Innovation and Performance Drivers of Business Clusters: An Empirical Study [1 ed.] 3030799069, 9783030799069

Table of contents :
Acknowledgements
Contents
Editors and Contributors
Chapter 1: Introduction
1.1 Topicality and Research Focus
References
Chapter 2: Evolving Insight of Localization Theories into Cluster Existence
2.1 Strategic Partnership
2.2 Initial Localization Theories
2.3 Modern Localization Theories
References
Chapter 3: Theory of Clusters
3.1 Development in the Field of Cluster Theory
3.2 Typology of Cluster Agglomerations
3.3 Industry Clusters in Terms of Externalities
3.3.1 Clusters and Macroeconomic Externalities
3.3.2 Formulation of Externalities in Terms of Microeconomics
References
Chapter 4: Innovation and Innovation Partnership
4.1 Factors of Economic Competitiveness
4.2 The Role of Innovation as Macroeconomic Externality: The Insight of J. A. Schumpeter
4.3 Innovation and Innovation Activities
4.4 Innovation Partnership: Beyond the Borders of Individual Entrepreneurship
4.5 Protection of Intellectual Property Rights
References
Chapter 5: Dynamic Development of Companies in an Industry Cluster
5.1 Production Functions and Economic Growth
5.2 Business Performance
5.2.1 Financial Performance
5.2.2 Innovation Performance
5.3 Performance Measurement by Data Envelopment Analysis (DEA)
5.3.1 Use of the DEA Method to Evaluate the Performance of Clusters
References
Chapter 6: Conceptual and Methodical Research Procedures
6.1 Research Motivation
6.2 Methodical Research Procedure
6.2.1 Procedure for Defining Natural Cluster Cores
6.2.2 Methodological Procedure for Assessing the Territorial Distribution of Cluster Organisations
References
Chapter 7: Specifics of Natural Industry Clusters
7.1 Historical Development of the Czech Economy
7.2 Glass Industry and the Manufacture of Bijouterie
7.2.1 Case Study of the Crystal Valley Natural Cluster
7.2.2 GLASS and BIJOUX Industrial Districts: Definition of their Members
7.2.3 Defined Specifics of the Natural Cluster of the GLASS and BIJOUX Industry
7.3 Textile Industry
7.3.1 TEXTILE Industrial Districts: Definition of their Members
7.3.2 Defined Specifics of the Natural Cluster of the TEXTILE Industry
7.4 Summary of Findings on Traditional Industries of Natural Clusters in North Bohemia
References
Chapter 8: Specifics of Institutionalised Cluster Organisations
8.1 History of Support for Czech Cluster Organisations
8.2 Territorial Distribution of Cluster Organisations
8.3 Specifics of Selected Cluster Organisations
8.4 Cluster Organisation and an Example of Management Quality Assessment
References
Chapter 9: Economic Impact of Clusters
9.1 Theoretical Ground of Research
9.2 Data and Methodology
9.3 Comparison of Innovation and Financial Performance of Clustered and Non-clustered Companies
9.4 Importance of Industry in Regions for the Existence of Institutionalised Cluster
9.5 Comparison of Change in Innovation and Financial Performance of Companies by Type of Cluster
9.6 Relation Between Innovation and Financial Performance
9.7 Influence of Clusters on Productivity Change
9.8 Macroeconomic Externalities of Cluster Existence
9.9 Other Factors Influencing Business Performance
9.9.1 Tradition, Industrial Districts, and Familiness in Business
9.9.2 Innovation Practices in the Industry
9.9.3 Existence of Cluster Support Programmes
9.10 Synthesis
References
Chapter 10: Approach to Innovation in Selected Industries
10.1 TI 2016 Innovation Survey
10.2 Methodical Procedure of the TI Survey: Evaluation of Innovation Activities
10.3 Characteristics of Companies in Selected Industries According to Innovation Activities
10.4 Approach to Innovation in Selected Industries
10.5 Cooperation Partners and Barriers to Innovation
10.6 Synthesis
References
Chapter 11: Tradition, Innovation, and Family Business as Factors of Sustainable Development of Industry Clusters
11.1 Survey Description
11.2 Family Business in the Given Industries
11.3 Tradition in the Industry Factor
11.4 Uniqueness in Industry Factor
11.5 Other Features of Family Businesses
11.5.1 Business Income
11.5.2 Interest of Family Members with Regard to What Is Happening in the Company
11.5.3 Multi-Generational Representation
11.5.4 Handing Over the Business to the Next Family Generations
11.5.5 Identification with the Values of the Company
11.5.6 Decision Rights in the Company
11.6 Innovation Factor
11.7 Cooperation Factor
11.7.1 Cooperation with Secondary Schools Depending on the Industry
11.7.2 Cooperation with Universities Depending on the Industry
11.7.3 Cooperation with Research Institutions Depending on the Industry
11.7.4 Cooperation with Associations, for Example, on Projects, Depending on the Industry
11.7.5 Cooperation of Family and Non-Family Businesses with Institutions
11.8 Summary of Survey Results
Reference
Chapter 12: Summary and Discussion of Research into Natural and Institutionalised Cluster Organisations: Conclusions
12.1 Confronting Theoretical Conclusions and Empirical Findings
12.2 The Glass (Selected Commodities) and Bijouterie Industry
12.3 Textile Industry
12.4 Spatial Proximity
12.5 Predominance of Institutionalised Clusters and Conditions of Meaningful Existence of Clusters
12.6 Cluster Innovation Potential
12.7 Innovation Stimuli
12.8 Innovation Stimulating Environment
12.9 Increasing Returns to Scale and Agglomeration Effects in General
12.10 Recommendation for Economic Policymakers
12.11 Conclusions
References
Index

Citation preview

Science, Technology and Innovation Studies

Miroslav Zizka Petra Rydvalova   Editors

Innovation and Performance Drivers of Business Clusters An Empirical Study

Science, Technology and Innovation Studies Series Editors Leonid Gokhberg, Moscow, Russia Dirk Meissner, Moscow, Russia

Science, technology and innovation (STI) studies are interrelated, as are STI policies and policy studies. This series of books aims to contribute to improved understanding of these interrelations. Their importance has become more widely recognized, as the role of innovation in driving economic development and fostering societal welfare has become almost conventional wisdom. Interdisciplinary in coverage, the series focuses on the links between STI, business, and the broader economy and society. The series includes conceptual and empirical contributions, which aim to extend our theoretical grasp while offering practical relevance. Relevant topics include the economic and social impacts of STI, STI policy design and implementation, technology and innovation management, entrepreneurship (and related policies), foresight studies, and analysis of emerging technologies. The series is addressed to professionals in research and teaching, consultancies and industry, government and international organizations.

More information about this series at http://www.springer.com/series/13398

Miroslav Zizka • Petra Rydvalova Editors

Innovation and Performance Drivers of Business Clusters An Empirical Study

Editors Miroslav Zizka Technical University of Liberec Liberec, Czech Republic

Petra Rydvalova Technical University of Liberec Liberec, Czech Republic

ISSN 2570-1509 ISSN 2570-1517 (electronic) Science, Technology and Innovation Studies ISBN 978-3-030-79906-9 ISBN 978-3-030-79907-6 (eBook) https://doi.org/10.1007/978-3-030-79907-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Acknowledgements

Supported by the grant No. GA18-01144S ‘An empirical study of the existence of clusters and their effect on the performance of member enterprises’ of the Czech Science Foundation.

v

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miroslav Zizka

1

2

Evolving Insight of Localization Theories into Cluster Existence . . . Marek Skala and Petra Rydvalova

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3

Theory of Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miroslav Zizka, Natalie Pelloneova, and Marek Skala

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Innovation and Innovation Partnership . . . . . . . . . . . . . . . . . . . . . . Petra Rydvalova and Marek Skala

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Dynamic Development of Companies in an Industry Cluster . . . . . . Marek Skala, Miroslav Zizka, and Natalie Pelloneova

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Conceptual and Methodical Research Procedures . . . . . . . . . . . . . . Miroslav Zizka, Petra Rydvalova, and Vladimira Hovorkova Valentova

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Specifics of Natural Industry Clusters . . . . . . . . . . . . . . . . . . . . . . . 101 Petra Rydvalova and Miroslav Zizka

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Specifics of Institutionalised Cluster Organisations . . . . . . . . . . . . . 121 Petra Rydvalova, Vladimira Hovorkova Valentova, and Natalie Pelloneova

9

Economic Impact of Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Miroslav Zizka and Eva Stichhauerova

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Approach to Innovation in Selected Industries . . . . . . . . . . . . . . . . 169 Petra Rydvalova and Miroslav Zizka

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Contents

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Tradition, Innovation, and Family Business as Factors of Sustainable Development of Industry Clusters . . . . . . . . . . . . . . 187 Petra Rydvalova, Denisa Skrbkova, Miroslav Zizka, and Vladimira Hovorkova Valentova

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Summary and Discussion of Research into Natural and Institutionalised Cluster Organisations: Conclusions . . . . . . . . . . . . 215 Marek Skala, Miroslav Zizka, and Petra Rydvalova

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

Editors and Contributors

About the Editors Miroslav Zizka is a professor of management and economics at the Faculty of Economics, Technical University of Liberec. In his research, he focuses on quantitative methods in economics and management, performance, and competitiveness of clusters. Petra Rydvalova is an associate professor in business economics and management at the Faculty of Economics, Technical University of Liberec. She has been dealing in the long term with the issue of clusters, outsourcing, and transfer of R&D results by a spin-off method.

Contributors Marek Skala is a PhD lecturer in the field of organisation and business management at the Faculty of Economics, Technical University of Liberec. He teaches economic theory. In his research and publication activities, he focuses on current microeconomics and macroeconomics issues. Vladimira Hovorkova Valentova is a PhD lecturer in the field of organisation and business management at the Faculty of Economics, Technical University of Liberec. She teaches statistical methods. In her research and publication activities, she specialises in methods of descriptive statistics and surveys. Eva Stichhauerova is a PhD lecturer of organisation and business management at the Faculty of Economics, Technical University of Liberec. Her field of research is business administration and management, quantitative methods in management, and lean management. ix

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Editors and Contributors

Natalie Pelloneova is a PhD lecturer in the field of business economics and management at the Faculty of Economics, Technical University of Liberec. Her work is focused on quantitative methods in management. Her further research includes business networks and clusters. Denisa Skrbkova is a PhD student of economics and management at the Faculty of Economics, Technical University of Liberec. Her research focuses on family business in developing countries.

Chapter 1

Introduction Miroslav Zizka

This book deals with the phenomenon of clusters which have been an important topic of the industrial economy since the 1990s. As early as at the end of the nineteenth century, Alfred Marshall described the reasons which lead to the concentration of industries in certain regions. They included sharing of workers, knowledge, and infrastructure, as well as informal contacts between people and companies, which created agglomeration savings resulting from the close proximity of economic activities. At the end of the twentieth century, Michael Porter combined this knowledge with the theory of strategic management and created the Diamond Model that defines the basic factors of competitiveness of the national economy and companies. The Porter Diamond gives economic policymakers guidance on how to influence and support an economy’s position in global competition. As a central idea, innovations affecting both the company’s strategy and structure, as well as suppliers, customers, and the environment in which these entities are located intertwine with individual factors of competitiveness. The very idea that managing the cooperation of companies, universities, research institutes, industry associations, chambers of commerce, and other entities can improve the competitive position of regional and national economies was at the birth of cluster policy. There are thousands of natural clusters in the world in industries such as microelectronics in Silicon Valley, California, the cork industry in Portugal, the yachting industry in Italy, shipbuilding in South Korea, and the manufacture of bijouterie in the Czech Republic. In addition, several hundred clusters have emerged as a result of the organised efforts of governmental and private institutions. Lindqvist, Ketels, and Sölvell (2012) counted more than 350 organised clusters in 50 OECD countries.

M. Zizka (*) Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_1

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How does the performance of companies in natural or organised clusters, industrial districts differ from non-clustered companies? What is the success of cluster organisations (hereinafter COs) in terms of increasing the performance of member companies? Is cluster policy worthwhile? Is it effective? These questions were asked by the authors of the book, who have been dealing with the issue of clusters in their country for more than twenty years. So, let’s dive into the fascinating world of clusters and the cluster initiatives that ask so many questions to answer. The book is organised as follows. The first three chapters (Chaps. 2–4) deal with the historical development of clusters in a broader view of individual localisation and economic theories, their typology and the positive and negative externalities in the market environment that their existence may bring. As the main importance of clusters is seen in the promotion of innovation, the text on innovation partnerships cannot be missing. Chapters 5 and 6 are devoted to research methodologies. The main aim of the research was an evaluation of the influence of clusters on the performance of companies and industries, therefore, a chapter on production functions and methods of measuring performance was included. Chapters 7–11 present the results of our own research, which sought answers to the above questions. The authors have used their many years of experience and knowledge about the development of the textile, glass, and bijouterie industries in their region. They have supplemented that knowledge with an extensive survey among entrepreneurs in these industries. In the book, the three industries represent an example of natural clusters. The industries and the specific factors that influence their competitiveness in the global environment are presented to the readers in the form of case studies. They mainly focus on the factors of tradition, family and innovation habits in the industries. The factors include social and frequently emotional parameters that influence business location decisions and which Alfred Marshall also mentioned in his work. The book then deals with the seven COs that emerged as a result of cluster policy initiatives. They examine the effects of the existence of these COs on the performance of member entities. They also address the companies’ approach to innovation in these industries and barriers to innovation. The last part of the book discusses the research findings and makes recommendations for those who affect industrial policy.

1.1

Topicality and Research Focus

The book is the result of three years of research supported by the Czech Science Foundation. The main goal of the research was to determine whether the existence of clusters (natural or institutionalised) has a positive effect on the innovation and financial performance of companies forming the core of the industry cluster. The research focused on those business entities that were members of COs in the automotive, engineering, furniture, nanotechnology, packaging, and textile industries, as well as in the IT industry. The aforementioned seven industries represent circumstances where there are both COs and other companies in the industry that for

1 Introduction

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various reasons have not joined the CO. Nevertheless, even these independent companies benefit from the educational and research infrastructure, specifically educated workforce and informal knowledge transfers that exist in the region. It is assumed that there are also natural clusters in the mentioned industries within the given regions. The research included companies from the bijouterie and glass industry, in a specific region of North Bohemia, which form natural industrial districts with several hundred years of tradition. Unlike previous industries, however, no COs were established in these fields, or their initiation was not successful. The third group consists of companies from the same industries that are not members of any group and do business in other regions. The research focuses on business entities with data available for evaluating financial and innovation performance. Special attention is paid to the relationship between innovation and the financial performance of companies. To achieve the main goal of the research, the following partial research questions (RQ) were determined: RQ1: Is the innovation and financial performance of companies in natural and institutionalised clusters different from companies that are not involved in any cluster? RQ2: Were the institutionalised clusters established in industries that were identified as significant in the region at the regional level (NUTS 3)? RQ3: Is there any difference between independent companies and companies in natural and institutional clusters in terms of their innovation and financial performance? RQ4: Is there a positive relationship between innovation and financial performance of companies? Is the relationship affected by the time lag and the specific industry? RQ5: Does the relationship between innovation and financial performance of companies depend on the type of interorganisational relationships (natural cluster, institutionalised cluster, or non-cluster companies)? RQ6: Does joining the cluster make a positive contribution to the total factor productivity change in member companies? Is there a difference in the total factor productivity change between natural and institutionalised clusters? RQ7: Do clusters have a positive effect on the shift in production-possibility frontier, pure technical efficiency, and economies of scale? RQ8: Are there any differences between companies in a natural cluster, an institutionalised cluster or independent companies in terms of their technological change, change in pure technical efficiency, and scale efficiency change? RQ9: Does the existence of clusters (especially institutionalised) bring positive macroeconomic externalities and is it therefore desirable to support them through economic policy makers? RQ10: What other factors (besides cluster membership) can affect a company’s financial performance? These factors could be, for example, time lags in relation to the type of innovation and industry. These research questions were operationalised into two basic hypotheses (H):

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H1: Core companies in a natural cluster achieve higher innovation and financial performance than major companies in an institutionalised cluster. Core companies are entities with the same subject of activity (according to the NACE statistical classification) which form the basis of the industrial focus of the cluster. The hypothesis was built on the assumption that the creation of a natural cluster is a long-term process based on natural market forces and links between companies, while institutionalised clusters often arise only on the basis of external stimuli such as subsidies from a specific public source. Institutionalised clusters also operate over a shorter time frame than natural clusters. H2: Core companies in both clusters achieve higher innovation and financial performance than independent companies. The hypothesis is based on the prevailing findings from the literature search on the positive impact of clusters on innovation and business performance (see Chaps. 2–4). In the European Research Area, clusters are perceived mainly as a tool for regional development and industrial policy. The research intent summarised in this book mainly draws on Michael Porter’s assumption on strengthening the competitiveness of companies within a cluster. The results of the research expand the knowledge base of the theory of the firm. In the book, the team of authors first focus on summarising the theoretical basis in the areas of localisation economics, cluster theory, the role of innovation, and innovation partnership (see Chaps. 2–4). Chapter 5 is at the interface of theoretical research and definition of the methodological procedure which, in relation to production functions, specifies the performance of companies in the cluster and justifies the use of the Data Envelopment Analysis method to measure the performance of companies. Based on this, a comprehensive concept and methodological procedures of research are presented in Chap. 6. Due to the extent of the data obtained, the approach of segmentation and model examples was chosen to present the procedure and selected outputs. Chapter 7 focuses on examples of procedures and on-going research results in the field of natural clusters and Chap. 8 on the specifics of institutionalised clusters. The research was carried out within the years 2018 to 2020 and the findings were published gradually as they occurred. Their summary is presented in Chap. 9. During the research, it was found necessary to supplement the research with other factors (apart from membership in clusters) that may have affected the financial performance of the company. These are, for example, tendencies to a certain companies’ approach to innovation, see Chap. 10, or to link the tradition of the industry with the methods of doing family business in the region. The question of the significance of the existence of family businesses in the cluster is addressed in connection with the statement (Basco, 2015) that family businesses have a close connection to the place of their operation and it is in their interest to further develop it.

1 Introduction

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For these reasons, a survey of family business in selected industrial districts was conducted, the results of which are summarised in Chap. 11. In his article, Porter (2000) introduces the concept of ‘cluster thinking’ as a broader and more dynamic view of competition between companies and localities based on productivity growth. Cluster thinking can help to set the right priorities for a given region and to manage science, technology, education, training, foreign investment, and many other policy areas. Finally, Chap. 12 brings implications for microeconomic and macroeconomic policies and the formulation of recommendations for corporate interorganisational behaviour.

References Basco, R. (2015). Family business and regional development—A theoretical model of regional familiness. Journal of Family Business Strategy, 6(4), 259–271. https://doi.org/10/gg98kw. Lindqvist, G., Ketels, C., & Sölvell, Ö. (2012). The cluster initiative greenbook 2.0. Ivory Tower Publishers. http://www.clusterobservatory.eu/system/modules/com.gridnine.opencms.modules. eco/providers/getpdf.jsp?uid¼c57a2f9f-aa59-4af8-a8f9-4fa99e95b355 Porter, M. E. (2000). Location, competition, and economic development: Local clusters in a global economy. Economic Development Quarterly, 14(1), 15–34. https://doi.org/10/fw7s76.

Chapter 2

Evolving Insight of Localization Theories into Cluster Existence Marek Skala

2.1

and Petra Rydvalova

Strategic Partnership

Over the last two decades, the literature on strategic partnerships has addressed various issues regarding interorganizational relationships, especially in relation to corporate performance. Interorganizational partnerships take a wide range of forms, from mutual cooperation within different types of cooperation (strategic partnerships, network organizations, and joint ventures) to integration (Golicic et al., 2003). Strategic partnerships are an alternative to market relations and integration and can be seen as essential to a network economy. Networks are focused on gaining a competitive advantage both for their members and for the networks as a whole (Fiala, 2008). Strategic alliances are usually made up of small and medium-sized enterprises, which form power groups through cooperation. These groups can also strengthen the protection of their interests vis-à-vis large companies. Such strategic alliances are especially formed by industry clusters (Vodáček & Vodáčková, 2009). The first research mapping the benefits of localizing particular economic activities dates back to the seventeenth and eighteenth centuries. Since the nineteenth century, many authors such as Alfred Weber, Alfred Marshall, Harold Hotelling, Julian Wolpert, Walter Christaller, August Lösch, and others have systematically addressed this issue. At the end of the twentieth century, Michael Porter followed up on the findings of these authors with his theory of clusters. As Porter (2000) himself states that industry clusters (agglomerations) have long been part of the economic environment, and the geographical concentration of trade and business in individual industries has been around for centuries. It is also worth noting that, in addition to the

M. Skala (*) · P. Rydvalova Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_2

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impact of industry groupings/agglomerations on business performance, it is necessary to perceive their benefits for the regional economy as well. The structure of this chapter respects the historical development of the issue. We will focus on the definition of terms within the framework of localization theories. The theory of clusters and the typology of clusters are discussed by Zizka, Pelloneova and Skala (2021, in this book).

2.2

Initial Localization Theories

Initial localization theories were focused on localization savings, which are achieved through a decrease in transaction costs due to the proximity of companies. Within these theories, agglomeration advantages were formulated, which are understood more broadly. The benefits can be understood as external savings obtained, thanks to the existence of other actors or public availability of resources, mainly thanks to the proximity of agglomerated companies. The first attempts to explain the location of particular economic activities date back to the seventeenth and eighteenth centuries. The central work of this generation of treatises is a study by von Thünen (1826) dealing with the regularity of the distribution of individual agricultural activities. Neoclassical localization theories are included in the works of Weber (1904/ 1928), Hotelling (1929), Wolpert (1964), Christaller (1933/1966), and Lösch (1944/ 1954). Alfred Weber tried to model the optimal location of the company by minimizing transport costs by defining localization factors (e.g., the location of raw materials, labour and transport costs). He introduced the concept of agglomeration economies achieved through the proximity of other companies, as a specific example of external savings. External savings were generally defined by Alfred Marshall (1890/1920) as savings resulting from the existence of other actors or the public availability of certain resources such as quality public education systems. Marshall detected three types of external savings (a traditional triad of external savings) achieved by the proximity of economic activities—sharing the labour market, disseminating technology (information and knowledge) from nearby companies, and sharing specialized infrastructure or specialized suppliers. Using these factors, Marshall tried to explain the region’s specialization in a particular industry. At the same time, Alfred Weber defined deagglomeration factors that contribute to its dispersion rather than to the concentration of production, see Zizka et al. (2021, in this book) for more information. Harold Hotelling examined the interdependencies of companies’ location decisions. He formulated his conclusions in a model of rival companies competing for market space (Hotelling, 1929). The model attempts a certain methodological realism by abandoning the neoclassical assumption of perfect competition and, conversely, assumes that even small companies may construct a strategy to achieve

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a monopoly position in some areas. The company thus tries to gain a part of the competitor’s space by approaching the areas where it sells. The absorption of the behavioural paradigm into neoclassical localization theories is represented by the model of Julian Wolpert (1964). It emphasizes the soft factors of perception, decision-making, and localization, as well as the subjectivity of the perception of the actors in general. Although the model attempts methodological realism, it continues to work with the methodology of individualism, which does not take into account influences outside the individual, however, they may affect his behaviour. It still assumes maximizing the behaviour of individuals, although it allows for a limited and modified process of perception and interpretation of information. Walter Christaller (1966) and August Lösch (1954) formulated central place theory, which is part of the neoclassical theory of regional equilibrium and deals with the spatial arrangement of the economy as a whole. Central place theory follows the work of Johann Heinrich von Thünen (1826) and Alfred Weber (1928). Christaller tried to explain the location and size of cities in the settlement system under idealized neoclassical assumptions about the rational behaviour of both customers and business owners, such as perfect information, perfect mobility, and perfect competition. Christaller’s theory was developed by August Lösch. It is based on Weber’s localization model (Weber, 1928), replacing the localization motif of companies minimizing costs with an effort to maximize profits. While Christaller formulated his retail model, Lösch added industry to his model. He also considered other forms of spatial arrangement than Christaller’s hexagon. This makes Lösch’s theory much more complicated.

2.3

Modern Localization Theories

Localization theories were sharply criticized during the 1970s through the lens of critical realism (Massey, 1979), mainly due to simplistic neoclassical assumptions and the neglect of institutional and behavioural aspects such as abstractly conceived companies, where the company is perceived as a profit-maximizing actor in a slightly differentiated environment. Neoclassical growth theories shift their attention from localization theories to the issue of explaining long-term growth with no emphasis on regional policy since, in their thinking, involuntary unemployment was determined primarily by not adapting the allocation of capital and labour to market principles. Similarly, macroeconomic conditions and technologies were perceived in these theories as uncontrollable, that is, without affecting the creation of imbalances and their impact on economic growth. New economic geography as a new localization theory from the 1990s epitomizes the renaissance of the neoclassical tradition of localization theories (Fujita & Krugman, 2004; Krugman, 1991a, 1994, 1995; Ottaviano & Thisse, 2005). It is a

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continuation of the neoclassical tradition of modelling localization factors and allows modelling under which conditions a concentration of economic activities in space occurs. The conditions can be the interaction of external and agglomeration advantages. However, these new localization theories replace traditional neoclassical postulates with more realistic assumptions. They replace perfect competition with imperfect competition, namely monopolistic competition, and consider increasing returns to scale instead of diminishing returns. Growing returns to scale (company size) is considered to be the most important cumulative mechanism (positive feedback) resulting in the concentration of production, which leads to an increase in external savings, namely agglomeration benefits and innovation potential. They describe three types of agglomeration benefits, which are the concentration of skilled and specialized labour, the interconnection of local businesses in the form of subcontracting, and the dissemination of information and knowledge. They further aim to explain what type of externalities contributes most to agglomeration (concentration of population and economic activities in cities, industrial zones, and production districts). In their models, they abstract from physical-geographical and other factors such as different climates, equipment with production resources and their availability. It is thus primarily a matter of understanding the spatial organization of the environment created by man. Paul Krugman (1991b) identified key factors leading to the concentration of economic activities, such as transport costs, economies of scale, mobility of production factors, and the size of markets. In particular, he states that returns to scale result in the concentration of production and transport costs lead to the location of production close to large markets (to the dispersion of production). These factors increase the competitiveness of companies and improve the functioning of markets. In his model, Krugman (1991b) tries to fill a theoretical gap explaining the tendency of economic activities to concentrate in certain localities. This tendency is a natural and logical consequence of the effort to maximize benefits and profits. An important postulate of Krugman’s model is the impact of rising returns to scale on the polarization of the economy—the higher the rising yields, the greater the tendency of the economy to polarize the core-periphery. Another postulate of the Krugman model is the impact of transport costs on core-peripheral polarity—the lower the transport costs, the greater the tendency for core-periphery polarity to occur. The significance of a given industry also contributes to the polarization of the core-periphery—the more important the branch, the greater the tendency to the polarity of the core-periphery to emerge. The model builds on the previous neoclassical localization models and is, therefore, a standard model in the spirit of mainstream economics or, more precisely, a model explaining the tendency to concentrate in space within the general model of equilibrium under conditions of imperfect competition. However, unlike the neoclassical theory, which assumes a single optimal arrangement of space, Krugman anticipates the existence of several possible equilibria. In Krugman’s model, an innovatively strong dependence on previous developments (path dependency) is assumed. It is the dependence on the chosen path (path dependency) caused by a random event in history that Krugman considers the main factor influencing

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localization. The concurrence of historical events determines which of the several possible equilibrium states will be achieved. The concept of strong dependence on previous developments (path dependency) represents the insight into reproaches against the traditional neoclassical theory that considers logical time, built on a high degree of abstraction and the neglect of historical contexts. Dependence on the chosen path (path dependency), on the other hand, emphasizes thinking in historical time. A random phenomenon in the past can have a permanent, cumulative effect on the organization of space. The chosen locality strengthens its dominance through the action of agglomeration forces and external advantages. It is the initial conditions that determine which of the possible equilibrium states will eventually be achieved. From the structuralist concepts Raymond Vernon’s (1966) theory of production cycles and Anna Markusen’s (1985) theory of profit cycles, which draws on Vernon’s theory can be defined as partially localization approaches. Using the method of abstract research and with the help of structuralist thinking, both theories try to find a general law of profit creation that could be used to explain either prosperous or lagging regions. Theory of production cycles is based on the premise that individual regions are differently disposed to locate the production of a given product depending on its life cycle. Specifically, there is a gradual shift of production from the core to the periphery. Profit cycle theory is based on behavioural strategies of corporations, namely oligopolies. These strategies then have an impact on regional development. Depending on the development of profit, the company sets a specific localization strategy. Markusen argues that oligopolies hinder decentralization during the initial innovation phase and, on the contrary it accelerates it during the decrease of profit phases. In the case of negative or zero profit, production is concentrated in a few locations. In the case of super profits, agglomeration will occur, as high demands on research and design will force companies to cooperate intensively with specialized companies. Normal profits will cause spatial dispersion motivated by moving closer to markets and using cheaper, less specialized labour. In case of loss (obsolete industries), there is a gradual reduction or even the end of production. In her theory, Markusen tries to take into account structural changes, innovations, and the increasingly significant existence of imperfect competition, namely oligopolies. The theory of flexible specialization (for example, Piore & Sabel, 1984; Scott, 1988) and the localization approaches of the California School (Scott & Storper, 1987; etc.) are also included in structuralist concepts. Both approaches belong to the group of regulatory theories. However, regulatory theories have not aspired to define the conditions that lead to successful economic development. On the contrary, the theory of flexible specialization clearly defines what an economically successful region should look like. The question of small innovative companies and regional agglomerations is of its main interest. Since the 1970s, there has been a fundamental change in the organization of production (post-Fordism) which is characterized by tendencies to increase specialization and greater flexibility. In this contemporary context, the theorists of flexible specialization postulate that competition based on innovation and flexible production, which, however, requires higher qualifications (re-skilling), is essential for the

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company. Piore and Sabel (1984) draw on the thesis, stating that the post-Fordist period is typical for its regional specialization in the form of industrial districts characterized by close specialization, intensive and long-term ties and partnerships of spatially close companies. The California School then attempts to document and map the complexity of political, social, and cultural influences that have led to the economic boom of highly specialized regional economies. They emphasize that the agglomeration effects, in their view, are a positive consequence of the disintegration of large companies (postFordism) and the mutual socio-cultural and spatial proximity of companies (non-commercial ties). The socio-cultural proximity of companies was perceived through the prism of the structuralist methodology, thus, differently from the institutional approaches. Massey’s Theory of Spatial Divisions of Labour (1984) works with localization decision-making. The model is one of the most important Marxist critical-realistic approaches dealing with regional differentiation (inequality). Changes in the spatial organization of relations in production are considered by the author to be the main cause of large changes in the spatial division of labour, thus increasing the concentration of less qualified (managed) professions in some regions, and conversely the concentration of qualified (managerial) professions in other regions. Massey dealt with the mechanisms of localization of economic activities and changes in economic and social differences between centres and peripheries in the broad context of social development. Companies use interregional differences in localizing their activities. Massey argues that this advantage will be used by companies that distribute their branches between regions based on the requirements of the various stages of production. At the same time, a hierarchy within the corporation on the one hand and a hierarchy on the other hand are assumed. Functions of the primary hierarchy (management, research, marketing, etc.) are placed in metropolitan areas (regions of the highest hierarchy). Subsidiary plants are usually located in peripheral regions, these relocations enabled deskilling (reduction of creative activities in a part of the workforce). Massey considers the position of a particular branch in the corporation’s hierarchy to be more important than its sectoral affiliation. Spatial patterns and regional differentiation are thus not the result of spatial relations, but social relations (dependence between managerial and managed parts of a company in a given region). Institutional localization concepts include the theory of industrial districts (e.g., Becattini, 1978; Brusco, 1982), the theory of learning regions (Lundvall, 1992; Saxenian, 1991, and others), the concept of regional innovation systems (e.g., Cooke, 1992, 2001), Porter’s cluster theory (e.g., Porter, 1990; Porter & Ketels, 2009), the concept of related variety (Boschma, 2011; Frenken et al., 2007, etc.) and a group of closely related localization approaches of the global commodity chain (e.g., Gereffi, 1994; Gereffi & Korzeniewicz, 1990), global value chains (Dolan & Humphrey, 2000; Sturgeon et al., 2008, etc.), and global production networks (Dicken et al., 2001; Henderson et al., 2002; etc.). The theory of industrial districts (e.g., Becattini, 1978; Brusco, 1982) was formulated at the turn of the 1970s and 1980s on the basis of an analysis of

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production organization, labour market function, and the role of institutions in Italian regions that have undergone a stage of rapid economic growth (endogenous approach to regional development). The authors (e.g., Becattini, 1978; Brusco, 1982) perceive the industrial district as a homogeneous entity that freely shares knowledge, is characterized by informal relationships of reciprocity and sharing, adaptation and innovation. It is mostly a spatial agglomeration of small companies. This theory has created space for an explanation based on an economic analysis sensitive to the specific cultural and social conditions of individual regions and to the historical process of their institutionalization. However, this method is criticized because of the replication crisis (for example, Rabellotti et al., 2009). Other reasons are non-transferability of the process of historical development on the one hand, and the uniqueness of socio-cultural and institutional environment of the region on the other. In the 1990s, the theory of learning regions was formulated (Lundvall, 1992; Saxenian, 1991; and others). It sees the source of competitiveness of industrial districts (production regions) in knowledge, the ability to learn and in creating a climate that stimulates innovation. The potential to learn and innovate is fundamentally influenced by the form of relations between the company and its environment (a network of relations, but also the general institutional framework) in which the company is rooted. The theory leaves the neoclassical postulate of agglomeration advantages resulting only from the spatial proximity of companies and replaces it with a more generally (broadly) formulated proximity—social, cultural, organizational, and spatial (for example Storper, 1995). Competitive advantage is primarily advanced by uncodified knowledge and skills, acquired only by one’s own experience and participation in the given activities. These are tied to specific regional contexts and institutional characteristics such as networks of contacts and forms of rooting. However, as Martin and Sunley (2003) point out, it is not sufficiently empirically proven that uncodified knowledge is indeed a source of business success. Local institutions, networks of contacts, and other forms of relationship assets (continuity) are the result of a complicated cultural and historical development (path dependency) that defines the specificity of the region, which means that history matters. In addition, it is typical for the research institutions to cooperate with local companies. Thereby, they increase the likelihood of innovation and stimulate the transfer of information and new ideas. From the methodological point of view, the theory of learning regions does not seek to find a general model, but rather a causal analysis of the reasons for success. The theory is criticized for failing to clarify the origins of innovation (Hudson, 1999) as well as demonstrating an empirically verifiable link between corporate cooperation and the degree of innovation (Maskell & Malmberg, 1999). In parallel (in the 1990s) with the theory of learning regions, the concept of regional innovation systems was formulated (for example, Cooke, 1992, 2001). It deals mainly with the role of the soft infrastructure of the host region (universities and educational institutions, innovation centres, business associations, government agencies, etc.) as well as with the role of intensive cooperation between spatially and

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socio-culturally close companies in the innovation process. It is a subsystem of knowledge-producing and knowledge-economic valuing. Regional innovation systems arise when there are systematic interactions between companies and knowledge organizations, which means knowledge contacts (continuity, innovation ecosystems). The concept pays great attention to formal and informal behavioural characteristics, such as customs, norms, values, and institutions. All these attributes may significantly influence the localization decisions of companies that create clusters in compliance with the postulate of the concept. It further emphasizes the support of the development of the innovation system (innovation, networking) in the given locality and the need for a broader concept of the innovation process, including the support of human and social capital. In comparison with the theory of learning regions, it deals with the issue of the company’s ability to absorb innovation. Simultaneously, this concept convincingly justifies why models from successful production regions cannot be copied (great heterogeneity). Unlike clusters, the concept can include a number of industries or clusters. In the professional literature, the concept is criticized for too much emphasis on networking at the regional level. Critics (for example, Harald Bathelt, 2003) oppose with the claim that the decisive part of knowledge contacts, on the contrary, directs to actors outside the region. See Zizka et al. (2021, in this book) for more information. Porter’s cluster theory (Porter, 1990; Porter & Ketels, 2009) has been continuously elaborated since the 1990s and is based on the premise that the localization of individual economic activities within the value chain is a strategic matter for the company, as its success depends on the environment in which it operates and, on the other hand, on key external factors (Porter’s competing diamond). The theory is based on the postulate that these competitive external factors are amplified when companies are geographically concentrated. Such geographical concentration of interconnected industries (clusters) thus allows a wide range of positive externalities to be achieved. With this postulate, Porter’s cluster theory builds on Krugman (1991a) who expected a deepening specialization at the local and regional level due to intensive globalization associated with lower transport costs and the removal of tariff barriers. He rediscovered Alfred Marshall’s idea of external returns to scale as agglomeration advantages (1920), which stemmed from the concentration of similarly specialized companies. He also incorporated into his concept the mechanisms and effects of the theory of production districts analysing the benefits resulting from the proximity of companies. Some conclusions from Porter’s cluster theory were critically analysed, such as the contribution of clusters to innovation (Simmie, 2004). Bathelt et al. (2004) question the benefits of regional networking, arguing that it is reasonable to expect that clusters which have established links for knowledge exchange with key global knowledge centres will be successful. Martin and Sunley (2003) criticize the ambiguous definition of clusters, which limits the empirical verification of to what extent clusters contribute to increasing the productivity, innovation, and profitability of participating companies.

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Since the 1990s, a group of closely related localization approaches was formed— global commodity chains (e.g., Gereffi & Korzeniewicz, 1990; Gereffi, 1994), global value chains (Dolan & Humphrey, 2000; Sturgeon et al., 2008, etc.), and global production networks (Dicken et al., 2001; Henderson et al., 2002), each of which aims to explain the localization (organization) of production in a globalized world. Compared to other institutional localization approaches—notably the theory of learning regions or the theory of production districts—these concepts emphasize the need for vertical links outside the region, while other institutional approaches focus on analysing horizontal links between actors in the region that are supposed to provide incentives for the development of the company. It is thus a global view of the organization of production, which contrasts with the predominantly regional view of other institutional approaches. Methodologically, the concept of global commodity chains evolved from the Keynesian macroeconomic approach of the core-periphery to the current form analysing the relationships between companies in the production of certain goods (micro-level). The concepts of the global value chain and the global production network have converged in recent years, which is why some authors no longer distinguish between them (for example, Pavlínek & Ženka, 2011). These two current concepts postulate that companies must be seen as actors that are interconnected by a complex network of relationships not only with other actors and the environment of the region in which they operate but also with actors from distant regions. These concepts are criticized (e.g., Coe et al., 2008) for their over-concentration on relationships between companies and neglecting endogenous corporate behaviour (e.g., the relationship between company management and subsidiaries). The contemporary (early twenty-first century) localization approach can be described as the concept of related variety (Boschma, 2011; Frenken et al., 2007, etc.), which draws on the concept of a learning region. It focuses on finding patterns that reveal the processes leading to the creation of companies in new industries and what roles the existing industry structures play. The concept postulates that most of these companies are formed from enterprises in related industries. Through the prism of cognitive psychology, they analyse the emergence of innovations in the interaction between actors with different socio-cultural characteristics, experiences, and patterns of thinking and behaviour. Simultaneously, they emphasize that a certain degree of cognitive proximity (related diversity, complementary industries) among companies is significant for the creation of innovations, while excessive proximity can cause the risk of lock-in. They postulate that the knowledge flow between related fields (industries) is important for the company. They thus respond to the localization dichotomy of institutional approaches—specialization or diversification in related industries. Table 2.1 summarizes the key personalities of localization theories, as well as the targeting of their studies, methods of solution, and defined outputs. These data are set in a time frame with a link to the dating of industrial revolutions and the definitions of key factors. The following key factors can be traced from the analysis of the development of localization economies, defining the causes of cooperation between institutions:

Personalities Johann Heinrich von Thünen

Alfred Weber

Alfred Marshall

Harold Hotelling

Theories Initial localization theory

Neoclassical localization theories

Neoclassical localization theories

Neoclassical localization theories

Interdependencies of localization decisions of companies

Generalization of the external savings definition

Modelling of the company optimal location

Aimed at Localization savings

Model of competing companies

Typology of external savings from the proximity of economic activities

Solution Reduction of transaction costs due to the proximity of companies Minimization of transport costs

Table 2.1 Summary of findings from the literature search of localization theories

Agglomeration savings as an example of external savings; in relation to A. Marshall’s definition of deglomeration factors Triad of external savings (labour market sharing, technology dissemination ¼ information and knowledge sharing, joint use of specialized infrastructure) Leaves the neoclassical assumption of perfect competition (methodological realism)

Defined outputs Agglomeration benefits, external savings (link to agriculture)

(1929) 2nd Industrial Revolution

(1890/1920) 2nd Industrial Revolution

Year of publication of the seminal work/ State of technical development of society (1826) The turn of the 1st and 2nd Industrial Revolution (1904/1928) 2nd Industrial Revolution

Proximity sharing region specialization for a specific industry

Key factor

16 M. Skala and P. Rydvalova

Paul Krugman

Critique of localization theories due to neglect of institutional and behavioural aspects Traditional modelling of localization factors ‘under what’ conditions

Massey Doreen

New economic geography (new localization theories)

Spatial arrangement of the economy as a whole

August Lösch

Neoclassical localization theories

Spatial arrangement of the economy as a whole

Walter Christaller

Neoclassical localization theories

Absorption of the behavioural paradigm

Julian Wolpert

Neoclassical localization theories

More realistic assumptions have been set: • Imperfect competition • Growing economies of scale

Location and size of cities in the settlement system, Christaller’s hexagon Shifting of localization motives: maximization of profit replaces the cost minimization

Emphasis on soft factors (perception, decision-making, localization)

Concentration of economic activities in space leads to an increase in external savings and an increase in innovation potential with a strong dependence on the concurrence of historical events

Critical realism

Defining the theory of central places—a model for industry

In maximizing the behaviour of individuals, it admits the influence of perception and interpretation of information Defining the theory of central places—a model for retail

(1991–2004) 3rd with an overlap to the 4th Industrial Revolution

(1944/1954) The end of 2nd Industrial Revolution (1979) 3rd Industrial Revolution

(1964) The end of 2nd Industrial Revolution, beginning of 3rd Industrial Revolution (1933/1966) 2nd–3rd Industrial Revolution

(continued)

• Company size structure (as positive feedback) • Innovation • Path dependency

2 Evolving Insight of Localization Theories into Cluster Existence 17

Personalities Raymond Vernon

Ann Markusen

Piore Sabel Charles Sabtel

Allen J. Scott Michael Storper

Theories Structuralist concept

Structuralist concept

Structuralist concept—A regulatory theory

Structuralist concept—A regulatory theory

Table 2.1 (continued)

Documenting and mapping the complexity of influences (political, social, and cultural)

Clear definition of an economically successful region

The influence of corporate behavioural strategies

Aimed at The region is differently disposed to locate production depending on the product life cycle

Innovative SMEs and regional agglomerations are of main interest Agglomeration effects based on disintegration of large companies (post-Fordism) and mutual socio-cultural and spatial proximity

Solution The method of abstract research—the effort to find the general pattern of profit generation and the resulting prosperity or lagging regions Takes into account structural change, innovation and imperfect competition

(1987) 3rd Industrial Revolution, overlap to 4th Industrial Revolution

(1984) 3rd Industrial Revolution

Theory of flexible specialization

Localization approaches of the California School

(1985) 3rd Industrial Revolution

Theory of profit cycles

Defined outputs Theory of production cycles

Year of publication of the seminal work/ State of technical development of society (1966) 3rd Industrial Revolution

Innovation requires cooperation between specialized companies • Innovation • Size structure of companies • Continuity

Key factor

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Massey Doreen

Giacomo Beccattini Sebastiano Brusco

Bengt-Ake Lundall Annalee Saxenian

Theory of spatial divisions of work—Marxist critical-realistic approach

Institutional localization concept—A theory of industrial districts

Institutional localization concept—A theory of learning regions

The competitiveness of industrial districts is in the knowledge, the ability to learn, in the innovative climate

The way production is organized, functioning of labour market, and the role of institutions in regions of rapid economic growth

Addressing regional inequalities

The neoclassical postulate of agglomeration advantages only from spatial proximity is replaced by more generally formulated proximity (social, cultural, organizational, and spatial). It does not look for a general model, but performs a causal analysis of the causes of success.

