Proximity and the Cluster Organization 1032046341, 9781032046341

Table of contents :
Cover
Half Title
Series Information
Title Page
Copyright Page
Table of Contents
Figures and tables
Author biographies
1 Introduction
Contextual Background and Motivation
The Research Problem and Aim of the Book
Structure and Contents
References
2 Prior and Contemporary Theories On Industrial Clusters
The Concept of an Industrial District
An Outline of the Marshallian Industrial District
An Outline of the Italian Industrial Districts
Theories of Regional Development Based On Knowledge and Innovation
Learning Region
Innovative Milieu
Innovation System
Innovation Ecosystem
Industrial Cluster
Conclusion
Notes
References
3 Dimensions of Proximity
The Concept of Proximity
Social Proximity
Organizational Proximity
Institutional Proximity
Cognitive Proximity
Geographical Proximity
Geographical Proximity and Social Proximity
Geographical Proximity and Organizational Proximity
Geographical Proximity and Institutional Proximity
Geographical Proximity and Cognitive Proximity
Proximity in Theories On Industrial Clusters
Conclusion
References
4 Research Methodology
Paradigm and Research Strategy
Research Stage I
Sample Selection
Data Collection Techniques
Data Analysis and Interpretation Techniques
Research Stage II
Sample Selection
Data Collection Techniques
Data Analysis and Interpretation Techniques
Research Stage III
Sample Selection
Data Collection Techniques
Data Analysis and Interpretation Techniques
Methodological Rigor
Qualitative Research
Quantitative Research
References
5 The Role of Proximity in the Development of Cooperation in Cluster Organizations: The Results of a Qualitative Research
Conceptual Categories
Development of Proximity in Cluster Organizations – the Results of the Empirical Research
Proximity at Cooperation Level I
“Input” Proximity
“Output” Proximity
Proximity at Cooperation Level II
“Input” Proximity
“Output” Proximity
Proximity at Cooperation Level III
“Input” Proximity
“Output” Proximity
Proximity at Cooperation Level IV
“Input” Proximity
“Output” Proximity
The Concept of Proximity in Cluster Organizations
Relations Between Levels of Cooperation and Dimensions of Proximity
Relations Between Dimensions of Proximity
Conclusion
Notes
References
6 The Role of Proximity in the Development of Cooperation in Cluster Organizations: The Results of Quantitative Research
Variable Operationalization
The Level of Proximity Development
Geographical Proximity
Competence Proximity
Social Proximity
Organizational Proximity
Commitment
Testing Research Hypotheses
Model 1 – Testing Hypothesis H1
Model 2 – Testing Hypothesis H2
Model 2' – Testing Hypothesis H2'
Conclusion
7 Application of the Generated Concept of Proximity to Selected Cluster Organizations in Europe
The Case of Techtera
General Information
Geographical Proximity
Social Proximity
Competence Proximity
Organizational Proximity
Commitment
The Case of Cluster Kybernetickej Bezpecnosti
General Information
Geographical Proximity
Social Proximity
Competence Proximity
Organizational Proximity
Commitment
The Case of the Bulgarian Fashion Association
General Information
Geographical Proximity
Social Proximity
Competence Proximity
Organizational Proximity
Commitment
Development of Proximity in the Analyzed COs
Geographical Proximity
Social Proximity
Competence Proximity
Organizational Proximity
Commitment
Conclusion
Reference
8 Conclusions
Final Remarks
Theoretical and Practical Contributions
Limitations and Further Research
References
Index

Citation preview

i

Proximity and the Cluster Organization

Including the category of proximity in theoretical considerations and empirical analyzes in cluster organizations is an attempt to integrate existing approaches to understand and explain the specificity of interorganizational cooperation developed in geographical proximity. The importance of geographical proximity to create a competitive advantage is emphasized in all theories on the establishment and development of industrial clusters. However, proximity should not be perceived only in the geographical dimension. The similarity of knowledge systems (cognitive proximity), relationships based on trust (social proximity), organizational links (organizational proximity), and, finally, the similarity of institutional operating conditions (institutional proximity) enable and facilitate the development of cooperative relationships between business entities. Each of these threads deals separately with issues that have much in common, namely they can be treated as different dimensions of the same concept –​proximity. Proximity provides a specific concretization of the features, processes, and mechanisms underlying interorganizational cooperation, and thus facilitates its understanding, increasing the possibility of its effective management. The study provides new important elements to the current system of knowledge, filling in the cognitive and research gaps in the scientific literature on problems related to proximity development in cluster organizations (COs). The new element includes a multidimensional concept of proximity explaining its role in the development of cooperative relationships in the COs. A strong point of the developed concept is its inductive-​abductive origin and the use of grounded theory methodology, which is rare in the studies of COs. The developed concept has also significant practical advantages since it allows us to consciously shape proximity in COs, thus contributing to the development of cooperation between cluster enterprises. Anna Maria Lis is a faculty member of the Department of Management and Economics at Gdańsk University of Technology, Poland. Adrian Lis is a faculty member at Collegium Civitas in Warsaw, Poland.

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Routledge Studies in Management, Organizations and Society

This series presents innovative work grounded in new realities, addressing issues crucial to an understanding of the contemporary world. This is the world of organized societies, where boundaries between formal and informal, public and private, local and global organizations have been displaced or have vanished, along with other nineteenth-​century dichotomies and oppositions. Management, apart from becoming a specialized profession for a growing number of people, is an everyday activity for most members of modern societies. Similarly, at the level of enquiry, culture and technology, and literature and economics, can no longer be conceived as isolated intellectual fields; conventional canons and established mainstreams are contested. Management, Organizations and Society addresses these contemporary dynamics of transformation in a manner that transcends disciplinary boundaries, with books that will appeal to researchers, students and practitioners alike. Recent titles in this series include: Business meets the Humanities The Human Perspective in University-Industry Collaborations Edited by Martina Skrubbeltrang Mahnke, Mikka Nielsen, Matilde Lykkebo Petersen and Lise Tjørring Business Groups and Strategic Coopetition Edited by Wioletta Mierzejewska and Patryk Dziurski Flexible Human Resource Management and Vocational Behaviour The Employability Market Orientation Model Anna Pawłowska Knowledge Communication in Global Organisations Making Sense of Virtual Teams Nils Braad Petersen Proximity and the Cluster Organization Anna Maria Lis and Adrian Lis

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Proximity and the Cluster Organization Anna Maria Lis and Adrian Lis

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First published 2023 by Routledge 605 Third Avenue, New York, NY 10158 and by Routledge 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2023 Anna Maria Lis and Adrian Lis The right of Anna Maria Lis and Adrian Lis to be identified as authors of this work has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-​in-​Publication Data Names: Lis, Anna Maria, 1976– author. | Lis, Adrian, 1977– author. Title: Proximity and the cluster organization / Anna Maria Lis and Adrian Lis. Description: New York, NY : Routledge, 2023. | Series: Routledge studies in management, organizations and society | Includes bibliographical references and index. Identifiers: LCCN 2022048911 | ISBN 9781032046341 (hardback) | ISBN 9781032046365 (paperback) | ISBN 9781003194019 (ebook) Subjects: LCSH: Space in economics. | Interorganizational relations. | Strategic planning. | Regional planning. | Cooperation. Classification: LCC HT388 .L573 2023 | DDC 338.6/042–dc23/eng/20230111 LC record available at https://lccn.loc.gov/2022048911 ISBN: 978-​1-​032-​04634-​1 (hbk) ISBN: 978-​1-​032-​04636-​5 (pbk) ISBN: 978-​1-​003-​19401-​9 (ebk) DOI: 10.4324/​9781003194019 Typeset in Bembo by Newgen Publishing UK

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Contents

List of figures and tables Author biographies

ix xi

1 Introduction

1

2 Prior and contemporary theories on industrial clusters

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Contextual background and motivation  1 The research problem and aim of the book  3 Structure and contents  4 References  5 The concept of an industrial district  8

An outline of the Marshallian industrial district  8 An outline of the Italian industrial districts  12

Theories of regional development based on knowledge and innovation  19 Learning region  21 Innovative milieu  21 Innovation system  22 Innovation ecosystem  27 Industrial cluster  29

Conclusion  35 Notes  36 References  38

3 Dimensions of proximity The concept of proximity  47 Social proximity  52 Organizational proximity  56 Institutional proximity  61 Cognitive proximity  64 Geographical proximity  69

Geographical proximity and social proximity  74 Geographical proximity and organizational proximity  74 Geographical proximity and institutional proximity  75 Geographical proximity and cognitive proximity  75

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vi Contents Proximity in theories on industrial clusters  78 Conclusion  82 References  84

4 Research methodology

Paradigm and research strategy  91 Research stage I  93

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Sample selection  93 Data collection techniques  96 Data analysis and interpretation techniques  97

Research stage II  98 Sample selection  98 Data collection techniques  99 Data analysis and interpretation techniques  100

Research stage III  101 Sample selection  101 Data collection techniques  103 Data analysis and interpretation techniques  105

Methodological rigor  105 Qualitative research  105 Quantitative research  106

References  107

5 The role of proximity in the development of cooperation in cluster organizations: The results of a qualitative research

Conceptual categories  109 Development of proximity in cluster organizations –​the results of the empirical research  117

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Proximity at cooperation level I  118 “Input” proximity  118 “Output” proximity  121 Proximity at cooperation level II  122 “Input” proximity  122 “Output” proximity  125 Proximity at cooperation level III  126 “Input” proximity  126 “Output” proximity  127 Proximity at cooperation level IV  127 “Input” proximity  127 “Output” proximity  134

The concept of proximity in cluster organizations  135 Relations between levels of cooperation and dimensions of proximity  136 Relations between dimensions of proximity  139

Conclusion  142 Notes  145 References  145

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Contents vii

6 The role of proximity in the development of cooperation in cluster organizations: The results of quantitative research Variable operationalization  147 The level of proximity development  150

147

Geographical proximity  150 Competence proximity  152 Social proximity  157 Organizational proximity  159 Commitment  161

Testing research hypotheses  164 Model 1 –​testing hypothesis H1  166 Model 2 –​testing hypothesis H2  168 Model 2′ –​testing hypothesis H2′  170

Conclusion  171

7 Application of the generated concept of proximity to selected cluster organizations in Europe The case of Techtera  175

General information  175 Geographical proximity  178 Social proximity  179 Competence proximity  181 Organizational proximity  183 Commitment  185

The case of Cluster Kybernetickej Bezpečnosti  188 General information  188 Geographical proximity  190 Social proximity  191 Competence proximity  192 Organizational proximity  193 Commitment  194

The case of the Bulgarian Fashion Association  196 General information  196 Geographical proximity  198 Social proximity  198 Competence proximity  200 Organizational proximity  201 Commitment  201

Development of proximity in the analyzed COs  202 Geographical proximity  202 Social proximity  204 Competence proximity  206 Organizational proximity  207 Commitment  209

Conclusion  210 Reference  212

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8 Conclusions

Final remarks  213 Theoretical and practical contributions  213 Limitations and further research  215 References  217

Index

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ix

Figures and tables

Figures 4 .1 Research strategy 5.1 Concept of the trajectory of development of cooperative relationships in cluster organizations 5.2 Links between dimensions of proximity 6.1 Conceptual models 6.2 Model 1 6.3 Model 2 6.4 Model 2′

92 117 139 165 167 169 171

Tables 2 .1 Industrial district definitions 3.1 The results of the review of scientific publications with the keyword “proximity” 3.2 Citations of publications concerning the concept of proximity 3.3 The results of the review of scientific publications with the keywords “proximity” and “cluster” 4.1 The sample characteristics (stage I) 4.2 Main interview topics (stage I) 4.3 The sample characteristics (stage III) 4.4 Similarities and differences in the research sample 4.5 Main interview topics (stage III) 5.1 Conceptualization of the main dimensions of proximity 5.2 Central categories in the generated concept of proximity in COs 5.3 Proximity at cooperation level I 5.4 Proximity at cooperation level II 5.5 Proximity at cooperation level III 5.6 Proximity at cooperation level IV 5.7 Relationships between the levels of cooperation and the dimensions of proximity in COs 6.1 Operationalization

16 50 52 81 95 97 102 102 104 114 115 119 123 128 130 137 148

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x  List of figures and tables 6 .2 6.3 6.4 6.5 6.6 6.7 6.8

The texted hypotheses Confirmatory factor analysis results for Model 1 Standardized parameter estimates for Model 1 Confirmatory factor analysis results for Model 2 Standardized parameter estimates for Model 2 Confirmatory factor analysis results for Model 2′ Standardized parameter estimates for Model 2′

165 167 168 169 170 171 172

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Author biographies

Anna M. Lis In 2005, she obtained her PhD in the field of Economic Sciences, in the discipline Management Sciences, followed by, in 2019, the degree of habilitated doctor in the field of Social Sciences, in the discipline of Management and Quality Studies. She was employed at the Faculty of Production Engineering at Warsaw University of Technology (2004–​2009). Between 2006 and 2007 she was granted a scholarship under the Dekaban Programme and participated in a scientific internship at the University of Michigan, holding the position of Visiting Assistant Research Scientist. After her scientific internship she was employed at the Faculty of Management and Economics, Gdańsk University of Technology (2008–​present). Her scientific interests have been focused on problems related to institutional forms of support provided for the development of interorganizational cooperation, including cluster organizations and business environment institutions. Her recent research concerns issues related to sustainability, the green economy, and the circular economy. Her scientific achievements include over 80 publications. She has participated in many research projects implemented at academic centers, as well as in projects requested by the public authorities. She was engaged in international cooperation, including participation in European programs (INNET, COSME, ERASMUS, Horizon) and many internships at foreign universities (including Nanjing University of Aeronautics and Astronautics, Aalborg University, Norwegian University of Science and Technology). She has cooperated with counseling and training companies, cluster organizations, and business environment institutions. She has also been involved in cooperation with social and economic environment agencies as part of numerous expert panels. Adrian Lis In 2006, he obtained his PhD in Humanities, in the field of Sociology, from Adam Mickiewicz University in Poznan. He was then employed at the Faculty of Social Sciences at University of Gdańsk between 2008 and 2018. He also worked with the Baltic Eco-​Energy Cluster, coordinated by The Szewalski Institute of Fluid-​Flow Machinery

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xii  Author biographies Polish Academy of Sciences. Since 2019, he has worked at Collegium Civitas in Warsaw. His scientific and research interests have been focused on problems related to cultural aspects of the functioning of the cooperation networks including industrial clusters. He has participated in six research projects implemented at academic centers and in eight research projects requested by the external entities (among others, government agencies and ministries). He has cooperated with counseling and training companies, cluster organizations, and business environment institutions.

