SUSTAINABLE BUSINESS DEVELOPMENT PERSPECTIVES

Denisa Gajdová¹ & Ľubica Foltínová¹

¹University of Economics in Bratislava

https://doi.org/10.5817/CZ.MUNI.P280-0197-2022-3

Abstract

Small and medium-sized enterprises are at a great disadvantage compared to large companies, they are not able to take advantage of, for example, economies of scale, they do not have sufficient capacity and resources for research, training of their employees, obtaining information, etc. Therefore, participating in such innovation clusters helps them survive and prosper for a long time. In essence, the cluster helps to increase specialization, encouraging governments to invest in the industry and the region at the same time. This, of course, has such a positive effect as regional development. For appropriate cluster identification, we have opportunity to use various methods, quantitative and qualitative, too. We have mainly focused on quantitative methods with the perspective to directly determinate achieved outcomes. Our objective in this this paper was to find possibilities of cluster creation and their identification in the area Slovak eight regions. With this aim we have used Location Quotient. Statistical data has been provided from statistic database and they were compared then with the real clusters operating in selected 8 regions within area of Slovakia. Identification of the economic opportunities of tourism clusters creation in individual regions is the precondition of the originality of this paper as the starting point for formation of clusters and cluster initiatives within the regions of Slovakia.

Keywords: Small and medium businesses, agricultural production, clusters, sustainability

1 Introduction

Clustering has several advantages for companies, including reducing their business constraints, which depend on their size. Clusters are usually perceived as an extremely important regional economic factor that supports the inflow of foreign direct investment, creates an environment conducive to innovation and knowledge creation (for this reason, regions with strong clusters are considered to be innovation leaders). Clustering can be called a merger of previously legally and economically independent enterprises into larger economic units, usually within a region, without having to remove the legal autonomy of such enterprises.

Michael Porter is the first to introduce the concept of cluster in The Competitive Advantage of Nations (1990). However, historically as early as 1890, Alfred Marshall was the first person to characterize the geography of economic activity and cluster analysis in his publication: Principles of Economics (1920, revised edition). The term cluster and its theoretical definition can be further found in the works of economists such as Perroux (1950), Hirschman (1958), Jacobs (1961) and Krugman (1991), (Breschi and Malerba, 2006).

2 Methods

For appropriate cluster identification, we have opportunity to use various methods, quantitative and qualitative, too. We have mainly focused on quantitative methods with the perspective to directly determinate achieved outcomes. Our objective in this this paper was to find possibilities of cluster creation and their identification in the area Slovak eight regions. With this aim were have used Location Quotient. Statistical data has been provided from statistic database and they were compared then with the real clusters operating in selected 8 regions within area of Slovakia. Identification of the economic opportunities of tourism clusters creation in individual regions is the precondition of the originality of this paper as the starting point for formation of clusters and cluster initiatives within the regions of Slovakia.

3 Results

The development of agricultural as well as other types of clusters can be seen in all corners of the world, including Europe. The concept of clusters is perceived as a specific area in the field of agriculture. The development and support of agricultural clusters can be used to eliminate size disadvantages in rural areas and can help businesses respond to increasing competitiveness, globalization and sectoral challenges. An agricultural cluster can be a local or regional network made up of farmers, suppliers, cooperatives, producers, transporters, universities, export associations, research institutions, research parks and associated initiatives. Rosenfeld (1997) emphasizes the need for cluster actors who have active channels for business transactions, support dialogue and information exchange. The geographical element of the clusters is preserved but is a non-limiting factor in the number of activities such as information, exchange of data and knowledge between cluster members, etc.

Barriers to the development of agricultural clusters include physical and technical infrastructure constraints, lack of capital and problems in accessing finance, lack of skilled labor, lack of organizational structure and lack of information channels and problems with information flow. The limited use of new technology is one of the most significant problems facing businesses in rural areas. A significant obstacle to the development of agricultural clusters is the lack of a suitable business concept between businesses. For many years, farms have been accustomed to conducting their business autonomously and independently. Due to the changing nature of competition, many companies are unable to trust other companies and tend to expect problems from cooperation.

In the context of the Europe 2020 strategy and the general objectives of the Common Agricultural Policy for the period 2014-2020, three long-term strategic objectives for EU agricultural policy have been identified:

1. Strengthen the competitiveness of agriculture

2. Ensure the sustainability of natural resource management and climate activities, a

3. Achieve a balanced territorial development of rural economies and communities, including growth and employment retention.