Economic analysis in the regions (Italy) in terms of the specifics of cultural, social conditions and historical process of their institutionalization

Context of social development

Spatial patterns are influenced by social relations, i.e. subsidiary plants are located in peripheral regions The production district is understood as a homogeneous entity freely sharing knowledge, has informal relationships, it is an agglomeration of SMEs with the existence of a family tradition (criticized for replication crisis) Regional innovation systems arise when there is a systematic interaction between companies and knowledge organizations. (1992) (1991) The overlap of 3rd and 4th Industrial Revolution

(1978) (1982) 3rd Industrial Revolution

(1984) 3rd Industrial Revolution, the overlap to 4th Industrial Revolution

(continued)

Uncodified (tacit) knowledge • Continuity • Path dependency • Innovation

• Continuity • Size structure of companies • Familiness • Path dependency

2 Evolving Insight of Localization Theories into Cluster Existence 19

Personalities Philip Cooke

Michael Porter Christian Ketels

Theories Institutional localization concept—The concept of regional innovation

Institutional localization concept—A Porter’s cluster theory

Table 2.1 (continued)

The localization of individual economic activities within the value chain is a strategic matter for the company

Aimed at The role of soft infrastructure in the region and the cooperation of spatially socioculturally close companies in the innovation process

Analysis of 5 forces, success depends on the environment in which it operates and on key external factors (Porter’s competing diamond)

Solution Focuses on the subject of corporate ability to absorb innovation.

Defined outputs Regional innovation systems arise when there is a systematic interaction between companies and knowledge organizations. Excessive emphasis placed on networking at the regional level is criticized. The theory is based on the postulate that these competitive external factors are strengthened if companies are geographically concentrated. Martin and Sunley (2003) criticize the ambiguous definition of clusters, which limits empirical verifications (1990) (2009) Beginning of the 4th Industrial Revolution

Year of publication of the seminal work/ State of technical development of society (1992, 2001) Beginning of 4th Industrial Revolution

Geographical concentration

Key factor • Continuity • Regional branch associations

20 M. Skala and P. Rydvalova

Koen Frenken, Ron Boschma

Institutional localization concept—The concept of related diversity

It seeks patterns that reveal the processes leading to the creation of companies in new industries

Explanation of localization (organization) of production in a globalized world

Analysis using cognitive psychology, searching for patterns of thinking and behaviour. They analyse the flow of knowledge between related fields.

Global commodity chains • Global value chains • Global production network These concepts emphasize the need for vertical ties outside the region. Concepts are accused (for example, Coe et al., 2008) of too much concentration on relationships between companies and the neglect of endogenous (internal) behaviour of corporations It postulates that most companies in new industries are formed from companies in related industries, but emphasizes that creativity requires a related diversity, i.e. that too close proximity can lead to the so-called locking (2007) 4th Industrial Revolution

(1990–1994) (2000–2008) (2001–2002) Beginning of the 4th Industrial Revolution

• Innovations • Lock-in

Note: The First Industrial Revolution (1784, the discovery of the 1st mechanical weaving machine). The Second Industrial Revolution (beginning around 1870) is associated with electrification. The Third Industrial Revolution (dating from 1969) is associated with automation and programmable machines in production. The Fourth Industrial Revolution is connected with the Internet (introduced in 1987) at the time of its commercialization (1994), which enabled further development of systems, networks, and communication not only of people, but also of things, and machines

Gary Gereffi & Miguel Korzeniewicz, Catherine Dolan & John Humphrey, Timothy Sturgeon, Peter Dicken, Jeffrey Henderson

Institutional localization concept—The concept of global approaches

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• • • •

Proximity sharing, allowing region specialization Benefits resulting from the diversity of the size structure of companies Existence of innovation activities in connection with the cooperation of entities Path dependency, i.e., the dependence of current economic performance on historical development • Continuity, tradition, companies with the existence of family tradition of the region • Geographical concentration enabling the use of infrastructure • Complexity of technologies, technological lock-in, bringing uniqueness at the same time These factors should also be examined in the light of cluster theory (Zizka et al. 2021, in this book) and in connection with the theory of innovation (Rydvalova & Skala, 2021, in this book). For this general analysis, research assumptions can be derived from localization theories: on the importance of familiness of businesses as a factor in the development of traditional industries in the region (e.g., in relation to industry districts); on unique innovation habits in groups of companies described by contemporary localization concepts (e.g., whether any typical behaviour of innovative companies in selected knowledge sectors can be defined); on the difficult imitation of the environment of a natural cluster by economic policy (e.g., whether the dependence of cluster organizations on public support is decreasing), and finally on the debatable benefits of such a policy (e.g., whether the support of institutionalized clusters is effective).

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Dicken, P., Kelly, P., Olds, K., & Yeung, H. (2001). Chains and networks, territories and scales: Towards a relational framework for analysing the global economy. Global Networks, 1(2), 89–112. https://doi.org/10/dvjgv8. Dolan, C., & Humphrey, J. (2000). Governance and trade in fresh vegetables: The impact of UK supermarkets on the African horticulture industry. Journal of Development Studies, 37(2), 147–176. https://doi.org/10/bvqrt4. Fiala, P. (2008). Síťová ekonomika. Professional Publishing. Frenken, K., Van Oort, F. G., & Verburg, T. (2007). Related variety, unrelated variety and regional economic growth. Regional Studies, 41(5), 685–697. https://doi.org/10/bt67f9. Fujita, M., & Krugman, P. (2004). The new economic geography: Past, present and the future. Papers in Regional Science, 83(1), 139–164. https://doi.org/10/cfm4x8. Gereffi, G. (1994). The organization of buyer-driven global commodity chains: How U.S. retailers shape overseas production networks. In Commodity chains and global capitalism. Praeger. Gereffi, G., & Korzeniewicz, M. (1990). Commodity chains and footwear exports in the semiperiphery. In Semiperipheral states in the world-economy. Greenwood Press. Golicic, S. L., Foggin, J. H., & Mentzer, J. T. (2003). Relationship magnitude and its role in interorganizational relationship structure. Journal of Business Logistics, 24(1), 57–75. https:// doi.org/10/bbqkch. Henderson, J., Dicken, P., Hess, M., Coe, N., & Wai-Chung Yeung, H. (2002). Global production networks and the analysis of economic development. Review of International Political Economy, 9(3), 436–464. https://doi.org/10/bjq29f. Hotelling, H. (1929). Stability in competition. The Economic Journal, 39(153), 41–57. https://doi. org/10/c9w7r9. Hudson, R. (1999). The learning economy, the learning firm and the learning region: A sympathetic critique of the limits to learning. European Urban and Regional Studies, 6(1), 59–72. https:// doi.org/10/fnzf7f. Krugman, P. (1991a). Geography and trade. MIT Press. Krugman, P. (1991b). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483–499. https://doi.org/10/fkmcbx. Krugman, P. (1994). Complex landscapes in economic geography. American Economic Review, 84 (2), 412–416. Krugman, P. (1995). Development, geography and economic theory. MIT Press. Lösch, A. (1954). The economics of location. Yale University Press. Lundvall, B.-A. (1992). National systems of innovation. Towards a theory of innovation and interactive learning. Pinter. Markusen, A. (1985). Profit cycle, oligopoly, and regional development. MIT Press. Marshall, A. (1920). Principles of economics. Macmillan. Martin, R., & Sunley, P. (2003). Deconstructing clusters: Chaotic concept or policy panacea? Journal of Economic Geography, 3(1), 5–35. https://doi.org/10/d97q6p. Maskell, P., & Malmberg, A. (1999). Localized learning and industrial competitiveness. Cambridge Journal of Economics, 23(2), 167–185. https://doi.org/10/b6r7n7. Massey, D. (1979). A critical evaluation of industrial-location theory. In Spatial analysis, industry and industrial environment. Wiley. Massey, D. (1984). Spatial divisions of labour: Social structures and the geography of production. Macmillan. Ottaviano, G. I. P., & Thisse, J.-F. (2005). New economic geography: What about the N? Environment and Planning A: Economy and Space, 37(10), 1707–1725. https://doi.org/10/ frv2pq. Pavlínek, P., & Ženka, J. (2011). Upgrading in the automotive industry: Firm-level evidence from Central Europe. Journal of Economic Geography, 11(3), 559–586. https://doi.org/10/ft3x8q. Piore, M., & Sabel, C. (1984). The second industrial divide: Possibilities for prosperity. Basic Books. Porter, M. (1990). The competitive advantage of nations. Macmillan.

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Porter, M. E. (2000). Location, competition, and economic development: Local clusters in a global economy. Economic Development Quarterly, 14(1), 15–34. https://doi.org/10/fw7s76. Porter, M., & Ketels, C. (2009). Clusters and industrial districts: Common roots, different perspectives. In A handbook of industrial districts. Edward Elgar. Rabellotti, R., Carabelli, A., & Hirsch, G. (2009). Industrial districts on the move. European Planning Studies, 17(1), 19–41. https://doi.org/10/d6smpb. Rydvalova, P., & Skala, M. (2021). Innovation and innovation partnership. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business clusters – An empirical study. Springer Nature. Saxenian, A. (1991). The origins and dynamics of production networks in Silicon Valley. Research Policy, 20(3), 423–437. https://doi.org/10/bvfxpc. Scott, A. J. (1988). New industrial spaces: Flexible production organization and regional development in North America and Western Europe. London: Pion. Scott, A. J., & Storper, M. (1987). High-technology industry and regional development: A theoretical critique and reconstruction. International Social Science Journal, 122(2), 215–232. Simmie, J. (2004). Innovation and clustering in the globalised international economy. Urban Studies, 41(5–6), 1095–1112. https://doi.org/10/fhfmf3. Storper, M. (1995). The resurgence of regional economies, ten years later: The region as a nexus of untraded interdependencies. European Urban and Regional Studies, 2(3), 191–221. https://doi. org/10/dczg44. Sturgeon, T., Van Biesebroeck, J., & Gereffi, G. (2008). Value chains, networks and clusters: Reframing the global automotive industry. Journal of Economic Geography, 8(3), 297–321. https://doi.org/10/dm3wnh. Vernon, R. (1966). International investment and international trade in the product cycle. Quarterly Journal of Economics, 80(2), 190–207. https://doi.org/10/fd4qr4. Vodáček, L., & Vodáčková, O. (2009). Moderní management v teorii a praxi. Management Press. von Thünen, J. H. (1826). Der isolirte Staat in Beziehung auf Landwirthschaft und Nationalökonomie, oder Untersuchungen über den Einfluss, den die Getreidepreise, der Reichthum des Bodens und die Abgaben auf den Ackerbau ausüben. Wirtschaft & Finanz. Weber, A. (1928). Theory of location of industries. University of Chicago Press. Wolpert, J. (1964). The decision process in spatial context. Annals of the Association of American Geographers, 54(4), 537–558. https://doi.org/10/cn9jsz. Zizka, M., Pelloneova, N., & Skala, M. (2021). Theory of clusters. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business clusters – An empirical study. Springer Nature.

Chapter 3

Theory of Clusters Miroslav Zizka

3.1

, Natalie Pelloneova

, and Marek Skala

Development in the Field of Cluster Theory

The development of cluster theory at the turn of the twentieth and twenty-first centuries is associated with Michael Porter, a professor at Harvard Business School. In his Competitive Advantage of Nations (Porter, 1990), he cites Italian ceramic tile manufacturers in the Sassuolo region as an example of how a diamond of competitive advantage works. The concentration of local suppliers, unique distribution channels and intense rivalry among local businesses creates constant pressure on innovation. Producers benefit from a large number of locally developed equipment suppliers and other supporting industries that provide materials, services, and infrastructure. The geographical concentration of the cluster completes the entire process. The number of experienced workers, technicians, developers, and service workers grows in the region over time. Porter linked the microeconomic theory of competition and the role of localisation in creating a competitive advantage. He built a model explaining the effect of localisation on competition by using four interrelated influences, which he graphically represented in the form of a diamond of competitive advantage (see Fig. 3.1). The basic attributes of a diamond are factor conditions (position in the production factor—qualified workforce and infrastructure), demand conditions (domestic demand for products and services), relating and supporting industries (existence or absence of supplier industries that are internationally competitive), and firm strategy, structure, and rivalry (conditions under which companies are established, organised, and managed). The diamond represents the environment in

M. Zizka (*) · N. Pelloneova · M. Skala Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_3

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Firm strategy structure and rivalty

Demand conditions

Factor conditions

Related and supporting industries Fig. 3.1 The diamond of national competitive advantage. Source: Porter (1990)

which companies are created and learn to compete. Competitive industries are intertwined with vertical and horizontal relationships. Porter in his 1990 work characterised the cluster in a relatively general way and it was further explained through case studies. Nevertheless, it is already clearly defined in a later article (Porter, 1998). Clusters are the geographical concentration of interconnected companies and institutions within a given area. They involve a number of interconnected industries and other actors important for competition. These include suppliers of specialised inputs, such as components, machinery, and services, as well as providers of specialised infrastructure. Clusters are also often expanded to include distribution channels, customers, and manufacturers of complementary products along with companies in industries with related skills, technologies, or common inputs. Finally, many clusters include government and other institutions, such as universities, standards-setting agencies, think-tanks, training providers, and trade/business associations. All of these contribute to provide specialised training, education, research, and technical support. The typology of clusters is further described in Sect. 3.2. Porter’s definition of an industry cluster contains two key elements. First, the companies in the agglomeration are somehow interconnected and complementary. The links between companies are both vertical (customer-supplier chain) and horizontal (use of similar inputs, technologies, work, etc.). Another key feature is a cluster’s geographic proximity. Co-location supports the creation of networks with direct and indirect relationships between businesses and increases the benefits that flow from them. Porter also supports the idea that clusters are not only an analytical concept but a key tool of economic policy as well (Asheim et al., 2009). However, the very concept of concentration of companies within a given locality that brings them advantages is not Porter’s finding, as evidenced by the analysis of the development of localisation theories. Before Porter, localisation advantages were

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already discussed by Weber (1928), Hotelling (1929), Christaller (1966), Lösch (1954), and mainly by Marshall (1920). As early as in 1890 Alfred Marshall (1920) described the tendency of industries to locate in areas with suitable physical conditions (work, soil, water, climate). He also observed that knowledge and ideas spilled over among people in such places. Other companies supplying materials, tools, or organising transport were established in the locality. Marshall used the term ‘industrial district’ for the geographical concentration of specialised industries. He explained this development of industrial districts as a result of the existence of positive externalities in concentrated and interconnected companies and industries. These externalities were triggered by hereditary skills, the growth of other affiliates, their use of highly specialised equipment, as well as the local market for specialised employees, the industrial atmosphere and leadership, and the introduction of innovations (Marshall, 1920). Marshall’s concept of industrial districts mentions the existence of dynamic complementarity within a system of interconnected economic entities. He expects that clusters of industrial companies will lead to better performance than the sum of their units as is the case in a more dispersed distribution of companies. The concepts of industrial districts and industry clusters show a number of similar features, as well as differences. The common feature is a centralised location of companies. However, industrial districts are typically small regions, usually not exceeding 500 thousand inhabitants (the Italian regions usually have around 100 thousand inhabitants), where the social environment and close informal relations between companies are of major importance. Clusters can develop over much larger areas, encompassing entire states, or even transcending national borders. For the industrial district, geographic concentration is a necessary but not a sufficient condition. Both in districts and clusters, companies cooperate as well as compete. For clusters, the main source of innovation and development is domestic rivalry. For districts, it is the existence of a high level of competition combined with cooperation in certain areas. The social environment (flow of information, level of innovation and relationships between companies) is a main source of success for local companies. Porter does not particularly emphasise the role of the social environment in the case of clusters, although he refers to Marshall’s ‘industrial atmosphere’ (Mottiar, 1997). According to Porter and Ketels (2009), industrial districts and clusters have a common denominator in that the agglomeration of related economic activities and types of interaction have an impact on economic performance. However, these concepts are not entirely identical. Industrial districts are characterised by a group of closely located small and medium-sized enterprises, usually operating within light industry. The absence of large companies in the region is typical, while a dense network of regional companies is created instead. Clusters mean a broader concept, encompassing many variations of business-institution relationships. Clusters include the configuration of companies that can be found in industrial districts. Industrial districts may be considered a cluster type.

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Typology of Cluster Agglomerations

Various typologies of cluster agglomerations can be found in the professional literature. The main reason for this is their great popularity. Studied by experts from various scientific disciplines and fields, each group tends to approach this phenomenon in different ways, by adding a specific perspective and terminology, as well as theoretical and practical frameworks (Šarić, 2012). The typology of clusters is not strictly given, but several concepts divide clusters according to partial aspects. The typology of clusters is based on the basic characteristics described, such as in alliances or corporate networks. Other aspects, such as size, depth, breadth or state of development or geographical aspect, and so on, are added to this. Cluster agglomerations can be characterised from different perspectives precisely because they exist in different forms and to different extents. Porter (1998) introduces a classification according to three aspects, namely size, breadth, or state of development. Naturally, clusters can be created in various ways, by the bottom-up approach, based on the initiative and real needs of companies and institutions that want to benefit from the synergistic effects of the factors of the region (Balog, 2016). A second approach, called top-down, is characterised by the cluster initiative of some mostly governmental organisations. The establishment and development of such clusters is usually funded from public funds. The aim of a cluster initiative is to support the growth and competitiveness of a cluster and its region (Lindqvist et al., 2012). To distinguish between natural and planned clusters, we use the designation ‘cluster organisation’ for entities established based on a cluster initiative. A cluster organisation (hereinafter CO) is an independent legal entity (e.g. an association) established as a result of a cluster initiative. The members of a CO are companies, research institutes, high schools and universities, development agencies, industry associations, and other institutions within the industry. A CO manages the joint activities of its members. At present, according to Palatková (2011), two basic types of clusters are most often developed, clusters based on the value chain, which are determined by their network of supply and demand activities. Support for this type of cluster is focused on the specific needs of the given industry. The second type are clusters based on competencies determined by knowledge, technology, and skills within a certain industry. Clusters focus on specific areas of technical skills or competencies in the region, such as research and training skills. These are not key supply relationships within a single industry, but rather the application of knowledge itself, often across different activities. Pavelková (2009) states that based on these facts, we can further divide clusters in terms of partnerships within the production process, which we divide into horizontal, vertical, and lateral clusters. The first type is the horizontal cluster, which is made up of a large number of manufacturers, mainly in the same industry who merge into a cluster to achieve better prices when purchasing materials and better sales opportunities. The second type is the vertical cluster which is formed through various supplier companies and institutions in a wide range of production

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programmes, which are connected with a larger company in a strategic chain. Thus, it is possible to introduce suppliers into the strategic intentions of finished product manufacturers well in advance and give subcontractors enough time for the development and preparation of new models of its subcontracts for the innovated final product. The last type of cluster is known as the lateral cluster because it signifies many companies that complement and modify the classic product models of a large company (Zaušková, 2010). Regarding the cluster typology, it is also appropriate to mention other authors such as Skokan (2004) who divided clusters according to their geographical aspects into local, regional, national, and international. From a geographical point of view, clusters can operate both within individual regions and the whole state. There is also a tendency to create clusters at the transnational level, mainly between neighbouring countries. Local clusters are a group of business entities operating in the same or related industries that serve the local market; regional clusters are made up of a group of business entities carrying out their business in the same or related industries, which serve small geographic areas (corresponding to, for example, regions). National clusters are formed by groups of business entities operating in the same or related industries throughout the country, and international clusters are formed by groups operating in the same or related industries that also extend to neighbouring regions abroad. When classifying clusters according to the development phase, it is also possible to distinguish emerging clusters that are in the early stages of their industrial development, producing for local markets with simple technological and work skills, as well as mature clusters composed of companies with more advanced technology and skills, producing for global markets and vulnerable to global pressures (Hernández Rodriguez & Montalvo Corzo, 2015). Another way to divide clusters is their classification in terms of developmental phases (i.e. the level of activity and self-realisation of the cluster) according to Enright (2003) supplemented by Skokan (2004). It is thus possible to define a functioning cluster that has already been identified and its members benefit from the synergistic effect of cooperation. Furthermore, the so-called latent cluster exists, in which there is an opportunity that has not yet been seized. Latent clusters are characterised by a high number of companies in related fields, however, with a relatively low level of interaction due to a lack of trust, not much cooperation and high transaction costs. Lastly, a ‘potential’ cluster is defined. It has promising economic potential and creates the conditions for the formation of a cluster, but lacks a critical number of necessary factors or specified inputs. Hernández Rodriguez and Montalvo Corzo (2015) provide a classification according to levels of knowledge and divide clusters into technically oriented that focus on cutting-edge technologies and clusters based on historical know-how that draw on traditional activities and maintain an advantage in know-how over the years. Different types of participants and their interrelationships make it possible to identify various types of clusters. Gordon and McCann (2000) identify three types or rather cluster models. In the pure agglomeration model, companies are

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geographically concentrated in a given locality but have no links with each other. There may be a spatial concentration of companies, yet, there is a lack of formal structure or strong long-term relationships between companies. Firms are essentially atomistic in the sense of zero market power. Gordon and McCann (2000) describe this type of cluster as a pure, classical, and atomistic agglomeration, characterised by open membership. The second model is the industrial complex, where small companies are concentrated around a large company in hierarchical supplier–customer relationships. The industrial complex is more stable than the model of pure agglomeration and is mainly characterised by long-term and predictable relationships between companies in the cluster (Kuchiki & Tsuji, 2011). The last type is the social network model, which is characterised by mutual trust between key decisionmakers in different organisations (Šarić, 2012). Pavelková (2009) states that another possible classification of clusters is according to the way they were created. The Natural (Porterian) clusters are created as natural units of interconnected companies in a given region and exist regardless of whether the companies are aware of it or not. The name Porterian is based on the fact that such a cluster fulfils the characteristics of a cluster as defined by Porter (1998). Constructed clusters (or an institutionalised cluster or a CO, in other words) arise as a result of an organised effort known as a cluster initiative. Another possibility is a combination of the previous two approaches, where a group of companies and other institutions create a CO within a natural cluster. This deepens cooperation between institutions and implements joint activities. Similarly, depending on the method of formation, Hernández Rodriguez and Montalvo Corzo (2015) also distinguish between spontaneously formed clusters, which by nature correspond to natural clusters and strategically created clusters, which, by their nature, correspond to constructed clusters. Furthermore, according to (Markusen, 1996), four general types of clusters can be distinguished depending on their industrial structure—network industrial districts (Marshallian cluster, Marshallian industrial districts) are the first and the oldest type of cluster (see the beginning of Chap. 3). The Marshallian cluster consists of many small companies operating in the same industry (Graham et al., 2009). They consist mainly of locally owned small and medium-sized enterprises which specialise in technology-intensive industries, especially high-tech. Member companies are supported by specialised services, labour markets, and institutions and create problem-solving networks. Hub and spoke clusters are controlled by one or more dominant key companies which are surrounded by smaller input suppliers and service providers. The dominant company creates a market for local suppliers and sets the conditions for their relationships and is also a central node in the local network, as well as its gateway to the outside world (Goetz et al., 2009). Satellite platforms are composed of subsidiaries of multinational companies, which maintain only sporadic productive ties with each other and the local business environment (Dooren, 2003). State-supported clusters are the last mentioned form of industry clusters; their business structure consists of a public or non-profit entity surrounded by suppliers and the service industry. These clusters owe their existence to government support for certain activities (e.g. military, research and development support).

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Table 3.1 Matrix of three types of clusters

Geographical proximity

Strong Weak

Strength of relations between individual members Strong Weak 1. Porterian cluster 2. Clusters based on local history and resources 3. Clusters without a 4. Scattered activities local base

Source: Own processing according to Stanculescu et al. (2013)

Another way to classify clusters is by their depth and width. A deep cluster contains an almost complete supply chain. A shallow cluster depends on inputs from outside the region. A broad cluster comprises several horizontally related industries (Hernández Rodriguez & Montalvo Corzo, 2015). Stanculescu et al. (2013) claim that the variable strength of relationships between individual cluster members and their geographical proximity allows the identification of three different types of clusters (see variants 1–3 in Table 3.1). The variant where there are great distances between subjects and mutual relations are not strong can be understood as a situation where the cluster does not exist and the subjects perform their activities in a scattered manner. As mentioned above, clusters can arise as a natural agglomeration of companies in a given region or they can result from an organised effort known as a cluster initiative. In the first case, there are clusters regardless of the official establishment of the group. Pavelková (2009) calls such clusters Porterian or natural. Cluster initiatives represent an organised effort to increase the growth and competitiveness of companies in the regions. This involves both companies and the government or regional administration, education, and research community. Based on the process of institutionalisation, all these institutions become an essential element of growth and competitiveness of the industry (Lindqvist et al., 2012). The resulting entity can be called a CO, which can be understood as a formalised entity that arises from a cluster initiative and provides services to support cluster development and member organisations (Pavelková, 2009). The aim of establishing a CO is to facilitate and manage the development of the cluster. The legal entity created acts as an intermediary between the various cluster members and adds value by stimulating cooperation both within the cluster and between the cluster and the outside world (Schretlen et al., 2011). The benefits of clusters are mainly reflected in the growth of efficiency, productivity and innovation and thus contribute to increasing the efficiency and competitiveness of companies and regions. The basis of this concept is the finding that sufficient resources and the ability to reach a critical concentration in a geographical location provide a sustainable competitive advantage over other locations in the industry (Tskalerou & Katsavounis, 2013). The cluster simply connects all the basic components - the availability of resources and the goals of individuals to achieve competitive success and share the ideas of proximity, networking, and specialisation.

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Clusters stimulate and support cooperation between businessmen. They exert competitive pressure, even between indirect competitors or non-competitive participants (Bialic-Davendra, 2011). Clusters also facilitate access to finding business partners, funds, and employees for their companies (Damborský & Wokoun, 2010). Lan and Zhangliu (2012) analysed the mechanism of interactive learning and linked the processes with knowledge sharing in clusters of participating small and mediumsized enterprises (hereinafter SMEs). They found that knowledge spillovers increased the dynamics of clustering. As a result of exchanging and sharing knowledge through mutual contacts in the cluster, companies significantly strengthen their innovation activities. A large number of empirical studies dealing with the effect of clusters on innovation can be found in the literature. Shu-en and Ming (2007) found, through the example of an optoelectronic cluster in China, that sharing knowledge with customers and suppliers improved product and process innovation within the company. In particular, companies with a higher level of knowledge sharing create more innovation, which is the principle of the open innovation model. Another study conducted on a sample of 166 automotive companies in China (Wu et al., 2013) examined the effects of cluster age, investment in research and development, and other variables in innovation. The analysis showed that the age of a company was the most important factor positively affecting innovation activities. An analysis of 1772 e-cluster companies in Korea found that clusters, open innovation, and rapid learning were the factors that enabled Korean electrotechnical companies to successfully face the competition in Japan and the USA (Won Park et al., 2012). Foley et al. (2011) consider clusters to be an appropriate form of a public–private partnership that has proven itself in promoting innovation in energyefficient buildings. Research by Hsieh-Sheng (2011) has shown a positive correlation between clustering in the high-tech industry and innovation in Taiwan. In this study, innovation was quantified by the number of patents. Huang and Rice (2013) conducted extensive research on 3468 European companies from 14 different industries focusing on the impact of various factors on innovation. The research results show that clustered companies have closer ties to universities, more efficient knowledge flows and exchanges of tacit knowledge, and are less dependent on internal research. Islam (2010) compared the performance of textile companies both inside and outside the cluster in Pakistan. He concluded that the companies in the cluster had achieved higher levels of productivity and innovation and invested more in the modernisation of their machines. The financial performance of the two groups of compared companies did not differ significantly from each other. However, some studies do not confirm the positive impact of clusters on innovation and business performance. Lang (2009) sees the negative aspects of clusters in creating homogeneous macro-cultures, inconsistencies in social identity, power imbalances, market rationalisation and the occurrence of negative externalities. Frohlich and Westbrook’s study (2001) of 322 companies from 23 countries around the world concluded that partial degrees of integration brought only small improvements in performance. The fact that the success of innovation in clusters requires the

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fulfilment of some basic success factors must also be taken into account. They include, in particular, the existence of highly qualified staff with technological skills, corporate R&D, financial resources, customer service and additional commercial services (Mohannak, 2007). In addition, the performance of clusters is closely linked to the method of management and to a number of formal and informal institutions that influence management. An important aspect is also industrial policy and its orientation to support clusters (Parto, 2008). Nishimura and Okamuro (2011) examined the effects of the ‘Industry Cluster’ project in Japan on the innovation and productivity of its participants’ research and development. The results of their research showed that participation in the project did not in itself guarantee an increase in the performance of research and development. It has not been confirmed that companies participating in the ‘Industry Cluster’ project have filed more patent applications than independent companies. Pavelková (2013) examined 1110 member companies of Czech clusters and, based on their analysis, they concluded that innovation activities in COs were at a low level. Krželj Čolović et al. (2016) examined the differences in the financial and non-financial performance of Croatian hotel companies in terms of cluster membership. The financial performance was defined by productivity and cost-effectiveness. The non-financial criteria included quality, market share, customer satisfaction, innovation, etc. They found that only customer satisfaction ratings were significantly better for cluster companies. For all other indicators (including financial indicators), no significant differences were found between intra-group and external group companies. This is largely in line with other studies that deal with the impact of different forms of business cooperation on their profitability. Particularly institutionalised clusters can produce effects common in vertical integration (see for example D’Aveni & Ravenscraft, 1994; Zhang, 2013). They include increased overhead costs due to increased internal coordination, inefficient purchasing of production inputs, problems with coordination of independent activities, unused and unbalanced capacities, and bureaucratic costs (Huang & Rice, 2013). Following the characteristics of clusters, externalities associated with industry clusters are addressed in Sect. 3.3.

3.3

Industry Clusters in Terms of Externalities

Based on the presented partial theoretical and descriptive analyses, it can be stated that the influence of clusters contributing to macroeconomic externalities is only sporadically discussed in the current economic theoretical literature. At the same time, however, contemporary economic theory provides tools for formulating conclusions about macroeconomic benefits in the subsequent theoretical-implicational and integrative part of the research. For this reason, a search of the current microeconomic literature was performed. Most modern publications and analytical articles dealing with the issue of

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externalities are built on the paradigm of mathematical and generalising abstraction. The phenomenon of clusters is currently absent in the microeconomic literature. Based on previous research, it seems that advanced tools of a microeconomic analysis can be applied to the phenomenon of clusters, and thus formulate positive or negative externalities arising from their existence.

3.3.1

Clusters and Macroeconomic Externalities

The analysis of localisation theories (Skala & Rydvalova, 2021, in this book) concludes that the agglomeration of companies allows the realisation of competitive advantages of the companies themselves, and thus increases regional and national competitiveness. In this context, the question arises as to whether the existence of clusters (especially institutionalised ones) is a positive macroeconomic externality. It is therefore desirable to support them through economic policymakers. Contemporary localisation theories, especially institutional localisation concepts, highlight positive agglomeration externalities in the immediate geographical area. These mapped positive macroeconomic externalities led economic policymakers to generalise universal structural economic policies. Their main motivation was an effort to transfer these positive agglomeration effects (sustainable economic growth) from a unique agglomeration to any region or area (Benneworth et al., 2003). It is either about recognising the presence of a natural cluster and its subsequent support or initiating the creation of a completely new group of companies (institutionalised cluster) in the existence of a significant industry in the region. However, the question of the transferability of positive macroeconomic externalities of clusters, such as higher productivity, prosperity, decentralisation, and entrepreneurship, to any region through economic policy arose in professional circles. For example, Martin and Sunley (2003) criticise the formal ill-considered support of clustering by economic policymakers. In a similar way, cluster policy in the European environment is critically assessed by Benneworth et al. (2003). In their view, the authorities of economic policy mistook the desirable agglomeration externalities for formally declared cluster policy. This context evokes a theoretical research question about the adequacy of economic policy which would optimally stimulate positive macroeconomic externalities through cluster support. From a theoretical point of view (a classical optimal-policy perspective), the economic policy measure (subsidy to the company) should correspond to an adequately (equi-proportionately) generated size of externality. In practical economic policy, however, quantifying the magnitude of such a positive externality is very difficult (even unworkable). As stated by Rodríguez-Clare (2007), cluster policymakers will, therefore, be satisfied with the criterion of the existence of a positive externality in the industry they support. Practical cluster policy (in the sense of regional policy) faces a fundamental dilemma in the treatment of economically less developed areas. On the one hand, the initiation of a completely new cluster without any tradition in the region is

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offered. On the other hand, it is possible to support traditional industrial activities firmly integrated with history. Ex ante, it is questionable to expect that a newly established cluster with extensive positive macroeconomic externalities in another area will show the same extent of externalities in the neglected region. On the contrary, from the point of view of economic policy, is it effective to further support traditional industrial activities that have negligible externalities? The choice of an appropriate policy concept depends on an understanding of the aggregation effect stemming from the cluster and externalities. The pitfalls of transferring the concept of a successful cluster with a high degree of macroeconomic externality to any other area face—as the analysis of localisation theories (Skala & Rydvalova, 2021, in this book) showed—the non-transferability, historical and geographical uniqueness of an agglomeration of companies in a model region. As Woodward and Guimaraes (2009) emphasise in practical cluster policy, Porter’s principles should be applied consistently: (a) cluster development should not be stimulated by top-down policy strategies, (b) non-selective support for all clusters (not only some), (c) cluster initiatives should originate from the private sector (the public sector only plays the role of a facilitator) and (d) preference for support of already established (especially promising) clusters before initiating new clusters. In localisation theories (Skala & Rydvalova, 2021, in this book), unique industrial agglomerations have been the focus of theorists for centuries. But it was not until Michael Porter (1990) strongly articulated the benefits of agglomerations for the multiplication of macroeconomic externalities (competitiveness, innovation and economic growth) and made clusters a tool of public policy. Porter’s principles are inherently a suitable compass for solving the dilemma of choosing an appropriate cluster policy (initiating the creation of a new cluster or supporting the clustering of traditional industries in the region) by guaranteeing maximum externalities in both options on condition that all attributes of the principles are met. Then, any cluster is functioning (alive). As Woodward and Guimaraes (2009) state, after Porter’s popularisation of clusters as instruments of public policy, cluster policy spread with great expectations in a number of countries but, as Martin and Sunley (2003) criticise, with faint macroeconomic externalities. Martin and Sunley (2003) cite the vagueness and definitional elusiveness of clusters as essential reasons. In their critique, economic policymakers inadvertently saw the cluster in any (even irrational) grouping of companies and did not subject the cluster policy issue to a thorough examination of Porter’s definition of a cluster as a ‘geographic concentration of interconnected companies, specialised suppliers, service providers, firms in related industries, and associated institutions (e.g., universities, standards agencies, trade associations) in a particular field that compete but also cooperate’ (Porter, 2000, p. 15). From the point of view of economic theory, only this unique grouping can guarantee a positive externality. In the list of macroeconomic externalities caused by the presence of clusters in the given area, possible negative macroeconomic externalities must be mentioned. Agglomeration benefits stemming from the spatial concentration of companies in

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the same or related industries can paradoxically cause a macroeconomically negative lock-in effect. Lock-in effect reduces the ability of a given cluster industry to absorb technological changes and modifications in the nature of business, but above all macroeconomic and global economic threats (Grabher, 1993). These negative shocks can then be demonstrated in a macroeconomic manner with significant intensity in such a region. There is no denial of the existence of urban advantages which arise in large economically prosperous agglomerations, where all actors (even outside the cluster) in the region benefit from modern infrastructure and a highly skilled workforce, especially in the services sector. Nevertheless, as Glaeser and Maré (2001) paradoxically claim, these urban benefits can lead to negative macroeconomic externalities. High living costs enormously increase the cost of labour for companies and thus reduce their competitiveness. High real estate and land prices have a similar effect. Macroeconomic growth in the area will thus reach these limits. In terms of macroeconomic externalities, the opinion on labour mobility in a region with the occurrence of a significant cluster is controversial. From the viewpoint of regional policy (employment policy), a permanent flow of labour between companies meeting the random requirements of their production is desirable. The highly specialised workforce of cluster companies equipped with unique knowledge and skills represents a pitfall for possible flexibility in the labour market in case of economic difficulties of the cluster business industry. Nevertheless, at the same time, this unique workforce is absolutely essential for the existence of a cluster (Krugman, 1991). It also seems an insurmountable obstacle to cluster policy in creating new clusters. As more broadly justified, successful clusters can be characterised by the spontaneity of formation, the interdependence of companies, spatial (geographic) proximity and the natural informality of mutual relations. These entities are difficult to imitate by state intervention through economic policy. In institutional localisation approaches (Skala & Rydvalova, 2021, in this book), most clearly in the concept of related variety, the synergistic benefits of different industries in the creation of innovations are emphasised. However, excessive fragmentation of these industries can cause negative macroeconomic externalities in terms of the inefficiency (fragmentation) of desirable economic policies. Regional policy will be fragmented and therefore ineffective. Duranton and Puga (2001) report specific macroeconomic implications, such as the reluctance of economic policymakers to implement targeted infrastructure initiatives. Macroeconomic externalities (especially economic growth associated with competitiveness) are an integral part of localisation theories, especially economic geography such as Myrdal (1957), Scott (1988), Porter (1998), and Maskell (2001). However, a paradigm shift can be traced in the scholarly articles of some important contemporary authors (Boschma & Martin, 2010; Martin & Sunley, 2003; and others). These authors focus their attention from the creation of macroeconomic externalities by clusters on the contribution of these clusters to the resilience of the region in the event of negative macroeconomic shocks various causes. See, for example, the crisis caused by the COVID-19 pandemic in 2020. However, if the geographical area is less prone to recession due to cluster agglomeration (Martin,

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2012), a macroeconomic positive externality is created from the perspective of economic policy.

