall’s coefficient was calcu-lated to test agreement of expert rankings. Exploratory factor analysis, calculation of factor scores using regression factor scores function and SAW index calculation method were used to calculate the value of national intellectual capital in EU countries. The Cronbach alpha coeffi-cient was used to test the reliability of the national intellectual capital measurement model. Descriptive statistic methods and graphical data visualization methods were used to analyse and present research results. 1. THE CONCEPT OF NATIONAL INTELLECTUAL CAPITAL

The term intellectual capital emphasizes the importance of knowledge resources. Firstly, this concept was developed in on the micro level of research, where intellectual capital is rec-ognized as a successful social construct holistically explaining the differences and shifts in the performance of the organization (Anskaitis et. al. 2006). Currently research focus shifts from intellectual capital within a firm to a longitudinal focus of how intellectual capital is utilised to navigate the knowledge created by countries, cities and communities (Serenko et. al, 2010; Dumay and Garanina, 2013). Intellectual capital is analysed in a more holistic approach not only as a companies’ asset bound in business relations and environment, but also as intellec-tual capital of a community, which is bound in relational and/or territorial proximity. As a conse-quence of this trend the concept of national intellectual capital was developed. National intel-lectual capital is described as “all intangible resources available to a country or region, that give relative advantage, and which in combination are able to produce future benefits“ (Andriessen and Stam, 2005). National intellectual capital is viewed as an essential element of wealth crea-tion (Bontis, 2004), as a source of competitive advantage and a factor determining future growth potential (Lin and Edvinsson, 2011), as a resource empowering economic, social and environmental development (Salonius and Lönnqvist, 2012). The importance of national intel-


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lectual capital is widely understood, but the nature of national intellectual capital is difficult to describe. Due to the complexity of the concept national intellectual capital is often character-ized by its structure.

There are several national intellectual capital structural models. Firstly, the intellectual cap-ital model of Scandia Navigator, proposed by Edvinsson and Malone (1997), is often applied for the analysis of national intellectual capital (Bontis, 2004; Lin and Edvinsson, 2011; Malhotra, 2000, 2003, Užienė, 2014, Beskese et.al. 2014, Stahle et. al. 2015); also the classification system of Stewart (1997) is used (Andriessen and Stam, 2005; Buračas, 2007; Stam and An-driessen, 2009; Buračas at. al 2012). The intellectual capital model of Scandia Navigator pro-vides hierarchical structure of intellectual capital components. In this structural model, firstly, intellectual capital is divided into human and structural capital, then structural capital further divided into market capital and organizational capital and lastly organizational capital is divided into renewal capital and process capital. Such hierarchical structure helps to reveal different importance of component capital types to final value of national intellectual capital. It is enough to measure four type of capitals (human, market, renewal and process) in order to evaluate national intellectual capital.

Intellectual capital classification system of Stewart (1997) defines three components: hu-man capital, structural capital and relational capital. Importance of those components to final value of intellectual capital needs to be investigated further. Once applied to measure countries intellectual capital system of Stewart (1997) gives too much importance to relational capital as this component is not so important for a country as it is for a company. These two classification systems were initially created for the analysis of business intellectual capital and later applied to the national level research. However scientists highlight a need to reconsider the suitability of these models to analyse national intellectual capital (Malhotra, 2003). It is argued that national the specificity of intellectual capital might not be revealed by models created to explain busi-ness resources. Recently a new structural model of national intellectual capital was proposed by (Kapyla et al., 2012), which was created especially for the macro level research. This model extends the Stewart's (1997) model of three components by adding one new component – so-cial capital (see Fig. 1).

Figure 1. The structure of national intellectual capital

Source: Kapyla et al., 2012.

National intellectual capital is divided into four parts: human capital, structural capital, rela-tional capital, and social capital.

Human capital represents knowledge, education and competencies of individuals in realiz-ing national tasks and goals (Bontis, 2004).

Structural capital is intellectual capital hidden in national organizational and technological structures (Malhotra, 2000). This capital consists of R&D and innovation systems, scientific and information communication technologies infrastructure. B. Batog and J. Batog (2015)


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shows that R&D spending is strongly related to the technological innovations and technolog-ical spillovers and is important source of economic growth.

Relational capital is a national asset hidden in a country’s international relations. It shows a country’s competitiveness in the external market, which is achieved by investments in for-eign relations and exports of quality products and services (Bontis, 2004).

Social capital refers to institutions, relations and norms, which compose quality and quantity of social interactions in specific society (Jianbin et al., 2014).

Incorporation of social capital into the structure of national intellectual capital reduces the scope of structural capital. Structural capital in the model of Scandia Navigator and Stewart (1997) described both internal relations, organizational and technological structures. Once so-cial capital becomes an independent component of intellectual capital, the concept of structural capital becomes narrower and describes the level of innovative infrastructure, but not the level of relational quality and quantity. Also the exclusion of social capital helps to keep external and internal relationships separate, the first described by social capital and the latter described by the term of relational capital. Kapyla et al. (2012) shows how the concepts of social capital and intellectual capital integrate together and in doing so enrich the research of national intellectual capital. Due to these reasons, the structural model of national intellectual capital proposed by Kapyla et al. (2012) was chosen as a basis for a measurement model. 2. NATIONAL INTELLECTUAL CAPITAL MEASUREMENT METHODOLOGY

The evaluation of national intellectual capital is a difficult task as there are no measurable metric parameters, which would allow us to measure this object directly (Užienė, 2010). Instead measurements are based on indirect indicators, which describe intellectual capacities, compe-tences and complexities of structure and relationships, etc. (Koch, 2011). The value of national intellectual capital is aggregated from values of indicators, which can be measured directly. Such approach is often used to evaluate abstract concepts such as competitiveness (Bruneckiene et. al. 2012), quality of life (Rakauskienė, Lisauskaitė 2009, Beslerova, Dzuričko-va 2014), leading economic properties (Pilinkus, Neimontaite 2013). While using this meas-urement approach the subjectivity of measurement arises in two areas. Firstly, the value of na-tional intellectual capital will depend on indicators selected to measure each concept. Indica-tors, which can be used to measure each concept, are not defined, and researchers use differ-ent indicators to measure the same concept. This happens due to the fact that measurement models are adjusted to a specific country or a group of countries; also the selected indicators represent the most important aspects of the time period when the measurement was taken. Characteristics of national intellectual capital and their importance changes with the level of development of a country, so naturally the used measures need to be adapted to track them.

Secondly, the final result is highly dependent on the aggregation function chosen to sum-marize the values of separate indicators into one value of national intellectual capital. Most often national intellectual capital measurement models use non-refined factor scores computa-tion methods: the Simple Additive Weighting (SAW) method (Beskese et al., 2014; Bontis, 2004; Užienė, 2014), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) (Balcerzak, 2016) or a sum of scores by factor (Lin and Edvinsson, 2011). Once using these methods it is very important to consider the weights of each component indicator. Indicators do not have the same importance, and also the additive rule is sometimes criticized due to its ina-bility to track the multiplying effect of different intellectual capital components. In order to min-imize the negative consequences arising from the use of non-refined factor scores computation methods scientists summarize the values of indicators depending on the hierarchical structure of national intellectual capital. Firstly, indicators are summarized into defined intellectual capi-tal components and later these components values are aggregated into one intellectual capital value. At this point the choice of the structural model of intellectual capital becomes very im-


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portant, because the weights of indicators for the final national intellectual capital value depend on this model. The structural model of national intellectual capital helps to control the weights of defined structural component factors, but it does not solve the problem of how the value of structural components was calculated. When aggregating indicator values some scientists use equal weights for all components (Andriessen and Stam, 2005; Hervas-Oliver and Dalmau-Porta, 2007; Lin and Edvinsson, 2011; Stam and Andriessen, 2009), others use expert evalua-tion to determine weights (Beskese et al., 2014; Bontis, 2004; Buračas et al., 2012; Užienė, 2014). Expert evaluation fits best once the number of evaluated objects is not high, but when the number of evaluated object increases it becomes more difficult for experts to accurately evaluate each of them (Ginevičius, 2005). Usually the number of indicators used to measure the components of national intellectual capital is high, and expert evaluation might not be able to accurately reveal the importance of each indicator. The authors suggest to use refined meth-ods of factor scores calculation in this aggregation level as a solution to this problem. Refined methods allow to calculate factor scores as a linear combination of the observed variables, which consider what is shared between the item and the factor and what is not measured (DiStefano et al., 2009). This method does not use any underlying model to predict the factor score but reflects the extent, to which the factor or component estimated is manifested by each individual case. Non-refined factor scores computation methods should be used only in order to aggregate small number of factors defined by refined factor scores computation methods. In this stage usually it is known small number of factors related with specific construct. To evalu-ate those factors importance to national intellectual capital perfectly fits expert evaluation method.

2.1 National intellectual capital measurement model In this article a national intellectual capital measurement model is proposed, which com-

bines two value aggregation functions depending on the level of aggregation. Firstly, the value of national intellectual capital components was calculated from initial indicators as regression scores using the factor analysis procedure. Regression factor scores reflect the location of each country on the factor. This method was chosen as it helps to maximize the validity of the calcu-lated factor scores. The calculated values of national intellectual capital components are standardized values, which have zero mean and unit variance. Secondly, the values of national intellectual components were aggregated using the SAW method. If indicators selected to measure structural part of national intellectual capital fall into several factors per factor analy-sis procedure, their values are summarized using SAW method applying equal factor scores. Later aggregation function is applied based on structural model of national intellectual capital. In this level of aggregation only four factors of national intellectual capital are defined. In order to define each of these factor weights we used direct expert evaluation. 21 experts from Lithu-ania, Latvia and Czech Republic were chosen, who evaluated the weight of each national intel-lectual component. Evaluation was performed in April of 2016. The calculated Kendall’s coeffi-cient of concordance is W=0.358, and this value is statistically significant with a level of signifi-cance of 0.01. This shows that the experts displayed a significant agreement of components' weights, and their evaluations can be considered as reliable.

Based on the results of the expert evaluation the national intellectual aggregation function is composed (refer with: Eq 1)

(1)

where NIC denotes national intellectual capital; SC is social capital, STC is structural capital, HC is human capital, and RC is relational capital. The highest weight was given to human capital and the lowest one to relational capital. The calculated Cronbach alpha score for defined components equals 0.859 and is above the threshold of 0.7 (Nun-nally, 1978). This shows that the composed measurement model is reliable.


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The national intellectual capital measurement model is composed of 28 indicators. These indicators were chosen based on literature review and data availability. After the first selection was made an indicator system was optimized by performing a factorial analysis. Only indicators that had a low level of correlation (less than 0.8) were kept. If indicators had a higher correla-tion one of them was removed from the measurement model.

Human capital is measured using education-based approach. Most popular indicators of human capital in this approach are average years of schooling, school enrolment rates, adult literacy rates and quality of schooling indicators (Le et al., 2005). After factor analysis of human capital indicators two factors was identified. First one covers quality of education indicators such as: student performance in reading (PISA), high-level computer skills, lifelong learning and satisfaction of education. Second one consist with education attainment related indicators: population with upper secondary or tertiary education attainment, participation rate of young people in education and high level internet skills.

Structural capital is measured with eight indicators: EPO patent applications, USPTO patent applications, community trade mark applications, share of innovative enterprises, fixed broad-band coverage, percentage of households with internet access, enterprises with internet access and total R&D personnel and researchers.

Relational capital is measured with ten indicators forming two factors. Openness of a coun-try factor is measured with seven indicators: foreign direct investment intensity, students (ISCED 5-6) studying in another EU-27, inflow of students (ISCED 5-6) from EU-27, EEA and Candidate countries - as % of all students in the country, foreign country citizens immigration per population, emigration rate, enterprises engaged in any type of innovation co-operation with a partner in all other countries except in EU countries EFTA or EU candidates countries, United States, China or India and exports of goods and services in % of GDP. Intensity of technology transfer factor contains two indicators: high-technology exports, enterprises engaged in any type of innovation co-operation with a partner in all other countries except in EU countries.

Social capital is measured with three indicators: level of institutions, satisfaction with the national government, general trust. Such measurement gives view not only to level of informal social interactions but also is able to assess level of institutional environment in a country. General trust level in society is measured according Akçomak and ter Weel (2009), Knack an Keefer (1997) recommendations to combine answers to three ESS survey questions.

Measurement scale of all indicators is comparable between counties. If indicator initially is measured in absolute scale, before any calculations it is transformed into relative value based on population of each country. One of advanced features of suggested measurement model is that it does not include input indicators (expenditures on development of intellectual capital elements). Including input indicators together with outcome indicators is criticized due to lack of construct validity (Malhotra, 2003). National intellectual capital inputs may not be valid ‘proxies’ for outcomes, since efficiency of inputs are not evaluated.

Data for this research is obtained from the Eurostat database, the World Data Bank, the World Economic Forum database, the European Social Survey database, OECD (PISA survey results), USPTO and EPO was the source of data on patent applications. The data panel had 9.92% of missing values, which were imputed using the multiple imputation procedure: predic-tive mean matching (PMM). This procedure was applied for the data of each country separately so the country’s values were not influenced by other countries’ indicator values.

The value of national intellectual capital was calculated in 25 EU countries from 2002 to 2012. Three EU countries (Croatia, Malta and Luxembourg) were not included in the analysis due to a lack of statistical data of defined indicators of national intellectual capital. These coun-tries are small in size and have very specific factors concerning their economies, so their exclu-sion from analysis could be justified.


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3. NATIONAL INTELLECTUAL CAPITAL VALUE IN EU COUNTRIES IN 2002- 2012

The calculated value of national intellectual capital is expressed in Z scores, where the av-erage value equals zero, values above average are positive, and below average values are neg-ative. The average value of national intellectual capital in EU countries during 2002-2012 is shown in Figure 2.

Figure 2. National intellectual capital value in EU countries in 2002-2012

Source: Authors' estimation.

The results have shown that the highest level of national intellectual capital was in Finland, Denmark, and Sweden. The lowest level of national intellectual capital was in Romania, Bulgar-ia, and Greece. Changes of the level of national intellectual capital during the period 2002-2012 are shown in Figure 3.

Figure 3. National intellectual capital value change in EU countries during 2002-2012

Source: Authors' estimation.

The level of national intellectual capital has increased in all evaluated countries except Fin-land. Finland has the highest level of national intellectual capital, but this level is not increasing further. In Figure 3 countries are divided into two groups depending on their average level of national intellectual capital. Countries with a higher than average national intellectual capital value level are marked in blue and countries with lower than average national intellectual capi-tal value level are marked in orange. This allows to graphically see how the level of national


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intellectual capital relates to the change of this level during 2002-2012. It could be seen that the change of national intellectual capital was above average in nine countries, which initially had a lower level of national intellectual capital, and in only three countries, which initially had a higher level of national intellectual capital. To test if there is a relation between the change of national intellectual capital during 2002-2012 and the initial level of national intellectual capi-tal in 2002 a correlation coefficient between these two variables was calculated. The results have shown that the correlation coefficient between these variables equals -0.401, and it is statistically significant with the significance level of 0.05. The negative sign of the correlation coefficient indicates that in the countries with a higher level of intellectual capital this level in-creased less than in the countries with a lower level of national intellectual capital. Such trend shows signs of convergence of national intellectual capital between EU countries.

The factors that compose the change of the level of national intellectual capital during 2002-2012 are shown in Figure 4. In this figure countries are ranked depending on the volume of the change of national intellectual capital from the lowest to the highest.

Figure 4. National intellectual capital value change in EU countries during 2002-2012

Source: Authors' estimation. It can be seen that the level of human capital increased in all analysed countries. Almost

the same trend applies for structural capital, which increased in all countries except Finland. These two factors determined the increase of the value of national intellectual capital value in many countries during this period. It can be noticed that the level of social capital was decreas-ing in many countries, and this slowed down the total increase of national intellectual capital in many countries.

Also a decrease of relational capital is measured in several countries. This decrease is very visible in Finland. In this country the decrease of relational capital was the highest comparing with the decrease of other national intellectual capital components. More detailed research is needed to explain the reasons of such a decrease.

CONCLUSIONS

The importance of national intellectual capital is widely recognized, though its conceptual models are still developing. Several structural models are used to explain this concept. Most of these models were developed for the business level of research and later directly applied to


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analyse intellectual capital on the national level. These models define a different number of structural components and use specific terms to describe them. However, these differences are most visible once defining components of non-human intellectual capital. Human capital is ex-cluded in all models as a structural component of intellectual capital. One of the models devel-oped especially for national intellectual capital research is the national intellectual capital mod-el of Kapyla et.al. (2012). This model consists of four components: human capital, structural capital, relational capital, and social capital. When comparing it with other national intellectual capital models this model incorporates the concept of social capital, which is widely recognized and helps to explain internal relational environment of countries better than the broad structur-al capital concept used in other models. Based on Kapyla et.al. (2012) a structural model of evaluation of national intellectual capital was proposed.

The analysis of models of national intellectual capital measurement has revealed that the main weakness of current measurement models is subjectivity arising in selecting measure-ment indicators and value aggregation functions. In order to reduce the subjectivity arising in the value aggregation process the authors suggest to combine two indicators values aggrega-tion functions refined methods of factor scores calculation should be used when aggregating indicators values into factor scores. In the next level factor scores could be aggregated using the non-refined method of SAW, though the importance of each factor to the final value of na-tional intellectual capital needs to be evaluated by experts. Such strategy of national intellectual capital value aggregation allows to increase the reliability of measurements.

The proposed national intellectual capital measurement model was used to evaluate na-tional intellectual capital in EU countries during the period of 2002-2012. During this period the level of national intellectual capital increased in all analysed EU countries except Finland. The main source of this growth was an increasing level of human capital and structural capital. However, the level of social capital decreased in many analysed countries and influenced a lower growth of the total level of national intellectual capital. When analysing the dependence of the initial level of national intellectual capital on its growth volume it was recognized that na-tional intellectual capital increased more in countries, which had a lower level of national intel-lectual capital in the beginning of the period (2002-2012). This means that national intellectual capital converges between EU countries. The speed of the change of national intellectual capital is different in all countries. Only absolute levels of change were identified in this article, and the sources of this change need to be investigated further by applying the case analysis method.

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Zhoomart K. Omonov, Anna Yu. Veretennikova / Montenegrin Journal of Economics,

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Quantitative Scaling of Social Innovations1

ZHOOMART K. OMONOV1, and ANNA YU. VERETENNIKOVA2 1 The research is supported by the grant of Russian Humanitarian Science Foundation, № 16-32-00003 2 Institute of Public Administration and Entrepreneurship, Ural Federal University, Russian Federation, E-mail: [email protected] 3 Institute of Public Administration and Entrepreneurship, Ural Federal University, Russian Federation, E-mail: [email protected]



Social innovation permeates the socio-economic dimension and it is the one of the effective instruments for improving the public wel-fare. Therefore, it has ignited growing academic and practical inter-est. However, despite of the increased interest in the designated research field, it has not yet formed a unified theoretical and meth-odological basis of social innovations. Besides, such an important aspect of social innovations as the evaluation of parameters, which can scale social innovations in the socio-economic dimension and expose lacks, is completely unexplored and unformed. Hence, the aim of this paper is to develop an original approach to quantitative scaling that allow analysing the allocation of social innovations in the socio-economic dimension. The concept of "social innovation" is interdisciplinary, so that the research included the integration of the modern economic theory, sociology, philosophy and political science for developing a common approach to the analysis of social innova-tions. We have used various methodologies as a groundwork such as: neo-institutional and neoclassical economic approaches, cate-gorical and instrumental apparatus of the institutional-evolution economic theory, the theory of public sector economy, and the eco-nomics of knowledge. To achieve the objective of our research we developed a definition of social innovations. Besides four the most important measurable parameters, which allow us to classify social innovations according to their signs, were developed. These param-eters were the basis for scaling of social innovations. Authors have analyzed thirty-two social in Ural region. It allows identifying spheres in public sector, which are less provided with social innovations.

JEL classification:

О35; Н41; R50 DOI:

10.14254/1800-5845/2016.12-4.7 Keywords:

social innovation, quantitative Scaling, institutional economics

INTRODUCTION

The contemporary socio-economic space exhibits a high degree of variability. A change is observed in the needs of society, both in terms of tangible and intangible benefits. In this re-gard, the set of goods consumed by different social groups varies significantly. As a conse-quence, the majority of economic models aimed at meeting the needs of the population have rapidly become outmoded and ineffective. In this context, particular importance is attached to social innovation that generates new and more effective ways of creating benefits at the lowest cost. Social innovations are starting to play an effective role as a tool for improving social wel-


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fare (Moore, 1995; Battistella C., Nonino F., 2012). The growing interest in this issue, both from the scientific community and from practitioners, may be referred to in terms of a global trend. However, the research community has not yet coalesced around a unified concept in this area, despite some aspects being presented in the works of Mulgan et al. (2007), G. Phils(2009) and M. Moore (1995). An example of a large-scale research project encompassing the various as-pects of innovation in the public sector is implemented in the European Union in the 2003-2006 research project entitled PUBLIN (Koch P., Cunningham P., Schwabsky N., 2005; Koch P., Hauknes J., 2005).

The above-mentioned fact shows that the indicated line of research is at the beginning of its development. An important question is: what position does this phenomenon occupy within the socio-economic system and what role is played here by civic initiatives (Dekker, 2009; Ja-ger, Schroer, 2014; Jäger, 2012)? The answers to these questions will not only reveal the basic features of social innovation, but also determine the characteristics of public management ini-tiatives supporting the development of social innovation (Cooney, 2006). Hence, the aim of this paper is to develop an original approach to quantitative scaling that allow analysing the alloca-tion of social innovations in the socio-economic dimension. To advance this aim, a review of approaches to the definition of “social innovation” is carriedout, considering the theoretical and methodological principles of economic theory, enabling a determination of the main parameters of the systematisation of social innovation and presenting a quantitative scaling of social inno-vation that highlights the main features of its distribution in social space.

1. THE DEFINITION OF SOCIAL INNOVATION One of the most generally accepted definitions of social innovation was provided by Mulgan

et al. (2007). The authors define social innovation in terms of “new ideas working to achieve social objectives”. However, this definition does not reflect the essential characteristics of social innovation or show where it contrasts with innovation in general. Often, business innovation contributes to the obtaining of an economic benefit in the form of profit, as well as the attain-ment of social goals. (Mulgan G., Tuckers S., Ali R., Sanders B., 2007).

G. Phils understands social innovation to consist of any new and useful solution aimed at meeting social needs (Phils, 2009). The main weak point in this definition is that it is based around the utility requirement of social innovation. However, in practice, the effects of innova-tion can be unpredictable. R. Heiscala understands social innovation to refer to changes in the cultural, legal and regulatory structures of a society that increase its collective power resources, as well as improving social and economic performance. The main value of this definition is the reference to “increasing the collective power resources” (Heiscala, 2007). Furthermore, the author understands social innovation not as an idea, but as a transformation that creates an alternative approach to the determination of the essence of social innovations (Gonzalez-Padron, Tomas, Hult , Calantone, 2008).

The concept of “social innovation” closely resembles the concept of “innovation in the pub-lic sector” – “Innovations in the public sector are new ideas, the embodiment of which lead to an increase in social value”. However, these definitions can be deceptive, implying that social innovation is one of the types of innovation that is aimed primarily at social needs (Koch, Hauknes, 2005;Koch, Cunningham, Schwabsky, Hauknes, 2005). Summarising the results of researchers, as well as taking into account the original approach to the definition of the term, it may be disclosed that social innovation possesses the following main general features: a cer-tain level of novelty; implementation in the social space; and a focus on improving the perfor-mance of contemporary activities aimed at solving social problems.

As such, we understand social innovation to refer to new ideas, opportunities and actions within the social space that increase the possibility of utilising resources to address economic, social, cultural and environmental issues. In this connection, we understand social space to


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refer to the multidimensional space of interrelated social processes, relations, practices and positions. Despite the fact that there is no single approach to the study of social innovation, it may be observed that the views of the majority of researchers coincide; for example, as regards the connections between social innovation, social value, activation of human potential, provi-sion of public goods and improved quality of life (McElroy, 2002; Fafchamps, 2006). In taking these aspects into account, the proposed definition of the term thus distinguishes social inno-vations from other types of innovation. We additionally observe that social innovation is an im-portant instrument for territorial development in the knowledge-based economy. 2. METHODS

In developing the original approach to the distribution of social innovation in the social and economic space, analysis and synthesis, compilation and systematisation methods – as well as a four-dimensional graphical representation technique – were applied within the context of re-search. The study procedure consisted primarily in the development of a theoretical basis for the original approach to the further distribution of social innovations. The theoretical analysis of the sources allowed the most important parameters of the systematisation of social innovations to be identified as presented below. Firstly, it includes a level of State participation. This pa-rameter is needed to track the effectiveness of social innovation, supported by public and pri-vate sources. It allows social innovation to be divided into the following groups:

with full State participation; with partial State participation; without State participation (Golubeva, Sokolova, 2010)

Secondly, the most important parameter of the systematisation of social innovation is initia-

tion level of the social innovation. This parameter characterises the source of the initiative to create a social innovation. In other words, it is necessary when analysing the effectiveness of social innovation to keep track of who is the initiating subject of this innovation: government, bureaucracy or ordinary citizens. Depending on their initiator, social innovations may be divided into:

innovations by public authorities; innovations by citizens.

The next parameter is thescope, indicating the application level of social innovation. The

significance of this parameter derives from the fact that it represents the degree of influence of social innovation. Depending on their extent, social innovations may be implemented at the following levels:

the level of the enterprise; micro level (industry sector) (Popov, 2005); local level (urban, rural settlement, municipal); regional level (separately selected region); national level; international level (social innovations cover a certain number of countries).