In this context, Member States are invited to formulate their policies on the basis of at least four of the six policies:

1. Strengthening knowledge transfer and innovation in agriculture, forestry and rural areas;

2. Strengthening the viability / competitiveness of all types of agricultural production and promoting innovative farming technologies and sustainable forest management;

3. Support for food chain organization, animal health and risk management in agriculture

4. Restoration, protection and strengthening of ecosystems related to agriculture and forestry;

5. Promoting resource efficiency and supporting a low-carbon and climate-stable economy in the agricultural, food and forestry sectors;

6. Promoting social inclusion, poverty reduction and economic development in rural areas.

Clusters can be identified by the very perception of the existence of clusters. Clusters, on the other hand, can be identified by their integration into five basic types: natural clusters, technology clusters, clusters based on historical know-how, low-cost industries and knowledge-based service clusters. Natural clusters arise in regions that have a comparative advantage due to a certain natural factor. This can be the soil, natural resources, the availability of the human factor and the size of the population of a particular nation or region. In the case of agriculture, wine-growing clusters in areas where there are natural conditions for growing vines could be such an example.

Technology clusters are clusters with a high concentration of technological production. These clusters are usually associated with universities, research institutes, etc. An example in this area could be clusters in the field of animal husbandry with a high share of technological production of milk, meat, etc.

Historical know-how clusters are clusters that perform traditional activities. Traditional techniques are the result of many years of experience and knowledge of previous companies in this field. An example of such a cluster could be e.g. traditional growers in crop production.

Low-cost manufacturing clusters are clusters that occur in developing countries within specific sectors. The driving force is the availability of cheap labor and the geographical proximity of consumers. Examples are poultry farms e.g. within the countries of Eastern Europe.

Knowledge clusters of services are similar to low-cost manufacturing clusters that are emerging in developing countries. These clusters are characterized by the availability of low-cost skills and experience. Such clusters meet the growing global demand for electronics, software development, analytics services, etc. Within agriculture, it could be the production of machinery and equipment for agricultural machinery. Thus, we can speak of four types of clusters, which can be identified by their various forms: geographic clusters, horizontal clusters, vertical clusters and sector clusters. Geographic clusters exist for geographical reasons, the location of certain types of resources attracts businesses that need this type of resources for their production processes. Horizontal clusters mean the interconnection between companies and industry at the horizontal level, it is a division of resources and knowledge. Vertical clusters are clusters with interconnections between companies and industry at the vertical level, usually the supply chain is castrated, a sector cluster is a cluster in which companies cooperate within the same sector. This type of cluster can occur both horizontally and vertically.

4 Discussion

Methods for identifying clusters and cluster initiatives can generally be divided into qualitative and quantitative. The first group, which is much more demanding on the processing itself, requires primarily experienced experts to evaluate the results, consisting of qualitative analyzes, which include expert assessment or the interview method. The second group mainly uses available data on the number of employees, value added, sales by industry, or is based on an input-output matrix.

Qualitative methods are often used to supplement the results of quantitative analyzes. These include, in particular, interviews, surveys and case studies.

1. Interviews with experts and business representatives - Regional experts and industry representatives are important sources of information on regional economic trends, strengths and weaknesses of the sector or their specific characteristics. These are people who know the industry in the region from their practical experience, supply chains, current investment patterns and potential opportunities for new products. Representatives can be considered to be representatives of the relevant employers' association (association), independent experts / consultants from the field, university teachers or workers in a field related to the research institute. Interviews with industry can take two forms:

- personal interviews with representatives of selected companies

- organization of round tables and seminars with representatives of selected companies

Interviews need to be well prepared in advance and based on mutual trust.

2. Surveys - are used to survey regional companies in order to identify local and non-local business features, cooperative alliances, etc. however, survey-based methods for industrial cluster analysis are very rare. Surveys are costly and the level of detail required in the survey documents (it is almost always impossible to fully clarify intercompany characteristics and informal links).

3. Case studies - an important component of qualitative analysis is the analysis of existing clusters using Porter's diamond as a framework for analyzing the competitiveness of local production structures. These case study analyzes examine the impact of clusters on the development of other regions.

Quantitative methods - their choice depends on the specific type of cluster and links between members. Frequently used procedures are localization coefficient determination and input-output analysis

1. Input-output analysis - this method does not examine the concentration of a particular industry in the region, but focuses on identifying links to other industries, thus obtaining a structure of interconnection of the department in the region. The most frequently sought-after sectors are supply and demand, and mutual relations are then quantified. Quantitatively, the relationships between industry inputs and outputs are described, ie. production from the industry. The disadvantage of this method is the considerable computational complexity and the limited database, because the input data are often unavailable for individual regions and are presented in a highly aggregated form. The application of graph theory is based on a similar principle, the output of which is an overview of significant links between industries.