3.3.2

Formulation of Externalities in Terms of Microeconomics

From the analysis of the section dealing with localisation theories (Skala & Rydvalova, 2021, in this book), it is apparent that clustering of companies causes a number of positive microeconomic externalities. Individuals and companies benefit from the production and innovation activities of neighbouring companies in the same or related industries. Microeconomic theory traditionally refers to these benefits as to positive externalities, where the consequences of industrial or commercial activities positively affect another entity without these benefits being reflected in the market price. Porter (1998) underlined in his standard corporate agglomeration theories the role of clusters in amplifying these positive externalities. From the point of view of microeconomic theory, it is possible to analyse those clusters generally described as groupings of companies, through the prism of positive externalities, which are thoroughly developed in contemporary theory. Nevertheless, clusters are unique among ordinary company groupings. These positive externalities are created by the unique coexistence of companies in the cluster as a whole. On the contrary, an individual company in the cluster cannot influence the emergence of these externalities to any large extent. The first characteristic of a cluster compared to any grouping of companies is the proximity of companies in the cluster. It is this spontaneous proximity of cluster participants that enhances the transfer of microeconomic externality resulting from the sharing of results in the field of research, innovation, and development, as well as knowledge-sophisticated inputs. Proximity enhances the multiplication of tacit knowledge. It gives the cluster a microeconomically competitive advantage in terms of a strong bargaining position vis-à-vis markets (both customers/customers and suppliers). Rosenfeld (2005) describes these unique microeconomic externalities derived from the proximity of companies in the cluster as exclusive ‘soft’ externalities. Voluntary proximity of companies in the cluster eliminates market failure in the case of positive externalities and, on the contrary, intensifies it. This anomaly is astonishing from the point of view of economic theory, especially when microeconomic policy, through state intervention, seeks difficult solutions to amplify these positive externalities. Voluntary proximity to a cluster participant can be anticipated namely in a natural cluster as opposed to the more institutionalised (Rydvalova & Zizka, 2021, in this book). In general, in microeconomics the grouping of companies is motivated by a common interest, namely profit. Positive externalities are accompanied by a negative phenomenon of the free rider. It is an entity that takes advantage of someone else’s positive externalities without paying market prices for that externality. As a result of

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this moral failure, an insufficient number of such positive externalities are created (e.g. research, development, and sophisticated inputs), putting the economy in a state of Pareto inefficiency. This insufficient creation of positive externalities is caused by the insufficient allocation of inputs for their creation, as the free rider knows that they do not have to participate in their creation in terms of costs, although they will participate in their utilisation in a profit-seeking way. In this context, as Rosenfeld (2005) emphasises, another feature of the cluster arises: the specific interdependence of companies and institutions in the cluster. This follows from Porter’s (1998, p. 78) definition of a cluster: ‘clusters are geographical concentrations of interconnected companies and institutions in a particular field’, where conscious and voluntary (wanted) interconnection paradoxically eliminates the phenomenon of the free rider in the informal and inclusive environment of the cluster. One of the theoretical microeconomic solutions to eliminate the free rider’s market failure in the case of a positive externality is internalisation. This consists in the agreement of the entity creating externality with the entity using externality, with both entities now valuing positive externalities at market prices. It is precisely this specific interdependence emphasised by Rosenfeld (2005) that guarantees the desired internalisation of positive externality in the cluster. The concept of a cluster, in comparison with a cluster, also shows a specific feature of social capital in terms of the relationship dimension and trust between its subjects. Microeconomic theory pays attention to the moral hazard arising from information asymmetry, where one subject abuses the information advantages at the expense of another subject. This market failure is associated with mistrust, but also a reluctance to create positive externalities. This specific dimension of social capital in a cluster is mentioned, for example, by Putnam et al. (1994) and is, according to the authors, associated with long-term trust. As Boschma (2005) emphasises, this trust is not automatic but depends on the intensity of communication between the cluster entities, the multi-generational tradition of mutual relations and repeated cooperation. Therefore, this unique type of social capital will be characteristic of a natural cluster in which it alleviates the reluctance to share knowledge and innovation as positive externalities. Krugman (1991) emphasised the unique positive externality associated with the transfer of tacit knowledge caused by the fluctuation (mobility) of specialised work in a closed cluster environment. Simultaneously, he emphasises that a highly and uniquely specialised (and therefore difficult to substitute) workforce forms the essence of a natural cluster. Thanks to their unique (tacit) knowledge, these employees fluctuate mainly in a closed cluster environment and bring them knowledge acquired in the previous company (or mediate their transfer). Microeconomically, the creation of positive externalities is thus intensified. Positive externalities are, in terms of economic theory, considered a market failure as they are created less frequently than the desired Pareto-efficient state requires. Potter and Watts (2011) anticipate growing returns to scale in clusters as a microeconomic externality caused primarily by knowledge spillovers. In this context, Glaeser and Gottlieb (2009) consider the cluster an accelerator of ideas. At the

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same time, microeconomic theory postulates diminishing returns to scale for a wellestablished company, caused by—provided the production function of companies does not change—wear and tear of the labour and capital used. The prism of microeconomic theory is this unique spillover of knowledge within the cluster shared by technological progress, that is, by the improvement of the production function. The analysis of agglomeration theories (see Skala & Rydvalova, 2021, in this book) shows that the absorption of positive externality is more intense in cases where the same type of industry or at least a related industry exists. In cluster agglomerations, positive externalities occur even when overcoming barriers during the accumulation of large investment capital. In the environment of a natural cluster, the results in research, development, and innovation are willingly shared, as well as knowledge-sophisticated inputs. These fragmented (small) investments are aggregated through the microeconomic internalisation of positive externalities (willing sharing of R&D results) into largescale innovative investments (Ruan & Zhang, 2009). The natural cluster thus spontaneously and remarkably internalises positive externalities and microeconomic policy looks for practical tools to induce the internalisation of externalities (Coase, 1960). This internalisation of positive externalities generally allows small companies in a cluster to achieve microeconomically such returns to a scale that only large corporations would achieve. Although positive externalities in the cluster environment are predictable through the prism of microeconomic theory, based on the analysis of localisation concepts (see Skala & Rydvalova, 2021, in this book), the most acute pitfall for cluster policy is the very essence of cluster existence which is primarily characterised by spontaneity, informality (ease) and tacit knowledge, which the best economic policy cannot imitate or transfer outside a unique region. From an empirical point of view, conclusions regarding the demonstration of externalities in the cluster environment are contradictory. Rosenthal and Strange (2004) demonstrated a strong multiplication of positive externalities. In contrast, Feldman (2000) came to controversial conclusions where, in addition to positive, negative externalities were detected in his research. These conclusions were later contradicted by de Groot et al. (2008) who mentioned inconsistencies in research methodology and sampling. As Potter and Watts (2011) and Henning et al. (2010) emphasise, the intensity and nature of microeconomic externalities also vary at different stages of industry maturity. Positive microeconomic externalities stemming from the proximity of companies will be characteristic of established (mature) industries, while externalities associated with modern institutional localisation concepts—such as the related variety concept—will be strongly demonstrated in the early dynamic stages of industry development. Neffke et al. (2008) concluded from empirical research that companies utilise only some types of externalities, rather than all. Numerous empirical studies examining the effects of localisation advantages on the performance of companies are criticised by some authors (e.g. Girma, 2005; Kostopoulos et al., 2011) for the fact that, from a theoretical point of view, they do

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not consistently define the ways of internalisation of microeconomic externalities by companies, nor the mechanisms of multiplication. The causality between microeconomic and macroeconomic externalities seems to be as follows: Localisation benefits are the first to generate microeconomic externalities in the business environment. Whether these benefits will be absorbed by firms or not also depends to a large extent on the readiness of given companies to internalize these microeconomic externalities. Van Oort (2013, p. 9) states on this issue: ‘Agglomerated firms can realize the potential benefits of location in an agglomeration only to the extent that they are capable of using and commercializing knowledge from co-located firms in combination with their own knowledge assets to create value’. The given capital and work in the economy represent limited inputs for the company. Just internalised microeconomic externalities arising from agglomeration advantages, therefore, can serve as a complementary source for the expansion of its production. Subsequently, and thus indirectly, these agglomeration benefits are demonstrated by macroeconomic externalities both in the region and in the economy as a whole. Similar influencing of externalities is accepted as a precondition in some empirical articles (Acs & Armington, 2004; Martin, 2012). Teece et al. (1997) establish the postulate that the higher the company’s ability to take advantage of agglomeration benefits, that is, to internalise microeconomic externalities stemming from agglomeration benefits, the higher ceteris paribus the economic performance of the company. The reason is, as seen through the prism of strategic management, the significant ability of this company to flexibly adapt its growth strategies to unexpected macroeconomic shocks and thus take advantage of a flexible and efficient range of positive externalities resulting from agglomeration benefits. At the same time, the literature (e.g. Beaudry & Schiffauerova, 2009; de Groot et al., 2008) accepts the view that the ability to absorb (internalise) microeconomic externalities varies across industries and changes over time and space. The individual specific transmission mechanisms for linking microeconomic and macroeconomic externalities are referred to in the professional articles in the field of regional economics as Marshall-Arrow-Romer (MAR) externalities, Porter externalities and Jacobs externalities. MAR externalities (Arrow, 1962; Marshall, 1920; Romer, 1986) arise from the spatial (geographical) proximity of companies in the same industry and unique industry specialisation and enhance the multiplication of knowledge and innovation among companies in the industry as well as economic growth in the industry. Glaeser and Gottlieb (2009) and Henderson et al. (1995) emphasise the key role of tacit knowledge in the case of MAR externalities. Porter’s externalities (Porter, 1990) are characteristic of geographically specialised and especially competitive industries. Such an industry generates and rapidly introduces innovations, thus stimulating economic growth. In the context of externalities, Jacobs (1969) expects that the spatial proximity of companies from diverse industries (in contradiction to the MAR assumption, which expects the spread of innovation in the same industry) stimulates the spillover of innovation, leading to economic growth. It is in this diverse environment that various creative perspectives stimulate invention and innovation.

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In the literature on regional economics (e.g. Frenken et al., 2007; Glaeser & Maré, 2001; Grabher, 1993), microeconomic externalities are referred to as agglomeration externalities, which include localisation, urbanisation, and Jacobs externalities. Urbanisation externalities are generated from the concentration of companies from unrelated industries in the environment of a rich metropolis. As it is claimed in localisation theories, Jacobs’ externalities arise thanks to cross-sectoral variety and are referred to as a variant (modification) of urban externalities. On the contrary, localisation externalities stem from specific sector specialisation. The terminology thus favours more the origin of agglomeration benefits than the microeconomic approach.

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Chapter 4

Innovation and Innovation Partnership Petra Rydvalova

4.1

and Marek Skala

Factors of Economic Competitiveness

The economic production of the national and regional economy depends on available resources, such as the volume of labour, available capital, and technologies used. However, technology must be viewed not only as a technical production process in a particular field, but rather in the broader sense as a system of management and organisation of the economy. Since the second half of the twentieth century, the importance of material factors influencing the competitiveness of the economy has been gradually and dramatically reduced. On the contrary, ‘intangible’ factors such as the quality of human resources, the ability to create and implement innovations, and cooperation between individual economic entities associated with the creation of added value for all stakeholders are gaining in importance. Material factors are characterised as essential to the existence of individual economic entities and thus also the regional and national economy, although not sufficient in affecting competitiveness. Greater added value is brought to the economy by strategic management, marketing, and financial management, as well as by R&D and the ability and speed with which to implement changes in factors affecting the economy. Growth in economic entities is primarily achieved through the successful application of the results of science and research into practice, the so-called knowledge and technology transfer. The concept of innovation in the sense of marketimplemented change and renewal in human activity in production began to be thought through in the 1920s. Joseph A. Schumpeter (Žižlavský, 2013) dealt specifically with that topic.

P. Rydvalova (*) · M. Skala Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_4

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One of the significant characteristics of cluster agglomerations is the creation and spillover of innovations that affect macroeconomic externalities through transmission mechanisms; see Zizka et al. (2021, in this book). Therefore, it is obvious to confront this positive macroeconomic externality with the theoretical conclusions of Schumpeter’s theory of innovation.

4.2

The Role of Innovation as Macroeconomic Externality: The Insight of J. A. Schumpeter

Joseph Schumpeter is the author of a trilogy of key scientific texts dealing with the role of innovation: Theorie der wirtschaftlichen Entwicklung (Schumpeter, 1912), Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process (Schumpeter, 1939), and Capitalism, Socialism and Democracy (Schumpeter, 1942). As Sirůček (2016) characterises the focus of his work, the pivotal Theorie der wirtschaftlichen Entwicklung formulates most of his central ideas which are both modified and improved in his following works. The research of cycles is thus captured in the Business Cycles monograph. The synthesis (completion) of his theoretical and methodological view is presented by Capitalism, Socialism and Democracy. His significance for theory of innovation illustrates that he is one of the most cited contemporary authors (Roncaglia, 2005). At the same time, together with J. M. Keynes, he is considered the most important giant of economics of the last century. His contribution to economic thinking stems from the interdisciplinary (theoretical economics transcending) nature of scientific thinking and thus includes economic theory, theory of social and institutional change, as well as theory of economic development (Sirůček, 2016). Schumpeter’s primary work (1912) presents his theory of economic development, with a central role of innovation. Sirůček (2016) highlights the methodological work of J. A. Schumpeter on the postulation of abstract dynamic theory of development. To illustrate the significance of his work, it is necessary to emphasise that contemporary economic theory is still built methodologically on the neoclassical static conception of general equilibrium theory by M. L. E. Walras. In Schumpeter’s view, market economy does not converge to a stable (static) Walrasian equilibrium (to the stationary model), but its equilibrium is constantly disturbed by purely endogenously generated innovation and institutional changes. Innovations cause a continuous disturbance of the static equilibrium of the market economy; they reoccur and at a higher level. Innovations thus cause both spontaneous and discontinuous changes. Schumpeter views innovations as spontaneous, clustering at certain points in time and in certain industries (i.e. they are unevenly distributed). They are cumulative in nature and cause chain reactions, which triggers economic expansion. It may even result in depressions, which in Schumpeter’s view represent an adaptation to technical and technological changes during the expansion.

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Schumpeter personally defined both innovation and invention. In his view, invention is the original idea and innovation is its initial embodiment. In Schumpeter’s triad, imitation is also mitigated, as are all other materialisations (the question of imitation can be one of the barriers to innovation partnerships; see Sect. 4.4 below). Unlike innovations, Schumpeter asserts that inventions are subject to a statistically random distribution. Therefore, they appear with a certain degree of predictability. His successor, G. Mensch (1979), even claims that inventions have a statistically and relatively uniform distribution over time. Entrepreneur-innovators cause economic development with their creative activity. As Sirůček (2016) emphasises, this type of entrepreneur is in contrast to the neoclassical entrepreneur-allocator who merely decides upon the distribution of inputs between various business activities. Thanks to these entrepreneur-innovators, companies in Schumpeter’s concept are creatively destructive, since companies not only passively accept technological innovations but change them and compete with others. As Sirůček (2016) states, creative destruction triggers the permanent destruction of the old and the creation of the new. The system thus evolves by reducing prices and eliminating weak and unsuccessful competitors. Subsequently, Schumpeter (1939) incorporates innovation as a purely endogenous factor caused by entrepreneurial activity within the theory of multicyclic development. Innovation clusters are thus demonstrated by the sudden alternation of up-and-down phases of the business cycle. Innovation clusters of a given technical and technological nature are reflected in the sudden rise of investment activities. In the case of profitability, these innovations are spread across the economy in the competitive environment of companies. The economy is thus in its expansion phase. However, this dynamic growth is gradually reaching its limits and slowing. It culminates in a phase of depression, which Schumpeter (1939) understands as a necessary adaptation to changes in terms of the overall economy. The way out of the trough of the business cycle is again a new wave of innovations emerging in the recovery phase. In Schumpeter’s view, as Sirůček (2016) points out, the economic cycle is perceived as a legitimate and irreversible phenomenon of the market economy, by means of which endogenously generated scientific and technical progress is permanently realised. J. A. Schumpeter works stricto sensu with three factors causing the business cycle: (a) external factors influencing the business environment (legislation, institutional changes, wars, and revolutions), (b) factors of long-term growth (capital accumulation and demographic change), and (c) innovation. However, for Schumpeter’s concept of multicyclic development, innovation is undoubtedly central. As Sirůček (2016) emphasises, innovation is the driving force of the system for Schumpeter. Based on a historical view of extensive statistical data and their analysis, Schumpeter (1939) pays considerable attention to the three long waves, which he regards as the most extensive business cycles caused by the clustering of basic (revolutionary) innovations in history. It is a long wave of 1787–1842 initiated by the first industrial revolution, furthermore, the long (bourgeois) cycle of 1842–1897 caused by the age of steam and steel and the third (neo-mercantelist) wave of

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1898–1924/1925 caused by the use of chemistry, engines or electricity. Methodologically, Schumpeter works with the premise that, in reality, cycles of different intensity and time length are intertwined. It thus works with short-term business cycles (3–5 years), the most common medium-term cycles (7–11 years), and longterm waves associated with basic innovations (45–60 years). The completion of Schumpeter’s (1942) concept of the dynamics of the development of society caused by the clustering of innovations is the interdisciplinary sociological theory of the self-destruction of a capitalist society. This is despite the fact that, as Sirůček (2016) highlights, Schumpeter was a supporter of the capitalist system and adored its competition, as well as the innovation activities of entrepreneurs. He finds its cause in the destruction of necessary institutional frameworks, values and morals. In his evolutionary economy, the very prosperity of the market economy has undermined these essential entities of capitalist society. Schumpeter is often viewed by theorists of economic thought as a contradictory person. He belongs to the (neo) Austrian school, whose elementary methodological paradigms he disrupts. He does not believe in the kinship of economics and psychology and glorifies mathematics and econometrics, although he himself hardly uses them. Therefore, he is called the ‘enfant terrible’. Although his methodological observations are original and ground-breaking, they are not accepted by the main currents of economics. Thus, his considerations were not incorporated into the concepts of neoclassical economic theory either in the field of microeconomics or in the superstructure of macroeconomics. Sirůček (2016) cites as the main obstacle a ‘war of axioms’ waged against the dominant neoclassical economics approach. His theory of innovation has become the basis for many theorists of business cycles (long waves). At the same time, we must mention Kuznets’ (1940) critical reaction to the explanation of all business cycles solely in terms of the dynamics (clustering) of innovations. In Schumpeter’s conception of his dynamic theory of development based on innovation, innovation is not only a positive microeconomic externality, the driving force of the economy that spreads across industries, but also a positive macroeconomic externality. Therefore, economic development under capitalism is based on innovation and comes from inside the system itself.

4.3

Innovation and Innovation Activities

As mentioned in Sect. 4.2, the theory of the innovation system was developed before World War I by J. A. Schumpeter who included in the term innovation: • The production of a new product, whether existing or of a new quality • The introduction of a new production process (technology) • The use of a new and hitherto unknown source of raw materials or semi-finished products

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• The acquisition or opening of a new market • Changes in production management and organisation As the focus of this publication is on addressing the issues of industry clusters rather than an analysis of the concept of innovation, it is further defined in relation to the Oslo Manual. This document is part of the so-called Frascati Family which forms the methodological basis for statistical surveys focused on R&D (Frascati Manual), innovation statistics (Oslo Manual), patents, human resources (Canberra Manual), etc., within OECD countries. The purpose of the Oslo Manual was not to create a model of innovation, but to define it in its entirety as among those diverse activities enabling the interconnection of individual elements of the innovation process. The Oslo Manual focuses primarily on the business sector and prioritises innovations at the company level. It does not deal with the specificity of innovation only in the sense of J. A. Schumpeter, i.e. the opening of a new market, the acquisition of a new source of raw materials or semifinished products, or even the reorganisation of the industry. It does not address innovation even from the point of view of neoclassical theory where innovation is an aspect of business strategy or part of a set of investment decisions in order to create capacity for product development or increase efficiency. The Oslo Manual discusses these topics in Chap. 3 (OECD & Eurostat, 2019) and draws attention to the further development of innovation theory, which focuses on the idea of so-called sunk costs, the creation of competitive advantages by relocating production or output in the value chain. As stated in the Oslo Manual 2018 (OECD & Eurostat, 2019), the evaluation of innovation theories points to four dimensions of innovation that can lead to measurement: knowledge, novelty, implementation, and value creation. Based on the above theoretical framework, the Oslo Manual focuses on changes in a company that imply a significant degree of ‘novelty’. The subject of analysis from the point of view of the Oslo Manual within the study of innovation processes can be divided into the following areas of the business environment (as an innovation ecosystem): • Defining the company's innovation strategy, although the classification of innovations is not easy. This is mainly due to the fact that, with the development of the industry, the combination of strategic options varies. • Description of the role of dissemination of findings: this is a problem of monitoring the flows of innovation and technical changes among industries, as well as monitoring their efficiency and impact on productivity in other localities, industries, etc. • Providing information sources on innovations and their barriers, which are important for the analysis of policymaking, in terms of possibilities to support factors that are essential for the emergence and implementation of innovations, or suppressing and overcoming barriers to innovation. • Evaluation of innovation inputs which allow understanding the benefits of the relationship between science and research and non-research inputs of the company, both in individual industries and among them.

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• Monitoring the role of public (state) policy in industrial innovation. R&D is financed mainly from public sources, so it is necessary to understand the impact of R&D on industry. • Evaluation of innovation outputs: this area is limited by the question of how to measure innovation outputs. It is an economic activity requiring resources that could be used for other purposes. In terms of opportunity costs, it can be assumed that the entities responsible for innovation activity will strive to create, or at least maintain, value. However, the results of innovation are uncertain and diverse (OECD & Eurostat, 2019). A key principle of the Oslo Manual is that innovation can and should be measured (OECD & Eurostat, 2019, p. 20). Besides, it is a dynamic system that is evolving, as evidenced by the definition of the term innovation as written in the Oslo Manual. Definition and typology of innovations according to the Oslo Manual (3rd Edition) until 2017: ‘An innovation is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations’ (OECD & Eurostat, 2019, p. 46). Definition and typology of innovation according to the Oslo Manual (4th edition) since 2018: ‘An innovation is a new or improved product or process (or a combination thereof) that differs significantly from the unit's previous products or processes and that has been made available to potential users (product) or brought into use by the unit (process)’ (OECD & Eurostat, 2019, p. 20). These definitions were accepted in the research to delimit the concept of innovation as basic, due to the possible comparison of statistical results; see Rydvalova and Zizka (2021, in this book). The success of an innovation depends upon two basic factors—resources (people, equipment, knowledge, finance, etc.) and the organisation’s ability to manage the resources (again, it is about people). Through experience, organisations create their procedures which, with repetition and time, become a model for ‘this is how it is done in our country’, the so-called routines. But what is the effect of innovation? This is, for example, a change in input, output, status (proportion between inputs and outputs), or result. The effects can be found in technical, economic, and other areas. We can use a number of financial and non-financial indicators to measure these effects, and there is no uniform guide for evaluation. To achieve a more objective evaluation, it is recommended to combine various indicators, such as in the Balanced Scorecard method; see Skala et al. (2021, in this book). The innovation process consists of many activities: scientific research, technology, organisational, and marketing. A simple innovation process, which makes it possible to present the individual phases of implementation, has been published, for example, by Tidd et al. (2005). They define the innovation process by its following elements: (a) exploring the stimuli of the internal and external environment; (b) a choice of incentives on the basis of a decision in relation to the conditions and eligibility of the company; (c) implementation (securing knowledge resources,

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marketing, and feedback); and (d) learning—the use of so-called tacit knowledge and acquisition of other knowledge resources. In connection with the development of society and technology, there is also a development in the field of innovation processes. The study of models of innovation processes was prepared by Rothwell (1992), in which he pointed out the connection between the development of technology and society with the implementation of innovation processes. He summarised his findings in 5 generations (hereinafter G) of models of innovation processes: Technology push (1G), market pull (2G), coupling of R&D and marketing (3G), integrated business processes (4G), and system integration and networking (5G). • The first and the second generation of a simple linear model (pull of demand or push of technology, from the 1950s to the 1970s). • The third generation is the so-called coupling model, including the interaction of process phases, and feedback between different phases. This model is a combination of the first two generations, complemented by feedback, more structured processes, cost reductions, market interactions, as well as research and development. The timeframe of the model is given in the late 1970s to the mid-1980s. • The fourth generation represents a parallel lines model—unification, integration within the company in both directions in the supply and demand chain, with an emphasis on interconnection and alliance. The model began to be used in the 1980s and the 1990s. • At the turn of the twentieth and twenty-first century, the fifth generation of the model with system integration appears. It contains extensive links, the emergence of networks, and a flexible response to adapt to customers, as well as the implementation of continuous innovation. As stated by Tidd et al. (2005), most critical to a company are those processes whose role is the effective involvement of resources and activities with continuous ‘learning’, hence the need for effective integration of information and knowledge. We can achieve this on the basis of a quality innovation strategy. The basic areas in which integration and learning are a prerequisite for the success of the strategy are listed below: • Location of research and development in the company. A question arises as to whether R&D departments should be localised in divisions, at company headquarters, in home institutions, abroad, and/or implemented in the framework of innovation partnerships, alliances, and consortia. • Defining the role of R&D in deciding on the allocation of corporate financial resources. • Specification of the link between innovation and company strategy. Two factors play an important role in deciding on the location and implementation of R&D activities inside/outside the organisation—the parameters of physical location and the method of financing in relation to the impact of the potential benefits of innovation. Rothwell’s (1992) five-generation models show that managing cooperation on innovation is challenging and brings potential drawbacks with it. The

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issue of the perspective/danger of imitation in innovation partnerships, as mentioned in Sect. 4.2, based on resources, is addressed by Foege et al. (2017). They draw attention to the issue in connection with the fact that innovation partnerships are important tools for learning and creating value, but at the same time they can increase a company's vulnerability to unwanted knowledge leakage and imitation by others. This danger mainly affects companies operating in technology-oriented industries.

4.4

Innovation Partnership: Beyond the Borders of Individual Entrepreneurship

As reported by Chesbrough (2006), cooperation in the innovation process increases the potential for problem-solving during the process and helps to identify the necessary knowledge for innovation in domestic and global markets. What, then, are the benefits of cooperation and networking of companies? The answer can be found in the development of models of innovation processes described by Rothwell (1992). Johnson (2011) also provides the following answer in his work: ‘Brilliant inventors stand on the shoulders of the giants who came before them, so virtually all important innovations are products of networking’. In his book, Johnson defines an innovation classification model. In his model, he classifies innovations from two perspectives—the environment for the creation of innovation and the motivation for innovation. In terms of the environment for the creation of innovation, he defined the following categories. An inventor, as an individual or a small team within an organisation, is characterised as an individual. If a larger number of groups (collective) participated in innovation, the authorship is characterised as a network. In terms of the motivation that led to innovation, Johnson distinguishes market-oriented motives (e.g. R&D was implemented due to the sale of a licence) and non-market motives (centralised, where the primary motive was not a commercial benefit). Due to the fact that innovation is connected with historical changes, the quadrants (see Fig. 4.1) of a given scheme take various forms. Quadrant 1 is characterised by the market/individual combination—a groundbreaking innovation of an individual in a private laboratory. That is rather a rarity. Willis Carrier with air conditioning and Alfred Nobel and his dynamite are given as examples. Quadrant 2 is represented by a market/network relationship—collective innovation, i.e. cooperation of several companies on the basis of a profit motive. This means creation of a new product within a decentralised network. An example is a light bulb, which was certainly not just the work of Edison, but rather a collective invention. Quadrant 3 is identified by the non-market relationship motive/individual. Individuals are understood to be amateur scientists or enthusiasts who share their ideas without any claim for compensation. An obvious example is the World Wide Web (www), but also the theory of the atom, blood groups, and many others.

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Fig. 4.1 The four quadrants. Source according to Johnson (2011, p. 231)

Quadrant 4 defines the non-market/network relationship, such as academic, opensource environments with many examples of innovation, such as the periodic table of elements, Braille, ECG, aspirin, children’s incubators, and GPS. Johnson considers the fourth quadrant to be literally the ‘seedbed of innovation’. As Johnson (2011) states, innovation networks are essentially an organisation’s response to the complexity or uncertainty of technologies and markets and, as such, innovation is not the result of a linear process. What is important in his work is that networking enables information spillovers which he regards as a phenomenon of the present. The operation of the resulting innovation network, the coordination of partners and thus its management, is a challenging matter even for those capable managers with the most experience. The choice of cooperation then depends on the competitive significance of a technology, its complexity, the credibility of organisations involved, their business strategies, internal competencies, corporate culture, and management's attitude to implementing change (Tidd et al., 2005). Foege et al. (2017) draw attention to the increase in the vulnerability of the central company (core companies or also cluster focal companies) to intellectual property rights (hereinafter IPR) infringements. Those are issues of ownership, utilisation strategies, uncovering the innovative capabilities of the focal company in the partnership, etc., during all stages of the innovation process. In their research in Germany, they made the following two primary findings. First, the most important finding is the fact that a broad portfolio of innovation partnerships increases the risk of becoming a victim of illegal copying. The intensity of the imitation threat depends on the specific configuration of the innovation partnership portfolio along three

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dimensions—type of a partner, location of a partner, and innovation phase (the greatest imitation threat is associated with the initial stages of an innovation partnership). They found that vertical innovation partnerships are associated with the greatest threat of imitation, as opposed to horizontal or scientific partnerships. In terms of partner placements, they showed a significant imitation-increasing effect among continental innovation partnerships (meaning European) versus domestic (local, regional, national) and international (outside Europe) partnerships. Second, companies can partially mitigate the threats of imitation posed by partnerships by leveraging IPR and setting up in-house research and development. Here, the authors point out the limits of their research, where they fail to explore alternative ways of protecting IPRs, such as confidentiality, partnership experience, and mutual trust, which manufacturing companies can rely on to protect themselves from imitation. That is the reason why these forms of IPR were included in the case study of the monitored clusters; see Rydvalova and Zizka (2021, in this book).

4.5

Protection of Intellectual Property Rights

Every business has its tangible and intangible aspects. Intellectual property is also a part of a company’s assets. This may take the form of protected solutions for creative technical activities (patents, utility models) or aesthetic activities (industrial designs), but includes the goodwill of the company and the labels under which its products are marketed and offered to the customer (trademarks). Some companies value their intangible assets (industrial property) above tangible assets. As mentioned in Sect. 4.4, one of the key issues network partnerships is the management of IPRs. They include both copyright and related rights (performers’ rights, audio recording producers, etc.) and industrial rights (trademark rights, patents, utility models, etc.). Occasionally, they also comprise trade secret rights, the right to protection against unfair competition or the right to Internet domains. However, the approach to IPRs varies from continent to continent and country to country. This must be addressed not only when creating an innovation strategy for the company in case of internationalisation, but also when preparing research and comparing its results in the academic environment. An example is the protection of software (hereinafter SW) rights in Europe under copyright if it is not inextricably linked to technology that can be protected by a patent on the American continent under industrial law. The general definition of a patent according to the American concept is broader than the European case. This results in a different IPR approach derived from the copyright concept of SW protection. Just as each intellectual property has a different nature, so do the reasons for protecting it. The basic reason is that the protection of IPRs can help a company increase its value and strengthen its position in the market and thus succeed in competition. As mentioned above, companies can use licences to protect their internal ideas outside their business process. In general, three basic forms of IPRs can be characterised:

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• IPRs in their own products • Enforcing patent claims against others • Sale of licenses The above framework delimitation of the topic of protection of IPRs was essential for subsequent chapters on the topics of dynamic development of companies in industry clusters, such as cluster organisations, industrial districts, and innovative behaviour in selected industries.

References Chesbrough, H. W. (2006). Open innovation: The new imperative for creating and profiting from technology (Nachdr.). Harvard Business School Press. Foege, J. N., Piening, E. P., & Salge, T.-O. (2017). Don’t get caught on the wrong foot: A resourcebased perspective on imitation threats in innovation partnerships. International Journal of Innovation Management, 21(03), 1750023. https://doi.org/10/ggh2wc. Johnson, S. (2011). Where good ideas come from: The natural history of innovation (1. paperback ed). Riverhead Books. Kuznets, S. (1940). Schumpeter’s business cycles. The American Economic Review, 30(2), 257–271. Mensch, G. (1979). Stalemate in technology: Innovations overcome the depression. Ballinger. OECD, & Eurostat. (2019). Oslo manual 2018: Guidelines for collecting, reporting and using data on innovation (4th ed.). OECD. https://doi.org/10.1787/9789264304604-en Roncaglia, A. (2005). The wealth of ideas: A history of economic thought. Cambridge: Cambridge University Press. Rothwell, R. (1992). Developments towards the fifth generation model of innovation. Technology Analysis & Strategic Management, 4(1), 73–75. https://doi.org/10/ct67rs. Rydvalova, P., & Zizka, M. (2021). Approach to innovation in selected industries. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business clusters – An empirical study. Springer Nature. Schumpeter, J. A. (1912). Theorie der wirtschaftlichen Entwicklung. Duncker und Humblot. Schumpeter, J. A. (1939). Business cycles: A theoretical, historical, and statistical analysis of the capitalist process. New York: McGraw-Hill. Schumpeter, J. A. (1942). Capitalism, socialism, and democracy. Harper & Row. Sirůček, P. (2016). Polozapomenuté postavy ekonomického myšlení – J. A. Schumpeter. Acta Oeconomica Pragensia, 24(3), 78–86. Skala, M., Zizka, M., & Pelloneova, N. (2021). Dynamic development of companies in an industry cluster. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business clusters – An empirical study. New York: Springer Nature. Tidd, J., Bessant, J. R., & Pavitt, K. (2005). Managing innovation: Integrating technological, market and organization change. Zizka, M., Pelloneova, N., & Skala, M. (2021). Theory of clusters. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business clusters – An empirical study. New York: Springer Nature. Žižlavský, O. (2013). Past, present and future of the innovation process. International Journal of Engineering Business Management, 5, 47. https://doi.org/10/gcm4pz.

Chapter 5

Dynamic Development of Companies in an Industry Cluster Marek Skala

5.1

, Miroslav Zizka

, and Natalie Pelloneova

Production Functions and Economic Growth

Empirical research in the field of regional sciences as well as economic geography (Ciccone & Hall, 1996; Ciccone, 2002) suggests that agglomeration benefits are embodied in changes in the production function of companies, specifically in productivity growth. The production function mathematically captures the transformation of limited (rare) inputs into output. In the theoretical approach to the production function, we abstract from technical inefficiency. It is therefore assumed that technological and managerial deficiencies are eliminated (technical efficiency). The production function can thus be understood more in terms of allocation efficiency as the relationship between the most technologically achievable output and the inputs necessary for the production of this output (Shephard, 1970). However, in professional literature (Banker et al., 1984; Seiford & Thrall, 1990) doubts can be traced concerning the correctness of the expectation of meeting the assumption of technical efficiency in the theoretical production function. The data envelopment analysis (DEA) method used for empirical research of production functions and in operational research reveals a number of hidden inefficiencies. Theoretical economics works with the following key production functions: Cobb–Douglas production function, production function with constant elasticity of substitution (CES), generalised production function, and the Leontief production function. For the theoretical uses of these production functions, properties such as returns to scale, homogeneity and homotheticity, marginal rate of technical substitution, the

M. Skala (*) · M. Zizka · N. Pelloneova Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_5

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degree of input substitutability, and separability of the production function are crucial. Due to the simplification of a complicated reality, theoretical production functions use specific types of mathematical functions, namely homogeneous and homothetic functions. A function is of a homogeneous degree r if the multiplication of all independent variables by the constant t is equivalent to the multiplication of the dependent variable tr: f (tx1, . . ., txn) ¼ tr f (x1, . . ., xn). The partial derivative of a homogeneous function is of the order r–1. The geometrical property of homogeneous production functions is the fact that the intersection of a radius from the beginning with any isoquant has the same direction. Thus, the slope of the isoquant homogeneous function depends only on the proportion in which the company uses the inputs in production, and not on the size of the product. In other words, the marginal rate of technical substitution does not depend on the volume of production. Another important feature of homogeneous production functions is the fact that they display identical returns to scale along any ray from the beginning, regardless of the combination of inputs. The homothetic function is obtained by increasing the monotonic transformation of a homogeneous function: H (zi) ¼ F ( f(zi), where f(zi) is a homogeneous function and F is a positive monotonic function. Every homogeneous function is a homothetic function, although every homothetic function may not be a homogeneous function. The same property with the same slope of its contour lines on the half-line drawn from the beginning applies to the homothetic function. Thus, a company can substitute inputs at a constant rate regardless of the size of the output. However, the second property no longer applies. At the beginning, the returns to scale may differ along the radius for homothetic functions. The study of production functions from a theoretical point of view has a thousand-year tradition. According to Blaug (1985), the topics of production and distribution were dealt with by the ancient Greeks and Romans, as well as later medieval scholastics. The first step for the implicit formulation of the production function was taken by the physiocratic Anne Robert Jacques Turgot, who in his Observations on a Paper by Saint-Peravy (1776) first formulated the concept of diminishing returns in a one-input production function. Adam Smith in The Wealth of Nations (1776) pays considerable attention to productivity and the distribution of wealth. This was followed by a number of classical economists (Thomas Malthus, David Ricardo, and others), as well as Marginalists (such as William Stanley Jevons, Carl Menger, and Leon Walras). All of the above-mentioned Smith’s followers simply assumed (contrary to reality) production functions with a fixed input ratio (with increasing output, the ratio of capital and labour used did not change). Johann von Thünen overcomes this shortcoming in the 1840s by formulating the first algebraic production function as a function of two inputs with a changing ratio of capital and labour. Blaug (1985) mentions that he uncovered the contemporary theory of marginal productivity by saying that in its production function at a constant level of one of the inputs, the total product grew, but with a diminishing rate. Philip

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Henry Wicksteed (1894) formulates an algebraic production function with constant returns to scale, specifically a linearly homogeneous function. The breakthrough is the Cobb–Douglas production function formulated in 1928 by the mathematician Charles Cobb and the economist Paul Douglas in their article (Cobb & Douglas, 1928). It was used to econometrically examine the quantitative relationships between production, labour, and capital in the time period of 1889–1922. The Cobb–Douglas production function can be written by the relation: Q ¼ ALαKβ, where Q is the output, A is the total factor productivity, L is the work, K is the capital, and α and β are output elasticities of capital and labour. Charles Cobb, the mathematician, formulates the production function algebraically as a homogeneous and concave function (i.e. with mathematically friendly properties). The homogeneous properties of the production function allow easy calculation of the output growth in the case of an increase in inputs (doubling, tripling, etc.). Furthermore, homogeneity ensures that all isoquants have the same slope at the point of their intersection with the line leading from the origin of the coordinate system. The slope is a marginal rate of technical substitution. In other words, at the points thus obtained, capital and labour are equally substitutable at different levels of output. In the case of the Cobb–Douglas production function, the authors determine the elasticity of capital substitution for labour equal to one. One percent reduction in capital is increased by one percent labour without changing the output produced. At the same time, the authors determine constant returns to scale, i.e. α + β ¼ 1. Their production function is therefore a homogeneous function of the first degree. The concavity of the function, in turn, facilitates the solution of optimisation problems by the fact that each local extreme of this function is also a global extreme (it is sufficient to find a stationary point that is also the sought extreme). David Durand (1937) incorporates increasing and diminishing returns to scale into the Cobb–Douglas production function by stipulating that the sum of the elasticities of output to labour and capital need not be equal to only one. It can therefore take the values α + β > 1 (increasing returns to scale, homogeneous functions of the order higher than 1), α + β < 1 (diminishing returns to scale, homogeneous functions of the order less than 1), and α + β ¼ 1 (constant returns to scale, homogeneous first-order functions). In their original production function, Cobb and Douglas set the elasticity of capital substitution for labour equal to one (one percent for one percent). However, Arrow et al. (1961) extend the mutual elasticity of input substitution from zero to infinity (from perfectly unsubstitutable to perfectly substitutable inputs), thus fundamentally contributing to the generalisation of the Cobb–Douglas production function. Their production function is referred to in production function theories as Arrow–Chenery–Minhas–Solow or the ACMS production function. By this generalisation, they created a general production function, where Cobb– Douglas, Leontief, and linear production functions are its special cases. Explicitly, the Cobb–Douglas production function is a special form of the ACMS production function if the mutual elasticity of the inputs takes on values equal to one. The Leontief production function (named after the American economist of Russian origin

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Wassily Leontief, 1905–1999) assumes that the inputs are perfect complements (any mutual substitution is absolutely excluded). In other words, a specific output can be produced with just one combination of inputs that cannot be changed. Thus, the Leontief production function is a special case of the ACMS production function if the elasticity becomes zero. The Walrasian general equilibrium model (1874) works with the Leontief production function. The linear production function derives its name from the shape of isoquants which are unusually straight lines. The inputs are therefore perfect substitutes (the output can be produced in one extreme view by one of the inputs). The linear production function is then a special case of the ACMS production function if the elasticity takes values to infinity. In theories of production functions, the ACMS production function is also referred to as the constant elasticity of substitution or CES production function. This term also reveals a methodological flaw in this production function. The elasticity of mutual substitution of inputs takes values from zero to infinity, but in a constant way—thus, it takes a given value which is unchanged along the isoquant and even in the whole isoquant map. In the following years (until the mid-1970s), theorists attempted to mathematically formulate a neoclassical production function (more general CES production function) reflecting the variable elasticity of input substitution, so that the generalised production function includes a change in the elasticity of the inputs both along the isoquants and when the size of the output changes (across the isoquants). At the same time, in these methodological works, it is possible to trace the effort to create a production function with multiple inputs. The transcendental logarithmic production functions received considerable attention. Their mathematical properties allow their authors to abstract from limited categories, such as elasticity of inputs, homogeneity, and more. Notable authors who have made a significant contribution to the generalisation of the CES production function include Sato (1975), Lu and Fletcher (1968), Revankar (1971), and Berndt and Christensen (1973). Subsequently, in professional theoretical circles (Just et al., 1983; Chizmar & Zak, 1983), the effort to further elaborate the production function continued in the sense of decomposition (separability) of the production function. This need stems from the fact that the production process is in reality multi-input, and theoretically, it is desirable to create a decomposition of the production process into individual phases with intermediates. The final product is thus the sum of intermediates. If we can detect separate production functions in this way (the original production functions can be decomposed in this way), it is possible to methodologically abstract from a number of parameters, which simplifies empirical analyses of production functions. For example, data envelopment analysis (DEA) is based on this principle (see Sect. 5.2). The original Cobb–Douglas production function also proved to be a suitable instrument for use in neoclassical income distribution theories during the twentieth century and later in neoclassical concepts of economic growth (Humphrey, 1997). Simultaneously, its macroeconomic application (the neoclassical production

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function) unleashed a storm of academic dispute. Piero Sraffa, Joan Robinson, Luigi Pasinetti, Pierangelo Garegnani, and others argue against the use of the neoclassical production function. Paul Samuelson, Robert Solow, Frank Hahn, and Christopher Bliss, on the other hand, prove the legitimacy of its use based on empirical studies of the neoclassical production function through time series. The subject of the dispute was the vague (not precisely defined) conception of capital and the related measurability. As Burmeister (2000) mentions, the neoclassical production function established itself in mainstream macroeconomics even after this dispute and forms not only the basis of the neoclassical model of economic growth, but also its elaboration. One of the significant theories which examine endogenously motivated progress are also theories of endogenous economic growth (Romer, 1986; Lucas, 1988).

5.2

Business Performance

As discussed in the previous chapters (Rydvalova & Skala, 2021; Zizka et al., 2021, in this book and Sect. 5.1), the phenomenon observed within clusters is to increase the performance of their members/companies. The term ‘performance’ is frequently used; however, its interpretation in the literature is not uniform. Lebas (1995) emphasises that performance, especially in management, generally does not take into account previous business performance, but rather its future and the capabilities that are being evaluated. However, performance evaluation is the basis for understanding the reasons for the competitiveness of companies and the implementation of corporate strategy. In general, the concept of performance can be characterised as the correct deployment and management of the components of the causal model (company), which leads to the achievement of set goals at the proper time, within the constraints specific to each company. Knápková et al. (2013) claim that organisational performance of the company includes all areas of business activities that need to be harmonised, so that the result is a functioning and prosperous company with a long-term perspective of its existence. In order to manage the company’s performance, it is necessary to create a system that will enable the quantification of the company’s performance (Dedouchová, 2001) and compare it with the reference phenomenon on a certain criterion scale. Performance has two dimensions: the choice of activities to achieve a particular goal and the way in which the chosen activity is carried out. The first dimension is usually referred to as effectiveness and the second dimension as efficiency (Kumar & Gulati, 2009). The terms ‘efficiency’ and ‘performance’ are often confused. The performance of a company should always be assessed simultaneously both in terms of the efficiency of resources spent and in terms of the effectiveness of achieving predetermined goals. Performance is therefore defined as an appropriate combination of efficiency and effectiveness. Efficiency expresses the company’s ability to achieve outputs with a minimum level of inputs. In this way, the efficiency of each

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production factor (human labour, machines, materials, etc.) or the overall efficiency of all production factors can be measured (Synek & Kislingerová, 2010). There are several concepts of efficiency: economic, technical, Pareto, and relative. Economic efficiency is related to value; it measures the value of output to the value of input. The output is usually the value of products and services sold or profit over a period of time. Inputs are the value of production factors consumed for a given output or spent capital (Synek & Kislingerová, 2010). The measure of economic efficiency is calculated simply according to the relation (5.1). Efficiency ¼

Output Input

ð5:1Þ

Technical efficiency is perceived as the use of minimum inputs to achieve the desired level of production or as maximising production at a given volume of input factors (Kodera & Pánková, 2002); see the relation (5.2). Real company production Max:potential output given

Technical efficiency ¼

ð5:2Þ

by production function of industry Pareto efficiency is such allocation of resources when the position of one entity cannot be improved without worsening the position of another entity. This is therefore the maximum use of resources available to society (Luenberger, 1994). In the physical sense, efficiency means the degree of work done in relation to the energy expended. The physical and economic concept of efficiency may not necessarily lead to the same result, as economic efficiency is affected by the value side of inputs and outputs. Thus, a situation may arise where a certain solution is relatively more efficient at the economic level than at the technical level. Effectiveness measures assess the company’s ability to achieve predetermined goals and objectives (Enright, 2012); see relation (5.3). Performance is then the product of efficiency and effectiveness; see the relation (5.4). Effectiveness ¼

Achieved outcome Desired outcome

Performance ¼ Effectiveness x Efficiency

ð5:3Þ ð5:4Þ

Various indicators can be used to measure individual performance components. Ho and Zhu (2004) use the Du Pont model and the ROA indicator (return on total assets before tax) to evaluate performance, which can be broken down into the product of the profit margin (return on sales) and the total assets turnover ratio; see the relation (5.5). The profit margin expresses efficiency in terms of the ability to achieve the expected goal (profitability) and the total assets turnover ratio measures efficiency as the ability to use assets to generate outputs (sales).

