KAUNAS UNIVERSITY OF TECHNOLOGY

LITHUANIAN ENERGY INSTITUTE

RASA VIEDERYTĖ

ECONOMIC EVALUATION OF LITHUANIAN MARITIME

SECTOR CLUSTERING PRECONDITIONS

Summary of Doctoral Dissertation

Social Sciences, Economics (04S)

2014, Kaunas

The Doctoral Dissertation was prepared in 2010-2014 at Kaunas University of

Technology, School of Economics and Business, Department of Economics.

Scientific Supervisor:

Prof. Dr. Vytautas JUŠČIUS (Klaipėda University, Social Sciences, Economics-

04S).

Dissertation Defence Board of Economics Science Field:

Prof. Dr. Vytautas SNIEŠKA (Kaunas University of Technology, Social

sciences, Economics-04S)-chairman;

Prof. Dr. Jonas MARTINAVIČIUS (Vilnius University, Social sciences,

Economics-04S);

Prof. Dr. Valentinas NAVICKAS (Kaunas University of Technology, Social

sciences, Economics-04S);

Prof. Dr. Violeta PUKELIENĖ (Vytautas Magnus University, Social sciences,

Economics-04S);

Prof. Dr. Gražina STARTIENĖ (Kaunas University of Technology, Social

sciences, Economics-04S).

The official defence of the Doctoral Dissertation will be held at 10 a.m., on 16th

of January, 2015 at the public meeting of the Board of Economics Science field

in the Rectorate Hall of Kaunas University of Technology.

Address: K. Donelaičio St. 73-402, LT-44209, Kaunas, Lithuania.

Phone (+370 37) 3000042, fax. (+370 37) 324144, e-mail doktorantura@ktu.lt.

The summary of the Doctoral Dissertation was sent on the 16th of December,

2014.

The Doctoral Dissertation is available at the Library of Kaunas University of

Technology (K. Donelaičio St. 73, Kaunas, Lithuania) and Lithuanian Energy

Institute (Breslaujos St. 3, Kaunas, Lithuania).

KAUNO TECHNOLOGIJOS UNIVERSITETAS

LIETUVOS ENERGETIKOS INSTITUTAS

RASA VIEDERYTĖ

LIETUVOS JŪRINIO SEKTORIAUS KLASTERIZACIJOS

PRIELAIDŲ EKONOMINIS VERTINIMAS

Daktaro disertacijos santrauka

Socialiniai mokslai, ekonomika (04S)

2014, Kaunas

Disertacija rengta 2010 – 2014 metais Kauno technologijos universiteto

Ekonomikos ir verslo fakultete, Ekonomikos katedroje.

Mokslinis vadovas

Prof. dr. Vytautas JUŠČIUS (Klaipėdos universitetas, socialiniai mokslai,

ekonomika-04S).

Ekonomikos mokslo krypties taryba:

Prof. dr. Vytautas SNIEŠKA (Kauno technologijos universitetas, socialiniai

mokslai, ekonomika-04S)-pirmininkas;

Prof. dr. Jonas MARTINAVIČIUS (Vilniaus universitetas, socialiniai mokslai,

ekonomika-04S);

Prof. dr. Valentinas NAVICKAS (Kauno technologijos universitetas, socialiniai

mokslai, ekonomika-04S);

Prof. dr. Violeta PUKELIENĖ (Vytauto Didžiojo universitetas, socialiniai

mokslai, ekonomika-04S);

Prof. dr. Gražina STARTIENĖ (Kauno technologijos universitetas, socialiniai

mokslai, ekonomika-04S).

Disertacija bus ginama viešame ekonomikos mokslo krypties tarybos posėdyje,

kuris įvyks 2015 m. sausio 16 d. 10 val. Kauno technologijos universiteto

Rektorato salėje.

Adresas: K. Donelaičio g. 73-402, LT-44209, Kaunas, Lietuva.

Tel. (8 37) 3000042, faksas (8 37) 324144, el.paštas doktorantura@ktu.lt

Disertacijos santrauka išsiųsta 2014 m. gruodžio 16 d.

Su disertacija galima susipažinti Kauno technologijos universiteto

(K. Donelaičio g. 20, Kaunas) ir Lietuvos energetikos instituto (Breslaujos g. 3,

Kaunas) bibliotekose.

5

INTRODUCTION

Relevance of the Research. Independent initiatives of clustering of business

entities are observed in Lithuania. Some of them are focused on development of

long-term economic goals, other are on their initial stage. Lithuania is a maritime

country located in a strategically important geographical place and having a

multi-purpose infrastructural object - Klaipeda State Seaport that is the

northernmost ice–free port on the Eastern coast of the Baltic Sea. Over the last

decade this Maritime sector has created and developed the infrastructure that

promotes entrepreneurship (that is, logistics system and logistics centres, free

economic zone), highly qualified specialists were educated, gained experience in

cargo storage and transportation sphere, programmes of modern quality

management are introduced. Business operators operating in Maritime sector

individually initiate branch associations or other joint structures by such means

reaching common and individual goals and implementation of economic interests

and reaching synergy effect of economic activities.

Clustering is recognized as the economy phenomenon of many advanced and

rapidly developing countries, clusters operating in many countries promote

economic growth, attract innovations, qualified personnel, investments into

scientific researches and experimental development, clustering promotes new

technologies. Clustering also brings together companies, public and research

institutions which social and commercial relations determine their specialization,

allow to benefit from the unique and specialized resources and thus, enhances the

advantage both of the cluster’s members and the whole country's advantage.

The cluster as a form of activity does not only change economic structure and

potential of a country or a region (district) or a particular city, but also,

strengthens capabilities of human, technical, scientific, capital, innovation,

partnership and other capabilities the individual members of a cluster. Increased

productivity, increased levels of competitiveness and innovative product

development and commercialization - these are the results which cluster

members can achieve working together. Clustering helps to develop new ideas

and businesses, accelerate knowledge and technology transfer and

implementation, product development. Clustering helps to improve labour and

product quality, technological content; it also helps to create favourable

conditions for improvement of the productivity, innovation. It helps to reduce

costs of small and medium-sized enterprises, particularly in research and

development, and innovation sphere; helps to promote the development of

exports, reduce risks and increase the probability of success in choosing new

investments, improve the efficiency of research and development processes;

helps companies and their representative organizational structures to join the

global expertise and innovation networks and to exploit the offered opportunities

by the development of higher added value, to increase innovation and

competitiveness.

6

Clustering as the process and the need and importance of cluster structures

have been started analysed already at the end of the nineteenth century.

Localization of economic activity ideas can be found in the 19th-century German

economist J. H. Thunen works. The focus is given to the value of land and how

this affects the agricultural production moving away from the trading place

(Šalčius, 1927). Aforesaid ideas are further analysed in the works of A. Marshall.

He introduced the concept of industrial districts, highlighting the benefit of

economic activities of businesses in small areas (Marshall, 1890). English

economist A. Marshall, 1890, in his work “Principles of Economics” analysed

the concentration of specialized industries centred in one area, and called this

phenomenon as the industrial districts. He also stated that due to the cost of one

unit, innovation activity and growth can have a positive impact on other parts of

the system, and the industrial districts as entire unit have to perform better than

the individual units.

In the middle of the twentieth century, researchers of cluster structures (Isard,

1956; Becattini, 1979) expanded the concept of industrial districts, with

emphasis on export-oriented industries in close liaison with other regional

industries, cost reduction of production and delivery, the ability to innovate and

become the dominant player in the global markets, the importance of the

clustering process. W. Isard (1956) described the phenomenon of clustering

using the export-oriented industries and their links with other industries in the

region. According to him, such a close industrial ties and show the existence of a

cluster. In the late 1970, the economist G. Becattini raised the idea of clustering

by applying this to northern Italian industrial organization. According to him, the

reason to concentrate geographically covers the economic aspects such as the

cost reduction of production and delivery, as well as the opportunity to become

the dominant player in the global markets, where the ability to innovate is a key

competitive advantage. S. Cruz and A. Texeira (2007), M. Porter (1990)

highlighted the enormous potential of industrial clusters. This was a major event

in the development of the cluster concept because Porter's ideas of cluster

successfully made their way to into the areas of science and politics by creating a

breakthrough of cluster initiatives in many countries.

At the beginning of the twenty-first century, the concept of clustering became

synonymous with the “knowledge economy”. The main argument was that the

knowledge-based economy process engines - the technological know-how,

innovation and the dissemination of information - develops favourably when

such development is localized (Martin and Sunley, 2001).

M. Porter (1998), one of the most influential economists who analysed the

importance of localisation to economy, stated that the country's leading export

companies are not isolated success stories but belong to the most successful

groups of competitors of related industries. He called these groups as “clusters”,

i. e., industries related by horizontal and vertical links and networks.

7

The idea of clustering in Lithuania was firstly developed by J. Činčikaitė and

G. Belazarienė (2001), Business Strategy Institute (Cluster.., 2002), Lithuania's..,

2003) and Č. Švetkauskas (2003). Their works formed the basis for further

development of clustering phenomenon in Lithuania. These and later (Jucevičius,

2007, 2008, 2009) studies and research works usually analysed development

opportunities of industry clusters (wood, textile, etc.), clusters of services

(tourism, etc.).

Recently, the scientific literature usually use M. Porter’s (1998) formulated

concept of the cluster – “geographic concentrations of interconnected companies,

specialised suppliers, service providers, firms in related industries and associated

institutions (for example universities, standards agencies and trade associations)

which not only compete with each other but also cooperate with each other.

Also, the networks that occur within a geographic location, in which the

proximity of companies and institutions ensure certain forms of commonality

and increases the frequency of interaction”. S. A. Rosenfeld (1997) marked the

importance of synergy between the organizations. T. Hertog and P. Roelandt

(1999) and J. Simmie and J. Sennett (2001) suggested a cluster analysis, looking

at them as to the value (cost) development chain.

Although clustering aims and goals of policy have not been reached yet in the

economies of the countries of the European Union, however in most countries’

strategic documents they are reasonably presented and moved to the industrial

sector development strategies and their implementation plans. Traditionally,

starting from a national or regional planning initiatives, “top-down” method

applies to the regional authorities by initiating and developing plans of clusters

formation, creation and development and by creating methodology for the

cooperation of enterprises in the particular geographical region. However, this

does not contribute to the practical cases of cluster development and creation and

this does not promote cooperation of enterprises.

In order to enhance the competitiveness of enterprises and strengthen the

competitive position in the market, cluster formation is increasingly becoming

one of the essential conditions for the development of business cooperation and

development, in particular the joint initiation of joint projects and the

strengthening of mutual trust between the companies. Cluster formation is a

dynamic process identified by common attributes and criteria, consisting of

successive stages and having different maturity phases.

Scientific problem and its level of investigation. In order to objectively

reveal the level of the investigation of the problem analysed in this dissertation,

the Matrix method of Garrard (2007) was adopted for the analysis of

bibliographic data. Application of this method helped to carry out search of

relevant publications in thirteen internationally recognized databases of scientific

journals and scientific journals: EBSCO, Emerald Insight, Springer Link, Sage

Journals, Science Direct, Oxford Journals, Wiley Science, Taylor and Francis,

8

ICPSR, electronic catalogue of Martynas Mažvydas National Library of

Lithuania, electronic catalogue of Virtual Library of Lithuania, Научная

Электронная Библиотека elibrary.ru, Каталог Электронных Ресурсов. The

search in English was performed in accordance with eight relevant key word

combinations based on the title of the dissertation. If less than 800 articles were

found which correspond to the keywords combinations, then after detailed

review, there was rejecting of the articles, which do not correspond to the scope

and object of the dissertation. The search was conducted according to the title of

the scientific article, keywords and summary. Publications of the chosen

databases were selected and the results of the search were analysed in the period

of 22 February 2014 and 30 May 2014.

With regard to the data of bibliographic analysis, keyword combination

“Lithuanian Maritime sector, Clustering, Preconditions, Economic analysis”

matched one scientific paper prepared by the author of this paper, which was

found in EBSCO database.

There were four books found which match key word combination according

to the keyword combination of “Lithuanian Maritime sector, Clustering,

Preconditions, Economic analysis”, however, on the scientific level they are not

0widely analysed. According to combination of “Maritime sector, Clustering,

Preconditions”, the introductory paragraphs of the scientific articles in many

cases discuss the importance of the Maritime clustering or the formation stages

of clusters but these articles lack the detailed analysis of clustering causes,

conditions, preconditions, risks and barriers. Brenner (2004), Hui (2005),

Lorenzen (2005), Hassink and Dong-Ho (2005), Nadaban and Berde (2009) and

other authors analysed various stages of the life cycle of the formation of

clustering and cluster. Clustering in many scientific publications is often

analysed as recognition of certain individual structural elements or signs and

connection with causal relationships and linkages while forming a statistical

clustering model.

It should be noted that there is no single economic approach to analyse the

Maritime clustering process. Different authors and different scientific and

political contexts differently identify clustering, the importance and stages of

cluster development and cluster formation often do not correlate with each other;

preconditions, reasons, demand and benefit motives are often treated as

synonyms of these concepts; the analysis of preconditions of clustering sector

usually is carried out by the evaluation of goals of clusters. This suggests that

there is no connectivity and continuity in respect of results of previously

published researches. The evaluation of proposed preconditions of clustering

sector lacks complexity and completeness; lack of a clear methodology for

evaluation of preconditions of concrete clustering sector; scientific works often

mistakenly equate sector and cluster and its evaluation continues in accordance

with one selected scientific research method or industry groups of different

9

countries are called clusters and their economic data are further compared.

Economic evaluation of preconditions of Maritime sector clustering is a

significant research object of this dissertation.

One of the main areas of the dissertation research - Lithuanian Maritime

sector clustering preconditions to increase Productivity, Innovations and

Competitiveness, factors influencing these preconditions and the level of

occurrence of the sector in the clustering process.