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1 Introduction

Contextual background and motivation In the scientific literature, there are many older and contemporary theories on the establishment and development of industrial clusters, which explain the development of cooperation based on geographical proximity. This group includes Marshall’s industrial districts (Marshall, 1890) and their Italian variant (Becattini, 2002; Bellandi, 2002; Pyke et al., 1990; Sforzi, 2002), and concepts such as the innovative milieu (Aydalot, 1986; Maillat, 1998), the learning region (Florida, 1995; Morgan, 1997), the regional innovation system (Cooke et al., 1997; Braczyk et al., 1998), the ecosystem of innovations (Adner & Kapoor, 2010; Autio & Thomas, 2014), as well as the concept of a cluster (Porter, 1998, 2000, 2008). One of the most popular and influential concepts from this group is the concept of a cluster as developed by Porter. It has become the basis of cluster politics, executed at different levels, from the international, through the national, to the regional level. Within EU cluster policy, a number of strategic economic programs were developed, the execution of which was tied to providing financial resources earmarked for supporting clusters. In effect, EU countries launched multiple cluster organizations (COs), also referred to as cluster initiatives (Sölvell et al., 2003; Lindqvist et al., 2013). Despite the popularity of COs in economic practice, however, this concept remains highly under-​researched. The vast majority of publications from the field refer to clusters from a geographical perspective (as industrial concentrations) and only a few are devoted to COs as such. Both cluster and CO are multidimensional concepts, the meaning of which should not be limited to coexisting on a given territory. The classic definition of a cluster alone (coined by Porter) leads to the conclusion that this is a structure which, to emerge, requires the “proximity” of the entities comprising the whole in other contexts (e.g., social relations, place in the value chain, etc.). However, despite the fact that “proximity” (in its different dimensions) can be considered to be a theoretically attractive category from the perspective of research and reflection on clusters and COs, it does not

DOI: 10.4324/9781003194019-1

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2 Introduction seem to be sufficiently exploited; it has enjoyed serious consideration on the part of researchers from the fields of economics and management only since the end of the 20th century. This relatively short period –​during which “proximity” was considered as the path to understanding clustering –​does not, however, imply there is homogeneity among the different approaches used. The final two decades of the 20th century witnessed the dominant position of topics focusing on proximity in reference to processes and phenomena present in the organization (Monge et al, 1985; Rice & Aydin, 1991), while the very end of the century brought about a shift of the balance in favor of the cross-​organizational context, which was significantly influenced by the so-​called French school of proximity (Rallet & Torre, 1999; Gilly & Torre, 2000; Torre & Rallet, 2005). The classic thinkers from this school underlined the fact that the category of proximity plays a fundamental role in the coordination of actions aimed at achieving an economic effect. The multidimensional nature of proximity was most present in the concept formulated by Boschma, who introduced a division of the main “contexts,” in which proximity is primarily present in the practice of the broadly understood economic life: namely, geographical, cognitive, social, institutional, and organizational (Boschma, 2004, 2005a, 2005b; Boschma & Frenken, 2010; Boschma et al., 2014; Balland et al., 2016). Each of the contexts is simply a different kind of proximity. The derived types of proximity are not enclosed realities, despite the fact that some researchers are interested in only some of them. Research and reflection on one aspect of proximity are, of course, possible and justified; however, it is important to be aware of the limitations of such an approach –​ a diagnosis made just in reference to one of the dimensions of proximity (without referring to the others) is similar to taking and interpreting a single photograph (i.e., one can observe the objects in the image and their placement, but it will be harder to fully grasp their significance without being able to tie them to other elements of reality). Relatively, the fullest and truest picture of the situation will only emerge when a diagnosis made for one dimension can be tied to knowledge of the other dimensions of proximity –​each change in dimension X will be both the consequence of changes in other dimensions of proximity as well as the cause of a remodeling of the situation itself. In other words, we are dealing with an interconnected system, in which influencing one element will result in specific changes to the remaining ones. Knowledge of these interrelationships –​that is, the identification of actions which, when taken with respect to one element, bring about a predictable effect with respect to another dimension/​other dimensions of proximity –​is of the utmost value in the context of the management of cluster structures.This applies both at the macro scale –​regarding decisions made far outside the scope of a given cluster/​CO (e.g., political decisions at different levels) –​as well as the micro scale (direct management in the cluster/​CO).

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Introduction 3

The research problem and aim of the book The main research problem concerns the development of proximity in COs, which determines the development of cooperation among entities comprising these organizations. Insufficient recognition of the issues related to the development of cooperation in COs and their poor description in the literature, as well as the lack of publications linking the concept of COs to the concept of proximity, juxtaposed with the dynamic development of these structures in the world, indicates a huge cognitive gap. The main aim of the research was to formulate and develop a multidimensional concept of proximity, explaining the role of proximity (and its various dimensions) in the development of cooperation in COs. Applying the concept of proximity to the concept of a CO allows –​at the theoretical level –​for the expansion of the current state of knowledge in the analyzed area, while at the practical level it helps these entities and their members to achieve higher levels of development. The research is based on the previously generated concept of the trajectory of the development of cooperative relationships in COs (Lis, 2018; Lis & Lis, 2021). The study conducted in COs showed that cluster cooperation can take different forms. Those forms, when divided into sets (according to a certain similarity), comprise a hierarchical four-​level system. The levels of cooperation were separated based on the identification of the main objectives guiding the key types of cluster activities. In turn, the identified levels were ranked based on the level of cooperation, reflecting the (individual or collective) approach adopted by cluster entities to the activities undertaken within a CO, the objectives set, and the interests determining them. Therefore, cluster cooperation was treated as a feature whose specific intensity characterizes a given CO and its constituent entities. The levels of cooperation identified and ranked in this way define the trajectory of development of cooperative relationships in COs, beginning with level I (Integration at the unit level) and ending with level IV (Creation and integration at the organizational level). Between these two extreme levels, there are two other levels that can be implemented simultaneously: level II (Allocation and integration at the process level) and level III (Impact on the environment). It was also noted that the achievement of successive identified levels of cooperation in COs (and thus passing through the various levels distinguished in the developed trajectory of development of cooperative relationships) can be explained with the abstract category of proximity. It turned out that “proximity” (or rather, its particular dimensions) manifests itself on all identified levels of cooperation, while its dynamics may determine the development of cooperative relationships in COs. Another important observation was that at each level of cooperation, a different combination of proximity dimensions produced the best results. The latter discovery provided the rationale for conducting an in-​depth investigation into the development of proximity in COs by posing the

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4 Introduction following two research questions: (i) What is the role of proximity in the development of cooperation in COs? (ii) Which dimensions of proximity are the most crucial from the perspective of the constitution of specific stages of the development of cooperative relationships (levels of cooperation) in COs? In order to verify the perceived links between the development of proximity and the development of cooperative relationships in COs, additional literature research was conducted to identify conceptual points of reference for the core categories derived within the formulated concept of the development of proximity in COs. The literature overview facilitated the conceptualization stage and enabled us to anchor the generated concept in existing scholarship. It also made it possible to determine the existence of significant differences between the generated concept of proximity in COs and the previously developed concept of proximity. One of these differences was predicated upon the nature of the collective to which it pertained –​ COs; previous scholarship fully omitted this type of organizations. A second difference was tied to the discovery of a new path in the conceptualization stage –​one of the dimensions of proximity identified in the study turned out to be the so-​called competence proximity, which did not appear in previously created concepts (neither in name, nor in understanding). An important task was to integrate both recalled concepts. The concept of the development of proximity was superimposed over the previously generated concept of development trajectory of cooperative relationships, and the development of identified dimensions of proximity and their interrelationships were presented in the context of the previously identified levels of cooperation in COs.

Structure and contents The book comprises eight logically structured chapters (including Chapter 1: Introduction). Below, we briefly explain the chapters’ content and contribution. Chapter 2 presents both older and contemporary theories on the establishment and development of industrial clusters (based on geographical proximity) to underlie the context of the implemented research. The focus here is on concepts developed within traditional geographical and economic literature. This group includes Marshall’s industrial districts and their Italian variant, and concepts such as the innovative milieu, the learning region, the regional innovation system, the ecosystem of innovations, as well as the concept of a cluster. Chapter 3 presents the state of knowledge on proximity and its selected dimensions. First, the concept of proximity is characterized and the results of the review of a collection of scientific publications on the concept of proximity are discussed. The focus then shifts to characterizing the individual dimensions of proximity. The final section shows the relationship between the previously discussed theories on industrial clusters and the presented concept of proximity.

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Introduction 5 Chapter 4 concerns the research methodology: the research paradigm and the research strategy. The research procedure, which has been applied to design and implement the research at each stage, is presented, including the method of selection of the research sample, the technique of data collection, and the analysis and interpretation. The methodological regime is described in the final section. Chapter 5 describes the results of qualitative research, concentrating on the role of proximity in the development of cooperation in COs. The first section presents the generated core categories of proximity and their conceptualization. The second section shows the development of five dimensions of proximity distinguished in COs. The third section covers the theoretical concept of proximity in COs, generated on the basis of the conducted research. Chapter 6 presents the results of quantitative research, the main purpose of which was to test the research hypothesis, reflecting the complex nature of the cause-​and-​effect relationships between the various dimensions of proximity in COs.The first part of the chapter contains the results of quantitative research on the development of proximity in the studied COs. In the second part, the conceptual models are tested based on the modeling of structural equations. Chapter 7 consists of three case studies, which are a practical exemplification of the generated concept of proximity in COs.The cluster organizations described as part of these individual case studies are located in three EU countries. Such geographical diversity of cluster organizations enables us to show the universality of the proposed concept. Chapter 8 summarizes the major findings and provides conclusions, including the epistemological contribution, current limitations, and directions for further research. It also presents practical implications and several policy recommendations regarding cluster policy.

References Adner, R., & Kapoor, R. (2010). Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations. Strategic Management Journal, 31(3), 306–​333 Autio, E., & Thomas, L. D. W. (2014). Innovation ecosystems: Implications for innovation management? In M. Dodgson, D. Gann, & N. Phillips (Eds.), The Oxford handbook of innovation management (pp. 204–​ 228). Oxford: Oxford University Press. Aydalot, P. (Ed.) (1986). Milieux innovateurs en Europe. Paris: GREMI. Balland, P. A., Belso-​Martínez, J. A., & Morrison, A. (2016). The dynamics of technical and business knowledge networks in industrial clusters: Embeddedness, status, or proximity? Economic Geography, 92(1), 35–​60. Becattini, G. (2002). Industrial sectors and industrial districts:Tools for industrial analysis. European Planning Studies, 10(4), 483–​493. Bellandi, M. (2002). Italian industrial districts: An industrial economics interpretation. European Planning Studies, 10(4), 425–​437.

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6 Introduction Boschma, R. (2004). Proximité et innovation [Proximity and innovation]. Économie Rurale, 280(1), 8–​24. Boschma, R. (2005a). Proximity and innovation: A critical assessment. Regional Studies, 39(1), 61–​74. Boschma, R. (2005b). Role of proximity in interaction and performance: Conceptual and empirical challenges. Regional Studies, 39(1), 41–​45. Boschma, R., Balland, P. A., & de Vaan, M. (2014). The formation of economic networks: A proximity approach. In A. Torre & F. Wallet (Eds.), Regional development and proximity relations (pp. 243–​267). Cheltenham: Edward Elgar. Boschma, R.A., & Frenken, K. (2010).The spatial evolution of innovation networks:A proximity perspective. In R. A. Boschma & R. Martin (Eds.), Handbook on evolutionary economic geography (pp. 120–​135). Cheltenham: Edward Elgar. Braczyk, H. J., Cooke, P., & Heidenreich, M. (Eds.). (1998). Regional innovation systems:The role of governances in a globalized world. London: UCL Press. Cooke, P., Uranga, M. G., & Etxebarria, G. (1997). Regional innovation systems: Institutional and organisational dimensions. Research Policy, 26(4–​5), 475–​491. Florida, R. (1995). Toward the learning region. Futures, 27(5), 527–​536. Gilly, J. P., & Torre A. (2000). Proximity relations: Elements for an analytical framework. In M. B. Green, & R. B. McNaughton (Eds.), Industrial networks and proximity (pp. 1–​16). Aldershot: Ashgate Publishing. Lindqvist, G., Ketels, C., & Sölvell, Ö. (2013). The Cluster Initiative Greenbook. Stockholm: Ivory Tower Publishers. Lis, A. M. (2018). Współpraca w inicjatywach klastrowych. Rola bliskości w rozwoju powiązań kooperacyjnych [Cooperation in cluster initiatives: The role of proximity in the development of cooperative relationships]. Gdansk: Wydawnictwo Politechniki Gdanskiej. Lis, A. M., & Lis, A. (2021). The cluster organization: Analyzing the development of cooperative relationships. London: Routledge. Maillat, D. (1998). Innovative milieux and new generations of regional policies. Entrepreneurship & Regional Development, 10(1), 1–​16. Marshall, A. (1890). Principles of Economics. London: Macmillan. Monge, P. R., Rothman, L. W., Eisenberg, E. M., Miller, K. I., & Kirste, K. K. (1985). The dynamics of organizational proximity. Management Science, 31(9), 1129–​1141. Morgan, K. (1997). The learning region: Institutions, innovation and regional renewal. Regional Studies, 31(5), 491–​503. Porter, M. (2000). Location, competition, and economic development: Local clusters in a global economy. Economic Development Quarterly, 14, 15–​34. Porter M. E. (1998). Clusters and the new economics of competition. Harvard Business Review, 76(6), 77–​90. Porter, M. E. (2008). On competition. Boston: Harvard Business School Publishing. Pyke, F., Becattini, G., & Sengenberger, W. (Eds.). (1990). Industrial districts and inter-​ firm co-​operation in Italy. Geneva: International Institute for Labour Studies. Rallet, A., & Torre, A. (1999). Is geographical proximity necessary in the innovation networks in the era of global economy? GeoJournal, 49(4), 373–​380. Rice, R. E., & Aydin, C. (1991). Attitudes toward new organizational technology: Network proximity as a mechanism for social information processing. Administrative Science Quarterly, 28(2), 219–​244.