5 Dynamic Development of Companies in an Industry Cluster

Earning before taxation Earning before taxation Net sales ¼ x Total assets Net sales Total assets

65

ð5:5Þ

This approach is based on the traditional method using indicators of financial analysis (in addition to the above, there are other indicators, such as profitability, activity, liquidity, and debt ratios). The drawback of this approach is that only the overall financial results of the company are taken into account, regardless of the level of market service and customer satisfaction (Lošťáková, 2009). Evaluation systems based only on financial indicators limit the possibilities of identifying key performance factors that characterise individual impacts influencing the creation of company value (Marinič, 2008). Increasing the value or wealth of business owners is currently considered the main business goal of the company, which should be taken into account when measuring its performance (Dedouchová, 2001). One of the suitable tools is economic value-added EVA representing essentially the economic profit generated by the company after paying all costs, including the cost of capital (Pavelková, 2009). The balanced scorecard (BSC) concept is also based on the principle of strategic value management, which measures a company’s performance using four balanced perspectives: financial, customer, internal business processes, and learning and growth (Kaplan & Norton, 1996). The BSC concept works with financial and non-financial measures, which show the degree of achievement of corporate goals in the above perspectives; see Sect. 5.2.1 below. Using objective financial measures is the simplest way to evaluate performance; on the other hand, data is mostly confidential and sometimes difficult to access (Haber & Reichel, 2005), especially for small and medium-sized enterprises, which are not required to publish financial statements. Due to the increased validity of data, subjective performance measures such as expected growth in market share, expected change in cash flow, or expected growth in sales are sometimes used (Haber & Reichel, 2005). Some authors distinguish three types of business performance: financial performance, operational performance, and overall effectiveness (Hult et al., 2008). Financial performance is measured by various absolute, proportional, differential, and composite financial indicators. Operational performance typically has four dimensions: delivery, production costs, quality, and flexibility (Sha et al., 2013; Terjesen et al., 2012). Each of these dimensions can be characterised by a set of sub-indicators. Overall effectiveness includes reputation, survival, goal achievement, and perceived overall performance relative to competition (Lewin & Minton, 1986). A company’s performance and efficiency can be assessed using simple ratios that include a single output and a single input (ROA, revenue per employee, earnings per share), or using multi-criteria approach, through multiple inputs and outputs. The DEA (data envelopment analysis) method is based on the latter principle. This method allows the evaluation of companies through a large number of financial and non-financial inputs and outputs and at the same time can identify the best companies, i.e. benchmarks. The DEA multi-stage method can be used to

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decompose the total performance into the individual components (Liu & Wang, 2009). In connection with the findings in Sect. 5.1, the DEA method was chosen as a carrier for the given research on the performance of companies in a cluster. It is specified in more detail in Sect. 5.3.

5.2.1

Financial Performance

The most commonly used tools of traditional systems include financial indicators based on profit or profitability. Currently, however, there is a growing interest in modern indicators of business performance, which are also based on non-financial measures (Hučka, 2011). Modern approaches take into account the concept of maximising wealth and other non-financial aspects, such as innovation (number of new products developed over a period, number of new patents), customer satisfaction, customer loyalty, or employee motivation (Arlbjørn & Haug, 2010). Most authors focus on measuring financial performance in their work. Another type of performance, innovation performance, is somewhat neglected in the literature. For example, Novák (2017) states that the most frequently used metrics of a company’s innovative ability are the return on innovation investment and Vitality Index indicators. Marr (2015) further adds that one of the most important key indicators of innovation performance is the innovation pipeline strength indicator, which measures potential future revenues from ongoing innovation projects. In a narrower sense, innovation performance refers to the results of companies and to the extent to which they market their inventions (Hagedoorn & Cloodt, 2003). It is this narrower approach to innovation performance that has been used in this research (see Sect. 5.2.2). Traditional business performance indicators can be used to evaluate financial performance. Traditional indicators of financial performance evaluation are mainly based on profit maximisation as a basic business goal. The traditional indicators of company financial performance include the following profit or loss levels (Wagner, 2009): earnings after tax (EAT), earnings before tax (EBT), earnings before interest and taxes (EBIT), and earnings before interest, taxes, depreciation, and amortisation (EBITDA). In addition, traditional indicators may include cash flow values and profitability indicators. The most commonly used profitability indicators include return on assets (ROA), return on equity (ROE), return on invested capital (ROIC), return on net assets (RONA), and return on sales (ROS) (Dluhošová, 2010). Traditional indicators of financial performance evaluation are relatively often used in practice, mainly due to their ease of calculation and the possibility of their access directly from the financial statements. On the other hand, traditional indicators have been criticised since the 1980s, mainly because they do not take into account risk, inflation, and the time value of money. When using traditional performance evaluation indicators, the possible impact of different depreciation methods, interest rates, and taxes should also be taken into account (Bachiller et al., 2011). In

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addition to the disadvantages already mentioned, the short-term orientation of the traditional system, its links to the past, insufficient attention to non-financial activities, and its inconsistency with corporate strategy have been criticised (Kaplan & Norton, 1996). For that reason, many authors emphasise the need for a multidimensional approach to performance measurement, integrating a financial and non-financial perspective (Chenhall, 2005). For measuring the performance of a company taking into account different dimensions and pillars such as financial and innovation, the mono-criterion approach is unsatisfactory. The results of comparing the performance of companies on the basis of one criterion are almost always contradictory, because we obtain different results when using various criteria. For this reason, more complex multi-criteria performance measurement methods have been developed taking into consideration a range of inputs and outputs that affect a company’s performance. The already mentioned BSC concept can be included in the group of multi-criteria methods, which measures the company’s performance using four balanced perspectives: financial, customer, internal business processes, and learning and growth. The BSC concept complements financial criteria on previous performance with non-financial criteria and drivers for future business performance. Goals and criteria are derived from the company’s vision and strategy. BCS is an alternative to traditional financial indicators that actually measure a company’s performance in the past. It is not only a method of measuring performance, but a strategic planning and management system linking business activities with the vision and strategy of the company (Lesáková et al., 2017). BSC enables managers to transform strategy into specific performance measures, align the strategy with the overall organisational mission and vision, as well as formulate and monitor the organisation’s activities to support the achievement of the strategy. As Murby and Gould (2005) and Madsen and Stenheim (2014) claim, measuring financial indicators is important to determine whether a company’s strategy and performance support the company’s overall mission. For private and profit organisations, financial metrics focuses mainly on profit and market share (i.e. company growth). BSC also measures how a company is perceived by customers, since it is the customers who bring direct revenues and their perception of the company is crucial to maintaining and further growing sales (Casey & Peck, 2004). BSC also measures internal processes focused on activities that increase customer satisfaction, innovation, and learning to improve employee skills (Bose & Thomas, 2007). Bose and Thomas (2007) add that the perspective of learning and growth is particularly important for strategic management in order to identify and improve the performance of intellectual capital. Growing intellectual capital is crucial for the development of innovative product designs, production, distribution, and promotion and for improving the market value of an organisation beyond the value of tangible assets. The researchers who have performed a study of methods for measuring cluster performance based on the concept of the BSC model are, for example, Cesar Ribeiro Carpinetti et al. (2008). Their conceptual framework consists of four performance perspectives—economic and social results, performance of individual companies,

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collective efficiency, and social capital. Within the framework of economic and social results, measures related to gross domestic product, labour force, and others are considered. As far as the performance is concerned, financial and non-financial performance indicators of individual companies are examined. To measure the performance of the cluster in terms of collective efficiency and social capital, indicators relating to cooperation between cluster members and mutual trust are monitored. In the Czech Republic, Pavelková (2009) are primarily involved in measuring the performance of clusters. They have developed their own model for measuring and managing the performance of clusters, which is focused on evaluating the performance of individual entities involved in the activities of clusters (with a focus on member companies). In addition, the model evaluates the effectiveness of individual activities implemented in the cluster, the performance of the cluster as a whole, the efficiency of cluster management, and the cluster policy of the region/country. The evaluation procedure is based on official statistics and a qualitative interview with individual cluster subjects using a purpose-built questionnaire. The performance of individual member entities is evaluated mainly on the basis of company accounting data. In the performance evaluation process, evaluation objectives are first formulated and appropriate evaluation criteria are selected. Then the data collection itself and their subsequent analysis take place. In the final phase of the evaluation, a final report is created and the results obtained are communicated. All stakeholders are involved in monitoring and interpreting the results.

5.2.2

Innovation Performance

In a broader sense, Werner (2002) explains the innovative performance of a company as the quality of value creation in a business innovation process, which begins with the generation of an idea and finishes with bringing a new product to market. In a narrower sense, innovation performance refers to the results of companies and to the extent they introduce their inventions to the market (Hagedoorn & Cloodt, 2003). Žižlavský (2013) defines innovation performance as the degree of realisation of a company’s innovation potential, i.e. the company’s ability to transform the potential of innovations (innovation inputs) into their market realisation (into outputs). This definition therefore contains a link to financial performance. Some authors characterise innovation performance as the success of companies in generating ideas, new equipment models, products, processes, and systems (Ernst, 2001; Freeman & Soete, 1997). Ryan (2010) describes innovation performance as the quantity and quality of innovative ideas and the efficiency and effectiveness of implementing those ideas in creating innovation processes. Other authors (Dautel, 2005; Jantunen, 2005) understand innovation performance as an increase in the performance of companies that is caused by product or process innovation. The concept of innovation performance emphasises in particular the process of creating, disseminating, and transforming ideas for generating new or improved

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economic or social results (products, services). As stated above, innovation performance can be defined as the successful transformation of inputs into outputs of the innovation process. In practice, the evaluation of innovation performance is carried out on the basis of the evaluation of economic and other indicators. Innovation activities are most often researched using: • Indicators of specific outputs of the innovation activity (number of patents, utility models, industrial designs, and trademarks). • Indicators of operational or process elements (customer satisfaction surveys, number of newly acquired customers). • Indicators of strategic success (increase in the financial performance of the company which is directly or indirectly caused by innovation activities). From the above options, it seems to be the simplest to use registered industrial property rights, which can be searched in the database of the patent or similar office. However, it should be taken into account that in some sectors, for example IT, other forms of protection are used in Europe to protect intellectual property, such as copyright protection or trade secrets. Innovation cannot be limited only to product issues, but also to processes (marketing, organisational). Even in some industries, process innovations are more common than product innovations (Rydvalova & Zizka, 2021, in this book). For this reason, it is desirable to monitor indicators of process innovation as well. A comprehensive view of innovation reveals how it will be reflected in increased financial performance. In the long run, innovation should be reflected in a shift in the production possibility frontier. The data envelopment analysis (DEA) method can be used to reveal the impact of innovation on performance; see Sect. 5.3.

5.3

Performance Measurement by Data Envelopment Analysis (DEA)

DEA is a multi-criteria method based on linear programming models that derive the relative efficiency or performance of compared decision-making units (DMUs) based on multidimensional inputs and outputs (Kocisova et al., 2018). Unlike the literature focused on performance, in the case of the DEA method, terms such as performance or efficiency are not strictly distinguished and depend on the context of the quantity used. The DEA method was originally developed mainly for evaluating the efficiency of public organisations, such as schools and hospitals (Kocisova et al., 2018; Mezősi et al., 2018). However, further development has shown that it can also be used for the evaluation of business entities. For example, Düzakın and Düzakın (2007) conducted the DEA for industrial companies in Turkey and demonstrated that the evaluation of business performance depends on the industry. In the cross-industry analysis, the number of executive companies was significantly lower than in the

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industry-by-industry analysis, which is related to the homogeneity of the units examined. Rydvalová and Žižka (2018), when examining the innovation performance of clustered companies, also came to the same conclusion. It should be emphasised that the DEA measures relative technical efficiency. This means that the best practice group of units is searched in a given evaluated set of all units. At the same time, the DEA shows the degree of inefficiency of other units and the necessary improvements in the inputs and outputs of these units (Yang, 2006). All feasible combinations of inputs and outputs form a set of production options. This is bounded by an efficient frontier, which shows the highest level of outputs achievable with a given volume of inputs (Düzakın & Düzakın, 2007). The best units serve as a benchmark (Ruiz & Sirvent, 2019). There are a large number of variants of DEA models. Their description goes beyond the possibilities of this chapter. The oldest model of CCR with the assumption of constant revenues to scale (CRS) was described by Charnes et al. (1978). In 1984, the method was generalised to variable returns to scale (VRS) conditions. The model is called BCC (Banker et al., 1984). It was the input-oriented BCC model that was used in the research described in this book. The model works with virtual inputs and virtual outputs, the weights of which are determined by linear programming so as to maximise the efficiency of each unit. The aim of the model is to maximise the objective function z (5.6) under constraints (5.6). The inputs xj of the unit q have weights vj. The outputs yi have weights ui. The variable μ indicates the deviation from the CRS. Units that are efficient have an objective function value equal to one. These units are located at the efficient frontier. Inefficient units have value z lower than one. Pr ui yiq maximise z ¼ Pmi j v j xjq

ð5:6Þ

Pr uy Pmi i ik  1, k ¼ 1, 2, . . . , n j v j xjk ui  ε, i ¼ 1, 2, . . . , r v j  ε, j ¼ 1, 2, . . . , m

ð5:7Þ

subject to

where ε is a non-Archimedean number securing that all input and output weights will be positive. The task (5.7) must be modified for the solution by linear programming using the Charnes–Cooper transformation into the form (5.8).

5 Dynamic Development of Companies in an Industry Cluster

z¼

r X

71

ui yiq

i¼1 r X i¼1 m X

ui yik 

m X

v j xjk, k ¼ 1, 2, . . . , n

j¼1

ð5:8Þ

v j xjq ¼ 1

j¼1

ui  ε, i ¼ 1, 2, . . . r; v j  ε, j ¼ 1, 2, . . . , m In the case of variable returns to scale, it is sufficient to add to the model the variable μ indicating the deviation from the CRS. In this case, the conical data envelope changes to convex, which leads to a higher number of efficient units being defined. The input-oriented BCC model has the form given by relations (5.9). z¼

r X

ui yiq þ μ

i¼1 r X i¼1 m X

ui yik þ μ 

m X

v j xjk, k ¼ 1, 2, . . . , n

j¼1

v j xjq ¼ 1

ð5:9Þ

j¼1

ui  ε, i ¼ 1, 2, . . . r; v j  ε, j ¼ 1, 2, . . . , m; μ2R It is advantageous to formulate dual models for the control of individual units and the search for savings in inputs. The dual CCR model with input orientation is given by relations (5.10); see Zhu (2014). minimise Θq subject to n X xij λ j  Θq xiq , i ¼ 1, 2, . . . , m j n X

yij λ j  yiq , i ¼ 1, 2, . . . , r

j

λ j  0, j ¼ 1, 2, . . . , n

ð5:10Þ

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where λ is the vector of weights of individual units. The variable ϴq indicates the required degree of input reduction to reach the efficient frontier for a given unit. In case of the BCC model, it is sufficient to add condition (5.11) to the model (5.10). Xn

λ j j

¼1

ð5:11Þ

Formulations of other types of models, with an orientation on outputs, additive models, slack-based models, and super-efficiency models can be found in the literature; see Zhu (2014).

5.3.1

Use of the DEA Method to Evaluate the Performance of Clusters

The DEA method can be used to evaluate the performance of companies in clusters, whether natural or institutionalised. Clusters usually consist of companies operating in one or more related industries. This is especially true for the companies that form the core of the cluster. Such companies can be considered as homogeneous units and can be well compared using the DEA method. When evaluating the performance of companies in clusters, some accounting variables can be chosen as inputs of the analysis, such as assets, long-term capital employed (equity and liabilities), numbers of employees, or company history, which can be considered a measure of intellectual capital (it aggregates knowledge, experience, and routines of employees and managers). To measure innovation performance, the outputs can be the number of patents, utility models, industrial designs, trademarks, and revenue from licences or new products. In the case of financial performance, sales, revenues, or economic value added can be selected as outputs. The model can be designed as a single-phase directly comparing the weighted inputs and the weighted outputs or as a two-phase. The two-phase model first evaluates how efficiently the company can transform economic inputs into the outputs of innovation activities (e.g. patents, new products). In the second phase, it is then evaluated how efficiently the company can commercialise the outputs of the first phase, i.e. convert them into economic outputs such as revenues or economic value added. Thus, in the multiphase DEA model, the outputs of the first phase also serve as inputs to the second phase. Using the DEA method, it is possible to compare how the performance of various types of clusters differs. For example, you can compare differences in performance between companies that are members of an institutionalised cluster and companies that operate in a natural cluster or in completely different regions. It is also possible to examine the differences between companies in the natural cluster and non-clustered companies in other regions. The analysis can be performed in one selected period or in a time series. The Malmquist index (hereinafter MI), based on DEA scores, can be used to evaluate

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panel data. The MI is a measure for assessing the change in relative productivity between different time periods. The index can be decomposed into the product of two components—a change in the technical efficiency and a technological change (Li et al., 2017). The first component expresses the company’s internal effort to improve its performance through various organisational measures. This means catching up the best companies in the industry. The second component, technological change, leads to a shift in the efficient frontier. It expresses the improvement in the performance of the whole industry, which is typically driven by innovation. Based on the MI, it is therefore possible to determine not only whether clusters (institutionalised or natural) have an effect on the growth of overall performance, but also which component contributed more to the growth.

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Lebas, M. J. (1995). Performance measurement and performance management. International Journal of Production Economics, 41(1–3), 23–35. https://doi.org/10/d3mgmq. Lesáková, Ľ., Dubcová, K., & Gundová, P. (2017). The knowledge and use of the Balanced Scorecard method in businesses in the Slovak republic. E+M Ekonomie a Management, 20 (4), 49–58. https://doi.org/10/gg55pd. Lewin, A. Y., & Minton, J. W. (1986). Determining organizational effectiveness: Another look, and an agenda for research. Management Science, 32(5), 514–538. https://doi.org/10/bcq3j9. Li, Z., Crook, J., & Andreeva, G. (2017). Dynamic prediction of financial distress using Malmquist DEA. Expert Systems with Applications, 80, 94–106. https://doi.org/10/gg2bvn. Liu, S.-T., & Wang, R.-T. (2009). Efficiency measures of PCB manufacturing firms using relational two-stage data envelopment analysis. Expert Systems with Applications, 36(3), 4935–4939. https://doi.org/10/dbr2cb. Lošťáková, H. (2009). Diferencované řízení vztahů se zákazníky: [Moderní strategie růstu výkonnosti podniku. Grada. Lu, Y., & Fletcher, R. F. (1968). A generalization of the CES production function. The Review of Economics and Statistics, 50(4), 449–452. https://doi.org/10/fjjqs4. Lucas, R. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22 (1), 3–42. https://doi.org/10/fpswz5. Luenberger, D. G. (1994). Dual pareto efficiency. Journal of Economic Theory, 62(1), 70–85. https://doi.org/10/ctbk3g. Madsen, D. Ø., & Stenheim, T. (2014). Perceived benefits of balanced scorecard implementation: Some preliminary evidence. Problems and Perspectives in Management, 12(3), 81–90. Marinič, P. (2008). Plánování a tvorba hodnoty firmy. Grada. Marr, B. (2015). Key performance indicators for dummies (1st ed). Wiley. Mezősi, A., Szabó, L., & Szabó, S. (2018). Cost-efficiency benchmarking of European renewable electricity support schemes. Renewable and Sustainable Energy Reviews, 98, 217–226. https:// doi.org/10/gfpq8k. Murby, L., & Gould, S. (2005). Effective performance management with the Balanced Scorecard. London: The Chartered Institute of Management Accountants. Novák, A. (2017). Inovace je rozhodnutí: Kompletní návod, jak dělat inovace nejen v byznysu : 12 praktických nástrojů, 40 příkladů z praxe. Pavelková, D. (2009). Klastry a jejich vliv na výkonnost firem. Grada. Revankar, N. S. (1971). A class of variable elasticity of substitution production functions. Econometrica, 39(1), 61–71. https://doi.org/10/b4gww5. Romer, P. M. (1986). Increasing returns and long-run growth. The Journal of Political Economy, 94 (5), 1002–1037. https://doi.org/10/cx8w5b. Ruiz, J. L., & Sirvent, I. (2019). Performance evaluation through DEA benchmarking adjusted to goals. Omega, 87, 150–157. https://doi.org/10/gg4d7n. Ryan, A. (2010). Innovation performance. Managed Innovation. http://www.managedinnovation. com/articles Rydvalova, P., & Skala, M. (2021). Chapter 4 Innovation and innovation partnership. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business clusters – An empirical study. Springer Nature. Rydvalová, P., & Žižka, M. (2018). Diskuse k problematice vymezení přirozených odvětvových klastrů. Trendy v Podnikání, 8(3), Article 3. https://doi.org/10/gg4g3s Rydvalova, P., & Zizka, M. (2021). Approach to innovation in selected industries. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business clusters – An empirical study. Springer Nature. Sato, R. (1975). The most general class of CES functions. Econometrica, 43(5–6), 999–1003. https://doi.org/10/bwf66g. Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA: The mathematical programming approach to frontier analysis. Journal of Econometrics, 46(1–2), 7–38. https://doi.org/10/ bw9jnr.

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Sha, D. Y., Liang, G. R., & Huang, K.-C. (2013). An empirical study on the influencing factors of design chain integration. Journal of Applied Sciences, 13(10), 1805–1810. https://doi.org/10/ gg4b2r. Shephard, R. W. (1970). Theory of cost and production functions. Princeton University Press. Synek, M., & Kislingerová, E. (2010). Podniková ekonomika. C.H. Beck. Terjesen, S., Patel, P. C., & Sanders, N. R. (2012). Managing differentiation-integration duality in supply chain integration*: Terjesen, Patel, and Sanders. Decision Sciences, 43(2), 303–339. https://doi.org/10/ggn6jr. Wagner, J. (2009). Měření výkonnosti: Jak měřit, vyhodnocovat a využívat informace o podnikové výkonnosti. Grada. Werner, B. M. (2002). Messung und Bewertung der Leistung von Forschung und Entwicklung im Innovationsprozeß [Dissertation, Technische Universität Darmstadt]. http://tuprints.ulb.tudarmstadt.de/200 Wicksteed, P. H. (1894). An essay on the co-ordination of the laws of distribution.. Macmillan. Yang, Z. (2006). A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies. Mathematical and Computer Modelling, 43(7–8), 910–919. https:// doi.org/10/bn35qq. Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking (Vol. 213). Springer International Publishing. https://doi.org/10.1007/978-3-319-06647-9. Zizka, M., Pelloneova, N., & Skala, M. (2021). Theory of clusters. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business clusters—An empirical study. Springer Nature. Žižlavský, O. (2013). Past, present and future of the innovation process. International Journal of Engineering Business Management, 5, 47. https://doi.org/10/gcm4pz.

Chapter 6

Conceptual and Methodical Research Procedures Miroslav Zizka , Petra Rydvalova Vladimira Hovorkova Valentova

6.1

, and

Research Motivation

The published literature on economics has not paid enough attention to the analyses of the cluster existence impact on the performance of individual entities in a broader context—innovative, financial, and organisational. Based on the general equilibrium theory, it can be assumed that the existence of a cluster evokes both positive and negative microeconomic externalities in companies that also operate outside the industry. As far as the economic policy institutions are concerned, a description and understanding of the effects of the supporting instruments and programmes is essential for them. They enable to minimise negative and stimulate positive externalities of microeconomic policies. At the macroeconomic level, it is desirable to check whether cluster support in the form of subsidies is justifiable; see Pavelková and Jirčíková (2008). The research carried out at the Technical University of Liberec within the Czech Science Foundation project from 2018 to 2020 sought to reveal the specific effects that clusters have on shifting the production function of all the companies involved as well as the entire industry. We can assume effects related to the improvement of the internal organisation of a company due to the existence of a cluster, and, subsequently in general, technological innovations realised as a result of more intensive cooperation of all entities in the industry. Production functions were modelled through linear programming, using data envelopment analysis (hereinafter DEA). Unlike other studies, the research focused not only on cluster organisations (hereinafter COs) established as a result of cluster initiatives, but also examined the effects of natural industry clusters in regions (so-called natural clusters). The

M. Zizka (*) · P. Rydvalova · V. Hovorkova Valentova Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_6

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research hypothesised that the existence of natural clusters could be a more effective tool in supporting corporate innovation activities than an artificially created CO. As previously observed, even though institutionalised COs in the Czech Republic are supported from the European and national sources, the resulting clusters are not very successful in joining other Knowledge and Innovation Communities (KICs); see Pittnerová and Rydvalová (2014). This leads to the question of whether cluster initiatives really support important industries in the Czech Republic. Previous research on the functioning of clusters in the Czech Republic primarily focused on the innovative activities of COs. The team of Pavelková (2013) concluded that, based on a survey of 1110 member companies in the Czech clusters, the innovative activities of companies in the COs are at a low level. Higher values were reflected only in the manufacturing industry. In evaluating the excellence of the Czech clusters, their role in the innovation environment was also examined in terms of cooperation with universities and research institutes, as well as in terms of companies’ participation in innovation projects, economic data, and the results of research, development, and innovation. The results obtained led to the conclusion that clusters achieve better results after several years of development (the minimum of 3 years since their establishment). The evaluation of the innovation role of clusters was positively influenced by the higher share of universities and research centres in the membership base and their participation in innovative projects. Rydvalová and Pittnerová (2013) analysed corporate innovation activities in the glass and bijouterie industry in the Czech Republic which have, especially in North Bohemia, the character of a natural cluster. The analysis showed that these companies mostly relied on marketing innovations. They mainly used private sources to finance these innovations. However, the overall companies’ innovation potential was very low. It was revealed that the primary priority of these companies was to cope with their current problems, and their thoughts on future development, including innovation, were often postponed. Further research by Žižka and Rydvalová (2014) did not confirm the dependence of the intensity of the innovation in the regions on the number of clusters, their average age, or the size of their membership base. The above-mentioned research was based on data covering a period of around 5 years. Czech COs have a relatively short tradition. Their development can be divided into two phases. In the first phase from 2002 to 2006, the concept of clusters was described in the Czech literature, and it was presented to the state administration and business managers in industries that were considered to have cluster potential. At the same time, mapping and clustering was supported through cluster initiatives with support from the European Structural Funds. Subsequently, over the period of 2004 to 2007, 53 cluster projects were supported from public funds, which mainly included support for mapping the possibilities of creating COs. In the second phase, which began in 2007 and has continued to the present day, support was focused on cluster activities aimed at innovation development and international competitiveness (Skokan et al., 2012). This means that the oldest institutionalised cluster organisations existed for a maximum of 10 years at the time of the published outputs around 2013. Thus, it was

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assumed that in this relatively short period of time published research results were influenced by too short a time period, during which it was impossible to demonstrate the positive impact of the CO existence. If the research is undertaken again after approximately five years (compared to the above-mentioned studies), the degree of validity of the results will increase.

6.2

Methodical Research Procedure

The research was divided into the following research activities; see Fig. 6.1. Numberof entities

Registered cluster organisations database (CO)

Inactive COs

Active COs

Number of employees

Identification of significant industires in regions

Is there anactive CO in the industry?

NO

Analysis of relations between companies, specialisations of education and research organisations, specifics of natural resources

YES Institucionalised

Are the above mentioned conditions satisfied?

NO

Partly

Subsidies

Other industries

Small region, SMEs, strong informal social environment

YES Public funding efficiency

Defining cluster core according to NACE

Technical innovation Number of employees History of the company

Macroeconomic externalities

Companies in other regions outside a cluster

Industrial district

Natural cluster

Inputs and outputs for the Data Envelopment Analysis

Balance sheet Profit and loss statement Economic Value Added

Data Envelopment Analysis

Efficiency and Performance Scores

Differencesamong the groups of companies

Dependence between innovation and financial

Performance change over time (Malmquist index)

Technical Efficiency Change

Technological Change (Innovation)

Microeconomic externalities

Recommendations for economic policy actors

Fig. 6.1 The framework of the research

Case studies

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1. Identifying the Regions in the Czech Republic with Important COs The identification was performed based on the data from the Czech Statistical Office (hereinafter CZSO) register of economic entities on the number of these entities in individual districts of the Czech Republic. As of 31 May 2018, they were classified according to the CZ-NACE industry classification (three-digit codes). The second group comprised data on the number of employees by districts, obtained on 31 May 2018, classified again according to the CZ-NACE industry classification (two-digit codes). It is important to note that the data in both the sets is complete for the entire Czech Republic, which means that it is a statistical population, so it is not necessary to use statistical induction methods (hypothesis testing or statistical estimates) when processing the data. When processing data on the number of economic entities in the districts of the Czech Republic, it can be assumed that if natural clusters are not formed in the given districts, in the given industry, the number of economic entities in them should be evenly distributed throughout the Czech Republic. However, it is necessary to take into consideration the fact that the size of individual districts varies, regardless of any comparison indicators (area, population, number of economically active people, etc.). As Brenner (2006) finds, the size of a region does not have a significant effect on the formation of clusters or the subsequent comparison of individual regions. Therefore, the total number of economic entities in a given district was chosen as the distinguishing criterion weight. For each district, the proportion of the total number of economic entities and the total number of economic entities in the Czech Republic was calculated (Proportion 1). This expresses the assumption of an even distribution of the number of economic entities in the given industry across all the districts of the Czech Republic. Furthermore, the proportion of the number of economic entities in a specific industry in a given district and the number of economic entities in a given industry in total was determined (Proportion 2). Proportion 2 thus expresses how a given district contributes by the number of economic entities in a given industry to the total number of economic entities that are registered in this industry throughout the Czech Republic. Subsequently, Proportion 1 and Proportion 2 were compared in the way that Proportion 1 was subtracted from Proportion 2. If the result of this comparison is positive, it means that the actual proportion of the number of economic entities in the industry is higher in the given district than when an even distribution of the number of economic entities in the industry is presumed. Therefore, it can be implied that natural clusters could occur. The same procedure was applied when processing the data on the number of employees in the districts of the Czech Republic. If there is no indication of the formation of a natural cluster in an industry, the number of employees in the industry should be evenly distributed across all the districts of the Czech Republic. In order to take into account different sizes of the districts in the Czech Republic, the proportion of the number of employees in the industry in the given district and the number of employees in the industry in the Czech Republic was established (Proportion A). Then the share of the current number of employees in the given industry in the

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district and the total number of employees in the industry (Proportion B) was determined. Both Proportion A and Proportion B were compared in the way that Proportion A was subtracted from Proportion B. A positive result indicates the possibility of a natural cluster formation in the industry as the number of employees here is higher than in the distribution which would not presuppose the existence of a natural cluster. Those industries which had a significant difference between Proportion 1 and Proportion 2 in a few districts were selected for further detailed analysis. This suggests that the activities in the given industry are concentrated in a limited space, and, therefore, it may signify activities of a natural cluster. The mathematical difference between Proportion B and Proportion A serves as a supporting evaluation criterion. Nevertheless, it does not provide as detailed information about the given industry as the difference between Proportion 2 and Proportion 1. The reason is that only data for the two-digit industry codes according to CZ-NACE were available. 2. Updating the Database of COs in the Czech Republic The input database was prepared as part of the team’s previous research activities. COs were searched for using the public register and the administrative register of economic entities. All organisations that contained the keyword ‘cluster’ in their name were identified. The database comprised the name of the CO, its identification number, industry, legal form, year of establishment, list of members, number of employees, the region of operation, registered office site, contact details, and a link to the website. The database contained data on 114 institutionalised COs in the Czech Republic, of which 16 already ceased their operation as of 1 January 2019. The remaining 98 organisations were further analysed in terms of their activities. There were identified 74 active COs with projects and up-to-date information on the cluster official website. Their financial statements in the public register and collection of documents can be traced. The remaining COs are inactive. They either do not have an official website or the website is down; thus, they do not have financial statements available in a public register and a collection of documents. 3. Identifying Other Potential Clusters in the Regions of the Czech Republic The identification is based on • National Report on Clusters from 2006 (Adámek et al., 2006). • Internal research based on the calculation of industry location quotient (Žižka, 2006). • Concentration of the industry in the regions (see phase 1). • Analysis of the relations among the companies. • Presence of research and educational institutions (see Sect. 6.2.1). The location quotients calculate the degree of employment concentration in the given industry and in the specified region. The data on employment by region were adapted from the CZSO regional statistics and the MagnusWeb commercial database (Bisnode). Financial data and information on links among companies were also obtained on the basis of the licence purchased. The output of this activity was to

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determine the status of a given industry in the region (a natural cluster, a CO, an industrial district, other—minor industries), i.e. a typology of the industry. Based on the intertwined results of phases 1–3, the regions were identified in which both natural clusters and institutionalised COs exist. This situation occurred, for example, in the NUTS 2 Northeast region, where there is a natural group of textile manufacturers and a CLUTEX CO (focusing on technical textiles). At the level of NUTS 3, these are, for example, clusters (natural and institutionalised) of packaging material producers in the Hradec Kralove Region, food and timber grouping in the Jihocesky Region (the South Bohemian Region), timber and energy clusters in the Moravskoslezsky Region (the Moravian-Silesian Region), an electrical engineering cluster in the Pardubice Region, an IT cluster in the Vysocina Region, as well as a packaging cluster in the Zlin Region. Some institutionalised clusters connect entities operating in several regions of the NUTS 3 level. It is complicated to define natural clusters which emerged historically and whose borders do not copy the administrative division of the regions in the Czech Republic. For this reason, based on a research case study method, authors’ own procedure that was tested in the conditions of the Czech Republic was prepared (see Sect. 6.2.1). For further research, seven randomly selected industries were chosen, which had to fulfil the condition that there was a CO or a natural cluster (see Table 6.1). In the case of a CO, the condition had to be satisfied that it was already in its maturity stage (established by 2010, at the latest). Furthermore, two industries in which there is only a natural cluster (glass and bijouterie industry) were added to the research. The hypothesis was also verified that the COs were only established in regions with a significant representation of the given industry (see Sect. 6.2.2). 4. Identifying the Core of Clusters For both natural clusters and COs, it was necessary to define the companies that make up the cluster core. These are companies from a given industry or related industries that can be considered homogeneous in terms of production inputs and outputs. The research focused on business entities, not on public or non-profit institutions which are usually members of clusters as well. The MagnusWeb database, a collection of documents and a public register that contains information on the main business activity of each entity according to the NACE statistical classification, served as a source of data. A list of rated companies for each group was compiled; each company could only be on one list. If a company is in an institutionalised cluster (CO), which also logically operates in the territory of the natural cluster, then it was included only in the list of the CO. The procedure for determining the economic entities of a natural cluster core is described in Sect. 6.2.1. 5. Preparing a List of Other Companies A list of companies operating in other regions (outside institutionalised or natural clusters) was also prepared for all the industries covered by the research. The list served as a control group for comparing performance with the clustered companies. 6. Collecting Data on Technical Innovations (According to the Oslo Manual) Data on protected results under industrial law, such as patents, utility models,

Central Bohemian, Pardubice Hradec Kralove Liberec, Hradec Kralove, Pardubice

Nanotechnology

Source: own processing (2020)

Total

Packaging Textile

IT Furniture

CO’s region of operation Moravian-Silesian Moravian-Silesian, Olomouc, South Bohemian Moravian-Silesian South Bohemian

Industry Automotive Engineering

172, 222 13, 141

620 161, 162, 310 721

Core NACE 293 251, 28

Table 6.1 Characteristics of the analysed industries

2005 2006

2010

2006 2006

Established in 2006 2003

104/15 275/38 4284/319

86/64

357/19

718/21 1794/46

No. companies in the CO’s region except of CO members 25/13 1011/167

16/9 19/19

6/3

8/6 16/12

No. companies in CO 11/9 10/6

17,489/747

764/162 1430/138

790/45

3409/85 9125/68

No. companies outside the CO’s region 135/52 1836/197

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industrial designs, and trademarks, were collected for all the companies in the examined industries. For this purpose, an extensive search in the database of the Industrial Property Office of the Czech Republic was performed, and the number of the above-mentioned industrial property rights was ascertained for all the companies listed in phases 4 and 5. Furthermore, information on the licensing of industrial property rights was also added when available. 7. Collecting the Data from the Balance Sheets and Profit and Loss Statements The accounting data necessary for the calculation of economic value added and a subsequent DEA analysis were obtained for all companies identified in phases 4 and 5. The MagnusWeb database and a collection of documents from the Commercial Register serve as the source of data. The data obtained cover the period of 2009–2016. The basic characteristics of the selected COs are listed in Table 6.1. The number of companies in the relevant groups is displayed first, followed by the numbers of companies for which financial data were obtained in the entire time series 2009–2016. The number of companies analysed is influenced by the fact that sole trader businessmen are not obliged to publish financial statements. Business corporations in the micro and small enterprises category publish their condensed financial statements without a profit and loss statement, which also impedes in-depth analyses of their financial performance. These two categories of companies are most highly represented in the economy. According to the CZSO (2019), 97.5% of entities in the manufacturing industry had less than 20 employees in 2018. However, even some larger corporations do not comply with legal obligations and do not publish their accounting data, or they do so with a significant time lag. As Table 6.1 shows, it was easiest to get accounting data on the CO member companies. Most of them were business corporations that have to publish annual reports and financial statements in the public register. 8. Collecting the Data on the Number of Employees and the History of the Companies Accounting data were supplemented by the number of employees, which is a significant input for further evaluation of technical efficiency and performance of the companies. The data were obtained from the MagnusWeb database and public registers. The history of a company can be considered a form of accumulated intellectual capital containing a technological trajectory, routines, know-how, skills, and experience of the owners and their employees. The history was identified based on the data in the public register of the Czech Republic and information on the companies’ websites. The information about family businesses was further added for an easier identification. This factor (see RQ10 in the introduction) is assumed to mainly appear in natural clusters. 9. Obtaining Information on Subsidies from Public Funds Spent on the Creation and Development of Cluster Initiatives The CEDR information system of the Ministry of Finance of the Czech Republic (hereinafter MF) was used as a data source. The information was obtained for the above-mentioned seven COs.

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10. Economic Value-Added (EVA) Calculations These were performed for companies with available financial statements. EVA is based on economic profit, which counts all the costs of the invested capital, both company’s own and borrowed (Grant, 2003). EVA is considered a modern measure of a company’s success because it expresses the true profitability of the company and is associated with the requirement to maximise shareholder wealth (Stewart, 1994). As the transformation of accounting profit into economic profit is a relatively complicated process, also influenced by national accounting standards, Neumaierová and Neumaier (2002) created a model for calculating the EVA indicator; see Eq. (6.1). The same calculation methodology has been used by the Ministry of Industry and Trade since 1999. This is an equity-based process in which EVA is defined as the product of equity and spread (i.e. return on equity minus the alternative cost of equity): EVA ¼ ðROE  r e Þ:E

ROE re E

ð6:1Þ

Return on equity alternative cost of equity Equity

The alternative cost of equity (re) can be calculated using formula (6.2), where a risk premium is added to the risk-free rate (rf). According to the MPO (2017), the risk premium consists of a risk premium for business risk (rbus), financial structure (rfinstr), financial stability (rfinstab), and the size of the company or the liquidity of its shares (rls). r e ¼ r f þ r bus þ r finstr þ r finstab þ r ls

ð6:2Þ

The EVA value was determined both individually for each business entity and for entire groups of companies (a CO, non-member companies operating in the same region as the CO, companies outside the COs region) based on the aggregation of company data. 11. Evaluating the Innovation and Financial Performance of Companies Using Data Envelopment Analysis (DEA) This phase included the definition of inputs and outputs, which should be independent (Düzakın & Düzakın, 2007). The relation between an input and an output (within and among the groups) was examined using correlation analysis. Assets, long-term capital, number of employees, duration of the existence of the company, etc., can be considered inputs for the evaluation of innovation activities. Outputs include patents, utility models, industrial designs, and trademarks; also the commercial success of patenting in the form of a licence is advantageous. The output of the first phase can serve as an input for the second phase of the evaluation that examines how businesses can commercially use protected results.

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Economic indicators (EVA, sales, and revenue to cost ratio) served as outputs from the second phase. After determining the suitable inputs and outputs, the formulation of mathematical models (under the conditions of constant returns to scale; hereinafter CRS and variable returns to scale; hereinafter VRS) was created and the results were calculated by using the OSDEA-GUI and MaxDEA Ultra software tools. The efficiency score in the innovation and financial areas and the overall performance score were defined for each company. The aim of the DEA model is to maximise the objective function z (6.3) under constraints (6.4). The inputs xj of the unit q have weights vj. The outputs yi have weights ui. Units that are efficient have an objective function value equal to one. These units are located at the efficient frontier. Inefficient units have value z lower than one. z¼

r X

ui yiq

ð6:3Þ

i¼1

r X i¼1 m X

ui yik 

m X

v j xjk, k ¼ 1, 2, . . . , n

j¼1

ð6:4Þ

v j xjq ¼ 1

j¼1

ui  ε, i ¼ 1, 2, . . . r; v j  ε, j ¼ 1, 2, . . . , m In the case of variable returns to scale, it is sufficient to add to the model the variable μ indicating the deviation from the CRS. In this case, the conical data envelope changes to convex, which leads to a higher number of efficient units being defined. The input-oriented BCC model has the form given by relations (6.5). z¼

r X

ui yiq þ μ

i¼1 r X i¼1 m X

ui yik þ μ 

m X

v j xjk, k ¼ 1, 2, . . . , n

j¼1

v j xjq ¼ 1

j¼1

ui  ε, i ¼ 1, 2, . . . r; v j  ε, j ¼ 1, 2, . . . , m; μ2R

ð6:5Þ

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12. Identifying the Differences between the Individual Groups of Companies within Each Industry This was performed by using the Kruskal–Wallis test. The test is a non-parametric variant of the variance analysis that tests differences between median performance scores. Furthermore, the differences in the means of the individual groups were examined by using the Games-Howell posthoc test. It is a non-parametric approach to comparing multiple groups of observations which works with the order of the original values. Its advantage is that it does not require compliance with the conditions of a normal distribution, homogeneity of variance, or the same group size. All tests were performed at the significance level of alpha 5%. 13. Examining the Relation Between the Performance and Innovation Activities of Companies The performance was measured by using a performance score which was the result of the DEA models solved separately within individual industries and subgroups of the companies. The value of the performance score was converted into a binary variable with values of 0 or 1. The value of 1 was assigned to companies that were marked as performance units or best practices in the group. Other companies, with the performance score lower than 1, were given a value of 0. Using the Chi-square test of independence for categorical variables, the connection between the registered results of innovation activities and the status of a high-performance unit was examined. The strength of the dependence was measured using Pearson’s correlation coefficient R. 14. Evaluating Performance Changes During the time line of 2009–2016, this evaluation was performed using the Malmquist index. The Malmquist index (hereinafter MI) evaluates the changes in relative productivity or performance of a decision-making unit between different time periods. One of the advantages of MI is the identification of the components that lead to a performance change. The index breaks down total productivity change into technical efficiency change EFFCH and technological change TECH; see Eq. (6.6). Companies aim to approach the closest to the efficiency frontier (the best companies in the industry) with the help of various internal organisational measures. The EFFCH component expresses this effort (Li et al., 2017). Simultaneously, however, the innovations in the industry cause the efficiency frontier shift over time. The TECH component expresses this shift. In general, it is desirable for the MI, EFFCH, and TECH values to be greater than one. In that case, industry productivity increases, efficiency improves, and technological progress takes place. The internal technical efficiency change (EFFCH) can be further decomposed into the product of pure technical efficiency change (PECH) and scale efficiency change (SECH). PECH expresses the company’s ability to improve its internal technical efficiency between two time periods, t and z, under the conditions of VRS. SECH measures the change in scale efficiency between these periods. The optimal value of SECH is unitary as, in this case, the company operates in conditions of the CRS and produces in the technically best range (Pantzios et al., 2011).