In Lithuania, research on Clustering and Maritime sector is very fragmentary

in comparison with other economic phenomena and scientific problems:

J. Činčikaitė and G. Belazarienė (2001), J. Bruneckienė and K. Pukėnas (2008),

J. Bruneckienė (2010) and others studied the impact of clusters on the

competitiveness of region. Recently the scientific literature (Jucevičius, 2009;

Stalgienė, 2010, Porter, 1998; Rosenfeld, 2002; Roelandt and Hertog, 1999;

Simmie and Sennett, 2001; Kamarulzaman and Mariati, 2008, etc.) have

extensively analysed the clustering processes taking place in the world, the

measures to promote clustering; the literature also discusses the business benefits

for the individual members of the group and for the state in which the cluster is

based on the bottom-up approach. Cluster formation initiatives bottom-up still

has not received the proper attention of scientists (Lorenzen, 2005). It is noted

that studies which analyse clusters in Lithuania (Jucevicius, 2009; 2012;

Jucevicius, Rybakov and Šajeva, 2007; Stalgienė, 2010, etc.) lack focus on the

stages of formation of clusters of common features and their isolation criteria,

maturity phase identification of clusters. Also it should be noted that the studies

analysing clusters in Lithuania (Jucevicius, 2009; Jucevicius et al, 2007; 2012;

Stalgienė, 2010 and others) do not pay enough attention to maritime sector of

Lithuania which is strategically important and economically viable for Lithuania.

However, there are not any scientific publications, which would analyse

maritime sector clustering and would conduct economic evaluation of clustering

or its preconditions.

The main research area of this dissertation is economic evaluation of

Maritime sector Clustering Preconditions for increase Productivity, Innovations

and Competitiveness.

M. Porter (2000a, 2000b, 2003), T. Andersson and G. Napier (2007), T.

Andersson et al. (2004) analysed different competitiveness preconditions

problems and proposed preconditions methodology. Althrough there is a lack of

research where Maritime sector clustering would be analysed as evaluation

object of Productivity, Innovations and Competitiveness. So far there is no such

preconditions methodology enabling economically evaluate preconditions of the

maritime sector clustering. This dissertation seeks to create such methodology

and empirically adapt it and verify this model in Lithuanian Maritime sector.

This dissertation deals not only with theoretical problems but also with

empirical ones. The practical significance of the dissertation research is justified

10

by opportunities of application of economic evaluation methodology of

Lithuanian Maritime sector clustering preconditions – the formulated model can

be the basis for making important decisions for Lithuanian Maritime sector on

political, managerial and economic issues: to prepare and implement National

strategy of Maritime sector clustering to promote Maritime sector organizations

of business, academic and public areas to cooperate and to form agglomerated

business structures - clusters in order to increase Productivity, Innovations and

Competitiveness and stimulating development of the Maritime sector. For

cluster-prone organizations, this model is an informative set of meaningful

indicators to help make decisions on cluster formation, involvement in the

clustering process or a new cluster formation as organizations which are not

related by clustering relations cannot use the significant advantages of cluster,

such as Productivity, Innovations and Competitiveness.

The problem of the scientific research - how comprehensively evaluate

preconditions of Lithuanian Maritime sector clustering.

Object of the researc - Preconditions of Lithuanian Maritime sector

clustering.

The aim of the research - to create combined evaluation methodics and to

conduct economic evaluation of Lithuanian Maritime sector clustering

preconditions.

The objectives of the research:

1. To identify and to systematize structural composition of economic

activities of Lithuanian Maritime sector.

2. To examine origin, formation and development of demand of Maritime

sector clustering and to distinguish Maritime sector clustering preconditions on

the increase of Productivity, Innovations and Competitiveness.

3. To evaluate economic significance of Lithuanian Maritime sector to whole

Lithuanian economy.

4. According to peculiarities of Maritime sector clustering economic

evaluation, to identify research initiatives of Lithuanian Maritime sector

clustering and to evaluate them.

5. To create combined economic evaluation methodology of Maritime sector

clustering preconditions.

6. To examine the created methodology by economic assessment of the

clustering assumptions in the context of Lithuanian Maritime sector.

Methods of research: systemic and comparative analysis and synthesis of

scientific literature, strategic documents and legislation; statistical analysis of

secondary data; empirical research: econometric analysis, expert evaluation and

questionnaire survey; mathematical and statistical methods, using of statistical

data processing applications: SPSS Statistics (v21.0) and Microsoft Excel (2010).

The analysis of scientific literature, legislation and strategic documents was

based on systematic (holistic) approach. The first and the second parts of this

11

dissertation are dedicated for systematic, logical and comparative analysis of

scientific literature, legislation, strategic documents and for synthesis of

scientific results. Formulation of scientific conclusions was based on logical

induction and deduction methods. The third part of this dissertation presents the

analysis of secondary data, questionnaire survey analysis and research by using

expert evaluation and data obtained by mathematical and statistical analysis

(including data structuring, processing, organization and calculation of statistical

indicators) and by using statistical data processing applications: SPSS Statistics

(v21.0) and Microsoft Excel (2010).

The structure of the Dissertation. Dissertation consists of three parts. The

first part analyses the origin, formation, development and economic significance

for Lithuanian economy of demand of Maritime sector clustering. The second

part of the dissertation analyses the evaluation features of clustering

preconditions and conducts the model formation of economic evaluation of

Maritime sector clustering preconditions. The third part of the dissertation

presents empirical decisions of economic evaluation of Maritime sector

clustering preconditions.

The structure of the dissertation is determined by the main aims of the

dissertation and the objectives set to reach the main aims. The conclusions

briefly summarize key findings of the dissertation.

Research base and used information sources. While analysing

preconditions of Maritime sector clustering, scientific works of Lithuanian and

foreign authors were used. In addition, published research results, publicly

available strategic Lithuanian and foreign documents and laws governing the

Maritime sector and the clustering process were used in this analysis. For the

identification of the latest preconditions of Maritime sector clustering, the latest

specialised literature, statistical data of Lithuanian Department of Statistics and

Eurostat statistics, studies and reports of international organisations (European

Commission, 2002; 2003; 2008; Organisation for Economic Cooperation and

Development, 2001; 2008; World bank, 2011; 2012; 2013) and specialized

research groups (European Cluster Observatory, 2014, Policy Research

Corporation, 2009; Ecorys SCS Group, 2009; 2012; Gallup Europe, 2006),

specialized publications (Sölvell, Lindqvist and Ketels, 2003; 2006; 2013;

Sölvell, 2008) and studies (Lithuania Cluster Concept 2014-2020, 2014;

Preconditions and Recommendations for Development of clusters in Lithuania,

2002; Lithuanian Industry clusters Developmental program Study, 2003).

Empirical quantitative results were obtained from an econometric evaluation

of the collected data, calculating Regional Coefficient, Coefficient of

Agglomeration, Rate of Production Specialization and the Geographic

Concentration Indicators also Index of Clustering. These indicators were chosen

because of their complexity and universality in terms of regional concentration,

clustering level, the extent of specialization and the extent of agglomeration. The

12

empirical qualitative results were obtained in the expert study where the experts

in the first stage conducted the direct evaluation of the preconditions and risks of

the Lithuanian Maritime sector clustering and granted weighted estimate. The

second stage conducts expert research based on “conversation-interview”

method. Empirical quantitative results were obtained by using questionnaire

survey method. The questionnaire was distributed via web channel because it is

universally available, the most convenient and the least cost requiring survey

tool. The questionnaire was posted on Lithuanian website using specialized

Internet access www.anketa.lt. The research was conducted in the period of May

- July 2014.

The Novelty of the Dissertation

Purified structure of Lithuanian Maritime sector. The paper provides

systematic structure of Lithuanian maritime sector according to groups of

industry groups distinguishing three main Lithuanian Maritime sector parts: the

Traditional Maritime sector, Coastal and Marine tourism and Fisheries. In order

to distinguish the main causal areas of industry groups of Lithuanian Maritime

sector, there is additionally provided the structure of Lithuanian Maritime sector

where industry groups are connected in accordance with their function and the

interconnection is presented.

The concepts of Sector, Maritime Sector, Clustering and Precondition are

summarized and presented. After identification of uncertainty of the concepts of

sector, maritime sector, clustering and preconditions, this paper presents the

classification and in accordance with keywords summarized and formulated

definitions of these concepts. Sector - this is part of the national economy, with a

certain general economic characteristics, combining similar economic behavior

codified in economic activities, groups of institutional units. Marine sector - a

combination of economic activities (which includes the traditional sectors of

marine, coastal and marine tourism and fisheries), a complex combining

economic activity groups (shipbuilding, marine works, marine services, marine

equipment and facilities maintenance, tourism and fisheries and aquaculture) and

assigned / or related business, academic and public sector groups of institutional

units. Clustering - this cluster formation process involving the relevant economic

activities in groups running vertically and / or horizontally integrated companies

and their tendency to concentrate on the general activities of the realization of

the value added chain to economic benefits. Precondition - the initial reasoned

argument based on assumptions given in evidence based on similar facts.

The risks and preconditions of Lithuanian Maritime sector have been

distinguished and systematized. This paper identifies and codifies the main

Lithuanian Maritime sector clustering preconditions and risks according to their

significant features, associated with increase of Productivity, Innovations and

Competitiveness. The work systematizes features of preconditions specific to

Maritime sector clustering. These features are combined into exploited formulas

13

of preconditions; the list made of preconditions is divided into 3 parts in

accordance with the impact of preconditions on the increase of Productivity,

Innovations and Competitiveness. The risks of Maritime sector clustering are

indicated as barriers of increase of Productivity, Innovations and

Competitiveness. Clustering risk equivalent is presented for each clustering

precondition. By concluding the list of risks and formulas, the same methodical

principals were followed as in systematization of preconditions: risks were

relatively divided into three parts: increase barriers of Productivity, Innovations

and Competitiveness.

The economic indicators were set which are significant to evaluation of

Maritime sector clustering preconditions. The author of the work created

database of results of economic activities of Lithuanian Maritime sector

enterprises and periodically added this database with the latest official data

announced by Statistics Lithuania and currently the database has got these

systematized indexes of economic activities of enterprises 2007-2012: Number

of companies acting Lithuanian Maritime sector, Number of workers in these

companies, Turnover, Added value (at factor costs), Gross operating profit,

Gross investment in tangible assets, R&D investments. According to available

data, indicators of Gross margin and Labour productivity are calculated. The

average method is used in order to determine which economic activities in the

Maritime sector exclude by the economic indicators. In order to identify the

Lithuanian Maritime sector clustering key features: the specialization and

concentration - used these quantitative indicators and indexes of assessment: the

Regional Coefficient, Agglomeration Coeffiient, Production Specialization

Index, Clustering Index and Geographic Concentration Indicators: Localization

Index, Herfindahl Index, Herfindahl-Hirschman Index, Ellison-Glaeser

Geographic Concentration Index.

Conceptual combined economic evaluation model of Maritime sector

clustering preconditions was designed. Designed conceptual combined economic

evaluation model of Maritime sector clustering preconditions is a visual method

(diagram) which presents causal relations between factors and stages, which are

significant for the problem analysed.

The methodology of combined economic evaluation of Maritime sector

clustering preconditions is composed. Taking into account the specification of

the subject, complexity of the analysed scientific problem and complication of

the thesis object, the composed methodology of combined economic evaluation

of Maritime sector clustering preconditions includes: Empirical quantitative

analysis by choosing econometric estimation method and by calculating the

Regional coefficient, Agglomeration Coefficient, Indexes of Production

specialization and Geographical concentration, Index of Clustering; Empirical

qualitative research - expert evaluation, which consists of two parts: the first part

presents ranking of preconditions and obstacles and direct evaluation method, the

14

second part presents qualitative research based on “conversation-interview”

method and empirical quantitative pilot study - questioning. The essence of the

combined economic evaluation of preconditions of Maritime sector clustering is

the systematic attitude towards the integrity and applicability of research

methods in order to by the empirical research to get clear and objective data on

Lithuanian Maritime sector clustering preconditions and on the basis of that, to

make conclusions about the results – the benefit or losses of Lithuanian Maritime

sector clustering preconditions for country, region, sector, economic activities

group, enterprise or related organisations.

Complex economic evaluation model of Maritime sector clustering

preconditionsis verified in the context of Lithuanian Maritime sector. According

to created and described combined economic evaluation methodology of

Maritime sector clustering preconditions, the created model was verified in

Lithuanian Maritime sector context: conducted evaluation of Lithuanian

Maritime sector impact on agricultural economy of the country, distinguished

and described methods of the empirical quantitative research, using selected

research instruments collected significant data, calculated identifying indexed

and indicators of clustering characteristics, carried out estimate weight analysis

and ranking of expert Maritime sector clustering preconditions and risks,

formulated conclusions on analysis of collected data during the expert

“conversation-interview”, carried out statistical analysis of collected data during

empirical research - questionnaire and presented conclusions of data analysis of

the pilot research.

Limitations of the Research. There are possible inaccuracies in

methodology of referring of companies for Lithuanian Maritime sector,

uncertainty of the preconditions of clustering concept, subjectivity of expert

evaluation and limited expert competence in a certain fields and unreliability of

publicly available statistical research data.

Continuity of Dissertation Research. In order to perform deeper analysis of

empirical qualitative research of economic evaluation of preconditions of

Lithuanian Maritime sector clustering in terms of content, it is appropriate to

expand the carried Pilot study and by ensuring the representativeness of the

study sample, to collect reliable data for the analysis of the research results. It is

appropriate to continue periodically to complement the Data base of the main

economic indexes of the companies (18.508 units) of Maritime sector in

Lithuania, updating the information in accordance with publicly available data of

the Statistics Lithuania, it is appropriate to include values of Exports and Imports

of the Lithuanian Maritime sector companies. It is planned in the future to create

a Methodology for verification of economic evaluation results of formed clusters

and it is planned to verify this methodology in the case analysis of Lithuanian

clusters. It is appropriate to initiate and maintain Lithuanian Maritime cluster

formation and take concrete steps to realize this idea. It is planned in the future

15

to conduct analysis of complexity, compatibility and optimization opportunities

of Lithuanian Maritime sector clustering, to conduct evaluation of Science,

Business and Public sector institutions operating in the Lithuanian Maritime

sector It is appropriate to analyse not only Economic but also Social and Political

impact on the Country’s economy of the Lithuanian Maritime sector.

It would be appropriate in the future while conducting research on

preconditions of Lithuanian Maritime sector clustering, to evaluate an economic

volume of Offshore business operating in the Maritime sector and to evaluate its

impact not only on Lithuanian Maritime sector but also on Economic

performance of all country.