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Introduction 7 Sforzi, F. (2002). The industrial district and the “new” Italian economic geography. European Planning Studies, 10(4), 439–​447. Sölvell, Ö., Lindqvist, G., & Ketels, C. (2003). The Cluster Initiative Greenbook. Stockholm: Ivory Tower. Torre, A., & Rallet, A. (2005). Proximity and localization. Regional Studies, 39(1), 47–​59.

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2 Prior and contemporary theories on industrial clusters

This chapter provides a traditional review of the subject literature, revealing the current state of knowledge of the prior and contemporary theories on the establishment and development of industrial clusters (based on geographical proximity) to underlie the context of the implemented research. The focus here is on the concepts developed within traditional economic geography and economic literature. This group includes Marshall’s industrial districts and their Italian variant, and other concepts such as an innovative milieu, learning region, regional innovation system, ecosystem of innovations, as well as the concept of a cluster. However, particular terms have been applied to highlight the contemporary approaches, namely “new industrial districts” (which indicates a clear connotation with their previous parallels –​the Marshallian industrial district) and the theory of innovation and knowledge-​ based regional development (regions as knowledge and innovation hubs).

The concept of an industrial district An outline of the Marshallian industrial district The literature indicates two convergent approaches concerning the location of economic entities –​by Hearn (1864) and by Marshall (1890). They both discuss issues related to the formation of aggregates of economic entities, namely industrial districts or, as referred to more recently, clusters in particular locations. According to Hearn, it is some economy in the cost of production that mostly determines the location of a certain industrial sector in an area. Depending on various circumstances –​natural conditions, the character of the inhabitants in the region, or purely accidental reasons –​one district (understood as a certain, clearly defined area separable from the whole territory where an economic activity of a specific nature is performed1) provides unusual benefits for a business or occupation (Hearn, 1864). As stated by Hearn: “Every person knows that the natural forces available for human uses which one country possesses, are often more abundant, or more accessible, or of a better kind than those in another country” (Hearn, 1864, p. 71). In other words, “the productive capacities of any two places are often very DOI: 10.4324/9781003194019-2

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Prior and contemporary theories on industrial clusters 9 different” (Hearn, 1864, p. 72), which has a particular influence on each location. Hearn noticed that in parallel to the process of spontaneous industry creation in large communities and the increase in facilities for the exchanges done by their members, a new (in those days) phenomenon began to gain in importance: specialization of local areas in performing specific functions (Hearn compared it to a developing specialization within a professional structure). The discussed notion allowed the conclusion that “the various branches of industry exhibit a strong tendency to fix themselves in, and confine themselves to, particular districts. Each district thus acquires a distinctive character and at the same time becomes dependent upon the other districts with which it deals” (Hearn, 1864, p. 305). Among the most important factors determining the degree of suitability of a given area for a particular industry, Hearn lists first the land (in terms of soil quality) and the climate specific to a given territory. As far as the subsequent issues are concerned, there are: plants and animals existing in the given area, the availability of wood, minerals, and water, as well as the existing communication network (in terms of facilities for the movement of people and goods) (Hearn, 1864, pp. 73–​76). Surprisingly, yet still in accordance with the further considerations of the scholar, the absence of negative factors that hinder doing business or performing work is a crucial positive premise for making a decision about locating a given economic entity in the very particular area (Hearn, 1864). Another important thread in Hearn’s thought concerns the superiority of the external (natural) conditions over human nature. Justifying the higher efficiency of English workers compared with the work done by citizens of other European countries (a specific reference concerned the Dutch), Hearn argues that it is not the biological distinction of the representatives of the English nation that decided their success in this area, but completely different living and working conditions prevailing in the British Isles in comparison to the Netherlands. Therefore, if employees from other countries moved to England, the efficiency of their work would increase significantly because of the conditions in which this activity would be undertaken (Hearn, 1864)2. The external (natural) conditions would determine such a development of the physical and mental forces of a person embedded in them that they would have the characteristics and skills fully analogous to the local specificity (Hearn, 1864). The importance of location in the process of establishing various branches of the economy is an important aspect also in Marshall’s considerations; however, similarly to Hearn’s reflection on this issue, it has a more complementary status rather than the mainstream one.The great agreement between the cited scholars can be seen in the list of factors that determine the location of economic entities with a specific business profile: Marshall, similarly to Hearn, acknowledges the primacy of the land and climate as the factors that most affect the location of a given branch of the economy in a specific place, supplementing them with natural resources valuable from the point of view of the specifics of the industry (e.g., coal, wood) as well as providing means

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10  Prior and contemporary theories on industrial clusters of transport (on the land and by water) to connect the given area with other territories. What deserves particular attention is the significant novelty in Marshall’s approach (when compared to Hearn’s reflections), who extended the list of factors by the opportunities created by “court patronage,” that is, the demand of the local aristocracy for products of a certain type (of high quality). In Marshall’s opinion, it was an element that allowed for a dynamic development of the labor market in a given location, creating a demand for a highly qualified workforce, which, in turn, owing to the diffusion of tacit knowledge characteristic of the district, acted as a catalyst for the development of local staff. It is also worth quoting a part of the scholar’s magnum opus since it contains the essence of what the distinction of the Marshallian districts meant in those days; that is, structures chronologically preceding the subsequent theoretical concepts connected with districts or directly resulting from them (such as clusters): When an industry has thus chosen a locality for itself, it is likely to stay there long: so great are the advantages which people following the same skilled trade get from near neighbourhood to one another. The mysteries of the trade become no mysteries; but are as it were in the air, and children learn many of them unconsciously. Good work is rightly appreciated, inventions and improvements in machinery, in processes and the general organization of the business have their merits promptly discussed: if one man starts a new idea, it is taken up by others and combined with suggestions of their own; and thus it becomes the source of further new ideas. And presently subsidiary trades grow up in the neighbourhood, supplying it with implements and materials, organizing its traffic, and in many ways conducing to the economy of its material. (Marshall, 1890, p. 225) While considering this quotation, it is worth emphasizing the sentence in which Marshall clearly supports Hearn’s thesis about the importance of location selection, not only of the entities of the leading industry in a given region but also industries that are complementary to it. The presence of complementary industries (or the “neighboring” industries –​as they are called by Marshall) is also a chance for a more efficient use of machinery and equipment as well as profitability of even high outlays incurred for this purpose: The economic use of expensive machinery can sometimes be attained in a very high degree in a district in which there is a large aggregate production of the same kind, even though no individual capital employed in the trade be very large. For subsidiary industries devoting themselves each to one small branch of the process of production, and working it for a great many of their neighbours, are able to keep in constant use machinery of the most highly specialized character, and to make it pay

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Prior and contemporary theories on industrial clusters 11 its expenses, though its original cost may have been high, and its rate of depreciation very rapid. (Marshall, 1890, p. 225) A district which is dependent chiefly on one industry is liable to extreme depression, in case of a falling-​off in the demand for its produce, or of a failure in the supply of the raw material which it uses. (Marshall, 1890, p. 227) The lack of neighboring industries in the district also contributes to narrowing the labor market profile in a given location and the potential dissatisfaction of the employees and their families. This dissatisfaction may result –​according to Marshall –​from the consequences of homogenization of the labor market in the district with the above-​mentioned characteristics, in particular due to no earning potential among those family members who do not closely match the narrow profile of the expected employee. This translates into lower family incomes, and thus a lower quality of life. According to Marshall, initiating a business activity in related supplementary industries would be the only remedy for such a situation: A localized industry has some disadvantages as a market for labour if the work done in it is chiefly of one kind, such for instance as can be done only by strong men. In those iron districts in which there are no textile or other factories to give employment to women and children, wages are high and the cost of labour dear to the employer, while the average money earnings of each family are low. But the remedy for this evil is obvious, and is found in the growth in the same neighbourhood of industries of a supplementary character. (Marshall, 1890, p. 226) The theoretical concepts presented above quite clearly take the purely economic consequences of location decisions as their main reference. Therefore, they emphasize, above all, how the location values of a given place affect success (here understood as the existence on the market) of the economic entities agglomerated in the area, paying less attention to the factors involved in this process. On the contrary, it is not only the very conditions specific to a given location that can be regarded as sufficient to build a strong market position by the enterprises anchored in them. The location conditions are a set of possibilities and restrictions characteristic of a specific place in a geographical and economic space –​each entity operating within them is forced to mobilize its resources (economic, social, cultural, human) in order to use them as efficiently as possible (when it comes to the positive features of location) or counteract them as effectively as possible (in the case of the negative features). It is worth emphasizing that these factors grow in importance proportionally to the more contemporary spectrum of the analyses and to the

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12  Prior and contemporary theories on industrial clusters higher degree of similarity to the Marshallian industrial district. Marshall, focusing on the external benefits of an industry location, did not consider the enterprises of his time as structures separated from socioeconomic relations with the area (or sector) from which they originated. An outline of the Italian industrial districts The impact of Marshall’s theory is evident in various contemporary concepts, particularly in the ones that perceive knowledge as the engine of regional development (as discussed in the subsequent part of the publication). This group also includes the notion of Italian industrial districts, which Italian economists and management experts have been applying to explain the specifics of the economic activity undertaken in some regions of Italy. It was the late 19th century when Hearn shed some light on the aforementioned specificity of the economic activity in Italy. His reflections, based on the reports of Laing, included in the work Notes of a Traveler (Laing, 1846), are a good starting point for the analysis of the contemporary approaches to Italian industrial districts3 (Pyke et al., 1990; Becattini 2002; Bellandi, 2002; Sforzi, 2002). Hearn pointed out that Italian products required a lot of hands to work (usually more than were really necessary) but only for a short time and the employees’ remuneration was largely paid in kind. Both of these factors as well as the warm climate of Italy (enabling the residents to get by in light, relatively makeshift shelters) and the country’s abundance of cheap food contributed to creating unfavorable conditions for the emergence and development of regular industry (Hearn, 1864). Meanwhile, the disappearance of the original Marshallian concept of industrial districts was determined by the nature of industrial development in the 20th century, when the emphasis was on the sphere of capital accumulation and the introduction of technical progress into each production process. Such a decrease of human economic activity in industrial production overshadowed the non-​economic elements of the reality that had been influencing those economic processes as well as depriving some theoretical concepts (including the concept of the Marshallian industrial district) of their relevance to and practical application in science. Nevertheless, this did not affect the very existence of the districts themselves, which maintained their activity but in the “shadow of major factories” (Becattini, 2002). It is worth taking into consideration those factors whose occurrence changed the optics on the analysis of the Italian industrial sector –​the previous focus on Taylor–​Ford determinants of industrial production efficiency switched to less homogeneous, more open and prosocial factors. The adjustment of the point of view to the reality resulted from the socioeconomic changes known as macroeconomic “surprises” (Bellandi, 2002). One of these surprising changes was the reversal of the trend (visible since the 1960s) of the increasing size of industrial plants, which was originally in line with the widely accepted “economy of scale.” A dominant part of Italian industrial plants reached their specific “growth limits,” while the strong dynamics of

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Prior and contemporary theories on industrial clusters 13 industry was evident in those areas of the country where numerous, though small, production entities played the dominant role. This concerned in particular those enterprises that had operated in the old sectors traditional for Italian manufacturing, such as the textile, clothing, ceramics, furniture, footwear, and machinery sectors. The latter became the most active in the production of light machinery and equipment necessary in various stages of production processes characteristic of the industries mentioned earlier. When taking into account the constantly decreasing production efficiency in the areas of the most industrialized triangle (i.e., Milan–​Turin–​Genoa) and the areas of the south of the country, which had been heavily supported from the state budget, the need to explain the trends discussed above appears obvious. An additional stimulus to provide explanations for this state of affairs concerns the changes in the labor market in Italy. Between 1961 and 1981, the shift of employment in industry from large business entities to small production plants was clearly visible (Bellandi, 2002). The discussions held at that time contributed to many explanations for the phenomenon. Following Brusco (1990), the main theoretical concepts that were born in Italy in the second half of the 20th century can be divided into four basic categories corresponding to the district visions dominating those days: the traditional artisan model, the dependent subcontractor model, the model of the industrial district Mark I, and the model of the industrial district Mark II. The traditional artisan model is the earliest attempt (in the 1950s and 1960s) to explain the tendency observed in a part of Italy to conduct production activities in compact agglomerations of numerous but small enterprises. It did not yet explicitly use the idea of the Marshall industrial district (such an application appeared in the late 1970s and early 1980s); the attempts were made to scrutinize the visible differences between the backward south of the country and the highly industrialized north. The enterprises from the north and the south basically operated in the same industries and produced goods for the same market, yet they differed in the way the goods were produced and exported (if that happened at all). The large production entities from the north (capital-​intensive, efficient, unionized, and paying relatively high wages) produced goods for the domestic and foreign markets, while the small enterprises from the south (labor-​intensive, inefficient, without union structures, paying relatively low wages) were mainly local-​market (or possibly domestic) oriented. It was believed that such a division would lead to further disparities between the north and the south, hence the large entities from the north were expected to expand their business activity in the south and, by means of their investments, not only increase the efficiency of the work and remuneration in the region but also bring a general improvement in the southern regions of Italy. The first analyses of the traditional artisan model were enriched in the following decade with some additional insights from which the dependent subcontractor model was born. It was created on the wave of structural changes in Italian industry in the late 1960s, when many large industrial

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14  Prior and contemporary theories on industrial clusters companies closed their production departments, shifting their manufacturing activities to the local small enterprises. Regardless of the way of explaining the market position of the small production companies, they were considered to be largely dependent on the policies that large production plants made in the market. As far as the dependent subcontractor model is concerned, the novelty concerned the break with the view of certainly lower efficiency of small production enterprises in comparison to large manufacturing plants. As Brusco (1990) pointed out, ten lathe machines under one roof (in large enterprises) can be as effective as ten lathe machines under ten roofs (in small enterprises agglomerated in a specific territory). What is more, this efficiency was achievable despite the fact that the employees of the small manufacturing entities clearly received a lower salary than their colleagues employed in the large factories, although their knowledge and skills were at a similar level. The early version (Mark I) of the industrial district model appeared in the second half of the 1970s as a response to the attempts to explain the economic success of those regions of Italy where small enterprises were the main production force –​Brianza and Cascina (the furniture industry), Puglia and Vigevano (footwear), Bologna (the machine industry), and Carpi and Prato (the textile industry). However, it was the article by Becattini (1979) that proved to be a breakthrough in the development of the industrial district model. It highlighted the need to change the scope of analysis applied in those days from single enterprises operating in a theoretical isolation to interconnected enterprises agglomerated in a specific location. This peculiar cluster –​as one might call such an aggregate today –​did not have one distinguishable executive center but was managed by a set of actions undertaken in each enterprise individually with respect to the existence and specificity of the other entities operating in the same area. It was the early version of this model that comprised a division of enterprises –​a crucial determinant to understand the specificity of the district. It included: • •

•

firms that produced the final product (approximately 30% of the companies operating in Italian districts at the time); stage firms, namely those involved in one specific phase of the production process (however, these were companies that not only dealt with the simplest stages of the production process of a given commodity; their scope of activities also included some production phases that required specialized knowledge and advanced technology); ancillary firms that aim at representing businesses not directly related to the industry (industries) involved in the production process characteristic for the district; these enterprises should be perceived as entities complementary to the key industries of the district not only because of supplementing the production process with useful elements or services –​i.e., by the direct impact –​but also due to the possibility of an indirect impact on the production process of final and phase enterprises (such a role is played by financial institutions, transport companies, etc.).