The fourth significant parameter is the degree of novelty of the social innovation (Tanimoto,

2012). Depending on the degree of novelty, social innovations are subdivided into:

gradual social innovations (minor improvements to existing services, processes, institu-tions);

radical social innovations (radical changes to existing services, processes, institutions);


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systemic social innovation, i.e the creation of a new system or a fundamental change to an existing system

The list of criteria constituting the basis for the formation of quantitative scalings for social

innovation is presented in Figure 1.

Figure 1. Quantitative scaling of social innovation

The application of the proposed theoretical development to the distribution of social inno-vation allows the location of the considered innovations to be determined in the social space, thus forming the basis for a prediction of its effects on the development of public sector man-agement at various levels (global, national, regional or municipal). As regards the collection of data by specific theme, an analysis of publicly available information about implementable social innovation was carried out. In order to analyze the most trending social innovations, authors have observed often mentioned and promoted social innovations by such organizations as Young Foundation, Social Innovation Center of Stanford University, Agency of Strategic Initia-tives in Russia, Center for Health Market Innovations, Grameen Foundation, Center of Social Innovations (CSI), Europe Tomorrow, Public Space. Moreover, all mentioned social innovations from Elibrary, Google Scholar, SCOPUS and Web of Knowledge databases, where key words “social innovations” was used, have been added to our selection. We believe that this sample of social innovations is going to significant influence to the development of practical and theoreti-cal aspects of social innovations. As a result, a list comprising 42 social innovations was formed; the list is presented in annex.

The last phase of the study consisted in the distribution of the 42 social innovations accord-ing to quantitative scaling criteria. The results of the distribution are graphically reflected in Figure 2. A separate schedule was constructed for each activity. The x-axis shows the degree of public participation. The y-axis represents the scope of social innovation. The size of the figures characterises the novelty of innovation, with a small circle representing incremental innova-tions, a medium-sized circle representing radical innovations and a large circle representing systemic innovations. The colour of the figure characterises the level of initiation of social inno-vation: coloured innovations are initiated from above and non-coloured innovations initiated from below.


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Insufficiently protected social innovations, as well as a list of supposed causes of this phe-nomenon, are as formed on the basis of the results of the allocation of identified areas. The main difficulty faced by the authors in the course of this study has been the formation of a sample of social innovation that reflects the objective tendencies of socio-innovative develop-ment; this is also a consequence of the lack of a generally accepted interpretation of social innovation in the scientific community. In addition, a certain barrier exists in terms of infor-mation availability concerning the measured characteristics. However, this problem can be ad-dressed through an analysis of the most significant socially innovative projects.

3. RESULTS The allocation of social innovations, identified according to the authors' proposed quantita-

tive scaling criteria, including type of activity, scope, degree of government involvement, level of innovation and type of initiation, allowed us to obtain the results presented below.

Figure 2: Dependency of the level of usage of social innovation on the proportion of State in-volvement.

Here, the size of the circle indicates the degree of novelty (small – process innovation; medium – radical innovation; large – system innovation); a dark colour indicates public authorities initi-


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ation, while a light colour represents citizens initiation. The numbering social innovations are presented in an annex to this article

Summing up on the presented list of social innovations, it can be concluded that 71.5% of the projects have a private character; of these, 33.3% are presented at the international level, 28.6% – at the national, but only 4.8% – at the regional and municipal levels. Projects with par-tial State participation accounted for 14.2%, while the proportion of projects with full public participation did not exceed 11.9%. Here, social innovations at the municipal level are com-pletely absent (Table 1). Table 1. Distribution of social innovation as a percentage according to the degree of govern-ment involvement and scope of the project

State participation Scope

Full Partial No State participa-tion

TOTAL

International 2.4% 7.1% 34.3% 43.8%

National 7.1% 7.1% 29.6% 43.8%

Regional 2.4% 0.0% 4.8% 7.2%

Municipal 0.0% 0.0% 4.8% 4.8%

TOTAL 11.9% 14.2% 73.5% 100%

Analysis of the distribution of social innovation by the level of initiation and degree of novel-ty is presented in Table 2. The largest share of innovation – 81% – was initiated by citizens. Of these, 67% are radical, 12% – improving and 2% – systemic. Innovations initiated by public authorities account for only 19%. Of these, 12% are radical, 5% – improving and 2% – systemic. Table 2. Distribution of social innovation as a percentage according to the level of initiation and degree of novelty

Novelty

Initiations

Gradual innovations

Radical innova-tions

Systemic inno-vations

TOTAL

Initiations by public au-thorities

12% 67% 2% 81%

Initiations by citizens 5% 12% 2% 19%

TOTAL 17% 79% 4% 100%

Thus, the majority of innovations are radical at the national level and are mostly of a private nature (71.5% of social innovations).

The analysis relationship between parameters of thescaling shows that parameters such as the state participation and the type of initiator iscorrelated. In other words, civic initiatives use only private finding while public authorities use state funding.


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Table 3. Correlation of parameters of the social innovations

The Scope State participation Degree of novelty The type of initiator

The Scope 1

State participation -0,19 1

Degree of novelty 0,05 0,13 1 The type of initia-tor -0,17 0,86 -0,03 1

4. DISCUSSION The results obtained in the previous section results of the study allow the following distribu-

tion patterns for the considered social innovations to be formulated.

In economic terms, the most common social innovation is initiated by citizens. As noted above, 81% of social innovations are initiated, not by public bodies or government agen-cies, but by ordinary citizens.

Most social innovations are carried out without State support; 71.5% are implemented without any government involvement at all.

The most popular innovations are of an international (42%) and national (42.8%) character. The share of regional innovation is only 7.2%, while the municipal level accounts for a mere 4.8% of social innovations.

The majority of social innovation is of a private character. Only 14.2% of social innovations are implemented with partial State support, while 11.9% of social innovations are imple-mented with full State support.

Only public authorities are supported by state funding

All of the above make it possible to rethink the role of this phenomenon in terms of the so-cio-economic development. The distribution characteristics of social innovations are defined in terms of the specifics of institutional development of the territory (North, 1989). Consequently, it is necessary to analyze the institutional basis of social innovations.

The presented scaling of institutions of social innovations can be applied in the construc-tion of an institutional atlas of social innovation, as well as to identify areas in the institutional social innovation basis for which current provision does not exist.

The most important aspectsof the process of developing of social innovations may be pre-sented according to the following propositions. The main purpose of social innovation is to ad-dress public sector failures. In the case of a lack of public sector institutions, their weaknesses or inefficiencies, the function of creating the necessary public goods takes on the character of social innovation. Since a large number of innovations are initiated by citizens, it is possible to conclude that it is precisely the citizens' initiatives that develop new forms of relationships for the solution of socially important problems (Chaudhry, 1993; Fafchamps, 2006). However, this trend applies mostly to developing countries with a weak public sector of the economy. For ex-ample, the abovementioned project Mothers2Mothers actually performs the function of a health institution.

Social innovation in developed countries has a slightly different function. These are focused on increasing the legitimacy of the people and new social requirements, which the public sector does not have time to respond to. For example, more than 80% of the adult population of the United Kingdom have taken part in a voluntary community police support programme at least


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once in their lives. This social innovation is focused primarily on increasing the legitimacy of the functioning of the rule of law system and the decisions of the authorities in this area (Dorword, Kydd, Morrison, Poulton, 2005; Fafchamps, 2006; Knowles, Owen, 2010; LU, 2013; Martinus, 2014).

Analysis has shown that, of the considered list of social institutions, developed countries account for only 12 innovations (Figure 2), while developing countries comprise 30 innovations. All the foregoing reaffirms the role of social innovations as an alternative to the production of public goods by the State.

Of special note is the role of social innovation in the elimination of State failures. Social in-novation can reduce the level of the state bureaucracy and the accompanying transaction costs when dealing with socially and economically significant problems (Tirole, 2015).

All of the above provides confirmation that, despite the fact that the production of public goods and the resolution of socially significant problems, the government is often unable to fulfil the main task of the public sector – i.e. provide the desired standard of living – due to inef-ficient institutional structures, high information asymmetry, weak institutional management and coercion. In such circumstances, citizens' initiatives contribute to the creation of the necessary institutions, which represent the socially innovative solution for ensuring the delivery of vital public goods. CONCLUSION

This study, which was conducted to develop an original approach to quantitative scaling, and it yielded the following results.

Firstly, it introduces a new, original concept of social innovation on the basis of the analysis of approaches to the consideration of social innovation.

Secondly, the four most important criteria for social innovation were identified, supporting the effective organisation of social innovation. The presented criteria formed the basis for the original approach to the allocation of social innovation. As part of this study, a quantitative scal-ing of social innovation was developed, serving as the foundation of the original approach.

Thirdly, the systematisation of 42 social innovations identified according to the proposed scaling criteria worldwide. The above analysis allowed us to determine the practical features of the distribution of social innovation. The study confirmed the specific role of civil initiatives for the development of the public sector.

Lastly, the key role of the level of institutional development, determining the features of the distribution of social innovation, is outlined. In addition, the role of social innovation in both developed and developing countries is revealed.

The theoretical significance of this study is in an extension of the theory of innovation in re-lation to the public sector, as well as in the formation of a platform for further analysis of social innovation.

The practical significance of the results consists in the ability of government to use the orig-inal approach to carry out an effective policy in the sphere of socially innovative development. In addition, the results of this study can be used to improve the effectiveness of innovators' activi-ties. APPENDIX 1-Open University, 2 - Cousera, 3 - Khan Academy, 4 - Zillion, 5 - italki, 6 - the flipped classroom, 7 -Médecins sans frontiers, 8 - Riders for health, 9 - Mother2Mothers, 10 -SaudeCrance, 11 -


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Grameen-Cure2Children, 12 – Graameen Healthcare Trust, 13 – Boligsocialnet, 14 - Hab-italSenour Service, 15 -Seniour Forum: a whole concept, 16 -Rehabitat, 17 - Batigere, 18 - Mi-crospaces of Solidarity and Youth Residential Inclusion in degraded Neighbourhoods, 19 -Grordbeznarkotikov, 20 - Voluntary and Community Sector Involvement in UK, 21 -YekîneyênParastina Gel,22 - Neighborhood Watching, 23 - Voluntary police in Sought Africa, 24 - Voluntary police in Israel, 25 - International Alert, 26 -Green Peace, 27 - Podorozhnik, 28 - Rus-sian Network of River, 29 - Nature of Sought Siberia and it’s defenders, 30 - Ecoline, 31 - WWF, 32 - The Big Issue, 33 - The ASA Project, 34 -Amnesty International, 35 - Oxfam, 36 - The Wom-an Institute, 37 - I Paid Bribe, 38 -Grameen Bank, 39 - Fairtrade, 40 -Participatory Budget, 41 - Linux, 42 – Zeleniy Koridor. ACKNOWLEDGEMENTS This study received support from the Russian Humanitarian Science Foundation, project № 16-32-00003. The authors would also like to express their gratitude to the editor in the chief and to anonymous reviewers for the peer review.

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Cooney, K. (2006). ''The institutional and technical structuring of nonprofit ventures: Case study of a US hybrid organization caught between two fields'', Voluntas, No. 17, pp. 143-161.

Dekker, P. (2009), ''Civicness: From Civil Society to Civic Services? '', Voluntas, No. 20, pp. 220-238.

Dorword A., Kydd J., Morrison J., Poulton C. (2005), ''Institutions, Markets and Economic Co-ordination: Linking Development Policy to Theory and Praxis'', Development and Changes, Vol. 36, pp. 1-25.

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Koch P., Hauknes J. (2005), ''On innovation in the public sector'', Publin, Oslo. LU, A.-D. (2013), ''Harnessing Social Innovation through Inclusive Thinking'', Japan Social Inno-

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Harvard University Press, Massachusetts. Mulgan G., Tuckers S., Ali R., Sanders B. (2007), ''Social innovation: what it is, why it matters

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Vilma Tamuliene, Egle Kazlauskiene, and Lina Pileliene / Montenegrin Journal of Economics,

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Ecologically-Conscious Consumer Purchases in Lithuania

VILMA TAMULIENE1, EGLE KAZLAUSKIENE2, and LINA PILELIENE3

1 Associate Professor, Vilnius University, Kaunas Faculty of Humanities, Kaunas, Lithuania e-mail: [email protected] 2 Professor, Mykolas Romeris University, Faculty of Economics and Finance Management, Vilnius, Lithuania e-mail: [email protected] Associate Professor, Vilnius Gediminas Technical University, Faculty of Business Management, Vilnius, Lithuania e-mail: [email protected]

3 Professor, Vytautas Magnus University, Faculty of Economics and Management, Lithuania e-mail: [email protected]


Received June 27, 2016 Received in revised from August 24, 2016 Accepted November 28, 2016 Available online December 15 2016

This study analyses the relationship between the ecologically conscious consumer behavior and ecological products purchase. Recent literature defines the ecologically conscious consumer behavior as a multidimensional construct and outputs of the ecologically conscious consumer behavior are attitudinal dimen-sion as intent to purchase ecological products or behavioral dimension as actual purchasing of ecological products. Authors of this study investigate outputs of the ecologically conscious consumer behavior in Lithuania. The purpose of this study – to investigate the relations between the ecologically conscious consumer behavior and ecological products purchase in Lithua-nia. The results of quantitative survey with 185 retail store cus-tomers prove the existence of both consumer behavior outputs: attitudinal and behavioral of the ecologically conscious consumer behavior. The study found that environmentally conscious con-sumer behaviors are expressed by attitudinal and behavioral dimensions. Results of the study revealed that strong actual purchase of ecological products are related to the energy saving and storage, and medium strength intent to purchase ecological products are related to products recycling, sorting, environmental friendliness and social responsibility among lithuanian.

JEL classification: M1, M30, M31.

DOI:

10.14254/1800-5845/2016.12-4.8 Keywords: ecological consciousness, consumer behavior, ecological product, intent purchase, actual purchase.

INTRODUCTION

For several decades ecological problems are issues in the world. In 1990, also known as the Earth or environmental decade "particular emphasis on ecological and social responsibility, because started to rise and consumer environmental awareness by acquiring products (Tiliki-dou et al., 2002, p. 47).

This environmental awareness growth remains to this day. The important roles of eco-consciousness formation have had the media, public organizations, rapid technological devel-


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opment, statutory regulation. It is in recent decades, the importance of ecology is so strongly emphasized, noticeable changes in consumer behavior - consumers began to realize that their shopping habits can have a direct impact on the living environment (Akehurst et al., 2012, p. 973).

There are authors who have analyzed the ecologically conscious consumer behavior as a multidimensional construct (Roberts, 1996; Roberts and Bacon, 1997; Tilikidou and Zotos, 1999; Tilikidou et al., 2002; Tilikidou and Delistavrou, 2007, Hamilton, 2010, etc.) or psycho-logical and ethical factors in the ecologically conscious consumer behavior (for example, Biel and Dahlstrand, 2005; Wheale and Hinton, 2007; Masouleh et al, 2013) as well as the rela-tionship between the construct, ecologically conscious consumer behavior, and this construct affect actual ecological product purchase or intent to purchase ecological product (Akehurst et al., 2012; Masouleh et al., 2013).

Most lithuanian authors dealing with ecological products and consumer behavior have ana-lyzed its impact on: consumer shopping habits, consumer environmental awareness by acquir-ing goods and consumers’ loyalty. The lithuanian scientist Skulskis et al., 2011, investigated the direct sales of ecological products, as well as information and knowledge of the influence of ecological products for sale. Lynikaite and Liesionis, 2010 are analyzed the factors affecting the ecological product purchase process. Waginger and Ruževičius, 2007 are analyzed the eco-labelling in Lithuania as a factor of gaining public recognition and powerful marketing tools, influencing customers behavior. This study is analyzed and generalized the practice of products and services ecocertification and eco-labelling in the world as well as to identify their better possibilities of usage in the Lithuania. Virvilaite et al., 2015 have also dealt with the problem of consumer behavior and loyalty. They have investigated consumer behavior and relations be-tween perceived value dimensions and attitudinal and behavioral loyalty. This study is basically oriented to loyalty of consumer behavior evaluation while the problem of the ecologically con-scious consumer behavior is not considered.

Although global interest in the ecologically conscious consumer behavior is increasing, none of this research has been conducted in Lithuania. An empirical study of the ecologically conscious consumer behavior is not developed in Lithuania.

The purpose of this study – to investigate the relations between the ecologically conscious consumer behavior and ecological products purchase in Lithuania.

The hypotheses are as follows:

H1: There is a direct significant influence between the ecologically conscious consumers’ be-havior and actual ecological product purchases. H2: There is a direct significant influence between the ecologically conscious consumers’ be-havior and intent to buy ecological products.

Research methodology. A quantitative, questionnaire-survey method for the research of the empirical research purpose has been chosen. To evaluate the ecologically conscious behaviour Roberts (1996) developed the scale, also used in Straughan and Roberts (1999), which consid-ered a wide variety of behaviours. We were adopted this ecologically conscious consumer be-havior scale from Straughan and Roberts, 1999 and was measured by 30‐items in a Lik-ert‐format, anchored by “Always True” (5) and “Never True” (1) and calculated as a sum of the responses to the 30‐items scale (with appropriate items reverse scored). The scope of the re-search sample is 185: retail store customers. The first step in the research was to answer the question: is a statistically significant relation between the ecologically conscious consumer be-havior (later ECCB) and the actual purchasing of ecological products (later APEP). There were ECCB as independent variable and APEP was the dependent variable. In addition, according to foreign researchers, were investigated relations between ECCB as independent variable and intent to purchase ecological products (later IPEP) as the dependent variable. The data were


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analyzed and administrated under a statistical package for social sciences (SPSS Statistic 17.0 version). Cronbach’s alpha coefficient for the assessment of data credibility was applied. Cor-relative (Spearman’s rank correlation coefficient) and regression analyses were also performed. 1. THE ECOLOGICALLY CONSCIOUS CONSUMER BEHAVIOR

The term of the ecologically conscious consumer behavior (ECCB), was first mentioned by Roberts (1996), Tilikidou (2002). According to Roberts and Bacon (1997), an ecologically con-scious consumer is recognized due to the fact that he/she is trying to purchase products, which do not have negative impact on environment. Although many people describe themselves being ecologically conscious; however, in reality, their actions do no reveal much ecology.

The term ecological consciousness or awareness defines the individual’s more respectful approach towards the set of questions, related to ecology (da Silva Tamashiro et al., 2013). Ecological consciousness – it is environmentally friendly and specific individual behavior. Eco-logically conscious behavior outlines individual efforts in terms of limiting the activities, which may negatively (hazardously) impact environment. The ecologically conscious individual behav-ior may be impacted by the ecological knowledge extent, possessed by an individual (da Silva Tamashiro et al., 2013).

The examples of the ecologically conscious behavior can be: saving of power energy and resources, selection of non-toxic materials and reduction of waste/exhaust materials. One of the ecologically conscious behavior examples is also ecological purchase that can assert through thoroughly purchasers’ analyzed product labels, selection of ecological packages and bags, selection of environmentally-friendly cleansers and body care (Tahir et al., 2011, p. 189).

Thus, the ecologically conscious consumer may be defined as a person, who seeks for the consumption of production that the least negatively impacts environment or has no negative impact on it at all (da Silva Tamashiro et al., 2013; Akehurst, et al. 2012).

According to Hailes (2007), a green consumer is one who associates the act of purchasing or consuming products with the possibility of acting in accordance with environmental preserva-tion. The green consumer knows that by refusing to purchase products that are harmful to the environment, she/he is contributing to environmental preservation. Therefore, according to this author, green consumers avoid purchasing products that they perceive as risky to health, harm the environment during production, use or final disposal, consume much energy, have exces-sive packaging, and contain ingredients coming from threatened habitats or species. Schlegelmilch et al. (1996), state that green consumers are individuals that understand the consequences of their actions and their environmental knowledge can explain their green buy-ing behavior.

In general, the ecologically conscious consumers avoid the consumption of products, which: 1) hazardous to their health; 2) in general, noxious to environment, throughout the whole prod-uct lifecycle or in the certain stage of it (production, consumption or recycling); 3) have an un-necessary (extra) package; 4) in the constitution of these products there are present elements, which invoke threat for some species of for their locations of living.

The consumers, who are most strongly motivated by such factors as worry in animal wel-fare, striving for the maintenance of sustainable and clean environment and other moral values directed towards the sustainability of environment and social welfare, are called the ecologically conscious consumers of ecological products. Besides, such consumer behavior is called ecolog-ical consumption. The ecological product purchase for the ecologically conscious consumers is a part of their lifestyle and manifestation of values (Pino et al., 2012 p. 159).


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Such consumers most frequently purchase the ecological productions of other kinds (hy-giene means and household goods), and they also sort out waste/rubbish and save electric power. It was notices that such consumers are likely to take personal responsibility for social and environmental changes; in other words, they consciously comprehend personal moral obli-gations to the society, environments and its sustainability. Consequently, they are often called idealists. So greatly expressed sense of responsibility is a part of those consumers’ personal identity (Pino et al., 2012 p. 159).

According to Akehurst et al., (2012) whenever there is a purchase decision, there is the po-tential that the final resolution can contribute to a more or less sustainable pattern of consump-tion. All purchasing actions have ethical, resource, waste and community impact consequences. According to Young et al. (2010), when consumers choose to adopt a sustainable lifestyle, their decision‐making process becomes increasingly complex. The author mentions that there is an “attitude‐behavior gap” or “values‐action gap”, because although 30 per cent of consumers report that they are very concerned about environmental issues, it is not necessarily translated to their purchase habits.

The ecologically conscious consumers – are such consumers, who are worried about the product friendliness to the environment (in the whole process of its production and consump-tion), and they emphasize this worry under their behavior: i.e. through the purchase of ecologi-cal products. However, not necessarily all ecologically conscious people turn into the ecological-ly conscious product consumers. First of all, as a result of the fact that different consumers have the ecological consciousness of different strength. Second, the ecological production con-sumer may be affected by different factors in a weakening way. 2. THE ECOLOGICALLY CONSCIOUS CONSUMER BEHAVIOR AND ECOLOGI-CAL PRODUCT PURCHASE

According to Nassani et al., (2013) the ecologically conscious behavior reflects consumers’ decisions during a purchase and at the same time determinacy to pay more for ecological prod-ucts. It is thought that the ecological consciousness is an indicator, capable of describing the consumer behavior during the purchasing of ecological production.

Mahesh, (2013) indicated that the consumers, who define themselves as ecologically con-scious ones, are more likely to purchase ecological production than ones, who do not describe themselves as ecologically conscious ones. There was also noted correlation between consum-er approach towards environment and their approach towards ecological products – the more manifested worry about environment is, the more positive approach towards ecological prod-ucts is too.

According to Akehurst et al., (2012) the consumers, distinguished by the ecologically con-scious behavior at a higher level, are also distinguished by a stronger intent to purchase an ecological product, i.e. the intent of the ecologically conscious consumer to purchase an ecolog-ical product, describes the manifested consumer readiness to behave positively in terms of ecology (Akehurst et al., 2012 p. 978). Besides, the revealed intent to purchase an ecological product means that consumers have a greater tendency to purchase an ecological product ac-tually (Akehurst et al., 2012, p. 983). As a result, the intermediate’s role falls on the factor “in-tent to purchase an ecological product” between a construct ECCB and an actual purchase of an ecological product. In case the ecological consciousness is high, the gap, present between the intent to purchase ecological production and actual its purchase is less. However, not nec-essarily, all ecologically conscious people turn into the ecologically conscious consumers of an ecological product. First of all, due to the fact that different consumers have the ecological con-sciousness of a different strength. Second, different factors may affect the consumer of ecolog-ical food production in a weakening manner, which are related to the very consumer approach


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and external circumstances. Nevertheless, an intent to purchase an ecological product is relat-ed to ecological decision, when an individual decides to take actual means in order to contrib-ute to the preservation of environment. An actual purchase of an ecological product is the com-pletion of actions, when a person takes means, under the aid of which, he can behave ecologi-cally responsibly – in the following case – to purchase ecological production.

In the research, there is followed the approach by Akehurst et al. (2012) that the ecological-ly conscious consumer behavior may reveal either in the purchase of an ecological product or at least an intent to purchase it that may also turn into the product purchase.

Research limitations. The ecologically conscious consumer behavior is related to the higher ecological consumer consciousness, when there is faced a series of suppressing factors as the brand, culture, demographic characteristics, finance, habits, lack of information, lifestyles and personalities (see more Biel and Dahlstrand (2005), Sener and Hazer (2008), which may stop/block the initiative of the developing ecological consciousness form the manifestation in ecological behavior. In the research, there have not been investigated factors, which suppress the developing decision to behave usefully in terms of ecology and the suppressing factors, which suppress the very purchase action of ecological production.

3. RESEARCH ANALYSE AND FINDINGS Reliability of questionnaire questions. Cronbach's alpha coefficient that is based on sepa-

rate questions, those complete the questionnaire, correlation and assessed if all scale ques-tions reflect the researched measurement sufficiently and enables the specification of the re-quested questions in the scale. Consequently for the assessment of variables reliability in ques-tions related to the ecologically conscious consumer behavior, intent to purchase ecological products and actual purchasing of ecological products, there was selected Cronbach’s alfa coef-ficient as shown in Table 1.

Table 1. Cronbach’s Alpha test results

Provided statements/ questions Quantity of questions

Cronbach’s Alpha

Statements related to the ecologically conscious consumer behavior (ECCB) 30 0,937 Statements related to intent to purchase ecological products (IPEP)

3 0,769 Statements related to actual purchasing of ecological products (APEP) 5 0,891

Source: own work.