There are available fields of economic evaluation methodology of

preconditions of certain complex Maritime sector clustering:

1. This methodology can be applied on the national (regional) level for the

preconditions of operating Maritime sector clustering evaluation. It can be

applied and for other countries’ research of preconditions of Maritime sectors

clustering. Improved methodology would also works in other countries to

evaluate clustering preconditions of Industry sectors but then certain

characteristics of industry clustering should be identified, to formulate the

clustering preconditions and risks statements to suit a particular industrial sector,

to evaluate optimal number of selected preconditions and risks, to select

appropriate methods for the analysis of clustering preconditions and risks, to

evaluate the need to involve experts into the study and identify current experts

and to consider need of concrete industry group in with regard to the

establishment of the cluster organization.

2. This method can help to evaluate national (regional) potential and

development opportunities of Maritime sector, to distinguish the main factors

determining and limiting preconditions of the clustering.

3. This methodology can be applied in order to compare preconditions of

Maritime sector clustering in the Baltic sea region countries.

4. The modified methodology could be a reference tool for business, science

and public sector entities which evaluate the clustering of industry sectors.

Scope of the dissertation. The dissertation consists of 298 pages (257 pages

without attachments), 53 figures, 61 table, 17 annexes. 395 references used in

Lithuanian, English, French, German and Russian languages.

16

CONTENT OF DISSERTATION

INTRODUCTION

1. RESEARCH CHARACTERISTICS OF MARITIME SECTOR CLUSTERING

PRECONDITIONS

1.1. Maritime sector clustering demand Emergence, Formation and Development

1.1.1. Structure and Development Predictions of Maritime sector

1.1.2. Peculiarities of Clustering Preconditions Formation

1.1.3. Interorganizational Communication Advantages for the Maritime sector

Organizations

1.2. Economic Significance of Maritime sector Clustering

1.2.1. Economic Significance of Maritime sector Clustering for Lithuanian Economy

1.2.2. Characteristics of the Maritime sector Policy based on Preconditions of Cluster

Formation

1.2.3. The Need for economic evaluation of Clustering Preconditions

2. ECONOMIC EVALUATION METHODOLOGY OF LITHUANIAN MARITIME

SECTOR CLUSTERING PRECONDITIONS

2.1. Peculiarities of Economic Evaluation Clustering Preconditions

2.1.1. Methods and Indexes of General Clustering evaluation

2.1.2. Evaluation Problems and Limitations of Clustering Preconditions in the Maritime

sector

2.2. Model formation of Economic evaluation of Maritime sector Clustering

Preconditions

2.2.1. Creation of Maritime sector Clustering Preconditions Combined economic

evaluation Methodology

2.2.2. Process and Structure of Lithuanian Maritime sector Clustering Preconditions

Combined economic evaluation Model

3. EMPIRICAL SOLUTIONS OF MARITIME SECTOR CLUSTERING

PRECONDITIONS ECONOMIC EVALUATION

3.1. Lithuanian Maritime sector Clustering initiatives and their Evaluation

3.2. Verification of Clustering Preconditions Combined economic evaluation Model in

the context of Lithuanian Maritime sector

3.2.1. Methodology of Clustering Preconditions Combined economic evaluation and

the Main principles of Data analysis

3.2.2. Evaluation of Clustering Preconditions by calculating Regional Coefficient,

Agglomeration Coefficient, Production Specialization Index, Geographic Concentration

Indexes and Clustering Index

3.2.3. Results and their interpretation of Clustering Preconditions Expert Evaluation

3.2.4. The results of Statistical research on Clustering Preconditions and their

Interpretation

3.2.5. Consideration of the Results of Combined economic Evaluation of Maritime

sector Clustering Preconditions

CONCLUSIONS

REFERENCES

LIST OF SCIENTIFIC PUBLICATIONS ON THE TOPIC OF DISSERTATION

ANNEXES

17

REVIEW ON DISSERTATION CONTENT

1. RESEARCH CHARACTERISTICS OF MARITIME SECTOR

CLUSTERING PRECONDITIONS

This part analyses the emergence of demand of Maritime sector clustering,

formation and development and the significance of Lithuanian maritime sector

clustering for Country's economy is evaluated.

1.1. Maritime sector clustering demand Emergence, Formation and

Development

This part analyses structure and development predictions of Lithuanian

Maritime sector, analyses peculiarities of formation of preconditions of

clustering, distinguishes inter organizational communication advantages for the

Maritime sector organizations.

1.1.1. Structure and Development Predictions of Maritime sector

The concept of Maritime sector is not used by the Statistics Lithuania in the

official accounts of the economic activities, this concept is not included into the

statistical annual reports of the banks operating in the Republic of Lithuania.

Therefore, “the Maritime sector” concept used in strategic documents of the

Republic of Lithuania and legal acts is conditional and strictly regulated.

Scientific literature and strategic documents (Lithuanian Dictionary, 2013,

Statistics Lithuania, 2011; A value chain..., 2011; ESaTDOR, 2013; Government

Resolution No. 786, 2008; the Regional Business..., 2012) the concept of the

Maritime Sector interpret in different ways, depending on the author of Strategy

document or belonging of an author of the scientific literature to a particular type

of organization.

According to the extracted significant features of the concept of the Maritime

sector, the description of concepts of the sector and Maritime sector to be

followed in this paper: Sector - a part of the national economy, which has certain

common economic characteristics and combining groups of institutional units of

similar economic behaviour, systematized into economic activities. Maritime

sector - it is a combination of economic activities (which includes traditional

maritime sector, coastal and marine tourism and fisheries) that complexly

combines for groups of economic activities (shipbuilding, marine works, marine

services, marine equipment and facilities maintenance, tourism, fisheries and

aquaculture) assigned and/ or related business, science and public sector groups

of institutional units.

Figure 1 shows that Lithuanian Maritime sector is divided into three

structural parts and this sector is defined in accordance with the EU studies

which use economic activities and features used to describe them.

18

Maritime sector

Traditional maritime sector Coastal and marine tourismFisheries

ShipbuildingMarine equipment Marine services

Exploitation of

marine aggregates

Offshore supply Marine works

Navy and coastal

safeguard

Shipping

Seaports

Inland navigation

Recreational boating

C

D

19.2046.69

25.93

26.51

G

C27.11

27.31

27.32

27.33

28.11

28.13

28.14

28.22

28.25

35.11

35.21

49.50

06.10

06.20

08.11

08.12

08.91

08.92

08.93

08.99

09.10

09.90

H

B

M

N

71.11

71.12

77.11

77.12

77.34

42.91

43.13

52.24

52.29

F

H

C

33.15

30.11

30.12

H50.10

50.20

H50.30

50.40

G46.14

H52.22

C33.12

33.19

O84.10

84.22

L

G

N

I

R

68.10

68.20

47.64

77.21

79.11

79.12

55.10

55.20

55.30

56.10

91.01

91.02

91.03

91.04

G46.38

47.23

03.12

03.11

03.21

03.22

10.20

10.84

10.85

10.91

A

10.92

C

Fig. 1. Structure of Maritime Sector

Sections: A - Agriculture, forestry and fishing; B - Mining and quarrying; C - Manufacturing; D - Electricity, gas, steam and air conditioning supply; F - Construction; G - Wholesale and retail trade;

repair of motor vehicles and motorcycles; G - Wholesale and retail trade; repair of motor vehicles

and motorcycles; I - Accommodation and food service activities; L - Real estate activities; M -

Professional, scientific and technical activities; N - Administrative and support service activities; O -

Public administration and defence; compulsory social security; R - Arts, entertainment and

recreation. Total - 13 sections.

Figure 2 provides the systematized structure of the main economic activities

of the Maritime sector.

Shipping

• Marine and coastal shipping

• Inland waterways

• Recreational shipping

Renewable energy sources and electricity

production •

Oil and natural gas production and

processing •

Fossil fuels and quarrying •

Marine cable and pipeline construction •

Dikes, ports and canals forming •

Peat and salt extraction •

Marine Supply • Cargo handling •

• Machinery and equipment

manufacturing and repair

• Machinery and equipment

manufacturing

• Wiring and installation materials

Marine equipment

and machineries

exploitation

Marine services

• Technical consulting, engineering

and design

• Motor vehicles and equipment rental

• Sea port activities

• Navy and coastal safeguard

Fisheries and

aquaculture

• Marine and freshwater fishing

• Marine and freshwater aquaculture

• Fish and fish products, wholesale and retail trade

• Raw Material Processing and preserving forage production

Coastal and

marine tourismAccommodation •

Catering •

Travel, sports and cultural activities •

Sporting goods for sale and rent •

Real estate for sale and for rent •

Marine works

and offshore

supply

Related fields of

activities• Research institutions and research centers

• Higher education institutions

• The public sector (national, regional and local) institutions

• The line and professional associations, consortia

• Financial organizations

• Non-profit organizations

• International Maritime Sector's activities abroad

• Ships and floating structures

• Recreational sports and

shipbuilding

• Different types of ship repair and

maintenance

Shipbuilding

Fig. 2. The structure of Lithuanian Maritime sector

19

In order to distinguish the main causal areas of industry groups of Lithuanian

Maritime sector, there is additionally provided the structure of Lithuanian

Maritime sector where industry groups are connected in accordance with their

function and the interconnection is presented.

1.1.2. Peculiarities of Clustering Preconditions Formation

The interest to agglomerate and geographically spread economic activities

was observed at the beginning of the Nineteenth century. The first scientific

works related to the studies of demand of economic concentration were

published by Ricardo (1817), von Thunen (1826), Launhardt (1882) and Weber

(1909) in published journals. Specialized industrial location research are

analysed in detail and presented in the works of Marshall (1890). The author

noted that clustering of activities in the geographic areas of focus will have a

significant impact on the performance of the companies (Hofe and Chen, 2006).

These cluster formation conditions can be distinguished. The first - the

Geographical proximity of the cluster elements (Doeringer and Terkla 1995;

Prevezer and Swann, 1996), which allows agglomerating (in regards of volume

and aim) with internal specialization and division of labour force. The second

condition is related to Social networks (Roelandts and den Hertog, 1999;

Rosenfeld, 2005) which includes global electronic communications network

intended to transfer technological knowledge and organize training in groups

(Asheim, 1999). The third criterion is related to the Institutional common values

and culture of expectations, business climate (trust of informal relationship,

cooperation). This allows the creation of new businesses and the formation of the

cluster itself (Maskell, 2001; Rosenfeld, 2005).

In this research, it is considered that the clustering - is the cluster formation

process involving combined companies which operate vertically and / or

horizontally in the groups of related economic activities and their tendency to

concentrate on the realization of the general activities in Value-added chain by

seeking the economic benefits.

The Clustering Process in five Steps: Common opportunities / problem

identification, the Recognition of need or opportunities for collaboration,

Cooperation development or the joint project initiation, Clustering by

implementing a number of joint projects and Cooperation formalization.

Assumptions, causes or hypotheses are sometimes identified as

preconditions. In the case of Maritime sector clustering it is often treated as

benefits or economic benefit is evaluated. This paper considers the

“precondition” as the initial reasoned argument based on predictions with regard

to reasoned evidence of similar facts.

While analysing motives of selecting preconditions of clustering for the

increase of Productivity, Innovations and Competitiveness, this paper analyses

the relations of Productivity, Innovations and Competitiveness.

20

According to the analysis of scientific literature, strategic documents and the

analysis of studies, Maritime sector clustering preconditions systematically are

combined into three groups (seven conditions for each group), respectively: “to

increase Productivity”, “to increase Innovations” and “to increase

Competitiveness” and presented in Table 1.

Table 1. The description of preconditions of clustering for the increase Productivity,

Innovations and Competitiveness. Description of Clustering Precondition

I group of preconditions - for the increase Productivity (P)

a) By disposing of the general business infrastructure, there is a possibility to reduce operating costs, to increase

indexes of productivity and efficiency, to ensure optimal the manufacturing process loads.

b) The ability to specialize and focus on the main activity by transferring secondary and additional activities to

the sector members who specialize in these activities.

c) Due to the migration of qualified specialists within the sector, business entities there are created conditions to

use and optimally use internal capacities of human resources.

d) By disposing of the general distribution channels, the opportunities are created for sector members to create

the overall supply chain or use them.

e) Co-operating companies in their respective fields are typical examples of synergy effect.

f) Clustering helps to achieve economies of production scale and scope.

g) Companies working together are in common marketing, distribution strategy and reduction of logistics costs.

II group of preconditions - for the increase Innovations (I)

a) Favourable conditions are created for transmission - take over of “good practice”, to search solutions for

solving common problems.

b) There emerges an opportunity to reduce various business risks, other costs related to investments, by

diversifying these costs between members of business systems.

c) During the sector clustering processes, the socialization is promoted and community-based culture is

developed between companies.

d) In cooperation there is formed favourable conditions for promotion of policy of innovation and the

development of innovation.

e) In cooperation there is on going promotion of research and experimental development (R&D) and there is an

opportunity of commercialization of higher education products (prototype) developed.

f) Clustering promotes innovative business creation and development, “spin-off” business occurring.

g) In collaboration, representatives of the clustering can reach higher level of innovation by cooperation in the

fields of research and technological development.

III group of preconditions - for the increase Competitiveness (K)

a) Cooperation gives an opportunity easier, cheaper and quicker to get specialised information about markets,

technologies and resources.

b) There are created conditions for the best prices to buy and sell high quality products and services.

c) Co-operating companies are in a strong bargaining power while searching for new clients and suppliers,

dealing with the supply or sales questions, raising and discussing issues relevant to business system at national

level, by providing designed applications for financial support or for other favourable business conditions.

d) The advantages of geographical concentration of enterprises and access to the shared infrastructure facilities

emerge (Port of Klaipėda, infrastructure of rail, roads and ferries).

e) The joint forces help easier to enter to new local and international markets, to compete, maintain and

strengthen positions in markets, develop channels of distribution of the production/ services, to look for potential

users, customers, suppliers.

f) Because of the unique intensity of knowledge exchange between members of the business system, innovative

ideas are stimulated, new products, services or/ and management systems are created and launched.

g) Cooperation between companies increase foreign direct investment opportunities.

Table 2 presents systematic Risk groups of clustering – “barriers to increase

Productivity”, “barriers to increase Innovations” and “barriers to increase

Competitiveness” as well as their significant characteristics.