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MI t,z ðxz , yz , xt , yt Þ ¼ EFFCH I TECH I ¼ ðPECH I SECH I ÞTECH I

ð6:6Þ

Suppose a company that works in period t with a vector of n inputs xt and produces m outputs yt. Then (xt, yt) represents the input–output pair of the given company in the period t, and (xz, yz) is the input–output pair of the same unit over the time period z. The Malmquist index between the time periods z and t is represented by the equations in (6.7) and (6.8). sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Dt ðxz , yz ÞDz ðxz , yz Þ MI t,z ðxz , yz , xt , yt Þ ¼ Dt ðxt , yt ÞDz ðxt , yt Þ

MI t,z ðxz , yz , xt , yt Þ ¼ EFFCH t,z  TECH t,z sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Dz ðxz , yz Þ Dt ðxz , yz ÞDt ðxt , yt Þ ¼ t t t  Dz ðxz , yz ÞDz ðxt , yt Þ D ðx , y Þ

ð6:7Þ

ð6:8Þ

The expressions Dtt(xt, xt) and Dz(xt, xt) are called distance functions. They express the distance between the input and output units over the time period t and efficient frontiers over the time periods t and z. Another pair of distance functions Dtz(xz, xz) measures the distance between input and output units over the time period z and efficient frontiers over the time periods z and t (Wang, 2019). The values of distance functions D are estimated with the help of DEA. Equation (6.8) shows the decomposition of MI into two components. The change in the technical efficiency EFFCHt,z assumes an efficient frontier in the same period and expresses the ratio of the efficiency of a given company over the period z to its efficiency over the period t. It shows how a unit tried to improve its internal performance through various measures in work organisation or production. The technological change TECHt,z characterises the frontier shift. If the efficient frontier shifts from a position over the time period t to a position over the time period z, the value of TECHt,z will be greater than one and technological progress will occur. The component TECHt,z expresses the group change in the efficiency caused by all the companies, i.e. innovations in the industry (Wang, 2019). 15. Comparing the Malmquist Index and its Components among the Groups of Companies and Industries As the final output of the previous phase was geometric averages, the original company values of MI and its components were logarithmised. The geometric mean is a monotonic function of the mean of logarithms. If there is a significant difference between the means of the logarithmised data, there is also a significant difference between the geometric means of the original variables (Alf & Grossberg, 1979). The Shapiro–Wilk test showed that the data did not have a normal distribution, and in some groups of companies the Levene’s variance check test found that the

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condition of homo-scedasticity was not satisfied. For these reasons, a non-parametric Games-Howell posthoc test was used to identify all the differences among all the company groups. The Games-Howell test works with the order of the original values and examines the differences in means of individual groups. Statgraphics XVIII software was used to test the differences. The alpha significance level was 5%. A statistical hierarchical cluster analysis was performed to identify the similarities among individual groups of companies in different industries. Ward’s method of clustering with squared Euclidean distances was used. The similarity of groups of companies was first assessed using a dendrogram of objects. Furthermore, the basic characteristics of MI and its components EFFCH and TECH were calculated for each statistical cluster. 16. Identifying and Formulating Macroeconomic and Microeconomic Externalities of a Company Clustering The question of whether the existence of clusters, especially the institutionalised COs, is a positive macroeconomic externality and whether it should be supported by economic policy actors was addressed. To answer this question, a literature review was carried out. The empirical verification of the positive externalities was done by analysing the additional tax and non-tax revenues in public budgets of the seven selected COs. The increase in corporate income tax of the clustered member entities was evaluated, as well as the increase in personal income tax of their employees, along with the increase in social and health insurance, paid by both companies and employees. The benefits of sharing the results in research, development, innovation, and tacit knowledge, as well as the multiplication of knowledge leading to increased economies of scale, are considered the positive microeconomic externalities of clustering. The data obtained from the literature review were empirically verified by the research on the companies’ approach to innovation in each industry, and changes in internal technical efficiency of the companies. 17. Synthesising the Outputs, Formulating the Consequences, and Recommendations for the Corporate Inter-organisational Behaviour Based on the data obtained, recommendations were formulated for the actors of the economic policy on how to approach support of clusters. The results identified were also processed via case studies for the textile, glass, and bijouterie industries. The case studies mentioned demonstrate the influence of other selected factors, such as tradition, or the type of innovation in the family businesses, on the development of the industries in which they create natural industrial districts and clusters in the regions studied.

6.2.1

Procedure for Defining Natural Cluster Cores

Due to further research, it is necessary to distinguish two basic types of industry clusters.

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The first type is those clusters that arose naturally in compliance with Porter’s definition, without any external state intervention. As mentioned, these are referred to as natural clusters. A natural cluster is a cluster of interconnected companies and research and educational institutions, which has existed in the given area for a long time, and no public resources were spent on its initiation. Then, there are COs that are the result of a certain cluster initiative. A cluster initiative is an organised effort to increase the growth and competitiveness of clusters in the region, involving the cluster companies, the government, and the research community (Lindqvist et al., 2012). The umbrella CO then provides the grouping with certain management services across the business and innovation processes. A CO can exist as a subset of a natural cluster as it generally does not include all industry organisations that operate in a region where a natural cluster exists. It is important to define the criteria for determining the existence of the natural cluster as this can help in evaluating the performance of both types of clusters. It can be assumed that natural clusters, which are not formalised and do not have contractual partnerships, will solve different problems than organised clusters, which are often set up to obtain some public support for their further development. In the case of industry clusters which arose based on the historically existing resources (labour, land, capital) in a given region, it can be stated that they have strong roots in the given region. The question is how to identify such a region, how to find out the importance of a given industry for its development, and, in particular, how to identify specific economic entities of a given natural cluster. Natural clusters must necessarily copy the administrative boundaries of the region to which the available data from the statistical office are linked. It is often necessary to first find the boundaries of a functional region which are defined by the local labour market and commuting areas; see Žižka (2013). Porter (1998) states that geographic borders of a cluster can vary from cities to states, to a group of neighbouring countries. Therefore, the problem is not to identify key, upstream, and downstream industries but to define geographic borders of a natural cluster. COs, established with the help of public resources, were geographically defined according to the administrative region borders in compliance with the statistical classification NUTS (NUTS 2 or NUTS 3). To define a natural cluster, pilot research was first conducted, using the method of a research case study based on local knowledge. The methodical procedure of setting up a research case study falls into the field of qualitative research including selecting a data collection method, choosing a research sample, creating a case study protocol for the database, contacting the research subjects, processing the data in the database, analysing and evaluating the obtained data, comparing the theoretical knowledge with the findings, and formulating research questions (Štrach, 2007). Based on the previous study (Rydvalová & Hotař, 2012), the glass and bijouterie industry was chosen for the pilot testing. The given industry was selected for several reasons. It is an industry which is historically typical for North Bohemia, and the authors have experience in cooperation with economic entities in this industry (manufacturing, educational, consulting, research). Furthermore, in the previous research, they

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developed a map of links among the entities in this industry (upstream, core, followup activities, including the infrastructure). It is common in studies mapping the existence of clusters (e.g. Delgado et al., 2014) to define industries with four-digit codes SIC (Standard Industrial Classification—an American system classifying industries). Such accuracy is not possible in the Czech Republic because it encounters the issue of the General Data Protection Regulation. Therefore, the following procedure for the identification of a natural cluster was used in a case study of the glass and bijouterie industry in North Bohemia. The first step was to identify the dominant industry within the geographically defined region. Ideally, municipalities would serve as the basis for industry analysis of data at the national level for the whole republic. Nevertheless, the Czech Statistical Office regards the data on the number of entities and the number of employees at the municipal level as individual data. The data on the number of economic entities (according to the NACE classification at the level of three-digit codes) and the number of employees (according to the NACE classification only at the level of two-digit codes) are thus available in the aggregate form only at the district level. Districts (NUTS4/LAU1) in the Czech Republic are relatively heterogeneous in terms of size (area, population, number of economic entities). The procedure for identifying significant industries in the districts was as follows. The proportion of the number of economic entities in individual districts in the total number of economic entities in the Czech Republic was determined. This proportion characterises the economic size of the district. For example, in Prague, there are 21.42% of all economic entities; the smallest number of them are in the Jesenik district, totalling only 0.37%. (a) The actual proportion of economic entities in a given district and industry (to the three-digit NACE codes) was compared with the proportion calculated in the previous step. If the actual proportion of the number of economic entities in the industry, in the given district, and the number of all economic entities in the given industry is greater than the one that expresses, the size of the district is, de facto, defined by the number of economic entities, thus indicating the possibility of the existence of a natural cluster. The difference need not be tested statistically; a basic set of all economic entities is sufficient for the needs of the analysis. The results for NACE 231 (manufacture of glass and glass products) and NACE 321 (manufacture of jewellery, bijouterie, and related articles) for selected districts are displayed in Table 6.2. (b) By analogy, the proportion of employment in the industry, in the given district, and the proportion of the district in the total employment were calculated, and the difference between the two variables was established; see Table 6.3. The fact that the data on employment are only available at the level of the two-digit NACE codes can be seen as a disadvantage. Thus, in many cases, they may cover a wider range of economic activities than in the case of the previous analysis by the number of economic entities. For example, the industry NACE 23 (manufacture of other non-metallic mineral products) includes, in addition to

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Table 6.2 The concentration of economic entities in the CZ-NACE 231 and 321 industries, in selected districts in the Czech Republic Industry CZ-NACE 231: Glass production and glass articles The proportion of the district The proportion of the district on the on the number of entities in number of economic entities in the District the industry Czech Republic Difference CZ0511 0.1988 0.0081 0.1907 Ceska Lipa CZ0512 0.1720 0.0085 0.1636 Jablonec n. N. CZ0631 0.0421 0.0071 0.0350 Havlickuv Brod ... Industry CZ-NACE 321: Manufacture of jewellery, bijouterie, and related articles CZ0512 0.1491 0.0085 0.1406 Jablonec n. N. CZ0514 0.0530 0.0069 0.0460 Semily CZ0513 0.0369 0.0178 0.0191 Liberec ... Source: own processing according to CZSO (2018)

glass and glass product manufacture, the manufacture of refractory, building, porcelain and ceramic products, cement, lime, plaster, and abrasive products, etc. NACE 32 represents other manufacturing, apart from the manufacture of jewellery and bijouterie, as well as manufacture of musical instruments, sports goods, games and toys, medical and dental instruments and supplies, etc. For this reason, the analysis of the number of entities was chosen as a determining factor for identifying natural clusters, and the analysis of the number of employees was considered complementary. The effect of the accuracy of the industry category is evident in the case of small industries. Table 6.2 indicates that entities in the bijouterie industry are mostly concentrated in the Jablonec nad Nisou district. However, this fact is difficult to notice in Table 6.3. (c) .As regards employment, other manufacturing is mainly concentrated in the Kladno district. A further examination reveals that out of almost 3800 employees in this broad industry, approximately 2500 are employed by Lego Kladno, the manufacturer of games and toys. In the second step, only those industries that are significantly concentrated (especially in terms of the number of entities) in individual districts were analysed. First, it was determined whether there was a CO established based on a cluster initiative in the given region and industry. The list of COs was created as a part of our research (see Sect. 6.2).

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Table 6.3 The concentration of employees in the CZ-NACE 23 and 32 industries in selected districts, in the Czech Republic Industry CZ-NACE 23: Manufacture of other non-metallic mineral products Proportion of the district on Proportion of the district on the the employment in the number of employees in the Czech District industry Republic CZ0423 0.6389 0.0232 Teplice CZ0512 0.3900 0.0171 Jablonec n. N. CZ0533 0.2473 0.0186 Svitavy ... Industry CZ-NACE 32: Other manufacturing CZ0203 0.1022 0.0096 Kladno CZ0311 0.0746 0.0195 Ceske Budejovice CZ0521 0.0679 0.0147 Hradec Kralove ...

Difference 0.6157 0.3729

0.2287

0.0926 0.0551

0.0532

Source: own processing according to CZSO (2018)

To identify natural clusters, we are interested in those industries where there are no institutionalised COs; they will be evaluated separately. In the case a given industry shows a significant concentration in the region (in terms of the number of entities and employees), and, at the same time, there is no support for a CO existence, then it is a candidate for creating a natural cluster. The subsequent analysis evaluates the number and profiling of the entities in the given industry in municipalities of the given region. In the third step, the fulfilment of other factors necessary for the existence of a natural cluster in the given region (the district level was chosen/NUTS4/LAU1) was checked by monitoring the publicly available data on: • The specialisation of the vocational and higher education (source: Ministry of Education Youth and Sports, hereinafter MYES—register of schools). • Industrial traditions in the region, including the existence of traditional and family businesses, monitoring customs, tacit knowledge, and skills. This factor is very demanding on the knowledge of the local environment, as there is no complete material mapping the historical development of individual industries in the Czech Republic, yet a study conducted at Masaryk University may help with this (Svobodová et al., 2013). • The specialisation of the research organisations (data source: MYES—register of research organisations).

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• Availability of local natural resources concerning the industry. • Existing networking. It should be noted that the analysis of industrial tradition and the structure of the region requires excellent knowledge of the local environment. Therefore, it is necessary to obtain data from local organisations. The data on natural clusters are further processed by research case studies. An example of such a situation is the ‘Crystal Valley’ natural cluster (Rydvalova & Zizka, 2021, in this book). In the fourth step, the municipalities were specified, through which it was possible to define the region of the natural cluster. The method for evaluating a systematic and predictable lack of balance according to Vilfred Pareto’s rule was chosen to select the important municipalities for the analysis of economic entities with the identified industry according to the NACE in the district. As Pareto proved (Koch & Novotná, 2008), the measure of such inequality is the ratio 80/20, which points out to the fact that approx. 80% of the outputs are the results of approx. 20% of the inputs. The procedure can be summarised as follows: • Preparation of data at the level of municipalities (the source of the data is the public register of the CZSO), the selection of the key industries at the two-digit NACE. • Arranging the data in descending order according to the size of all the municipalities in the observed regions. • Building a histogram (cumulative sum). • Expressing the partial sums in the percentage of the total number. • Setting the criteria for decision-making. • Separation of the essential from the insignificant. Graphically, it is represented by the Lorenz curve which expresses the degree of concentration of the observed phenomenon. It is a graph of quantiles related to the uniform distribution. The evaluation focused on industries, which according to the classification of economic activities (NACE) were classified by Eurostat as key industries in terms of innovation activities, whereas all wholesale activities were excluded. The included industries are expressed by two digits: • • • • • • • • • •

Mining and quarrying—B/05-09/. Manufacture of food products—C/10-34/. Electricity, gas, steam, and air conditioning supply—D/35/. Water supply, sewerage, waste management, and remediation activities—E/3639/. Transformation and storage—H/49-53/. Information and communication—J/58-63/. Financial and insurance activities—K/64-66/. Architectural and engineering activities; technical testing and analysis—M/71/. Scientific research and development—M/72/. Advertising and market research—M/73/.

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Fig. 6.2 The significance of knowledge industries in the Jablonec nad Nisou district, in compliance with CZ-NACE (two digits), excluding wholesale in %. Source: own processing according to CZSO (2018)

These industries were analysed in a pilot study in 34 municipalities of the Jablonec nad Nisou district, according to the economic entities indicator. As displayed in Fig. 6.2, NACE 32 and NACE 23 industries ranked in the first and second places according to the number of economic entities, followed by the industries which subsequently provide only services to the entities in these industries. Following these findings, the number of entities indicator was observed for individual municipalities only in the above-mentioned industries NACE 23 and NACE 32, and again, in compliance with Pareto’s rule, Therefore, the municipalities from the Jablonec nad Nisou district were chosen for an in-depth analysis of economic entities. These municipalities can be viewed as the core of the evaluated natural cluster from the geographical point of view. The industry NACE 23: Zelezny Brod, Jablonec nad Nisou, Pencin, Smrzovka, Tanvald, Mala Skala, Koberovy, and Desna. The eight municipalities make up 24% of the municipalities bringing 80% of the economic entities in the given industry CZ-NACE 23 in the district (see Fig. 6.3). Industry NACE 32: Jablonec nad Nisou, Zelezny Brod, Pencin, Smrzovka, Tanvald, and Rychnov u Jablonce nad Nisou. The six municipalities make up 15% of the municipalities bringing 80% of the economic entities in the given industry CZ-NACE 32 in the district (see Fig. 6.4).

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Fig. 6.3 The significance of municipalities according to the number of economic entities with CZ-NACE 23 in % in the Jablonec nad Nisou district

However, there is a certain risk that a significant entity in the municipality can be overlooked and not included in the analysis. The significance threshold must be intuitively set based on the knowledge of the local environment. Therefore, for example, the municipality of Zasada must be included in the analysis of NACE 23 (after all, the number of economic entities (16) in Zasada does not differ so much from the number of entities in the municipality of Desna (18), included in the analysis automatically according to the above algorithm). In the case of NACE 32, the municipalities of Lucany and Mala Skala were added to the analysis for similar reasons. In the fifth step, a detailed analysis of economic entities was conducted, and the data were obtained from the publicly available registers in the Czech Republic (ARES, 2020) in the above-identified municipalities and industries. Based on this, a database of economic entities was created. It enables a consequent evaluation within a natural cluster.

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Fig. 6.4 The significance of municipalities according to the number of economic entities with NACE 32 in % in the Jablonec nad Nisou district. Source: for both figures: own processing according to CZSO (2018)

6.2.2

Methodological Procedure for Assessing the Territorial Distribution of Cluster Organisations

When assessing the territorial distribution of COs, the intention was to prove that COs prevail in districts or regions where there is a significant representation of the industry in which the CO operates. This means that the proportion of the members of a CO sample operating in a district/region, where the representation of the given industry is significant, must be greater than 50%. The process of preparatory and implementation work was scheduled as follows: • Assigning a district and a region to the place of operation of each member of all the seven monitored COs. • Assessing whether the place of operation of the CO members belongs to the district/region, in which there is a significant representation of the given industry. • Calculation of the proportion of the CO members who operate in the district/ region, where the industry is significant. • Assessing individual COs in terms of their place of operation in the district/region with a significant representation of the given industry. The assessment was run by a simple comparison of the calculated proportions in the region/district with a value of 50%. The research was carried out for seven randomly selected clusters. A simple comparison was used because the authors de facto worked with seven populations, where the observed feature was checked in all their units. • One sample was created out of all the members of the seven monitored COs. Two hypothesis tests for π were run, which are described in detail for example in Black

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(2010). The reason for using this test was the fact that from the set of COs that were considered suitable for the analysis, these seven were randomly selected. Based on that, it is possible to work with this set as with one-stage cluster sampling, where during the first step a number for groups was chosen, and in the second step, all the units of the selected groups were assessed, as described in Lohr (2010). Based on the above procedure, it was examined whether business entities that are members of cluster organisations have their registered office mainly in districts or regions, where a significant concentration of the industry is identified. Spatial concentration was measured using a localisation coefficient. The coefficient compares the relative employment in a given industry at the regional level to the relative employment in the industry at the national level. An industry is considered significant in a region if the value of the localisation coefficient exceeds 1.1 (Skokan, 2004). At the district level, out of seven randomly selected cluster organisations, only two cluster organisations (CLUTEX and IT Cluster) were mostly located in areas with a significant concentration of the industry. In the case of regions, four such organisations were identified. On average, 51% of the members of institutionalised clusters were based in a region with a significant representation of the industry. These were IT Cluster, Czech Machinery Cluster, and MS Automotive Cluster and the CLUTEX cluster organisations. Subsequently, at the level of significance of 5%, it was tested whether the members of cluster organisations significantly occur in the territory where the given industry predominates, namely at the regional level. This hypothesis could not be proved. It is obvious that, in fact, natural or functional regions may differ from administratively defined units. Therefore, it was determined whether the members of cluster organisations are located near the border of the administrative region, specifically within a distance of 40 km from it. This distance was selected because commuting radius is supposed to be within one hour. It was found that the proportion of members of cluster organisations within commuting distance increased to 74%. Subsequently, at a significance level of 5%, it was demonstrated that the proportion of members with the registered office within 40 km from a district where the industry is significant is higher than 50%. It can be concluded that Czech institutionalised clusters were indeed created in areas where the industry traditionally occurs.

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Chapter 7

Specifics of Natural Industry Clusters Petra Rydvalova

7.1

and Miroslav Zizka

Historical Development of the Czech Economy

The Czech Republic is one of the countries where the driving force of the economy was private business until the period before the Second World War. Subsequently, however, private businesses (often of the family type) were virtually forced by historical development to submit to a controlled economy for almost 50 years (two generations). That began on 15 March 1939, when the German occupation of Czech Lands took place through the Nazi invasion of Bohemia and Moravia and the regulations and plans imposed by German wartime economics. After the end of the Second World War in May 1945, the elimination of private business was gradually completed within the systems of a controlled socialist economy. A period of privatisation of large companies and the chaotic establishment of mainly minor and small enterprises took place after the end of communism in 1989. A large number of new companies were founded within a very short period. The situation in transforming economies in the 1990s may be viewed from the perspective of an analogy with the renewal theory as an environment with a non-homogenous age structure of family businesses. Unlike the circumstances in developed economies that experienced no disruption of their market economy, a major disruption of ownership conditions occurred in the post-communist countries. The number of registered companies in Czechoslovakia in 1990, following the ‘Velvet Revolution’, was approximately 179,000, while two years later this number increased by nearly sevenfold. Most of these companies were formed as small businesses and were financed by individual families.

P. Rydvalova (*) · M. Zizka Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_7

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Therefore, those entities that succeeded in the market are all of approximately the same age and deal with similar problems regarding their growth, professionalisation, and succession. The age structure of these companies in the Czech Republic (and other countries with similar historic development) may be seen as non-homogenous. The oldest among them is exactly 30 years old, and there are a large number of them of this age. Hence, they are simultaneously facing similar problems regarding the professionalisation of these companies while, in essence, these are large firms that launched their activities with the support of a family. To the contrary, in economies with no discontinuation of private enterprise development, the age structure of companies is stabilised and distributed across longer periods. Despite the change in the system of management of the state’s economy, which lasted for two generations, it was possible to maintain the development of traditional industries in individual regions, and we can speak of the existence of natural conditions for the development of industry clusters. Examples of such industries are the glass, bijouterie, and textile industries in the region of North Bohemia. A case study was chosen as a method of presenting the specifics of these natural clusters. The reasons for choosing the industrial district for the case study are closely connected with the knowledge of the field, location, and close cooperation with the economic entities of the researched phenomenon. Without this, it would not have been possible to carry out the studies.

7.2

Glass Industry and the Manufacture of Bijouterie

(Lasvit, Internal materials, 2018) North Bohemia—GLASSWORKS/GLASS INDUSTRY has always belonged here. As the glassmaker Martin Šikola said in a document by Leon Jakimič from Lasvit company: ‘There was nothing else to do here. . .’ Historically, it was about family ties, the coexistence of Germans and Czechs, the transfer of crafts from generation to generation, but then World War II and regime change came, instead of family glassworks, factory halls appeared. After the revolution in 1989, a huge enthusiasm, which was replaced by disappointment and crisis. Surprisingly, in the 2020s, we can say that tradition has survived it all. It is inscribed in the blood of the people of the region. Glassmaking is a traditional industry that has become a significant phenomenon for the development of the Czech economy, especially in North Bohemia. It is an industry with extensive know-how and specific technologies created over many generations. Since about the seventeenth century, it has been a pro-export-oriented industry influenced by tough foreign competition, the prices of imported commodities due to changes in exchange rates, tariff barriers, and other external influences.

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Looking back through history, the industry is accustomed to going through alternating periods of booms with severe recessions. Let us focus, for example, on the eighteenth century. The first half is associated with the boom and significant development of the glass industry. Glass production was dispersed in small ‘handicraft companies’ associated with the economy of large estates. These glassworks were dependent on the supply of wood as a raw material for heating furnaces, until the middle of the eighteenth century. Around the end of the eighteenth century, glassmakers faced a crisis caused by the distance of production from sales, competition from England, and the inability to adapt to demand for new products. Further stagnation of the glass industry occurred at the beginning of the nineteenth century due to the Napoleonic Wars and the continental blockade. The way out of this crisis was to use the stimuli of the industrial revolution, which enabled the transition from piece production to serial consumer production from manufactories. Despite the fact that this industry is accustomed to crises (see Nový, 2013), it was significantly shaken by the closure of several glass companies in the first decade of the twenty-first century. At the end of the twentieth and the beginning of the twenty-first century, glass companies occupied the third most important place in the manufacturing industry in Liberec in terms of employment. At the end of 2003, however, the first signs of the commencing problems in the glass industry were already recorded. These gradually deepened, and at the beginning of 2006, it was possible to characterise the situation as a crisis, and the global economic crisis only completed it. It can be assumed that this was due to insufficient innovation activities. This caused a decline in competitiveness, especially vis-à-vis Southeast Asian producers. The average number of employees in the Czech glass industry fell by 61% in 2009 compared to 1991 (from approximately 42,000 to 16,000 employees). This decline occurred mostly in utility glass, where there was a decrease of 90% of employees over the same period. There is a wide variety of glass technologies as well as raw materials used and products produced. The following glass products can be defined: car glass, building glass, mirrors, rods, tubes and technical glass, glass fibres, glass bijouterie and chandelier trimmings, optical glass, packaging glass, household glass, utility glass, replicas and historical glass, art glass, decorative glass, handmade glass bijouterie, and glass Christmas decorations. It was the manufacture of bijouterie in North Bohemia that created a separate industry, originally as an imitation of precious stones. The industry using glass technology was preceded by the existence of natural resources—precious stones. Their shortage and high price stimulated a demand (from the townspeople in the sixteenth century) for a quality imitation of precious stones. So, the replacement of rare jewellery was created as a result of an innovative idea. However, glass stones gradually became a specific product and are perceived as a product with its own value (Rydvalová & Hotař, 2012). The source of raw materials for the glass batch, the source of energy (forests), knowledge of mostly utility glass production, and at the same time the skill and craftsmanship of originally precious stone cutters were important for the natural development of the glass and bijouterie industry. Companies in the glass industry in North Bohemia are concentrated geographically and are also linked to specialised

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Fig. 7.1 Geographic delimitation of Crystal Valley natural cluster. Source internal material of Preciosa Group

educational and other support institutions in the region. The industry focus in the investigated locality of the Liberec Region in North Bohemia is mainly ‘decorative glass and chandeliers, and jewellery and bijouterie’ and, based on the above, can b understood as a natural cluster as defined by Porter (1998). This functional region can be defined on the historical availability of natural resources; see the map in Fig. 7.1.

7.2.1

Case Study of the Crystal Valley Natural Cluster

It concerns the generational transfer of craft and tradition, as the example of Crystal Valley shows. The area called ‘Crystal Valley’ is an area of the region with historically the oldest knowledge of glassmaking in the Czech Republic. It is located in the north of the Czech Republic and is home to many companies focused on glassmaking, specialised schools, museums, and other institutions, which are associated with the names of world-renowned glassmakers. Crystal Valley has a more than a 460-year tradition in the history of Czech glass and is a world heritage site. In 2017, Crystal Valley was nominated by the Czech Republic for registration in the UNESCO World Heritage List of Intangible Cultural Heritage. It is not an institutionalised cluster organisation (hereinafter CO), although the natural grouping has a dominant player, which is the Preciosa Group. As you can read on the Preciosa website (Preciosa, 2017): ‘Glass factory after glass factory was being built in Crystal Valley, and customers around the world were

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demanding higher and higher quality products from Bohemian glassmakers. Simply handing down the traditions of glassmaking from father to son was no longer enough. And so, the world’s first School of Glass was founded in Kamenicky Senov in 1856. Two decades later, the School of Jewellery was added in Jablonec nad Nisou.’ From the map in Fig. 7.1, it is clear that this is a region that is not defined by the administrative boundary of one district or the whole region, but by selected areas. The region of the natural cluster consists of several industrial districts. In 2014, Preciosa (IPO, 2018) registered the trademark for the given area with the designation ‘Crystal Valley’ with the Industrial Property Office. Subsequently, this trademark was bought by the Liberec Region administration which, on the basis of this, launched an initiative to connect the glass and bijouterie industry and the development of tourism (for more, see https://crystalvalley.cz/en). In terms of the definition of industries according to the CZ-NACE classification, these are industries: 231—Manufacture of glass and glass products (hereinafter referred to as GLASS-(NACE)231) and 321—Manufacture of jewellery, bijouterie, and related articles (further referred to as BIJOUX-(NACE)321). Both dominant industries can be further divided into more detailed classes according to the CZ-NACE classification. Complementary industries are 259—Manufacture of other fabricated metal products, 274—Manufacture of electric lighting equipment, and 17,290—Manufacture of other articles of paper and paperboard and others. Geographically, from the administrative point of view, these are selected municipalities of the Liberec Region in the administrative districts of municipalities with extended powers (hereinafter also AD MEP) Ceska Lipa, Novy Bor, Liberec, Jablonec nad Nisou, Zelezny Brod, Turnov, Desna, and Tanvald. This is a relatively interesting finding. It can be said that the definition of AD MEP is closer to the territorial unit, which respects the natural development of the region. However, data for industry analysis (two-digit CZ-NACE) codes are available at the national level in aggregate form for individual municipalities (LAU2), district (NUTS4 or LAU1), or regions (NUTS3). The Vilfredo Pareto’s rule was chosen to select the important municipalities for the analysis of economic entities with the identified industry according to the NACE in the district. As Pareto proved (Koch & Novotná, 2008), the measure of such inequality is the ratio 80/20, which points out to the fact that approx. 80% of the outputs are the results of approx. 20% of the inputs. The procedure can be summarised as follows: • Preparation of data at the level of municipalities (the source of the data is the public register of the CZSO), the selection of the key industries at the two-digit NACE. • Arranging the data in descending order according to the size of all the municipalities in the observed regions. • Building a histogram (cumulative sum). • Expressing the partial sums in percentage of the total number.

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• Setting the criteria for decision-making. • Separation of the essential from the insignificant. The NACE 32 and NACE 23 industries ranked in the first and second places in the Jablonec nad Nisou district according to the number of economic entities, followed by the industries which subsequently provide only services to the entities in these industries. Following these findings, the number of entities indicator was observed for individual municipalities only in the above-mentioned industries NACE 23 and NACE 32. Therefore, the municipalities from the Jablonec nad Nisou district were chosen for an in-depth analysis of economic entities. These municipalities can be viewed as the core of the evaluated natural cluster from the geographical point of view. The industry NACE 23: Zelezny Brod, Jablonec nad Nisou, Pencin, Smrzovka, Tanvald, Mala Skala, Koberovy, and Desna. The eight municipalities make up 24% of the municipalities bringing 80% of the economic entities in the given industry CZ-NACE 23 in the district. Industry NACE 32: Jablonec nad Nisou, Zelezny Brod, Pencin, Smrzovka, Tanvald, and Rychnov u Jablonce nad Nisou. The six municipalities make up 15% of the municipalities bringing 80% of the economic entities in the given industry CZ-NACE 32 in the district (Zizka et al., 2021, in this book). Given that this is a traditional industry, the indication of family businesses in the industry was also monitored. The term is not generally defined either in terms of theory or legislation (as is the case, for example, of small and medium-sized enterprises, hereinafter SMEs). Available definitions of family businesses are always defined for the purpose of monitoring the phenomenon, which the authors of the text do not contradict. In the Czech Republic, the subject of a family business in 2018 was given higher attention at the beginning of the project activities (2018 was the year of a family business in the Czech Republic) in both research and media areas, with an effort to define the term within the Czech legislation as part of the activities of the Association of Small and Medium-Sized Enterprises and Crafts CZ (hereinafter also AMSP CR). In the Crystal Valley case study, a more general understanding of the term was used, which was defined in the previous research of the given phenomenon: Family business is understood as a gainful activity of family members carried out systematically, independently, on their own account, at their own risk, in a trade licence, or a similar manner to generate profit/value for a given family, assuming generational transfer. Family members are spouses, parents, siblings, children, grandchildren, brothers-in-law, and grandparents. In the case of the family business, family relationships are assessed in relation to the founder or owner of the economic entity (Jáč et al., 2017).

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GLASS and BIJOUX Industrial Districts: Definition of their Members

In the Liberec Region (NUTS 3), which has four districts, the most business entities in BIJOUX-(NACE)32 are in the Jablonec nad Nisou district. In the GLASS(NACE)23 industry, the district is in the second place, after the Ceska Lipa district. The mere frequency of subjects without comparing the size of the district was compared according to the number of inhabitants (Jablonec nad Nisou is the second smallest district in the region, after Semily), which only confirms the importance of both branches of glass in the region. The district of Jablonec nad Nisou is dominant in BIJOUX-(NACE)32; therefore, the pilot research and, at the same time, the presented demonstrations were focused on this district. The district has 34 municipalities. The total population as of 31 December 2017 was 90,357, of which 45,771 inhabitants lived in the municipality of Jablonec nad Nisou (statutory town), i.e. more than 50% of the district’s population. In the Jablonec nad Nisou district, a total of 881 economic entities were registered under the BIJOUX-(NACE)32 category, of which 484 (again more than 50%) have their registered office or place of business in the Jablonec nad Nisou municipality (data source: CZSO, 2020b). Based on this finding, the municipality of Jablonec nad Nisou was chosen as an area for further research. Other significant numbers of economic entities in the industry occur in the municipalities of Pencin, Smrzovka, Tanvald, and Zelezny Brod. Subsequently, it was necessary to define the industry in a more detailed division of CZ-NACE. Table 7.1 shows the numbers of economic entities in the municipality of Jablonec nad Nisou in the industry BIJOUX-(NACE)32 only for group 321—Manufacture of jewellery, bijouterie, and related articles. The table no longer provides data for

Table 7.1 Number of economic entities by groups and classes of CZ-NACE 32 industry in the municipality of Jablonec nad Nisou CZNACE 32 321 3211 32,110 3212 32,120 3213 32,130

Industry Other manufacturing industry Manufacture of jewellery, bijouterie, and related articles Striking coins Striking coins Manufacture of jewellery and related articles Manufacture of jewellery and related articles Manufacture of imitation jewellery and related articles Manufacture of imitation jewellery and related articles SUM category 321 to 32,130

Number of economic entities 484 4 0 2 2 120 0 268 396

Source: own processing according to (ARES, 2020) Note: In NACE Class 3213, the term bijouterie is replaced by the term imitation jewellery

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Table 7.2 Ceska Lipa GLASS-(NACE)231 industrial district Researched activity within NACE 231 231 Manufacture of glass and glass products 23,110 Manufacture of flat glass 23,120 Shaping and processing of flat glass 23,130 Manufacture of hollow glass 23,190 Manufacture and processing of other glass, including technical glassware Total

No. of entities 638 1 8 177 51 875

Source: own processing according to (ARES, 2020)

industry 322—Manufacture of musical instruments, 323—Manufacture of sports goods, 324—Manufacture of games and toys, 325—Manufacture of medical and dental instruments and supplies, and 329—Manufacturing n.e.c., in which remaining 88 economic entities have its share within the municipality. Using a case study, a survey of 120 economic entities in the BIJOUX-(NACE) 321 industry was carried out as a part of a pilot study. In terms of legal form, there are 98 entities carrying out their activities as sole traders, 22 juridical persons, mostly with the legal form of private company, limited by shares, and three of them were joint-stock companies (PRECIOSA GS, Czech Mint, and SOLITER). Ten private companies limited by shares were established in 2017 and 2018, and trade licences show there were recurring names of persons responsible. It can be assumed that this may be a type of ready-made companies, i.e. ‘empty’ companies ready for sale and these have not been further investigated. However, this situation may also indicate an expected interest in doing business in the industry and region. The final range of companies adjusted for companies with a short history includes 110 business entities in the industrial district under the designation of BIJOUX(NACE)321. The procedure was similar in all districts of the Liberec Region, for both industries GLASS-(NACE)231 and BIJOUX-(NACE)321. The database of companies contains data identifying the subject, in terms of place or seat of business, legal form, records of obtaining funds from public sources, registration for value-added tax (hereinafter also VAT), subsequent contact details, existence of websites, possible family ties, public benefit, and specification of the year of establishment of the business activity of the given company, and was traced via monitoring on the Internet. In total, the database contains data for 2500 economic entities. The following are the numbers of entities in individual industrial districts: 1. Ceska Lipa GLASS-(NACE)231 industrial district is defined by 875 economic entities, where 92% of companies were without employees, i.e. only the founder or other family members work in the company. The overview of the number of entities in terms of activities according to NACE is given in Table 7.2. 2. Jablonec nad Nisou GLASS-(NACE)231 industrial district is defined by 847 economic entities, where 91% of companies were without employees, i.e. only the founder or other family members work in the company. The overview of the number of entities in terms of activities according to NACE is given in Table 7.3.

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3. Jablonec nad Nisou BIJOUX-(NACE)321 industrial district is defined by 564 economic entities, where 87% of companies were without employees, i.e. only the founder or other family members work in the company. The overview of the number of entities in terms of activities according to NACE is given in Table 7.4. 4. Semily BIJOUX-(NACE)321 industrial district is defined by 214 economic entities, where 86% of companies were without employees, i.e. only the founder or other family members work in the company. The overview of the number of entities in terms of activities according to NACE is given in Table 7.5.

Table 7.3 Industrial district Jablonec nad Nisou GLASS-(NACE)231 Classification of economic activities within NACE 231 231 Manufacture of glass and glass products 23,110 Manufacture of flat glass 23,120 Shaping and processing of flat glass 23,130 Manufacture of hollow glass 23,140 Manufacture of glass fibres 23,190 Manufacture and processing of other glass, including technical glassware Total

No. of entities 700 2 6 96 3 40 847

Source: own processing according to (ARES, 2020)

Table 7.4 Jablonec nad Nisou BIJOUX-(NACE)321 industrial district Classification of economic activities within NACE 321 321 Manufacture of jewellery, bijouterie, and related articles 32,110 Striking coins 32,120 Manufacture of jewellery and related articles 32,130 Manufacture of imitation jewellery and related articles Total

No. of entities 3 2 191 368 564

Source: own processing according to (ARES, 2020)

Table 7.5 Semily BIJOUX-(NACE)321 industrial district Researched activity within NACE 321 321 Manufacture of jewellery, bijouterie, and related articles 32,120 Manufacture of jewellery and related articles 32,130 Manufacture of imitation jewellery and related articles 32,990 Other manufacturing n.e.c. Total Source: own processing according to (ARES, 2020)

No. of entities 1 172 39 2 214

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Defined Specifics of the Natural Cluster of the GLASS and BIJOUX Industry

In the next step, the fulfilment of the criteria defining the natural cluster composed of industrial districts of the given industries was verified, in terms of the existence of the core, as well as soft and hard infrastructure. In all four industrial districts forming the Crystal Valley industrial cluster, both the glass industries (aggregation of 231 to 23,190 industries) and the manufacture of jewellery, imitation jewellery (i.e. bijouterie), and related articles (aggregation 32,110, 32,120, 32,130 industries) were analysed. In both industries, the existence of SMEs was found, with a predominance of small entrepreneurs with up to 10 employees, excepting business corporations such as Preciosa GS, Czech Mint, and SOLITER, which are medium-sized and large companies. In the region, there has historically been an industry-oriented secondary and continuing education, including university education in the nearby city of Liberec within commuting distance (Technical University of Liberec, Department of Glass Machines and Robotics, Department of Design). There is demonstrable cooperation of economic entities with education at all levels, both at the educational and research level. There are technical and non-technical innovations associated with the industries, without which related companies in the region would not survive the economic crisis. In the 1960s, the Research Institute of Glass and Bijouterie was established in the Jablonec nad Nisou municipality, and it is currently the development office of the Preciosa company. In the region, there are specialised institutions such as museums, galleries, and institutions of leisure activities, documenting the tradition of the industries. An industry association (the Association of the Glass and Ceramic Industry of the Czech Republic) was established for the given industry in 1990, dealing with providing and sharing information, research in the industry, publishing a professional magazine Sklář a keramik (Glassmaker and ceramist). An independent Association of Glass and Bijouterie Manufacturers was established in Jablonec nad Nisou, which brings together 50 bijouterie and glass companies, four art schools, and the Museum of Glass and Jewellery in Jablonec nad Nisou. The main goal of the association is to represent the interests of the manufacture of bijouterie and glass industry of the Czech Republic. It also offers a catalogue of glass companies on its website. The Chamber of Commerce in Jablonec nad Nisou states that primarily in the region of its operation there are entities with the main activity in the manufacture of bijouterie and in the glass industry, followed by the automotive and engineering industries. The region has a tradition of more than 400 years, despite changes in political and economic systems. In 1945, the seven most important and eighteen smaller

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companies in the Jablonec region were merged, thus creating the basis of today’s Preciosa Group. Knowledge, skill, and love of the craft of the bijouterie and glass industry are passed on from parents to children. In both industries, there is a strong connection to the region with the tendency to involve all members and generations of families in the business. The presumption of the existence of a family business was verified. Concerning the self-employed sole trader without employees, the existence of a family business can be expected (i.e. the business of a sole trader with the involvement of other family members). Concerning legal entities, the existence of the same surname within the ownership, managerial, or control structure in the company or in the historical context of most of the 12 companies was examined.