Pukenas (2009) suggested that minimum alpha of 0,7 is sufficed for research. As the Cronbach’s alpha in this research were all higher than 0,7, the constructs were assessed as adequately reliable.

Correlation analysis. In order to identify connection among variables there was carried out the computing of variables and question statements were divided into three blocks ‘the ecologi-cally conscious consumer behavior’ or ECCB, ‘intent to purchase ecological products relation-ship quality’ or IPEP and ‘actual purchasing of ecological products’ or APEP. The estimated co-efficients are provided in Table 2.

The results of the correlation analysis reveal that all correlations are statistically significant (p=0,000). Due to the fact that all coefficients are positive, thus the correlation among variables


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is positive too. Correlation coefficients among variables vary from 0,598 to 0,816, i.e. there is mean or strong relationship. In the following case the strongest correlation (r = 0,816) is be-tween ‘the ecologically conscious consumer behavior’ and ‘actual purchasing of ecological products’.

Table 2. Correlations coefficient between variables

ECCB IPEP APEP

Spearman's rho ECCB Correlation Coefficient 1,000 ,602** ,816**

Sig. (2-tailed) ,000 ,000 N 185 185 185

IPEP Correlation Coefficient ,602** 1,000 ,598** Sig. (2-tailed) ,000 . ,000 N 185 185 185

APEP Correlation Coefficient ,816** ,598** 1,000

Sig. (2-tailed) ,000 ,000 . N 185 185 185

**Correlation is significant at the 0.01 level (2-tailed). Source: own work.

Following correlation coefficients it can be seen there is present quite strong correlation be-tween ‘the ecologically conscious consumer behavior’ and ‘intent to purchase ecological prod-ucts’ (r = 0,602), There is also medium strength inter-correlation between ‘actual purchasing of ecological products’ and ‘intent to purchase ecological products relationship quality’ (r = 0,598). Consequently, it can be stated that there exists positive and statistically significant line-ar relationship among the factors and the analysis of correlation showed that there is present positive, meaningful connection between excluded variables. Thus there was carried out an analysis of regression.

Results of linear regression analysis. There was carried out a simple linear regressive anal-ysis (one independent variable) due to which there was done testing of hypotheses. As it is stat-ed in the literature, during the performance of the linear regressive analysis attention is paid to three key indexes: coefficient of determination (R2), coefficient of impact (Non-standard β) and relevance of the model (p). Thus the key data, enabling the solution related to impact and its meaningfulness are provided in Table 3.

Table 3. Main influence on variable

Independent variable R2 β p Dependent variable The ecologically conscious consumer behavior 0,697 0,835 0,000 Actual purchasing of ecological

products The ecologically conscious consumer behavior 0,373 0,511 0,000 Intent to purchase ecological

products Source: own work. As it can be seen under the data provided in Table 3, all coefficient signs are positive as well as correlation coefficients are, thus regression analysis is relevant. The determination coefficient is


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quite high in all cases (R2 > 0,20), consequently that means that the model describes the data quite well the simple linear regression model can’t be rejected as one not relevant for data. In all cases p meaning of the criterion of ANOVA is less than 0,05 (p = 0,000), and as a result it can be stated that there is present dependence among variables, i.e. in case of the customer satisfaction increase their respectively increases relationship quality, switching costs, customer retention, etc. The very estimates of coefficients: unstandardized Coefficients β are presented in the column. It is also seen that the signs of all coefficients are positive as it had to be under the correlative connection analysis. Besides, coefficients are statistically meaningful, i.e. have some independent impact on the level of dependent variable.

Testing of hypotheses. H1: There is a direct significant influence between the ecologically conscious consumers’ behavior and actual ecological product purchases. As it can be seen in Table 3, during the testing of the hypothesis: with reference to H1 it has been found out that during improvement of the ecologically conscious consumer behavior increases the actual eco-logical product purchases (accepted Ha, p<α), i.e. there is present linear regression. Besides, regression is statistically meaningful, the coefficient of determination is be to, R2 = 0.697>0.20. The ecologically conscious consumer behavior impact of the actual ecological product purchas-es is equal 0.835. Thus, the first hypothesis is tested and approved: the ecologically conscious consumer behavior has impact on the actual consumers’ ecological product purchases.

H2: There is a direct significant influence between the ecologically conscious consumers’ behavior and intent to buy ecological products. The regressive analyses revealed that there is present linear regression between the ecologically conscious consumer behavior and intent to purchase ecological products (p<0.05), regression – statistically meaningful, (R2 = 0,373 > 0,20). The impact of the ecologically conscious consumer behavior on intent to purchase eco-logical products is 0,511. Thus it is stated that the hypothesis is tested and approved and the ecologically conscious consumer behavior impacts the intent to purchase ecological products.

The ecologically conscious consumer behavior. The ecologically conscious consumer behav-ior was researched in accordance with Straughan and Roberts, (1999) who developed a scale for the ecologically conscious consumer behavior in 1999. In the following research instrumen-tarium there are provided statements, related to the factors of product purchase and consump-tion, and energy saving and storage, recycling of products, sorting of products, product friendli-ness to the environment and to social responsibility. The data, gained during the research are presented in Table 5.

Table 5. Distribution of actions influencing the ecologically conscious consumer behavior by purchasing and using products

Statement of the ecologically conscious consumer behavior Mean*

Std. Devia-tion**

To save energy, I drive my car as little as possible. 3,41 1,199

I normally make a conscious effort to limit my use of products that are made of or use scarce resources. 3,05 1,219

I try to purchase energy efficient household appliances. 3,57 1,006

I always try to use electric appliances (e.g. dishwasher, washer and dryer) before 10 a.m. and after 10 p.m. 3,1 1,447

I will not purchase products which have excessive packaging. 2,44 1,366

When there is a choice, I always choose that product, which contributes to the least amount of pollution. 3,6 1,445

I have tried very hard to reduce the amount of electricity I use. 3,74 0,873


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If I understand the potential damage to the environment that some products can cause, I do not purchase these products. 3,25 1,372

I have switched products for ecological reasons. 3,06 1,296

I use a recycling center or in some way recycle some of my household trash. 3,5 1,256

I make every effort to purchase paper products made from recycled paper. 2,59 1,144

I have purchased a household appliance because it uses less electricity than other brands. 3,88 0,921

I use a low-phosphate detergent (or soap) for my laundry. 2,66 1,249

I have convinced members of my family or friends not to buy some products which are harmful to the environment. 3,59 1,333

I have replaced light bulbs in my home with those of smaller wattage so that I will conserve on the electricity I use. 3,01 1,304

I have purchased products because they cause less pollution. 3,16 1,392

I do not purchase products in aerosol containers 2,9 1,467

Whenever possible, I purchase products packaged in reusable containers. 2,92 1,421

When I purchase products, I always make a conscious effort to purchase those products that are low in pollutants. 3,37 1,349

When I have a choice between two equal products, I always purchase the one which is less harmful to other people and the environment. 3,84 1,282

I purchase toilet paper made from recycled paper. 3,02 1,128

I purchase Kleenex made from recycled paper. 3,48 1,277

I purchase paper towels made from recycled paper. 3,35 1,103

I will not purchase a product if the company that sells it is ecologically irresponsible. 4,08 1,145

I have purchased light bulbs that were more expensive but saved energy. 4,48 1,002

I try only to purchase products that can be recycled. 3,76 1,197

To reduce our reliance on foreign oil, I drive my car as little as possible. 3,89 1,288

I usually purchase the lowest priced product, regardless of its impact on society. 3,49 1,239

I do not purchase household products that harm the environment. 3,83 1,113

I purchase high efficiency light bulbs to save energy. 3,73 0,803

*Mean –reveals average significance assessment of every aspect; **Std. Deviation – standard deviation. The smaller it was, the more unanimously respondents assessed the factor. Source: own estimation based on Straughan and Roberts, (1999) a scale. After having carried out a deeper significance analysis of the assessment related to actions influencing the ecologically conscious consumer behavior (see Table 5), it was noticed that the respondents’ opinions were very unequal (standard deviation of the motivator assessment >1). Only three actions influencing the ecologically conscious consumer behavior coincided: I have tried very hard to reduce the amount of electricity I use, I have purchased a household appli-ance because it uses less electricity than other brands and I buy high efficiency light bulbs to save energy (standard deviation of the action assessment 0,873< 1, 0,921< 1, 0,803 < 1). The respondents’ actions influencing the ecologically conscious consumer behavior were very scat-tered. Consequently, the purchases made by the ecologically conscious consumers are related to the saving and storage of electric power/electricity.


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CONCLUSION In accordance with the empirical research it was defined that the ecologically conscious

consumer behavior manifests in two dimensions: behavioral and attitudinal. In the ecological consumer purchases, the behavioral dimension is defined as an ecological product purchase and the attitudinal dimension is defined as intent to purchase an ecological product. In the study the ecological product is comprehended as a product that saves energy and/or consumes less energy that can be recycled, that is environmentally-friendly, pollutes environment less and that is produced using less natural resources and in preservation of environment. In purchases, the ecologically conscious consumer behavior reveals as consumer actions, while giving prefer-ence to the purchase and consumption of an ecological product.

The empirical research found that the environmentally conscious consumer behavior is ex-pressed by attitudinal and behavioral dimensions. It was identified that there is statistically sig-nificant linear regression, which shows strong relation between the ecologically conscious con-sumer behavior and ecological products purchase.

The results of the study revealed that strong actual purchase of ecological products as be-havioral dimension are related to the energy saving and storage, and medium strength intent to purchase ecological products as an attitudinal dimension are related to products recycling, sort-ing, environmental friendliness and social responsibility among Lithuanians.

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‘

The Strategic Directions of Innovative Economy Development in Russian Agribusiness

VLADIMIR I. TRUKHACHEV1, VITALIY Z. MAZLOEV2, IGOR Y. SKLYAROV3, YULIYA M. SKLYAROVA4, ELENA N. KALUGINA5,

and ANNA V. VOLKOGONOVA6

1 Doctor of Economics, Professor of Stavropol State Agrarian University, rector 2 Doctor of Economic, Professor of the Russian Institute of Agrarian Problems and Informatics mane A.A. Nikonov, Russian Academy of Agricultural Sciences 3 Doctor of Economics, Professor of Stavropol State Agrarian University [email protected] 4 Doctor of Economics, Professor of Stavropol State Agrarian University [email protected] 5 PhD in Theory of Language, Associate Professor of Stavropol State Agrarian University, Master of Economics, Mas ter of Business Administration in Agribusiness and Commerce [email protected]

6 Lecture of Stavropol State Agrarian University, Master of Economics, Master of Business Administration in Agri business and Commerce [email protected]


Received June 12, 2016 Received in revised from September 02, 2016 Accepted November 24, 2016 Available online December 15, 2016

The article describes the evolution of waves of innovations in the global economy and the development of multiculturalism in agri-business of the Russian Federation. The author gives an estimate of innovation activities in Russian agriculture and defines the main directions of innovative activity development.

It was noted that the solution of the problem of innovative activity activation will ensure the effective development of agricultural production in all directions at the expense of labor productivity growth, resource savings, cost savings and cost reduction of agro-food products, increasing the volume and efficiency of agricultural production. The growth of innovation potential will increase the competitiveness of Russian agricultural enterprises on the domes-tic and foreign markets and make agricultural sector of the Rus-sian economy more economically sustainable.

JEL classification:

Q24; Q29; E22; E24 DOI: 10.14254/1800-5845/2016.12-4.9 Keywords:

innovations, scientific and technical potential, waves of innovation, investments, agribusiness

INTRODUCTION

As international experience shows, there is no other way of development for the economy based on knowledge, but innovative one. Creation, implementation and wide expansion of new


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products, services and processes are the key factors in output growth, employment and invest-ment. These are the most essential provisions of product quality improvement, labor and mate-rial costs saving, labor productivity growth, advancement of production management and its efficiency. Ultimately, these factors determine competitiveness of enterprises and their prod-ucts on the domestic and international markets, and enhancement of the socio-economic situa-tion.

Meanwhile, contrary to the laws of the market economy, where demand stimulates the in-crease of innovative activity of the enterprises, Russia is facing the opposite reality. During so-called pre-perestroika period, the country was struggling for the scientific and technical pro-gress, corresponding to planned state economy with its inherent centralized control of decision-making, which was not focused on the enterprise, eliminating the risk of a business venture. As a result, nowadays the innovative sphere, like many others, is not ready to operate in new con-ditions. Only 6% of Russian companies can be called innovative. In industrialized countries the number of such enterprises is 80-90%. In Japan, for example, only 20% of research and devel-opment projects are funded by the state, in Russia this level is up to 80%. And it's not a matter of pride but evidence of the unsuitability of science and technology policy to a market economy. (Morozov et al., 2003)

As a result of systemic wide collapse there was formed a set of objective preconditions for perfection of organizational-economic management in the innovative activity of AIC in Russia. The solution to this problem would provide an effective development of agricultural production in all areas, thanks to the growth of labor productivity, saving of resources, reducing of expens-es and decreasing of agro-food products costs, increasing the level and efficiency of agricultural production. The growth of the innovation potential will increase the competitiveness of local agricultural business on the domestic and foreign markets will make agricultural sector of the Russian economy more economically sustainable.

Currently, there is an increase in production efficiency of the Russian agriculture; due to implementation of domestic and international scientific and technical progress achievements into practice. So, the strategic role of agriculture development belongs to innovation activity. The main premise of investment activity in agriculture is that everything that exists has been aging. It is therefore necessary to update systematically all that is worn out, that is become an obstacle to progress, as well as to take into account mistakes, failures and errors. For this it is required to carry out periodical certification of products in agriculture, technologies and jobs, to analyze markets and distribution channels. In practice, there is nothing that makes agribusi-ness focus on the idea of innovation as the realization that the product produced in the near future will be obsolete.

With regard to the AIC, innovative process is a constant and continuous flow of technical or technological transformation of ideas into new technologies, or their components and bringing them to be used directly in the production in order to obtain new products of high quality. The innovation process involves agricultural research and educational organizations, production controls, supporting and implemental enterprises of various types, as well as agricultural pro-ducers by themselves. Development of innovative processes is caused by market demands; the producers of innovative products in order to create profit promote innovations to their custom-ers. The innovation process begins with the birth of the idea and ends with the commercial im-plementation.

1. METHODS A variety of methods, integrated by systems analysis and analysis of the basic resources of

agriculture were used in the research. The study was carried out by using the following meth-ods: abstract logic, monographic, method of system analysis and expert method.



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2. DISCUSSION The analysis of the various definitions allows us to conclude that, depending on the object

and the subject of the study, various economists consider the category "innovation" in different ways, while research can identify the main directions, depending on the core of innovation: the change, the final result, the progress, the set of activities. (Drucker, 2004; Santo,1990; Ilyenko-va, 2008; Schumpeter, 1982; Nelson and Winter, 2002; Twiss, 1989). If changes constitute the specific content of innovation, the main function of innovation is the function of change. Joseph Schumpeter distinguished five typical changes: the use of new equipment, new technological processes, or new market production support (purchase - sale); the introduction of products with new features; the use of new raw materials; changes in the organization of production and logistics; the emergence of new markets. (Schumpeter, 1982). M. Porter has suggested dividing all operations of organization into two blocks: the main activities for the creation of the final product and support activities. The main activity of an organization represents a cross-cutting process and consists of: procurement activities, operations, distribution, marketing and sales, as well as providing product support services. Supporting business processes consist of corpo-rate-wide infrastructure, human resource management, technological development and general corporate purchases. (Drucker, 2004)

Before the industrial revolution in human history, successive technological methods of pro-duction can be traced, which characterize the various stages of the development of civilization. (Kolpakova, 2011) All of these production methods were directed at the development of agricul-ture and included, on the one hand, soil cultivation, and on the other, the improvement of soil cultivation implements. In Russia, the formation of technological methods of production took place with a certain delay, which is primarily due to the severe natural and climatic conditions. The initial stage of development of agrarian and industrial complex is connected with processes of industrialization. Industrialization was carried out in different countries and at different peri-ods of their development. Previously countries had the status of agrarian. Turning the countries into agrarian-industrial happened under the influence of specific instruments in the period as-sociated with the industrial revolution. However, it coincides over time only in Great Britain. By the middle of the XIX century in these countries the formation of the industry of factory type had already completed, which allowed them to transfer to the status of agro-industrial. Germany had come to this type of development by the end of the XIX century, France at the beginning of the XX century. The industrial revolution led to the emergence of the first wave of innovations (1770-1830). Its formation began with the creation of machinery in England. During this period, spinning had been evolving and the agrarian reforms in the country took place in the 60-70s of the XIX century; it was the beginning of the transformation of countries in the direction of the dynamic type of social development. By 1880 there had been the completion of the first stage of development, during which the developed countries emerged as the agro-industrial. Russia had emerged as the agro-industrial country only by 1913.

The second wave of innovations was associated with the construction of an industrial socie-ty, as well as with the development of regional systems. The epicenter of the third wave of inno-vations (1880-1930) was in the USA and Western Europe. To the special features of this way of life relegated the emergence of electricity, the invention of electric motors and internal combus-tion engines, steel production, the reduction of the share of the primary sector of the economy, increased competition both domestically and on world markets, the development of state-monopoly ownership, increasing the value of the services sector, etc. The development of tech-nologies of the third wave of innovations was associated with the achievements of the natural sciences: physics and chemistry. On the basis of the achievements of these sciences through the use of tools such as the invention and introduction of new technology (modernization, re-placement of equipment with modern technology), provided rapid progress in the industrial pro-cessing of agricultural products. The development of soil cultivation in agriculture was achieved through the tool “production of capital goods for agriculture”. With the development of mechan-



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ical engineering grew the use of machinery in a large agricultural production, but it had not yet acquired a systemic character.

The third industrial wave of innovations was based on the use of electrical energy, the de-velopment on this basis of heavy mechanical engineering, electrical and radio engineering in-dustry. Basic activity of developed foreign countries at this stage in the economy was govern-ment regulation of both public and private sectors, that is, the state was actively involved in the contemporary multicultural mixed market economy in the formation of the most important so-cio-economic processes (Gordeev, 2009). The extent of state redistribution of financial and other resources in foreign developed countries ranges from 30% of gross domestic product in the US and 50% of gross domestic product in Europe and Asia and more than 60% in Scandina-vian countries. This shows the enormous role of the state and its potential to impact on the socio-economic and scientific-technical development. Development of agribusiness in foreign countries was due to the increasing competition in the system. Thus, farmers competed with each other in all directions. Development of the USSR after the transformational changes in 1917 passed with accelerated pace. The Soviet Union, due to the transformational changes, lagged behind the developed foreign countries, in which they had been continuing to build the type of capitalist industrial society. In the Soviet Union in this period there was the formation of the public administration system. From occasional exposure the country moved to state man-agement system. At the same time the regional dimension emerged in Russia. Directive plan-ning was the main instrument of state regulation during that period. As a result of the revolution and the socialist transformation of production relations, electrification plan was realized. For-mation of the third wave of innovations in the USSR was greatly accelerated, the state actively intervened in the process of technical and economic development and in the 20s state order came into operation.

The fourth wave of innovations (1930-1980) actively developed in the United States, Japan, Western Europe and the USSR. The features of the wave were the development of mechanical engineering, discovery and production of synthetic materials and the production of durable goods. Means of labor and technology were based on the use of computers and automated lines using electricity. This mechanical engineering and, in particular, the capital goods produc-tion for agriculture was a key sector, which determined the pace of technical development of the fourth wave of innovations.

The fourth wave of innovations was marked by an active transition of world agriculture on the road of intensification, which was reflected in the rapid increase in global food production by 3.2 times (from $ 765 trillion in 1950 to $ 2.475 trillion in 2000). The maximum growth rates were recorded during the study period in China (6.5 times), India (3.2 times), Japan (2.9 times) and Western Europe (2.8 times). In agriculture, they actively began to use fertilizers as means to increase soil fertility. Automated lines were applied in the production of animal feeds, in the processing of meat products, in the dairy industry. Food production of the persistent provision should be mass. This led to the development of automated lines for processing, food packaging, and creation of the conditions for long-term storage of products. During this period agribusiness was finally formed.

The fourth wave of innovations led to the development of state-monopoly capitalism. The role of the state as a regulator of economic processes was growing, and the main tool became the distribution and redistribution of budgets. In developed countries the system of national development objectives were elaborating, as well as scenarios and forecasts with the controls, the development of programs such as the public sector and its associated with other economic sectors.

The fifth wave of innovation, which began in 1980-1990 period in the same countries, was characterized by rapid development and use of the achievements of the electronics industry, computer technology, as well as the use of nuclear energy. Technical achievements of the fifth wave of innovation could reduce losses during the production, transport and processing of agri-



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cultural products and raw materials. Food production was transformed into industrial transfor-mation of primary agricultural resources in the final product. In this period of time the estab-lishment of international political alliances was created in the interests of the financial oligarchy of the “Group of Seven” member countries. There was a need to harmonize the financial and economic policies of the leading capitalist countries (the “Group of Seven”).

The fifth industrial wave of innovations (1990-2030) has been based on the electronic and nuclear energy capabilities, innovations in the field of microelectronics, information technology, genetic engineering, biotechnology, has led to space exploration, the emergence of satellite communications and other opportunities. Technical achievements of the fifth wave of innova-tion can reduce losses during the production, transport and processing of agricultural products and raw materials. Food production has been moved to industrial transformation of primary agricultural resources in the final product. Formation of the fifth wave of innovations in the USSR was more and more constrained by scarcity of productive resources associated with the reproduction of outdated innovations wave (the second, the third). The consequence of this process is a mixed economy, slowing the pace of its growth and the formation of the fifth wave in Russia. In the USSR, as well as in the developed capitalist countries, the preconditions of fifth wave of innovations were formed.

Transition to the technology of the fifth wave in all sectors, especially in agricultural sector has happened with a significant delay. The only exception was the military industrial complex and space industry. Operation of technology of the third and fourth ways led to a widening of the repair base, strengthened intersectoral interdependence; predetermine excessive resource consumption of their functioning. The reproduction of the second, third and fourth waves were the brake on the development of the economy and resulting in a systemic crisis in the late 80s. Reforms of the mid 80-90s led to the collapse of the USSR and Russia and only made worse the position of farmers. (Ushacheva et al., 2007) Long-term inter-industry connections were broken, many companies lost the commodity markets, and all areas of the economy have regressed. Long-term planning was cancelled; there was a rejection from the forecasting, funding for sci-ence and development of advanced technologies were significantly reduced.

At present time, there is a formation of the sixth wave of innovation in the world (about 2030-2090). Its key factors are Informatics and Microelectronics, on the basis of which the artificial intelligence system will be formed. At the heart of the sixth wave are there is agricultur-al production based on implementation of nano-, bio-, and energy-saving technologies. Nano-technology include: molecular, cellular and nuclear technologies (nanotechnology, nanobi-otechnology, nanobionika, microelectronic technology, nanomaterials, nanorobotization and other nanoscale technology and nanostructures). Technologies based on nano energy will pro-vide even greater performance capabilities for the economy and citizens. Biotechnology will be broadly developed (Dolgushkin, 2010), what raises the question of the development of agricul-ture and agro-industrial complex. Let us compare the levels of development of agribusiness technologies with the core of wave of innovations (Table 1).

Development of agricultural sector of economically developed foreign countries practically is unchanged. In modern Western economy, a government actively supports agriculture as one of the most important sectors of economy (Krylatyh, 2011). In this area of production the main principle of free market is complemented by active state regulation (Gataulina, 2007), namely, minimum prices for major agricultural products are reviewed annually (thus producers are pro-tected from the sharp fall in prices). At the same time the domestic market is protected from the external low-cost product by import duties. Therefore, the EU food prices are above the ones in other world countries, agricultural policy costs are charged to the budget. Support for agricul-ture is practiced in all developed countries. For example, in Canada, the US, Germany, France, the government invests from 42 to 52% of the cost of agricultural production, in the Nordic countries up to 80%. As for Russia, it is only 1.4% of the total state budget is invested in agricul-ture. Starting from 1990s, the science draws the concept of innovation waves in assessing the



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state of the economy and prospects of its development, that express the certain level of scien-tific and technological development. (Bilkov, 2012)

Table 1. Main characteristics of technological structures in agribusiness

Wave of innovations (period)

The core of wave of inno-vations

Countries and leaders of wave of innovations

Particularities of wave of innovations

I Wave of innovations (1770-1830)

The simplest mechaniza-tion of manual labor

Great Britain

-processing of iron and obtaining agricultural tools

made of iron; -use of wood fuel;

-water engine; -mechanization of agricul-

ture.

II Wave of innovations (1830-1880)

Internal combustion engines

England, USA, Germany, France, Russia

- The steam engine; -Use trains and other

transport; -The emergence of ma-

chine tools; -Growth of the scale of

mechanization and agri-business concentration.

III Wave of innovations (1880-1930) Electric Energy USA, England, Germany,

Austria, Russia

- Enhancing the develop-ment of agricultural sci-

ence and agro-consulting; - The beginning of the

active phase of the devel-opment of agricultural

machinery.

IV Wave of innovations (1930-1980)

Energy of hydrocarbons, internal combustion engine

USA, Japan, England, Germany, USSR

-complex mechanization, electrification,

-use of chemicals and reclamation of agricultural

production; - "Green revolution."

V Wave of innovations (1980-2030)

Electronic and nuclear power, genetic engineering

USA, Japan, England, Germany

- Intellectualization and automation of the active agricultural production

("cyber-farming", inbingre); resources-saving, agro-

and Zootechnology.