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Prior and contemporary theories on industrial clusters 15 The development of the model of the industrial district Mark I was considerably affected by Becattini’s emphasis on the cultural background of a district’s functioning (e.g., the historical tradition of producing a specific type of commodity in a given region), which attributed high importance to knowledge and its derivatives (e.g., innovation) for the development of a district and prosperity of its entities in the market (Becattini, 1979). It was indicated that the relations between final-​product and stage firms (more importantly, supporting the relationship of the qualified employees and the owners of end-​product enterprises with the employees and the owners of stage firms) had a huge impact on the creation of innovation (also on a global scale). Due to cooperation in the district, the small enterprises were able to create and take advantage of the scale effect in some of their activities (excluding sales and marketing). The cooperation was more intensive in the case of entities representing various stages of the production process, whereas firms embedded in one stage usually competed with one another. With regard to the model of the industrial district Mark II (dating back to the early 1980s), the role of broadly understood knowledge as a factor strongly determining the specificity of the functioning of enterprises within a district was even greater. The beginning of the 1980s was a period when Italian enterprises underwent intensive restructuring processes (regardless of their size) aimed at improving the absorption of new technologies by economic entities and improving their competitive position both in the European market and in the world economy. It was noticed that enterprises grouped in industrial districts, when compared with large industrial plants, reacted differently to the changes in the conditions they had to operate in. It resulted from a dissimilar approach to management of the production processes in the district and large firms: in large entities, these were top-​down decisions of the management boards of the individual enterprises, whereas for small enterprises from the district, the production process was a social process strongly anchored in the local community and thoroughly understandable. Although the changes in the production process of the district enterprises were slower than in the large firms, they were definitely deeper, more durable, and complete in nature, since they resulted from the reflection and arrangements of the entire community involved in it. It had a positive influence on the level of creativity and innovation of the businesses in the districts since their employees (and, at the same time, members of the community) fully understood the technology they had been using with all its advantages and weaknesses. However, the slow pace of changes occurring in the districts, which is characteristic of the industrial district model in the later version, was the basis for an intervention of the central authorities in the internal processes and functioning of the district (this factor was absent in the earlier version of the discussed model) (Brusco, 1990). The changes in the districts that took place in Italy in the subsequent decades had a positive impact on the notions applied to describe them. The

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16  Prior and contemporary theories on industrial clusters following interpretations of the term “industrial district” in conjunction with the Italian experience allow us to recognize several basic threads that make up a relatively complete picture of this socioeconomic structure (see Table 2.1). Table 2.1 Industrial district definitions Author

Definition

Sforzi, 2002

“A local system characterized by the active co-​presence of a human community and a dominant industry constituted by a set of small independent firms specialized in different phases of the same production process. This ‘active co-​presence’ consists in the fact that the local society exerts an influence on the organization of production which springs from its social culture. A system of values and norms –​dominated by a spirit of initiative and largely reflected in the principal aspects of life, like work, consumption, saving, attitudes to uncertainty –​produces a cultural environment favourable to economic enterprise, influencing industrial relations and the activities of local government and administration.” “A socio-​territorial entity which is characterised by the active presence of both a community of people and a population of firms in one naturally and historically bounded area. In the district, unlike in other environments, such as manufacturing towns, community and firms tend to merge.” “The industrial district combines, then, a very active kind of competitive behaviour on the part of its individuals, with a semi-​ conscious and semi-​voluntary cooperation among them, resulting from the special way in which the sociocultural system permeates and structures the market in the district.” “A complex, inextricably economic and social form of organization which contains within itself the essential factors of its own formation and development.” “The competitiveness and the dynamism of industrial districts’ firms are dependent from social integration. Social integration, however, is usually the result of a conscious co-​ ordination among the local institutions: i.e., the ‘high road’ to competitiveness is not the outcome of market mechanism, but of a combination of market and concerted collective action among the representatives of the principal district categories and the local establishment.” “Local systems strongly structured socially and economically around a productive process or several integrated productive processes. […] The feature shared by all local systems is that they comprise mechanisms which shape and transmit knowledge, and which reduce the transaction costs hinging on relations that are not solely market ones; mechanisms able to amalgamate firms’ production economies with economies of territorial concentration.”

Becattini, 1990

Ottati, 2002

Goglio, 2002

Source: Authors’ own study

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Prior and contemporary theories on industrial clusters 17 The industrial district can be considered not only by means of a simple analysis of the available definitions but also by identifying its constituent factors. A list of such components has been formulated by Ottati (2002), according to whom the industrial district consists of: •

•

•

•

phase firms (i.e., business entities specialized in one or several phases of the district’s key production process, as well as enterprises performing auxiliary roles for the firms involved in this production process –​it is phase firms where most of the district’s know-​how is located; they also have the greatest potential for a comprehensive and innovative use of their knowledge and skills); final firms (i.e., business entities responsible for the design and/​or distribution of completed products offered by the district –​their main role is to mediate between the local production system and markets of a wider (national, continental or global) scale); district employees, who are a source of knowledge and skills both formal (codified) and informal (contextual) –​they hold this knowledge regardless of the form of employment (therefore, this applies to both the employed and the self-​employed) and it is the knowledge and skills that are the sources of the competitive advantage of the industry (industries) located in a given district; local intermediary institutions whose task is to harmonize and facilitate activities within the district’s key production process (or production processes). The local government authorities and all the public and private institutions (e.g., chambers of commerce, banks, training agencies, etc.) are the main players in the sphere of the local institutions –​they have the greatest impact on the functioning of the district; but for them, the existence of the district would be questioned.

Capecchi (1990), analyzing the success reasons of the districts in the Italian region of Emilia-​Romagna, presented an interesting list of factors whose occurrence in a given area would allow to recognize the socioeconomic structure functioning there as an “industrial district.” Although Capecchi put the main emphasis on the specifics of the production activity in a district area, he also maintained the thread of communitarization determining this activity. According to Capecchi, a district is recognized when: • • •

the district boundaries are clearly defined and limited to a specific geographical space characterized by a specific type (method) of production; in the area where the district is to operate, there are many small and very small enterprises that, due to their flexibility, can meet the requirements described in the section below; the production capacities of the enterprises in a given area allow them to meet diverse demands of retail customers as well as to provide a proper quantity of products to customers interested in wholesale purchases;

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18  Prior and contemporary theories on industrial clusters •

•

•

•

among the enterprises operating in the district, there is a clear division between economic entities offering “finished” (completed and ready to use) products and entities focused on one phase of the district’s key production process or on production of a specific part for the “finished” product; the flexibility of small businesses that make up the district lies not only in their ability to meet the growing demands of individual customers or the expectations of wholesalers but also in their ability to change their role in the district –​the division into “final” and “phase” firms does not have a rigid, unchangeable character but is only a temporary system whose structure is modified depending on the individual needs of the business entities involved in it and constantly changing market requirements in the region, country, continent, and world; the firms operating in the district do not exist in a social vacuum but establish many and varied relationships with other economic actors –​ they compete with some economic entities as well as cooperate with others within the district structure and the supreme goal of every firm operating in the district is to take actions that do not bring negative effects to other entities in that location (which is because of the factor described in the next subsection); there is a close internal connection between its production sphere and the area in which the sphere is located –​moreover, this area is not only a place in geographical and physical space but a kind of conglomerate of social, political, and family bonds that interconnect there.

With regard to Bellandi’s concept, the term “industrial district” has been replaced by a newer theoretical structure (“cluster”); however, its essence is still in line with the main characteristics of the district (Bellandi, 2002). It is also worth mentioning that neither the small business cluster nor the industrial district is a structure to be found only in Italy. In addition, they are not products of their times. The earlier part of this chapter scrutinized the universal tendency of economic entities of a certain specificity to localize in a single place (the Italian industrial districts or Italian clusters are no exception). Nevertheless, as Bellandi points out, although two main engines of economic growth (industrial areas composed of large plants and industrial districts consisting of small and very small enterprises) can be distinguished in most national economies, it is only Italy where the engine in the form of industrial districts performed a role similar to that of large firms. This is evidenced by the Italian statistics from the late 1990s, according to which approximately 200 districts were distinguished in Italy at that time. They employed over 40% of the entire national workforce and accounted for 43% of Italian exports. It is essential to add that the brands of products originating in the Italian districts constituted the core of the brands that have made up the positive image of the “Made in Italy” label (Goglio, 2002). In Bellandi’s opinion, when analyzing districts (clusters), the attention should be primarily paid to such factors as:

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Prior and contemporary theories on industrial clusters 19 • • •

the cluster acts as the core of the economic and social activity of the local system (i.e., the area in which a specific community of people lives and works); inside the cluster, there are groups of very small, small, or at most medium-​size independent enterprises, most of which are complementary to one another; enterprises in the district (cluster) are interconnected at two levels: at the market mechanism level and in the non-​market sphere (Bellandi, 2002).

The analysis of the definitions presented above has facilitated the identification of the main theoretical spheres raised in the individual concepts (yet never alienable) of the Italian industrial district, namely the spheres of: the place, economic entities, the labor market, and knowledge, as well as the sphere of common norms and values. The sphere mentioned first clearly refers to the considerations discussed earlier in this c­ hapter –​they highlight the natural necessity of taking into account the location of the created business entity during the decision-​making process on initiating its activity. Just as individual locations were expected to differ, so are the “local systems” in which industrial districts used to (or still) operate. Nonetheless, whereas the previously discussed location concepts focus on the “traditional” factors determining the specificity of a given place (natural resources, proximity of water, location near major communication routes), the industrial districts and the related local systems are more concerned with the community of people living in a given area. It is people who hold two features: creating and maintaining relationships with other entities (both individual as well as collective ones) and creating and disseminating knowledge indispensable for the production process characteristic of a particular district.

Theories of regional development based on knowledge and innovation With regard to the issues of the study (with a special consideration of the concept of the cluster and the concept of the cluster organization that directly arises from it) and in accordance with the integrating nature of the literature review, this section presents selected concepts convergent with the concept of the cluster. They are included in the group of regional development theories, where a region is perceived as the hub of knowledge4. In addition to the Italian industrial district described earlier, there are other common concepts assigned to the group mentioned above, namely (in chronological order): •

innovative milieu (Aydalot, 1986; Camagni, 1991; Maillat & Perrin, 1992; Maillat et al., 1993; Ratti et al., 1997; Maillat, 1998; Crevoisier & Camagni, 2000);

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20  Prior and contemporary theories on industrial clusters • • • •

learning region (Florida, 1995; Asheim, 1996; Morgan, 1997; Hudson, 1999); regional innovation system (Cooke et al., 1997, 1998; Asheim & Isaksen, 1997; Braczyk et al., 1998; Cooke, 2001; Carlsson et al., 2002; Doloreux, 2002; Doloreux & Parto, 2005); industrial cluster (Porter, 1998, 2000, 2003, 2008; Porter & Ackerman, 2001); innovation ecosystem (Adner, 2006; Adner & Kapoor, 2010, 2016; Autio & Thomas, 2014; Gobble, 2014) that arose from the business ecosystem concept (Moore, 1993, 1996; Iansiti & Levien 2004)5.

The first important step in the development of the concepts based on knowledge and innovation was to understand that “the most fundamental resource in the modern economy is knowledge and, accordingly, that the most important process is learning” (Lundvall 1992, p. 1)6. The development of modern technologies, an increasing competition in the market, and continuously increasing customer requirements force entrepreneurs to implement regular changes, which in turn raises the need for permanent knowledge improvement and competence renewal. According to the basic assumption of this concept, learning takes place through interactions with a whole group of various entities (learning by interacting) in connection with the routines undertaken in each area of the company’s activity: in the field of R&D, production, marketing, or distribution. The relationships occurring inside and outside an organization facilitate the accumulation and flow of knowledge, especially the non-​codified (unwritten) and tacit knowledge, based on experience, which is difficult to transfer over long distances and is mainly shared in the form of direct contacts. Therefore, it is not only the spatial proximity of the partners that is essential to establish such interactions but, above all, trust and willingness to cooperate arising from the cultural context and the local environment. Interactive learning translates directly into increasing the knowledge base as well as pro-​innovation activities undertaken by enterprises. This is indicated by the co-​creators of the learning economy concept, who claim that “innovation is rooted in processes of interactive learning” (Andersen et al., 2002, p. 187) –​that is, all actions aimed at strengthening the network of connections for acquiring knowledge and skills simultaneously contribute to increasing efficiency in innovation. This particularly applies to the institutional environment of innovation enterprises. Regional and local social networks and organizations can significantly influence the mutual interactions of market entities and thus support the process of implementing and commercializing innovations. According to Rothwell (1992), successful enterprises are usually connected to external sources of technical knowledge and consultancy, which, as emphasized by Capello (1999), is the main condition for success primarily in the SME sector (innovation of small and medium-​size enterprises is based on interactive learning processes).