21

Table 2. The description of clustering risks as barriers of increase of productivity,

innovation and competitiveness Description of Clustering Risks

I group - obstacles of clustering preconditions - barriers to increase Productivity

a) Lack of infrastructure level unsatisfying cooperating business needs. Clustering as an advanced instrument of

economic policy requires a high level of infrastructure.

b) Raising additional questions on contributions of property, for example, question on results of investment

projects and division of property of created infrastructure.

c) The business entity specialization can lead to reduction of the part of qualified personnel, economic indicators

rise by lower percentage because the part of certain functions are removed or transferred to other companies.

d) Raising other administrative and financial obligations in different stages of business entity's involvement into

the clustering.

e) The vast majority of companies in the sector focus on the medium and low value-added products or services

does not increase the income of companies in the short term and in the long term - limits development

opportunities of companies in the sector.

f) Even seeing the total potential benefits of cooperation, companies individually often are reluctant to show the

initiative of formation of the cluster and assume the associated costs and responsibility.

g) The additional administrative and financial burden - maintenance of cluster governing body and funding of

additional package of strategic action: costs for organisation of meetings, costs for administrative facilities,

marketing techniques and so on.

II group - obstacles of clustering preconditions - barriers to increase Innovations

a) In practice non-functioning business information systems - are the main obstacle for the dissemination of

information. Low awareness of business entities about other businesses in the same region, about opportunities to

provide specialized services, about available technologies, implemented projects and other regional business

information stop clustering process.

b) Cluster activities are poorly regulated by legal framework which does not systematically and completely cover

EU legislation and the realization of the strategies and legislative acts of Lithuania.

c) Lack of entrepreneurship determine low involvement into networking processes, lack of leadership, lack of

initiatives and capacity of penetration into markets and domination.

d) Low professional skills of workers and lack of competence - the successful functioning of clusters requires

qualified labour force, continuous training and capacity-building.

e) Many companies which are prone to clustering usually lack competence to determine possible cooperation

fields, to discern the potential synergies integrating the separate parts of the value chain.

f) Uncertainty of patenting and intellectual property protection of advanced technologies (copyright of products

or services, trademarks of goods or services, design) developed within the cluster.

g) Non- confidence culture in Lithuanian business is still widespread, Lithuanian companies are relatively closed

for cooperation with competitors, it is difficult to effectively combine interests and mutual benefits. Confidence

among the cluster entities is critically important factor in the functioning of the network organization.

III group - obstacles of clustering preconditions - barriers to increase Competitiveness

a) Inactive professional and sectoral associations do not adequately represent the interests of businesses, therefore

sceptical attention of companies is formed towards other associated business structures and formations.

b) There is a rise in likelihood to buy the product / service at higher than market prices. There is a possible threat

of cartel agreements.

c) Different level of technologies and management between separate business entities is related to dissatisfaction

of progressive businesses about the quality of additional provided services of other businesses due to low

technological and managerial levels.

d) An obvious exclusiveness and isolation of region, the lack of accessibility and lack of dissemination of good

practice specialists and other elements essential for clustering.

e) Raising threat of power asymmetry - cluster members have different technological equipment, production

resources, infrastructure, capital and so on.

f) The emerging asymmetry of risk diversification by the size of business entity, generated incomes, production

and marketing scale and so on. Large business likely will have to take greater risk than the medium or SMB.

g) The associated business structures are relatively of limited availability of financing (cost of financing, access to

capital and liquidity, confidence in market participants and individual lending strategy of banks).

22

1.1.3. Interorganizational Communication Advantages for the Maritime

sector Organizations

In practice, various interorganizational joint forms are found. Alliances,

networks of organizations and partnerships are the closest concepts for cluster.

Clusters, from other cooperation forms (alliances, networks of organizations and

partnerships) differ in that cluster members share technology-related activities

that are innovative and common economic interests in the presence of the

product value chain. The cluster includes more than simple horizontal networks

where companies operating in the same market and belonging to the same

industry group cooperate in such spheres like scientific research and

experimental development, implementation of innovations, creations of products

or transfer of technologies.

This part of the paper distinguishes potential motives of clustering benefits

and threats for educational institutions, public sector and business organizations.

1.2. Economic Significance of Maritime sector Clustering

This part analyses economic significance of Maritime sector clustering for

Lithuanian economy, presents the analysis of the characteristics of the Marine

and Maritime sector policy based on preconditions of cluster formation and this

part presents the analysis of economic evaluation of clustering preconditions

need.

1.2.1. Economic Significance of Maritime sector Clustering for Lithuanian

Economy

Lithuanian Maritime sector organizations are concentrated in the coastal

region and the Baltic Sea Area, which belongs to Lithuania. However, it through

the shipping and freight routes, mineral and biological resources, scientific

cooperation is directly related not only to all the Baltic Sea countries, but also to

the Global waters. In Lithuanian Economic zone and Territorial waters there are

concentrated energy, oil, sand and gravel resources, developed fishery,

developed international transport (shipping) corridors activities, engineering

communications are being built and other economic and social activities are

being carried out. Particularly significant potential of coastal region and sea

recreation. All of this is an important sphere of the state's strategic and

geopolitical interests.

Indexes of economic activity evaluating economic importance of Lithuanian

traditional Maritime sector to Lithuanian economy (Number of companies

operating in Lithuanian Maritime sector, Number of employees working in the

Maritime sector, Turnover, Value added at factor costs, Gross operating profit,

Gross investment in tangible assets, Total investment in R&D, Gross

profitability and Labour productivity are expressed in absolute and relative

23

values by comparing them with indicators of other Lithuanian sectors economic

activities.

1.2.2. Characteristics of the Maritime sector Policy based on Preconditions

of Cluster Formation

Clustering is based on the essential precondition that the country’s or

region’s economic well-being is not determined by the individual companies, but

performance of groups of companies related by productive relations in certain

geographical regions. Thus, the main object of clustering policy is not single

individual companies but all of the industrial systems of the region that supports

such a productive business contacts.

By implementing the cluster-oriented policy, the main focus is paid on the

following aspects: (1) creation of conditions for entrepreneurship and formation

of clusters and support of potential clusters; (2) promoting the development of

clusters when policy measures aimed at the existing clusters but for some

reasons experiencing difficulties (Jucevičius, 2009; Stalgienė, 2010). So there

must be selected appropriate policy measures to reduce or eliminate problems

caused by barriers.

As foreign experience shows, various clustering processes, especially

formation of clusters and creation and development, are not directly regulated by

law. General clustering conditions are affected by all laws which regulate

general economic, business, innovations and other environment, especially those

legal acts which are horizontal policy tools: laws on competition, innovation,

technology and so on. Cluster policy regulation is carried out through joint

program documents. Major clustering policy instruments in the European Union

are: Europe INNOVA Cluster Observatory, Cluster Alliance, EU Structural

Funds and various research and development programmes, the core policy

makers of the EU’s Maritime sector clustering processes associated policy are:

Cluster policy of each member state, Maritime Industries Forum and European

Technology Platform Waterborne.

1.2.3. The Need for economic evaluation of Clustering Preconditions

So far, there is no detailed empirical research of clusters in Lithuania, seeking

to determine the main preconditions of clustering for increase of Productivity,

Innovations and Competitiveness, to identify potential or current clusters, to

analyse their development conditions and opportunities. However, it should be

noted that the recent period of Lithuania has produced several studies which

analyse certain aspects of clusters and their environment.

SWOT analysis of Lithuanian Maritime sector showed that preconditions for

increase Productivity and Competitiveness (related to access to the common

business infrastructure, common distribution channels, high-quality products and

24

services, geographic focus and corporate involvement in the associate structures)

are the strong side of companies of Lithuanian Maritime sector.

Group of preconditions of increase Innovations (uncertainty of intellectual

property protection, lack of cooperation and lack of effectiveness of business

informational systems, weak networking, poor legislative framework) is the

weakest part in the Maritime sector.

2. ECONOMIC EVALUATION METHODOLOGY OF LITHUANIAN

MARITIME SECTOR CLUSTERING PRECONDITIONS

This part analyses peculiarity of clustering economic evaluation and model of

economic evaluation of Lithuanian Maritime sector clustering preconditions is

being formed.

2.1. Peculiarities of Economic Evaluation Clustering Preconditions

This part analyses general methods and indexes of evaluation of clustering

and the main problems of evaluation of preconditions of clustering and

limitations in Maritime sector.

2.1.1. Methods and Indexes of General Clustering evaluation

Different authors treat the impact and input of clustering for the country and

its members differently - one of them (Foray et al., 2009; Jucevičius, 2008;

Jucevičius et al., 2007, Poon, 2003; Moreno et al., 2005) emphasize quantitative

impact indexes (concentration and dispersion methods of assessment, cost-

benefit assessment methods and other quantitative methods), others authors

(Turok, 1990; Brodzicki et al., 2003; Cooke, 2006) tend to analyse qualitative

indexes of cluster impact and the third one (Hill and Brennan, 2000, Wang et al.,

2005) tend to choose the combined indexes. The aim is to combine qualitative

and quantitative clustering evaluation methods.

However, despite the abundance of methods and indexes for identifying

industry clusters, there is no generally accepted methodology for clustering

studies, universally suitable for regional industrial clustering process to identify

and evaluate. Successful clustering provides many concrete benefits of cluster

companies - this reflects the benefits of productivity, innovations and

competitiveness in the business. This dissertation presents research methods,

which are applied for research of benefits and needs of clustering. There are a

great number of such methods.

2.1.2. Evaluation Problems and Limitations of Clustering Preconditions in

the Maritime sector

These problems and limitations are faced while evaluating preconditions of

Maritime sector clustering:

25

1. Uncertainty of preconditions of clustering concept; 2. Unjustified

application of evaluation methods for preconditions of clustering;

3. Measurement indicators and indexes data failure and systemic deficiencies of

this measurement; 4. Expert evaluation subjectivity and limited competitiveness;

5 Uncertainty of evaluation of differences of preconditions of clustering and

clusters’ benefit. Unreliable publicly available statistical study data; 7. Maritime

sector, as statistically important economic unit, absence; 8. Insufficiency of

results of Maritime sector lobbying activity; 9. Preference of cluster results

analysis against studies of clustering process and preconditions of clustering;

10. absence of earlier economic evaluations of preconditions of Maritime sector

clustering.

2.2. Model formation of Economic evaluation of Maritime sector Clustering

Preconditions

This part of the paper analysis creation of combined economic evaluation

methodology of preconditions of clustering, conducts selection of criteria and

variables significant to preconditions of Maritime sector clustering, conducts

determination of financial indexes, presents structure of combined economic

evaluation model of preconditions of Lithuanian Maritime sector clustering.

2.2.1. Creation of Maritime sector Clustering Preconditions Combined

economic evaluation Methodology

Regarding the specification of the thesis topic, analysis of thesis problem

complexity and thesis object complexity, combined economic evaluation

methodology of preconditions of Maritime sector clustering includes:

1) Empirical Quantitative Research, selecting an econometric evaluation method

and calculating Regional Coefficient, Agglomeration Coefficient, Production

Specialization Index and Index of Geographic Concentration, as well as

Clustering Index. 2) The Empirical Qualitative Research - Expert evaluation

consists of two parts: in the first part, Ranking of preconditions and obstacles

(risks) and Direct method of assessment is conducted, in the second part,

Qualitative research is conducted which is based on “Conversation-interview”

method; c) Empirical Quantitative Research - Questionnaire (Pilot Study).

2.2.2. Process and Structure of Lithuanian Maritime sector Clustering

Preconditions Combined economic evaluation Model

Model of combined economic evaluation of preconditions of Lithuanian

Maritime sector clustering structurally consists of these stages: 1. Identification

of macroeconomic factors influencing Lithuanian Maritime sector. 2. Lithuanian

Maritime sector hierarchy in accordance with industry groups. 3. Determination

of significant indexes for Countries economy of Lithuanian Maritime sector.

4. Identification of characteristics of Lithuanian Maritime sector clustering.

26

5. Selection of research methods for the analysis of preconditions and risks of

Lithuanian Maritime sector clustering. 6. Identification of the main

characteristics of preconditions and risks of Maritime sector clustering for the

increase Productivity, Innovations and Competitiveness. 7. Expert and statistical

evaluation of the importance of signs of preconditions. 8. Statistical evaluation of

correlation of preconditions and risks of Lithuanian Maritime sector clustering.

9. Evaluation of need of cluster management organization, which would manage

Maritime sector risks and preconditions. 10. Formulation of evaluation findings

of clustering preconditions.

In view of the above-described stages, there is created Combined economic

evaluation Model of Lithuanian Maritime sector clustering preconditions, which

is presented in Figure 3. Macroeconomic factors

Lithuanian Maritime sector

Gro

up

s o

f ec

on

om

ic

act

viti

es

Lithuanian Maritime sector‘s impact on the Lithuanian economy Number of companies Number of companies in classes, according to the size Number of employees Turnover Value added at factor costs

Gross operating profit Material investments Foreign direct investment Gross profitability Labour productivityInd

ica

tors

To increase Productivity

1.

2.

3.

4.

5.

6.

7.

To increase Innovations

1.

2.

3.

4.

5.

6.

7.

To increase Competitiveness

1.

2.

3.

4.

5.

6.

7.

Ma

in a

ttri

bu

tes

of

ass

um

pti

on

s

Clustering asumptions and risks analysisA questionnaire survey

Assumptions clustering

evaluation: survey

Expert evaluation

Clustering assumptions

assessment: „conversation-

interview“

Assumptions attributes manifestation importance expert evaluation Assumptions attributes manifestation statistical evaluation

Shipbuilding Marine equipment Shipping Fisheries

Offshore supply

Marine works

Marine services

Inland navigationRecreational

boating

SeaportsCoastal and marine tourismExploitation of marine

aggregatesNavy and coastal safeguard

Clustering assumptions evaluation result

BENEFIT

For region

For sector

For economic activity group

For partner organizations

For organization itself

LOOSES

For region

For sector

For economic activity group

For partner organizations

For organization itself

Assumptions and risks correlation statistical evaluation

Assumptions and risks managing cluster organizations' needs importance assessment

To increase Productivity

1.

2.

3.

4.

5.

6.

7.

To increase Innovations

1.

2.

3.

4.

5.

6.

7.

To increase Competitiveness

1.

2.

3.

4.

5.

6.

7.