VI Wave of innovations (2030-....) Nano energy USA, Japan, England,

Germany

-Active implementation of the principles of sustaina-

ble development of the agricultural sector, molecu-

lar biology, genetic engi-neering, nanotechnology; -productional agriculture.

Formation of the fifth wave of innovation in agriculture and agribusiness of Russia is only beginning now. Technology of the fifth waves of innovation can significantly reduce the level of energy and material consumption of agricultural products. The use of obsolete equipment and technology in agricultural and agro-industrial complex is the reason for the large production costs (energy and material level of agricultural products in Russia is 3-4 times higher than the level of resource consumption of similar products originating in countries in developed market economies) and significantly less than the efficiency of domestic production. This raises the problem of technical and technological re-equipment of agricultural enterprises and the search for sources of funding. (Zhidkov, 2014). The main deterrent of innovation is the lack of financial



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resources. In some countries this problem is solved by a public-private partnership (PPP). They were used to enhance agricultural innovation, to attract public funds, improving the efficiency and adaptation of innovations requires promoting wider and more rapid spread.

Most programs are not specific to a system of food and agriculture, but it is applied to the economy-wide innovation system. The basic conditions for the formation of a successful part-nership between public and private parties are the presence of common objectives, the ex-change of mutual benefit and complementarities of human and financial resources. Institution-al mechanisms should be clear, but the degree of formalization may vary. The elements of good governance include setting clear goals and rules, as well as the implementation of regular moni-toring and evaluation, which use proven, open and competitive process to select the PPP for public participation. Transparency is desirable at all stages of implementation. Increasing the capacity of partners to design, manage and participate in the PPP is an important success fac-tor, and this is especially true for agricultural innovation. 3. RESULTS AND RECOMMENDATIONS

In accordance with the Global Innovation Index 2014 ( Cornell University, International Business School INSEAD, World Intellectual Property Organization, Global Innovation Index) Russia ranked 49-th place in the list of 143 countries, which is 13 items higher than in the pre-vious year. Let’s consider the advantages and disadvantages of technological structures prevail-ing at the present time in modern Russian agriculture (Table. 2).

Table 2 - Advantages and disadvantages of technological structures in modern Russian agricul-ture (Sandu et al., 2012)

Type Basic economic entities Advantages Disadvantages

Strongly technocratic

Leading enterprises of various organizational

and legal forms, includ-ing peasant farm enter-

prise

The possibility of production in large quantities at low cost. Susceptibility to the achieve-

ments of scientific-and-technological advance. Possibil-ity of selection of personnel due to high earnings. The ability to

extract large profits

Goods are usually produced by a massive use of agro-

chemicals, resulting in rela-tively low quality of the food. Limited ability to attract the rural population to work in enterprises, entailing an in-crease in unemployment,

aggravation of social tension.

Naturally innova-tive

Collective enterprises and peasant farm en-terprise of different

levels of development

The ability to manufacture eco-friendly products. The absorp-tion of a large number of work-

ers in rural areas, the removal of social tensions. Susceptibility to the achievements of scientific

and technical progress, various kinds of innovations. The possibility of obtaining of

ecological rent.

Limited capacity of production. Relatively high cost of food, which can be offset by in-

creasing prices as a result of environmental cleanliness

products.

Natural

Personal subsidiary plot, partially peasant

farm enterprise

Freedom of choice for people and the type of production work. The ability to produce exclusive

and environmentally friendly products. The absorption of labor in unlimited quantities. Obstruction to unemployment

Primitive production condi-tions. Heavy, monotonous, poorly mechanized labor. Weak susceptibility to the

achievements of scientific and technical progress. The ab-sence of a range of social guarantees (paid holidays,

sick leave, etc.). Low profitabil-ity.



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Now a day, the political situation in Russia is in relatively difficult situation in terms of the initiation of the food embargo in response to actions of the European Union, the United States and some other countries, lead to rejection of imported food produced by these countries. These measures entail a number of inevitable consequences in the development of the domes-tic agro-industrial complex (AIC). In this connection, the main actions of the Russian government in the process of regulation of agricultural sector should be aimed at the stabilization and de-velopment of agricultural production, food security of the population, maintenance of economic, including price, parity between agriculture and other sectors of the economy, reducing differ-ence in the income of agricultural workers and industry, comprehensive support and protection of domestic agribusiness enterprises (Baranova and Tuskov, 2012).

Its advantages are connected with the quality of human capital (30-th position), business development (43-th), the development of knowledge and technology (34-th). Indicators of infra-structure development remain at an average level (51-th), they interfere with the development of innovation imperfect institutions (88th position), poor performance results of creative activity (72-th) and the development of the internal market (111-th). It should be mentioned that the agricultural sector in Russia is undergoing a difficult financial and economic period, because of the high cost of production and a large share of the overdue accounts payable, there is no mar-gin in many areas of the agricultural production. In addition, a high level of depreciation of fixed assets, there is a shortage of working capital, insufficient use of effective organizational and economic mechanisms of economic activity of agricultural enterprises with modern production technology, and management. Of course, the main economic and internal impediments to inno-vation are the lack of own funds, the high cost of innovation, a high economic risk, poorly devel-oped institutional environment. Therefore, the development of science and innovation in the field of agriculture is one of the main directions of the state agrarian policy, which is reflected in the concepts of modern economic policy (Golichenko, 2011).

Innovations in agriculture are new technologies, new equipment, new plant varieties, new breeds of animals, new fertilizers, protection of plants and animals, new methods of prevention and treatment of animals, new forms of organization, financing and crediting of production, and new approaches to training, retraining and skills upgrading (Dokholyan and Umavov, 2011). Experience has shown that many agricultural enterprises, which are embedded in its manufac-turing process advances in science, achieve a significant increase in performance in the manu-facturing and the financial sector.

Unfortunately, innovation potential of AIC is used only in 4-5% in our country, while this numbers in the USA are 50%. The share of high technology products in Russian agro-industrial complex does not exceed 0.3% of the total and more than 20% in developed countries (Golichenko, 2011). The main indicator that measures the innovation activity is the amount of expenses that the state spends on innovation. Figure 1 shows the dynamics of the internal structure of spending on research and development of economic activity in Russia in recent years by economic activity.



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Figure 1. Changes in the structure of domestic spending on research and development of eco-nomic activity of Russia

The presented data indicate that there is a tendency to reduce overall cost of research and development. The biggest share falls on the research and development and in the branches of agriculture expenditure amount to 0.05% of the total costs. Thus, the need to address the prob-lem of increasing innovation activity in agriculture is vital. In modern conditions of worldwide innovation is regarded as one of the main conditions of modernization of the economy (Naydanova and Polyanskaya, 2015). Traditional manufacturing techniques have exhausted the possibility of both extensive and intensive development. Today in many countries there are de-veloping industries that actively apply innovative technologies, the use of every dollar of which 45 returns eight dollars or more. In the last decade, many industrialized and newly industrial-ized countries, including China, have begun to set goals in the area of science and innovations in program and strategic documents of their policies, both in qualitative and quantitative terms. This fixation has served as a kind of catalyst for research and innovation activities in the public and private sectors (Kozlov, 2008). The main feature of the innovation development period is considered to increase the investments volume for R & D. In today's world, the costs of research and development in the United States account for 2.9% of gross domestic product, in Japan it is 3%, in Germany - 2.35%, in Sweden - 4%. Expenditure on research activities in the Russian Fed-eration in 2012 amounted to 1.12% of GDP (Ushachev, 2013). Let’s consider the dynamics of expenditures on research and development over the last decade in our country (Figure 2). Figure 2. Dynamics of expenditure on research and development in the Russian Federation, billion rubles.



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Thus, it is clear that the costs in considered area were increased by 83.6% in 2012 relative-ly to 2000. But despite the positive dynamics of the cost of innovation their share of GDP re-mains low compared with other developed countries. In comparison with the highly developed agrarian countries, Russian farmers have lack in use of technology, engineering, genetic and other scientific achievements and advanced experience. Indicators of crop and livestock productivity of European level can be achieved thanks to innovative modernization, integration of agricultural science, financial, logistical and other resources to achieve the goals set by the State Program of innovative development of agriculture. But, unfortunately, there is no system that provides innovative progress in agriculture. This is due to several factors hindering the de-velopment of innovative activity in agriculture. The amount of financial support applied to R & D is low and does not meet the innovation model of economic development.

Recently there are some active attempts on implementation of innovation systems, which are based on new knowledge, and scientific development. This long-term objective is aimed at replacing the raw material model into innovation model and economic growth. In this regard direct investment should be sent into equipment and science which are required to be the main drivers of economic growth. Improvement of the system is enshrined in the Strategy of equip-ment-technological modernization of agriculture of Russia for the period until 2020. The central part of this system should be innovative companies formed under the laws of a small business. As international experience shows, they are acting in an environment of agricultural producers; they are more competitive and dynamic in the sphere of intellectual services. In order to create a supportive innovative environment at the federal and regional levels the creation of agro technological park promoting the integration of scientific ideas, technologies and development of innovative projects’ production. Currently, such formations have been created in the agro-industrial complexes of Tambov, Rostov, Voronezh, Novosibirsk, Tula, Belgorod Perm regions. There is also the project of technological park Biotehnopolis in the Republic of Buryatia. Busi-ness plan and project documentation have been developed. On a short-term horizon, the Gov-ernment of the Republic should start the construction of engineering infrastructure of industrial park as the main condition of its co-financing from the federal center.

One of the elements that were not examined enough during the formation of agro-industrial complex of the innovation market is evaluation of the demand for innovation. As a rule, the se-lection of innovative projects is carried out without economic expertise, calculation of perfor-mance indicators of development and promotion schemes practiced in the production of the results. According to statistics, almost every year, agricultural producers remain unclaimed at about 50% of completed scientific and technical projects. And this indicator shows that there is no effective system of organizational and economic mechanism of management of innovative activity, encouraging developer to create innovative projects, and consumers to use them. Schematically, the relationship between science and industry in Russia can be represented as follows (Figure 3). Figure 3. The innovation cycle in Russia

Fundamental research Applied research

Inventions Export of inven-tions

The absence of an intermediary, which commercializes R & D

Technology import Sectors of the economy (industry, agriculture)



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In the Russian Federation authorities have created practically all forms of support for inno-vation used in the world. But the chain of innovative products in Russia is opened: the basic researches do not transfer into the applied ones, the applied researchers do not transfer into R & D, and the last ones do not move into industrial production (Ushacheva et al., 2008). For ef-fective innovative activity the important links are the scientific-technical and innovative pro-grams that are implemented on the basis of fundamental and applied research in the priority sectors of the agricultural economy. They should ensure the creation of new generations of equipment and technology to improve the technological level of agribusiness industries.

Active participation in the development of innovation policy should be taken by the regions and the regional legislature, as the most appropriate mechanism, is indirect stimulation of in-novation development. Legal framework regarding tax benefits, organizational forms, and incen-tive innovation mechanism in regional agriculture should also be developed. Unfortunately, in many parts of innovation system are poorly realized and there is nothing done to create the infrastructure of innovative activity not only in agriculture but also in the whole region's econo-my. To improve innovation activity of agribusiness entities, as well as for growth of investment attractiveness of agricultural production joint efforts by all levels of government and the agricul-tural business are required, aimed at creating the innovation infrastructure. By our opinion, it will be possible to implement in case of great interest from the government and business, and certain capital injections in companies that define the scientific, technical and innovation policy in agriculture. It is also necessary to stimulate the introduction of research results by providing funds for their purchase or lease. In addition, training of highly qualified specialists in the field of innovation management in the agricultural sector should not be ignored. It is necessary to develop measures to attract them and consolidate the rural areas. When the necessary condi-tions are adopted there is a hope that those figures, which are planned by the Russian govern-ment in the development of programs for innovation, will be successfully met and agriculture receive "new blood" in its innovative development.

Russia is currently experiencing a sharp need to enhance and intensify innovation. Accord-ing to Rosstat data, in the beginning of 2014 the indicator of innovative activity in the whole country decreased to 10.1% compared to 10.4% in 2012 (The official website of the Federal Service of State Statustics). Innovative activity indicator of organizations is an indicator repre-senting the proportion of organizations implementing technological, organizational and market-ing innovations, the total number of organizations surveyed. Innovative activity of organizations in Russian regions in recent years has a marked tendency to some decrease, as indicated by the pattern data 4.

Among the Russian federal districts, the leader in innovative activity indicator is the Volga Federal District: the proportion of organizations engaged in technological, organizational and marketing innovations, the total number of surveyed companies, at the beginning of 2014 there were up to 11.7%, while in 2011 this indicator was equal to 12.3% (Fig. 4).

The second position on this indicator are Central and Northwestern Federal District (10.7% respectively), the third is Urals Federal District (9.6%). The lowest value of this indicator has a North Caucasian Federal District, where the level of innovation activity was only 5.9%.at the beginning of 2014. At the same time, unlike other districts, North-Caucasian, Siberian and Southern Federal Districts have a positive trend in this indicator during the years. Such a situa-tion is due to a significant socio-economic divergence of Russian regions, the qualitative and quantitative parameters of their existing economic potential, and now it is clear, that this poten-tial is not fully used for innovative development.

A certain problem and an obstacle in the development of innovation activity is underdevel-oped domestic economic institutions, the high risks associated with equity of investments in innovation, a high level of shadow operations in the Russian economy. To reduce the level of the risks, connected with investments in innovation, it is necessary to ensure the development



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of property interest protection mechanism of subjects of innovation activity and innovation in-frastructure organizations. This is possible through the formation of work model of a venture capital funds. Venture funds invest in securities or companies with a high or relatively high risk in anticipation of extremely high profits. Typically, these investments are made in the latest sci-entific and technical developments and high technologies. Typically, about 70-80% of projects do not generate profit, but the profit of the remaining 20-30% pays all the damages.

Figure 4. The level of innovation activity by federal districts of Russia, % (was made by the au-thors according to Rosstat data).

For the purpose of the joint struggle for getting bookings for innovation by subjects and or-ganizations of innovation infrastructure a consortia can be created, which represent an organi-zational form of temporary association of independent companies in order to coordinate their business activities. In world practice, consortia created for the implementation of major capital projects. Obviously, in the next few years of the race of "innovation arms" between the economic powers of the world, as well as between regions within the country will only gain “momentum”. And all the country, each of its regions, every enterprise and every citizen will have a tremen-dous job, firstly, to change mentality and model of economic thinking and behavior, and second-ly, the implementation of practical actions in line with the innovation and modernization of the economic activity. In the field of economics and agribusiness land relations it is advisable to allocate the following main areas for research and development to ensure the development of innovative processes.

CONCLUSION It is possible to build an innovative economy based on knowledge through joint efforts of

the state, business community and science. The development of innovation is always exacer-bates the problem of lack of qualified personnel, so it is necessary to make a bid for training of Russian specialists. In this case the invitation of highly qualified specialists from other countries



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does not completely solve this problem, firstly, they are expensive employees, and secondly, there is no guarantee that the work they perform will be done efficiently and will be brought to an end, and we have to compete in the recruitment of staff with the United States, Europe and Japan. Currently, there are two scenarios for the development of innovations in Russia: depres-sive one and innovational breakthrough scenarios. There are more preconditions in Russian economy for the development according to the first scenario: almost destroyed scientific and technical basis, there is no necessary support of innovation, business has no interest in innova-tion, and the investment environment is not favorable. (Ushacheva et al., 2007)

For the realization of the second scenario, the innovation and breakthrough, we do not have much time, as the supply of economic and technological strength since the Soviet era is almost exhausted, the scientific base has used all its resources, there is no young skilled per-sonnel, equipment is obsolete and physically worn-out, and created technological parks do not give effective output, which is required of them. In this context it should be:

Strong government and corporate innovation policy focused on the innovative development of the country, technological breakthrough, and large-scale development of modern tech-nologies, increasing the competitiveness of domestic finished goods, modernization and re-newal of fixed capital investment mainly on its own scientific and technical base.

A system of public-private partnership should be created where the civil officer is interested in the development of the real sector of the economy of the region. To do this, a system of evaluation of the regional officials’ performance should be elaborated, including indicators such as the number of newly established enterprises, the number of newly created jobs, the employment rate in the region, the number of incoming taxes to the federal, regional and local budgets, the volume of investments in the region. Criteria assessment of effectiveness of regional officials should consist of indicators, characterizing in general socio-economic situation in the region, including indicators, no more than ten. They should have a simple method of calculation, publicity, possibility of operational testing, control of the state regula-tory bodies and public organizations, also it should be strengthened the role of social con-trol by means of the media.

There should be created a national system of education, which will prepare a competent, active and qualified experts and there have been done a lot by the government in this direc-tion: transition to a two-levels system of education, establishment of national research uni-versities, scientific and industrial clusters. The government should ensure effective econom-ic and social guarantees for highly qualified young professionals, thereby solve the problem of departure in search of jobs to foreign countries, where living standards are higher (guar-anteed employment after graduation within two years on the specialty, preferential loans for tuition without interest loan for young families with a child under the condition that parents have higher education received in educational institutions with state accreditation).

The conditions should be created through (subsidies, preferential tax treatment, and more

effective anti-monopoly regulation) for the development of innovative and high-tech production in Russia, including the participation of foreign partners. (Sklyarov et al., 2016). It is necessary to develop healthy competition and to create multiform domestic economy by supporting small and medium-sized businesses. In this case the main emphasis is on the development of pro-duction in the country, and for high-tech industries, provides tax holidays and payment in in-stallments for loans from 3 to 5 years or to provide for 100% subsidizing of interest rates on loans. Streamline the licensing process and reduce the number of activities subject to licensing to a minimum. Development of a system of national standards, or adopting of already existing standards in other developed countries aimed at improving the quality of products, works and services. Transfer the function of the development of modern licensing regulations and stand-ards to professional organizations and business communities. In the USA and Europe, such a scheme works effectively. For example, there are car manufacturing standards in the USA that



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exist from 1920, but this does not prevent a global automaker to produce the modern high-tech cars in America.

China requires particular attention and learning of experiences in stimulating of production with its booming economy; for example, purchasing a car of Chinese production is it is possible to receive a discount of up to 20%; when agricultural producer takes a credit on the needs of production, the loan rate is 0%; if the entrepreneur who is Chinese citizen wants to do business, it will be offered several options for obtaining a soft loan, etc. At the same time regional officials are very interested in business development, as an important criterion for evaluating their per-formance is the growth of GDP and the number of new jobs created in subordinated regions. The sharp decline of this index leads to a very tough measures by the central leadership.

These measures in the complex will ensure the development of international cooperation, attraction of investments, thereby expanding the scope of the introduction of new technologies into the Russian economy. The implementation of innovation policies will make it possible in 2015 to begin development of the sixth way of innovation, and by 2030 to increase its share in the structure of the economy to 20%. All this will enable the Russian Federation to carry out a technological breakthrough and to increase the competitiveness of the domestic economy.

REFERENSES Baranova, O.A., Tuskov, A.A. (2012), “The system of state stimulation of innovative activity in

the AIC”, Modern problems of science and education, No. 6, available at: http://www. sci-ence-education.ru/106-7722 (accessed 08 November 2014).

Beketov , H. (2004), “Prospects for the development of the national innovation system in Rus-sia”, Questions of economy, No. 7, pp. 96–105.

Bilkov, V.A. (2012), “Sustainable production of milk as a factor of food security in the region”, AIC: economy, management, No. 3. pp. 69-72.

Dokholyan, S.V., Umavov, Y.D. (2011), “Innovative approaches to improve the efficiency of use of resource potential of the agro-industrial complex”, Problems of development of agribusi-ness in the region, No. 4 (8), pp. 73-76.

Dolgushkin, N. (2010), “What hinders the development of innovative agribusiness?”, The Rus-sian Federation today, No. 7, pp. 3-8.

Drucker, P. (2004), Management Encyclopedia, Williams, Moscow. Gataulina, E.A. (2007), “Do loans for the construction of mega - farms in Russia recoup?”, Eco-

nomics of agricultural and processing enterprises, No. 5, pp. 26-29. Global Innovation Index 2014, http://www.globalinnovationindex.org/ Golichenko, O.G. (2011), The main factors of development of the national innovation system:

lessons for Russia, Central Economic Mathematical Institute, Nauka, Moscow. Gordeev, A.V. (2009), “The state program of agricultural development: the first year of imple-

mentation”, Economics of agricultural and processing enterprises, No. 1. Ilyenkova, S.D. (2008), Innovative management, UNITY-DANA, Moscow. Kolpakova, Y. (2011), “Research Global Reach Consulting (GRC) of Russian dairy market”, Rus-

sian Food Market, No. 2, pp. 15-23. Kozlov, V.V. (2008), “Organization of innovative development of Russian agricultural business”,

The role of innovation in the development of agriculture, Encyclopedia of Russian villages, Moscow, pp. 44-48.

Krylatyh, E.N. (2011), “To the development of a global food security strategy: the principles of partnership of countries and civilizations”, Nikonovskye chtenia - 20119 Globalization and the agrarian economy of Russia: trends, risks and possible strategies.

Morozov, Y. P., Gavrilov, A.I., Gorodnov, A.G. (2003), Innovation Management: A manual for schools, UNITY -DANA, Moscow.



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Naydanova, E.B. (2014), “Questions of agricultural policy of Russia , in Proceedings of the All-Russian scientific conference Baikal economic reading, pp .25-27.

Naydanova, E.B., Polyanskaya N.M. (2015), “Innovative activity in Russian region”, available at: http://novainfo.ru/archive/30/innovatsiii-v-agropromyshlennom-komplekse (accessed 7 September 2015).

Nelson, P.P., Winter, J.C. (2002), Evolutionary Theory of Economic Change, Delo, Moscow. Porter, M.E. (1980), Competitive Strategy: Techniques for analyzing industries and competitors,

New York. Sandu, I.S., Nechayev, V.I., Birman, V.F. (2012), Organization of innovative activity in the AIC,

KolosS, Moscow. Santo, B. (1990), Innovation as means of economic development, Progress, Moscow. Schumpeter, J. (1982), The Theory of Economic Development, Progress, Moscow. Sklyarov, I.Y., Sklyarova, Y.M., Berezhnaya, M.V. (2016) “Approaches to evaluating the effec-

tiveness of technology transfer”, Economy and Entrepreneurship, No. 1. (2), pp. 562 - 567. Sklyarov, I.Y., Sklyarova, Y.M. (2014), “Evaluation of the agricultural production market and

determining the development of perspectives of the rural area entrepreneurship” in Pro-ceedings of International scientific conference Sustainable agriculture and rural develop-ment in terms of the republic of Serbia, goals realization within the Danube region, Insti-tute of agricultural economics, Belgrade, pp. 529-546.

Twiss, B. (1989), Management of scientific and technical innovations, Economika, Moscow. Ushacheva, I.T., Trubilina, I.T., Ogloblina, E.S., Sandu, I.S. (2007), Innovation activities in the

agricultural sector of Russia, KolosS, Moscow. Ushachev, I.G. (2013), “Agrarian Policy of Russia: Problems and Solutions”, All-Russian Acade-

my of agricultural Sciences, PI Nasirddinova V.V., Moscow. Ushacheva, I.G., Sandu, I.S., Savenko, V.G. (2008), Innovative development of agribusiness of

the Russian Federation: experience and problems, Stolichnaya typographiya, Moscow. Zhidkov, A.V. (2014), “Integrated implementation of innovations - accelerated path of changing

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Impact of Organization Culture on l-Learning

EDMUNDAS JASINSKAS1, BIRUTA SVAGZDIENE2, and ARTURAS SIMANAVICIUS 3

1 Professor, PhD., at the Department of Sports management, Economics and Sociology, Lithuanian Sports Universi-ty, Kaunas, Lithuania, e-mail: [email protected] 2 Professor, PhD., at the Department of Sports management, Economics and Sociology, Lithuanian Sports Universi-ty, Kaunas, Lithuania, e-mail: [email protected] 3 Lector, PhD., at the Department of Sports management, Economics and Sociology, Lithuanian Sports University, Kaunas, Lithuania, e-mail: [email protected]


Received June 22, 2016 Received in revised from August 24, 2016 Accepted October 30, 2016 Available online December 15, 2016

Organization culture is an integral part of every organization. It is a whole of principles, beliefs, expectations, norms, attitudes, and habits characteristic only to its members, treated as a group con-sciousness of these people, which determines their reaction to the processes inside and outside the organization and causing their behavior. It may be stated that formation of culture is a dualist process, which forms naturally under the influence of external and internal factors, or may be formed, altered artificially realizing the corresponding measures by the organization employees and espe-cially by its managers.

Organizational learning is very important factor for the survival and persistence of organization in today world market, since it is one of the main factors of competitive advantage. In constantly changing market the technologies, new products causing revolution, new needs of customers and employees, financing possibilities appear. Learning organization is qualified to create, obtain and transfer knowledge, and not to be afraid of changes in order to reflect and apply new knowledge and skills. This forms the basis for disclosure of employees’ potential, the possibilities for regular and continuous development of organization occur. Thus modern society and learn-ing organizations are oriented towards continual change and learn-ing. Therefore, it is important to find out how organization culture influences learning.

JEL classification: O19, D8

DOI:

10.14254/1800-5845/2016.12-4.10 Keywords:

Organisation culture, organisational learning, knowledge transfer, learning organisation.

INTRODUCTION

Scientists quite in detail analyze the issues of both individual and organizational learning. A direct benefit of learning and organization results is identified; therefore it is important to create an appropriate organization culture promoting organizational learning. A problematic question: what is the impact of different organization culture types on organizational learning?


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Object of paper – impact of organizations’ culture on learning.

Objective of this paper – to determine the impact of organisation culture on learning.