7.3

Textile Industry

The textile industry is one of the oldest industries in the Czech Republic. In the Czech lands, the technique of hand-weaving was extant as early as the fifteenth century. At the end of the seventeenth century, the first larger manufactories began to appear, in which manual labour predominated. But the division of labour made it possible to increase the quality and volume of production while reducing production costs. In 1784, a protectionist measure banning the import of foreign products for public sale helped domestic textile production. In 1806, an embargo was imposed on the import of goods from England, which caused a shortage of fabrics and stimulated the development of domestic draperies. The first drapery master came to Liberec in 1579, and by 1599, the Liberec drapers received the status of a guild (Bergmanová, 2008). The concentration of the industry in North Bohemia since the eighteenth century was caused by favourable natural conditions (sufficient water energy, later brown and black coal, domestic raw materials—flax and wool), available labour, and the technological influence of neighbouring Saxony and Prussian Silesia. At that time, several large industrial enterprises were established here—dyeing, spinning, and weaving mills (Johann Georg Berger, Clam-Gallas, Franke & Comp., Karl Ballabene & comp.). A great impetus for the development of textile production was the purchase of spinning mills and dye-works from the Ballabene company by the Liebig brothers in 1828. The Liebig brothers founded the renowned Johann Liebig & Comp. (1833), which was inherited from fathers to sons until nationalised in 1945. In 1898, they employed 4120 workers and were one of the largest textile factories in Austria-Hungary (Bergmanová, 2008). The Liebigs also contributed to the development of housing construction, textile education, gas, energy, and rail transport in the region. The development of the textile industry reached its peak before the First World War when it was in first place in the structure of the industry in Austria-Hungary. When independent Czechoslovakia was founded in 1918, more than 80% of the textile production of the former Austria-Hungary was located on its territory

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(Häufler, 1984), of which a fifth of production was concentrated in the Liberec Region. Johann Liebig & Comp., with its 5000 employees, was one of the most important textile companies in Central Europe. As successor states built their first textile industry, the Czechoslovak textile industry found itself in a sales crisis after 1918. Liebig responded to the crisis by modernising its operations and making numerous technical and organisational improvements. The company focused mainly on the mass production of cheaper fabrics. Employment and production peaked in 1929. The global economic crisis hit the textile industry particularly hard. Between 1929 and 1937, the number of textile companies in Liberec decreased from 42 to 25 and the number of employees from 18,551 to 6445. The share of textile goods in total Czechoslovak exports fell from 15% to 8% in cotton, and from 10% to 6% in wool. At the Liebig Company alone, the number of employees was reduced from 5000 to 3200 and a three-day workweek was introduced (Bergmanová, 2008). Another decline occurred during the German occupation when the textile industry was transferred to war production. In 1945, the textile industry became obsolete, and after the expulsion of the German population from the borderlands, it lacked a skilled labour force. Within the years 1945–1948, companies not only from the textile industry were confiscated, nationalised, and gradually merged into large units. From the original 3000 separate textile plants in 1945, only 55 national companies were established by 1983, with more than 2.5 thousand employees (Häufler, 1984). Johann Liebig & Comp., which belonged to German owners, was taken into national administration as early as June 1945. Due to the war, it employed only 1300 workers in June 1945. The number of employees continued to decline. There was also a shortage of raw materials, especially wool. In 1946, the former group was divided according to industry. The Liberec plant became part of the Czech Wool Mills. Other plants were incorporated into the national company of Jizera-River Cotton Mills. In the post-war period, the textile industry was closed to the world due to its loss of customers. As far as the imports of raw materials and exports of finished products were concerned, the textile industry became heavily dependent on the market of the Soviet Union. In 1948, the national Czech Wool Factories company was renamed Textilana. At that time, it had 52 plants throughout North and East Bohemia and employed 10,000 workers who produced 6.5 million metres of fabric. During the socialist economy of 1948–1989, companies underwent numerous restructurings aimed at concentrating production. Economy management mechanisms were very inflexible. Companies were grouped into production and economic units, and the entire textile industry was centrally managed from the ministry. Production and export plans were set by the state planning commission. Wage policy favoured heavy industry. Wage conditions in the textile industry were, therefore, worse. State-regulated prices did not correspond to world prices. As the centrally planned economy required foreign exchange, the state subsidised exports. Western clothing companies were willing to buy Czechoslovak textile production at the rate of 12% to 15% lower than comparable products in Western Europe. Towards the end of the planned economy in 1989, the largest textile companies were located in North Bohemia—Bytex employed 8006

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workers, Textilana had 5370 employees, and Seba Tanvald 4052 employees (CZSO, 1990). After the transformation of the Czechoslovak economy in 1990 was initialised, the import of raw materials, fuels, and energy became more expensive due to the termination of foreign exchange interventions, and the subsequent easing of the exchange rate. Production costs climbed, and therefore, it was necessary to increase sales prices. Czech producers were no longer attractive in terms of prices for customers in Western Europe. The market of the Soviet Union remained; the country was interested in importing large quantities, yet, it became insolvent. The domestic market has also opened up to the import of cheap textiles, mainly from Asian countries. Demand and textile production fell to 63% of its original volume by 1999 (Kunc, 2000). The main reasons were lower product quality and high production costs. In December 1990, Textilana, the state-owned company, was transformed into a joint-stock company, and in 1993, it was privatised. The first reduction in the number of employees (by 734 people) took place at the beginning of the transformation in 1991. By the end of the 1990s, the textile industry was still operating in the Liberec Region: Interlana and Licolor in Frantiskov, Larisa in Rochlice, Mykana in Chrastava, Hoflana in Machnin, Textilana and Libea in Liberec, Bekon and Elas in Hradek nad Nisou, Slezan in Frydlant, and Naveta and Retex in Straz nad Nisou. In Vratislavice nad Nisou, Intex was producing loop carpets. Associated Weavers had a production of tufted carpets, and Pro-Tex focused on bathroom carpets. However, the sales situation gradually deteriorated, and the companies gradually ceased their activities: 1996 Elas, 2002 Intex, 2003 Textilana, 2006 Mykana, 2014 Hoflana, and 2016 Interlana and Slezan (Bisnode, 2019). From a territorial point of view, the textile industry has historically been concentrated in the area of North and North-East Bohemia. In 1902, 55% of all textile companies were located in this area, employing over 60% of workers in the textile industry. At that time, the textile industry accounted for 37% of total industrial employment (Kunc, 2000). However, due to technical progress and the development of other industries, the share of employment in the textile industry was decreasing— in the 1960s it employed only about 10% of industrial workers and this share decreased to 7% by 1989 (CZSO, 1990). However, the territorial distribution of the textile industry has remained practically unchanged over time. By the mid-1980s, most workers in the textile industry were employed in the districts of Decin, Liberec, Trutnov, and Nachod (Mištera et al., 1985). This geographical concentration, even after the closure of several textile companies, remained essentially the same until the present. In 2017, most workers in the textile industry were employed in the districts of Trutnov, Nachod, Usti nad Orlici, Liberec, and Decin (CZSO, 2018).

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TEXTILE Industrial Districts: Definition of their Members

In the Liberec Region, the textile industry is predominantly represented in two industrial districts: Liberec and Semily. Almost 90% of the textile industry employees in the region worked in these two districts. Unlike in the glass and bijouterie industries, a cluster of technical textiles CLUTEX operates in the region. Its geographical scope is wider as well, including entities from the three regions (Liberec, Hradec Kralove, and Pardubice) forming the NUTS2 North-East Region. The CLUTEX CO currently has 35 members, including research institutions, a university, and an industry association. Therefore, it includes only a relatively small proportion of companies in the textile industry, as there are 665 entities related to NACE 13 activity (Bisnode, 2019) in the North-East Region. In the Liberec Region, two natural industrial districts were identified with the occurrence of NACE 13 ‘Manufacture of textiles’ (hereinafter referred to as TEXTILE-(NACE)13): 1. Industrial district Liberec TEXTILE-(NACE)13, where 135 entities operated, of which 60% were micro-enterprises without employees. The structure of activities according to the NACE classification is given in Table 7.6. The vast majority of entities (73%) were based in Liberec. 2. Industrial district Semily TEXTIL-(NACE)13, where 57 enterprises operated, of which 67% were micro-enterprises without employees. Municipalities with a significant occurrence of companies in the textile industry include Turnov,

Table 7.6 Industrial district of Liberec TEXTILE-(NACE)13 Research activity within NACE 13 13 Manufacture of textiles 13,100 Preparation and spinning of textile fibres 13,200 Weaving of textiles 13,300 Finishing of textiles 139 Manufacture of other textiles 13,910 Manufacture of knitted and crocheted fabrics 13,920 Manufacture of made-up textile articles, except apparel 13,930 Manufacture of carpets and rugs 13,940 Manufacture of cordage, rope, twine, and netting 13,950 Manufacture of non-wovens and articles made from non-wovens, except apparel 13,960 Manufacture of other technical and industrial textiles 13,990 Manufacture of other textiles n.e.c. Total Source: own processing according to (ARES, 2020)

No. of entities 22 9 3 9 25 4 46 2 1 2 8 4 135

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Table 7.7 Industrial district of Semily TEXTILE-(NACE)13 Research activity within NACE 13 13 Manufacture of textiles 13,100 Preparation and spinning of textile fibres 13,200 Weaving of textiles 13,300 Finishing of textiles 139 Manufacture of other textiles 13,920 Manufacture of made-up textile articles, except apparel 13,960 Manufacture of other technical and industrial textiles 13,990 Manufacture of other textiles n.e.c. Total

No. of entities 2 7 3 4 7 23 4 7 57

Source: own processing according to (ARES, 2020)

Lomnice nad Popelkou, Jilemnice, and Semily. The structure of activities according to the NACE classification is apparent from Table 7.7.

7.3.2

Defined Specifics of the Natural Cluster of the TEXTILE Industry

The textile industry in the region has a more than two-century tradition. However, since 1990 traditional textile production has been going through a crisis caused mainly by competition from cheap Asian producers. Nevertheless, production focused on technical textiles for the construction industry, medicine, the automotive industry, nanofibres, glass, and basalt fibres remain competitive on world markets. A high density of small and medium-sized specialised enterprises concentrated mainly in the eastern part of the Liberec Region and the neighbouring regions of the Hradec Kralove and Pardubice is typical for the industry. The main research institutions include the Faculty of Textiles of the Technical University of Liberec (TUL), which also represents the main educational base, the Research Institute of Textile Machines in Liberec (VUTS), and INOTEX in Dvur Kralove nad Labem. Based on the cooperation of TUL and VUTS, the TEXTIL research centre was created. Virtually all major producers are associated at the national level in the Association of Textile– Clothing–Leather Industry. The secondary textile school in Liberec operates in the region as an educational institution in the secondary sphere. The other two secondary textile schools operate in the neighbouring regions. Companies in the textile industry were aware of the fact that the creation of a CO could provide a chance for the industry to survive in the market. Unlike in other industries in the region (glass and bijouterie industry), company representatives were willing to agree on the establishment of COs from the very beginning. A civic association was chosen as the legal form, later changed into a registered association. The Faculty of Textile Engineering TUL was the project coordinator. Another significant partner was the Association of Textile–Clothing–Leather Industry. In 2006, the CO had 17 members. Currently (2020), the cluster has 35 members

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(Clutex, 2020). Medium and large companies of Czech owners predominate. The largest company is MOSILANA with 1000 employees. In addition to business entities, a member of the cluster is the Technical University of Liberec, ATOK the professional association, the textile testing institute, the research institute of textile machines, and the cotton research institute. The aim of the cluster is to create optimal conditions for technology transfer, ensuring higher-order innovations and business developments in the areas of research, development, and production of technical textiles, including materials and semi-finished products used for their production (Clutex, 2020). However, the CLUTEX CO unites only a small part of the textile industry’s business entities in the region. The vast majority of SMEs are not members of an institutionalised CO. These companies operate independently within defined industrial districts. Thus, there is a coexistence of natural and institutionalised clusters in the textile industry. Both types of clusters benefit from a common educational and research base, tradition, and know-how.

7.4

Summary of Findings on Traditional Industries of Natural Clusters in North Bohemia

All three industries described have a centuries-old tradition in the region and have undergone the same processes of nationalisation, restitution, and privatisation. Nevertheless, it is clear that their success varies in the national and global markets. The glass industry appears to be relatively the most stable, where the number of entities over time is more or less the same (see Fig. 7.2). In the manufacture of bijouterie, the decline in the number of entities was already more pronounced (see Fig. 7.3) and the textile industry underwent the deepest decline (see Fig. 7.4). All 2500 2000 1500 1000 500 0

Ceska Lipa

Jablonec n. Nisou

Liberec

Semily

Fig. 7.2 Development of the number of businesses in the glass industry

Total

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2000 1500 1000 500 0

Ceska Lipa

Jablonec n. Nisou

Liberec

Semily

Total

Fig. 7.3 Development of the number of businesses in the manufacture of bijouterie. Note: In the manufacture of bijouterie, data for districts have only been available since 2008

600 500 400 300 200 100 0

Ceska Lipa

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Semily

Total

Fig. 7.4 Development of the number of businesses in the textile industry. Source for all three figures: own processing according to ARES (2020)

three industries created industrial districts of different sizes in the region. The specificity of the glass and bijouterie industry is that no institutionalised CO has been created here. In the glass industry, an attempt was made for a cluster initiative, supported by public funds, and the CO Czech Glass Cluster was even established. However, from the beginning, this CO had a small number of members and never showed significant progress. After a few years, it ended up in liquidation. In the manufacture of bijouterie, attempts at a cluster initiative ended quickly. There was no interest in the cluster initiative among the owners of mostly micro and small companies in the glass and bijouterie industries. Trust between members and their facilitator is essential for the functioning of the CO. Such trust has not been gained in these industries. Both operate within industrial districts, where informal relations and the quality of the social environment are important. The specificity of the bijouterie

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industry is its high degree of concentration in only two districts. Virtually nowhere else in the Czech Republic do companies manufacturing bijouterie occur. A significant specificity of the bijouterie industry is its product. Especially during times of economic crisis, the industry experiences profound declines in production, as it produces both fashionable and redundant goods. This is still the case today; as a result of the COVID-19 pandemic, many companies face existential problems and reduced staff numbers. The situation is better in the glass industry, as it also focuses on the production of technical glass. The textile industry in the region has undergone a significant decline in production since the 1990s. At one time, it seemed that the textile industry in the region would not survive and end up only in the memories and exhibitions of technical museums. To some extent, this expectation was met in the case of the mass production of textiles. However, some textile companies managed to reorient themselves to products with high added value (especially technical textiles with applications in healthcare, sports, construction, automotive, etc.). The research base significantly helped the Technical University of Liberec, the Faculty of Textile Engineering, and other research institutions that were engaged in research and development of new materials (nanomaterials) and their applications. After considerable stagnation, specific textile production has been recently revived. During the COVID-19 pandemic, there was also an increase in domestic demand for textiles (production of protective equipment). To some extent, the situation of the great economic crisis of the 1930s is repeated, when domestic demand for textiles helped revive the textile industry much faster than it did in the glass and bijouterie industries in particular. The textile industry also differs from the glass and bijouterie industry in that a functional and active CO CLUTEX was established here. This CO has been awarded the Cluster Management Excellence label at the international level. Although CLUTEX brings together only a small proportion of textile manufacturers, it contributes to the promotion of the industry and the interconnection of the industrial, educational, and research base in the industry. Other entities operating in the natural textile cluster can then profit from these benefits.

References ARES. (2020). Administrativní registr ekonomických subjektů – ekonomické subjekty. Ministry of Finance. https://wwwinfo.mfcr.cz/ares/ares_es.html.cz Bergmanová, V. (2008). Textilana v obrazech a datech. Technická univerzita v Liberci. Bisnode. (2019). MagnusWeb: Komplexní informace o firmách v ČR a SR. Bisnode. https:// magnusweb.bisnode.cz Clutex. (2020). Clutex—Cluster of technical textile. Clutex. http://www.clutex.cz/&lang¼EN CZSO. (1990). Pracovníci a mzdové fondy socialistického sektoru národního hospodářství v krajích a okresech podle resortů a ústředních orgánů v ČSR za rok 1989. Czech Statistical Office.

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CZSO. (2018). Registr ekonomických subjektů—Počty subjektů podle okresu a CZ-NACE k 31. 5. 2018. Czech Statistical Office. Häufler, V. (1984). Ekonomická geografie Československa. ACADEMIA. IPO. (2018). Trademark databases. Industrial Property Office. https://www.upv.cz/en/clientservices/online-databases/trade-mark-databases.html Jáč, I., Rydvalová, P., Karhanová Horynová, E., Zbránková, M., Petrů, N., Vacek, J., & Štichhauerová, E. (2017). Typologie a hodnocení vitality rodinného podnikání. Technická univerzita v Liberci. Koch, R., & Novotná, J. (2008). Pravidlo 80/20: Umění dosáhnout co nejlepších výsledků s co nejmenším úsilím. Management Press. Kunc, J. (2000). Změny v rozmístění textilního, oděvního a kožedělného průmyslu v České republice v období let 1989–1990 [Rigorous Work.]. Masarykova univerzita. Mištera, L., et al. (1985). Geografie Československé socialistické republiky. Státní pedagogické nakladatelství. Nový, P. (2013). Krize a konjunktury ve sklářském a bižuterním průmyslu. Sklář a Keramik, 63 (5–6), 111–115. Porter, M. E. (1998). Clusters and the New Economics of Competition. Harvard Business Review, 76(6), 77–90. Preciosa. (2017). Kamenický Šenov dává světu první sklářskou školu. Preciosa. https://www. preciosa.com/cs/history Rydvalová, P., & Hotař, V. (2012). Podnikání ve sklářství: Inovace jako cesta z krize s příklady ze severních Čech. VÚTS. Zizka, M., Rydvalova, P., & Hovorkova Valentova, V. (2021). Conceptual and Methodical Research Procedures. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business clusters – An empirical study. Springer Nature.

Chapter 8

Specifics of Institutionalised Cluster Organisations Petra Rydvalova , Vladimira Hovorkova Valentova Natalie Pelloneova

8.1

, and

History of Support for Czech Cluster Organisations

Cluster organisations (hereinafter COs) are a relatively new form of business groupings in the Czech Republic whose origin and subsequent development is, as Pavelková (2009) claim, dated to 2001–2006. This period also includes the announcement of the first cluster programme called Clusters supported by the European Union’s Structural Funds. The Czech Republic was able to first draw subsidies from European funds in the regime of an associated country, and then, from 2004, as an EU member. The main institutions focusing on cluster support in the Czech Republic are the Ministry of Industry and Trade (hereinafter MIT) and the Agency for Business and Investment Support (CzechInvest). Other organisations that are also involved in cluster policy are, for example, the Ministry of Education, Youth and Sports, the Ministry of Labour and Social Affairs, and the Ministry of Regional Development of the Czech Republic. Furthermore, clusters are also supported by European cluster policy and its projects and other supporting initiatives, including the National Cluster Association (NCA), which seeks to integrate and interconnect individual COs. In the Czech Republic, the form of support is mainly financial. The establishment and development of COs have been actively supported since 2004, when the Operational Programme Industry and Enterprise and its sub-programme Clusters, which ran until 2006, were approved. This programme was the first to focus on the development of clusters in the Czech Republic, and its main goal was to support projects for the establishment and development of clusters at the regional and supra-regional levels (MPO, 2010a).

P. Rydvalova (*) · V. Hovorkova Valentova · N. Pelloneova Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_8

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In 2007 the Clusters programme was followed by the Cooperation—Clusters support sub-programme within the Operational Programme of Entrepreneurship and Innovation, which operated until 2013. The managing authority again was MIT and the programme was implemented through the CzechInvest agency. Newly established clusters or already established clusters which, for example, used support from the previous operational programme (MPO, 2010b), could apply for this programme. Since 2014, clusters have been supported by the Operational Programme Enterprise and Innovation for Competitiveness, which lasts until 2020 (MPO, 2019). Clusters can draw funds under the Cooperation—Clusters sub-programme, which aims to support the creation of clusters and technology platforms that focus on the development of innovation and international competitiveness (MPO, 2019). In addition to the operational programmes described above, COs could also use other grant programmes, such as the Operational Programme Education for Competitiveness managed by the Ministry of Education, Youth and Sports or the Operational Programme Human Resources and Employment and follow-up Operational Programme Employment of the Ministry of Labour and Social Affairs. Although these operational programmes were not primarily targeted at COs, some organisations expressed interest in this support. How a cluster institution is established will, among other things, also affect the internal organisation of the cluster. The internal structure of the CO is formalised because it was developed based a certain initiative that had to be formally established. This was also due to registration in the public register and by the subsequent possibility of drawing the subsidy programmes described above. It is also necessary to determine the legal form of emerging COs. Most COs in the Czech Republic have the legal form of a registered association and/or a specialinterest association. The quality and scope of the CO’s activities also depend on its interactions with the academic sphere, which make it possible to improve cooperation in the field of education, knowledge sharing, and research activities. Despite their significant role in clusters, universities are not the main initiators of COs. Effective implementation of activities by the CO is a key element for its successful development. One of the indicators of success is the ability of a CO to expand its membership base. If the organisation stops providing services and support, many members can easily lose interest in membership. Section 8.4 deals with evaluating the management quality of such an organisation. Seven COs were randomly selected to answer research questions. In order to be included in the research, the condition that they were all in their maturity stage (i.e. established by 2010, at the latest) had to be met. Those organisations are as follows: • • • • •

Nanoprogress Cluster; Moravian-Silesian Automotive Cluster (hereinafter MS Automotive Cluster); CLUTEX—Cluster of Technical Textile; IT Cluster; Czech Machinery Cluster;

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Fig. 8.1 Overview of members of the Cluster of Czech Furniture Manufacturers by region of their registered office. Source: own processing, see http://www.furniturecluster.cz/kcn-structure-EN125

• OMNIPACK Cluster (Packaging Manufacturers Cluster); • Cluster of Czech Furniture Manufacturers. Individual COs are described in Sect. 8.3. First, however, the territorial distribution of these COs is introduced.

8.2

Territorial Distribution of Cluster Organisations

Prior to analysing the territorial distribution of clusters, the basis of research was first defined. The question was: Are COs mainly represented in districts/regions where the industry in which the CO operates? The territorial distribution of clusters is addressed here from two points of view of its administrative arrangement. First, from that of belonging to a region (territory at the NUTS 3 level) and then to a district (designation of a NUTS 4 or LAU 1 territory). The first step was to assign a district and a region to each member of all seven COs surveyed. Figure 8.1 shows the overview of the members of the Cluster of Czech Furniture Manufacturers according to the regions where they are based. It shows that from the 14 regions of the Czech Republic, its members are based in 12 of them, although the representation of the last four regions is quite small. Figure 8.2 provides a graphic demonstration of the overview of members of the same cluster. In this case, according to the districts in which the members have their registered office. There are only five districts in which more than one member of the cluster is based—Brno, Praha, Svitavy, Hradec Kralove, and Tabor. Figure 8.3 shows the overview of IT Cluster members in relation to the district of their registered office. It is obvious that the members of this cluster are grouped in a

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Fig. 8.2 Overview of members of the Cluster of Czech Furniture Manufacturers by district of their registered office. Source: own processing, see http://www.furniturecluster.cz/kcn-structure-EN125

Fig. 8.3 Overview of IT Cluster members by district of their registered office. Source: own processing, see https://itcluster.cz/clenove

few districts of the Czech Republic with a substantial proportion in the district of Ostrava, where almost 69% of the members of this cluster are located. Figure 8.4 gives an overview of the number of IT Cluster members in relation to the region of their registered office. Even here, there is only minor fragmentation of the registered office of cluster members, as we find them only in four regions of the Czech Republic, mostly in the Moravskoslezsky (Moravian-Silesian) Region.

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Fig. 8.4 Overview of IT Cluster members by region of their registered office. Source: own processing, see https://itcluster.cz/clenove

Fig. 8.5 Overview of members of the Packaging Manufacturers Cluster by district of their registered office. Source: own processing, see http://klastromnipack.cz/en/cluster-members

Figure 8.5 shows the distribution of members of the Packaging Manufacturers Cluster according to the district where they are based. Significant representation can be seen in only eight districts of the Czech Republic—Praha, Hradec Kralove, Jihlava, Nachod, Rychnov nad Kneznou, Brno-mesto, Svitavy, and Usti nad Orlici. In other districts, only a single member company has its registered office. Figure 8.6 provides a clear graphic illustration on the distribution of members of the Packaging Manufacturers Cluster in relation to the region of their registered office. Over 60% of the cluster members are based in the Hradec Kralove, Vysocina, and Praha regions. A significant proportion of members are also from the Pardubice,

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Fig. 8.6 Overview of members of the Packaging Manufacturers Cluster by region of their registered office. Source: own processing, see http://klastromnipack.cz/en/cluster-members

Fig. 8.7 Overview of members of the Czech Machinery Cluster by district of their registered office. Source: own processing, see http://www.msskova.cz/en/Lists/Membership/AllItems.aspx

Stredocesky (Central Bohemia), and Jihomoravsky (South Moravian) Regions. To sum up, members of the cluster are from 11 regions of the Czech Republic out of a total of 14 regions, including Praha. Figure 8.7 shows the distribution of the members of the Czech Machinery Cluster according to the district in which they are based. As the graph shows, the most significant proportion of members can be found in only four of the given districts, namely in Ostrava, Frydek-Mistek, Praha, and Brno-venkov (Brno-Country) Region. Figure 8.8 supplements the previous information on data on the regions in which the members of the Czech Machinery Cluster have their registered office. It is

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Fig. 8.8 Overview of members of the Czech Machinery Cluster by region of their registered office. Source: own processing, see http://www.msskova.cz/en/Lists/Membership/AllItems.aspx

Fig. 8.9 Overview of MS Automotive Cluster members by district of their registered office. Source: own processing, see http://autoklastr.cz/en/members

apparent that the members of the cluster are from only six regions of the Czech Republic, where the predominant proportion of members is from the Moravskoslezsky (Moravian-Silesian) Region (73.5%). Figure 8.9 shows the distribution of the members of the MS Automobile Cluster related to the district of their registered office. The highest number of cluster members is based in the districts of Ostrava and Novy Jicin, in total it is almost

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Fig. 8.10 Overview of members of the MS of the Automobile Cluster by region of their registered office. Source: own processing, see http://autoklastr.cz/en/members

Fig. 8.11 Overview of members of the CLUTEX CO by district of their registered office. Source: own processing, see http://www.clutex.cz/list-of-members

40% of members. Other more represented districts are Frydek-Mistek, Brno-město (Brno-City), Praha, Opava, and Prerov, where a total of 33% of members are based. Figure 8.10 also focuses on a graphical representation of the distribution of the members of the MS Automotive Cluster, but this time in relation to the region of their registered office. More than 60% of cluster members are based in the Moravskoslezsky (Moravian-Silesian) Region, however, a significant proportion of members is also in the Olomouc Region (15.9%). 12.7% of the total number of cluster members are based in the Moravskoslezsky (Moravian-Silesian) Region and in Praha. In Fig. 8.11 we see the distribution of the members of the Clutex CO, where the classification is made according to the district of their registered office. The most

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Fig. 8.12 Overview of members of the CLUTEX CO by region of their registered office. Source: own processing, see to http://www.clutex.cz/list-of-members

represented districts are Liberec, Svitavy, and Usti nad Orlici, which represent almost 41% of the total number. Figure 8.12 supplements the previous information again with the regions in which the members of the CO are located. It illustrates that the members of the Clutex CO are based in 10 regions of the Czech Republic, with the Pardubice Region being the most significantly represented, with 27.3% of members based. Only the first three regions have a significant number of members—Pardubicky, Královehradecky, and Liberecky, where we find the registered office of almost 60% of the cluster members. Figure 8.13 displays an overview of members of the Nanoprogress CO per the district where they have their registered office. The districts of Praha, Pardubice, Brno-mesto (Brno-City), Jindrichuv Hradec, Kladno, and Olomouc are more significantly represented. A total of 55.2% of cluster members can be found there. Figure 8.14 shows the distribution of the members of the above-mentioned Nanoprogress CO in relation to the regions in which they are based. We see that the members of the cluster are spread in 12 regions of the Czech Republic, with most of them based in the Pardubice Region and Praha (37.9%). Three other regions are also more significantly represented—Jihocesky (South Bohemian), Jihomoravsky (South Moravian), and Zlinsky, where a total of 31% of members have their registered office. In the following step, the district and region of the registered office of members of the COs were compared with the district and region in which the industry is significant. It should be noted here that a significant industry in the district is considered to be one for which the value of the location quotient is greater than 1.1 (Skokan, 2004). Based on this comparison Table 8.1 was created. It shows the proportion of members of COs who operate in a natural region—district or region (as a territory at the NUTS 3 level).

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Fig. 8.13 Overview of members of the Nanoprogress CO by district of their registered office. Source: own processing, see https://www.nanoprogress.eu/members

Fig. 8.14 Overview of members of the Nanoprogress CO by region of their registered office. Source: own processing, see https://www.nanoprogress.eu/members

Table 8.1 shows that the proportion of those cluster members who are based in the district where the industry in which they operate is significantly represented (localisation coefficient is higher than 1.1) is higher than 50% only for CLUTEX and IT Cluster organisations. For other organisations, this proportion is less than 50%, so it cannot be described as ‘predominant’. If we focus on the proportions of cluster members based in a region that is considered a natural region of the industry, we can define four COs for which this proportion is higher than 50%. These are CLUTEX, IT Cluster, Czech Machinery Cluster, and MS Automotive Cluster. Thus,

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Table 8.1 Proportion of members of individual clusters based in the natural region of the industry (district/region)

CO CLUTEX—cluster of technical textile IT Cluster Czech Machinery Cluster Moravian-Silesian Automotive Cluster Nanoprogress Cluster OMNIPACK Cluster Cluster of Czech Furniture Manufacturers

Proportion of members operating in the natural region of the industry— district (in %) 72.7

Proportion of members operating in the natural region of the industry — region (in %) 59.1

87.5 35.3

75.0 82.4

34.9

60.3

27.6

44.8

47.1

27.5

35.0

30.0

Table 8.2 Results of two tests on the parameter π of the alternative distribution Natural region of the given industry DISTRICT REGION

Hypothesis H0 : π ¼ 0.5 H1 : π > 0.5 H0 : π ¼ 0.5 H1 : π > 0.5

Input parameters p ¼ 0.431 n ¼ 255 p ¼ 0.510 n ¼ 255

pValue 0.9879

Test result at a significance level of 5% We do not reject H0

0.4011

We do not reject H0

if COs are considered as separate units, the hypothesis has not been confirmed that they occur mainly in districts/regions where there is a significant representation of the industry in which they operate. However, on the condition that the COs are evaluated as a whole, this set can be considered as a random sample from a set of all COs that were identified as suitable for the survey planned. The proportion of those members of all COs located in a district considered a natural region of the given industry is 43.1%. This sample proportion is further used in the hypothesis test for π. The aim is to prove that the proportion of members of all COs that are based in a natural region (district) of the industry is higher than 50%. The intention is to prove the same in the second test which concerns the proportion of those members of all COs that are based in a natural region of the industry, this time defined as a region at NUTS 3 level. The results of both tests are shown in Table 8.2. Apparently, as far as the district is concerned, it was not possible to prove that there would be a prevailing proportion of companies located in the natural region of the industry. The result concerning the size of the sample proportion (marked p) was

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expected. On the other hand, in this case, it was proved at the 5% level of significance that this proportion was smaller than 50% (P-Value ¼ 0.0166). The second test, concerning regions with a significant representation of the industry also led to the null hypothesis rejection, although the sample proportion has a value of 0.510 (i.e. 51%). Thus, in none of the cases was it possible to prove that the proportion of cluster members established in regions (NUTS 3 or LAU 1) with a significant presence in the industry is predominant. Looking at the more detailed territorial distribution of COs, the administrative structure of the country undoubtedly has a significant influence on the proportions identified. There is a certain territorial discrepancy in terms of the distance of cluster members to the districts in which the monitored industry is significant. An example is the Cluster of Czech Furniture Manufacturers. When traced back, other 16 members are located within a distance of 40 km from the nearest district, where the industry is significant. If the localisation criterion included not only affiliation in a district with a significant industry, but also a certain maximum distance from this district, then the number of such members would increase by 16 and the proportion would therefore rise to 75%. We select a 40 km distance of here since it is expected to have a commuting distance of up to 45 min or up to 60 min.1 It follows that there may be a number of other cluster members who are not from the district or region where the industry under investigation is identified as significant but are located at a relatively short distance from them. In the case of another CO, the IT Cluster has one more member within the 40 km commuting range. Therefore, the proportion of members based in the district with a significant industry would now increase to 93.8%. Another 18 members who have a commuting range of up to 40 km to a district with a significant industry can be found in the Packaging Manufacturers Cluster. This would mean that the proportion observed will increase from 47.1% to 82.4%. Another four members with a commuting range of up to 40 km were found in the Czech Machinery Cluster. This means that although the proportion monitored will increase to 47.1%, it will not exceed the 50% limit. However, the MS Automotive Cluster will undergo a significant change, as there are another 32 members based within 40 km of the district where their industry is significant. The proportion monitored is 87.3%. In the case of the Nanoprogress CO, the number of members based in a district with a significant industry or located within 40 km from the district would be only twice higher with a proportion of 34.5%. Finally, at the CLUTEX CO (where the proportion of members belonging to districts where their industry is significant is high), three more members can be found

1

Here, the distance was adjusted in accordance with the knowledge of local customs and the current situation and condition of the transport infrastructure in the Czech Republic. The ECEI material (2012), which evaluates the quality of cluster management, see Sect. 8.4, considers the commuting distance in terms of geographical concentration of cluster participants max. 150 km, or 1.5 h of commuting time from the registered office of the cluster organization.

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Table 8.3 Proportion of members of individual clusters located in the natural region of the industry (district) and at a commuting distance of 40 km from it

CO CLUTEX IT Cluster National Engineering Cluster MS Automotive Cluster Nanoprogress Packaging Manufacturers Cluster Cluster of Czech Furniture Manufacturers

Proportion of members of individual clusters located in the natural region of the industry (district) and at a commuting distance of 40 km from it. 90.9 93.8 47.1 87.3 34.5 82.4 75.0

Table 8.4 Test results on the parameter π of alternative distribution after adding one more condition Natural region of the given industry DISTRICT

Hypotheses H0 : π ¼ 0.5 H1 : π > 0.5

Input parameters p ¼ 0.737 n ¼ 255

P-Value 287,548 ∙ 10–14

Test result at a significance level of 5% We do not reject H0

within 40 km of the nearest district with a significant industry. The proportion monitored is 90.9%. These new findings are summarised in Table 8.3. From the above data, it is clear that if we perceive the district with a significant industry in a broader way, then it was confirmed in five of the seven selected clusters that member entities are located in this catchment area. In light of the above results, the test of the hypothesis on the parameter π of alternative distribution for the entire set together was performed again. The monitored property is now the affiliation of the cluster member’s registered office to the district where his industry is significant or a distance of up to 40 km from it. Out of the total number of 255 members of selected COs, 188 of them showed this property. The sample proportion is thus 0.737 (73.7%). The test results are summarised in Table 8.4. The test results show that at a significance level of 5%, it was proven that the proportion of cluster members based in a district where the industry is significant or within 40 km of it is higher than 50%. In conclusion, it can be stated that on condition only the administrative division of the country is monitored, then the importance of the industry in the region does not have a significant effect on the territorial location of COs. However, after the extension of the original requirement by the condition of a maximum commuting distance of 40 km to the district border, it was clearly demonstrated that the significance of the industry has a substantial impact on the territorial location of COs. It should also be noted that the distance of a cluster member’s registered office

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from a region where the industry is significant has not been assessed in relation to the size of such an area. Assessing the distance of a cluster member’s registered office from the district with a significant industry can therefore be considered meaningful. Following the territorial distribution of seven clusters, randomly selected as representatives of institutionally established organisations, these institutions are specified in more detail in Sect. 8.3.

8.3

Specifics of Selected Cluster Organisations

Nanoprogress, a registered association, focuses on research and development of functionalised nanofiber structures and their application to industry and medicine. This CO was founded in 2010 as a special-interest association of legal entities. In addition to its registered office in Pardubice, the cluster has three business premises, in Bustehrad (biomedical research), and technological research in Liberec and Roudnice nad Labem. As supported by (Hartmanová et al., 2013), research and development are provided by specialised workplaces or institutions such as the Institute of Experimental Medicine of the ASCR, the Technical University of Liberec, the Faculty of Biomedical Engineering of the Czech Technical University, or the second Medical Faculty of Charles University. At present (2019/2020), the cluster has expanded its operations from the NUTS 2 Northeast region to the entire Czech Republic, and foreign institutions also became members of Nanoprogress. In 2020, this CO had 55 members. Nanoprogress is proud of its many successes in research, but also in the field of international cooperation, internationalisation, and cluster development. It is one of the excellent European clusters and is a co-founder of the European Strategic Cluster Partnership ‘AdPack’. Some aspects of the existence of this CO are further addressed in Sect. 8.4. More information about Nanoprogress can be found on the website https://www.nanoprogress.eu/. The Moravian-Silesian Automotive Cluster, a registered association, was founded in 2006 with its registered office in Ostrava to support innovation and increase the competitiveness of entities operating in the automotive industry. It was the largest CO in the Czech Republic (88 members) as of 1 January 2020. The members of this CO are mainly companies producing automotive components and modules at various levels of the value chain. Its great success is, among other things, the award of the Gold Mark of Cluster Excellence in 2014. For more, see http:// autoklastr.cz/en. CLUTEX—Cluster of the Technical Textiles, a registered association, is based in Liberec, where it was founded in 2006. The founding members were entities concentrated in Northeast Bohemia (manufacturing companies, development and innovation organisations, the Association of Textile, Clothing and Leather Industry, and the Technical University of Liberec). The mission of the cluster is the coordination and cooperation of activities of textile and clothing companies, organisations engaged in development and research in order to create optimal conditions for

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technology transfer, ensuring innovation and business development in the field of research, development and production of technical textiles. This cluster is the only CO in the Liberec region, in 2020 it had 64 members, including the Technical University of Liberec. For more, see http://www.clutex.cz/&lang¼EN. IT Cluster, a registered association, brings together business entities and educational institutions in the field of information and communication technologies. The cluster was established in 2006 and operates in the Moravian-Silesian Region, where it provides consulting services to its members, organises educational activities, and ensures the preparation of grant projects. In 2020, the CO numbered 30 member entities. More information, only in the Czech language, at https://itcluster.cz/. Czech Machinery Cluster, a registered association, was founded in 2003 as the Moravian-Silesian Engineering Cluster as a civic association in engineering and related fields in the Moravian-Silesian Region. It was later transformed into the legal form of a registered association. The cluster is focused on supporting the increase of competitiveness of companies from the engineering industry in the MoravianSilesian Region and strives to build a highly prestigious and modern engineering base. At present, the membership base consists of a total of 45 business, educational and research, and development organisations focusing on the industries of conventional and nuclear energy, chemical, petrochemical, and metallurgical industries, transport and transport infrastructure, as well as ecological engineering. More information about this association at http://www.nskova.cz/. Cluster of Packaging Manufacturers was founded in 2005 as a cooperative and is also known as OMNIPACK. The cluster brings together companies engaged in the design and manufacture of industrial packaging and other entities in the field of packaging technology, logistics, service organisations, and educational institutions. It carries out its activities mainly in the Hradec Kralove Region to strengthen the competitiveness and economic growth of entrepreneurs in the field of packaging and logistics services by supporting their innovation activities. Other member entities come from the Pardubice Region and the Vysocina Region. It can, therefore, be stated that it is an interregional CO. In 2020, it consisted of a total of 45 member entities. More about this cooperative at http://klastromnipack.cz/en/omnipack. Cluster of Czech Furniture Manufacturers was established in 2006 as a cooperative. The cluster is the result of the efforts of furniture companies associated in the Union of Czech and Moravian Production Cooperatives, the Association of Czech Furniture Makers, and Mendel University focused on export orientation, development, innovation, and education. In 2020, with its 42 members, the base consists of manufacturers of furniture, semi-finished products, accessories, consulting companies, Mendel University, Czech University of Life Sciences Prague, etc. The CO is based in Brno, and most of the member entities come from the traditional furniture regions of the South Moravian Region, the Vysocina Region, the Pardubice Region, and the Hradec Kralove Region. More at http://www.furniturecluster.cz/? language¼en.

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P. Rydvalova et al.

Cluster Organisation and an Example of Management Quality Assessment

Unlike a natural cluster of companies and other institutions, COs are created as an economic entity, the essence of which is the management and administration of a cluster on the basis of someone’s initiative. This is a method of cooperating that is demanding regarding managerial skills. It is necessary to manage not only the issue of project and product portfolio, but also relations and protection of intellectual property rights. How to evaluate the management of a given group? One of the possibilities is to undergo an audit following the ECEI methodology (2012/2013), the revision of which took place in 2019 (ESCA, 2020a). As the European Secretariat for Cluster Analysis (hereinafter ESCA), established in 2010, states, it is professional cluster management that can contribute to the successful development of the industry through projects and services that exploit the potential of the cluster. The European Cluster Excellence Initiative (ECEI) developed a methodology and tools to support COs to improve their management capacities and capabilities. These are available as an online self-assessment tool. The evaluation has been carried out since 2014 and is divided into three categories: bronze, silver, and gold. The condition for obtaining the Gold Label quality mark is the implementation of a standardised quality management model EFQM (ESCA, 2020b). The evaluation of cluster excellence is first carried out at the ECEI Bronze Label level in the following areas: Cluster structure; Typology, management, cooperation; Cluster management financing; Strategies, goals, services; Achievements and reputation of the cluster. These areas are described by 36 indicators. To obtain the ECEI Silver Label ‘Dedicated to Cluster Excellence’ rating, improvement processes that began after previous Bronze Label benchmarking must be successfully implemented. If a CO wants to receive an ECEI Gold Label rating, it must demonstrate excellence in the management of its processes. The audit takes place for two days at the cluster’s registered office. It is managed by two independent ESCA experts who focus on fundamental quality indicators (the same as for the Bronze Label category). More than 1000 clusters expressed interest in evaluating the ECEI. Their achieved ECEI levels are summarised in Table 8.5. On average, 25 clusters participated in the quality assessment in one country—regardless of the size of the country. In the Czech Republic (CZ in Table 8.5), 23 clusters were evaluated, and three of them received an additional evaluation higher than Bronze Label. This indicates the fact that Czech COs place great importance on quality assessment. The evaluation of the quality of Nanoprogress cluster management is given as a best-practice example. It is further supplemented by an interview on the reasons that led the cluster to audit management according to the ECEI methodology. Nanoprogress Example The cluster was briefly introduced in Sect. 8.3. The key members of the cluster at the time of its establishment were the following economic

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Table 8.5 An overview of the number of successful clusters in the selected period ECEI— Label Bronze Silver Gold

Worldwide (situation in 2020) 1165 clusters from 46 countries 139 clusters from 22 countries 119 clusters from 18 countries

CZ situation in 2014 13

CZ situation in 2018 19

CZ situation in 2020 23

0

0

1

0

2

2

Source: own processing using ESCA data (ESCA, 2020a)

entities: Student Science, Sindat, Nanopharma, KPL invest, CB Bio, Technical University of Liberec, SinBio, BioInova (Hartmanová et al., 2013). Nanoprogress first obtained a bronze level evaluation valid for the Bronze Label until 2015, then Gold Label valid until 2019, the extension of which was defended successfully by the cluster management for another three years until 2022. The first cluster evaluation for the Gold Label level took place in December 2017, in compliance with the ECEI methodology from 2012 (ECEI, 2012). Karel Havlíček, Minister of Industry and Trade, stated that ‘The Gold Label award means that Nanoprogress is the number one cluster in the Czech Republic and at the same time one of the 5% best clusters in Europe, and proves that this cluster is one of the most important entities in the Czech Republic in the field of nanotechnology and nano-industry’ (Pirkl, 2018, the authors’ translation). In April 2018, in an interview for our book, the cluster manager Luboš Komárek described the evaluation procedure: ‘The whole management team took part in the preparation, but I and three other employees were the most active. It was necessary to prove the facts for the individual indicators a year or two backwards. The preparation took several years. If I count the last two years, I believe that archiving for excellence took about two man-months. Document updates, records, operations, etc., took about four man-months, and in the final month, an average of three people worked on it. That is another three man-months. In total, it is the equivalent of one full-time employee working for nine months. Yet, we must realise that a lot of management processes are implemented outside this process of evaluating excellence, important if you care about quality’. The Gold Label award means that Nanoprogress is the number one cluster in the Czech Republic and at the same time one of the 5% best clusters in Europe. This proves that this cluster is, in addition to universities in the field of nanotechnologies and nano-industry, one of the most important entities in the Czech Republic.