Ma

in a

ttri

bu

tes

of

risk

s

Risks attributes manifestation importance expert evaluation Risks attributes manifestation statistical evaluation

Political

environment:

Political stability;

Strategic objectives;

Implementation of

policy; Bureaucracy

Economic environment:

The stability of the

macroeconomic situation;

The investment climate;

Offshore conditions; FEZ

specialization

SME‘s and OAC promotion

Geographical environment:

Geographical location;

Natural resources; Quality of

the environment and ecology

Cultural

environment:

History and traditions;

Cultural

characteristics; Impact

of globalization;

Lifestyle

Technological

environment:

ICT infrastructure and

development; Technological

development; Transport

infrastructure; Logistics and

distribution centers

Social - demographic environment:

Human resources qualification;

Science, education and research

centers infrastructure; Supply of

human resources

Clustering attributes identification

1. Clustering Index 2. Location Quotiens 3. Agglomeration Coefficient 4. Production Specialisation Index 5. Geographical

concentration indicators: 5.1. Localization Index, 5.2. Herfindahl Index, 5.3. HHI Herfindahl-Hirschman Index,5.4. Ellison-Glaeser

Index, 5.5. Dartboard Approach Index 6. M.Porter Diamond Model 7. T.Padmore and H.Gibson GEM model

Fig. 3. Model of economic evaluation of Maritime sector clustering preconditions

27

While evaluating preconditions of clustering, it is aimed to determine the

importance of signs manifestation of preconditions of clustering and to evaluate

the importance manifestation of the main risk signs, which are related to

preconditions. After identification of the main preconditions and risks, it aimed

to evaluate them with empirical research methods by using expert evaluation and

statistical evaluation of the data processing, which were collected during the

questionnaire survey. After expert and statistical evaluation of importance of

manifestation of signs of preconditions and risks of Lithuanian Maritime sector

clustering, statistical evaluation of preconditions and risks correlation is done by

including into formulated evaluation results of preconditions of clustering and

evaluation results of demand importance of cluster organization operating

preconditions and risks of Maritime sector clustering.

For the increase of Productivity, Innovations and Competitiveness of

preconditions of Lithuanian Maritime sector clustering, economic evaluation is

oriented into identification, systematization, justification and verification of

manifestation importance of preconditions of clustering. Additionally, economic

evaluation is oriented into characteristics of risks and their verification of

importance for realisation of preconditions of clustering and at a later stage - into

correlation analysis of preconditions and risks which results would justify

clustering benefit or loss for region, sector, group of economic activities,

partners-organizations and a company itself.

3. EMPIRICAL SOLUTIONS OF MARITIME SECTOR CLUSTERING

PRECONDITIONS ECONOMIC EVALUATION

This part analyses initiatives of Lithuanian Maritime sector and conducts

their evaluation as well as combined model of economic evaluation of

preconditions of clustering is verified in the context of Lithuanian Maritime

sector.

3.1. Lithuanian Maritime sector Clustering initiatives and their Evaluation

All over Lithuania, clustering initiative is in similar condition in different

industry sectors: currently there is conducted economic evaluation of traditional

industry sectors, maps of clusters are being created, studies on clusters are being

conducted and models of facilitation of clusters are provided. Innovative

companies show initiative by assuming the status of cluster organisation and co-

integrating the corporate resources and capabilities and offer Lithuanian and

foreign markets new products and services.

After conduction of evaluation of initiatives of various Lithuanian industry

sectors’ clustering, there is observed the increasing awareness in the importance

of clusters and positively changing position of system members. There are a

number of micro clusters and clusters at the formation stage, the support and

lobbying initiative of sectoral and trade associations is growing. Major clustering

28

initiatives interferences: small, though growing, education, business and public

sector cooperation, lack of professional information systems, weak links with

other industries, weak sectoral associations, there is no policy system promoting

cooperation, lack of (almost none) specialists of creation and management of

networks and clusters, lack of networking competence.

3.2. Verification of Clustering Preconditions Combined economic evaluation

Model in the context of Lithuanian Maritime sector

In view of the the studies carried out in the first part and the concluded model

structure of combined economic evaluation of preconditions of Lithuanian

Maritime sector clustering in the second part, this part describes methodology of

research “Economic evaluation of preconditions of Lithuanian Maritime sector

clustering”, the main principles of data analysis and research results are

presented and interpreted.

3.2.1. Methodology of Clustering Preconditions Combined economic

evaluation and the Main principles of Data analysis

Regarding the specification of the thesis topic, for the analysis of thesis

problem complexity and thesis object complexity, these types of research were

chosen: a) Empirical quantitative research, selecting an econometric evaluation

method. b) Empirical qualitative research - expert evaluation consists of two

parts: in the first part, Ranking of preconditions and obstacles and Direct method

of preconditions is conducted, in the second part, Qualitative research is

conducted which is based on “Conversation-interview” method; c) Empirical

Quantitative research - Questionnaire (Pilot Study).

Evaluation of clustering preconditions in this empirical quantitative research

is conducted by calculating Regional coefficient, Agglomeration coefficient,

Production Specialization Index and Index of Geographic Concentration, as well

as Clustering index. These indexes were chosen for their complexity and

versatility while evaluating regional concentration, clustering level and extent of

specialization and agglomeration. These indicators are important and affect

evaluation of Lithuanian Maritime sector status and are integral part of integral

economic evaluation of preconditions of Lithuanian Maritime sector clustering.

While applying empirical research methods, firstly empirical qualitative

research was selected - it is an expert evaluation comprising of two parts: in the

first part, ranking of preconditions and obstacles (risks) is conducted and direct

evaluation method, in the second part, qualitative research based on

“Conversation – interview” method is conducted. Also, empirical quantitative

study was conducted - Questionnaire- designed for representatives of companies

of Lithuanian Maritime sector. The study is attributed to the Pilot study group.

29

3.2.2. Evaluation of Clustering Preconditions by calculating Regional

Coefficient, Agglomeration Coefficient, Production Specialization Index,

Geographic Concentration Index and Clustering Index

Calculated indicators showed a relatively high level of industry clustering

(Clustering Index value), higher than the national average - the relative

employment in the Maritime sector in Klaipeda region, however, these indexes

did not show the regional Maritime sector specialization (Regional Coefficient

value). In regard of data and calculations according to Production Specialization

Index of Lithuanian Maritime sector production specialization in Klaipeda

region, it is seen that production specialization in Maritime sector, according to

2010-2012 data, is increasing, agglomeration blurred (Agglomeration

Coefficient) and calculated indexes of Geographical Concentration Index show

that Lithuanian Maritime sector industry localization in Klaipeda region is

increasing, Lithuanian Maritime sector concentration in region was not identified

and it can be stated that all regional shared similar industry parts, sector’s market

in Klaipeda region is evaluated as medium concentrated and evaluating market

concentration in respect of other regions of country, although it is insignificant,

but fixed. The resulting valuesof indicators show the region's relative industrial

density, relative region size and relative established companies’ size differences

in respect of other Lithuanian regions.

During this phase, the collected data analysis allowed the identification of

Lithuanian Maritime sector clustering characteristics (for example, calculated

index value of clustering - 1,2 and this allows to declare about unity and

specialization of Lithuanian Maritime sector so it gives the ground for further

analysis of preconditions and characteristics of clustering and other clustering

process aspects and its impact factors).

3.2.3. Results and their interpretation of Clustering Preconditions Expert

Evaluation

Statistical evaluation of preconditions and risks correlation of Lithuanian

Maritime sector clustering was conducted during the Empirical study. Expert

evaluation data of preconditions and risks of Lithuanian Maritime sector

clustering of the first questionnaire part were systematized, analysed and

summarized while calculating these statistical indexes: granted estimate amount

of every precondition and risk of Lithuanian Maritime sector clustering, place in

general and group ranking scales, average of expert evaluation, median, mode,

standard deviation and variance.

Weighted averages of preconditions and risks of clustering were calculated

after calculation of relative weight coefficients of preconditions of Lithuanian

Maritime sector clustering. For calculation of estimates of cumulative weighted

averages of preconditions and risks of clustering, formula created by the author

are offered and after calculating these weighted averages of preconditions (P)

30

and risks (K) of clustering (𝐼𝑆𝑉𝑃 and 𝐼𝑆𝑉𝐾), the comparative analysis of

preconditions and risks of clustering is conducted by verifying offered

hypotheses of the author. After summing up the results of the study, it is seen

that evaluation of 21 averages of expert estimate weighted preconditions with 21

indexes of expert estimate weighted risks, 18 preconditions are more significant

and therefore influence the Productivity, Innovations and Competitiveness

enhancement and the clustering effect is positive in terms of region, sector,

group of economic activities, organization and organizations-partners.

(=”benefit”). These preconditions are considered to be catalysts for the process

of clustering, since they occurred as the most significant in the evaluative

conditional pairs together with the risks.

Thus, the main preconditions of Maritime sector clustering are related with

innovation policy promotion and development of innovations in cooperation,

transfer of "good practice" and strengthening of bargaining power. Averages of

expert estimate weighted 3 preconditions were lower than averages of expert

estimate weighted risks. (=”losses”).

Through qualitative evaluation indicators to identify the the characteristics of

clustering, Porter’s Diamond competitiveness assessment model and Padmore

and Gibson GEM model which are combined and combined into semi-structured

questionnaire for experts, methodology of “diamond” model study by

complementing with significant Padmore and Gibson GEM model question

groups. In this semi-structured questionnaire for experts, questions were divided

into six relative groups, recommended in the methodology of competitiveness

evaluation: 1 (question group) - to determine demand conditions, 2 - to evaluate

company’s strategy, structure and competitiveness, 3 - determinants, 4 - to

identify related and each other supporting industry groups, 5 - 6 (question

groups) - to evaluate respective influence of government and opportunities. In

this part, empirical quantitative research was conducted and results used in

further studies.

3.2.4. The results of Statistical research on Clustering Preconditions and

their Interpretation

The data collected during the statistical political research showed that almost

all of the economic activity groups which were interrogated about collaboration

among the enterprises of Lithuanian Maritime sector replied – “we do not

collaborate at all”. When indicating type of organisation, with which enterprises

of represented respondents collaborate most, the respondents chose international

partners of maritime sector and institutions of national importance. Comparing

the collaboration with institutions of science and studies, the results showed, that

collaboration with institutions of studies is closer. After having analysed the

systemized data, it is obvious, that the greatest discrepancy between the

importance of subjects affecting the productivity and the factors affecting the

31

productivity of currently represented enterprise is in regard with competitors and

organizations of business cluster, and the least discrepancy is in regard with

public sector institutions and non-affiliated enterprises. The respondents claimed,

that in order to increase the productivity, it is essential to invest in the production

technique, to introduce new technologies and to form qualitative working

conditions. The greatest discrepancy between the importance of factors affecting

the innovation and factors affecting innovation of currently represented

enterprise is in regard with cluster organization and institutions of science and

studies, and the least discrepancy is in regard with the clients and public sector

institutions. The respondents indicated, that in order to increase the innovation it

is essential to actively participate and manage innovation networks, it is

important to involve the suppliers in the initial stage of innovative projects, and

it is important to keep in touch with clients, observe the changes of their

priorities and conduct surveys. It was determined, that great discrepancy between

the importance of factors affecting competitiveness and factors affecting the

competitiveness of currently represented enterprise was not noticed, only the

greatest importance of the difference regarding associations and consortium

could be singled out. According to the respondents, in order to increase the

competitiveness it is important to observe the competitors, analyse their

mistakes, observe the clients, the change of their priorities and needs, and to

develop and improve the strategy of enterprise marketing, actively participate in

exhibitions of Lithuania and foreign countries.

Having asked the respondents, would they agree with the initiative of

creating Maritime business cluster, most of the answers were positive. Having

summed up and arranged the estimate averages of the importance of reasons and

benefit motives, it can be seen that conditionally, most important reasons and

benefit motives are: to access the newest specialised information on the industry

you work in, to decrease expenditure of logistics and storage, and to level up

your knowledge by collaborating with the professionals from close ground

industries working near your enterprise.

3.2.5. Consideration of the Results of Combined economic Evaluation of

Maritime sector Clustering Preconditions

Following the created methodology of combined economic evaluation of

Maritime sector clustering preconditions, the results received in every stage are

generalised and the most essential economic data are interpretated.

32

CONCLUSIONS

The analysis of Lithuanian Maritime sector clustering preconditions was

conducted, which was the ground for formation of the model of combined

economic evaluation of Maritime sector clustering preconditions and grounded

on empirical research, which allows drawing these conclusions:

1. Having evaluated and systemised the structural composition of Lithuanian

Maritime sector, its structure according to economic industry groups is presented

in this research, singling out three main parts of Lithuanian Maritime sector:

traditional Maritime sector, coastal and marine tourism, and fisheries. The

traditional Maritime sector consists of 11 economic industries: that of

shipbuilding, marine equipment, marine services, exploitation of marine

aggregates, marine works, offshore supply, navy and coastal safeguard, inland

navigation, recreational boating, seaports and shipping. Coastal and marine

tourism consists of two economic industries: coastal tourism and marine tourism.

Fisheries include the industries of fishery and aquaculture. In total, Lithuanian

Maritime sector includes 13 sections, 28 sectors, 49 groups and 71 economic

industry classes. This research additionally contains the structure of Lithuanian

Maritime sector, in which the economic industries are combined according to

their functions and their mutual entanglement.

2. Having examined the rise, formation and expansion of the need for

clustering of Maritime sector, it was determined that though geographical

concentration of enterprises is a very important criteria for rise of the need for

clustering, but also the rise of the need is the concentration of industry nature.

Strengthening of reliance among the enterprises is substantial condition for

formation of Maritime sector clustering, which decreases the need for

geographical concentration of enterprises, but forms favourable conditions for

collaboration of enterprises in potential economic industries of Maritime sector.

The formation of clustering starts from the recognition of need or possibilities to

collaborate, accentuation of value added and strengthening of reliance among the

enterprises. Often the market is not able to regulate all the processes of clustering

development, therefore, the government must help to regulate it: both creating

favourable conditions for free enterprise and formation of business clusters, and

stimulating processes of clustering expansion by using financial and consultative

means; different levels of government may use different policies, which may

differ according to the degree of intervention in the market. In process of work,

the main preconditions and risks of clustering of Lithuanian Maritime sector are

singled out and systemized according to their significant features, related with

the increase Productivity, Innovations and Competitiveness. Such way of

systemising clustering preconditions and risks was fallowed because of the

forces influencing the increase of Productivity, Innovations or Competitiveness,

accented in their formulation, because of their strategic functions that are singled

out in the working process and a more clear presentation of their formulation to

33

the experts. The risks of Maritime sector clustering in this work are named as the

barriers of the increase of the Productivity, Innovations and Competitiveness.