To reach this purpose the following key tasks were set:

To evaluate exploration level of organization culture impact on learning;

To reveal impact of different organization culture types on organizational loop learning;

Research methods: analysis of scientific literature, questionnaire and statistical analysis. 1. THEORETICAL GROUNDING

Various scientists (Schein, 2001; Camero & Quinn, 2011; Denison, 1990; Morden, 1995; Singh, 2010; Vveinhardt, 2011; Šimanskienė 2008) have analysed, described and recom-mended quite a lot of various models and methods for creation and formation of organization culture. The major part of them are based on cultural values or values, on the basis of which the efforts of organization members may be unified to implement the strategies, herewith ensuring the survival of organization itself in the competitive environment. Most authors note that culture formation is a dualistic process. In one case the organizational culture forms naturally, under the objective functioning of internal and external factors, and in other case it is formed and al-tered artificially by the organization employees and especially managers realizing the corre-sponding measures.

As it was mentioned before, a variety of studies about organization culture often includes the creation of successful organization culture. This perfectly reflects in the study of researchers Kotter & Heskett (1992), in which 200 organizations took part. The purpose of the research was to determine what factors have contributed to higher success of organizations’ culture. Kotter & Heskett (1992) in their research established two culture levels, i.e., visible and invisible, which are more properly shown in theoretical part of paper. The first, visible level includes models and style of employees’ behavior. The second, invisible level involves generally accepted long-term values and attitudes. Although the elements of the second level may be changed with more difficulties, Kotter & Heskett (1992) claim that changes of the first and second level elements – behavioral models and style – eventually may initiate the alteration of common values.

Besides, the study of scientists has proved that the impact of culture on organization activi-ty is strong and the following conclusions were made:

Organization culture may have a significant impact on company’s long-term economic activi-ty;

Organization culture will probably be even more significant factor determining success or failure of companies in coming decades;

Organization cultures impeding a strong long-term financial activity are not rare; they easily appear even in the companies where many intelligent and wise people work;

Though it is difficult to change this, however organization cultures may be formed in such a way to enhance organization activity.

Šimanskiene and Sandu (2013) confirm the results obtained by Kotter and Heskett (1992), i.e., the organization culture consists of visible (physical environment, symbols) and invisible (employee’s feeling) elements, which proves the difficulty of its analysis. The other important step in the assessment process is the selection of measures for data collection. In the opinion of E. Schein (2001), cultural studies may be analyzed in two ways – qualitative and quantitative. Qualitative studies help to form a full image of culture, what cannot be achieved through quanti-tative studies. However data not always may be compared objectively. Quantitative studies are more suitable to compare data, and to verify various theories.


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Šimanskienė (2008) having analyzed various scientific attitudes states that their great va-riety allows managers and owners of many business companies, especially those who are theo-retically prepared, successfully pursue the enterprise activity results applying scientifically grounded certain managerial models.

As Singh (2010) claims, to keep pace with rapid environment changes, organizations must adjust to them, otherwise there is a risk of survival in present-day environment. Organizations have to be flexible, constantly developing, to obtain a competitive advantage, be able to adapt, and try to forge ahead. In order to fulfil the challenges in this rapidly changing environment, organization must create and absorb new knowledge, promote innovations, and learn compet-ing in new ways. Organization culture plays a very important role in order to retain learning pro-cess in organization, to make it learning one. In the research made by author the link was es-tablished between organization culture and learning organization in business organizations and the conclusion was made that organization culture plays an essential role in the development of learning organization.

Singh (2010) analyzed the dimensions of organization culture and relations of learning or-ganization in Indian business organizations. The organization culture in the study was identified and analyzed as significant variable affecting the creation of learning organization. Singh (2010) sets eight hypotheses, one for each culture dimension, claiming that it has a significant and positive relation with learning organization.

Culture dimensions having a significant relation with learning organization:

Openness; Confrontation; Confidence; Authenticity; Action „before“ (anticipation in advance); Autonomy; Cooperation; Experimentation.

The results of Singh’s (2010) research have shown that there are three organization culture dimensions positively related with learning organization: openness, action „before“, and exper-imentation. Thus, it may be deemed that these three organization culture aspects are directly affecting the creation and maintenance of learning organization environment. Besides, from the results obtained it may be concluded that openness and action „before“, in the organization culture are the most important variables, which explain the variance of learning organization and were significant. Experimentation variable also positively contributes to the creation of learning organization. The dimensions of authenticity, autonomy and cooperation are signifi-cant; however they are not positively related to learning organization. Other organizational cul-ture dimensions, namely confrontation and confidence, are not significantly related to learning organization. Although these aspects are essential requirements for culture, their contribution to organizational learning is not direct.

However it may be noticed that there is no single opinion of authors regarding the impact of learning organization in respect of factors’ culture. Duden (2011) notices that the basis of or-ganizational learning culture is namely confidence and leadership. While in the research carried out by Singh (2010) the hypothesis, claiming that confidence has a significant and positive rela-tion with learning organization was rejected. The research results have shown that confidence is not significantly related with learning organization. Though this aspect is essential requirement for culture, however the contribution to arrangement of learning is not direct. As Duden (2011) states, according to theory, many possibilities to manage learning processes are described where learning culture manifests through confidence. However, this is practically not easily re-


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stored or accessible, since the grounds of learning culture are implemented inside the entire enterprise and involve all employees (leaders, managers, officials, employees, etc.) It is claimed that learning culture conducted by confidence ensures constant growth, innovations, leadership in the market, what bring enterprise to success.

Fard, Rostamy & Taghiloo (2009) carried out empirical research to analyze the impact of organization culture on learning. The research focused on five organizational learning dimen-sions: personal competence, psychiatric model improvement, vision sharing, team formation, systemic thinking, which were suggested by Senge (1990) as criteria for the assessment of learning organization. After the research following results were obtained:

Great positive relation between organization culture and organization when forming a learn-ing degree.

Great negative relation between bureaucratic culture and organization when forming a learning degree.

Great negative relation between competitiveness culture and organization when forming a learning degree.

Great positive relation between participation culture and organization when forming a learn-ing degree.

Great positive relation between learning culture and organization when forming a learning degree. Referring to studies made by scientists it may be claimed that organization culture has a

significant impact on a long-term economic activity of company. The organization significance is forecasted to grow even more in future, what will affect the success or failure of companies. It is important to note that organization culture may be formed but it is a difficult process where a leader plays an important role. Also more than one culture may prevail in the organization. To speak about organizational learning it is positively related with innovations, and organization culture may promote both organizational learning, and technological innovation, but also may act as a barrier. There is no single opinion of authors regarding the impact of learning organiza-tion in respect of factors’ culture. On the basis of different empirical studies of scientists, the following most mentioned features of organization culture positively affecting organizational learning could be singled out: experimentation, open culture, employees’ expression possibili-ties, environment observation and responding to changes. As the barriers inhibiting develop-ment of learning organization, the bureaucracy and rules were mostly accentuated. 2. METHODOLOGY AND RESULTS OF THE RESEARCH

The research was made to reveal the impact of organisation culture types on organizational learning on the basis of questionnaire data of private sector enterprises in Lithuania. The re-search was performed in 2015.

Research sample. To determine a research sample, non-probability formation of sampling group under the principle of “snowball” applying the method of convenient selection was cho-sen. First, a part of persons featuring by certain characteristics interesting to the research (a person working in private sector enterprise or organization) was selected. Later these persons at the request of the researcher have indicated other persons with analogous features, and the latter indicated the other, etc. Thus, a test group has grown as a “snowball” up to 155 respond-ents from 58 different enterprises.

Questionnaire characteristics. To form questions with the aim to find out the organization culture type, the model of Cameron & Quinn (1999) was taken, in which organization cultures are classified into four groups and coded accordingly: A-Creativity; B-Hierarchy; C-Market; D-Clan. The questions for assessment of organization culture have four possible answers, where


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for the assessment of one question 100 points are given in total. In the second part the an-swers related to the level of organizational learning and loop learning are presented. Organiza-tional learning loops are established using models presented by Argyr and Schon (1978), Akhtar & Khan (2011), Liepė & Sakalas (2008). The research tries to analyze how different types of organization culture affect organizational learning. The hypotheses were raised:

H1 – creativity culture has a positive relation to organizational learning;

H2 – hierarchy culture has a negative relation to organizational learning;

H3 – market culture has a negative relation to organizational learning;

H4 – clan culture has a positive relation to organizational learning.

First it was determined what organizational learning type prevails in the organizations of re-spondents. 2–5 questions were invoked for this, in the answers of which the culture types of clan, creativity, market, and hierarchy were inscribed. To reveal organizational learning level the 7’th question was analyzed, where in the named statements the learning levels of three loops were inscribed on the basis of which the conclusion is made whether organization is learning. Respondents assessed the statements under the principle of Likert scale. Grouped data of or-ganizational culture and learning loops is presented in Figure 1. Figure 1. Distribution of organizational culture and loop learning (P < 0,05* except market cul-ture)

In general the diagram of presented summarized data of private sector organizations indi-cates that organizations are distributed in different ways among all learning loops, however we may see the dominance of certain cultures in the first, second or third learning loop. First of all it is worth to mention that the second loop may be attributed to learning organization since, as Love & Gunasekaran (1999) state, double-loop cycle learning not only monitors present pro-cesses but also includes the changes in organizational culture, politics, objectives, strategy and structure. While one loop learning does not promote studying, problems are solved in present norms and according to Mason (1993) this is non-strategic learning, which is not attributed to learning organization. Three loop learning still is little analysed however it is considered to be the most effective learning level.


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Thus hypothesis H1 states that the creativity culture has a positive relation to organizational learning. Under the research results 19% respondents indicated that the creative type culture prevails, where double-loop learning flourishes and 12% indicated three loop learning, while only 2% of respondents indicated one loop learning level. Therefore it may be concluded that the type of creativity culture positively affects organizational learning. In the study made by Singh (2010) where organization culture dimensions positively affecting learning organization were analyzed, the experimentation, action “before” and openness have proved. These dimen-sions are also characteristic to creativity culture, where environment is learning-friendly, and employees feel themselves able to learn and make experiments with new ideas.

Hypothesis H2 approaches that hierarchy culture has a negative relation to organizational learning. The obtained answers of respondents show that in major part (14%) of hierarchy cul-ture, one loop learning is developed, and two and three loop learning are 5% and 1% respective-ly. Thus this hypothesis was also proved, since hierarchy culture affects the organizational learn-ing as a barrier. The employees of organization where such culture type prevails are interested in safe work and predictability, and long-term concern of organization is to keep stability and smooth activity. Observing such conditions enterprise is not capable to remain competitive in today’s market.

According to hypothesis H3 market culture has a negative relation to organizational learning. As data obtained shows, under the one loop learning the market culture is the second after hierarchy, i.e., 9%. However this culture prevails in both of two and three loop learning cycles, 8% and 4% respectively, what makes more after summing up compared to 1 cycle learning, but these differences are not significant. After such data hypothesis H3 cannot be proved. Though according to results of the study carried out by Sanz-Valle, Naranjo-Valencia, Jime´nez-Jime´nez, Perez-Caballero (2011) it was stated that market culture acts as a barrier for learning organization to develop, however assessing from the perspective of learning organization loops it resulted that market culture does not have a great negative impact on organizational learning.

Hypothesis H4 states that clan culture has a positive relation to organizational learning. By the highest percentage clan culture is attributed to the second loop learning, i.e. 19%, however this culture also leads in three loop learning level (12%). While the clan culture makes only 1% between one loop learning cycles. Therefore we conclude that clan culture makes the strongest positive impact on learning organization.

Thus referring to the research results, a manager, who seeks to make the organization as learning, has to promote culture with strongly expressed feeling „we“, a team work, valuable team, participation of employees in organization activity, transfer of more powers to employees, creation of humane, friendly working environment, open communication of members should be encouraged and appreciated. It is advisable to avoid structured and formalized organization culture, where stability and permanence are appreciated, and the members are related by for-mal rules. This may become a barrier for creation and development of learning organization. CONCLUSION

Referring to studies carried out by scientists it may be claimed that organization culture has a significant impact on a long-term economic activity of organization. It is predicted that signifi-cance of organization culture will grow even more in the future, what will affect the success or failure of organization. It is important to note that organization culture may be formed but this is a complicated process, where a leader plays an important role. Also it is noticeable that more than one culture may predominate in organization. After the analysis of organization culture types reflecting the main values, desirable behaviour and criteria of successful activity, it may be claimed that types of creativity and clan culture form the environment favorable for devel-opment of learning organization in respect of the characteristics of learning loops.


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Empirical research results have revealed that creativity and clan culture positively affect or-ganizational learning, the market culture has no significant impact on organizational learning, and hierarchy culture negatively affects organizational learning. Learning organization has to inseparably link to such values which are close to creativity and clan culture. Therefore a per-sonal initiative as well as freedom to make decisions should be supported, and individual as well as organizational learning must become one of the basic elements of organization culture.

REFERENCES Akhtar, N., Khan, R.A. (2011), “Exploring the Paradox of Organizational Learning and Learning

Organization”, Interdisciplinary journal of contemporary research in business, Vol. 2, No. 9, pp. 373-385.

Argyris C., Schön D. (1978), Organizational Learning: a Theory of Action Perspective,. San Fran-cisco, CA, Jossey-Bass (Chapter 2).

Cameron K., Quinn, R. (1999), Diagnosing and Changing Organizational Culture, New York: Ad-dison Wesley Longman, Inc. (Chapter 3).

Denison, D.R. (1990), Corporate culture and organizational effectiveness, Wiley, New York. (Chapter 1).

Duden, A. (2011), “Trust and leadership learning culture in organizations”, International Journal of Management Cases, Vol. 13, No. 4, pp. 218-223.

Fard, H.D., Rostamy, A.A.A., Taghiloo, H. (2009), “How types of organisational cultures contrib-ute in shaping learning organisations”, Singapore management review, Vol. 31, No. 1, pp. 49-61.

Kotter. J.P., Heskett J.L. (1992), Corporate Culture and Performance, Free Press, Chapter 1, NY. Liepė, Ž., Sakalas, A. (2015), “The three-loop learning model appliance in new product devel-

opment”, Engineering Economics, Vol. 58, No. 3, pp. 73-80. Love, P.E., Gunasekaran, A. (1999), Learning alliances: a customer-supplier focus for continu-

ous improvement in manufacturing. Industrial and Commercial Training, Vol. 31, No. 3, pp. 88-96.

Mason, R.M. (1993), “Strategic information systems: Use of information technology in a learn-ing organization” In System Sciences, 1993, Proceeding of the Twenty-Sixth Hawaii Interna-tional Conference, No. 4, pp. 840-849.

Morden, T. (1995), “International culture and management”, Management Decision, Vol. 33, No. 2, pp. 16-21.

Sanz-Valle, R., Naranjo-Valencia, J. C., Jiménez-Jiménez, D., Perez-Caballero, L. (2011), “Linking organizational learning with technical innovation and organizational culture”, Journal of Knowledge Management, Vol. 15, No. 6, pp. 997-1015.

Schein, E. (2001), Organizational Culture and Leadership, San Francisco: Jossey-Bass (Chapter 3).

Senge P.A. (1990), The Fifth Discipline: the Age and Practice of the Learning Organization, Lon-don: Century Books, (Chapter 2).

Singh, K. (2010), “An analysis of relationship between the learning organization and organiza-tion culture in Indian business organization”, Organizations and markets in emerging econ-omies, Vol. 1, No. 1, pp. 142-165.

Šimanskienė, L. (2008), “Organizacinės kultūros poveikis organizacijų valdymui”, Vadybos mok-slas ir studijos–kaimo verslų ir jų infrastruktūros plėtrai, Vol. 15, No. 4, pp. 1-6.

Šimanskienė, L., Sandu, L. (2014), “Organizacinės kultūros vertinimo metodai”, Regional For-mation and Development Studies, Vol. 10, No. 2, pp. 201-210.

Vveinhardt, J. (2011), “Organizacijos kultūros ir organizacinės kultūros charakteristikos”, Man-agement theory and studies for rural business and infrastructure development, Vol. 5, No. 29, pp. 221-230.

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Travel Service - an Innovative Solution if Exist

BIRUTA SVAGZDIENE1, EDMUNDAS JASINSKAS2, and ARTURAS SIMANAVICIUS3

1 Professor, PhD., at the Department of Sports management, Economics and Sociology, Lithuanian Sports Universi-ty, Kaunas, Lithuania, e-mail: [email protected] 2 Professor, PhD., at the Department of Sports management, Economics and Sociology, Lithuanian Sports Universi-ty, Kaunas, Lithuania, e-mail: [email protected] 3 Lector, PhD., at the Department of Sports management, Economics and Sociology, Lithuanian Sports University, Kaunas, Lithuania, e-mail: [email protected]


Received June 04, 2016 Received in revised from August 27, 2016 Accepted October 30, 2016 Available online December 15, 2016

Tourism is an important key for development of many regions in Europe. Development of the economic growth and other related activities, promote more and more representatives of such sector areas to take a sustainable and environmentally friendly activities. The assessment of tourism in the context of sustainable develop-ment becomes an important goal to preserve and enhance the cultural and natural heritage. This should lead to the development of the management of all the resources that allow the satisfaction of the economic, social and aesthetic needs, while maintaining cultural integrity, essential ecological processes, biological diversity and life support systems. Cooperation in various ways, one of them - the tourism-based alliances, must begin at an early strategic tourism management process. At one of such stages, an important role for learning is played by tourism organization, where tourists using the service switches to the learning process, while have acquired knowledge, develop their skills and values. Scientific studies in Lithuania and abroad permit to assert that there is an increasing focus on expression quality of service problem for learning tourism organization. However, the tourism service quality is not properly investigated rather than theoretical or empirical approaches. In particular, there is a lack of studies, hat analyze educational action expression coherence of the tourism service’ providers and users. Formulated and highlighted statements are supported by the scien-tific literature and studies of the official documents, where the rele-vance of the topic is based on, underlining the fact that there is no sufficient scientific publications, presenting necessary expression of the educational potential of tourist services in the context of a learn-ing organization. Therefore, there is a fundamental problematic question – does tourism organization divert its activity to the ex-pression of the educational potential of the tourism services, and does it perceive the interactions with the educational and manage-rial processes of a learning tourism organization?

JEL classification: O19, D8, Z30

DOI:

10.14254/1800-5845/2016.12-4.11 Keywords:

learning tourism organization, educational and managerial aspects, educational potential, tourism services.


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Biruta Svagzdiene, Edmundas Jasinskas, and Arturas Simanavicius/ Montenegrin Journal of Economics,

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INTRODUCTION The General Assembly of United Nations (2010) accepts the role of a tourism organization

in the meaning of social, cultural and educational contexts: tourism promotion and develop-ment, economic development, international understanding, peace keeping, society honor for human rights and freedoms regardless of race, sex, language, or religion.

In Lithuania the formation process of learning organizations in the contexts of lifelong learn-ing stays a challenge for business organizations. The competitiveness of business organizations is determined by changes of personnel training processes, competence development (B.Simonaitiene, 2001), ability to flexibly choose programs of human resource training, career and development (G.Dessler, 2001; J.Bagdonavicius, 2002; A.Sakalas, 2003; N.Kvedaraite, 2009; D.Bukantaite, 2005).

Often an organization, seeking to adapt to dynamic, innovation-based competitive environ-ment applies non-traditional organizational management forms. It is important to focus on or-ganizational management forms such as personnel motivation (L.Marcinkeviciute, 2003), intel-lectual capital and human resource management (J.Karaleviciene and K. Matuzeviciute (2008); R.Mikuleniene, 2004) ; J.Siugzdiniene, 2008; L.Vaskeliene, 2005; M.Vienazindiene, 2009), organizational identity, image and relation management (A.Pikčiūnas, 2002), consumer loyalty (L.Pileliene, 2009). One of them is a model of a learning organization. Its authors (P.S.Chinowsky and K.R.Molenaar, 2006) offer continued training for personnel and managers, because it makes organization become attractive and helps to survive in a competitive dynamic market.

Scientists (I.D.Stoniene, 2008 ; R.Hopeniene, 2009; A.R.Ligeikiene, 2003) paid a special attention to an educational role of a tourism service. B.Svagzdiene (2013), E.Musneckiene (2007) citing P.Juceviciene (1997) indicates that a learning process is unconscious, mechanical in various environments, especially in museums and cognitive excursions.

A special attention is paid to diverse contacts: environment, society, economic and cultural, past-time, present and future, learning at school and real world, personal way of life and its impact on other people and environment.

The formulated and highlighted statements are based on scientific literature and official document analyses that substantiate the topicality of the theme emphasizing the fact that in Lithuania there are no scientific research works published that would analyze the expression of educational potential of a tourism service in the context of a learning organization. Therefore, a research question is formulated – whether Tourism services are being created by looking for an innovative solution, to let it exist.

The research object is the expression potential of a tourism service.

The research aim is to innovative solutions of tourism services, to exist.

The research objectives are as follows:

To analyze and on the theoretical basis substantiate theoretical access of an organization and tourism organization as a learning organization, and to present theoretical concept of a service.

To investigate the expression of educational potential of a tourism service supplier and a consumer, focusing on knowledge, abilities and values. 1. THEORETICAL GROUNDING

A tourism service can and must be meaningful as: learning and educational means; society promotion; promoting development of less acknowledged values such as imagination, and


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breadth of attitudes; implanting generality, feeling of necessity; team formation; helping tourists to develop their ethical and aesthetical relation to environment, nature, cultural and moral val-ues, nation, and other people (V.Aramaviciute, 2005; V.Aramaviciute and E.Martisauskiene, 2006).

Activity theories of travel organizers and agencies. Mediators play the most significant role in tourism system in order to satisfy customer needs satisfaction and to provide services and products to customers. Even though the impact of presented factors on tourism service groups varies, it is true to state that the development of tourism service creation, supply, and imple-mentation is a multiple social and economic phenomenon.

Travelling organizers and agencies’ functions as mediators’ are quite wide; they encounter peculiarities and priorities of tourism business participators’ and service consumers’ lifestyles, interpersonal communication creation, dynamics of recreation and consuming culture and forms. Mediators are travelling organizers and agencies that can have a huge impact on the choice of consumer target place and tourism kind through communication processes with cli-ents.

Travel organizers on the basis of legal regulations and conditions regularly implement tour-ism business and directly or through mediators (travel selling agents) publicly offer tourism ser-vices for any individual or a special group. Travel organizers directly implement tourism whole-sale agents’ functions, and travel agencies are tourism retail agents. Travel organizers do not have a direct contact with tourism service customers, except the cases, when organizers have their own retail offices. Travel agencies are mediators, selling separate parts or their complexes created by travel business. On marketing point of view, travel organizers and agencies are de-scribed as mediators acting for the use of clients and making contracts with service providers (airlines, hotels, recreation and entertainment centers, museums, etc.) and receiving commis-sions from suppliers. On the legal basis travel organizers and agents act as legal mediators, representing a special geographic region. The educational point of view states that activities of travel organizers are substantiated by the process of tourism services creation by a manager and a persistent learning process of tourism services customers, enabling to overcome chal-lenges, involving into knowledge collection and learning interpretation competences: sharing knowledge, being able to learn through training how to transfer and accept knowledge. This allows understanding such a process as the most important factor for organization to survive.

In a modern society needs are rapidly changing for tourism services, their structure and quality; moreover, new stereotypes of social behaviour, new values, new models of lifestyles. All of that make organizations as learning ones. A tourism organization as a learning organization when three knowledge groups that are implemented and interact together: technical / analyti-cal, problem-based and context-based knowledge.

A group of technical / analytical knowledge is fundamental and essential for a tourism or-ganization as a learning one; however, this group is not valuable and informative enough with-out other groups of knowledge. The group of problematic knowledge requires understanding of real problems rising against the manager and personnel. This knowledge group exists in reality and is connected to budget formation, activity planning and analysis, resource studies, relation with customer creation, and cognition of a customer. Knowledge that has to do with procedures is more likely to be of a political manner rather than technical, and their implementation activi-ties become more theoretical. Contextual knowledge requires understanding of not only organi-zational culture and customer needs, but also environments, where employers act. Essentially, this is a form of organizational wisdom, whose contents encounters technical competence and ways of administration. Contextual knowledge has to be strongly related to other two knowledge groups, as people have to effectively solve organizational projects.

The expression dimensions of a tourism service potential. Situation nowadays indicated a new type of a consumer for tourism organizations. From one point of view, an economic crisis


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has limited consumer possibilities to travel, visit places of interest, revel in resorts and water. On the other side, the new type of a consumer is a lively enough human being, waiting for af-fordable services in order to acquire knowledge. A new view towards a consumer proves that service quality may be strengthened having a management relation with educational factors. Tourism organizations seeking to keep their customers will create a service that customers, reflectively examining environments, experience, and knowledge, will be able to create partner-ship based relations on mutual agreement.

Theoretical and praxeologic contexts of a tourism service consumer and consuming factors. Fundamentally changed national economic activities and principles brought main cultural changes into human consciousness. In market economy a consumer is formed, and he or she is affected by separate organizations’, creating products and services, inter-competition that is used in order to attract consumers. That is why a consumer is usually attacked by masses of information about better decisions for their being. The relationship between knowledge, values, viewpoints, and attitudes is revealed in the processes of consuming.

Knowledge is very valuable in a consuming process. A service consumer learns out that af-ter feeling a need, it is necessary to buy one or the other tourist trip which allows acquiring knowledge. What is more, a loyalty to a service organizer rises without a real experience, just because of thinking, reasoning, watching, listening, etc. A learning process forms certain persis-tent habits; for example, a person travels buying a tourist trip from the same travel organizer.