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References ECEI. (2012). European Cluster Excellence Initiative (ECEI): The quality label for cluster organisations—Criteria, processes, framework of implementation. European Cluster Excellence Initiative. https://cluster-analysis.org/downloads/copy_of_GOLDAssessment.pdf ESCA. (2020a). Benchmarking of Cluster Organisations. The European Secretariat for Cluster Analysis. https://www.cluster-analysis.org/ ESCA. (2020b). Pricing of the processes to obtain a label related to the ‘European Cluster Excellence Initiative’. European Secretariat for Cluster Analysis. https://www.clusteranalysis.org/pricing-information Hartmanová, K., Karas, M., Kotková, J., Tuháčková, K., & Vlček, L. (2013). Analýza klastru NANOPROGRESS. University of Economics. https://nca.cz/Resources/Upload/Home/nca/ aktuality/nanoprogres.pdf MPO. (2010a). Program podpory Spolupráce – Klastry. Ministry of Industry and Trade. http:// www.mpo-oppi.cz/spoluprace-klastry MPO. (2010b). Úspěšné projekty v OPPP – program KLASTRY. Ministry of Industry and Trade. http://www.mpo-oppi.cz/155-uspesne-projekty-voppp-program-klastry.html MPO. (2019). Operační program Podnikání a inovace pro konkurenceschopnost. Ministry of Industry and Trade. https://www.mpo.cz/cz/podnikani/dotace-a-podpora-podnikani/oppik2014-2020/operacni-program-podnikani-a-inovace-pro-konkurenceschopnost/operacni-pro gram-podnikani-a-inovace-pro-konkurenceschopnost%2D%2D157679 Pavelková, D. (2009). Klastry a jejich vliv na výkonnost firem. Grada. Pirkl, R. (2018). Evropská zlatá známka pro klastr Nanoprogress. T-UNI. https://tuni.tul.cz/ rubriky/veda-a-vyzkum/id:93611/evropska-zlata-znamka-pro-klastr-nanoprogress Skokan, K. (2004). Konkurenceschopnost, inovace a klastry v regionálním rozvoji. Repronis.

Chapter 9

Economic Impact of Clusters Miroslav Zizka

9.1

and Eva Stichhauerova

Theoretical Ground of Research

Clusters can be understood in two basic forms. Clusters may arise as a natural grouping of interconnected companies in a given region, or they may result from an organised effort known as a cluster initiative. In the first case, clusters exist regardless of whether the companies are aware of them or not. Pavelková (2009) calls such clusters ‘Porterian’ or natural. Cluster initiatives are organised efforts targeted at increasing the growth and competitiveness of the clusters in regions in which cluster companies, government, or the research community participates and have become the fundamental element in increasing the growth and competitiveness of clusters (Lindqvist et al., 2012). Entities which result from cluster initiative are referred to as institutionalised clusters or as cluster organisations (hereinafter COs). A cluster organisation is an entity that manages the joint activities of cluster members (e.g. their collaborative research). It can have the legal form of, for example, a registered association. The benefits of clusters are particularly reflected in the growth in efficiency, productivity, and innovation activities, and thus contribute to increasing the performance and competitiveness of companies and regions. What is fundamental in this concept is the finding that sufficient resources and capability to reach the critical value of concentration in the geographic location ensure a sustainable competitive advantage against other localities in the given industry (Tskalerou & Katsavounis, 2013). A cluster simply interconnects all the basic components—resource availability and the individuals’ objectives to achieve competitive success and shares the ideas of proximity, networks, and specialisation. Clusters stimulate and promote

M. Zizka (*) · E. Stichhauerova Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_9

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cooperation among entrepreneurs. They stimulate competitive pressure, even between indirect competitors or non-competitive participants (Bialic-Davendra, 2011). Clusters also make access to seeking trading partners, funding, and employees easier for their companies (Damborský & Wokoun, 2010). Lan and Zhangliu (2012) analysed the mechanism of interactive learning and the processes associate with sharing knowledge in clusters of small and medium-sized enterprises. They discovered that knowledge spill-over increases the dynamics of cluster formation. Companies inside a cluster exchange and share knowledge through face-to-face interactions, which significantly strengthens innovation. The economic benefits of the existence of clusters can thus be twofold. In the case of cluster organisations, a positive impact on the performance of their members can be expected. Natural clusters should show positive microeconomic externalities consisting of a functioning business environment, a skilled workforce and informal links between entrepreneurs. This should ultimately also be reflected in higher business performance. In both cases, the impact of clusters should have a positive effect on the competitiveness of the region in which the cluster operates. In literature, it is possible to find a large number of empirical inquiries into a specific cluster’s effect on innovation. Shu-en and Ming (2007) discovered through an example of an optoelectronic cluster in China that the sharing of knowledge with customers and suppliers enhanced product and process innovation within the company. Specifically, companies with a higher level of knowledge sharing create more innovation, which is a principle of the open innovation model. Another study carried out on a sample of 166 companies from the automotive industry in China (Wu et al., 2013) examined the effects of cluster age, R&D investment, and other variables on innovation. The analysis showed that the age of a company is the most important factor that has a positive and significant effect on company innovation. Based on an analysis of 1772 companies in an electronic cluster in Korea, it was discovered that clusters, open innovation, and quick learning were the factors that most allowed Korean electrical companies to successfully face Japanese and US competition (Won Park et al., 2012). Foley et al. (2011) consider clusters to be a suitable form of public–private partnership that proved successful in supporting innovation in the area of energy efficient buildings. Research carried out by Hsieh-Sheng (2011) proved a positive correlation between clustering in the high-tech industry and innovation in Taiwan. In that study, innovation was quantified by the number of patents. Huang and Rice (2013) conducted extensive research on 3468 European companies from 14 different industries focused on the effects of various factors on innovation. The research results showed that clustered companies had closer links to universities; more efficient knowledge flows, tacit knowledge exchange, and were less dependent on internal research. Islam (2010) compared the performance of textile companies both in and outside a cluster in Pakistan. He came to the conclusion that companies in a cluster had reached a higher level of productivity, innovation, and had invested more in upgrading their machinery. The financial performance of both groups of compared companies was not significantly different from each other.

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However, there are also studies that do not confirm a positive effect of clusters on innovation and company performance. Lang (2009) sees the negative aspects of clusters in the creation of homogeneous macro-cultures, discrepancies in social identity, power imbalances, rationalisation of the market, a lack of untraded interdependencies, and the occurrence of negative externalities. In their study containing 322 companies from 23 states from around the world, Frohlich and Westbrook (2001) came to the conclusion that partial degrees of integration had only brought little improvement in performance. It is necessary to take into consideration that the success of innovation in clusters requires meeting some basic factors of success. They particularly include the existence of highly educated workers with technological capabilities, corporate R&D, financial resources, addressing customers, and complementary commercial services (Mohannak, 2007). It is possible to add that cluster performance is closely associated with the method of governance and the range of formal and informal institutions that influence the governance. An important aspect also lies in the industrial policy and its orientation towards the support of clusters (Parto, 2008). Nishimura and Okamuro (2011) examined the effects of the Industrial Cluster project in Japan on innovation and the R&D productivity of its participants. The research results showed that project participation itself did not guarantee an increase in R&D performance. It was not confirmed whether companies participating in the Industrial Cluster project had filed more patent applications than independent companies. Pavelková (2013) examined 1110 member companies of Czech clusters, and based on their analysis, they came to the conclusion that innovation activities in cluster organisation companies were at a low level. Krželj Čolović et al. (2016) examined the differences in the financial and non-financial performance of Croatian hotel companies from the viewpoint of cluster membership. Financial performance was defined by productivity and cost efficiency. The non-financial criteria included quality, market share, customer satisfaction, innovation, etc. They discovered that only the evaluation of customer satisfaction was significantly better for cluster companies; with regard to all other indicators (including the financial ones), significant differences between companies inside and outside cluster were not proved. This corresponds, to a large extent, to other studies that deal with the effects of various forms of cooperation among companies on their profitability. Institutionalised clusters in particular can induce effects common in vertical integration (for example, see D’Aveni & Ravenscraft, 1994; Zhang, 2013). They include increases in overhead cost as a result of increased internal coordination, the inefficient purchase of production inputs, problems with coordinating independent activities, unused and unbalanced capacities, and bureaucratic costs (Huang & Rice, 2013). The main research objective was to find out whether the existence of clusters (natural or institutionalised) in the conditions of the Czech Republic has a positive effect on the innovation and financial performance of those companies that create the core of a cluster. The research was targeted at entrepreneur entities in clusters for which it is possible to evaluate financial performance. Special attention was paid to the relationship between the innovation and financial performance of companies.

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For achieving the main research objectives, the following research questions (RQ) have been determined: RQ1: Is the innovation and financial performance of companies in natural and institutionalised clusters different from companies that are not involved in any cluster? RQ2: Were the institutionalised clusters established in industries that were identified as significant in the region at the regional level (NUTS 3)? RQ3: Is there any difference between independent companies and companies in natural and institutionalised clusters in terms of their innovation and financial performance? RQ4: Is there a positive relationship between innovation and financial performance of companies? Is the relationship affected by the time lag and the specific industry? RQ5: Does the relationship between innovation and financial performance of companies depend on the type of inter-organisational relationship (natural cluster, institutionalised cluster or non-cluster companies)? RQ6: Does joining a cluster make a positive contribution to the total factor productivity change in member companies? Is there a difference in the total factor productivity change between natural and institutionalised clusters? RQ7: Do clusters have a positive effect on the shift in production-possibility frontier, pure technical efficiency and economies of scale? RQ8: Are there any differences between companies in a natural cluster, an institutionalised cluster or independent companies in terms of their technological change, change in pure technical efficiency and scale efficiency change? RQ9: Does the existence of clusters (especially institutionalised) bring positive macroeconomic externalities and is it therefore desirable to support them through economic policy makers? RQ10: What other factors (besides cluster membership) can affect a company’s financial performance? These factors could be, for example, time lags in relation to the type of innovation and industry. The answers to the individual research questions are given below in separate subchapters.

9.2

Data and Methodology

Seven branches (automotive, engineering/machinery, IT industry, furniture, nanotechnology, packaging, textiles), in which natural and institutionalised clusters exist in the Czech Republic, were selected for the research. These industries were selected at random. However, the assumption that both types of clusters exist in the industry had to be met, with an institutionalised cluster having to operate for at least three years. The positive effects of the cluster’s existence can be expected only with a certain time lag. In each industry, an overview of companies, which were divided

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into three groups, was compiled. The first group includes member companies of a cluster organisation—an institutionalised cluster. The second group includes companies that operate in the same industry and region as the institutionalised cluster, but are not members. These are companies in a natural cluster. The third group includes companies operating in the industry in regions other than those of the institutionalised cluster. Each company can logically be a member of only one group. The sizes of the individual groups were as follows: the first 86, the second 4284, and the third 17,489 companies. Accounting and other data were collected for the mentioned companies: the number of employees and the history of the company (this can be considered as a form of accumulated intellectual capital). The source of this data was the commercial database of MagnusWeb (Bisnode, 2019) and the collection of documents from the Commercial Register. Furthermore, data on registered industrial rights (patents, utility models, industrial designs, and trademarks) were obtained, the source of which was the database of the Industrial Property Office of the Czech Republic. Data were collected for the period of 2009–2016. When collecting financial data, the problem is that small businesses are not required to publish financial statements. However, even larger companies often do not comply with the legal obligation and publish financial statements with considerable delay or not at all. For the first group of companies, financial data were obtained for 64 entities, for the second group for 319 and for the third for 757 units. Data Envelopment Analysis (hereinafter DEA) and the related Malmquist index (hereinafter MI) were used to evaluate the performance of companies. MI allows the change in performance to be broken down into a component expressing the catch-up effect and a component expressing technological progress. Non-parametric tests (Kruskal-Wallis, Games-Howell) were applied to compare differences in performance between individual groups of companies.

9.3

Comparison of Innovation and Financial Performance of Clustered and Non-clustered Companies

First, a comparison of the innovation performance of companies generally operating in clusters (regardless of the type of a cluster) with non-clustered companies was made. We are looking for an answer to RQ1 (see above). The results of the research show that the assumption that companies in clusters perform better than non-cluster companies cannot be, in general, applied in all industries and on both types of performance (innovation and financial). Results can be influenced by the performance measures used (Pavelková et al., 2020). When innovation performance was measured by the number of registered industrial rights (patents, utility models, industrial designs, trademarks), then the positive influence of the institutionalised cluster for existence on the innovation performance was detected only in the textile industry. Members of the textile cluster organisation

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showed a better ability to use resources to create industrial property rights while also being able to commercialise them more effectively, which is reflected in their better economic performance compared to companies that are not part of a cluster organisation (Žižka et al., 2018). No significant impact on innovation performance was confirmed in six other industries. The registered results of innovation activities only partly cover the innovations. It is almost impossible to determine the precise number of the outcomes of other innovation activities (e.g. marketing, organisational) in individual companies as they frequently do not register them. Therefore, the DEA method was applied to measure innovation in the industries. The MI considers the technological change (hereinafter TECH) component, which expresses the influence of innovation. A significant shift in the TECH component was detected in the COs operating in the automotive, furniture, and packaging industries. Such a shift has not been identified in any other industries (Stichhauerova et al., 2020). Table 9.1 displays all total changes in performance according to a particular industry over the 2009–2016 period. The MI, which combines the components of internal technical efficiency change EFFCH and technological change TECH expresses the total performance change. The internal technical efficiency change can be further decomposed to pure technical efficiency PECH (if the variable returns to scale condition is satisfied) and scale efficiency change SECH. The groups of COs member companies are shown in Table 9.1, they are marked by the letter C. The groups of non-member companies, which operate in the same region as a CO are marked by the letter N. Other (non-cluster) companies are marked by the letter O. The first letter of a group indicates the industry (e.g. A is automotive). The names of the industries are stated under the individual groups. The identical identification is used in Tables 9.2, 9.3, 9.4, 9.5. In terms of financial performance, no statistical impact of the CO’s existence was detected (at the level of significance 5%). Although in five industries (automotive, furniture, engineering, textile, and IT), the MI values in the groups of COs are higher than in the group of independent companies, which operate in the regions outside the clusters (see Table 9.1), these differences, despite one exception, are not significant. The natural clustering led to the improvement of the overall performance in the automotive industry only when compared to companies in other regions (Štichhauerová & Žižka, 2020a).

9.4

Importance of Industry in Regions for the Existence of Institutionalised Cluster

As part of the research, we also addressed the question (see RQ2) of whether institutionalised clusters were established in regions where the industry is important. The territorial distribution of the COs was looked at from two angles—the regional affiliation (NUTS 3) and the district affiliation (LAU 1). The first task

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Table 9.1 Average scores of the Malmquist index and its components by industry in the 2009–2016 period Group AC AN AO Auto total EC EN EO Engineering total IC IN IO IT total FC FN FO Furniture total NC NN NO Nano total PC PN PO Packaging total TC TN TO Textile total Core total Natural total Other total Industry Total

Count 9 13 52 74 6 167 197 370 6 21 85 112 12 46 68 126 3 19 45 67 9 15 162 186 19 38 138 195 64 319 747 1130

MI 1.082 1.108 1.011 1.036 1.073 0.984 1.021 1.005 1.023 0.926 0.972 0.966 1.032 0.976 0.980 0.984 0.971 0.996 0.994 0.994 0.978 1.022 0.982 0.985 1.012 0.961 0.934 0.947 1.025 0.984 0.984 0.986

EFFCH 1.018 1.020 1.030 1.027 1.104 1.006 1.015 1.013 0.978 1.009 0.949 0.962 0.994 0.990 0.981 0.986 1.021 0.975 1.010 1.000 0.926 1.011 1.032 1.025 1.008 0.935 0.936 0.942 1.001 0.994 0.994 0.994

TECH 1.063 1.086 0.981 1.009 0.972 0.978 1.006 0.993 1.046 0.917 1.023 1.004 1.038 0.986 0.999 0.998 0.951 1.022 0.984 0.993 1.055 1.011 0.952 0.961 1.004 1.028 0.998 1.005 1.024 0.989 0.991 0.992

PECH 1.005 1.045 1.050 1.043 1.020 1.103 1.061 1.079 0.989 0.992 1.004 1.001 0.985 1.015 1.060 1.036 1.021 0.960 1.026 1.007 0.998 0.998 1.120 1.103 1.006 1.048 1.084 1.069 1.001 1.059 1.068 1.062

SECH 1.013 0.977 0.981 0.984 1.082 0.913 0.957 0.939 0.989 1.018 0.945 0.961 1.009 0.976 0.925 0.951 1.000 1.015 0.985 0.994 0.928 1.013 0.922 0.929 1.003 0.892 0.863 0.881 1.000 0.939 0.930 0.937

Source: Štichhauerová and Žižka (2020a) Note: The abbreviations are explained in the text above

was to assign a district and a region to each member of all the seven investigated COs. In the next step, the district and the region of residence of the members of the COs were compared with the district and the region in which the industry, the member operates in, is significant in terms of location quotient. An industry was considered significant if its location quotient was higher than 1.1. Location quotient expresses the degree of concentration of a given industry in the region in terms of employment. Calculated according to relation (9.1), see for example (Tohmo, 2004).

T

N P

F

I

E

Industry A

A AN > AO, AC > AO

E AN > EN, AN > EO EC < EO

I AC > IN, AN > IN EC < IC, EO < IC, EO > IN IC > IN, IN < IO FN < IC, FO < IC FC > FN, FC > FO

F AC > FN, AN > FN EN < FC

FC > PO x PC > PO, PC > PN, PN > PO

NC < NN

x

x x

IN < TC, IN < TN x

EN < TN

EC < PC, EO < PC IC > PO, IN < PC

T x

P AN > PN

x

IC > NC

x

N AN > NC

Table 9.2 Significant differences in the TECH component between groups of companies over the 2009–2016 period

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Table 9.3 Significant differences between groups of companies in individual years (GamesHowell test) Year 2010/2009

MI AC < AN, AN > AO, TN > TO

EFFCH AC < AO, EC > EN, PC > PN

2011/2010

AC < AN, EC > EN

AC < AN, FC < FN, PC < PN, PN > PO, TC > TN, TN < TO

2012/2011

EC > EN

EC > EN, TC < TN, TN > TO

2013/2012

AC > AO, AN > AO,

EN < EO, TC > TN, TN < TO

2014/2013

EC > EN, PC > PN

AC > AN, AN < AO, IC > IO, NN < NO, PC > PN

TECH AC < AN, AC > AO, AN > AO, EN < EO, FC < FN, PC < PN, PC > PO, PN > PO, TC > TO, TN > TO EC > EN, FC > FN, FC > FO, FN < FO, NN < NO, PC > PN, TC < TN, TN > TO IC > IN, IC > IO, FC > FN, FC > FO, NN < NO, PC > PN, PC > PO, TC > TN, TC < TO, TN < TO AC < AN, AC > AO, AN > AO, EN > EO, IN < IO, NN < NO, PC < PO, PN < PO, TC < TN, TC > TO, TN > TO AC < AN, EC > EN, EC > EO, IC < IO, FC > FN, FC > FO, NC > NN, NC > NO, NN > NO,

PECH AC < AO, PN < PO

SECH EC > EN, PC > PN, PC < PO

AC < AN, EN > EO

PN > PO

IC < IO, IN < IO, NC > NO, NN > NO

TC < TN, TN > TO

EC > EN

AN < AO, NC < NO, NN < NO, TN < TO

IC > IO, IN > IO, PC > PN

(continued)

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Table 9.3 (continued) Year

MI

EFFCH

TECH

2015/2014

AN < AO

AC < AN

2016/2015

EC > EN, NC > NN

EC > EN, FC > FO, NC > NN, TC > TO

PN > PO, TC > TO AC > AN, AN < AO, EN > EO, IN < IO, FC > FN, FC > FO, PC > PN EC < EO, IC > IN, IN < IO, FC < FN, FN > FO, PN > PO, TC < TN, TC < TO

PECH

SECH

AC < AN, EC < EO,

AC < AN

FC < FO, NN < NO, PC < PO, PN < PO

AN < AO, EC > EN, IC > IO, IN > IO, FC > FO, NC > NN, TC > TO

Source: own processing Table 9.4 Significant differences in the SECH component between groups of companies over the 2009–2016 period Industry A E I F N P T

A x

E x EC > EN

I x x x

F x x x x

N x x x x x

P x EC > PC x x x x

T AC > TO, AO > TO, EC > TN x FC > TO x x TC > TO

Source: Štichhauerová and Žižka (2020)

Location quotient ðLQÞ ¼

REi,n REn NEi,n NE

REi,n REn NE i NE

ð9:1Þ

employment in industry i and region n, total employment in region n, national employment in industry i, total national employment.

It was found that the proportion of the members of COs residing in the district where the industry in which they operate is significantly represented (the location quotient is higher than 1.1) is higher than 50% in only two COs. Namely in CLUTEX—a cluster of technical textile and IT Cluster. Other organisations’ proportion is lower than 50%. Thus, it cannot be labelled ‘prevailing’. When evaluating

TECH Sig. *

Difference 0.034422 0.032952 0.00147

Results for individual companies Source: Štichhauerová & Žižka (2020) * Denotes a statistically significant difference

Group Contrast C–N C–O N–O

Table 9.5 Games-Howell post-hoc test +/ Limits 0.01912 0.036918 0.034231

PECH Sig. * * Difference 0.05653 0.06471 0.00818

+/ Limits 0.052338 0.042283 0.051469 *

SECH Sig.

Difference 0.063431 0.072267 0.008836

+/ Limits 0.06461 0.071872 0.078706

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the significance of the industry at the regional level, four COs, whose more than half of the members reside in the given region, were identified. These are CLUTEX—a cluster of technical textile, IT Cluster, Czech Machinery Cluster, and MoravianSilesian Automotive Cluster. Since the choice of organisations was random, the hypothesis that COs occur mainly in districts/regions where there is a significant representation of the industry they operate in could not be confirmed. This strict conclusion is valid only in the situation when the industry significance is assessed within the administrative borders of the region. If the regions were to be understood more broadly, i.e. functionally in terms of the catchment area, different results can be expected. If the regions were widened by 40 km, then seven COs showed that their member entities operate in areas where the given industry was significantly concentrated. At the level of significance of 5%, it was statistically confirmed that more than half of the cluster members reside in regions with a significant representation of the surveyed industries.

9.5

Comparison of Change in Innovation and Financial Performance of Companies by Type of Cluster

The research focused on the question (RQ3) of whether there is a difference in the change in innovation and financial performance among all groups of companies. This means between companies in institutionalised and natural clusters and in comparison with other companies outside the cluster region. At the same time, the performance of companies in institutionalised clusters was expected to grow faster than in natural clusters. Logically, corporate performance should also grow faster in natural clusters than in non-clustered companies in other regions. The change in performance was assessed using the MI and its components. Differences between groups of companies were assessed using the GamesHowell post-hoc test. In terms of the overall MI, significant differences (at a significance level of 5%) were only identified between three groups of companies (AN > AO, AN > IN, AN > FN). This means that the hypothesis that clustered companies had faster performance growth rates was only confirmed in one case, namely in the automotive industry. However, the hypothesis that members of the automotive cluster organisation would perform better than non-member companies was not confirmed. This conclusion is also apparent when making a comparison of the MI values for the AC and AN groups of companies in Table 9.1. For the EFFCH component, a significant difference was only found for one pair of values (PC < PO). This means that in the packaging industry, companies outside the cluster showed a significantly lower rate of decline in technical efficiency than companies in the cluster organisation. No significant difference was confirmed for the other pairs of values. A higher incidence of significant differences was found in the case of the TECH component, see Table 9.2. If the shift in TECH is considered a measure of innovation

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performance, the assumption that innovation performance is better in the group CO members compared to other groups (a natural cluster, non-member companies) was confirmed only in the packaging and furniture industry, see the diagonal in Table 9.2. In the automotive industry, the innovation shift is more evident in COs (institutionalised and natural) than in non-COs. However, no difference in innovation performance within the clusters was noticed (i.e. the difference between AC and AN). Similarly, the IT and furniture industries show a more significant innovation change in the group of COs compared to the naturally located companies. No cluster effect on any type of performance has been found in the nanotechnology industry (Štichhauerová & Žižka, 2020a).

9.6

Relation Between Innovation and Financial Performance

In the next part, the research focused on the question of whether there is a positive relationship between innovation and financial performance of companies (RQ4). Where applicable, whether this relationship is affected by time lag, industry or type of inter-organisational relationship (RQ5). To this end, companies were divided into two groups—innovative and non-innovative. Companies that have at least one result of the industrial rights type are considered innovative. The innovation activity of a company in this research was evaluated in a simplified manner, due to the low number of registered rights to industrial property and is primarily expressed by the binary variable with the values 0 or 1. In case the company had no registered industrial rights, the variable value was 0. In case the company had acquired rights to industrial property, this variable value became 1. The level of financial performance obtained using the DEA model was also transformed into a binary variable with values of 0 or 1. The value 1 was assigned to companies that were identified as efficient units within the group, meaning those with best practices. Other companies with the performance score lower than 1 obtained the value 0. The relation between whether the company has registered results of innovative activities and whether it has been identified as a high-performance unit was examined using the Chi-square test of independence for categorical variables. The null hypothesis was formulated in such a way that no relation exists in the population between whether the company has registered results of innovative activities and whether it has been identified as a high-performance unit. The alternative hypothesis states the opposite. The degree of dependence was measured using the Pearson’s R correlation coefficient (Stichhauerova et al., 2020). No positive connection was found between innovation and financial performance. The results of our research suggest that the protection of industrial rights does not necessarily have a direct relation to corporate financial performance (Stichhauerova et al., 2020). The stated conclusion is valid for all types of inter-organisational relations (natural cluster, institutionalised cluster or non-cluster companies).

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The input data defines the innovation performance act as a limitation here. The availability of data is given by the registration of intellectual property rights, which is publicly available in Europe only in the field of industrial property rights (patents, utility models, industrial designs, etc.). By contrast, registration or records of rights under copyright law is necessary. Yet, in the IT industry, for example, the software innovation outputs are considered copyright protection. Another reason for the limited availability of data is their sensitivity related to the innovation strategies adopted by the companies. Some companies decide to protect their intellectual rights in the form of secrecy or trade secrets. Even in the case of the application of a more general procedure, where innovation activities were simply derived from the shift of the efficient frontier, it was not possible to state a universal positive link between technological progress and the improvement of the internal technical efficiency of companies. Only the automotive industry shows a positive shift of both components (Štichhauerová & Žižka, 2020a, 2020b). To define whether the performance of companies in clusters could be affected by time lag, Malmquist indices, on a year-by-year basis, were determined (see Table 9.3). The confirmed assumed relations (C > N, C > O, N > O) are in bold in Table 9.3. If there is a significant difference, yet in the opposite direction, then it is indicated in plain text in the table. Bear in mind that during the entire 2006 to 2016 period, a significant change in the MI was only identified in the AN > AO pair. The change in this given (i.e. automotive) industry was also noted in 2010/2009 and 2013/2012. Some significant performance growth in the textile, engineering, packaging, and nanotechnology industries was monitored during some years. However, most of them are just random pairs, it is almost impossible to monitor any systematic trend. An exception could be an engineering cluster where significant differences appear in four periods. Each time, there is a relation where the performance change of the CO companies is higher than in the non-member companies in the given industry in the region. Although the overall performance change of the CO member in the engineering industry was not significantly higher in 2016 compared to the non-member companies in 2009, it is possible to detect an improving performance trend. Notably, in 2016/2015, for the first time, the performance change of the members of the nanotechnology cluster was higher compared to the non-member group. However, it may just be a random fluctuation. Analogical conclusions can be drawn on the development of the individual MI components. Significant differences were mainly detected at random pairs in the given periods. The internal technical efficiency change appears with a higher frequency of fundamental differences in the engineering and textile industry. However, the internal technical efficiency did not radically change during the entire 2019/ 2009 period. In terms of the TECH, a higher frequency of significant differences was noted in the automotive, furniture, and packaging industries. These three industries show a significant positive movement in the efficient frontier, during the entire 2016/ 2009 period. However, the differences did not increase over time. The COs showed faster growth in the TECH component already at the beginning of the monitored

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period, i.e. 2009 and 2010. Therefore, the future research should monitor the performance trends in these three industries. Yet, it is impossible to generally conclude that the performance growth in the cluster companies is delayed.

9.7

Influence of Clusters on Productivity Change

The research focused on the question of whether clusters (institutionalised or natural) contribute positively to the growth of the productivity of their members (RQ6), and whether there is a difference between the two types of clusters in this respect (RQ8). If the positive effect of clusters was demonstrated, it was determined where the effect would manifest itself—whether in the shift of production-possibility frontier, pure technical efficiency or in economies of scale (RQ7). This research was examined in seven industries in which both a CO and a natural cluster exist. The given assumption about the positive impact of the productivity growth was confirmed only at one surveyed industry (automotive industry). The assumption was not confirmed in the other six industries. In the automotive industry, it was found out that natural clusters led to the growth in productivity factors of member organisations compared to non-member companies. However, no significant difference in the growth rate among the COs and non-member companies operating in the same region was identified (Štichhauerová & Žižka, 2020a). It was found that in the majority of the industries, the existence of COs or natural clusters has a positive impact on pushing the production-possibility frontier in comparison with the non-member organisations from other regions. This concerns the automotive, furniture, packaging, and IT industries (see Table 9.2). On the contrary, no positive impact of the existence of clusters on the growth of pure technical efficiency was noticed. A positive impact on economies of scale was identified in two industries (engineering and textile), see Table 9.4 (Štichhauerová & Žižka, 2020a). In terms of the overall comparison of all three groups of companies across all industries (members of cluster organisations, non-members operating within the same region, and companies in other regions), some differences can be observed in the trends in performance and its components. However, most differences are not significant at a 5% level. This applies both to the overall trends in performance MI and to changes in internal efficiency EFFCH. It was confirmed with the help of the Games-Howell post-hoc test that a desired significant shift (at the level of 5%) of the efficient frontier (i.e. technological change) only appeared in the group of CO companies, all across the examined industries. The pure technical efficiency even showed the opposite development to the expected one. Namely, the pure technical efficiency change was significantly worse in the group of COs. It was more or less stagnant in contrast with the group of natural clusters or other companies, where there was a year-to-year increase in the pure technical efficiency. In the economies of scale, a significant change was noted

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in the group of COs in comparison with the group of other independent companies, see Table 9.5 (Štichhauerová & Žižka, 2020a).

9.8

Macroeconomic Externalities of Cluster Existence

In the next part, the question was examined whether the existence of clusters (especially institutionalised) brings positive macroeconomic externalities and is therefore desirable to support them through economic policy makers (RQ9). The analysis of localisation theories showed that establishing clusters enables the realisation of competitive advantages of companies, and consequently, regional and national competitiveness should increase. In this context, the question is whether the existence of COs generates positive macroeconomic externalities (competitiveness, innovation, and economic growth). Based on the analysis of the shift in production functions in the individual industries, it was found that only two out of seven industries (furniture and packaging industries) experienced a desirable efficient frontier shift when the CO existed. Therefore, it might be assumed that the existence of a CO has caused a positive macroeconomic externality in the two mentioned industries. From the theoretical point of view of economic policy, an adequately generated size of an externality should correspond to a certain measure (subsidy to a company). In practical economic policies, quantifying the magnitude of such a positive externality is very difficult. Therefore, a survey was run to find out whether subsidies, which were drawn for the establishment and development of COs, generated an increase in tax and non-tax revenue to public budgets. The social security expenses and health insurance paid by companies and employees have the character of a compulsory tax in the Czech Republic, despite being officially called insurance. Table 9.6 shows the number of subsidies received by individual COs from the beginning of their existence until the year 2017. It was further investigated how much corporate income tax (tcorp) the members of the CO paid, how big their social security expenses were, and how much they spent on health insurance paid by the employer (icorp). Moreover, the wage costs of the member companies were investigated. Based on that, the amount of natural person income tax on employment income (tinc), social security expenses, and health insurance of employees (iempl) were estimated. The starting point was the year when the CO began to draw subsidies for its development. In the following years, the increase in taxes and insurance compared to the base year was monitored. The calculation is simply based on the premise that this increase is a result of the CO existence. The seven COs have received more than CZK 602 million (approx. EUR 23 million) in subsidies from public budgets since 2006. The total increase in taxes and the insurance paid by companies and their employees to the public budgets was over CZK 32.346 million (approx. EUR 1.244 million). At first sight, it could be stated that the funds spent by the state to support COs have returned. Table 9.6 clearly shows that the result is influenced mainly by the balance of the automotive cluster.

Source: Žižka et al. (2019)

Name of the cluster Cluster of Czech Furniture Manufacturers IT Cluster OMNIPACK—Cluster of Industrial Packaging Manufacturers Czech Machinery Cluster Moravian-Silesian Automotive Cluster CLUTEX—Cluster of technical textiles NANOPROGRESS—the Nanotechnology Cluster Total 38.760 2523.297

21.639 22.350 85.656 104.962 602.181

2007–2013 2011–2017

2010–2017

2012–2017

124.009

8.905

3061.720

98.418

482.267 1644.587

395.859 293.231

Increase in tinc (CZK mil.) 23.349

259.867 1875.309

15.844 2.816

72.155 205.251

2010–2017 2006–2015

Increase in tcorp (CZK mil.) 339.606

Total amount of subsidies (CZK mil.) 90.168

Years of drawing subsidies 2010–2017

Table 9.6 Subsidies received by COs and revenues paid to public budgets by their members

12,327.571

246.398

250.792

2160.896 5046.227

1099.621 910.117

Increase in icorp (CZK mil.) 64.969

14,434.304

82.276

78.764

632.213 1635.382

355.752 253.715

Increase in iempl (CZK mil.) 21.252

32,346.892

465.853

444.661

3535.242 10,201.504

1867.076 1459.879

Total increase in revenues paid to public budgets (CZK mil.) 276.734

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Companies in the automotive industry prospered in the period of 2011–2017, for example, labour costs increased by more than 80% during this period. The average growth rate of wage costs of 10.5% per year reflects this situation. However, even in the other monitored COs, the increase in revenues paid by their members to public budgets is higher than the paid public aid. From this perspective, it seems that COs have created a positive externality. However, it is necessary to take into account the fact that this development could have taken place even without the existence of a CO. It is difficult or even impossible to find out how the industry would have developed without the CO. Due to the historical and geographical uniqueness of the agglomeration of companies in the given region, it is impossible to transfer the concept of a successful cluster with a high degree of macroeconomic externality to any other area. The practical cluster policy should strictly respect Porter’s principles: (a) cluster development should not be stimulated by top-down policy strategies, (b) non-selective support for all clusters (not just some); (c) cluster initiatives should be launched by the private sector (the public sector only plays the role of a facilitator), (d) preferences for supporting the already established (especially promising) clusters before initiating new clusters. From this point of view, it seems problematic that the Czech COs were mostly initiated by a top-down approach. The analysis of the Czech COs in the maturity stage showed that 24 of 32 organisations were established based on a top-down approach with significant public support. In terms of the financial performance of the member companies in both types of clusters, some differences were detected (see Fig. 9.1), however, in two exceptions in 2015, they were not statistically significant (Žižka & Pelloneová, 2019). Thus, it has not been confirmed that top-down created clusters have a more dominant effect on performance. From the industrial perspective, the support for establishing clusters in the Czech Republic was broadly-based. It included various branches of the manufacturing industry. However, cluster initiatives were mainly initiated by the public sector and subsequently supported by massive subsidies from the operational programmes. In the previous programme periods, from 2004 to 2013, almost CZK 1300 million from public funds were spent on supporting clusters. In the current programme period until 2020, another almost CZK 696 million was released from the Operational Programme Enterprise and Innovation for Competitiveness (API, 2020). It reached in total almost CZK 2 billion (about EUR 77 million). The principle that clusters should develop from the naturally functioning industry clusters was not followed. This supports the fact that macroeconomic externalities of clusters are relatively weak in the conditions of the Czech Republic. From the macroeconomic point of view, the effect of significant clusters on labour mobility in the region is also questionable. On the one hand, from the regional policy (employment policy) point of view, a constant flow of labour between companies from different industries is desirable. On the other hand, the highly specialised workforce of cluster companies, equipped with unique skills and knowledge, presents a barrier to flexibility on the labour market. Yet, this skilled workforce is essential for the existence of a cluster (Krugman, 1991). This may be evidenced,

Fig. 9.1 Development trends for the financial performance indicators for the bottom-up and top-down clusters. Source: Žižka and Pelloneová (2019)

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for example, by the existence of a natural glass and bijouterie cluster in the territory of North Bohemia, where the craftsmanship of glassmakers has been inherited from generation to generation for 300 years. Yet, at the same time, it seems to be an insurmountable obstacle of a cluster policy when creating brand new clusters. Perry (2010) explains that a CO can be characterised by a spontaneous origin, the interdependence of its companies, their unique (geographic) proximity, and informal relations. COs cannot be imitated by state intervention through economic policy.

9.9

Other Factors Influencing Business Performance

It follows from the previous text that the effects of the existence of clusters on business performance are only partial. Therefore, we asked the question (RQ10) of what other factors can affect the financial performance of companies. Such factors may be, for example, the traditions of the industry, the family business, innovative practices in the industry or the existence of support programmes. For this reason, four more research sub-questions were formulated as follows. RQ10-1: Can familiness be specified as a factor in the development of a traditional industry in the region, e.g. in relation to industrial districts? RQ10-2: What are the innovation practices in the industry? Is there any typical behaviour of the surveyed industries? RQ10-3: Is the establishment of cluster organisations related to the existence of support programmes? Is the dependence of cluster organisations on public support declining? RQ10-4: Is the support of institutionalised clusters from public funds effective?

9.9.1

Tradition, Industrial Districts, and Familiness in Business

Factors that are close to clusters can have a significant impact on business performance. This is a tradition of the industry in the region, which is often associated with the presence of industrial districts. Family businesses play an important role in industrial districts. Traditions, industrial districts, and family businesses are therefore the other three sub-factors that we examined in relation to clusters and performance (RQ10-1). In the context of the viability of family businesses, we aimed to find out whether it was possible to define conditions which ensure the subsequent continuity of family businesses. Such a condition is, for example, an industry which is relevant for the given region (the existence of an industrial district or a natural cluster), even though the continuity of business activities was disrupted due to the change of the economic system in the Czech Republic (40-year-long socialism). The initial approach was a

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resource-based view defining the specification of family businesses in the institutional context. The behaviour of family businesses was assessed from three perspectives—legal, managerial, and financial. The results of the evaluation showed considerable agreement with the assumptions stated in the literature. These regarded family businesses that resumed doing business continuously. However, in contrast to these countries, the economies where private sector was disrupted do not have enough experience with generational changes in family businesses. A large number of companies in the transition economies are addressing this situation at a similar time. This situation raises questions about the institutional support of the process, for example, in the legislative definition of a family business, in specific forms of education, etc. Within the framework of quantitative research, it was assumed that there was a connection between an industrial district in a region and family businesses in the traditional industry that forms the core of the industrial district. This situation is reported by Cucculelli and Storai (2015) who point out the importance of tacit knowledge and values generated in industrial districts over a long time. The specialisation of a region in a certain industry in terms of localisation theories has been addressed by many experts. Based on these findings, a survey examining the young generation’s relationship to the family business in the context of innovation performance was carried out. It focused on their parents’ companies, where the members of the young generation worked. Another essential factor is the industry in which the companies operate. This was a key factor for the research on the impact of family businesses on the traditional industrial districts. An industrial district is understood as a network of small and medium-sized enterprises (hereinafter SMEs) with certain specific knowledge, education, traditions, and skills, where a highly specialised concentration of SMEs very often operates. The results of this research emphasise the importance of sharing knowledge among generations (Rydvalova & Antlova, 2020). The next step was the implementation of the selected industrial districts survey, which form a natural industry cluster in the region. The survey was conducted in three industrial districts in North Bohemia, focusing on the textile, bijouterie, and glass industries. At the turn of 2019/2020, 124 companies took part in the survey. There is a natural and institutionalised cluster in the textile industry in the given area. In the glass industry, this is a natural cluster. An attempt to establish an institutionalised glass cluster was unsuccessful in the region. It failed due to the lack of interest of glass companies. Bijouterie represents an industrial district concentrated in a relatively small area of Jablonec Region. In the case of the glass and textile industries, it can be said that clusters consist of several industrial districts. It was found that 44% of respondents from all three industries consider their business to be traditional in the region. It mainly concerns the production of bijouterie, where 48% of entrepreneurs consider this activity to be traditional. In contrast, only 38% of respondents expressed this opinion in textile production. In the glass industry, 43% of respondents consider their business to be traditional. Tradition is one of the attributes associated with familiness. It was found that entrepreneurs who described their business as family were more likely to choose a

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positive answer to the question of whether it is a traditional family activity. Across industries, 69% of respondents considered their company as family (most in the textile industry—75%, in bijouterie 73% and in glassmaking 66%). Tradition is closely related to uniqueness. A total of 65% of respondents considered their production to be unique, mostly in the glass production industry (72%). In the case of bijouterie industry, 56% of respondents answered in the affirmative, and so did 63% of respondents in textile production. In conclusion, it can be stated that tradition, familiness, and uniqueness are important qualities that have contributed to maintaining the viability of these industries in the region of North Bohemia. All three industries are located in highly competitive global markets. Despite a certain decline accompanied by the demise of a number of companies in the 1990s, the textile, bijouterie, and glass industries are still among the most important industries in North Bohemia. Innovation activities in the area of products and processes also contributed to this, see the next chapter.