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Prior and contemporary theories on industrial clusters 21 Learning region The growing focus on the role of regions in stimulating the innovation capacity and competitiveness of enterprises has led to the development of numerous concepts that view a region as a hub of knowledge and innovation. This is particularly marked in the concept of the learning region, according to which regions are the key element of the new era of global “knowledge-​ based capitalism.” By adopting the role of centers of creating and storing knowledge and ideas, they become learning regions (or regions of knowledge), providing the environment and the necessary infrastructure with the flow of knowledge and ideas as well as learning processes (Florida, 1995). Florida points out that globalization goes hand in hand with regionalization since it is at the regional level that the knowledge support and innovation infrastructure works. This infrastructural environment consists of: production infrastructure (based on a network of enterprises and suppliers as the sources of innovation); labor market infrastructure (based on the employees’ knowledge, continuous improvement of their competences, lifelong learning and training); physical and communication infrastructure (facilitating a global flow of various types of resources, including information, human, and material resources); financial market infrastructure (financial support for knowledge and innovation); and industrial policy (all formal rules and informal relations among enterprises and between enterprises and institutional entities) (Florida, 1995). Innovative milieu The other distinguishing concepts draw more attention to the role of interactive relationships among the entities operating in geographical proximity. These relationships are not only economic in nature but also, above all, social, which facilitates the development of innovation –​a largely collective process (Camagni & Capello, 2005).The first of the discussed concepts –​the innovative milieu –​was created as a result of changes taking place in the economy, such as the decline of old industrial regions and the emergence of new dynamically developing ones (Maillat & Lecoq 1992; Maillat, 1998).The key role in the development of these regions was played by small and medium-​ size enterprises (more specifically, by a system of co-​ related enterprises within the SME sector) capable of innovation. Being increasingly perceived as active territorial organizations capable of creating strategic, specific, and diverse resources as well as launching development and innovation processes, territories gained their competitive advantage (Maillat, 1998). Therefore, regional development depended, to a large extent, on an environment conducive to innovation (Maillat, 1995), functioning as a kind of innovation incubator (Aydalot, 1986) predominantly affected by the connections among the business entities, leading to the development of specific skills and know-​ how (Camagni, 1991).

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22  Prior and contemporary theories on industrial clusters The concept of the innovative milieu was introduced into the literature in the mid-​1980s by the Groupe de Recherche Européen sur les Milieux Innovateurs (GREMI) (Aydalot, 1986)7 and is well interpreted by Camagni, according to whom the innovative milieu creates “the set, or complex network of mainly informal social relationships on a limited geographical area, often determining a specific external ‘image’ and a specific internal ‘representation’ and sense of belonging, which enhance the local innovative capability through synergistic and collective learning processes” (Camagni, 1991, p. 3). The innovative environment consists of three defining elements (Fromhold-​ Eisebith, 2004). First, it is the network of informal social contacts developed both on professional and private grounds (based on trust). Second, it is the geographical proximity of the partners, which is the kind of (sociocultural) ground on which innovation processes are jointly implemented. Location proximity also facilitates employee mobility and knowledge transfer; it is also the basis for the emergence of the third element –​that is, a sense of belonging, which facilitates an informal coordination of entities in networks of cooperation operating on the basis of common (for a given territory) rules of conduct, shared values, and conventions. All the elements mentioned here create an innovative environment, which provides conditions for an informal exchange of hidden information and knowledge as well as observation of the partners and inspiration from their successes (which significantly accelerates the processes of collective learning and the development of innovation) (Camagni, 1991; Lawson, 1997). Innovation system The subsequent concepts –​the regional innovation system and the innovation ecosystem –​highlight the systemic nature of innovation8. The innovation system can be understood as “the elements and relationships which interact in the production, diffusion and use of new, and economically useful, knowledge” (Lundvall, 1992, p. 13) or, in a broader sense, as “all important economic, social, political, organizational, and other factors that influence the development, diffusion, and use of innovations” (Edquist, 1997, p. 14). The notions by Freeman and Metcalfe define innovation systems through the prism of the institutions that create them as “the network of institutions in the public and private sectors whose activities and interactions initiate, import, modify and diffuse new technologies” (Freeman, 1987, p. 1), or also as “a system of interconnected institutions to create, store and transfer the knowledge, skills and artefacts which define new technologies” (Metcalfe, 1995, pp. 461–​462). Each innovation system consists of four basic components: • •

enterprises as the core of the system –​responsible for creating, implementing, and diffusing knowledge and innovation; R&D institutions –​involved in new knowledge creation processes and educational activities;

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Prior and contemporary theories on industrial clusters 23 •

•

business environment institutions (i.e., science and technology parks, business incubators, technology transfer centers, financial institutions) that stimulate the innovative activity and initiate cooperation on the science–​business line; public authorities that can create appropriate incentives for the development of innovation.

Analyzing the concepts based on the systemic approach toward innovation, one cannot ignore the assumptions guiding the concept of national innovation systems (NIS) as they became the foundation for the concept of regional innovation systems (RIS).The concept of NIS developed simultaneously in Europe and the United States in the 1990s (Freeman, 1987, 1995; Lundvall, 1992; Nelson, 1993; Edquist, 1997; Dosi et al., 1998), being a synthesis of two other concepts: national production systems (NPS)9 and national business systems (NBS)10. Its aim is to capture the co-​evolution of the structural and institutional features in systemic terms –​it takes into account both the institutional dimension (omitted in NPS) and the structural dimension (which does not appear in NBS) (Lundvall & Maskell, 2003). Considering the NIS concept, the success of innovation at each level of aggregation (at the level of individual organizations, regions, and entire nations) is increasingly dependent on the interrelationships (to a large extent culturally conditioned) among the entities involved in the process of generating, distributing, using, and diffusing commercially useful knowledge. This means that national culture and the institutions determined by it affect not only the effectiveness of innovation processes within a single organization but also the performance of the entire national innovation system. The efficiency of NIS also results from the structure of the economy and the degree of its adjustment to the institutional environment. There are three main stages in the development of the concept of national innovation systems (Lundvall et al., 2002; Lundvall & Maskell, 2003) that determined the contemporary perception of NIS (and the RIS concept connected with it). The first stage consisted of rejecting the technology push and market pull innovation models, which implied a linear course of the innovation process, and accepting a new paradigm based on the interactive chain-​linked model of innovation by Kline and Rosenberg (1986). According to this model, innovations are generated by numerous interactions and feedback among all the units (human and organizational ones) involved in innovation activities. The complexity of the innovation process and a high risk of innovation failure requires maintaining effective relations between the subsequent phases and frequent returns to the previous phases. The interactive model is therefore a combination of the technology push and market pull models. The considerations concern both the demand aspects (i.e., the needs and opportunities created by the market –​including the users’ requirements and opinions), as well as supply, such as the scientific and technical base and a company’s capabilities. The model by Kline and Rosenberg as well as the prior empirical research conducted in the

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24  Prior and contemporary theories on industrial clusters 1970s as a part of the Sappho Study by Freeman and his colleagues from the SPRU (Science Policy Research Unit) (Rothwell, 1977) were convincing arguments to conclude that the success of innovation is largely determined by long-​term relationships and a close interaction with external stakeholders. The second phase of NIS development meant accepting that the relationships and interactions with external entities also include non-​market contacts. A long-​term cooperation among partners, shared experiences, and the confidence developed over the years facilitate an exchange of knowledge and experience, which may reduce transaction costs and increase the efficiency of innovation. These non-​commercial connections that are an important complement to “pure” market relations have been described by Lundvall (1985) in the form of “organized markets” with elements of power, trust, and loyalty. Due to many differences among countries, the latter introduce diverse conditions for the development of “organized markets.” One of the most important variables that differentiate nations is the culture, which determines how all of the institutions and organizations operate in a given community. It is reflected by the fact that institutions (both formal and informal) are built in accordance with the norms, values, and the national character of the community in which they are operating. The cultural dimension also affects the capability of establishing non-​commercial relationships, and thus the possibility of building social capital. Understanding these regularities was the third (and last) step in building the NIS model. The importance of the institutional environment for innovation activities has been strongly emphasized by Edquist and Johnson. In their work, they apply a broad approach to defining institutions, describing them as “sets of common habits, routines, established practices, rules, or laws that regulate the relations and interactions between individuals, groups and organisations” (Edquist & Johnson, 1997, p. 46). The institution category includes all “formal structures with an explicit purpose and they are consciously created” (Edquist & Johnson, 1997, p. 47): for example, enterprises, universities, R&D companies, business-​related institutions, as well as financial institutions. The NIS concept also highlights specific national interactions between the existing economic structure and the set of institutions. On the one hand, it is assumed that national differences in the institutional environment result from the structural features of the economy (Breschi & Malerba, 1997); on the other hand, there are voices suggesting that it is exactly the opposite: national institutions determine the development of specific industries in a given country (Guerrieri & Tylecote, 1997). Lundvall recognizes an interdependence of both dimensions in the innovative system, namely the structural and institutional ones, claiming that the structure of the economy shaped by historical evolution affects the institutional framework, which has a reversible impact on the economy, creating conditions for the development of sectors that are able to fit in best (Lundvall & Maskell, 2003). Such an emphasis on the role of culture, institutions, and economic structure in

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Prior and contemporary theories on industrial clusters 25 the national economy is the heart of the concept of national innovation systems. In Lundvall and Maskell’s opinions, the contemporary theories of endogenous growth noticeably underestimate the national aspect of culture and institutions. It is assumed that national differences in these areas have an average or insignificant impact on innovation development, investment size, and economic development (Lundvall & Maskell 2003). Meanwhile, as the NIS scholars argue, it is culture and the institutions influenced by it that influence the way particular entities (people, enterprises, or other organizations) behave, interact, learn, and use their knowledge. The concept of regional innovation systems can be regarded as an extension of the concept of national innovation systems (RIS are often treated in the literature as a subset of the national system). In the RIS approach, similarly to the concept of national innovation systems, the emphasis is put on the systemic dimension of innovation and the importance of interactions in innovation processes; however, unlike NIS, the focus is shifted to the level of the region. As Doloreux and Parto claim, specific competencies of enterprises and learning processes, provided they are based on local capabilities (such as specialized resources, skills, institutions, sharing common social and cultural values), can lead to the competitive advantage of regions (Doloreux & Parto, 2005). The essence of the regional innovation system is best reflected in the definition introduced by Cooke and colleagues, according to which RIS is a system “in which firms and other organizations are systematically engaged in interactive learning through an institutional milieu characterized by embeddedness” (Cooke et al., 1998, p. 1581). This notion highlights the three most important features of RIS: learning by interacting, institutional environment, and embeddedness. What is worth highlighting in the concept of regional innovation systems is the environment, more often referred to as the “milieu,” which, in accordance with the basic assumptions of the RIS concept, shapes innovative processes and affects their efficiency. The RIS approach assumes that innovation is an evolutionary process influenced by many different entities and factors –​both internal and external from the perspective of an organization. Such considerations also imply the social aspect of innovation, which addresses collective learning –​the cooperation of various entities (not just within a single organization), the application of the knowledge it creates, as well as interactions with external partners. In other words, innovations depend on the way organizations interact with the milieu, which is originally influenced by the socio-​institutional environment in which they are embedded. Therefore, such an environment can be treated as a network of entities (such as enterprises, scientific units, business-​related organizations, local and regional public authorities) that jointly participate in the processes of generating, using, and disseminating knowledge and innovation. The interactions among these entities (in the form of knowledge and information flow, creation of partnerships, or implementation of joint projects), facilitated by geographical proximity of the partners, form the basis for the

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26  Prior and contemporary theories on industrial clusters development of the regional innovation system11.The environment may also be understood as “an open territorialized complex, which involves rules, standards, values, and human and material resources” (Doloreux, 2002, p. 246). This ambiguity in the sense of “environment” is expressed by Malmberg, who claims that “the environment may either be seen as a network of actors (other firms and organizations) with which the firm interacts or as a general framework for firm action: institutional structures, social values and political cultures, etc.” (Malmberg, 1997, p. 575). The entities that make up the regional innovation system are in fact “embedded” in a specific network of connections, which, due to the unique set of factors that make up social and cultural capital within a given community, can positively affect learning processes and, consequently, the development of innovation in enterprises. Therefore, innovations are embedded in social relations that are characteristic of a particular community and result from economic and sociocultural factors (such as rules, shared values, trust). They not only enable but also facilitate the establishment and development of cooperation and the flow of knowledge and information among individual elements of RIS (limiting market imperfections for enterprises; e.g., reducing market costs). The RIS concept emphasizes the importance of untraded interdependencies (Storper, 1995, 1997), which facilitate effective trade links. According to Storper, these include “labor markets, public institutions and locally or nationally determined customs, conventions and agreements that enable effective information transfer and knowledge development” (Storper, 1995, p. 293). Untraded interdependencies are critical for collective and interactive learning because they are strongly embedded in a specific institutional, political, social, and cultural context12 that cannot be reproduced in another location. This context is primarily made up of proximity, affinity, and stable relationships based on trust (Malmberg & Maskell, 1997). Proximity –​as Malmberg and Maskell claim –​does not only concern physical distance. Since the acquisition of certain forms of knowledge requires partners to have a high degree of mutual trust and understanding (which in turn is not only related to the language of the message but also applies to shared values and culture), proximity is considered here in social and cultural terms. Therefore, the RIS concept emphasizes not so much the very geographical proximity that enables the development of personal relationships, especially relevant for innovation, but primarily draws attention to natural embeddedness of economic links in a specific institutional environment, in social relations (Steiner, 2011). The concept of embeddedness, which is mentioned many times in this part of the study, is the heart of the RIS concept and applies to all the economic processes (especially those related to the creation, acquisition, use, and dissemination of knowledge) that are implemented (inside and outside the organization) in the context of various forms of social interaction, which hinders their multiplication (Doloreux, 2002). Therefore, embeddedness is closely related to the concept of networking scrutinized in a publication by Grzeszczak (1999). According to Grabher, “embeddedness refers to the