3. Having evaluated the economic significance of Lithuanian Maritime

sector for the economy of Lithuania, a significant benefit of this sector and its

part in Lithuanian economy structure. While evaluating gross margin of

Maritime sector and gross margin of all sectors in the country it is important to

pay attention to the fact, that the gross margin of Maritime sector enterprises in

the period of 2007 – 2012 was higher than the gross margin of all sectors in the

country by approximately 4,75 percentile points, and the productivity of the

Maritime sector enterprises in the period of 2007 – 2012 was higher than the

productivity of all sectors in the country by approximately 22,73 percentile

points. Lithuanian Maritime sector is observed emergent and correlated

connections between defferent business enterprises in a variety of mutual

economic relations, gained professional experience, developed highly qualified,

specialized and periodically raising the professional competence specialists,

developed long-term relationships with Lithuanian and foreign suppliers and

customers, introduction of innovative technologies and business modern quality

management processes and other system improvements. Lithuanian Maritime

sector is relatively easier to react to economic crisis, which shows the period of

2008-2010 the global economic crisis, while in 2009, Lithuanian Maritime sector

increased turnover, value added (at factor cost), investments in tangible assets,

gross operating profit ratios, compared to the whole country economy.

4. Having analysed the peculiarities of economic evaluation of Maritime

sector clustering it was determined, that the most often used methods for

evaluation of industry clustering are: cost-benefit analysis, case study, interview

of experts, interrogations and various statistical and econometric methods. Most

countries are widely adjusting the methodology of Porter Diamond model of

evaluation of competitiveness or its modified versions (GEM, model of Nine

factors and others). In the process of evaluation of Maritime sector clustering

preconditions the indefiniteness of conception of clustering preconditions, the

validity of methodology of evaluation of clustering preconditions, the

insufficiency of dada measured by indexes and systematic shortage of this

measurement, the indefiniteness of differences in evaluation of clustering

preconditions and benefit received from the cluster, the insecurity of publically

accessible statistical data of research, the preference of analysis for cluster

results over process of clustering and research of clustering preconditions, and

absence of earlier economic evaluations of Lithuanian Maritime sector clustering

preconditions are met. The analysis of Lithuanian Maritime sector initiatives has

showed, that concrete stages and processes of Lithuanian Maritime sector are

being analysed and evaluated in the paper works of foreign scientists and

involved in project studies and the accounts of regional thematic industries,

though the practical result-orientated initiatives of Lithuanian Maritime sector

34

clustering, that would lead to the real formation of Maritime sector, are missed.

The foreign experts quite often present the initiatives and rudiment of Lithuanian

Maritime sector clustering as a working cluster and having systemised the results

of Lithuanian Maritime sector economic industry according to their own

methods, present them as results of Maritime cluster, while analysing and

comparing them to the results of other countries. It was determined that the

applied single quantitative, qualitative and combined research methods examine

the stages of clustering only fragmentally and episodically, without evaluating

the complexity of scientific problem and the object of research.

5. The essence of combined economic evaluation methodology of Maritime

sector clustering preconditions is the systematic attitude towards the integrity

and appliance of the research methods, in order to get as much exact and

objective data, that ground the preconditions of Lithuanian Maritime clustering,

as possible by carrying out empirical research and to draw conclusions about the

received result that is the benefit or detriment of the country, region, sector,

economic activity group, enterprise or affiliated organisations conditioned by

Lithuanian Maritime clustering preconditions. The methodology of combined

economic evaluation of Maritime sector clustering preconditions embraces

empirical quantitative researches and the empirical quantitative research. When

evaluating regional solidarity, level of clustering, scale of specialisation and

agglomeration, the quantitative indicators of empirical research were chosen

because of their complexity and universality. These indicators are important and

have influence over the evaluation of Lithuanian Maritime sector state and are a

part of combined economic evaluation of Lithuanian Maritime sector clustering

preconditions. Empirical researches were chosen because of their informative

nature, causality and opportunities to analyse the data applying the principles of

correlation, regression, dispersion and comparative statistical analysis. After

having checked the economic evaluation methodology of complex Maritime

sector clustering preconditions it was determined that this methodology can be

fully applied for evaluation of clustering preconditions of countrywide working

Maritime sector, because under the grounds of this methodology it is possible to

evaluate the potential and expenditure possibilities of Maritime sector, to single

out the main factors conditioning and limiting the preconditions of clustering.

This methodology can be applied in order to compare the clustering

preconditions of the Baltic States. The suggested methodology of combined

economic evaluation of Maritime sector clustering preconditions can be applied

for the researches on Maritime sector clustering precondition of other countries.

6. The indexes assessed during this empirical quantitative research of

combined economic evaluation have showed a quite high clustering level of the

sector, which is higher than the average of the country – relative employment in

the Maritime sector of region of Klaipeda, but it did not showed the

specialisation of regional Maritime sector. According to the data of

35

manufacturing specialisation received in the Maritime sector in the period of the

year 2010–2012 it is increasing, the agglomeration is not considerable, and the

assessed indexes of Geographical concentration have showed, that localisation

industry of Lithuanian Maritime sector in the region of Klaipeda is increasing,

the concentration of Lithuanian Maritime sector in the region was not fixed, it

possible to say that all the regions had similarly equal parts of the industry, the

sector market in the region of Klaipeda is valuated as moderately concentrated,

and while evaluating the market concentration regarding other regions of the

country, though it is not notable, but it is fixed. The received indexes showed the

differences of the relative industrial density, the relative size of the region and

relative size of the created enterprises regarding other regions of Lithuania. The

clustering preconditions chosen for evaluation during the empirical quantitative

research were arranged according to their importance and the weighted averages

of estimates were compared with the risks, which were analysed according to

analogical methodology, with the help of experts. That allowed grinding the

importance of the singled out preconditions and risks argumentatively. The stage

of transcription analysis of empirical quantitative research, which is half

structured “conversation-interview” with the experts has helped to reveal the

attitude of respondents towards the determining factors of Lithuanian Maritime

sector, demand conditions, strategies of enterprises, structures and

competitiveness, the interrelated and mutually supportive branches of industry,

the influence and opportunities of the government, while prescriptive analysis

allowed to form the suggestions for the improvement of Lithuanian Maritime

sector clustering conditions. The data collected from representatives of the

enterprises during the empirical quantitative research allowed to analyse the

provisions and need for collaboration of Maritime sector, as essential conditions,

necessary for realisation of clustering preconditions, to get recommendations for

increasing of Productivity, Innovations and Competitiveness, to examine the

attitude towards participation in Maritime cluster, to evaluate the need for

creation Maritime cluster organisation and the motives for its benefits.

36

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01/dissertation.pdf>.

LIST OF SCIENTIFIC PUBLICATIONS ON THE TOPIC OF

DISSERTATION

1. Viederytė, Rasa. Lithuanian Maritime Sector‘s clustering economic

impact evaluation // Procedia - Social and Behavioral Sciences. ISSN 1877-

0428. 2014 [priimta ir patvirtinta spausdinimui; pažyma pridedama].

2. Viederytė, Rasa. Lithuanian Maritime Sector‘s economic impact to the

whole Lithuanian Economy // Procedia – Social and Behavioral Sciences.

Amsterdam: Elsevier. ISSN 1877-0428. 2014, Vol. 143, p. 892-896.

3. Viederytė, Rasa; Skeivytė, Milda. Lithuanian Maritime Sector‘s

Economic impact evaluation: Methods and Comparative analysis // Trends

Economics and Management. ISSN 1802-8527. 2014 [priimta ir patvirtinta

spausdinimui; pažyma pridedama].

4. Viederytė, Rasa. Maritime Cluster Organizations: Enhancing Role of

Maritime Industry Development // Procedia – Social and Behavioral Sciences.

ISSN 1877-0428. 2013, vol. 81, p. 624-631.

5. Viederytė, Rasa; Didžiokas, Rimantas. Cluster Models, Factors and

Characteristics for the Competitive Advantage of Lithuanian Maritime Sector //

Economics and Management. ISSN 1822-6515. 2014, vol. 19, no. 2, p.162-171.

6. Viederytė, Rasa. Economic implications on the basis of Lithuanian

maritime sector’s clustering // Regional formation and development studies.

ISSN 2029-9370. 2014, no. 2 (13), p. 118-126.

7. Viederytė, Rasa. Klasterio formavimas: bendrieji požymiai, kriterijai,

etapai ir brandumo fazės // Management Theory and Studies for Rural Business

and Infrastructure Development. ISSN 1822-6760. 2014, vol. 36, no. 3, p. 688-

700.

8. Viederytė, Rasa; Strakšienė, G. Practice of cross border cooperation in

capacity building project: ensuring sustainable development // Regional

formation and development studies. ISSN 2029-9370. 2012, no.1 (6), p. 147-159.

9. Viederytė, Rasa. Maritime sector impact on the Economy of Lithuania //

Economics and Management = Ekonomika ir vadyba [elektroninis išteklius].

ISSN 1822-6515. 2012, no. 17 (1), p. 244-249.

41

10. Viederytė, Rasa; Juščius, Vytautas. Jūrinio sektoriaus klasterizacijos

skatinimas: prielaidos ir pagrindinės kliūtys // Ekonomika ir vadyba: aktualijos ir

perspektyvos. ISSN 1648-9098. 2012, Nr. 4 (28), p. 99-107.

11. Viederytė, Rasa; Didžiokas, Rimantas; Juščius, Vytautas. Enhancing

innovations importance in Lithuanian Marine sector: interdisciplinary approach

// Human resources – the main factor of regional development. ISSN 2029-5103.

2011, no. 4, p. 158-168.

12. Viederytė, Rasa; Dikšaitė, Loreta. Maritime clusters productivity and

competitiveness evaluation methods: systematic approach // Economic and social

development: 5th International Scientific Conference (ESD-Belgrade): Book of

Proceedings. ISBN 978-953-6125-08-1. 2014, p. 313-321.

13. Viederytė, Rasa. Competitive economic grow abilities through maritime

cluster development in Lithuania // Advances in Business-Related Scientific

Research Conference 2013: Conference proceedings. Venice, 2013. ISBN 978-

961-269-957-4.

14. Didžiokas, Rimantas; Viederytė, Rasa; Gintalas, Marius. Potencialios

Lietuvos Laivų statybos sektoriaus plėtros iniciatyvos sektoriaus strategijos

LeaderSHIP 2015 kontekste // Technologijos mokslo darbai Vakarų Lietuvoje:

[konferencijos medžiaga]. ISSN 1822-4652. 2010, [d.] 7, p. 10-17.

42

INFORMATION ABOUT THE AUTHOR OF THE DISSERTATION

Name:

Contacts:

Rasa Viederytė

rasa.viederyte@ktu.edu

Academic Background:

2010 – 2014

2002 – 2004

2002 – 2004

1998 – 2002

Doctoral studies at Kaunas University of Technology,

Faculty of Economics and Management, Department of

Economics

Full-time Master’s degree studies at Faculty of Social

Sciences, Klaipeda University. Master degree of Marketing

Management.

Full-time Master’s degree studies at Faculty of State

Management, Mykolas Romeris University. Master degree

of Public Administration.

Full-time Bachelor’s degree studies at Faculty of Social

Sciences, Klaipeda University. Bachelor degree in

Management and Business Administration.

Work experience:

2012 - current

2011– current

2013 - 2014

2012 – 2014

2012 – current

2010 – 2012

2008 – 2009

2007 - 2011

2006 – 2013

2005 – 2008

General project manager and Junior researcher in Klaipeda

University scientific project „Lithuanian Maritime sector

technologies and environment research development“.

Head of Klaipeda University Development department.

Deputy director of Klaipeda University Marine sciences and

technologies Centre.

Lecturer at Economics Department in the Faculty of Social

Sciences, Klaipeda University.

Owner and manager of Consulting company „Maritime

Cluster“, Ltd. (Small community status).

Owner and director of logistic company ”Elvitransa”, Ltd.

Assistant at Bachelor degree studies in ISM University of

Management and Economics, Vilnius.

Head of Klaipeda University Projects management

department.

External Consultant and Lecturer. Services provided under

the business license and individual performance certificate.

Manager of projects and Administrator at Mechatronics

Science institute, Klaipeda University.

Fields of scientific interest: Clustering, Maritime sector analysis, productivity, innovations and

competitiveness, marketing, public–private management coherence, knowledge

and technology transfer.

43

REZIUMĖ

Tyrimo aktualumas. Lietuvoje pastebimos savarankiškos verslo subjektų

klasterizacijos iniciatyvos. Vienos jų yra kryptingai vystomos ilgalaikiams

ekonominiams tikslams siekti, kitos – užuomazgos stadijoje. Lietuva yra jūrinė

valstybė, esanti strategiškai svarbioje geografinėje padėtyje ir valdanti

multimodalinį infrastruktūrinį valstybinės svarbos objektą, labiausiai į šiaurę

nutolusį neužšąlantį rytinės Baltijos jūros uostą - Klaipėdos valstybinį uostą. Per

pastarąjį dešimtmetį šiame jūriniame sektoriuje yra sukurta ir vystoma verslumą

skatinanti infrastruktūra (logistikos sistema ir logistikos centrai, laisvoji

ekonominė zona), išugdyti jūrinės srities aukštos kvalifikacijos specialistai, įgyta

krovinių saugojimo ir transportavimo patirtis, diegiamos šiuolaikiškos kokybės

valdymo programos. Jūriniame sektoriuje veikiantys verslo subjektai

savarankiškai inicijuoja šakines asociacijas ir kitas jungtines struktūras, tokiomis

priemonėmis siekdami bendrų ir individualių tikslų bei ekonominių interesų

įgyvendinimo ir ekonominių veiklų sinerginio efekto.

