Analysis of the Effectiveness of the Cluster Environment

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Victoria Mamatova

Trends and analysis of the effectiveness of the cluster environment

Abstract

Today competitiveness is a common? topic in the world, and it is used as a device to reach the optimal economic growth and stable development. Among the main problems for the development of industrial clusters in the absence of an obvious approach is to evaluating its performance. This research has reviewed the economic effectiveness indicators of innovative clusters. This work provides a review of several methods and approaches of evaluating the cluster performance. Then the most appropriate and fully describing approach will be chosen as the most optimum one.

Introduction

Clusters, due to their inherent capability to support cooperation between different innovation actors in a region, provide a favourable environment encouraging R&D driven innovation closely linked to the markets’ needs. Clusters are now a new trend in the field of innovation and new technologies. In developed countries, they have long been a platform where innovative ideas are born and implemented into practice. Russia also recently discovered a similar phenomenon, but already rapidly developing more and more opportunities in this area.

The aim of this work is to study the methods for assessing the effectiveness of innovation clusters, and identifying common trends in the development of the cluster environment. This research proposal expects to have the following outcomes by the end of the project. First, it is important to define the measurement of the economic effectiveness of each cluster. The next objective is to develop quantitative and qualitative indicators that evaluate the effectiveness of clusters, to apply these indicators to the analysis of clusters and confirm the validity of these indicators. Then the analysis of the mechanism and methods required for analysis of cluster environment will be discovered.  In addition, it will be discussed the different approaches towards clusters and find the most appropriate one. Then given a review on the consequences of these concepts for economic and industrial policy. Finally, it will provide description of national and regional approaches in supporting clusters and identifies the main challenges that clusters meet today for its proper functioning and development.

Nowadays there is no any system or standard accepted, where would be a unite criteria for estimating performance of the cluster. Clusters can be estimated by each criteria separately. However, it is hard to define the whole effectiveness of cluster with separated values such as profit or performance or sustainability etc.

Recent publications in foreign literature offer a variety of ways and methods to assess performance and efficiency of the cluster. However, many of them are evaluated in only one cluster parameter.

Literature Review

The origin of clusters. The current state of research regarding the cluster concept has evolved based on the studies conducted on agglomeration of firms. The first studies in this field started with four empirical observations (Marshall, 1890; Krugman, 1991; Malmberg, Solvell & Zander, 1996): most part of the world, national economic and industrial areas are concentrated in very few regions. In these regions investors, universities, government or any other stakeholders of companies are operating. These companies have a longer life and effectiveness than isolated one due to the concentration of resources in this area. The later studies conducted by Porter (1990) and Krugman (1991) highlighted and added new dimensions to Marshall’s observations. Despite criticisms regarding the generality of the approach, the widely accepted descriptions regarding clusters are: “Geographic concentrations of interconnected companies and Institutions in the particular field” (Porter, 1998, p. 45). Clusters are not seen as fixed flows of goods and services, but rather as dynamic arrangements based on knowledge creation, increasing returns and innovation in a broad sense (Krugman, 1991). Porter (2000) redefines the cluster concept in a new analysis, concentrating on the type of relations that exists between cluster members ―a geographically proximate group of inter connected companies and associated institutions in a particular field, linked by commonalities and complementaritiesâ€- (Porter, 2000), and defining its boundaries that can ―range from a single city or state to a country or even a group of neighbouring countries (Porter, 2000).

Modern clusters. Now there is a cluster concept (Porter, 2007) that says that companies gain more competiveness and therefore effectiveness inside the cluster. It also claims that firms in the cluster have a longer life than other isolated companies. There is competition within the cluster. This competition can be among cluster members in an advanced way in the international market. It is worth mentioning that reducing competition is the most important goal among members of the cluster. The idea of reducing competition means ensuring agencies to cooperate more clusters to have an easy access to commercial inputs. Reaching the above goals in cluster facilitates outer-cluster competition and also business and enterprise firms can make the cluster ready for international competition (Kim, 2002).

Nowadays there are two problems concerning clusters: economic integration and cluster effectiveness. Economic integration of clusters should be supported by the government with laws (Litzel, 2009), while clusters should consider the intensive intra-regional relationship between its elements (businesses, suppliers, institutions etc.).