Values and viewpoints are oriented towards service understanding: beliefs of object-characteristics, beliefs of use-characteristics, and beliefs of object-use. Speaking about con-sumer viewpoints, they can be described in several ways. A viewpoint can be a belief of what a person likes or dislikes; what is more, a viewpoint can be understood as a basis for individuals or groups’ likes and dislikes. Therefore, keeping such a conception, it is meaningful for a service provider to be able to form consumer viewpoints and values through services and so create the basics for consumer loyalty.

Attitudes. Consumers are often used to make decisions paying attention to quite insignifi-cant things, such as physical attraction of a product or a seller. First of all, knowledge and in-formation reach consumers and motivate them, and only then emotional relation rises. A con-sumer feels a special emotional reaction before a cognitive process, and later he or she con-firms or denies this emotional reaction a priori because of cognitive processes.

The buying process of tourism services is connected to consumer attitudes that he or she has, and which are affected by advertising and public opinion means. In order to understand why a consumer buys one or the other tourism service and how he or she buys, it is important to understand consumer attitudes. And vice versa, the recognition of consumer attitudes allows modelling how, why and what tourism services would be bought by consumers. 2. METHODOLOGY AND RESULTS OF THE RESEARCH

Expression of as a learning tourism organization features. The research data of tourist or-ganizations as learning organizations’ feature expression allow stating that all features of a learning organization is revealed in a tourism organization; however, in a different manner.

Experience of a teamwork activity is presented on the basis of system and human factors. This strengthens personnel orientation towards sustainable knowledge gathering. Barriers and difficulties often emerge in successful teamwork in an organization. They become present be-cause of insufficient understanding of needs, aims, incorrect sharing of roles, making incorrect decisions, ineffectively managing, incorrectly spreading information as individual conflicts arise among personnel, giving awards and not waiting for changes in organizational activities. All


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teamwork activities in a tourism organization have to be properly evaluated. This is linked to objectives and their completion depends on tourism service consumer needs.

A tourism organization is a global organization, democratically, rapidly changing and react-ing to outside factors, that means an open system. It is important that personnel would be able to learn individually and in teams.

The creation of a learning organization starts at individual learning encouragement. There-fore a new functions reveals for personnel; it is not only provide tourism services, but also learn themselves. This is the fundamentals for a learning organization creation.

The more employees learn how to learn, the quicker a tourism organization comes closer to a learning organization.

Qualitative potential of tourism services. For the systematic point of view tourism acts as an economic system consisting of tourism subjects and objects.

Knowledge, abilities and values of a service consumer in the context of potential expres-sion. Consumer attitudes in the context of tourist service consumption reveal cultural-social realities of society. Consumer attitudes are statistically meaningful and connected with only one demographic factor – a living place. This allowed identifying consumer attitude types: conserva-tive, modern, and super-innovative. The research data shows that conservative consumers live in the largest cities and are modern enough. They value service quality and learning tourist or-ganizations.

Mostly super-innovative, modern and dynamic attitudes were revealed by citizens of district centres. Such consumers more value tourist organizations as learning ones, providing good services and good servicing.

3 typed cluster was made, while analysing the results of the research. The users were di-vided into three groups by similarity – clusters and named them conservative, economic and superinnovative clusters. They are showen by graph in Figure 1.

Figure 1. Users typology by the factors of provision.

1 – conservative 2 – economic or based on the quality 3 – superinnovative or modern.

By the Figure 1, we can see, that users who are more conservative, value the quality of the

service, orientated on the knowledge they gain, are cautious, focus on the safety of the trip. While being skeptic on the values and the innovations. Economic class users stay in the mid-


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dle. Meanwhile super innovative travelers – are orientated into values and innovative, they like the risk are low cautious, they don’t take much notice to the quality of the service or the knowledge and skills they get and the safety of the trip. In conclusion we can tell that people differentiates by the place that they live.

The research revealed: a) consuming attitudes are connected to demographic and socio-cultural factors; b) individuals living in different regions are differently impacted by knowledge and abilities.

Creation of value to customers of tourism services ‘potential. The peculiarity of tourism or-ganizational activity comes from motives, specific relations with service providers, values that are quite individual in the context of personality self-actualization. The way the personnel wish to change and is able to self-interpret in professional activities impacts business and consumer future.

The learning process of a learning organization is present when an organization accepts new principles and paradigms that usually convert into a competitive advantage. This research work includes: results of organizational data, organizational learning awards, and effective organiza-tional activity results. This helps to identify the most important success factors that may be use-ful to creation of organizational modern learning system development. The relation between these factors and organizational results may form fundamentals for further research.

CONCLUSION

Every supplier of tourism services, depending on service quality and quantity, receives a certain pay rate from a buyer that is a tourist. A certain process is in progress, which can be called a peculiar economic branch or area. The most important element of such an activity is a tourism product, which connects tourism service suppliers and consumers into one singular system. Tourism enables to study people far from their everyday natural environment after set-tlement in a new environment, which satisfies travelers’ needs. This includes tourist’s experi-ence, motivation, regulation, completing the coordination of agencies and firms, cooperating together. Services of travel organizing are provided by travel organizers and travel agencies that are called suppliers of travel organizers’ services.

Activity theories of travel organizers and agencies. A travel organizer does not communicate with tourism service consumers directly, except the cases when they have their retail offices. A travel agency is a mediator selling separate parts of or their complexes of tourism industry. On marketing point of view a travel organizer and agent are described as mediators operating for clients use, making contracts with travel suppliers (airlines, hotels, recreation and entertain-ment centers, and museums, etc.) On the legal basis travel organizers and agents act as legal mediators, representing a special geographic region. The educational point of view states that activities of travel organizers and agents could be as mediators (looking for tourists) between excursion and travelling heads. Excursion and travelling heads complete functions of a lecturer: tell stories, communicate knowledge, show objects, present national values

The theoretical context of a learning organization and a tourist organization is substanti-ated by management and education ideas. The main functioning characteristic of a tourism organization as a learning one is abilities and desire for personnel to develop. A tourist organi-zation becomes a learning organization as it perceives development as a learning function. Such an organization is open in tourism business system and able to not only to accept new information, transform it, but also identify and utilize unnecessary information. Only this way is important to strengthen organizational attractiveness of a tourist organization as a creator. A learning organization’s characteristics are loyalty to personnel and consumers. This is ex-pressed by a variety of learning ways in an organization. Personnel loyalty is clear in teams.


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Teamwork helps to strengthen organizational functioning that broadens possibilities of innova-tive service creation.

The meaning of consumer loyalty is very important to an organization. As time has changed, consumer viewpoints changed as well, and consumers and organizations became learning ones. Producer’s role was based on loyalty between a producer (seller) and a consumer in pre-industrial era. During time change, a concept of consumer and viewpoints towards a sell-er had also changed. Mass consumers are presented as priorities, while needs of individuals were ignored. In the 20th century marketing encountering managerial and educational poten-tial, consumer behavior and trade views emerged. Consumer loyalty is the main aim of relation marketing. Sometimes it is compared to relation marketing conception itself. Relation market-ing conception, with reference of comparative and non-economic perspective, is described as a strategy to keep and strengthen relations with consumers. Educational and managerial aspects are stressed in relations with consumers. These aspects are usually described as a whole of essential principles and elements. Consumer loyalty as the main aim of relation stimulation between consumers of tourism services and service providers. The aim is equally important to a new type of consumer as a learner and a tourism organization, participating in a creation pro-cess of a consumer as a learner. The cross-relational views allow broadening satisfaction receiv-ing tourism services through educational characteristics’ expression, which give a better value to a service. A scientific discussion reveals that a modern organization can strengthen consum-ing views as it is a learning organization.

REFERENCES The General Assembly of United Nations, (2010), Sustainable Tourism. http://www.un.org/

esa/dsd/susdevtopics/sdt_susttour.shtml. Simonaitiene, B. (2001), Besimokancios organizacijos veiklos bruozu raiska gimnazijoje, Kauno

technologijos universitetas, Vilniaus universitetas, Vilniaus. Dessler, G. (2001), Personalo valdymo pagrindai, Poligrafija ir informatika, Vilnius. Bagdonavicius, J. (2002), Zmogiskasis kapitalas, PVU, Vilnius. Sakalas, A. (2003), Personalo vadyba, Vilnius Kvedaraite, N. (2009), Siuolaikines mokyklos kaip besimokancios organizacijos bruozu raiska

personalo savivaldaus mokymosi sklaidos procesuose. Siauliu universitetas. Bukantaite, D. (2005), Besimokancios tinklines organizacijos modeliavimas, Vytauto Didziojo

universitetas. Marcinkeviciute, L. (2003), Lietuvos imoniu darbuotoju motyvavimo modeliu ypatumai besikei-

ciancios rinkos salygomis, Lietuvos zemes ukio universitetas ir Kauno technologijos univer-sitetas.

Karaleviciene, J., Matuzeviciute K. (2008), “Organizacijos intelektinio kapitalo lygio tyrimas musu salies pramones imonese”, Ekonomika ir vadyba: aktualijos ir perspektyvos, Vol. 3, No. 12, pp. 133-140

Mikuleniene, R. (2004), Intelektinio kapitalo valdymas:strateginis aspektas, Kauno technologi-jos universitetas.

Siugzdiniene, J. (2008), Zmogiskuju istekliu vystymo sistema viesojo valdymo reformos kontek-ste, Kauno technologijos universitetas.

Vaskeliene, L. (2005), Organizacijos intelektinio kapitalo vertinimo modelis, Kauno technologi-jos universitetas.

Vienazindiene, M. (2009), Zmogiskuju istekliu valdymo kaita pereinant is viesojo administra-vimo į Naujaja viesaja vadyba (Lietuvos savivaldybiu pavyzdziu), Kauno technologijos uni-versitetas.

Pikciunas, A. (2002), Organizacijos identiteto, ivaizdzio ir rysiu valdymas. Daktaro disertacija, Vytauto Didziojo universitetas.


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Pileliene L., Zikiene K., Svagzdiene B. (2009), “The Formation of Customer Loyalty Conception in the Context of the Evolution of Schools of Marketing Thought”, Socialiniai tyrimai. Mok-slo darbai,No. 2(16), pp. 67- 75.

Chinowsky, P.S., Molenaar, K.R., Realph, A. (2006), “Learning Organizations in Construction”, Journal of Management in Engineering, In Press.

Stoniene, I.D. (2008), Ekskursiju rengimo ir vedimo metodika Mokymo priemone gidu special-izacijos studentams. Klaipedos universitetas.

Hopeniene, R. (2009), Turizmo verslo sistemos dinamines strukturos modelis, Kauno tech-nologijos.

Ligeikiene, A. R. (2003), “Turizmo pletra ir valdymas. Socialiniai mokslai”, Vadyba ir administra-vimas Vol. 131, No. 13, pp. 35-39.

Svagzdiene, B., Jasinskas, E., Fominienė, V.B. Mikalauskas, R. (2013), “The situation of learning and prospects for improvement in a tourism organization”, Engineering econom-ics=Inžinerinė ekonomika, Vol. 24, No. 2, pp. 126-134.

Musneckiene E. (2007), “Siuolaikines vizualines kulturos edukacinis reiksmingumas ir jos pro-jekcija i menini ugdyma”, Jaunuju mokslininku darbai, No. 5 (16), 7-13.

Aramaviciute, V. (2005), Auklejimas ir dvasine asmenybes branda, Vilnius: Gimtasis zodis. Aramaviciute, V.,Martisauskiene, E. (2006), “Tendencies of change in youth spirituality in the

context of globalisation”, Socialiniai mokslai. No. 3 (53), pp. 41-48.

Evgeny Lisin, Wadim Strielkowski, and Evgeniya Krivokora / Montenegrin Journal of Economics,

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Economic Analysis of Industrial Development: a Case of Russian Coal Industry

EVGENY LISIN1, WADIM STRIELKOWSKI2, and EVGENIYA KRIVOKORA3

1 Assoc. Professor, Department of Economics in Power Engineering and Industry, National Research University “Moscow Power Engineering Institute”, e-mail: [email protected] 2 Research Associate, Cambridge Judge Business School, University of Cambridge, e-mail: [email protected] 2 Assoc. Professor, North-Caucasus Federal University, Institute of Economics and Management, e-mail: [email protected]

1. INTRODUCTION

The long-term development of fuel and energy industry should completely satisfy the internal demand for fuel and energy resources in any given country and provide their export to external markets. There are some countries abundant in coal but experiencing institutional difficulties related to the development and modernization of that industry nevertheless. One of the typical examples is the Russian coal industry which has become the main focus of our paper.

One would agree that there are some problems of the use of the immense natural potentials in Russia. These problems relate to extremely uneven allocation of primary energy resources

Received August 02, 2016 Received in revised from – October 15, 2016 Accepted November 09, 2016 Available online Dec. 15, 2016

JEL classification:

O14; O32; O38 DOI: 10.14254/1800-5845/2016.12-4.12

Keywords:

Coal power production industry, Social and environmental aspects, Power generation technologies, Economic mechanisms, Policy of the government, Development factors

This paper focuses on the economic framework of industrial development with a special focus on the assessment of prospects for coal power production industry development in Russia over the short-term and medium-term horizons. We analyse both social and environmental aspects of coal power industry from the standpoint of energy safety and sustainable development of the national economy. We distinguish the lines of improvement of technologies of coal power production as well as provide their economic assessment. Our economic assessment of the coal industry in Russia demonstrated that prospects for coal power industry development would be determined, in many respects, by coal price, conventional pollutant emission limits, stimulating policy of the government, and availability of high-performance technologies of coal-fired energy products generation.

ARTICLE INFO ABSTRACT


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within the territory of regions, reduction of exploration works over recent years, increased depreciation and ageing of the capital equipment in fuel and energy industry.

Estimated natural gas output will significantly vary depending on different ways of social and economic development of Russia. At the moment, major natural gas fields in the Western Siberia, providing the main actual output, are largely exhausted. The necessity of development of new complicated natural gas fields in the Eastern Siberia and the Far East; the need to establish the appropriate infrastructure under worsened geographic, geological, natural and climatic conditions of the natural gas production; and also the increased length of gas transmission routes will negatively impact the economy of the natural gas production and transportation (Lisin et al., 2015; Smil, 2015).

At the same time, the raw material source capacity in the coal mining industry is sufficiently more favourable. Russia has significant balanced coal reserves, which is over 200 bln tonnes (12% of world reserves) with 105 bln tonnes of those being explored at the moment. However, coal reserves are allocated extremely unevenly: over 80% of all of those are in Siberia, whereas the share of the European part of Russia is 10% only (Russian Federal State Statistics Service, 2016).

According to the Russian Energy Strategy for the period up to 2030 (Ministry of Energy of the Russian Federation, 2010; Kuznetsova et al., 2015), the consumption of power-generating coal in the power industry will grow, which will increase its production. Thus, the coal production in 2015 was 360 mln tonnes, including the coal stock of 115 mln tonnes for thermal power plants. The coal production in 2020 should rise up to 415 mln. tonnes, including 138 mln. tonnes as fuel for thermal power plants.

Meanwhile, there is a number of problems that hinder the development of coal power production industry. Increased ash content of coal from many deposits with poorly developed enrichment technologies decrease their competitiveness when compared to natural gas production and give rise to some problems when used as fuel. Also, significant number of coal deposits are characterised with unfavourable mining and exploration conditions. At some particular deposits, significant impairment of coal-mining capacities is expected, which requires commissioning of new facilities considering their technical re-equipment and intensification of production. It is worth to mention the fact that the enhancement of coal production would stimulate the increase in the volume of the interregional solid fuel traffic related to additional coal supply for thermal power plants (Lisin et al., 2015; Gorbacheva et al., 2015; Bugge et al., 2006).

According to the necessity to arrange the reliable provision of organic fuel for the power production industry requiring the maximum inclusion of coal into fuel balance of power plants, and, on the other hand, due to challenges related with coal production and increased use of coal as fuel for power plants, the problem of assessment of short-term prospects for coal power production industry development arise. The problem is discussed in this paper.

2. SOCIAL AND ENVIRONMENTAL ASPECTS OF COAL POWER PRODUCTION INDUSTRY DEVELOPMENT

Thermal power plants as enterprises performing their industrial and economic activities for heat and electric power production influence the social sphere through creation of new employments and via pricing for energy products.

The number of new job opportunities directly depends on technological level of energy products manufacturing and is determined by the installed capacity and number of generating units of the power plant (see Table 1).


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Table 1. Headcount standards for thermal power plants

Installed capacity of the unit, MW

Staff required for 1st power unit maintenance

Additional staff required for 2nd unit maintenance

150 - 210 125 5 300 140 10

500 - 800 170 40 Source: Own development based on (TsOTenergo, 2016)

As some advanced technology is implemented, the single unit output grows. However, on a scale of the countrywide energy system, due to specific features of the energy products, the implemented output is fully determined by customers, i.e., additional employment is created due to increased demand for electric power, not switching to another generating technology. It is also worth to note that the implementation of any advanced power production technology doesn’t lead to qualitative changes in automatic control system of the power plant; therefore, the staff structure and headcount corresponded to installed capacity don’t undergo any change.

On the other hand, the implementation of advanced technology results to the improvement of electric power production efficiency as well as to the decrease of fuel consumption rate. However, at the same time, the cost of coal-fired power production facility grows, and depreciation and maintenance costs, which are included in electric power production cost, also increase. Depending on the ratio of efficiency growth due to fuel consumption decrease and the cost of power plant construction, the unit cost of electric power production when switching to more advanced technology may either decrease or increase.

Apart from technical and economic parameters, which define the level of technology, external factors, particularly fuel price, sufficiently affect the alteration of the unit cost of electric power production. At the low price of coal, the use of high-performance but expensive energy equipment may become economically unsound.

The cost of energy products, which has to lay within the range of socially affordable prices, is also limited by the development of coal-fired technologies. The energy product prices for the population of Russia are regulated by the government by using tariff restrictions. Tariff restrictions are figured up by the Russian Federal Tariff Service (FTS), which assigns the price limits (upper and lower) in accordance with the social policy of the government.

Considering that the cost of the natural gas is growing more rapidly than tariffs for energy products (Lisin et al., 2015; Russian Federal State Statistics Service, 2016), a threatening situation for power plants arises since fuel cost is the principal cost item for thermal power plants. In this case, switching to the prior use of coal as fuel in the future will allow to avoid the decline in profits of power generating companies. This means the reduction of investment programmes for technical re-equipment in the industry, which is one of important criteria of provision of the energy safety to the country. Also, the implementation of effective coal-fired technologies will allow to rein in energy product tariffs growth and to fulfil obligations of the government with regard to social programmes.

Another important aspect of development of coal power production industry is the impact of energy products on environment. Production activities of thermal power plants related to pollutant emissions such as sulphur oxides SOX and nitrogen oxides NOX. Non-exceeding of threshold limits of allowable pollutant concentrations in emissions is the criteria of environmental cleanness of a power generation facility.


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In the absence of the emissions quota system no economic incentive for implementation of additional environmental measures as well as for switching to more advanced coal-fired technologies is provided. Table 2 demonstrates the values of CO2/SO2 emission rates for various carbon-fired power production technologies.

Table 2. CO2/SO2 emission rates for various thermal power production technologies

Technology Fuel consumption rate, TOE/kWh

Emission rate of combustion products, m3/kWh

CO2 SO2

Coal-fired generating units at supercritical steam conditions (SC) 215.25 0.304 0.00076

Coal-fired generating units at ultra-supercritical steam conditions (USC) 190.05 0.268 0.00067

Coal-fired generating units at advanced ultra-supercritical steam conditions (A-USC)

172.2 0.243 0.00061

Source: Own development based on (Sargent & Lundy, 2009; Franco et al, 2009; Breeze, 2014)

Targeting the construction of thermal power plants with elevated steam conditions as the way of development of power production industry will provide saving non-renewable resources (power-generating coal) and will decrease pollutant emission rate, which is extremely important within the context of permanently growing demand for electric power.

3. PROSPECTS OF RENEWAL AND DEVELOPMENT OF COAL POWER PLANTS

Coal-fired power production sufficiently contributes into the diversification of production structure of Russian electric power industry. However, within the last 10 years its share diminished from 29% to 24% over the country and from 19% to 16% in the European part of Russia (Russian Federal State Statistics Service, 2016; Ministry of Energy of the Russian Federation, 2016).

The share of the natural gas in fuel balance of power plants continuously increased within the last decade, whereas the share of coal diminished from 29% to 24%. The increase in the share of the natural gas in fuel consumption of thermal power plants in the European part of Russia was even more significant (from 78% to 87%) while the share of coal there dropped down from 12% to 9% (Russian Federal State Statistics Service, 2016).

Active power plants are characterised by wide spread of semi-fixed costs and fuel costs, which requires calculations based on variant models for substantiation of various standard solutions for renewal and optimisation of the structure of power generating facilities. Due to the ratio of the natural gas and oil prices (despite the intensive rate of increase of the natural gas price, the average cost of 1 TOE of the natural gas was less than the same of the coal), the improvement of coal-combusting technologies and solving of economic problems if coal is used at thermal power plants have fallen out of interest.

Nevertheless, Russia is one of the leading countries in the world in coal usage at conventional steam units of power plants, which is cost-effective today and will be profitable in the foreseeable future. Nowadays, there are 116 coal-fired thermal power plants in operation. Their basic facilities are units with 150–800 MW output capacity.


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There is a severe need in improvement of technical performance of coal-fired units. The prerequisites are achievements in development of new materials and aspiration to reduce the negative impact on environment, including the CO2 emission.

The efficiency of coal-combusting power unit may be increased from 37% to 42% by enhancement of turbine and boiler units, optimisation of thermal circuit, and reduction of auxiliary power consumption. Further raising of unit efficiency up to 43–46% (depending on the quality of fuel) is achievable by the increase in steam parameters (Dykas et al., 2011; Zhang et al., 2013).

Enhancement of reliability and cost-efficiency of actively operated equipment, modification of capital and auxiliary units, implementation of new technologies also contribute to the increase in the performance of coal-fired thermal power plants (Sargent & Lundy, 2009).

Usage of them depends on the equipment conditions, competitiveness of coal-fired generating facilities on the energy market, availability of funds, and other parameters of both internal and external environments.

In order to improve reliable and cost-effective operation of active equipment, well-known and proven technical solutions are applied. These include organising of examination and monitoring of technical conditions of coal-fired units with maximum age-dating. Commissioning of up-to-date automated control systems at coal-fired units is another way of enhancement of efficiency of coal power plants. It sufficiently improves their reliability, possibilities of operational control, economic and operational parameters.

Technological methods of nitrogen oxides NOX suppression, particularly three-stage combustion, are the most mastered ways for improvement of environmental characteristics of coal-fired units. Also, the concentration of NOX in the flue gases may be reduced by 30–65% by using the selective non-catalytic reduction technology which is widely applied in the EU, USA and Japan (Daood et al., 2014).

Table 3 represents the data on pollutant emissions by thermal power plants in Russia and abroad.

Table 3. Comparative data on environmental performance of high-capacity coal-fired thermal power plants in Russia and abroad

Emissions via flue gases Active thermal power plants in

Russia

The EU standard on emissions

Japan (the best achieved result)

NOx mg/m3 300-570 200 80-100 SO2 mg/m3 700 200 70-100

Ashy particles mg/m3 50 30 10-20 Source: Own development based on (Wilde, 2009; Daood et al., 2014; Romanov et al., 2012)

Considering further toughening of standards on solid particles emission and implementation of norms on fine particles emission (PM2.5 and PM10), the application of innovative ash collection technologies becomes economically viable. However, due to liberal environmental legislation, desulphurisation systems are almost completely absent at Russian coal-fired thermal power plants. Today, diminishing of SO2 emissions by coal power plants and development of effective desulphurisation facilities for them is still very actual in Russia.

A question of the need for reducing the concentration of post-combustion CO2 and its capture from the power production cycle arises. Russia holds the fifth place in the world by its


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emissions. It generates about 8% of overall world emissions; power production and transportation industries contribute about 84% of those (International Energy Statistics, 2016).

Significant reducing of CO2 released into atmosphere by coal-fired thermal power plants is achievable by joint production of electric power and heat. Thermal power plants that implement such combined production of energy products and provide customers with district heating are known as heat and power plants (CHPs). These plants are widely spread in Russia and, according to adopted assessment, they reduce fuel consumption over the country by 1.4 mln TOE per annum. Gain in economic performance of coal-fired units of heat and power plants may reduce fuel consumption and CO2 emissions by 20% and more (Lisin et al., 2016).

Sufficient increase in efficiency of operating thermal power plants may be achieved due to in-depth overhaul of their equipment. Some constructive solutions are required, taking into account specificities of combustion of particular Russian sorts of coal; thermal circuits of power generating units should be optimised; usage of expensive steels and alloys during development and manufacturing of new cost-effective power units has to be minimised.

At the present time, Russia, in spite of its extensive experience, noticeably lags behind in manufacturing and commissioning of up-to-date coal-fired power units. According to updated General Layout Plan for Electric Power Facilities for the period up to 2030 (Energy Forecasting Agency, 2010), the commissioning of up to 15 modern coal-fired power units was scheduled. However, in the coming years there is no orders for them from power generating companies. At the same time, for example, in China there are over 100 modern coal-fired power units already operated, with up to 1,000 MW capacity, up to 46% efficiency, and emissions of NOx=90 mg/m3, SO2=120 mg/m3, ashy particles=11 mg/m3 (Mao et al., 2012; Yue, 2012)

The need in technical modernisation of coal-fired CHPs became extremely crucial. These plants are economically unsound for their operation without heat load and thus they are operated during the heating season only, which is about 4,000–5,000 hours per year (Lisin et al., 2016). Heat and power plants work according to heat load schedule and have no flexibility necessary for power adjustment in energy systems. Equipment and systems of heat and power plants were designed and manufactured about 50 years ago, they are worn, insufficiently automated and require the numerous staff for their operation and maintenance. Replacement of obsolete equipment of coal-fired heat and power plants with the new heating units with modern boiling and turbine equipment is a durable solution of the problem. Implementation of production technologies based on elevated steam parameters is necessary for improvement of economic efficiency of coal-fired power units.