9.9.2

Innovation Practices in the Industry

To answer the question whether innovation behaviour influences performance, secondary data analysis was carried out to examine companies’ approach to innovation (RQ10-2) since the availability of the data in the field of innovation activities has its limits, for example, in terms of individual data anonymity, low returns from own research, etc. The source of the data was a statistical survey of innovation activities carried out by Eurostat and the CZSO National Statistical Office with the designation TI-2016. Data in the industries of the nine examined clusters (seven abovementioned plus two industries in which there are only natural clusters—glass and bijouterie) were evaluated in order to define the tendency of approach to innovation of economic entities. The choice of tools for protecting intellectual property rights, types of innovations, ways of implementing innovations (an internal or an external form), forms of partnership, and barriers to innovations were analysed. It was observed that in the industries with a high proportion of innovative companies, the impact of clusters (nanotechnology, engineering) on the technological progress was weaker (case of an IT cluster) or even non-existent. This finding can be understood as that there is a high proportion of innovative companies in these industries, and the impact of the cluster existence on the further increase in their innovation activities is probably negligible. As regards the type of the implemented innovations (product, process, marketing, and organisational), product innovations prevailed, marketing innovations occurred the least frequently. Minor differences were noted in two of the monitored industries. The differences were identified in the furniture industry, which indicated the largest proportion of organisational innovations. On the contrary, the automotive industry showed the smallest proportion of organisational innovations.

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From the point of view of individual performance factors, the results of the findings support the conclusion that the impact of clusters on the change in internal technical efficiency is negligible. Efficiency is understood mainly as a result of various organisational and procedural changes. For example, in the textile industry, a sharp decline in internal technical efficiency was observed, while it also showed low activity in the process and marketing innovations. A similar conclusion can be drawn for the furniture industry, where a decrease in internal efficiency was also identified. On the contrary, automotive, packaging, and engineering manufactories experienced growth in internal technical efficiency and showed higher proportions of process innovations. From the point of view of cooperation with other businesses, most industries showed a low level of cooperation (cooperation here was perceived as a potential to create network links and clusters). Logically, then, the possible effects (externalities) of COs’ existence on increasing innovation and, consequently, the financial performance in these industries must be limited. It is a positive factor that universities are members of each assessed CO. In most of the industries, universities with other institutions of higher education are the key partners when implementing innovations. The intensity of cooperation with universities is not the same in all the industries—it is relatively high in the textile, engineering, packaging, and nanotechnology industries. The cooperation with universities is close within the clusters, while outside them, it is not. The examined natural clusters behave differently in terms of their approach to innovation. The textile industry showed the lowest intensity of technical innovation in all the examined industries, which consequently showed a gradual loss of competitiveness and the decline in the number of companies and employees in this, once traditional, industry in North Bohemia. On the contrary, the intensity of technical innovation in the glass industry is slightly above the average and in the bijouterie industry on average. The number of companies in the examined industries reflects this situation. The glass industry shows almost the same number. However, the number of companies is declining in the bijouterie industry. Yet, not as notably as in the textile and clothing manufacture.

9.9.3

Existence of Cluster Support Programmes

As part of the research, we also investigated whether the establishment of institutionalised clusters is related to the existence of government support programmes (RQ10-3). Furthermore, whether the dependence of institutionalised clusters on government support decreases over time. The question of whether public support for clusters is effective (RQ10-4) is also important. The number of established institutionalised clusters is temporally related to support programmes, see Fig. 9.2. The most established COs were in 2006 (19 in total). The second-highest number was in 2009 (14 organisations) and then in 2012 (11 organisations). The enormous increase in the number of COs in 2006 was related

M. Zizka and E. Stichhauerova

20 19

15

2014

8 2016

3

2013

2012

4

2015

10

5

2 2018

11 8

2011

2010

2009

5 2008

4

2007

0

2006

1

2005

1

2004

0

10

9

2003

5

2017

14

10

2002

Number of cluster organisations

162

Fig. 9.2 Number of COs by foundation year. Source: Pelloneová and Žižka (2019) 1.73% 3.09%

32.95%

7.93%

0.21% 3.99%

0.03% 0.01%

1.98%

50.05%

Cluster founding Development of innovation potential Product innovation Support for collaborative research Publishing activities

Cluster development Cross-border cooperation Education Non-investment transfers

Fig. 9.3 Subsidies by purpose. Source: Pelloneová and Žižka (2019)

to subsidy support through the Clusters sub-programme under the Operational Programme Industry and Entrepreneurship. The same connection can be noted in 2009 and 2012 when cluster support was the subject of the Cooperation sub-programme within the Operational Programme Enterprise and Innovation. The highest part of public support was spent on the development of COs (see Fig. 9.3). A positive finding was that the dependence of financing the operation of institutionalised clusters on subsidies from the public budgets is decreasing. About 70% of the revenues of Czech COs come from the sale of their own products and services, see Fig. 9.4 (Pelloneová & Žižka, 2019). Public support is effective and will return revenues to the state relatively quickly in the form of growing tax and non-tax revenues from the member companies of institutionalised clusters. The conclusion applies to all the examined COs. Table 9.7 evaluates the efficiency (by individual sources of revenue paid to public budgets) and the payback period for public support. On average, the efficiency rate is high, with CZK 3.37 of annual increase in the amount of revenue paid to public budgets

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163

4.92%

9.30%

90%

17.82%

80% 70% 60%

65.20%

50%

81.93%

80.80%

70.53%

40% 30% 20% 10%

1.75% 23.41%

2.21% 10.95%

0% 2014 Operating subsidies

2015 Membership fees

1.45% 8.45% 2016 Revenues from own products and services

1.15% 10.49% 2017 Other revenues

Fig. 9.4 Sources of financing the operation of COs. Source: Pelloneová and Žižka (2019)

from taxes and mandatory insurance per one crown of public support. However, there are significant differences between the individual cluster organisations. In the case of the automotive cluster, the annual increase in the revenue paid to public budgets amounts to CZK 76 per one crown of public support; in the case of the furniture cluster, it is just CZK 0.44. The efficiency rate is especially positively influenced by non-tax revenue from insurances. Taking into account subsidies paid (see Table 9.6) so far for the development and functioning of cluster organisations and the increase in revenue from taxes and insurances that has been theoretically achieved as a result of public support, the payback period for public support is short. The funds spent will return to the state in 0.29 years on average. However, the result is influenced by three clusters—IT, engineering, and automotive—which, however, only drew a small part of the subsidies (19%) but show strong economic results. However, the payback period is short even for other clusters, ranging from one to two years (Žižka et al., 2019).

9.10

Synthesis

The results of the research show that clusters can be one of the factors influencing the performance of companies. However, based on our findings, it can be stated that this conclusion does not apply universally. The effects of cluster existence are different, varying by cluster type and industry. In terms of overall performance, significant growth was demonstrated only in the automotive industry, specifically in companies in the natural cluster. There was also a slight increase in performance in the group of institutionalised clusters in the engineering, furniture, textile, and IT industries. However, this improvement was not strong enough to be statistically significant. However, in terms of the existence of

Source: Žižka et al. (2019)

Name of the cluster Cluster of Czech Furniture Manufacturers IT Cluster OMNIPACK— Cluster of Industrial Packaging Manufacturers Czech Machinery Cluster Moravian-Silesian Automotive Cluster CLUTEX—Cluster of technical textiles NANOPROGRESS—the Nanotechnology Cluster Total 3.14 0.12 120.09 1398.42 1.49 7.39 50.45

1633.73 7607.28 74.16 88.77

337.47

Efficiency coefficient tcorp 53.81

369.66 64.66

Total efficiency coefficient 43.84

56.84

222.87 1226.37 20.68 18.75

78.37 12.99

Efficiency coefficient tinc 3.70

174.86

99.86 3762.98 41.83 46.95

217.71 40.31

Efficiency coefficient icorp 10.29

Table 9.7 Efficiency coefficient (%) and payback period for public support provided to cluster organisations

55.32

292.16 1219.51 13.14 15.68

70.43 11.24

Efficiency coefficient iempl 3.37

0.29

0.06 0.01 1.35 1.13

0.27 1.55

Payback period (years) 2.28

164 M. Zizka and E. Stichhauerova

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cluster organisations in the Czech Republic, it is possible that a significant effect will occur only in the longer term. Some foreign experience confirmed the positive impact of clusters on economic performance after 20 years of the cluster’s existence (Branco & Lopes, 2018). From this point of view, Czech clusters are young and their impact on performance needs to be further monitored in the future. However, even for existing clusters, some partial effects on performance were identified. Specifically, in terms of technological progress or innovation, a positive impact of clusters in three industries can be observed. In the automotive industry, companies in a cluster organisation and in a natural cluster showed faster technological change than companies in other regions. In the furniture industry, technological progress was found in companies within the cluster organisation, both in comparison with other companies within the same region and in comparison with companies in other regions. However, unlike the automotive industry, the technological growth of companies within the natural regional cluster was not significantly better than that of other companies in other regions. In the packaging industry the technological progress was strongest among companies within the cluster organisations. At the same time, it was also stronger in non-member companies operating near the cluster as compared to packaging companies from other regions. Only companies from other regions showed technological regress (Štichhauerová & Žižka, 2020a). On the contrary, the fundamental influence of both types of clusters was not demonstrated in the case of a change in technical efficiency, which reflects the impact of internal organisational measures on performance. Only the positive influence of institutionalised clusters in the engineering and textile industry on scale efficiency was found. In the first case, the difference is significant compared to non-member companies of the cluster. In the textile industry, the difference is significant compared to other textile companies that operate in other regions than that in which the cluster operates. In terms of the overall comparison of all three groups of companies across all industries (members of cluster organisations, non-members operating within the same region, and companies in other regions), some differences can be observed in the trends in performance and its components. Specifically, in the group of institutionalised clusters, there was a significant desired shift in the efficient frontier. In terms of scale efficiency, a significant change was identified in the group of cluster organisations in comparison with the group of other companies. From the perspective of the various industries, some benefits were identified in four industries in the component expressing technological shift. This applied to the automotive, furniture, packaging, and IT industries, where the group of companies in cluster organisations showed faster growth in the efficient frontier in comparison with companies that are not members of a cluster organisation (furniture, packaging, IT) or in comparison with other companies outside the region (automotive, furniture, packaging). This suggests that cluster organisations have a positive effect on innovation within each industry. When forming cluster organisations, an additional benefit could also be expected in the area of economies of scale, e.g. due to the joint organisation of the procurement of raw materials and other materials, joint

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usage of research infrastructure, or the joint promotion of production. This effect was successfully demonstrated in the case of the engineering and textile industries. No effect of clusters on performance was found only in the nanotechnology industry. It can be assumed that the reason is the fact that highly innovative companies operate in this industry, regardless of membership in a cluster organisation. The nanotechnology cluster is also specific as it has a virtually national scope. Unlike other clusters, it is not narrowly defined regionally.

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Chapter 10

Approach to Innovation in Selected Industries Petra Rydvalova

10.1

and Miroslav Zizka

TI 2016 Innovation Survey

The purpose of the research was to determine the tendency of companies’ behaviour and decision making in selected industries in the Czech Republic within the implementation of their innovation activities (hereinafter approach to innovation). The aim was to get further information on the approach to innovation of economic entities in industries, which were identified as the cores of the clusters and were selected for further surveys. The Czech Statistical Office (CZSO) database was used as a source of the data for evaluating the innovation activities in the given industries. The data is updated every two years. The majority of the survey was conducted by EUROSTAT (CZSO, 2018b). This fact consequently allows a comparison of the data at the EU level. The survey is labelled TI 2016. It was undertaken in 2017; the data was published in 2018 (the individual microdata was bought for research purposes); the reference period was 2014–2016. The CZSO carried out a further survey in 2019, where it examined the period of 2016–2018. The survey is labelled TI 2018. The TI 2018 survey results were not available at the time of completing the research (2019–2020). Within the framework of the above-mentioned aim, further questions addressing the specifics of the economic entities’ approach to innovation (in the selected industries) were formulated: What R&D management system (internal, external) is used by the economic entities? What are the typical ways of protecting intellectual property rights (hereinafter IPRs)? What innovations are used the most frequently (technical, non-technical)? What barriers do the economic entities encounter?

P. Rydvalova (*) · M. Zizka Technical University of Liberec, Liberec, Czech Republic e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Zizka, P. Rydvalova (eds.), Innovation and Performance Drivers of Business Clusters, Science, Technology and Innovation Studies, https://doi.org/10.1007/978-3-030-79907-6_10

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The answers will help complement the previous research findings. Further, they will also allow identifying possible problems of cooperation between companies, which are pointed out in the professional literature, which deals with, for example, the issue of the perspective of imitation in resource-based innovation partnerships (Foege et al., 2017). This fact should be taken into account, as innovation partnerships are important tools for learning and creating values, but, at the same time, they can increase a company’s vulnerability to unwanted knowledge leakage and imitation by others. This danger mentioned mainly concerns companies operating in technology-oriented industries. The present research focuses on these industries. There were 114 institutionalised clusters identified in the Czech Republic within the context of this research (they have an identification number and are listed in the public register)—74 of them are active. Apart from these, other possibly natural clusters were identified. The industries, which are displayed in Table 10.1 (according to the NACE classification), were chosen for the economic entities’ approach to innovation survey using the TI 2016 data. The reasons for selecting these clusters were the following. First, it was a traditional industry. Second, in the case of cluster organisations (hereinafter COs), these were clusters in the maturity stage, i.e. established by 2010 latest. Finally, they were active and operating. Each cluster is defined by the industry which forms its core, by its location, type of cluster, and classification. The definition of these clusters with their characteristics is shown in Table 10.1.

10.2

Methodical Procedure of the TI Survey: Evaluation of Innovation Activities

The first joint and harmonised survey of innovation in the EU countries was conducted in 1993. Nowadays, a statistical analysis of innovation in compliance with the EU Commission Regulation No. 995/2012 is carried out in all the European Union countries every two years with a three-year reference period. The Czech Republic participated in this survey, as an associated EU state, for the first time in the reference period of 1999–2001 with the designation ‘Pilot survey TI 2001’ (CZSO, 2020). Individual surveys emphasise societal needs and changes. For example, starting with the TI 2008 survey (for the years 2006–2008), a revised version of the ‘CZNACE Rev. 2’ sector codes was used in the statistical survey on innovation activities in companies based on legislative amendment. The latest TI 2018 survey (for the years 2016–2018), from which only aggregated data is available at the time of processing, the methodology is already followed in compliance with the updated Oslo Manual (OECD & Eurostat, 2019). The innovation thus newly differentiates the research into the product (products and services) and process (internal processes, organisational, and marketing).

620 (IT)

MoravianSilesian Hradec Kralove CO

CO

CO

CO

CO

Type of cluster

Liberec (Jablonec

Top-down

Top-down

Value chain

Value chain

Value chain

Bottom-up

Top-down

Top-down

Top-down

Competences Top-down

Value chain

Latent

Active

Active

Active

Active

Active

Active

Active

Location

Based on local history Group of industrial districts

Established on local history Scattered activities Established on local history Based on local sources

Scattered activities

Local

Regional

National

Regional

Regional

Regional

Technically orientated

Technically orientated

Historical know-how

Technically orientated Historical know-how

Approach to Innovation in Selected Industries (continued)

Horizontal Based on historical know-how

Vertical

Vertical

Vertical

Vertical

Horizontal Historical know-how

Multiregional Vertical

Historical know-how

Type of Type of integration knowledge

Established Multiregional Vertical on local history

Establishing Development Industrial approach phase structure

Competences Top-down

Value chain

Established based on

Cluster characteristics

Natural Value chain cluster comprising

28x, MoravianCO 251 (engineering) Silesian Olomouc, South Moravian, Zlin 293 (automotive) MoravianCO Silesian

Bijouterie (Man- 32-other ufacture of jew- 321 (BIJOUX) ellery, bijouterie,

MS Automotive cluster

National engineering cluster

Location NUTS 3 region

13x, 141 (textile) Liberec, Hradec Kralove, Pardubice 721 (NANO) Central Bohemian, Prague, Pardubice 310, 161, 162 South (furniture) Moravian

Packaging manu- 222, facturers cluster 172 (packaging)

Czech furniture manufacturers cluster IT Cluster

Nanoprogress

CLUTEX

Cluster name / identification

Industrial core, NACE code (acronym)

Table 10.1 The selected clusters

10 171

nad Nisou, Semily) Liberec (Ceska Lipa, Semily)

Location NUTS 3 region Established based on

industrial districts Natural Value chain cluster comprising industrial districts

Type of cluster

Bottom-up

Latent

Group of industrial districts

Establishing Development Industrial approach phase structure

Cluster characteristics

Local

Location

Note: The numerical coding of NACE with the ‘x’ means that all categories at the third digit of the NACE coding were included

and related articles) Glass (Glass and 231 (glass) glass articles)

Cluster name / identification

Industrial core, NACE code (acronym)

Table 10.1 (continued)

Horizontal Based on historical know-how

Type of Type of integration knowledge

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Approach to Innovation in Selected Industries

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The respondents are business entities with ten and more employees which are either registered or not in the commercial register in selected industries (B, C, D, E, G46, H, J, K, M71–73) in conformity with the Statistical Classification of Economic Activities (NACE). The monitored variables are coded under the Eurostat (CZSO, 2018b). The type of survey is a combination of a full population survey and a sample survey. The full population survey was run for companies with more than 250 employees, and the sample survey for companies with fewer than 250 employees. The number of the companies in the population was 25,103. The data was obtained from the Business Register of the Czech Republic, and the number of companies in survey (to which the questionnaire was sent) was 6638. Therefore, 26% of the population was covered. Net response rate was 85%. An Important Note on the Data Processing (Research Limitation) The evaluation of the individualised microdata from the TI 2016 survey is related to the set of respondents within the proceedings presented. In other words, it relates to the net response rate (5620). The published results of the CZSO, in an aggregated form (CZSO, 2018b), present the total data obtained by the TI 2016 random sample. These data were added to the statistical population by the CZSO with the help of mathematical and statistical methods. The indicator VAHAK, i.e. the weight for converting data to the total population, is described in the methodology of the CZSO TI 2016 survey. The aggregated published data of the CZSO take into consideration the VAHAK coefficient (CZSO, 2018b). It is not just a pure multiplication with the help of this coefficient; the calculation is based on considering the non-response rate in individual strata of samples. The research team did not have access to this data. According to the CZSO, the following factors influenced the survey sample stratification: • CZ-NACE 2-digits; • The size of companies (small, medium, large); • NUTS 2—statistical regions. The limit of the research carried out thus derives from the above-listed factors. The calculations of the defined indicators from the microdata of the CZSO survey were performed with the knowledge that they would not be truly representative. Companies can be given weight (according to the VAHAK indicator) and thus get closer to representativeness. However, the results are related to the replying respondents, not to the statistical population.

10.3

Characteristics of Companies in Selected Industries According to Innovation Activities

At first, the key industries of the defined clusters were evaluated in terms of the existence of innovative companies. An innovative company in the TI 2016 research was defined according to the Oslo Manual from the year 2005, in compliance with

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Table 10.2 Innovation companies in industries—according to the cluster core Selected industries for the whole CR (CZ-NACE) All CZ-NACE 13X a 141-textile 231-glass 32-others incl. BIJOU 721-NANO 310, 161, 162-furniture 620-IT 172, 222-packaging 251, 28x-engineering 293-automotive

Total number of respondents 5620 266 57 164 72 313

Innovative company according to the definition of Oslo Manual 2960 114 29 85 55 145

Proportion of innovative companies in the industry (%) 52.67 42.86 50.88 51.83 76.39 46.33

278 278 286 207

189 158 195 118

67.99 56.83 68.18 57.00

Source: own processing of the CZSO data, TI 2016

the Commission Implementing Regulation No. 995/2012 of 26 October 2012. It is a company, which implemented at least one innovation during the observed period. Table 10.2 shows the number of respondents and defined innovative companies in the selected industries; their proportions are more notable in Fig. 10.1. Figure 10.1 shows that the largest proportion of innovative companies is reported by the NACE 721 industry, which is the core of the Nanoprogress industry cluster. It is followed by the engineering industry (NACE 251 and 28x), followed by the IT industry (NACE 620). It is noteworthy, in this context, that the impact of the clusters on the technological process is little (in the case of the IT cluster), or non-existent (nanotechnology, engineering). There is a high proportion of innovative companies in these industries, and the impact of the clusters’ existence on a further increase in their innovation activities is negligible.

10.4

Approach to Innovation in Selected Industries

In connection with the further evaluation of the selected industry clusters, it was necessary to define the economic entities’ approach to innovation in the observed industries. Namely, they were the chosen tools for protecting intellectual rights, types of innovations, methods of innovation (internal and external), forms of partnership, and barriers to innovation. Figure 10.2 presents the results of the evaluation of the approach to the protection of IPRs during the innovation process in the companies in the selected industries, in years 2014–2016. The tools, within the TI 2016, were monitored according to the detailed division of the industrial law in four categories; namely the patent application, an application for a utility model (the so-called small patent), the industrial

10

Approach to Innovation in Selected Industries

175

100

Occurrence of innovative companies (%)

90 80 70 60 50 40 30 20 10 0

CZ-NACE industry designation - cluster cores

Fig. 10.1 The proportion of innovation companies in industries. Source: own processing of the CZSO data, TI 2016

design registration, and the trademark registration. The tools were, during the TI2016, monitored with the help of a detailed division of the industrial law in four categories. Furthermore, the respondents could choose the option to use trade secrets (valid in the Czech Republic, in compliance with the Civil Code, with effect from 1 January 2014), copyright protection (in Europe including software (hereinafter SW)), and an IPR licence (except the licence for SW and copyright). Figure 10.3 shows data from Fig. 10.2 in a simplified form, categorised to the industrial right, trade secret, copyright, and IPR licence purchase. As seen in the figures, the indicator of the number of patents and other industrial rights are the least appropriate for calculating innovation performance in the IT industry. Trade secrets are a common choice for protecting IPRs, especially in the two mentioned industries—IT and engineering. In this context, it is worth recalling the findings of research (Stichhauerova et al., 2020), that the protection of IPR is not directly linked to a company’s performance (innovation and financial), especially in the industries where it is traditional to protect the IPR in a different form. Table 10.3 presents the approach to innovation of companies within selected industries in terms of the type of innovation. Based on the proportion of individual innovations (product–process–marketing–organisational), product innovations

0

7,37 7,83 5,06 20,02 41,23 5,11 13,38

All CZNACE

10

8,93 8,93 7,14 35,71 25,89 2,68 10,71

13X a 141textile

20

9,09 9,09 12,12 21,21 33,33 3,03 12,12

231 - glass

30 32- others incl. bijouterie 11,88 8,91 12,87 20,79 33,66 2,97 8,91

50

60

21,71 18,42 7,89 7,89 25,66 4,61 13,82

4,08 11,22 8,16 24,49 36,73 5,10 10,20

310, 161, 162 721 - NANO - furniture

40

3,28 2,55 1,46 15,69 50,73 9,85 16,42

620 - IT

70

7,64 10,42 7,64 14,58 39,58 2,78 17,36

172, 222 packaging

90

14,14 13,09 4,71 7,85 43,98 1,57 14,66

251, 28x engineering

80

100

9,62 6,73 3,85 6,73 50,96 1,92 20,19

293 automotive

Fig. 10.2 The proportion of IPR tools used in the monitored industries—detailed categorisation. Source own processing of the CZSO data, TI 2016

Patent application Application for a utility model Industrial design registration Trademark registration Trade secrets, confidentiality agreements Copyright protection Acquisition of an IPR license

All CZ-NACE

13X a 141- textile

231 - glass

32- others incl. bijouterie

721 - NANO

310, 161, 162 - furniture

620 - IT

172, 222 - packaging

251, 28x - engineering

293 - automotive

176 P. Rydvalova and M. Zizka

0

10

30

40

Trade secrets, confidentiality agreements

20

50

Copyright protection

60

70

90 Acquisition of an IPR license

80

100

Fig. 10.3 The proportion of IPR tools used in the monitored industries—simplified categorisation. Source: own processing of the CZSO data, TI 2016

Total industrial property rights

All CZ-NACE

13X a 141- textile

231 - glass

32- others incl. bijouterie

721 - NANO

310, 161, 162 - furniture

620 - IT

172, 222 - packaging

251, 28x - engineering

293 - automotive

10 Approach to Innovation in Selected Industries 177

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Table 10.3 The proportion of individual types of innovation in the selected industries—according to the core of the cluster To the number of respondents To the number of all innovations Selected industries for the Proporon of Proporon of Proporon of Proporon of Proporon of Proporon of Proporon of Proporon of proces s whole CR (CZ-NACE) product i nnova on i n orga ni s a ona l product proces s i nnova  on i n orga ni s a ona l i nnova on (%) i nnova on (%) ma rkeng (%) i nnova on (%) i nnova on (%) i nnova  on (%) ma rkeng (%) i nnova  on (%)

All CZ-NACE 13X a 141- texle 231 - glass 32- others incl. BIJOU 721 - NANO 310, 161, 162 - furniture 620 - IT 172, 222 - packaging 251, 28x - engineering 293 - automove

33.88 28.57 40.35 39.63 63.89 24.28 52.52 34.17 49.30 40.10

34.50 26.32 31.58 32.93 43.06 24.92 43.17 37.05 48.95 44.93

22.62 14.29 31.58 14.63 31.94 12.46 33.81 24.46 32.87 27.05

30.89 30.83 35.09 32.32 38.89 30.99 42.45 31.65 36.36 13.53

27.80 28.57 29.11 33.16 35.94 26.21 30.54 26.84 29.44 31.92

28.31 26.32 22.78 27.55 24.22 26.90 25.10 29.10 29.23 35.77

18.55 14.29 22.78 12.24 17.97 13.45 19.67 19.21 19.62 21.54

25.34 30.83 25.32 27.04 21.88 33.45 24.69 24.86 21.71 10.77

To the number of respondents of innovative companies Proporon of Proporon of Proporon of Proporon of product proces s i nnova  on orga ni za  on i nnova  on i nnova  on (%) i n ma rke ng a l i nnova on (%) (%) (%)

64.32 66.67 79.31 76.47 83.64 52.41 77.25 60.13 72.31 70.34

65.51 61.40 62.07 63.53 56.36 53.79 63.49 65.19 71.79 78.81

42.94 33.33 62.07 28.24 41.82 26.90 49.74 43.04 48.21 47.46

58.65 71.93 68.97 62.35 50.91 66.90 62.43 55.70 53.33 23.73

Source: own processing of the CZSO data, TI 2016 Note: The biggest proportion—dark green colour, the smallest proportion—dark red colour

prevail in all implemented innovations in the monitored industries, whilst the marketing innovations appear the least frequently. Two industries indicate a slight deviation. The furniture (310, 161, 1612) industry shows the highest proportion of organisational innovations; the automotive (293) industry, on the other hand, indicates the smallest proportion of organisational innovations. As regards the individual performance factors, the stated findings support the conclusions of this research about a negligible impact of clusters on the internal technical efficiency change, which can be perceived as a result of various organisational and process changes. The textile industry shows notable results as the internal technical efficiency change decreased by almost 6% annually. The mentioned industry also demonstrated a low activity in its process and marketing innovations. A similar conclusion can be drawn about the furniture industry, where the internal efficiency decreased on average by 2% annually (Štichhauerová & Žižka, 2020). On the contrary, the automotive, engineering, and packaging industries experienced a growth in their internal technical efficiency, and show a higher proportion of process innovation. The respondents (2444) implemented at least one technical innovation (i.e. a product innovation and/or a process innovation). In the case the respondents implemented innovations other than the technical ones, they could choose more options in the type of innovation implementation. The study also compared the following ways of implementing innovations (those were omitted where the frequency was so low that it was not plausible to link the data in the sector with a specific institution): • The proportion of in-house innovation implementation (in %); • The proportion of implementation by a company together with the companies within the group of companies (in %); • The proportion of implementation by a company together with companies outside the group of companies (in %); • The proportion of implementation of a company together with a university and a research institute (in %);

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• The proportion of innovation via imitation, where an entity developed an innovation of a process by adjusting an already existing product/service, which had been developed by a different entity (in %). In terms of cooperation with other organisations and thus, the potential of creating network links and clusters, it is evident that the majority of the industries were reluctant to cooperate. It is then logical that the possible effect of the COs existence on increasing innovation and then, consequently, financial performance is poor. The only exceptions are the nanotechnology industry and, to some extent, the packaging industry. The nanotechnology industry is intrinsically very innovative and progressive. The statement, however, applies to all companies, regardless of the membership in a CO. The packaging industry demonstrated a positive impact of clusters on innovation and financial performance. Regarding product innovation, the respondents first commented on product innovation implementation, and then on the service innovation implementation. The product innovation implementation prevailed in the industries monitored. The exceptions were two industries. These types of innovation were evenly presented in the NANO-721 industry. In the IT-620 industry, service innovations prevailed over product innovations. That was due to the line of business and the specificity of the products. The data in the study was merged and is presented as data in the field of implementing product innovation. In the case of process innovation, the respondents commented on the following types: • implementation of a new production method; • implementation of new methods of logistics, supplies, or distribution, • implementation of new support activities. The summary assessed the following number of ways of innovation implementation, which the respondents commented on: • 3533 cases of product innovation implementation, see Table 10.4; • 2605 cases of process innovation implementation, see Table 10.5. Subsequently, the ways technical innovations (product and process) were implemented contributed to the evaluation of the specifics of each industry. It was checked whether internal or external forms of R&D implementation were utilised, see Table 10.6. It shows that the largest proportion of external expenses on R&D was in the IT industry and the textile industry. On the contrary, in the glass, the bijouterie (incl. Other manufacturing), and the furniture industries, in-house R&D was carried out significantly. Another essential aspect of evaluation is a question of what the most important forms of cooperation in terms of innovation activities are (see Table 10.7). A university is a member of each of the COs evaluated. This fact supports the finding that universities or other higher education institutions, in most of the industries, are more strategic partners in the implementation of innovation than other institutions such as consultant companies, private R&D institutions, private

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Table 10.4 The proportion of selected ways of PRODUCT innovation implementation in industries—according to the cluster core Implementation of PRODUCT innovation

Number of Frequency of Proportion of Proportion of res pondents product s el f-di rected i mpl ementation i nnova tion i mpl ementation i n one group(%) (%)

All CZ-NACE 13X a 141- textile 231 - glass 32- others incl. BIJOU 721 - NANO 310, 161, 162 - furniture 620 - IT 172, 222 - packaging 251, 28x - engineering 293 - automotive

5,619 266 57 164 72 313 278 278 286 207

2,376 90 29 75 69 95 201 117 174 94

45.00 52.89 38.00 58.06 33.06 57.36 52.56 37.87 41.67 26.40

Proportion of i mpl ementation wi th compa ni es outs i de the group (%)

19.81 14.88 28.00 15.05 19.01 11.63 15.06 19.53 25.40 42.40

Proportion of i mpl ementation wi th a uni vers i ty a nd a res ea rch orga ni s a tion (%)

14.49 8.26 16.00 11.83 17.36 13.18 16.03 15.38 10.32 14.40

Proportion of i mpl ementation i n the form i f imitation (%)

13.05 13.22 8.00 10.75 22.31 9.30 8.97 15.38 17.86 11.20

7.64 10.74 10.00 4.30 8.26 8.53 7.37 11.83 4.76 5.60

Source: own processing of the CZSO data, TI 2016

Table 10.5 The proportion of selected ways of PROCESS innovation implementation in industries—according to the cluster core Implementation of PROCESS innovation

Number of Frequency of res pondents proces s i nnova tion i mpl ementation

All CZ-NACE 13X a 141- textile 231 - glass 32- others incl. BIJOU 721 - NANO 310, 161, 162 - furniture 620 - IT 172, 222 - packaging 251, 28x - engineering 293 - automotive

5,619 266 57 164 72 313 278 278 286 207

Proportion of Proportion of s el f-di rected i mpl ementation i mpl ementation i n one group (%) (%)

3,321 122 35 80 47 122 197 195 254 179

46.87 63.64 29.03 58.06 50.00 66.30 50.90 48.30 44.50 35.94

Proportion of i mpl ementation wi th compa ni es outs i de the group (%)

20.77 15.91 32.26 22.58 9.62 5.43 22.16 20.41 24.61 36.72

Proportion of i mpl ementation wi th a uni vers i ty a nd a res ea rch orga ni za tion (%)

17.93 10.23 22.58 14.52 15.38 15.22 14.37 19.05 16.75 16.41

Proportion of i mpl ementation i n the form of imitation (%)

6.41 4.55 9.68 0.00 21.15 5.43 2.99 5.44 6.81 5.47

8.02 5.68 6.45 4.84 3.85 7.61 9.58 6.80 7.33 5.47

Source: own processing of the CZSO data, TI 2016 Note: The largest proportion—dark green colour, the smallest—dark red

Table 10.6 Implementation of technical innovations in terms of internal and external R&D in selected industries—according to the cluster core Industry

All CZ-NACE 13X a 141- texle 231 - glass 32- others incl. BIJOU 721 - NANO 310, 161, 162 - furniture 620 - IT 172, 222 - packaging 251, 28x - engineering 293 - automove

Number of respondents

2,625 95 24 73 86 95 205 131 209 111

A) Internal (own, in-house) R&D, frequency of occurrence

1,638 64 17 50 54 67 141 80 129 64

A1) Interna l s ys tem type wi th own empl oyees , frequency of occurrence

885 23 6 28 50 17 88 37 77 38

A2) Type of i nterna l funcona l onl y occa s i ona l l y, frequency of occurrence

753 41 11 22 4 50 53 43 52 26

B) External R&D (frequency of occurrence)

987 31 7 23 32 28 64 51 80 47

Costs A) of internal R&D (including wages and investments other than depreciaon), in thous. EUR

Cos ts a s s oci a ted wi th i nterna l R&D per 1 res pondent i n thous . EUR

914,719 8,452 13,984 14,782 43,502 1,739 52,223 8,305 73,210 77,973

558 132 823 296 806 26 370 104 568 1,218

Costs B) for external R&D services, in thous. EUR

613,078 3,397 673 567 3,145 413 19,906 3,116 19,454 52,446

Cos ts a s s oci a ted wi th externa l R&D per 1 res pondent i n thous . EUR

TOTAL costs of technical innovaons, (including the purchase of licenses, training and addional acvies) in thous. EUR

Cos ts for the i mpl ementat i on of techni ca l i nnova  on per 1 res pondent i n thous . EUR

621 110 96 25 98 15 311 61 243 1,116

3,076,380 20,130 66,439 27,145 56,459 15,182 94,885 63,115 147,141 300,495

1,172 212 2,768 372 656 160 463 482 704 2,707

Exchange rate 1 EUR ¼ 27.02 CZK (CNB, 31. 12. 2016) Source: own processing of the CZSO data, TI 2016 Note: Number of respondents who commented on the topic in the questionnaire

WEIGHTED total costs of technical innovaons including purchase of licenses, training and addional acvies) in thous. EUR

4,198,395 30,195 67,749 38,518 76,947 32,610 192,046 95,291 277,148 355,020

Total revenues of enterprises with technical innovaon in thous. EUR

155,024,239 1,268,829 1,173,650 799,031 466,667 791,475 1,596,247 2,880,318 6,711,042 12,437,218

Innovaon costs to revenues in 2016 in %

2.0 1.6 5.7 3.4 12.1 1.9 5.9 2.2 2.2 2.4

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Table 10.7 The proportion of the most important cooperation partners in implementing innovations in selected industries—according to the cluster core Industry

A Other compa ni es wi thi n the group of compa ni es %

All CZ-NACE 13X a 141- textile 231 - glass 32- others incl. BIJOU 721 - NANO 310, 161, 162 - furniture 620 - IT 172, 222 - packaging 251, 28x - engineering 293 - automotive

B Suppl i ers of C Pri va te s ector D Publ i c s ector equi pment, cl i ents or cl i ents or ma teri a l s , cus tomers cus tomers components or s oftwa re %

28.52 19.44 61.54 24.24 33.33 17.95 27.50 19.05 32.00 56.25

%

27.45 16.67 7.69 33.33 5.56 43.59 26.25 28.57 17.00 15.63

%

12.77 11.11 7.69 15.15 16.67 17.95 16.25 20.63 20.00 15.63

2.14 5.56 7.69 6.06 5.56 2.56 3.75 0.00 1.00 0.00

E Competitive a nd other compa ni es from the s a me i ndus try

F Cons ul tants , commerci a l l a bora tori es or pri va te R&D ins titutions

%

%

3.13 5.56 7.69 6.06 11.11 0.00 6.25 3.17 1.00 0.00

G Uni vers i ties H Publ i c I Pri va te or other hi gher res ea rch res ea rch educa tion orga ni za tion orga ni za tion ins titutions %

8.87 8.33 0.00 6.06 0.00 7.69 8.75 14.29 5.00 4.69

%

12.69 27.78 7.69 9.09 19.44 7.69 7.50 12.70 20.00 7.81

%

2.22 2.78 0.00 0.00 5.56 0.00 2.50 1.59 2.00 0.00

2.22 2.78 0.00 0.00 2.78 2.56 1.25 0.00 2.00 0.00

Source: own processing of the CZSO data, TI 2016 Note: The largest proportion—dark green colour, the smallest—dark red colour

or public research institutes. The intensity of the cooperation with universities is the same in all industries—relatively high in the textile, nanotechnology, packaging, and engineering industries. A positive impact of clusters on innovation was further found in other industries (automotive and furniture), which shows that the cooperation with universities is mainly close within COs, whilst outside them, it is not. As regards innovation barriers, lack of qualified employees and internal finance, in combination with unsure return on investment due to low purchasing power parity or small market size, was the most challenging barrier in 2016–2018. Some industries showed other predominant barriers, for example, more than 50% of the respondents in the 721 NANO industry stated that it was difficult for them to obtain public grants and subsidies (the second and third rank out of three). To a lesser extent, the respondents from the packaging, furniture, and engineering industries also pointed at the significance of the above barrier. Unlike in other industries, decision making about new product development was identified as a challenging barrier in the 293-automotive industry. This barrier was indicated by almost half of the respondents in the industry. The statement might be linked to the fact that companies in the automotive industry are, in the Czech Republic, frequently owned by foreign entities, which determine their business strategies, including research and development. It is possible to reduce difficulties in obtaining public support by joining COs. Based on an analysis of the amount of public support received by COs in 2006–2018 (Pelloneová & Žižka, 2019), it was revealed that, during that period, more than EUR 77 mil was spent on establishing and developing COs. The most important recipients of support were mainly clusters in the packaging industry (approx. EUR 8 mil. EUR), the nanotechnology industry (approx. EUR 4 mil.), the furniture industry (approx. EUR 3.5 mil.), and the textile industry (approx. EUR 3 mil.). These are industries, where respondents (apart from the textile industry) stated the access to public support as a barrier in innovations. Perhaps, these were the respondents who were members of COs.

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Table 10.8 The intensity of technical innovations in 2016 in selected industries

Source: own processing of the CZSO data, TI 2016 Note 1: Number of respondents who commented on the topic in the questionnaire Note 2: The largest proportion—dark green colour, the smallest—dark red

From the published data, it is also possible to determine the ratio indicator of innovation intensity (CZSO, 2018b), which is defined as the ratio of the costs of technical innovations to the revenues of the given companies that introduced technical innovation. As can be seen from Table 10.8, an extreme deviation compared to other industries and the national average was found in the 721-NANO industry. The 231-GLASS and 620-IT industries are also well above average. The data in Table 10.8 confirms the conclusion regarding the high innovation activity of most of the companies in the nanotechnology industry, in which it was not confirmed that clusters have a specific impact on increasing innovation performance. Furthermore, Table 10.8 indicates the different approach to innovation in the selected natural clusters. The textile and clothing industry had the lowest intensity of technical innovation of all the industries examined. That might be the cause of a gradual loss of competitiveness in the industry. It could also be the explanation for the decline in the number of companies and employment in this once traditional industry in North Bohemia. On the contrary, the intensity of technical innovations in the glass industry is slightly above average, and average in the bijouterie industry. This stated situation is reflected in the number of companies in the industries examined (see Fig. 10.4). The number of textile companies in the region decreased by 65% compared to 2002, the number of glass companies, on the other hand, is more or less the same, and in the bijouterie industry, the number of companies decreased by 43%. All the industries operate on global markets with fierce foreign competition.

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1.2000 1.0000

0.8000 0.6000

0.4000 0.2000 0.0000

Textile

Glass

Bijouterie

Fig. 10.4 Development of number of businesses by industries (2002 ¼ 100%). Source own processing according to the RES data (CZSO, 2018a)

10.5

Cooperation Partners and Barriers to Innovation

In the TI 2016 survey, companies also commented on the barriers to innovation activity. At the same time, they mentioned the partners with whom they cooperate on innovations. A multiple linear regression analysis was performed, and regression coefficients were estimated to determine whether the type of cooperation affects the barriers to innovation. The dependent variable was the type of barrier. The independent variables were the types of cooperating entities. Stepwise regression was applied to determine significant regression coefficients (at a significance level of 5%). Table 10.9 shows the strong links between a particular type of barrier to innovation and the extent of cooperation with individual entities. For example, companies that perceive a lack of good ideas as a significant barrier to innovation work more closely with universities and suppliers. Negative regression coefficients could mean that stated factors reduce the perception of the severity of a given barrier to innovation. Cooperation with clients and customers has a positive impact on the perception of the seriousness of insufficient decision-making competences and limited opportunities to decide on the development of new products. Cooperation with universities reduces difficulties in obtaining public support. Table 10.9 clearly shows that, from the range of various cooperating entities, links to universities, suppliers, clients, and customers are considered vital. These are

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Table 10.9 Dependence between barriers to innovation and the types of cooperation Barriers to innovation Lack of good ideas Lack of internal finance Lack of qualified employees

Lack of decision-making power in the field of business investment Lack of decision-making power in new product development Difficult access to information on market needs

Low or uncertain return on investment Lack of collaboration partners

Difficulties in obtaining public support

Important influencing factors Cooperation with universities Cooperation with suppliers Cooperation with universities Cooperation with suppliers Cooperation with universities Cooperation with clients and customers Cooperation with suppliers Cooperation with counsellors, commercial laboratories, and private research institutes Cooperation with other companies within a group Cooperation with clients and customers Cooperation with other companies within a group Cooperation with clients and customers Cooperation with universities Cooperation with counsellors, commercial laboratories, and private research institutes Cooperation with clients and customers Cooperation with universities Cooperation with suppliers Cooperation with universities Cooperation with private research institutes Cooperation with suppliers Cooperation with clients and customers Cooperation with universities Cooperation with the competition and other companies from the same industry Cooperation with other companies within a group Cooperation with suppliers

Regression coefficients 0.0869852 0.170567 0.129546 0.354657 0.185986 0.233159 0.154998 0.371515

P-value 0.0252 0.0001 0.0059