A cluster model. In order to understand the cluster model from the viewpoint of relations between firms, researchers have defined different models that take into account supplier chains relations, directly based on specific characteristics of urban areas, and clusters which define a typology (Malmberg, Solvell, & Zander, 1996) that describes four different agglomerations, which highlights the conceptual differences between the clusters and the other three models.  Based on the role of different cluster members and the interaction between them, Markusen (1996) has defined four models of clusters. Markusen compares its models of modern clusters with the Marshal one, in which the cluster is rather comprising small firms that collaborate with each other, are in direct competition or in a supplier-producer relation. In a hub-and-spoke cluster, there are few dominant firms that represent the core of the cluster and are surrounded by numerous small firms that are linked directly to them. In a satellite platform cluster, a group of branch facilities of externally based multi-plant firms (Markusen, 1996) are located in a particular geographic region in order to benefit from governmental facilities or low costs with supplies and workforce. The last category, the state centered (He & Fallah, 2011) or state anchored cluster (Markusen, 1996) is defined around a public, governmental or non-profit organization that dominates the region and the economic relation between cluster members.

In short, the industrial cluster literature highlights the importance of cluster governance operating horizontally between cluster firms and institutions in local contexts, be it learning and innovation for economic upgrading or implementing CSR measures for social upgrading. This horizontal governance can be contrasted with the vertical governance in GVCs that links global lead firms to both first-tier and local suppliers in international production networks (see below). Cluster firms in developing economies often find themselves confronted by conflicting demands from global buyers, which seek lower labor costs while simultaneously requiring suppliers to comply with higher quality or social standards that would incur additional expenditures (Barrientos & Smith 2007; Lund-Thomsen & Pillay 2012). The fear of global buyers being ‘foot-loose’ can keep cluster actors from making sustained investments in infrastructure or workforce development, thereby hindering local joint action. Such anxiety has grown in the face of global economic recessions (Ruwanpura & Wrigley 2011).

Industrial clusters. A number of studies have been conducted to show the investment criteria for choosing the industrial clusters in decision-making, which can be separated into several strands. A main group of studies presents that firms will select investment location depending on the development of an innovation system or a technological system in a region (Braunerhjelm et al., 2000, Malerba, 2002, Cooke, 2002, Yeh & Chang, 2003, Fleming and Sorenson, 2003, Chang and Shih, 2004, Bell, 2005, Asheim & Coenen, 2005). It is reasonable to expect that industrial clusters will emerge from the location where innovation opportunity is available and accessible, as in the link between firms clustering and their probability to innovate (Baptista and Swann, 1998). These building blocks in the innovation system research institution, infrastructure, innovation network, and technology transfer mechanism, will affect the competitiveness of the industrial cluster. Network externality (Dayasindhu, 2002) and market proximity (Krugman, 1995, Cook et al., 2001) are sometimes the critical criteria when creating a new start-up in an industrial cluster.

Innovational clusters. Innovation through industrial clusters can be defined as a way to increase the competitiveness of small and medium enterprises by reaping the benefits generated by the local structures and synergies via cooperative relationship (Idrissia, Amaraa and Landrya, 2012). The clusters provide alliances, which among other things promote flexibility in terms of production volume and variety, reductions in investment costs, reduction in transaction costs and increase in operational efficiency, increased bargaining power, and the development of technology innovation processes (Rabellotti, 1999; Solvell, Ketels and Lindqvist, 2008; Bas, Amoros and Kunc, 2008). The synergy of industrial clusters is also recognized as a relationship network including companies in the same industry sector and that offer them the possibility to achieve innovation and improve product and process development. According to Kuei-Hsien, Miles and Ghung-Shing (2008), network relationships can differentiate the value of the productive chain when the partners are engaged in activities of common interests allowing the improvement of pro-active actions in the final product or service, which creates a stimulating environment for the innovation process.

Engel and del-Palacio (2009) extended Porter’s (year) definition of industrial agglomeration to delineate a Global Cluster of Innovation Framework that describes business clusters defined not primarily by industry specialization but by the stage of development and innovation of the cluster’s constituents. While industry concentrations do exist, they are not definitive. It is rather the nature and the behavior of the components that is distinctive-the rapid emergence of new firms commercializing new technologies, creating new markets, and addressing global markets.

Methodology

The purpose of my research is to estimate the economic effectiveness of clusters performance. Therefore, the main methods of evaluating cluster effectiveness would be economic methods of evaluating project effectiveness. These methods allow seeing the economic feasibility of investment and detecting one of the most financially advantageous of clusters. It is accepted to divide methods of evaluating to dynamic, those that take into account the time factor, and static: accounts.

The first group to overview is static methods. The rate of profit is the ratio of the average annual income to total investment costs (Rutherford, 2002). This method can be used to compare several alternatives to capital investment. The most profitable cluster will be considered as one if its rate of profit is not less than the rate of return alternative. The second method is method of determining the payback period. Payback period is a period of time through which the full return on investment due to income from the project (Rutherford, 2002). The payback period of a given cluster is an important determinant of whether to undertake the position or project, as longer payback periods are typically not desirable for investment positions.