4. INDICATOR TARGETS OF THE PROGRAMME OF MODERNISATION OF THERMAL POWER PLANTS

The Programme of Modernisation of Electric Power Industry in Russia for the period up to 2030 is a key document determining the development strategy for coal-fired thermal power plants in Russia.

According to this document with reference to thermal power plants, the following is expected:

reduction of fuel consumption per output of electric power by thermal power plants from 0.233 to 0.210 kgoe/kWh in 2020 and to 0.189 kgoe/kWh in 2030;

decommissioning (dismounting, conservation) of deteriorated and outdated equipment (5.7 GW up to 2020, 40.1 GW up to 2030);

commissioning of new facilities at power plants (16.7 GW up to 2020; 57.5 GW up to 2030);


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efficiency of new natural gas-fired thermal power plants should be at least 50% by 2020 and at least 53% by 2030;

efficiency of new coal-fired thermal power plants should be at least 38% by 2020 and at least 41% by 2030.

The Programme funds for the period up to 2030 are equal to 17.6 trillion roubles, including 10.2 trillion roubles for generating facilities, with 6.3 trillion roubles of those for thermal power plants.

Table 4 represents indicator targets of the Programme of Modernisation of Electric Power Industry in Russia reflecting significance of coal-fired technologies for the development of thermal power plants according to short-term and medium-term forecasts.

Table 4. Indicator targets of the Programme of Modernisation of Electric Power Industry in Russia

# Indicator target 2016-2020 2021-2030 1 Decommissioning of deteriorated equipment of thermal power plants,

GW 5.7 40.1

- by fuel: natural gas-fired thermal power plants, GW 3.99 38.62 coal-fired thermal power plants, GW 1.7 1.42 - by type: condenser type electricity-only thermal power plants, GW 11,2 heat and power plants, GW 11,5 2 Commissioning of new facilities of thermal power plants, GW 16.7 57.5 - by fuel: natural gas-fired thermal power plants, GW 10.71 51.11 coal-fired thermal power plants, GW 5.86 6.36 - by type: condenser type electricity-only thermal power plants, GW 8.98 36.09 heat and power plants, GW 7.76 21.47 3 Average efficiency of thermal power plants, % 46.4 49.4 - by fuel: natural gas-fired thermal power plants, % 50 53 coal-fired thermal power plants, % 38 41 4 Average fuel consumption per output of electric power by thermal

power plants, kgoe/kWh 0.210 0.189

Source: Own development based on (Ministry of Energy of the Russian Federation, 2011; Energy Forecasting Agency, 2010)

As it is demonstrated by the data hereinabove, despite the necessity of development of coal power industry from the standpoint of energy safety of the country, the Programme of Modernisation of Electric Power Industry in Russia gives preference to the development of the natural gas-fired thermal power plants production facilities. This circumstance is due to chosen economic mechanisms of implementation of the Programme.


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5. ECONOMIC MECHANISMS OF IMPLEMENTATION OF THE PROGRAMME OF MODERNISATION OF THERMAL POWER PLANTS

Economic mechanisms of thermal power plants modernisation under the implementation of the Programme of Modernisation of Electric Power Industry in Russia and within the approach used for management in electric power industry presume the following: economically viable ratio of yields and risks of long-term investments into modernisation

and construction of new power generating facilities; leading role of the government in the investment process for the purpose of secure

provision of energetic safety of the country and its regions under the limitation of direct budget expenditures for investments into electric power industry;

containment of additional investment burden on electric power tariffs for end customers; reduction of costs of energy equipment and construction of power generating facilities due

to reorganisation of credit portfolio with acquisition of “long-term” loans and refinancing, including resources of Russian banks with state participation. Financing of commissioning of main generating facilities of electric power plants is provided

within terms of concluded contracts for power provision (CPP). Commissioning of auxiliary capacities is managed through modified mechanism of the long-term power market, which presumes the following:

separated competitive capacity take-off (CCT) for operating facilities of power plants with limitations on technical and economic access conditions (it stimulates modernisation of generating assets);

competitive selection of projects of new capacities for auxiliary CCT with further conclusion of CPP;

pricing limitation on the cost of standard projects and yields for CCT concerning new capacities;

guaranteed investments into commissioning of new capacities dictated by balanced requirements of energy system;

inclusion of new projects on modernisation and construction of facilities of power plants in the programme of public-private partnership in electric power industry. The following methods are applied for selection of new projects on modernisation and

construction of power plants: Assessment of comparative efficiency of standard solutions on technical modernisation and

new construction of power plants (optimisation of technology varieties) on the basis of calculation of discounted expenditures and ranging according to efficiency of power generating technologies. Discounted unit costs per life cycle of applied technology of energy resources production are the criteria of assessment.

Systematic assessment of balanced and economically viable ways of development of electric power industry (optimisation of the scope of development of technologies) on the basis of dynamic optimisation of electric power industry development model. The minimum discounted cost of energy supply to the economy per planning cycle is the criteria of assessment.

Assessment of rational option of the generating capacities structure on the basis of balanced and circuit-operational calculations. The minimum deviations from the optimal capacity structure considering the cost of the single capacity of power generating units, operational factors and resource limitations are the criteria of assessment.


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Adopted economic mechanisms of thermal power plants modernisation are primarily focused on commissioning of new capacities characterised by minimal operational costs. In this case, prospects of development of coal-fired generating technologies will be defined by coal price forecast, the ratio of coal and natural gas prices and pollutant emission quotas.

Obtained results of comparison of threshold power-generating coal prices that determine the economic viability of implementation of coal-fired generating technology, considering or not the pollutant emission quota system, are represented in Table 5. Table 5. Coal price ranges determining the economic viability of implementation of coal-fired technologies from the standpoint of the minimum operational costs, considering or not the pollutant emission quota system

Technology level of coal-fired power generation

Coal price range providing the minimum unit cost of electric power production, roubles/tonne

excluding pollutant emission quota system

including pollutant emission quota system

Coal-fired generating units at supercritical steam conditions (SC)

0 - 662 0

Coal-fired generating units at ultra-supercritical steam conditions (USC)

662 - 7400 0 - 4280

Coal-fired generating units at advanced ultra-supercritical steam conditions (A-USC)

>7400 >4280

Source: Own development based on (Lisin et al., 2015)

The results represented in Table 5 allow to conclude that implementation of pollutant emission quota system results to market signals addressed to owners of power plants, indicating the necessity of implementation of more advanced technologies of coal-fired power production. Commissioning of higher-level technologies of coal-fired power production reduces pollutant emissions into atmosphere and also provides the lean usage of non-renewable natural resources. This matches the principles of the concept of sustainable development and creates the basis for the long-term sustainable development of the national economy.

At that point, consideration of emission quotas will lead to increase of prices on electric power generated by coal-fired thermal power plants. However, a single rise in prices on energy products for the purpose of development of the mechanism of power industry functioning which would comply with the principles of sustainable development should be considered acceptable. Assessment of possibility of implementation of systems stimulating care for the environment is one of objectives of Ministry of Energy, which in turn should determine the growth of electric power production cost and put it in line with the social policy of the government.

In order to match the principles of sustainable development, economic mechanisms of modernisation of thermal power plants should also include criteria of resource adequacy as well as environmental and social factors, not just those of commercial performance. With stimulating policy of the government and favourable economic conditions (e.g., coal price, power generating equipment price, heat electric power costs, terms of crediting, construction and maintenance, transportation abilities and costs), the coal power industry is able to dominate in the energy system of the country in the medium term. However, under existing conditions, the prominence of the natural gas over coal would continuously grow, and it will result to continuing trend of progressive exclusion of coal-fired power production from the market.


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6. CONCLUSIONS AND DISCUSSIONS At present, too little attention is paid to the institutional framework of the coal power industry

in Russia. Its role and scope are not defined for the short-run or medium-run, while its economic preconditions have not been formulated properly by the policy-makers.

There are large coal deposits in Russia with favourable mining conditions. In turn, regions where those deposits are located need electric power and heat for their development. In order to satisfy such demand in energy products, the stimulating technical policy of the government is needed, as well as economic conditions for implementation of projects for thermal power plants construction and modernisation.

The programme of technical modernisation of operated coal-fired units should provide the gradual transition to higher-level coal-fired power production technologies. Implementation of such technologies results to significant reduction of fuel consumption as well as to improvement of environmental performance of the enterprise; however, it requires a bigger share of expensive steels and alloys for the construction of power generating units. This leads to significant increase of capital costs per installed capacity unit. From the economic standpoint, implementation of high-level technologies is viable nowadays for the construction of high capacity power plants.

According to the Russian Energy Strategy for the period up to 2030, the maximum inclusion of coal into fuel balance of power plants is provided. Given this, despite the severe necessity of diversification of fuel and energy resources consumption and decrease of dependence of power industry upon the natural gas, the transition to the prior use of coal-fired technologies does not occur. Moreover, exclusion of coal-fired power production by the natural gas-based one is carrying on. The existing trend in turn may result to incompliance with the energy safety requirements and, thus, to unsustainable development of the national economy. This circumstance has been caused by adopted mechanisms of the state policy on construction and modernisation of thermal power plants, since, first of all, these mechanisms take into account commercial performance of power generating facilities.

With stimulating policy of the government and competitive price on coal fuel, the coal power industry may achieve the dominant place in the energy system of the country in the medium term. However, under existing conditions, the prominence of the natural gas over coal would continuously grow, and it will result to continuing trend of progressive exclusion of coal-fired power production from the market. Acknowledgements:

The reported study was partially implemented within the framework of research project No. 26.1795.2014/K supported by the Ministry of Education and Science of the Russian Federation.

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combustion efficiency and fly ash improvement for coal fired power stations”. Fuel, No. 134, pp. 293-306.

Dykas, S., Rulik, S., Stępczyńska, K. (2011), “Thermodynamic and economic analysis of a 900 MW ultra-supercritical power unit”, Archives of thermodynamics, No. 32, pp. 231–244.


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Energy Forecasting Agency (2010), General Layout Plan for Electric Power Facilities for the period up to 2030. Available at: http://www.e-apbe.ru/scheme/ (accessed: 2016, August, 1).

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Russian Power Utilities Industry”, Sustainability, Vol. 7, No. 9, pp. 11378-11400. Lisin, E., Sobolev, A., Strielkowski, W., Garanin, I. (2016), “Thermal efficiency of cogeneration

units with multi-stage reheating for Russian municipal heating systems”, Energies, Vol. 9, No. 4, pp. 269-288.

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Russian Federal State Statistics Service. Statistical Yearbooks. Available at: http://www.gks.ru/ wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1135087342078 (accessed: 2016, August, 1).

Romanov, A., Sloss, L., Jozewicz, W. (2012), “Mercury emissions from the coal-fired energy generation sector of the Russian Federation”, Energy & Fuels, Vol. 26, No. 8, pp. 4647-4654.

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Smil, V. (2015), Natural Gas: Fuel for the 21st Century, John Wiley & Sons, N.Y. TsOTenergo. “Staffing Standard”, Available at: https://esistems.ru/?page_id=5478 (accessed:

2016, August, 1). Wilde, M. EU (2009), “Emission regulation on coal-fired power plants”, Report Presentation,

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Žaneta Karazijienė, and Artūras Jurgelevičius / Montenegrin Journal of Economics,

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Expanded Concept of Human Capital as Intangible Resource at Macro Level

ŽANETA KARAZIJIENĖ1, and ARTŪRAS JURGELEVIČIUS2

1 Assoc. prof., Mykolas Romeris University, Faculty of Economics and Finance, Institute of Economics, Ateities str. 20, LT-08303, Vilnius, Lithuania; [email protected] 2 Phd student in Economics, Mykolas Romeris University, Faculty of Economics and Finance, Institute of Economics, Ateities str. 20, LT-08303, Vilnius, Lithuania; [email protected]

Received August 11, 2016 Received in revised from – October 10, 2016 Accepted November 29, 2016 Available online December 15, 2016

JEL classification:

E24, E2, A13 DOI: 10.14254/1800-5845/2016.12-4.13

Keywords: Human capital, Quality of human capital, Value orientation, Composite indicator, European countries

The subject of the research is the human capital as intangible resource at macro level in European countries. The aim of the research paper is to propose expanded concept of human capital differentiating it into three dimensions (qualitative, quantitative and value orientation) and empirically apply this concept for European countries. In the research paper, it is believed that classical understanding of human capital as skills, knowledge and educational attainment is incomplete. The values and attitudes are embodied in individuals the same as skills and knowledge. Having high level of educational attainment can be treated only as necessary but not sufficient requirement for human capital’s value growth. It is assumed that value orientation is complementary for human capital value creation. To assess these relationships the composite indicator methodology has been applied. The results revealed that societies with higher qualitative dimension of human capital possess with more tolerance, more openness for different individuals with various cultural backgrounds, trust people more and are more motivational. The strong and positive relationship has been identified between these two dimensions. Scandinavian and Western European countries scored the highest points. Meanwhile Eastern, Central and Southern European countries were categorized as countries with low and very low scores. The conducted research points out the benchmark group of the level of accumulated human capital and lies strategic orientation for policy makers. It is essential and critical to understand the significance of expanded human capital concept as a key factor for economic development especially in the context of knowledge based economy.

ARTICLE INFO ABSTRACT


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INTRODUCTION A great number of various scholars attempting to analyze from different aspects the nature

of human capital and has been using cases studies, experiments and a variety of research methods. While these tremendous efforts have significantly contributed to understanding of the subject itself, nevertheless there are many unclear aspects on the concept and many hypothesis to be tested. The great challenge lays in the nature and definition of human capital as a fundamental source in knowledge driven economies. Broad, complex and multi-angle concept of human capital can take many forms in various contexts from cultural to economic or technological ones.

In recent economics, sociology and related disciplines the concepts of intangible capital and its various forms have emerged. The scholars analyzed these forms of capitals as a factor to economic growth or impact to economic development in general: health capital (Grossman, 1972), religious capital (Azzi and Ehrenberg, 1975), linguistic and cultural capital and symbolic (Bourdieu, 1977), reputational capital (Veljanovski and Whelan, 1983), social capital (Bourdieu, 1986; Coleman, 1988, 1990; Putnam, 1995) academic capital (Bourdieu, 1988), cultural or consumption capital (Becker and Murphy, 1988), cognitive capital (Rescher, 1989),), network capital (Sik, 1994), personal capital (Dei Ottati, 1994; Becker, 1996), political, social and cultural capital (Mouzelis, 1995), intellectual capital (Edvinsson and Malone., 1997), resource capital institutional capital (Oliver, 1997), spiritual capital (Verter, 2003), cultural or creativity capital (Florida and Tinagli, 2004), collective trust capital (Castelfranchi et al., 2006).

However, deeper analysis of these forms of capitals suggests that human capital, fundamentally, is the source for other forms of intangible capital. Since everything has become the capital there is no point to create unclear and confusing forms of capitals but focus and improve the concept of existed human capital. The aim of the research paper is to propose expanded concept of human capital differentiating it into three dimensions (qualitative, quantitative and value orientation) as intangible resource and empirically apply this concept for European countries

1. PROPOSAL FOR EXPANDED DIMENSIONS OF HUMAN CAPITAL CONCEPT In a vast majority of studies human capital refers to the following definition: the skills the

labor force possesses and is regarded as a resource or asset. Most of researcher view human capital as knowledge, skills, education and abilities embedded in an individual (Kagochi and Jolly, 2010; Beach, 2009, Alan et al., 2008; Garavan et al., 2001). These dimensions can be acquired by formal and informal education or working experience (Barney, 2011; Kagochi and Jolly, 2010).

Another view point on human capital is linked to production oriented perspective. Authors claim that human capital is a key source of economic productivity (Romer, 1990). Skills represent individual capacities contributing to production as an argument in the production function (Bowles and Gintis, 2001). Frank and Bemanke (2007) define human capital as a bunch of factors such as education, experience, training, intelligence, energy, work habits, trustworthiness, lifelong learning and initiative that affect the value of a worker’s marginal products. Sullivan and Sheffrin (2003) argue skills and knowledge are necessary for human capital to produce economic value. In this context, human capital studies focused on investment – return approach and the main determinants were analyzed in three ways: formal schooling (individual devotes his time to learning at school and universities), learning at the job place (trainings provided by employer) and off the job training (learning new skills not at work place). All these researches support idea that higher skills, more experience and higher level of educational attainment increase the earnings of individual and make higher productivity for the firms and economic growth in more general.


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One more view on human capital tries to integrate more qualitative aspect. The most popular qualitative dimension of human capital is considered to be health (Akbari et al., 2012, Barney, 2011; Nureev, 2010; De la Fuente and Ciccone, 2002). Some empirical studies human capital’s quality interpret as relative cognitive skills measuring by scores of students in math and science (Laabas and Razzak, 2011). Hanushek and Woessmann (2015, 2012) to the scores on math and science additionally introduce reading achievements as cognitive skills as well. Authors claim that not only years of schooling is important but the quality of schools as well. After correlation analysis, Balcerzak (2016) produces six indicators that reflects the quality of human capital: effectiveness of labor force, employment rate among people in the age 55-64, educational attainment, participation rate in education and trainings, R&D expenditure, tertiary graduates in science and technology.

Meanwhile authoritative economic international organizations World Bank and World Economic Forum and later OECD introduce qualitative measures for human capital. The scope of dimensions acquire quality of education system, quality of primary schools, healthy years beyond 65, high skills employment shares. Quality of math and science education has become very significant dimension in human capital’s quality analysis. Math and science education associates with the cognitive skills which is measured by IQ tests and presents human ability to learn, memorize and connect abstract ideas (Kauts et al, 2015). However, above mentioned economic organizations do not provide deeper relationship analysis of human capital quantity and quality dimensions. Very recent studies suggest that analyzing human capital without non-cognitive skills is incomplete and does not provide full understanding of human capital theory. Authors contributing in human capital theory (Kauts et al., 2015; Lunberg, 2015) emphasis openness, conscientiousness, extraversion, agreeableness and neuroticism as personal traits. Research shows that scores on IQ tests much depends on people motivation rather than level of education (Borghans et al., 2008; Segal, 2012). The higher motivation of person, the higher IQ test results.

This paper suggests to expand and empirically test the concept of human capital claiming that traditional dimensions of human capital do not reflect the whole idea of human capital theory and human values, attitudes are not least important than skills and education. In the paper, it is believed that the increase of the stock of human capital by educational or skills dimensions alone will not ensure social or economic progress. Some values embodied in individual might stimulate or hinder the qualitative dimensions of human capital. In general authors agree that values or attitudes are a part of human capital. Rastogi (2002) and Youndt et al (2004) conceptualizes the human capital as knowledge, competency, attitude and behavior embedded in an individual. Gižienė and Simanavičienė (2012) conducted literature review of historical developments of the term of human capital summarize that human capital is composed not only of knowledge, skills and experience but motivation, personality type, genes, value system, health and many other personal traits. Similar definition is proposed by Potelienė and Tamašauskienė (2014) that human capital apart its classical components implies motivation, insightfulness, attitudes, behavior, physical and emotional health, energy. All these traits are oriented in order to increase individual productivity and generate income.

Figure 1. Proposed dimensions of expanded concept of human capital at macro level

Quality of human capital

Quantity of human capital Values and attitudes of human capital

Source: Authors’ creation


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However, there is a great lack of empirical attempts to evaluate its level and impacts in the context of human capital theory. In this paper the concept of human capital is expanded and presented in three dimensions at macro level: qualitative, quantitative and value orientation dimensions Fig. 1.

Value orientation presents several critical values recently researched in scientific literature: tolerance, cultural openness and diversity, trust. Recent literature suggest that these values encourage individuals be more initiative, ensures social interactions, treat people from various backgrounds as equal (Florida, 2002; Florida and Tinagli, 2004; Berggren et al., 2016; Berggren and Nilsson, 2015; Berggren and Nilsson, 2013; Berggren and Elinder, 2012; Correia and Costa, 2014; Global Creative index, 2015; Hui et al., 2005). Culturally more diverse environment more intensively create and transfers knowledge. Also these values are important for economic freedom and self expression. It is observed that regions with more tolerant and diverse environment more easily attract creative class and fuel more economic growth.

In the context of human capital, tolerance, trust and openness ensure the access to various people, facilitate the interaction and communication with them. It helps to receive more exchange with more knowledge and stimulate the cognitive skills of human being.

2. RESEARCH METHODOLOGY

The methodology of the research has been planned and carried out based on views of Rajasekar et al. (2013), Ginevicius and Podvezko (2008a, 2008b, 2008c), Singh (2006), Kumar (2005), OECD (2008). The research has been divided into eight parts: in the first part, scientific literature in the area of intangible capital was analyzed and the expanded concept of human capital theory has been proposed. In the second part, preliminary indicators for evaluation have been selected and data has been collected. In the third part, imputation of missing data has been conducted. In the fourth part, normalization of data was done. In the fifth part, after correlation matrix final indicators were selected, weighting and aggregation process was accomplished. In the sixth part, composite indicator of human capital was estimated. In the seventh part, raised hypothesis have been tested. Finally, in the eighth part, results have been interpreted and conclusions have been drawn.

To estimate the level of three dimensions, composite indicator has been constructed for each dimension. Composite indicator (sometimes named as summary or synthetic indicator) is increasingly used method to evaluate complex social and economic phenomena (Novickytė et al., 2016; Sarkar, 2013; Prascevic and Prascevic, 2013; Žvirblis and Rimkevičiūtė, 2012; Jokšienė and Žvirblis, 2011; Simanavičienė 2011; Ginevičius and Podvezko, 2008b; OECD, 2008). OECD (2008) has pointed out the main advantages of the method: can summarize complex, multi-dimensional realities with a view to supporting decision makers; are easier to interpret than a battery of many separate indicators; can assess progress of countries over time; reduce the visible size of a set of indicators without dropping the underlying information base; thus make it possible to include more information within the existing size limit; enable users to compare complex dimensions effectively. However, one of the most important preconditions to avoid mistakes while constructing composite indicator remains that the process must be transparent and sensitivity of selected indicators to final results must be conducted.

The choice of selection of indicators to evaluate multi-dimensional phenomena is subjective process and essentially depended on the author’s subjective understanding as well as review of scientific literature (Ginevicius and Podvezko, 2008a; OECD; 2008). Combining both scientific literature review and data availability at macro level, in order to evaluate human capital’s level in European countries, indicators have been selected in a way to present three dimensions of human capital: quantity of human capital, quality of human capital and values, attitudes and non-cognitive skills of human capital which was named as value orientation.


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To collect necessary indicators, secondary data from following databases were used: World Development Indicators (for indicators X6, Z2 ), UNESCO (for indicator X4), EUROSTAT and Life Expectancy Database (for indicator X5) European Value Survey (for indicators, V1, V2, V3, V4, V5, V6,

V7, V8), World Economic Forum reports (for indicators X1, X2, X3), United Nations (for indicator Z1). All in all, 16 preliminary indicators where integrated in human capital’s evaluation system.

The period of analysis was chosen 2008-2010. Mainly it was determined by the latest available indicators of European Value Survey. The arithmetic averages of these three consequent years were estimated in order to identify the level of researched dimensions. The arithmetic averages also contribute in reducing potential negative impact of possible outliers for chosen years. The sample seize of 37 countries based on the continent of Europe was selected. While determining the sample seize, main criteria was following. The states should be as homogeneous as possible. The key criteria for homogeneity is hold relatively similar cultural values, market economy orientation and geographical location in Europe. It is believed that these countries are similar enough in comparison with the rest world’s states. The availability of qualitative data was considered as well.

As a result, 1184 observations were taken into account while analyzing primary data.

Table 1. Preliminary formed system of indicators for measuring the level of human capital at macro level in European countries

Dimension Subdimension Indicator

Quality dimension

of HC

Cognitive skills

Quality of math and science education max X1

Quality of the educational system max X2

Quality of primary education max X3

Health

Life expectancy at birth max X4

Life expectancy at 65 age max X5

Health expenditure by GDP max X6

Quantity dimension

of HC

Education attainment

Mean years of schooling max Z1

Labor force with tertiary education max Z2

Values orientation

of HC

Tolerance

People that wouldn’t like to have Muslims as neighbours min V1

People that wouldn’t like to have homosexuals as neighbours min V2

Openness

People that wouldn’t like to have people of a different race as neighbours

min V3

People that would say they are not a religious person max V4

Trust People that say, generally speaking, most people can be trusted max V5

Diversity People that wouldn’t like to have immigrants / foreign workers as neighbours

min V6

Motivation Optimism

Degree of satisfaction with one’s life max V7

Degree of satisfaction with one’s job max V8

Source: Authors’ creation Selected indicators presented in the Table 1. The indicators had both directions of its values:

maximizing and minimizing ones. Indicators X1, X2, X3, X4, X5, X6, Z1, Z2, V4, V5, V7, V8 were classified


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as maximizing indicators. That means that higher value of indicator presents better performance of analyzed object. Indicators V1, V2, V3 V6 were classified as minimizing indicators which means that higher value of the indicator presents worse performance of analyzed object. Indicators X1, X2, X3, and V4 deserve additional explanations. In the analyzed context, regarding to indicator V4

it is assumed that people that are less religious they are less dogmatic and more open. On contrary, more religious people treated as more conservative, hold traditions and customs. Less religious people are more likely accept new ideas, question accepted social order and will not be afraid to act in a way which is not particular for community. As a result, indicator V4 was classified as maximizing.

