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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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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
50
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
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