2. Location quotients - this is a relatively simple method suitable for statistical search of local and regional clusters. Its advantages include the fact that recalculations can usually be based on available statistical sources. Localization coefficients, on the other hand, cannot express the interconnectedness of businesses. The localization coefficient (LQ) expresses how many times the share of the sector in employment in the region is higher than the national average.

LQ = (x/X) / (y/Y)

LQ - employment localization coefficient in the region

x - number of employees working in the sector in the region

X - total number of employees in the region

y - number of employees working in the sector in the country

Y - total number of employees in the country

If the LQ is greater than one, it means that the industry is over-represented in the region. Localization coefficients exceeding 1.2 are then perceived as initial evidence of regional specialization in the industry. The disadvantages of localization coefficients are that they do not offer any deeper insight into the interdependencies between sectors, which is often considered an unsystematic approach.

3. Other methods - other methods of cluster identification can be used quantitatively, such as Shift-share analysis, Gini localization coefficient, Ellison and Glaeser agglomeration index or Maurel-Sédillot index (Žižka 2006).

The localization coefficient, despite being attributed to an unsystematic approach in examining cross-sectoral interactions, is one of the most widely used and simplest tools to identify a region's potential for clustering in a given sector. For this reason, we used this method in the analysis of the potential of individual regions (regions) of Slovakia for the creation of clusters in the field of Agriculture, Forestry and Fisheries.

Our goal was not only to determine the localization coefficient for a certain period but also to monitor the development of this coefficient year-on-year. We have decided for the last three years, i. 2011-2013, for which we had data from the Statistical Office of the Slovak Republic (hereinafter also the Statistical Office of the Slovak Republic). The results were as follows:

Table 1

Agriculture, Fischery and Forestry

Note. Own processing from data reached from Statistical office, 2021

It can be seen from the table that the localization coefficient is greater than 1 in three regions (Trnavský, Banskobystrický and Prešovský), even in these regions it is possible to speak of regional specialization on the basis of LQ> 1.2.

Conclusion

Employment within individual EU regions in the field of agriculture is thus a prerequisite for the emergence of such types of clusters in these localities. A prerequisite for the effective long-term functioning of clusters, as well as the emergence of new clusters within regions, is the correct identification of the potential for the emergence of such clusters. Several analyzes were performed in Slovakia, the aim of which was to map the potential of individual regions for clustering, but their results mainly reflect the operation of large companies in the regions and therefore we decided to analyze the potential of regions for clustering separately. Within the agricultural sector, we wanted to examine the potential of this sector, which in our opinion is suitable for the creation of clusters within Slovakia not only for the development of the regions themselves but also the municipalities associated with them.

Agricultural production has changed considerably within individual countries in recent years. It is no longer a traditional crop and animal husbandry, individual regions are increasingly characterized by peculiarities for the emergence of production, which is a specialization of the region or municipality, encourages governments to invest in the industry and the region at the same time. Clusters are evaluated by several experts in theory and practice as a significant microeconomic factor. A prerequisite for the creation of clusters is also the fact that small and medium-sized enterprises, unlike large ones, are not able to take advantage of, for example, economies of scale, do not have sufficient capacity and resources for research, training of their employees, obtaining information, etc. For this reason, it is appropriate for them to create clusters as a potential for their own development within the region. The adequacy and possible use of regions for the creation and existence of clusters is the subject of many studies and analyzes.

As part of our contribution, we examined the agriculture, forestry and fisheries sectors. Using a localization coefficient that can clearly identify the region's potential by comparing employment within the department, we not only determined the coefficient for the sector itself, but also tried to compare the development of this coefficient over a three-year period to eliminate any "accidental occurrence". . From the results we presented, it is possible to more or less accurately determine the potential of the regions of Slovakia in terms of regions for the creation of clusters in this area. In many cases, it makes sense to form clusters by bringing together several sectors that can support and influence each other. That is why, in our view, further research in this area is particularly appropriate and necessary.

Acknowledgements

The paper was created with the support of the project „Sociálno-ekonomické determinanty trvalo udržateľnej spotreby a výroby z hľadiska vplyvu na výkonnosť a konkurencieschopnosť podnikov“, VEGA no. 1/0708/20 (100 %).

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Corresponding author

Dr. Denisa Gajdová

University of Economics in Bratislava, Faculty of Business Management, Department of Business Economy

Dolnozemska cesta 1, 852 35 Bratislava, Slovak Republic

denisa.gajdova@euba.sk