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Prior and contemporary theories on industrial clusters 27 fact that economic action and outcomes, like all social action and outcomes, are affected by actors’ dyadic relations and by the structure of the overall network of relations” (Grabher, 1993, p. 4). In the approach presented by Granovetter (1990), embeddedness means that all economic activities, results, and institutions are influenced by the personal relationships of actors and the entire structure of the relationship network. Granovetter suggests that the main difficulty of modern economics results from neglecting the social nature of economic life –​therefore, in order to properly capture the external relationships among various entities (located at different levels of aggregation), more attention should be focused on specific personal relationships and structures (networks) since a major part of human behavior is strongly embedded in interpersonal networks (Granovetter, 1985). Consequently, the RIS concept refers, as Wiig concludes, to the “sociological” approach to the innovation process. In line with this approach, innovation means “interactive learning between actors that are socially embedded, who act in an institutional and cultural context” (Wiig, 1999, p. 15). A similar point of view is shared by Camagni, who claims that “technological innovation … is increasingly a product of social innovation, a process happening both at the intra-​regional level in the form of collective learning processes, and through inter-​ regional linkages facilitating the firm’s access to different, though localised, innovation capabilities” (Camagni, 1991, p. 8). Innovation ecosystem The concept of innovation ecosystem is the latest one in the discussed group of theories of regional development based on knowledge and innovations. This concept finds its origin in the business ecosystem (Moore, 1993). Discussing it should therefore begin with a closer look at the basic assumptions of Moore’s ideas. The business ecosystem was built upon the natural ecosystem –​that is, a community of living organisms interacting with one another as well as with the environment in which they reside. An ecosystem comprises living organisms (biotic constituents) supported by abiotic constituents (such as water) and all other non-​living elements (e.g., climate). In a natural ecosystem, living organisms can occupy different trophic levels (positions in the food chain). On this basis, producers (autotrophic organisms producing organic compounds from inorganic compounds), consumers (e.g., herbivores, predators), and decomposers (organisms that make dead organic matter decay, providing producers with minerals) can be distinguished (Moore, 1996). An ecosystem must adapt to a constantly changing environment –​therefore, for the purpose of maintaining its balance, it should have a large variety of species so that at least some of them could survive in new conditions (Peltoniemi & Vuori, 2004). Achieving species diversity is possible if the non-​living components of the environment provide favorable conditions. The laws that govern the biological world have been translated into the world of business (Moore, 1993).As defined by Moore, the business ecosystem

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28  Prior and contemporary theories on industrial clusters is “an economic community supported by a foundation of interacting organizations and individuals –​the organisms of the business world” (Moore, 1996, p. 26). Enterprises operating in a business ecosystem are compared to living organisms, while industries correspond to species that coexist to form a vast and living ecosystem (Rothschild, 1990). Similarly to a natural ecosystem, business efficiency is rewarded with survival, while inefficiency results in business extinction (Rothschild, 1990). In a business ecosystem, we can distinguish subgroups of supporting entities that perform various, complementary roles such as customers (end product or service users within a given ecosystem), suppliers, leading manufacturers, financial institutions, industry associations, and government institutions (Moore, 1998). All the elements of a business ecosystem evolve together, developing their abilities and adapting to the surrounding environment. This often means adapting to the rules set by enterprises occupying the central positions in this ecosystem (Moore, 1996). The fact that a business ecosystem may include many different industries (and organizations) means that its boundaries are set by relations of cooperation and competition that are developed by the organizations (components of the ecosystem) striving to achieve their business goals (Moore, 1993). Interaction and competition of the entities that are a part of an ecosystem have an impact on its balance and dynamics (Valkokari, 2015). However, as long as a natural ecosystem can achieve a state of equilibrium, a business ecosystem can do nothing but strive to achieve such a state (Jackson, 2011). Another characteristic feature of a business ecosystem, also concerning specialization, is the division of labor among the organizations (Papaioannou et al., 2009). Organizations in a business ecosystem co-​create a value, using the complementary skills and resources of the other ecosystem participants. The stronger the specialization in the ecosystem, the greater the interdependence of the components of this system (which is intensified by the complexity of the relationships among them).A business ecosystem is a self-​regulating system with a decentralized decision-​making process (Moore, 1998) and changes that occur dynamically and are often difficult to control (Gobble, 2014). As far as the innovation ecosystem is concerned, it is important to emphasize its similarity to the concept of the business ecosystem described above. In some works, the innovation ecosystem is even treated as a synonym for the business ecosystem13. It is believed by de Vasconcelos Gomes et al. (2018) that the basic difference is that the innovation ecosystem refers to value creation, while the business ecosystem refers to value capture. Regarding the literature analysis, scholars have been able to identify common features of the business ecosystem and the innovation ecosystem. Both systems have their own life cycles (mirroring the co-​evolution process), consist of interconnected entities that cooperate and compete with one another, apply the same platform (e.g., a technological one), and can be led by a key actor (e.g., a big central enterprise) providing this common platform and establishing a set of goals to be achieved (de Vasconcelos Gomes et al., 2018). The innovation ecosystem can be understood as a set of diverse organizations that through complex interactions and complementarity

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Prior and contemporary theories on industrial clusters 29 participate together in creating a value (e.g., technology, competence) (Moore, 1993; Adner & Kapoor, 2010, 2016; Autio & Thomas, 2014; Gobble, 2014). The three elements distinguished in the name of the innovative ecosystem: “eco,” “system,” and “innovation” indicate the basic assumptions of this concept (Ritala & Almpanopoulou, 2017). The “eco” prefix emphasizes the relationships with the biological ecosystem and refers to the interrelationships among the elements of the innovation ecosystem and their co-​evolution (Moore, 1993). The term “system” indicates the existence of a specific set of components that are interdependent –​actions and decisions taken by one entity may affect the other entities that are a part of the same ecosystem. The boundaries of the innovative ecosystem can be marked by identifying the central enterprises or innovations (around which the system is created), geographical scope (e.g., local, regional, national, global), time scale (e.g., development dynamics), permeability (open or closed), or types of flow (e.g., knowledge, value, material) (Ritala & Almpanopoulou, 2017). The third part –​innovation –​refers to the creation and commercialization of knowledge and inventions. Nevertheless, there is no consensus on what exactly the innovation ecosystem is (Oh et al., 2016; de Vasconcelos Gomes et al., 2018). There are many different definitions used in the literature; many other concepts on the issue coincide14. Jing and Xiong-​Jian claim that, despite the differences in defining this concept, it is possible to identify some common features such as a large group of organizations (making up the ecosystem), interconnections, interdependence, and co-​evolution (Jing & Xiong-​Jian, 2011). Nambisan and Baron (2013) supplement this list with a set of common goals as well as complementary resources of knowledge and skills. The innovation ecosystem is often identified with the previously described national innovation system but unlike NIS (and the other knowledge and innovation-​based concepts mentioned earlier) it has its roots in biology and focuses on co-​evolution processes. The fact that innovations are strongly embedded in the market process means that consumers are an important element of the innovation ecosystem (Autio & Thomas, 2014), while innovation systems mainly emphasize the non-​market aspects and the role of institutions (Papaioannou et al., 2009; Mercan & Goktas, 2011). The difference between the discussed concepts is also visible in the area of system regulations: innovation ecosystems are dynamic structures which, unlike innovation systems, cannot be regulated by a public policy since they evolve in line with changing market conditions (Mercan & Goktas, 2011). In the innovative ecosystem, a great emphasis is also put on creating a platform (tools, technologies, manufacturing processes, and services) that enables the parties (including manufacturers and end users) to exchange products or provide services (Gawer, 2014). Industrial cluster The concept of a cluster originates from previous studies on international competitiveness15 by Porter (1998, 2000, 2003, 2008). In both academic

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30  Prior and contemporary theories on industrial clusters circles and the business and political environment, Porter is regarded as one of the most influential researchers of corporate strategy and competitiveness. In his opinion, the location of the place of business and, precisely, the broadly understood quality of the business environment (Porter, 1985, 1990) determine whether national companies are successful or not. He presented a model of the effects of the location on competition in the form of a diamond of competitive advantage, which includes the four most important components: factor conditions, demand conditions, related and supporting industries, and strategy, structure, and rivalry. Individually, and as a whole, these components form a national environment in which companies learn to compete. It is the Porter Diamond Theory of National Advantage that became the starting point for the development of the cluster concept. In his assumptions, Porter argued that the intensity of interaction in the diamond is higher if companies are geographically close and form clusters, which can be observed in the most competitive industries. Therefore, the diamond of competitive advantage, originally used to analyze the competitiveness of the national economy, became a metaphor for a cluster, with the components highlighted in it and their interrelations being recognized as primary determinants of the creation and development of clusters. Porter defines clusters as “geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions (for example, universities, standards agencies, and trade associations) in particular fields that compete but also cooperate” (Porter, 2008, pp. 213–​214). In his definition, Porter highlights three core cluster attributes, which include geographic concentration, sectoral concentration, and development of relationships between constituents. An additional determinant of a cluster includes strong specialization, division of labor and key competences, and exchange of complementary resources. Using the definition provided by Porter, however, it is difficult to determine the geographical boundaries of a cluster accurately because they are smooth and variable. The same goes for industrial boundaries –​as cluster companies generally represent one or more related sectors, cluster cooperation is usually of a multisectoral nature. Clusters vary in size, scope, and degree of development and may assume different forms, which is reflected in the composition of a cluster. The structure of every cluster results from the specific activity of the companies that form its backbone. Porter offers a wide approach to the definition of a cluster structure, including in it both companies and specialist institutional infrastructure serving an auxiliary role in comparison to the primary business of the cluster (Porter, 2008). A similar approach is taken by other authors dealing with the issues of an industrial cluster (among others, Feser, 1998; van Dijk & Sverrisson, 2003; Gorynia & Jankowska, 2008). Definitions developed by international organizations such as the European Commission –​Enterprise Directorate General (2003) and OECD (2004) are also in line with such an approach. Another highlighted approach assumes a narrower view of a cluster structure (Enright, 1992, 1996; Rabelotti, 1995; Swann & Prevezer, 1996; DeBresson, 1996; Rosenfeld, 1997; Padmore &

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Prior and contemporary theories on industrial clusters 31 Gibson, 1998a, 1998b; Simmie & Sennett, 1999; Roelandt & den Hertog, 1999; van den Berg et al., 2001; Cooke, 2002; Steinle & Schiele, 2002; Maskell & Kebir, 2005, Lis & Lis, 2014). In this approach, the designator of the concept of a cluster is the geographical concentration of companies, upon which other regional development concepts based on knowledge and innovations are used to describe the infrastructure supporting the cluster, mainly the concept of a regional innovation system and innovation environment. Strong and lasting interactions between cluster entities bring a number of benefits, but, most of all, lead to the synergy effect.This is reflected by Porter in his definition, according to which a cluster is “a system of interconnected firms and institutions whose value as a whole is greater than the sum of its parts” (Porter, 2008, p. 229). Referring to the diamond model, Porter points to the competitive advantage of clusters in three main areas: productivity, innovation, and entrepreneurship (Porter, 2008, p. 229). The benefits obtained in each of these areas are closely associated with geographical proximity. In the context of productivity growth, it is easier for companies belonging to clusters and the same or similar economic sectors as well as operating in different parts of the supply chain to develop cooperation, also making it possible to reduce transaction costs. Through the availability of specialist suppliers, cluster companies have easier and cheaper access to specialized inputs, while close cooperation facilitates a clear definition of mutual expectations. In addition, cluster companies have easier access to a specialized labor market, information, knowledge, institutions, and public institutions and goods. They are also characterized by complementarity and flexible specialization to avoid duplication of effort.The second area defined by Porter includes the innovation capabilities of cluster entities. According to Porter, the concentration of companies in a cluster favors innovation development through the establishment of close cooperation with various entities engaged in the processes of generation, commercialization, transfer, and diffusion of innovation. The presence of local rivals requires continuous development and searching for competitive advantages, but also makes cooperation in the innovation process possible, as well as enabling easy and quick dissemination of knowledge and information. The availability of specialist suppliers means cluster companies gain access to inputs in the innovation process (tangible and intangible knowledge). The institutions of the R&D sector in the region are responsible for generating and transferring knowledge and innovation. Another stimulus to increase innovation includes demanding customers –​the large local demand and access to information enable cluster companies to notice market trends faster and exploit market opportunities related to innovation development in a way isolated companies cannot. Finally, the development of a specialized labor market has a positive impact not only on productivity but also on the innovation potential of cluster companies. With respect to entrepreneurship, Porter highlights two key competitive advantages of a cluster. The first includes low barriers to entry and ease of obtaining market information, which makes it easier for entrepreneurs to collect inputs, resources, and skills necessary to start a

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32  Prior and contemporary theories on industrial clusters business in a cluster. Another competitive advantage is the ease of establishing spin-​offs based on the resources developed in a cluster. The popularity of Porter’s cluster concept encouraged researchers to study the nature of this phenomenon. With no unambiguous and clear definitional frames, many attempted to develop their own definitions of a cluster, which led to considerable morphological diversity. The terminological confusion over the concept of a cluster was also intensified by the association of a cluster with other regional development concepts (mainly, pole of growth, innovation system, innovation environment, local production system). What is more, the literature defines the term cluster as various forms of cooperation (e.g., strategic alliances, R&D consortia, industry associations, networking, industrial districts).The literature does not include one universal cluster model –​clusters are characterized more by their diversity than by their similarity (Özcan, 2004), especially since they are a dynamic phenomenon. This led numerous researchers of the cluster phenomenon to develop cluster typologies. They can be divided into two main groups: typologies referring to the dynamics of development processes within a cluster (dynamic approach); and typologies that do not introduce dynamics to their divisions, classifying clusters based on their specific characteristics instead (static approach). The most notable typologies in the first group include those based on an evolutionary approach (van Dijk & Sverrisson, 2003) and cluster life cycle (e.g. Pouder & John, 1996; Maggioni, 2002; Rosenfeld, 2002; Maskell & Kebir, 2005; Sonderegger & Täube, 2010; Menzel & Fornahl, 2010; Martin & Sunley, 2011), typologies that consider the stage before and after a cluster reaches critical mass (Markusen, 1996; Knorringa & Meyer-​ Stamer, 1998), or typologies that relate to the level of self-​awareness, activity, and self-​fulfillment of a cluster (Enright, 1996, 2003; Rosenfeld, 1997). The other group includes typologies that consider how a cluster was established (Mytelka & Farinelli, 2000), profile of regional resources (John & Pouder, 2006), type of cluster companies (Altenburg &Meyer-​Stamer, 1999), nature of the links between cluster companies (Knorringa & Meyer-​Stamer, 1998), or the power structure of a cluster and the international location of a cluster (Rugman & Verbeke, 2003), as well as typologies developed on the basis of basic cluster dimensions (Enright, 2003; Peters & Hood, 2000). The study led by Porter, carried out at the end of the last century in the United States and aimed at the identification and development of clusters (Porter & Ackerman, 2001), made it possible to formulate best practices supporting the development of clusters and laid foundations for the creation of a cluster-​based policy. According to Martin and Sunley (2011), the concept of a cluster was promoted by Porter from the very beginning not only as an analytical concept but also as a policy tool. Clusters are currently promoted at all policy tiers –​starting from international organizations and ending up with governments of individual states and regional and local authorities. The cluster policy of the European Union –​implemented for many years and transposed to the national and regional level –​is also based on the cluster concept. Clusters were considered an important tool that