Klasteris kaip veiklos forma ne tik keičia šalies ar regiono (apskrities) ar

tam tikro miesto ekonominę struktūrą ir potencialą, bet ir stiprina atskirų

klasterio narių žmogiškuosius, techninius, mokslinius, kapitalo, inovacinius,

partnerystės ir kitokius pajėgumus. Didesnis produktyvumas, išaugęs

konkurencingumo lygis, inovatyvių produktų kūrimas ir komercializavimas – tai

yra rezultatai, kurių klasterio nariai gali pasiekti veikdami išvien. Klasterizacija

padeda plėtoti naujas idėjas ir verslus, spartinti žinių ir technologijų perdavimą

bei diegimą, produktų kūrimą, gerinti darbo ir produktų kokybę, technologinį

turinį, sukurti palankias sąlygas didinti įmonių produktyvumą, inovatyvumą,

sumažinti mažų ir vidutinių įmonių veiklos kaštus, ypač mokslinių tyrimų ir

eksperimentinės plėtros bei inovacijų srityje, skatinti eksporto plėtrą, mažinti

riziką ir didinti sėkmės tikimybę pasirenkant naujas investicijų kryptis,

efektyvinti mokslinių tyrimų ir eksperimentinės plėtros procesus, padėti

įmonėms ar jas atstovaujančioms organizacinėms struktūroms įsijungti į

pasaulinius kompetencijų ir inovacijų tinklus, išnaudoti jų teikiamas galimybes

kuriant didesnę pridėtinę vertę, didinti inovatyvumą ir konkurencingumą.

Klasterizacijos kaip proceso ir klasterinių struktūrų poreikis ir svarba

pradėti nagrinėti jau devyniolikto amžiaus pabaigoje. Ekonominių veiklų

lokalizavimo idėjų galima aptikti jau 19 amžiaus vokiečių ekonomisto J. H.

Thunen darbuose. Didžiausias dėmesys kiriamas žemės vertei ir kokią tai daro

įtaką žemės ūkio gamybai tolstant nuo prekybos vietos (Šalčius, 1927). Minėtos

idėjos toliau nagrinėjamos A. Marshall darbuose. Jis pristatė pramoninių rajonų

sąvoką, išryškindamas mažoje srityje sutelktų įmonių ekonominės veiklos naudą

(Marshall, 1890). Anglų ekonomistas A. Marshall 1890 metais veikale

„Ekonomikos principai“ analizavo specializuotų pramonės šakų koncentraciją

vienoje sutelktoje teritorijoje, šį reiškinį pavadinęs pramoniniais rajonais ir

teigęs, jog dėl to vieno ekonominio vieneto inovacinė veikla ir augimas gali

44

daryti teigiamą įtaką kitoms sistemos dalims, o pramoniniai rajonai kaip visuma

turi veikti geriau nei atskiri vienetai.

XX a. viduryje klasterinių struktūrų tyrėjai (Isard, 1956, Becattini, 1979)

išplėtė pramoninių rajonų sąvoką, akcentuodami į eksportą orientuotų pramonės

šakų glaudžių ryšių su kitomis regiono pramonės šakomis, gamybos ir

pristatymo išlaidų mažinimo, gebėjimo kurti naujoves bei tapti dominuojančiu

žaidėju pasaulio rinkose svarbą klasterizacijos procesams. W. Isard (1956)

apibūdino klasterizacijos reiškinį, naudodamas į eksportą orientuotas pramonės

šakas ir jų ryšius su kitomis pramonės šakomis regione. Pagal jį, tokie glaudūs

pramoniniai ryšiai ir rodo klasterio egzistavimą. 1970-ųjų pabaigoje,

ekonomistas G. Becattini iškėlė klasterizacijos idėją, taikydamas tai šiaurės

Italijos pramoninei organizacijai. Pasak jo, priežastis koncentruotis geografiškai

apima tokius ekonominius aspektus, kaip gamybos ir pristatymo išlaidos

mažinimas, taip pat galimybė tapti dominuojančiu dalyviu pasaulio rinkose,

kuriose gebėjimas kurti naujoves yra pagrindinis konkurencinis pranašumas S.

Cruz ir A. Texeira (2007), M. Porter (1990) išryškino didžiulį pramoninių

klasterių potencialą. Tai buvo pagrindinis įvykis klasterio sąvokos vystymosi

raidoje, kadangi Porterio klasterio idėjos sėkmingai skynėsi kelią į mokslo ir

politikos areną sukurdamos klasterio iniciatyvų proveržį daugelyje šalių.

XXI amžiaus pradžioje klasterizacijos koncepcija imta tapatinti su „žinių

ekonomika“. Pagrindinis argumentas buvo tas, jog žiniomis grįstos ekonomikos

procesų varikliai – technologinis know-how, inovacijos ir informacijos sklaida –

palankiausiai vystosi tada, kai tokia plėtra yra lokalizuota (Martin ir Sunley,

2001). Vienas iš įtakingiausių ekonomistų, analizavusių lokalizacijos reikšmę

ekonomikai, M. Porter (1998) teigė, jog šalies pirmaujančios eksporto įmonės

yra „ne pavienės sėkmės istorijos, tačiau priklauso sėkmingiausioms susijusių

pramonės šakų konkurentų grupėms“. Jis šias grupes pavadino „klasteriais“, t.y.

pramonės šakų, susijusių įvairiais horizontaliais ir vertikaliais ryšiais, tinklais.

Lietuvoje klasterizacijos idėją vieni pirmųjų pradėjo plėtoti J. Činčikaitė ir

G. Belazarienė (2001), Verslo strategijos institutas (Klasterių..., 2002),

Lietuvos..., 2003) ir Č. Švetkauskas (2003). Jų atlikti darbai suformavo pagrindą

tolimesniam klasterizacijos reiškinio pažinimui Lietuvoje. Šiose ir vėlesnėse

(Jucevičius, 2007; 2008; 2009) studijose ir moksliniuose darbuose dažniausiai

buvo tyrinėjamos pramonės (medienos, tekstilės ir kt.), paslaugų (turizmo)

klasterių plėtros galimybės.

Pastaruoju metu, mokslinėje literatūroje dažniausiai naudojama M. Porter

(1998) suformuluota klasterio sąvoka – „geografinė koncentracija tarpusavyje

susijusių įmonių, specializuotų tiekėjų, paslaugų teikėjų bei asocijuotų institucijų

(pvz., universitetų, standartizavimo agentūrų, profesinių sąjungų), kurios

tarpusavyje ne tik konkuruoja, bet ir kooperuojasi. Taip pat – tinkliniai ryšiai,

kurie pasireiškia geografinėje vietovėje, kur įmonių ir institucijų artumas

užtikrina bendrumą ir padidina sąveikos dažnumą“. S. A. Rosenfeld (1997)

45

pabrėžė sinergijos svarbą tarp organizacijų. T. Roelandt ir P. Hertog (1999) bei J.

Simmie ir J. Sennett (2001) pasiūlė analizuoti klasterius, žvelgiant į juos kaip į

vertės (sąnaudų) kūrimo grandinę.

Regioninės ir vietinės ekonomikos žinių, inovacijų ir technologijų

plėtojimo atvejai, kuomet įmonės bendradarbiauja su vietiniais kompetencijų

tinklais ir pasinaudoja „sėkmės istorijomis“, tokiomis kaip „Silikono slėnis“,

„128 greitkelis“, „Kembridžas“ ir kt., veda ekonomikos augimo, vystymosi ir

klasterių formavimo poreikio link (Castells, 1996; Porter, 1998; Segal 2000;

Chen ir kt., 2008; Jakobsen ir kt., 2012; Portsmuth ir kt., 2012). Klasterių, kaip

besivystančios ekonomikos konkurencingumo ir produktyvumo kūrimo šaltinio,

formavimo poreikio palaikymas susiformavo pirmiausiai aukštojo mokslo

institucijose ir buvo analizuotas įvairių mokslininkų grupių darbuose (Porter,

1990; 1998; Rosenfeld, 1995; Ketels ir kt., 2013). Kaip pažymi Cooke ir Morgan

(1998, p. 185), mokslininkų akademinė parama turėjo didžiulės įtakos

pirmiausiai klasterio apibrėžimo, tikslų ir veikimo sąlygų apibūdinimui, ypač

daug darbo įdėjus mokslininkui M.Porter (1990). Įvairias konkurencingumo

vertinimo problemas analizavo ir vertinimo metodikas pasiūlė Porter (2000a;

2000b; 2003), Andersson ir Napier (2007), Andersson ir kt. (2004) ir kt.

Mokslinė problema ir jos ištyrimo lygis. Siekiant objektyviai atskleisti

disertacijoje analizuojamos mokslinės problemos ištyrimo lygį, buvo pasitelktas

Garrard (2007) matricinis tyrimo metodas bibliografinių duomenų analizei.

Pritaikius šį metodą, buvo atlikta aktualių publikacijų paieška trylikoje

tarptautiniu mastu pripažintų mokslinių leidinių duomenų bazių ir mokslo

žurnalų: EBSCO, Emerald Insight, Springer Link, Sage Journals, Science Direct,

Oxford Journals, Wiley Science, Taylor and Francis, ICPSR, Lietuvos

Nacionalinės Martyno Mažvydo bibliotekos el.katalogas, Lietuvos virtualios

bibliotekos el.katalogas, Научная Электронная Библиотека elibrary.ru,

Каталог Электронных Ресурсов. Paieška anglų kalba buvo vykdyta pagal

disertacijos pavadinimo pagrindu sudarytas aštuonias aktualių raktinių žodžių

kombinacijas. Iš visų raktinių žodžių kombinacijas atitinkančių straipsnių, jei jų

buvo rasta mažiau nei 800 vienetų, po peržiūros atmesti disertacijos tyrimo

srities ir objekto neatitinkantys moksliniai straipsniai.

Paieška vykdyta pagal mokslinio straipsnio pavadinimą, raktinius žodžius

ir santraukos tekstą. Pasirinktų duomenų bazių publikacijos buvo atrinktos ir

paieškos metu gauti rezultatai išanalizuoti per 2014 m. vasario 22 d. – 2014 m.

gegužės 30 d. laikotarpį. Atsižvelgiant į atliktos bibliografinės analizės

duomenis, raktinių žodžių kombinaciją „Lietuvos jūrinis sektorius, klasterizacija,

prielaidos, ekonominis vertinimas“ atitiko vienas EBSCO bazėje rastas mokslinis

straipsnis, paruoštas šios disertacijos autorės.

Pagal raktinių žodžių kombinaciją „Lietuvos jūrinis sektorius,

klasterizacija, prielaidos, ekonominis vertinimas“ rastos 4 knygos, kuriose

sutinkamos raktinių žodžių sąvokos, tačiau moksliniu lygmeniu jos nėra plačiau

46

analizuojamos. Pagal kombinaciją „Jūrinis sektorius, klasterizacija, prielaidos“

daugeliu atvejų įvadinėse rastų mokslinių straipsnių dalyse diskutuojama apie

jūrinio sektoriaus klasterizacijos svarbą arba apie klasterių formavimo etapus,

tačiau straipsniuose pasigendama detalesnės klasterizacijos priežasčių, sąlygų,

prielaidų, kliūčių ar rizikų analizės. Įvairūs klasterizacijos ir klasterio

formavimosi gyvavimo ciklo etapai analizuoti Brenner (2004), Hui (2005),

Lorenzen (2005), Hassink ir Dong-Ho (2005), Nadaban ir Berde (2009) ir

kituose darbuose. Klasterizacija rastose mokslinėse publikacijose yra dažniau

analizuojama kaip tam tikrų atskirų struktūrinių elementų ar požymių

atpažinimas ir jungimas priežastiniais ryšiais ir sąsajomis formuojant

klasterizacijos statistinį modelį.

Būtina pažymėti, kad nėra vieningo ekonominio požiūrio klasterizacijos

procesams analizuoti – skirtingų autorių ir įvairiame moksliniame bei

politiniame kontekste skirtingai identifikuojami klasterizacijos, klasterių kūrimo

ir klasterių formavimo svarba ir etapai dažnai nekoreliuoja tarpusavyje;

prielaidos, priežastys, poreikis ir naudos motyvai yra dažnai prilyginami šių

sampratų sinonimams; analizuojant sektoriaus klasterizacijos prielaidas,

atliekamas klasterio tikslų vertinimas ir pan. Tai leidžia teigti, jog nėra susietumo

ir tęstinumo anksčiau paskelbtų mokslinių tyrimų rezultatų atžvilgiu.

Pasiūlytoms sektoriaus klasterizacijos prielaidoms vertinti trūksta

kompleksiškumo ir išbaigtumo; pasigendama aiškios metodikos konkretaus

sektoriaus klasterizacijos prielaidoms įvertinti; moksliniuose darbuose dažnai

sektorius yra klaidingai prilyginamas klasteriui ir toliau atliekamas jo vertinimas

pagal pasirinktą vieną mokslinių tyrimų metodą arba skirtingų šalių ekonominės

veiklos grupės yra pavadinamos klasteriais ir toliau atliekamas jų ekonominių

duomenų palyginimas.

Viena iš pagrindinių disertacijos tyrimų sričių – Lietuvos jūrinio

sektoriaus klasterizacijos prielaidos produktyvumui, inovatyvumui ir

konkurencingumui didinti, jas įtakojantys veiksniai ir pasireiškimo lygis

sektoriaus klasterizacijos proceso metu.

Lietuvoje klasterizacijos ir jūrinio sektoriaus tyrimai atliekami tik

fragmentiškai, kitų ekonomikos reiškinių ir mokslinių problemų kontekste:

klasterių poveikį regiono konkurencingumui tyrė Činčikaitė ir Belazarienė

(2001), Bruneckienė ir Pukėnas (2008), Bruneckienė (2010) ir kt. Pastaruoju

metu mokslinėje literatūroje (Jucevičius, 2009; Stalgienė, 2010; Porter, 1998;

Rosenfeld, 2002; Roelandt ir Hertog, 1999; Simmie ir Sennett, 2001;

Kamarulzaman ir Mariati, 2008 ir kt.) plačiai analizuojami pasaulyje vykstantys

klasterizacijos procesai, jų skatinimo priemonės, diskutuojama apie šių verslo

sistemų sukuriamą naudą kaip jos pavieniams grupės nariams, taip ir valstybei,

kurios teritorijoje kuriasi klasteris principu „iš apačios į viršų“. Klasterių

formavimo iniciatyvos „iš apačios į viršų“ vis dar nesulaukia deramo

mokslininkų dėmesio (Lorenzen, 2005). Pastebėta, kad ir Lietuvoje klasterius

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analizuojančiose studijose (Jucevičius, 2009; 2012), Jucevičius, Rybakovas ir

Šajeva, 2007; Stalgienė, 2010 ir kt.) nepakankamai dėmesio skiriama klasterių

formavimo etapų bendrųjų požymių ir jų kriterijų išskyrimui, klasterių brandumo

fazių atpažinimui. Taipogi atkreiptinas dėmesys, kad Lietuvoje klasterius

analizuojančiose studijose (Jucevičius, 2009; 2012), Jucevičius ir kt., 2007;

Stalgienė, 2010 ir kt.) nepakankamai dėmesio skiriama Lietuvos jūriniam

sektoriui, kuris valstybei yra strategiškai svarbus ir ekonomiškai gyvybingas, ir

jūrinio sektoriaus veiklų ekonominėms grupėms bendradarbiaujant tarpusavyje,

besiformuojančioms klasterizacijos užuomazgoms. Tačiau Lietuvoje nėra

paskelbta mokslinių tyrimų, kuriuose būtų analizuojama jūrinio sektoriaus

klasterizacija ir atliktas klasterizacijos ar jos prielaidų ekonominis vertinimas.