Regarding to indicators X1, X2, X3, primary data was available in rankings from World Economics Forum’s reports which value would be interpreted as minimizing. As a result, these indicators were transformed in maximizing interpretation as well. For multi-criteria evaluation all other minimizing indicators must be transformed in a way that higher value of indicator would present better performance for the object. To do such transformations, the formulas 1 and 2 have been applied.

= in (1)

And

- rij = in (2)

Where - is normalized indicator in a given country i by feature n, - is an actual value of the indicator in a given country i by the feature n, max( ) – the highest value of the indicator of given country i of the sample by the feature n, min( )- the lowest value of the indicator of the given country i of the sample by feature n. However, if the 1 formula is proposed by other authors (Hwang and Yoon, 1981; Podvezko, 2008c) as the correct one for such transformations, it was irrelevant for this research. After transformation, correlation coefficients of newly transformed data and primary one were unacceptable because in all cases coefficient was less than 0.6. Meanwhile correlation coefficient was -1 in all cases using formula 2 proposed by authors of the paper. This means that data transformation was done without loss in data validity.

In some cases, missing data imputation was accomplished. Missing data was filled by the average of nearest neighbor method. In other cases, the averages of indicators where estimated in order to not distort the data (OECD, 2008). Missing data imputation was done less than to 1% of all observations. It is possible to conclude that missing data imputation does not impact deviation of final estimations and only support validity of it. Having selected and transformed necessary data, the further step is the normalization of the indicators. As all indicators are of different measurement units, the data aggregation must be done in order to be able to compare objects among themselves and estimate composite indicator.

In scientific literature, a number of normalization methods exists: ranking (Fagerberg, 2001), standardization (z-scores) (OECD, 2008), methods for cyclical indicators (EC, 2004) etc. However, every method implies bias. One of the most reliable and widely applied normalizations method is mini-max method presented in the formula 3.

(3)

Where - is normalized indicator in a given country i by feature n, - is an actual value of the indicator in a given country i by the feature n, max( ) – the highest value of the indicator of given country i of the sample by the feature n, min( )- the lowest value of the indicator of the given country i of the sample by feature n.


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In this way, the countries could be ranked in respect of other countries. The ranking of performance of one country depends on the ranking of performance of other countries. Using mentioned formula, the highest score is 1, the lowest 0. If a country scored maximum ranks in all dimensions and indicators, it would have maximum score of 1.

For further analysis for estimations of composite indicators, the information value should be assessed. Information value means that indicators should not present the similar information otherwise it might distort estimations. Further, the correlation matrix was applied for each of three dimensions’ indicators. In the case of current research work, indicators V3 and V6, V7 and V8, X2 and X3 had high correlation because correlation coefficient was above 0.8 (Hellwig, 1972).

Regarding to X2 and X3 indicators, summary statistics have been done. It was identified that these two variables distributed almost by normal distribution because key criteria obey to the rule of normal distribution. To decide which indicator should be left and which should be removed from analysis, the qualitative dimension of human capital was estimated without X2 but with X3 and then with X2 but without X3. As a result, indicator X3 was removed since correlation coefficient between X2 and other indicators was slightly higher. From logical point of view, it was assessed that quality of education system more worth to be introduced to further analysis than quality of primary education system.

Regarding to V6 and V3, indicators showed very similar normal distribution results. Consequently, indicator V6 was removed because indicator V3 has deeper logical meaning in the analyzed context. It is believed that the value and attitude of an individual can be better presented by neighbor with different race than workers with different race in general since the first one is closer to an individual everyday life. Regarding to indicators V7 and V8, high correlation coefficient was observed as well. However, summary statistics proposed that indicator V8 should be removed from analysis because coefficient of variation and skewness of V7 was closer to criteria for normal distribution. After these estimations, the weights to composite indicators can be given. A number of methods to estimate weights is used in empirical research: COPRAS, TOPSIS, VIKOR, SAW, PROMETHEE, budget allocation processes, analytical hierarchy processes, “benefit of the doubt” (Bogdanovic and Miletic, 2014; Drejeris, 2014; Podvezko et al., 2010; Ginevičius et al., 2013, 2009, 2008; Tvaronavičienė et al., 2008, OECD, 2008).

All of methods, basically, can be grouped in two categories: objective and subjective ones. Objective methods are based on neutral mathematical estimations eliminating the risk of human mistake or subjective opinion. The subjective methods are based on subjective opinion of experts or groups of people. It may vary according to experts’ experience, mood, educational or cultural backgrounds. Particularly, each single method implies advantages and disadvantages for final estimations. A number of authors have compared the validity and sensitivity of each method. Opricovic and Tzeng (2002) compared VIKOR and TOPSIS methods and came to conclusion that TOPSIS is more sensitive to the initial data instability than VIKOR. Another authors came to similar results (Simanavičienė, 2011). V. Podvezko (2011) has performed calculations and claim that SAW and COPRAS methods provide the same results while all indicators are maximizing. However, authors claims that including minimizing indicators, SAW methods becomes more stable than COPRAS. Meanwhile Ginevičius and Krivka (2009) in their analysis concluded that analyzed methods provide coincided results (SAW, VS, COPRAS, TOPSIS).

To conclude, it is important to mention that there is no only one the best method to give weight to composite indictor. However, as many previous discussed authors agree that in most of the cases, there is hardly difference among subjective methods (TOPSIS, COPRAS, VIKOR, PROMETHEE). However, probably the greatest drawback of these methods is that for estimations experts’ opinions should be evaluated. In analyzed context, it is believed that these methods are not appropriate.

The significance of quantitative, qualitative and value orientation dimensions in European countries capture different impact on economic and social developments in these countries.


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Using subjective methods for weights evaluations, countries should be divided into various groups as clusters and from each cluster representative number of experts should be interviewed. It is estimated that following this methodology more than 50 experts’ opinions should be evaluated to have reliable results. Authors of the research paper believe that it is efficient enough to apply less time and cost consuming methods which are reliable, validate and widely used as well. The SAW (Simple Additive Weighting) was used to estimate the weights of indicators. All variables are given the same weights. Essentially, this implies that all variables are worth the same in the composite indicator. This method reduces the risk of subjective opinion. Formula of estimations presented in formula 4.

∑ (4)

Where – value of composite indicator, - is normalized indicator in a given country i by feature n, - weight for indicator n. Equal weights does not mean that there is no weights at all. Since there are some 13 indicators chosen in total, the weights for each indicator is estimated by formula no 5. It is worth mentioning that equal weights were different for all three dimensions.

∑ (5)

Where - weight for indicator n, - number of indicators. Finally, hypothesis of the

research were formulated as following. (see table 2)

Table 2. Hypothesis of the research

H1 There is statistically significant relationship between value orientation and qualitative dimension of human capital.

H0: ρ = 0 H1: ρ ≠ 0

H2 There is statistically significant relationship between human qualitative and quantitative dimensions.

H0: ρ = 0 H2: ρ ≠ 0

H3 There is statistically significant relationship between value orientation and quantitative dimension of human capital.

H0: ρ = 0 H3: ρ ≠ 0

Source: Authors’ creation

The correlation coefficient was interpreted based on views of Čekanavičius and Murauskas (2004) (see table 3). Correlation coefficient can show the relationship between two linearly on each other depended variables. However, correlation relationship does not imply causality explanations. To explain causal relationship other methods should be applied. Table 3. Interpretation of correlation coefficient

│r│ 0 < r < 0.3 0.3 < r < 0.5 0.5 < r < 0.7 0.7 < r < 0.9 0.9 < r < 1 Relationship status Very weak weak modest strong Very strong

Source: Adopted by Čekanavičius and Murauskas (2004) All estimations have been completed using Microsoft Excel program and econometrical program GRETL. 3. RESULTS AND DISCUSSIONS


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Conducted analysis and estimations presented in table 4. Countries have been grouped in the clusters from 1 to 5 which essentially can be interpreted as following. Countries scored the highest rankings grouped in first cluster with very high dimension values. In the second cluster countries with relatively high scores are grouped. Countries with average scores were classified in third cluster. Low and very low scores were presented in fourth and fifth clusters respectively.

Table 4. Rankings of countries by qualitative, quantitative and value orientation dimension No Qualitative dimension of HC Quantitative dimension of HC Value orientation of HC Country’s name C* Value Country’s name C Value Country’s name C Value 1. Switzerland 1 0,93 Russian Feder. 1 0,91 Norway 1 0,89 2. Norway 1 0,84 United Kingdom 2 0,76 Sweden 1 0,85 3. Iceland 1 0,83 Norway 2 0,73 Denmark 1 0,85 4. Denmark 1 0,83 Ireland 2 0,72 Iceland 1 0,82 5. Sweden 1 0,80 Switzerland 2 0,72 Switzerland 1 0,81 6. Belgium 2 0,79 Lithuania 2 0,70 Netherlands 2 0,78 7. France 2 0,77 Estonia 2 0,68 United Kingdom 2 0,76 8. Netherlands 2 0,77 Denmark 2 0,67 Finland 2 0,74 9. Ireland 2 0,77 Luxembourg 2 0,66 France 2 0,73 10. Finland 2 0,77 Germany 2 0,66 Spain 2 0,71 11. Luxembourg 2 0,76 Belgium 2 0,65 Belgium 2 0,70 12. Austria 2 0,70 Cyprus 2 0,65 Germany 2 0,68 13. Malta 2 0,67 Sweden 2 0,63 Ireland 2 0,67 14. Cyprus 3 0,66 Netherlands 2 0,62 Luxembourg 2 0,65 15. United Kingdom 3 0,65 Latvia 3 0,57 Czech Republic 3 0,55 16. Germany 3 0,62 France 3 0,55 Hungary 3 0,55 17. Slovenia 3 0,60 Finland 3 0,55 Austria 3 0,53 18. Czech Republic 3 0,59 Slovenia 3 0,54 Portugal 3 0,51 19. Montenegro 4 0,49 Poland 3 0,53 Malta 3 0,50 20. Albania 4 0,46 Hungary 3 0,50 Greece 3 0,47 21. Greece 4 0,46 Czech Republic 3 0,48 Italy 3 0,47 22. Estonia 4 0,46 Iceland 3 0,48 Slovak Republic 3 0,45 23. Spain 4 0,46 Bulgaria 3 0,45 Montenegro 3 0,44 24. Poland 4 0,45 Spain 3 0,45 Estonia 3 0,44 25. Italy 4 0,43 Slovak Republic 3 0,43 Croatia 3 0,42 26. Bosnia and Herz. 4 0,42 Montenegro 3 0,43 Poland 4 0,39 27. Macedonia 4 0,42 Greece 3 0,43 Bosnia and Herz. 4 0,39 28. Croatia 4 0,40 Croatia 4 0,39 Slovenia 4 0,38 29. Serbia 5 0,38 Austria 4 0,38 Latvia 4 0,37 30. Romania 5 0,35 Serbia 4 0,35 Bulgaria 4 0,36 31. Lithuania 5 0,35 Romania 4 0,32 Russian Feder. 4 0,36 32. Russian Fed. 5 0,33 Italy 4 0,27 Romania 4 0,35 33. Hungary 5 0,33 Malta 4 0,27 Cyprus 4 0,32 34. Bulgaria 5 0,33 Albania 5 0,17 Macedonia, FYR 4 0,28 35. Portugal 5 0,32 Macedonia, FYR 5 0,15 Serbia 4 0,28 36. Latvia 5 0,29 Portugal 5 0,10 Lithuania 5 0,23 37. Slovak Rep. 5 0,28 Bosnia and Herz. 5 0,00 Albania 5 0,16

*the number of clusters

Source: Authors’ estimations Cluster analysis was conducted using k-means method. All variation was divided into 5 equal

intervals taking maximum and minimum values of the variation into account. After that, the center points of each cluster was estimated. The differences of the nearest object of two clusters’ middle points were estimated.


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The country was grouped in that cluster where it had smaller difference between two nearest cluster’s centers. Having newly clustered objects, the centers and differences between centers and objects where re-estimated. In this case, only two countries had to change primary clusters. For qualitative dimension, Croatia was classified from 5th cluster to 4th cluster and for value orientation dimension, Lithuania was classified from 4th cluster to 5th cluster.

For qualitative dimension, Switzerland and all Scandinavian countries presented very high scores. This group can be treated as benchmark group for the rest variation.

Apparently, through very well established all level of education systems, these countries developed cognitive skills. Cognitive skills may lead people to live healthier life style which reflects in longer duration of life. On contrary, poorly developed education systems are not able to generate outcomes as human capital with high cognitive skills and reflects in less rational life living. Such countries grouped in 4th and 5th clusters.

It is important to note that a great number of countries have scored higher rankings on quality of math and science education and less on quality of education systems. These are some countries that had high differences and mismatch by these two indicators: Bulgaria, Bosnia and Herzegovina, Croatia, Greece, Hungary, Lithuania, Romania, Slovak Republic and Serbia. Very likely that inefficient and ineffective education systems and institutions can hinder the organic potential of human capital of these countries and stimulate brain drain.

For quantitative dimension, the variation of countries are more polarized. On one hand, some countries have reached knowledge based economic development stage and possess high qualitative dimensions. On other hand, countries experience the development stage by increasing its quantitative potential which can be transformed later into higher qualitative dimension. However, countries in 4th and 5th clusters can be at high risk while transiting to the next stage of economic development.

In value orientation dimension, countries have split similarly as in qualitative dimension. The 1st and 2nd clusters are strictly formed only by Scandinavian and Western European countries leaving Central, Eastern and Southern European countries in the rest three clusters. Societies of the countries in these first two clusters are more open, more tolerant, trust people more, and accept diversity of values of other people, being more optimistic and motivational. It seems that qualitative and value orientation dimensions may have reasonable correlation. However, the statistical relationship of them are analyzed below.

It was doubted whether Russian Federation quantitative dimension can be interpreted as outlier or not and whether it can hinder the results in further analysis or not since only one country formed one cluster. To test possible impact on results, correlation matrix has been done with and without Russian Federation’s observed values (see table 5). Table 5. Correlation coefficient comparison

Correlation coefficients, using the

observations 1 – 37 Correlation coefficients, using the

observations 1 – 36

Quality Quantity Values Quality Quantity Values 1,0000 0,4336 0,7864 1,0000 0,5491 0,7814 Quality 1,0000 0,4458 1,0000 0,5367 Quantity 1,0000 1,0000 Values

Source: Authors’ estimations

Since after analysis the correlation coefficients has changed even by 0.1 points, it was

decided to remove this observation from hypothesis testing procedures while testing H2 and H3.


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The correlation coefficient for value and quality orientation has not changed significantly so in further analysis this object has been left.

Table 6. Descriptive statistics of estimated composite indicators

Variable Mean Median Minimum Maximum

quality 0,561118 0,494547 0,280000 0,955993

quantity 0,501080 0,528353 0,00000 0,908333

values 0,536454 0,500000 0,160000 0,893017

Variable Std. Dev. C.V. Skewness Ex. kurtosis

quality 0,197116 0,351292 0,221275 -1,33420

quantity 0,201889 0,402908 -0,531997 -0,0843308

values 0,199536 0,371953 0,161902 -1,10753

Variable 5% Perc. 95% Perc. IQ range Missing obs.

quality 0,289000 0,851599 0,383024 0

quantity 0,0869223 0,773184 0,267414 0

values 0,223000 0,855881 0,345000 0

Source: Authors’ estimations

Descriptive statistic and distribution of estimated composite indicators is presented in the

table 6.

Finally, hypothesis has been tested.

First hypothesis stated that there is statistically significant relationship between value orientation and qualitative dimension of human capital.

H0: ρ = 0 H1: ρ ≠ 0

It was identified that correlation coefficient is 0.78 which presents strong and positive relationship between two variables and two tailed p-value 0,0000. As a result, under the null hypothesis of no correlation, hypothesis H0 must be rejected and alternative H1 accepted.

Second hypothesis stated that there is statistically significant relationship between human qualitative and quantitative dimensions.

H0: ρ = 0 H2: ρ ≠ 0

It was identified that correlation coefficient is 0.55 which presents modest and positive relationship between two variables and two tailed p-value 0,007. As a result, under the null hypothesis of no correlation, hypothesis H0 must be rejected and alternative H2 accepted even if relationship is modest.

Third hypothesis stated that there is statistically significant between value orientation and quantitative dimension of human capital.

H0: ρ = 0 H3: ρ ≠ 0

It was identified that correlation coefficient is 0.53 which presents modest and positive relationship between two variables and two tailed p-value 0,005. As a result, under the null hypothesis of no correlation, hypothesis H0 must be rejected and alternative H3 accepted even if relationship is modest.

Figure 2 shows the visual distribution of countries by qualitative (X axis) and value orientation (Y axis) dimensions. The chart is relatively divided into four quadrants by middle points in each axis.


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Figure 2. Combination of value orientation and qualitative dimension of human capital

Source: Authors’ creation In the I quadrant countries appear with higher value orientation than quality orientation. In

the second quadrant both value orientation and quality dimensions are very high. In the third quadrant both value orientation and qualitative dimensions are low. Meanwhile in the fourth quadrant quality are higher than value orientation.

A great discussion of the matrix lies how countries can move from one quadrant to another. Namely it is very important for countries being in the third and fourth quadrants. Cognitive skills and value orientation cannot be improved over short period of time. Basically, it can be the matter of a few generations.

Recent literature studies and conductive empirical research can propose that people with higher cognitive skills are more open, tolerant and more trust people in general. Perhaps this is because people are more confident and feel that they are able to behave in a way to protect themselves and reduce the level of uncertainty in the life.

People possessing less knowledge and having less cognitive skills feel uncertainty and fear more and attempt to isolate themselves from social interaction with others. Societies with less qualitative dimension are more archaic and conservative. It is more believed in the traditions and customs, societal structure are more hierarchical, it is more believed in the fatality.

Apparently, such societal structure and values embedded in individuals do not stimulate creativity, knowledge creation and transfers. Having access to smaller number of informational sources do not empower cognitive skills of individuals. And on contrary, more open societies sharing their knowledge and information which diversify the source of received knowledge and that stimulate cognitive skills of individuals.

ALB

AUT

BEL

RUSBIH

CHE

CYP

HRV

CZE

DEU

DNK

ESP

EST

FINFRA

GBR

GRC

HUN

IRL

ISL

ITA

LTU

LUX

LVA

MKD

MLT

MNE

NLD

NOR

POL

PRT

ROUBGR

SVK

SVN

SWE

SRB

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

1,00

0,20 0,30 0,40 0,50 0,60 0,70 0,80 0,90 1,00

Y‐ value dimension, X qualitative dimension

II

III IV


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CONCLUSIONS To sum up, the objective of the paper was to propose expanded concept of human capital

as intangible resource at macro level by dividing human capital into three dimensions: quality of human capital, quantity of human capital and value orientation. It was claiming that skills and knowledge -as it is understood in classical concept of human capital – alone does not form the complete concept of human capital.

Values, attitudes, personal traits and behavior of human can be as significant as gained skills and attained education. It is believed that values and attitudes are embodied in individual.

It was discovered that value orientation and qualitative dimension of human are closely interact with each other. On one hand, more open, more tolerant, diverse societies who trust other people more possess with higher cognitive skills and better health. On other hand, people with more developed cognitive skills and better health more willing to accept diverse people with different cultural background. This leads to higher level of communication, idea and knowledge generation and exchange. The multi polar sources of knowledge encourage the improvements of cognitive skills.

For the time being it is too early to suggest the causal relationship between these two dimensions. It should be understood as complementary ones. The analysis of causality relationship is seen as future research.

Apparently, Scandinavian countries with Western European countries possess the highest level of human capital which lay foundation for economic development especially for knowledge based economy. Meanwhile Eastern, Central and Southern European countries need almost double its human capital in order to achieve current level of human capital of Scandinavian and most of Western European societies.

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Author Guidelines

Submit to the journal Submissions should be sent via email to: Professor Veselin Draskovic E-mail: [email protected] Review process Each paper is reviewed by the editor and, if it is judged suitable for this publication, it is then

sent to two referees for double blind peer review. The authors’ names are anonymous to the review-ers. Based on their recommendations, the editor then decides whether the paper should be accept-ed as is, revised or rejected. The Editorial Board retains the right to methodologically adjust the arti-cle to the journal propositions and standards of the English language, as well as not to consider articles which do not meet the requirements of these guidelines.

Copyright Articles submitted to the journal should not have been published before in their current or sub-

stantially similar form, or be under consideration for publication with another journal. Use this in conjunction with the points below about references, before submission i.e. always attribute clearly using either indented text or quote marks as well as making use of the preferred Harvard style of formatting. Authors submitting articles for publication warrant that the work is not an infringement of any existing copyright and will indemnify the publisher against any breach of such warranty.

The author is responsible for ensuring the authenticity of data, facts, quotations and other in-formation. The Editorial Boards may publish articles for discussion, without necessarily sharing the author’s views.

Manuscript requirements Please prepare your manuscript before submission, using the following guidelines: Format All files should be submitted as a Word document, A4 format, Franklin Gothic Book, font size 11 pt. Article Length Articles should be between 5000 and 10000 words in length. For long articles, compliance of

editor-in-chief is required. Pictures, graphics and other attachments should be marked and sent as separate files, or in text, and must not exceed the journal format with margins.

Article Language It is strongly recommended to send articles in the English language. However, upon the compli-

ance of the Editorial Board, it is possible to publish articles in some of the other world languages. Authors from Montenegro and surrounding countries should submit articles both in English and mother tongue due to the bilingual nature of the website.

Article Title Page - An Article Title Page should be submitted alongside each individual article.

This should include:

Article Title - A title of not more six eight words should be provided. Author Details - Details should be supplied on the Article Title Page including: Full name of

each author, Affiliation of each author, E-mail address of the corresponding author


E.V. Popov, I.S. Katz, A.Y. Veretennikova / Montenegrin Journal of Economics,

Vol. 12, No. 4 (2016), 157-158

158

Structured Abstract - Authors must supply a structured abstract: Purpose, Methodology, Ap-proach, and Findings. Maximum is 250 words in total.

Keywords - Immediately after the abstract, provide a maximum of 6 keywords. Article Classification - The articles are classified in the following categories: original scientific

paper, preliminary communication, review, professional paper, and book review. Classification codes - Please provide up to 6 standard JEL codes. The available codes may

be accessed at JEL: http://www.aeaweb.org/journal/jel_class_system.html Article structure - The structure of article should comprise: the title, abstract, key words, in-

troduction, subtitles, conclusion and bibliography. Articles can also be structured in the following way: introduction, starting hypotheses, solutions, discussion, conclusion and bibliography. Divide your article into clearly defined and numbered sections (1, 2, 3, ...). Subsections should be num-bered 1.1 (then 1.1.1, 1.1.2 ...), 1.2, etc. (the abstract is not included in section numbering).

Footnotes - Footnotes should be used as least as possible, and only for the necessary ex-planations, with the continuous use of Arabic numbers.

References

Literature is not to be numerated. It is to be arranged in alphabetic order of authors and chrono-logically for the articles of the same author. Literature is to be quoted according to the examples for books, magazines and other sources.

References to other publications must be in Harvard style and carefully checked for complete-ness, accuracy and consistency. You should cite publications in the text: (Ilic, 2009) using the first named author's name or (Ilic and Tot, 2009) citing both names of two, or (Tot et al., 2009), when there are three or more authors. At the end of the paper a reference list in alphabetical order should be supplied:

For books Surname, Initials (year), Title of Book, Publisher, Place of publication. e.g. Bagdikian, B. H. (1983), The Media Monopoly, Beacon Press, Boston. For book chapters Surname, Initials (year), “Chapter title”, Editor's Surname, Initials, Title of

Book, Publisher, Place of publication, pages. e.g. Picard, R. G. (2005), “Money, Media, and the Public Interest”, in Overholster, G., Jamieson,

K. H. (Ed.), The Press, Oxford University Press, Oxford, pp. 337-350. For journals Surname, Initials (year), “Title of article”, Journal Name, volume, number, pages. e.g. Thacher, D., Rein, M. (2004), „Managing Value Conflict in Public Policy”, Governance Vol.

17, No. 4, pp. 457-486. For published conference proceedings Surname, Initials (year of publication), "Title of paper",

in Surname, Initials (Ed.), Title of published proceeding which may include place and date(s) held, Publisher, Place of publication, Page numbers.

e.g. Draskovic, V., Grego, Z., Draskovic, M. (2011), “Media Concentration, Neoliberal Paradoxes and Increase in Virtuality”, in Media Concentration proceedings of the international conference in Podgorica, Montenegro 2011, Elit, Podgorica, pp. 33-45.

For working papers Surname, Initials (year), “Title of article”, working paper [number if avail-able], Institution or organization, Place of organization, date.

e.g. Draskovic, V. (2007), “Specificities and problems of Montenegrin transition”, working paper, Leeds University Business School, TIGER, Warsaw, September.

For newspaper articles (authored) Surname, Initials (year), “Article title”, Newspaper, date, pages. e.g. Miller, M. C. (1997), “The Crushing Power of Big Publishing”, The Nation, 17 March, p. 10. For newspaper articles (non-authored) Newspaper (year), “Article title”, date, pages. e.g. Vijesti (2011), „The New Media“ 2 December, p. 5. For electronic sources If available online, the full URL should be supplied at the end of the

reference, as well as a date that the resource was accessed. e.g. Compaine, B. M. (2005), „The Media Monopoly Myth: How New Competition is Expanding

our Sources of Information and Entertainment”, available at: http://www.NewMillennium Re-search.org//archive/ final_Compaine_Paper_050205. pdf (accessed 10 december 2011).

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