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Prior and contemporary theories on industrial clusters 33 enhances the innovation of the European economy, and the cluster policy was included in subsequent EU strategies, namely in the Lisbon strategy (2000–​2010) and the “Europe 2020: A strategy for smart, sustainable and inclusive growth” (2010–​ 2020) strategy. The EU cluster initiatives were launched under the COSME (Competitiveness of Small and Medium Enterprises) and Horizon 2020 programs to support SME innovation and growth.The industrial cluster policy is also being continued in the EU in the coming years (2021–​2027) within the framework of interregional innovative investments proposed by the European Commission. Support for clusters is to be provided, among others, in the EU’s research and innovation framework program Horizon Europe. Regions with smart specialization funding are to be supported in establishing pan-​ European clusters in key areas (including big data technology, circular economy, advanced manufacturing technologies, and cybersecurity). Further, the European Union focuses on cross-​sectoral and cross-​regional collaboration and innovation to develop new industrial value chains with the aim of creating the highest levels of industrial competitiveness. Strategic programs implemented by the EU and focused on supporting cluster structures required the establishment of entities to act as intermediaries in the transfer of various forms of aid to clusters, and at the same time to coordinate activities undertaken within a cluster. This led to the development of numerous cluster initiatives understood as “organised efforts to increase the growth and competitiveness of clusters within a region, involving cluster firms, government and/​or the research community” (Sölvell et al., 2003, p. 15). Porter believes that “cluster initiatives provide a new way of organizing economic development efforts that go beyond traditional efforts to reduce the cost of doing business and enhance the overall business environment” (Porter, 2008, p. 278). The literature also uses the term “cluster organizations” (COs) for cluster initiatives (Coletti & Di Maria, 2015; Balog, 2016; Jankowska et al., 2017; Morgulis-​Yakushev, & Sölvell, 2017; Dore, 2018; Lis 2018; Modenov et al., 2018; Horak et al., 2020; Lis & Rozkwitalska, 2020; Lis & Lis, 2021; Pavelkova et al., 2021) to highlight their organizational attributes. In this perspective, a CO should be understood as “a formally established organization which functions at a higher level of aggregation, is composed of institutional members (and their units) who purposefully and voluntarily joined it, being engaged in cooperation to achieve common goals (concerning the development of a specific cluster) and/​or individual goals (related to their own development)” (Lis & Lis, 2021, p. 19). This means that –​in contrast to a cluster –​a CO may be managed by coordinating activities undertaken by the entities belonging to such a cluster (CO members) that generally represent all three components of the triple helix structure (i.e., companies, R&D institutions, and public authorities). Due to their obvious connotations, in the literature the terms “cluster initiative” or “cluster organization” are often used interchangeably, with the term “cluster” pointing to the equality of both meanings. Meanwhile, a cluster is a term used to describe the geographical concentration of companies related

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34  Prior and contemporary theories on industrial clusters commercially and non-​ commercially, while a cluster initiative refers to organized activities undertaken for the development of a cluster. Therefore, due to the differences, a cluster is a category that is more often used in economics, socioeconomic geography, and spatial economy, while a CO is an issue mostly addressed in management and sociological sciences. COs have their own life cycles, which are independent of cluster life cycles. What is more, they can be established at any cluster development state –​both at an early development stage and at a maturity stage to serve as reinforcement (Sölvell et al., 2003). Using the terms introduced by Enright (1996, 2003) and Rosenfeld (1997), COs should be regarded as working clusters. Following the typology prepared by them, clusters may be divided into three groups according to their level of activity and self-​ fulfillment: potential, latent, and working clusters. Although potential clusters have basic cluster structure attributes (such as, e.g., geographical and sectoral concentration), they lack critical mass. Latent clusters have the right potential in comparison to potential clusters but are unable to obtain the full benefits of clustering. Low levels of social capital, translating into weak ties and rarer interactions between cluster entities, are often an issue. Companies belonging to the cluster do not see themselves as a part of a larger cluster structure and operate as separate market entities focused on achieving their own objectives. Working clusters have the best conditions for development. They form agglomerations of interconnected companies that are aware of their interconnectedness, enabling them to achieve synergy effects. Clusters of this type are characterized by a developed and specialized infrastructure that facilitates the establishment and development of relations in a cluster, as well as knowledge and information flows. As in the case of a cluster, cooperation within a CO leads to diverse benefits, mainly triggering the synergy effect, which should be understood as the ability of CO members to create additional value through cooperation-​ based relations with other entities, with this value being higher than the sum of values that would be created by each of these entities separately (Lis, 2018, p. 88). With this in mind, COs may be considered an important tool to develop a cluster-​based policy implemented at the point of contact between three separate areas, which include regional, industry, and SME policies, foreign direct investment (FDI) attraction policies, and science, research, and innovation policies (Sölvell et al., 2003). A cluster-​based policy is built on stimulating competition and cooperation not only at the micro level (between companies) but also at the meso level (i.e., between sectors and regions) (Porter, 2008). COs operate in various economies in developed, developing, and transition countries. They can operate as informal creations united by joint objectives or interests, or formalize their activity based on various agreements provided for in the legislation of a given country (Knorringa & Meyer-​ Stamer, 1998). The manner of establishment of COs is well illustrated by the typology developed by Mytelka and Farinelli (2000) who distinguished between spontaneous clusters and clusters induced by public policies. The

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Prior and contemporary theories on industrial clusters 35 first group includes clusters resulting from spontaneous events and developed based on endogenous factors in a given location (such as natural resources, industrial traditions, scientific base, etc.). They are developed on the basis of bottom-​up initiatives, in which companies eager to develop formal cooperation are involved. The other group includes clusters established in a top-​ down manner –​often as a result of political decisions. Enright (2003) refers to them as policy-​driven clusters and “wishful thinking” clusters, arguing that they are artificially stimulated creations, selected to be supported based on political pressure, and they lack both critical mass and factors that encourage natural development. COs are characterized by great diversity but are united by a joint orientation toward the regional context and microeconomic business environment; ambition to improve the competitiveness of clusters via the development of social capital and the cooperation network based on it; competition manifested in simultaneous relations of cooperation and competition; involvement of various groups of entities in jointly undertaken activities; and a systemic approach to innovation development (Sölvell et al., 2003).

Conclusion Three main conclusions can be drawn from the literature review. The first pertains to the benefits of clustering in a single geographical area of commercial entities characterized by various cooperative ties. Marshall was the first to point to the existence of positive effects (or so-​called externalities) in the industrial clusters he observed, comprising small and medium-​size enterprises with a strong sectoral focus. These benefits depend on the development of the industry as a whole and are equal in strength to the internal benefits reaped by large enterprises. According to Marshall, the most important externalities are: availability of a workforce with specialist skills, access to non-​trade inputs specific to the given industrial branch, and knowledge flow. Research on the phenomenon of Italian industrial districts is in a sense a continuation of the topics discussed earlier by Marshall himself. Not only did Italian economists resurrect Marshall’s concept, they also reconstructed it in some way. They supplemented Marshallian externalities with new elements, underlining the significance of such concepts as: localization (very broadly understood as a “territorial community” intertwined in a network of relationships between entities clustered in a given area), commercial entities cooperating with each other thanks to phase-​based production processes, a flexible labor market and high workforce mobility, processes of the dissemination of tacit knowledge as the result of common interactions, and the relocation of human resources, as well as the feeling of trust resulting from, among other things, a common system of norms, values, and experiences in cooperation. The second conclusion pertains to the coexistence in a given area of different industries, which is a necessary condition for the area to be considered a district both from the Marshallian and Italian perspectives. In

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36  Prior and contemporary theories on industrial clusters order for all of the identified external benefits to arise in the context of the clustering of economic entities, it is necessary –​according to Marshall –​to achieve a specific kind of industry diversification on the given area: a diversification rooted in the networked economic entities, which both cooperate and compete with one another.These entities represent different parts of the economy: both leading and supplementary. Furthermore, they are located at different parts of the value chain of the leading industry and themselves create additional chains, indirectly tied to the leading chain. A similar perspective is provided by Italian researchers, who associate a district with a conglomerate of cooperating industries, which see this cooperation as a possibility of achieving beneficial results. The specific nature of a given district is conducive to the emergence and development of phase-​based production processes, executed by three cooperating groups of entities. The first comprises firms that produce the final product and offer these products to the local and external markets. The second comprises stage firms, concentrated on one or several stages of the production process. Finally, the third group comprises ancillary firms, representing industries which are not directly tied to the districts’ leading industries, albeit which essentially support the key production process by offering complementary services. The coexistence of the aforementioned groups provides a perfect opportunity to undertake diverse forms of cooperation in the district. The third conclusion pertains to the essence of modern concepts of “new industrial districts,” which, despite differences in terminology, for the most part refer to very similar matters. First, all of the discussed concepts center around “territory” as an area of “localized” possibilities, emerging from geographical proximity, as well as social and cultural capital. Second, they ascribe the most weight to knowledge and innovation. It is these two components of districts which are the most decisive with regard to their strength: they determine cooperation between entities functioning within a given district, while also contributing to the development of the entire region itself. Each of the discussed concepts treats innovations as an interactive process of acquiring knowledge, embedded not just territorially but also socially in networks of mutual relationships and the cultural and institutional context.The efficiency of this process is determined by local resources, such as industrial traditions, the specialized labor market, partner networks, and the institutional environment, supporting the development of innovations. Of great significance are non-​tangible assets, and specifically the set of common principles, norms, and values. All of the above-​mentioned factors comprise an innovative environment, which generates external benefits for companies, primarily tied to the phenomenon of knowledge spillover as described by Marshall.

Notes 1 Hearn did not directly define the district in his work; he used the term in a broader sense than the very colloquial term means in English.The proposed definition is intended to reflect the sense of the term which was applied by Hearn.

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Prior and contemporary theories on industrial clusters 37 2 An idea that appears interesting concerns the link between work efficiency and the climate (see Hearn, 1864, pp. 46–​47). 3 In order to distinguish the two constructs: the Marshallian industrial district and its Italian variety, in the latter case the term “Italian industrial district” is applied (Markusen, 1996). 4 Martin (2003) recognizes three groups of basic conceptions of regional competitiveness: regions as sites of export specialization, regions as sources of increasing returns, and regions as hubs of knowledge. 5 It is worth mentioning that there is also a relationship between the concept of clusters and polarization theories. This primarily applies to the original growth pole theory by Perroux, which refers to the economic dimension (Perroux, 1955, 1970). The concept of clusters is even recognized by some researchers as a development of Perroux’s theory (Popa & Belu, 2009) –​clusters can be treated as general centers constituting poles of positive development impulses. 6 This became the leitmotif of the learning economy concept introduced to the literature by the Nordic school (Lundvall & Johnson, 1994; Johnson & Lundvall, 2003; OECD, 2000) to highlight the main changes that took place in the economy due to rapid technical progress and the processes of globalization and internationalization. The notion of learning economy refers primarily to highly developed countries, with a dominant share of the high-​tech sector in the structure of the economy, where the ability to learn is the key to economic success (people, enterprises, regions, and the entire national economy). 7 In subsequent years, the concept of the innovative milieu was constantly developed within: GREMI II (Maillat & Perrin, 1992), GREMI III (Maillat et al., 1993), GREMI IV (Ratti et al., 1997) and GREMI V (Crevoisier & Camagni, 2000). 8 There are some other concepts based on the systemic approach to innovation, namely: technological innovation systems (Carlsson & Stankiewicz, 1995), sectoral innovation systems (Breschi & Malerba, 1997; Malerba, 2002, 2004), and Triple Helix (Etzkowitz & Leydesdorff, 2000). 9 According to the NPS approach, different sectors of the economy shape economic growth in different ways, whereas vertical relationships are crucial for the efficiency and results of the production system (Dahmén, 1970; Hirschman, 1958; Perroux, 1969; GRESI, 1976; Stewart, 1977). The NPS concept was developed by scientists from the IKE-​group (Lundvall et al., 2002; Lundvall & Maskell, 2003), being initially focused on the innovative potential and life cycle of the NPS in order to lay the foundations for the NIS concept. The Danish researchers advanced the concept of the French economists: they emphasized the importance of interactions and flow of information between the producer and the user sectors for the development of new technologies, introduced the concept of learning by doing and learning by searching; they also indicated the need to take into account the level of development of industrial subsystems. 10 The NBS concept, developed at the interface between economic sciences and sociology, emphasizes the importance of institutions for economic growth. This concept derives primarily from the works by Whitley (1994). As the starting point, it takes the thesis that international differences in the organization and activity of enterprises and markets result from the differences in culture and formal institutions (mostly at national levels). In the concept of national business systems, a lot of attention is paid to the role of social capital as the main determinant of the effectiveness of cooperation within a system. The NBS model formulated by Whitley describes the most important cause–​effect relationships

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38  Prior and contemporary theories on industrial clusters related to institutions and businesses. With regard to this model, the state, by establishing various trust-​ creating institutions and institutions that provide financial assistance in the form of loans, can stimulate enterprises to cooperate with other market entities. This results in initiating and developing networks of cooperation, which facilitates the synergy effect and reduces risk by sharing it among particular partners. 11 Interaction as an important attribute of innovative systems is emphasized in the literature –​for example, by: Lundvall, 1992; Cooke, 2001; Doloreux, 2002; Carlsson et al., 2002. 12 The concept of “contextual embedding” appears in many publications on innovation systems, including those by: Doloreux & Parto, 2005; Coriat & Weinstein, 2002; Lundvall, 1992; Asheim & Coenen, 2005; Braczyk et al.. 1998; Malmberg & Maskell, 1997; Maskell & Malmberg, 1999; Asheim & Isaksen, 1997; Cooke et al., 1998. 13 Based on a literature review of the concept of the innovation ecosystem, de Vasconcelos Gomes et al. (2018), carried out a detailed bibliometric analysis as well as an analysis of the content of publications concerning the discussed issues. Among other features, they identified similarities and differences between the innovation ecosystem and the business ecosystem. 14 In this group, it is worth mentioning the digital ecosystem (Rao & Jimenez, 2011), hub ecosystem (Nambisan & Baron, 2013), open innovation system (Chesbrough et al., 2014), and platform-​based ecosystem (Gawer, 2014). The differences among various coinciding concepts of the innovative ecosystem are described in detail by de Vasconcelos Gomes et al. (2018). 15 The concept of cluster and cluster initiative /​organization is discussed in detail in: Lis & Lis 2014 (Chapter 1, Koncepcja klastra [The cluster concept]); Lis & Lis 2021 (Chapter 2, Theoretical foundations of clusters and cluster organizations).