The next group is dynamic methods. The first is method of net present value (NVP). This method compares the investment volume of the cluster with a total sum of the discounted net cash flows generated during the period of the intended investment. NPV shows if used in the calculation of the rate of return reach R (where R is Interest rate) of the investment within the project life cycle. It should be noted that disadvantages of this method is impossibility to assess which of the alternative cluster are better with great NPV and a long payback period, or a lower NPV, but faster payback. The method of internal rate of return (IRR) This method shows the rate at which the present value of the net revenue from the investment of the project is equal to the present value of the investment and the value of net present value: zero. The disadvantage of this method is that the IRR analysis is not suitable for ordinary investment flows.

Then all quantitive and qualitive should be summerised to get number that actually describes effectiveness of each cluster. Each number describes one cluster, so there will be 5 numbers for 5 clusters. The method that will be used to summerise all the criteria is analytic hierarchy process developed by Saaty (2008). This method helps to determine the root of any problem through a hierarchical view of the elements. The main idea of the method is to split the problem into smaller elements, the next step they are compared in pairs. Then the next level is estimated by stating priorities and values each criteria. The output is a relative degree of interaction of the elements in the hierarchy.

Overall, these are the main methods that will be used in my study. All of them are quantitative methods. That means that it requires quantifiable data involving numerical and statistical explanations. That is why firstly there will be data collected and analysed from financial and performance monthly reports. Statistica or MS Excel will be used to proceed the data and define the main financial and performance values and then count PP, NVP, IRR that mentioned above. There is no particular soft to build the analytic hierarchy process, so MS Excel is an appropriate option.

Anticipated Results

By the end of this project there will be five values that fully describe the effectiveness of each (of five) cluster. According to these values, it is possible to choose the most effective one. Therefore, it is very useful tool for investors that can easily define the cluster to invest. Companies also can define their weak sides and where they should improve performance, as priority system is used in the hierarchy analytical method.

As it was mentioned before, there is no union system to identify the overall cluster effectiveness. This project will be an example of another working tool for measuring the most effective cluster.

Moreover, after finishing project there will be gaps and mistakes revealed (if there any). And then recommendations will be given considering these gaps and how to fix them or improve it.

Conclusion

The purpose of this review was to view trends in evaluating the economic effectiveness of clusters performance within the past five years and see the rapid improvement in innovation clusters. It is clear from the research that creating and integrating clusters and cluster policy is widely practiced in today’s economy. That is why there are plenty of methods of estimating cluster effectiveness such as static and dynamic methods of effectiveness analysis. These methods will be analyzed to find out the one that can fully describe the effectiveness. In this research the most appropriate method will be found and reviewed on the consequences of concepts for economic and industrial policy, as today clusters meet some challenges for its proper functioning and development.

References

Baptista, R., & Swann, P. (1998). Do firms in clusters innovate more?. Research policy, 27(5), 525-540.

Krugman, P. (1991). History and industry location: The case of the manufacturing belt. The American Economic Review, 81(2), 80-83.

Kuei-Hsien, N., Miles, G., & Chung-Shing, L. (2008). Strategic development of network clusters. Competitiveness Review: An International Business Journal, 18(3), 176-81.

Litzel, N., & Möller, J. (2009). Industrial clusters and economic integration: theoretic concepts and an application to the European Metropolitan Region Nuremberg, 44-47.

Malmberg, A., Sölvell, Ã-., & Zander, I. (1996). Spatial clustering, local accumulation of knowledge and firm competitiveness. Geografiska Annaler. Series B. Human Geography, 85-97.

Marshall, A. (1890). Principles of economics: An introductory volume (Eighth Edition). London: Macmillan.

Ortega-Colomer, F. J., Molina-Morales, F. X., & Fernández de Lucio, I. (2016). Discussing the Concepts of Cluster and Industrial District. Journal of Technology Management & Innovation, 11(2), 139-147.

Porter, M. E. (2000). Location, competition, and economic development: Local clusters in a global economy. Economic Development Quarterly, 14(1), 15-34.

Porter, M. (2007). Clusters and economic policy: Aligning public policy with the new economics of competition. Cambridge: Harvard Business School, 2.

Rutherford D. (2002). In Routledge Dictionary of Economics (2d ed.). London, New York: Routledge.

Ruwanpura, K. N., & Wrigley, N. (2011). The costs of compliance? Views of Sri Lankan apparel manufacturers in times of global economic crisis. Journal of Economic Geography, 11(6), 1031-1049.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.

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