Pasigendama tyrimų, kuriuos Jūrinio sektoriaus klasterizacija būtų

analizuojama kaip produktyvumo, inovatyvumo ir konkurencingumo didinimo

objektas. Iki šiol nėra sukurtos vertinimo metodikos, įgalinančios ekonomiškai

įvertinti jūrinio sektoriaus klasterizacijos prielaidas. Tokią metodiką sukurti ir

Lietuvos jūrinio sektoriaus pavyzdžiu empiriškai pritaikyti ir patikrinti siekiama

disertacijos teorinėje ir praktinėje dalyse.

Disertacija sprendžia ne tik teorines, bet ir empirines problemas. Praktinę

disertacijos tyrimų reikšmę pagrindžia Lietuvos jūrinio sektoriaus klasterizacijos

prielaidų ekonominio vertinimo metodikos taikymo galimybės – sudaryto

modelio pagrindu galima priimti reikšmingus jūriniam sektoriui politinius,

vadybinius ir ekonominius sprendinius: parengti ir įgyvendinti nacionalinę

jūrinio sektoriaus klasterizacijos strategiją, skatinančią jūrinio sektoriaus verslo,

mokslo ir viešojo sektoriaus organizacijas bendradarbiauti ir jungtis į

aglomeruotas verslo struktūras – klasterius, siekiant padidinti sektoriaus

produktyvumą, inovatymumą ir konkurencingumą, stimuliuojančią jūrinio

sektoriaus plėtrą. Klasterizuotis linkusioms organizacijoms šis modelis yra

informacinio pobūdžio reikšmingų rodiklių visuma, padedanti priimti

sprendimus dėl klasterio formavimo, įsitraukimo į klasterizacijos procesus arba

naujo klasterio formavimo, kadangi klasteriniais ryšiais nesusietos organizacijos

negali pasinaudoti reikšmingais klasterio teikiamais produktyvumo,

inovatyvumo ir konkurencingumo didinimo pranašumais.

Mokslinio darbo problema – kaip kompleksiškai įvertinti Lietuvos

jūrinio sektoriaus klasterizacijos prielaidas.

Mokslinio darbo objektas – Lietuvos jūrinio sektoriaus klasterizacijos

prielaidos.

Mokslinio darbo tikslas – sukurti Lietuvos jūrinio sektoriaus

klasterizacijos prielaidų kompleksinio vertinimo metodiką ir atlikti šio sektoriaus

klasterizacijos prielaidų ekonominį vertinimą.

Mokslinio darbo uždaviniai:

1. Nustatyti ir susisteminti Lietuvos jūrinio sektoriaus ekonominių veiklų

struktūrinę sudėtį.

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2. Išnagrinėjus jūrinio sektoriaus klasterizacijos poreikio atsiradimą,

formavimąsi ir plėtrą, išskirti jūrinio sektoriaus klasterizacijos prielaidas

produktyvumui, inovatyvumui ir konkurencingumui didinti.

3. Įvertinti Lietuvos jūrinio sektoriaus ekonominę reikšmę Lietuvos ūkio

ekonomikai.

4. Atsižvelgiant į jūrinio sektoriaus klasterizacijos ekonominio vertinimo

ypatumus, įvertinti Lietuvos jūrinio sektoriaus klasterizacijos tyrimų iniciatyvas.

5. Sukurti ir pagrįsti jūrinio sektoriaus klasterizacijos prielaidų

kompleksinio ekonominio vertinimo metodiką.

6. Patikrinti jūrinio sektoriaus klasterizacijos prielaidų kompleksinio

ekonominio vertinimo metodiką, atliekant klasterizacijos prielaidų ekonominį

vertinimą Lietuvos jūrinio sektoriaus kontekste.

Tyrimo metodai: sisteminė ir lyginamoji mokslinės literatūros,

strateginių dokumentų ir teisės aktų analizė ir sintezė; antrinių duomenų

statistinė analizė, empiriniai tyrimai: ekonometrinė analizė, ekspertinis

vertinimas ir anketinė apklausa; matematinis ir statistinio apdorojimo metodai,

naudojant statistines duomenų apdorojimo programas: SPSS (v21.0) ir Microsoft

Excel (2010).

Atliekant mokslinės literatūros ir teisės aktų bei strateginių dokumentų

analizę, vadovautasi sisteminiu (holistiniu) požiūriu į tyrimo problemą.

Pirmojoje ir antrojoje disertacijos dalyse buvo atliekama mokslinės literatūros,

teisės aktų ir strateginių dokumentų sisteminė, loginė ir lyginamoji analizė,

mokslinių rezultatų sintezė. Mokslinių išvadų formulavimas buvo atliekamas

vadovaujantis loginės indukcijos ir dedukcijos metodais. Trečiojoje disertacijos

dalyje buvo atliekama antrinių duomenų statistinė analizė, anketinės apklausos

analizė ir tyrimai, pasitelkiant ekspertinio vertinimo metodą bei gautų duomenų

matematinė ir statistinė analizė (įskaitant duomenų struktūrinimą, apdorojimą,

sisteminimą ir statistinių rodiklių skaičiavimą), naudojant statistines duomenų

apdorojimo programas: SPSS Statistics (v21.0) ir Microsoft Excel (2010).

Disertacijos struktūra. Disertaciją sudaro trys dalys. Pirmojoje dalyje

analizuojamas jūrinio sektoriaus klasterizacijos poreikio atsiradimas,

formavimasis, plėtra ir ekonominė reikšmė Lietuvos ūkio ekonomikai. Antrojoje

disertacijos dalyje analizuojami klasterizacijos prielaidų vertinimo ypatumai ir

atliekamas jūrinio sektoriaus klasterizacijos prielaidų ekonominio vertinimo

modelio formavimas. Trečiojoje disertacijos dalyje pristatomi jūrinio sektoriaus

klasterizacijos prielaidų ekonominio vertinimo empiriniai sprendimai.

Disertacijos struktūrą lemia suformuotas tikslas ir jam pasiekti numatyti

uždaviniai. Išvadose koncentruotai pateikiami apibendrinti esminiai disertacijos

tyrimo rezultatai.

Tyrimų bazė bei naudoti informacijos šaltiniai. Analizuojant jūrinio

sektoriaus klasterizacijos prielaidas, buvo naudojamasi Lietuvos ir užsienio

autorių moksliniais darbais, skelbiamais tyrimų rezultatais, viešai prieinamais

49

strateginiais Lietuvos ir užsienio dokumentais ir teisės aktais,

reglamentuojančiais jūrinį sektorių ir klasterizacijos procesus. Naujausioms

jūrinio sektoriaus klasterizacijos prielaidoms identifikuoti buvo nagrinėjama

naujausia specializuota literatūra, Lietuvos statistikos departamento ir Eurostat

statistiniai duomenys, tarptautinių organizacijų (European Commission, 2002;

2003; 2008; Organisation for Economic Cooperation and Development, 2001;

2008; World bank, 2011; 2012; 2013) ir specializuotų mokslinių grupių (Cluster

Observatory, 2014; Policy Research Corporation, 2009; Ecorys SCS Group,

2009; 2012; Gallup Europe, 2006) tyrimai ir ataskaitos, specializuoti leidiniai

(Sölvell, Lindqvist ir Ketels, 2003; 2006; 2013; Sölvell, 2008) ir studijos

(Lietuvos klasterių koncepcija 2014 – 2020 m., Klasterių kūrimo Lietuvoje

prielaidų analizė ir rekomendacijų parengimas, 2002; Lietuvos pramonės

klasterių plėtros programinė studija, 2003).

Disertacijos naujumas

• Išgryninta Lietuvos jūrinio sektoriaus struktūra.

• Apibendrintos ir pateiktos sektoriaus, jūrinio sektoriaus, klasterizacijos ir

prielaidų sampratos.

• Išskirtos ir susistemintos Lietuvos jūrinio sektoriaus klasterizacijos

prielaidos ir rizikos.

• Nustatyti ekonominiai rodikliai, reikšmingi jūrinio sektoriaus

klasterizacijos prielaidų vertinimui.

• Sukurtas jūrinio sektoriaus klasterizacijos prielaidų kompleksinio

ekonominio vertinimo modelis.

• Sudaryta jūrinio sektoriaus klasterizacijos prielaidų kompleksinio

ekonominio vertinimo metodika.

• Patikrintas jūrinio sektoriaus klasterizacijos prielaidų kompleksinio

ekonominio vertinimo modelis Lietuvos jūrinio sektoriaus kontekste.

Tyrimo apribojimai: (1) Įmonių Lietuvos jūriniam sektoriui priskyrimo

metodikos galimi netikslumai. (2) Klasterizacijos prielaidų sąvokos

neapibrėžtumas. (3) Ekspertinio vertinimo subjektyvumas ir ekspertų tam tikrose

srityse ribota kompetencija. (4) Viešai prieinamų statistinių tyrimo duomenų

nepatikimumas.

Disertacijos tyrimų tęstinumas. Siekiant atlikti Lietuvos jūrinio

sektoriaus klasterizacijos prielaidų ekonominio vertinimo empirinio kiekybinio

tyrimo gilesnę analizę turinio prasme, tikslinga atliktą pilotinį tyrimą išplėtoti ir,

užtikrinant tyrimo imties reprezentatyvumą, surinkti patikimų duomenų tyrimo

rezultatų analizei. Tikslinga ir toliau periodiškai papildyti sukurtą Lietuvos

jūrinio sektoriaus įmonių (18508 vnt.) pagrindinių ekonominių rodiklių duomenų

bazę, atnaujinant informaciją pagal viešai skelbiamus Statistikos departamento

duomenis, tikslinga į šią duomenų bazę įtraukti Lietuvos jūrinio sektoriaus

įmonių eksporto ir importo apimtis. Ateityje planuojama sukurti metodiką

suformuotų klasterių ekonominės veiklos rezultatų patikrinimui ir ją patikrinti

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Lietuvoje veikiančių klasterių atvejo analizei. Tikslinga būtų inicijuoti ir

palaikyti Lietuvos jūrinio klasterio formavimo iniciatyvą ir imtis konkrečių

priemonių šiai idėjai realizuoti. Ateityje planuojama atlikti Lietuvos jūrinio

sektoriaus klasterizacijos prielaidų kompleksiškumo, suderinamumo ir

optimizavimo galimybių analizę, atlikti Lietuvos jūriniame sektoriuje veikiančių

mokslo, verslo ir viešojo sektoriaus institucijų bendradarbiavimo prielaidų ir

skatinimo galimybių vertinimą, bendradarbiavimo kultūros vystymui įtakos

veiksnių tyrimus. Tikslinga būtų ateityje, atliekant Lietuvos jūrinio sektoriaus

klasterizacijos prielaidų tyrimus, įvertinti ir jūriniame sektoriuje veikiančio

ofšorinio verslo ekonomines apimtis ir jo įtaką ne vien Lietuvos jūrinio

sektoriaus, bet ir visos šalies ekonominiams rodikliams.

Galimos kai kurios kompleksinio jūrinio sektoriaus klasterizacijos

prielaidų ekonominio vertinimo metodikos taikymo sritys:

1. Ši metodika gali būti taikoma šalies (regiono) mastu veikiančio jūrinio

sektoriaus klasterizacijos prielaidoms įvertinti. Ji gali būti pritaikyta ir kitų šalių

jūrinių sektorių klasterizacijos prielaidų tyrimams. Patobulinta metodika tiktų ir

kitų šalies pramonės sektorių klasterizacijos prielaidoms vertinti, bet tuomet

reikėtų identifikuoti tam tikro pramonės sektoriaus klasterizacijos požymius,

suformuluoti klasterizacijos prielaidų ir rizikų teiginius, tinkančius konkrečiam

pramonės sektoriui, įvertinti optimalų išskirtų prielaidų ir rizikų skaičių, parinkti

tinkamus klasterizacijos prielaidų ir rizikų analizės tyrimo metodus, įvertinti

ekspertų įtraukimo į tyrimą poreikį ir identifikuoti esamus ekspertus bei

atsižvelgti į konkrečios pramonės sektoriaus poreikį klasterio organizacijos

steigimo atžvilgiu.

2. Šios metodikos pagrindu galima įvertinti šalies (regiono) jūrinio

sektoriaus potencialą ir plėtros galimybes, išskirti pagrindinius klasterizacijos

prielaidas sąlygojančius veiksnius. Ši metodika taipogi gali būti taikoma siekiant

palyginti jūrinio sektoriaus klasterizacijos prielaidas Baltijos jūros regiono šalių

tarpe. Modifikuota metodika galėtų būti informaciniu įrankiu verslo, mokslo ir

viešojo sektoriaus subjektams, vertinantiems pramonės sektorių klasterizaciją.

Disertacijos apimtis. Disertaciją sudaro 298 psl. (257 psl. be priedų), 53

paveikslai, 61 lentelė, 17 priedų. Panaudoti 395 literatūros šaltiniai lietuvių,

anglų, prancūzų, vokiečių ir rusų kalbomis.

Disertacijos mokslinių rezultatų pristatymas. Disertacijos tyrimų

rezultatai pristatyti Lietuvos ir tarptautinėse mokslinėse konferencijose ir

paskelbti pripažintuose Lietuvos bei užsienio mokslo leidiniuose. Tyrimo

rezultatai paskelbti 14-oje mokslinių publikacijų.

51

UDK 338.45:656.61+334.5](424.5)(043.3)

SL344. 2014-12-08, 3,25 leidyb. apsk. l. Tiražas 70 egz. Užsakymas 624.

Išleido leidykla „Technologija“, Studentų g. 54, 51424 Kaunas

Spausdino leidyklos „Technologija“ spaustuvė, Studentų g. 54, 51424 Kaunas