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42  Prior and contemporary theories on industrial clusters Jankowska, B., Götz, M., & Główka, C. (2017). Intra-​cluster cooperation enhancing SMEs’ competitiveness: The role of cluster organisations in Poland. Investigaciones Regionales, 39, 195–​214. Jing, Z., & Xiong-​Jian, L. (2011). Business ecosystem strategies of mobile network operators in the 3G era: The case of China Mobile. Telecommunications Policy, 35(2), 156–​171. John, C. H., & Pouder R. W. (2006). Technology clusters versus industry clusters: Resources, networks, and regional advantages. Growth and Change, 37(2), 141–​171. Johnson, B., & Lundvall, B.A. (2003). Promoting innovation systems as a response to the globalised learning economy. In J. E. Cassiolato, H. M. M. Lastres, & M. L. Maciel (Eds.), Systems of innovation and development. Cheltenham: Edward Elgar. Kline, S. J., & Rosenberg, N. (1986). An overview of innovation. In R. Landau & N. Rosenberg (Eds.), The positive sum game: Harnessing technology for economic growth (pp. 275–​305). Washington, DC: National Academy Press. Knorringa, P., & Meyer-​ Stamer, J. (1998). New dimensions in local enterprise cooperation and development: From clusters to industrial districts. In UNCTAD, New approaches to science and technology cooperation and capacity building. New York and Geneva: United Nations. Laing, S. (1846). Notes of a traveller: On the social and political state of France, Prussia, Switzerland, Italy, and other parts of Europe, during the present century. Philadelphia: Carey and Hart. Lawson, C. (1997). Territorial clustering and high-​technology innovation: From industrial districts to innovative milieux. Cambridge: ESRC Centre for Business Research, University of Cambridge. Lis, A. M. (2018). Współpraca w inicjatywach klastrowych. Rola bliskości w rozwoju powiązań kooperacyjnych [Cooperation in cluster initiatives: The role of proximity in the development of cooperative relationships]. Gdansk: Wydawnictwo Politechniki Gdanskiej. Lis, A. M., & Lis, A. (2014). Zarządzanie kapitałami w klastrach: Kapitał społeczny, kulturowy, ekonomiczny i symboliczny w strukturach klastrowych [Capital management in clusters. Social, cultural, economic and symbolic capital in cluster structures]. Warszawa: Difin. Lis, A. M., & Lis, A. (2021). The cluster organization: Analyzing the development of cooperative relationships. London and New York: Routledge. Lis, A. M., & Rozkwitalska, M. (2020). Technological capability dynamics through cluster organizations. Baltic Journal of Management, 15(4), 587–​606. Lundvall, B. Å. (1985). Product innovation and user–​producer interaction. Aalborg: Aalborg University Press. Lundvall, B. Å. (Ed.). (1992). National systems of innovation: Towards a theory of innovation and interactive learning. London and New York: Pinter. Lundvall, B. Å., & Johnson, B. (1994). The learning economy. Journal of Industry Studies, 1(2), 23–​42. Lundvall, B. Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31(2), 213–​231. Lundvall, B. Å., & Maskell, P. (2003). Nation states and economic development: From national systems of production to national systems of knowledge creation and learning. In G. L. Clark, M. S. Gertler, & M. P. Feldman (Eds.), The Oxford handbook of economic geography (pp. 353–​372). Oxford: Oxford University Press. Maggioni, M.A. (2002). Clustering dynamics and the location of high-​tech firms. Heidelberg and New York: Springer.

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44  Prior and contemporary theories on industrial clusters Morgulis-​Yakushev, S., & Sölvell, Ö. (2017). Enhancing dynamism in clusters: A model for evaluating cluster organizations’ bridge-​building activities across cluster gaps. Competitiveness Review: An International Business Journal, 27(2), 98–​112. Mytelka, L. K., & Farinelli, F. (2000) Local clusters, innovation systems and sustained competitiveness. UNU/​INTECH Discussion Papers, No. 2005. Nambisan, S., & Baron, R. A. (2013). Entrepreneurship in innovation ecosystems: Entrepreneurs’ self-​regulatory processes and their implications for new venture success. Entrepreneurship Theory and Practice, 37(5), 1071–​1097. Nelson, R. R. (Ed.). (1993). National innovation systems: A comparative analysis. New York and Oxford: Oxford University Press. OECD (2000). Knowledge management in the learning society. Paris: OECD. OECD (2004). Clusters in transition economies. OECD LEED Programme. Paris: OECD. Oh, D.-​S., Phillips, F., Park, S., & Lee, E. (2016). Innovation ecosystems: A critical examination. Technovation, 54(August), 1–​6. Ottati, G. D. (2002). Social concertation and local development: The case of industrial districts. European Planning Studies, 10(4), 449–​466. Özcan, S. (2004). Institutions, institutional innovation and institutional change in clusters. Paper presented at DRUID Academy, Winter PhD Conference, January 22–​24, Aalborg, Denmark. Padmore, T., & Gibson, H. (1998a). Modeling regional innovation and competitiveness. In J. de la Mothe & G. Paquet (Eds.), Local and regional systems of innovation (pp. 45–​79). Boston, Dordrecht, and London: Kluwer Academic. Padmore,T., & Gibson, H. (1998b). Modelling systems of innovation II: A framework for industrial cluster analysis in regions. Research Policy, 26(6), 625–​641. Papaioannou, T., Wield, D., & Chataway, J. (2009). Knowledge ecologies and ecosystems? An empirically grounded reflection on recent developments in innovation systems theory. Environment and Planning C: Government and Policy, 27(2), 319–​339. Pavelkova, D., Zizka, M., Homolka, L., Knapkova, A., & Pelloneova, N. (2021). Do clustered firms outperform the non-​clustered? Evidence of financial performance in traditional industries. Economic Research–​Ekonomska Istraživanja, 1–​23. Peltoniemi, M., & Vuori, E. (2004). Business ecosystem as the new approach to complex adaptive business environments. In Proceedings of eBusiness Research Forum (pp. 267–​281). Tampere, Finland: Tampere University of Technology and University of Tampere. Perroux, F. (1955). Note sur la notion de “pôle de croissance” [Note on the notion of the “growth pole”]. Economie Appliquée,VIII, 1–​2. Perroux, F. (1969). L’Économie du XXe siècle [20th-​century economics] (3rd ed.). Paris: Presses Universitaires de France. Perroux, F. (1970). Note on the concept of growth poles. In D. McKee, R. D. Dean, & W. H. Leahy (Eds.), Regional economics:Theory and practice. New York: Free Press. Peters, E., & Hood, N. (2000). Implementing the cluster approach: Some lessons from the Scottish experience. International Studies of Management & Organization, 30(2), 68–​92. Popa, I., & Belu, M. G. (2009). Growth poles and national competitiveness. Annals of the University of Oradea, Economic Science Series, 18(1), 33–​39. Porter, M. E. (1985). The competitive advantage: Creating and sustaining superior performance. New York: Free Press. Porter, M. E. (1990). The competitive advantage of nations. New York: Free Press.

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46  Prior and contemporary theories on industrial clusters Steiner, M. (2011). Regional knowledge networks. Handbook of regional innovation and growth. In P. Cooke, B.T. Asheim, R. Boschma, R. Martin, D. Schwartz, & F. Tödtling (Eds.), Handbook of regional innovation and growth (pp. 222–​233). Cheltenham: Edward Elgar. Steinle, C., & Schiele, H. (2002). When do industries cluster? A proposal on how to assess an industry’s propensity to concentrate at a single region or nation. Research Policy, 31(6), 849–​858. Stewart, F. (1977). Technology and underdevelopment. London: Macmillan. Storper, M. (1995). Competitiveness policy options: The technology–​ regions connection. Growth and Change, 26(2), 285–​308. Storper, M. (1997). The regional world: Territorial development in a global economy. New York: Guilford Press. Swann, P., & Prevezer, M. (1996). A comparison of the dynamics of industrial clustering in computing and biotechnology. Research Policy, 25(7), 1139–​1157. Valkokari, K. (2015). Business, innovation, and knowledge ecosystems: How they differ and how to survive and thrive within them. Technology Innovation Management Review, 5(8), 17–​24. Van den Berg, L., Braun, E., & van Winden, W. (2001). Growth clusters in European cities: An integral approach. Urban studies, 38(1), 185–​205. Van Dijk, M. P. V., & Sverrisson, Á. (2003). Enterprise clusters in developing countries: Mechanisms of transition and stagnation. Entrepreneurship & Regional Development, 15(3), 183–​206. Whitley, R. (1994). Societies firms and markets: The social structuring of business systems. In R. Whitley (Ed.), European business systems. London: SAGE. Wiig, H. (1999). An empirical study of the innovation system in Finnmark: STEP Report. Studies in Technology, Innovation and Economic Policy. Oslo: STEP Group.

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3 Dimensions of proximity

This chapter presents the state of knowledge on proximity and its selected dimensions. Its existence was dictated by the will to make an overview of the available relevant literature and to determine the present state-​of-​the-​art thinking with respect to the concept of proximity.To begin with, the concept of proximity is characterized and the results of a review of associated scientific publications, supplemented with the results of bibliometric analysis, are discussed. The focus then shifts to characterizing the individual dimensions of proximity within a uniform mode of description –​from determining the semantic field of a given dimension, through its definition, to the analysis of its impact on cooperative ties. In the final part, the previously discussed theories on the formation and development of industrial clusters are used to show their connections with the presented concept of proximity.

The concept of proximity The rich tapestry of issues pertaining to and the complexity of the entire social world, which includes its economic and commercial sphere, has forced (and continues to force) theoreticians and practitioners alike from this field of expertise to search for continually emerging factors that explain changes in this area. Departure from purely economic explanations of actions and processes present in the commercial world and a focus on “soft” (non-​ economic) factors of influence have opened the way toward using various new explanatory variables, but also resulted in researchers being faced with another problem –​namely, how to select factors from among such a large number of possibilities, which in a relatively full way contribute to understanding the analyzed phenomena and which, in turn, provide a strong foundation for anticipating future events. The inclusion of the category of proximity in theoretical thought and empirical analysis was an attempt to integrate existing ideas pertaining to the non-​economic understanding and explanation of the specificity of phenomena and processes of an economic nature. The small distance between economic entities and the broadly understood support institutions, and the market itself, have a positive influence on the development of these companies and their sector as a whole. Another known thesis pertained to DOI: 10.4324/9781003194019-3

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48  Dimensions of proximity the role of trust and personal relationships in establishing and developing cooperative ties between economic entities, or the relatively lower chance of initiating cooperative ties between companies with very different organizational structures or functioning in radically different institutional conditions. Each of these threads independently pertained to issues which –​as it turned out –​have a lot in common, that is, can be treated as different dimensions of the same term –​namely, proximity. It is worth noting that the term proximity –​in contrast to neighborhood –​was never limited to physical proximity (Cooke, 2006). The use of the “proximity” category in considerations and analyses embedded in the field of economy and commerce gained popularity at the end of the 20th century. As Klimas (2011) underlines, as late as in the 1980s and at the beginning of the 1990s, the dominant approach in relevant literature was one that focused on relationships within organizations themselves (Monge et al., 1985; Rice & Aydin, 1991). It is only from the mid-​1990s onward that we can speak of a shift of accents to the context of proximity across organizational boundaries. The development of the term “proximity” itself, as well as its dissemination in the awareness of theoreticians and practitioners alike, was influenced to a large degree by the so-​called French school of proximity (represented by, among others, André Torre, Alain Rallet, Jean-​Pierre Gilly, and Yannick Lung), which stressed the idea that the similarity of the features of engaged actors is the key element in the process of coordinating their economically oriented actions (as it facilitates the transfer of knowledge and the mechanisms of sharing strategic information, as well as positively influencing conflict resolution) (Boschma et al., 2014). Furthermore, proximity is considered to be a factor that considerably facilitates processes of cooperation across entities (Petruzzelli et al., 2009) and is conducive to developing innovation capacity and reducing uncertainty in relationships (Boschma, 2005a; Paci et al., 2014). At present, it would be hard to imagine an analysis of network structures without references to the concept of proximity and its different dimensions. The above-​ mentioned relationships between proximity and specific directions of scientific research have found their reflection in the classification put forward by Aguilera, Lethiais, and Rallet, which presented the three main directions that the then-​recent relevant literature on the issue of proximity in relationships undertaken by economic entities could be reduced to. The first thread pertained to studies on the relationship between proximity and network formations; the second to the analysis of the influence of proximity on the companies’ economic results; and the third to considerations of the influence of different dimensions of proximity on the creation and sharing of knowledge (Aguilera et al., 2012). One characteristic feature of proximity is the fact mentioned earlier that this is not a term of a homogeneous nature. Considering proximity as a homogeneous term would rid this category of most of its explanatory power and would therefore question the merit of its use in the analysis of relationships not only between economic entities but also between entities

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Dimensions of proximity 49 of any other nature. The term “proximity” itself is only a common term that is commonly used to refer to a set of specific aspects through the lens of which we can perceive specific interacting entities. The list of such aspects is by no means complete nor defined. In the relevant literature, the most popular is Boschma’s division of proximity into five dimensions. According to this approach, we can distinguish between geographical, cognitive, social, organizational, and institutional proximity. This division is the result of theoretical research and empirical analyses published since 2004 by Boschma and colleagues (e.g., Boschma, 2004, 2005a, 2005b; Boschma & Frenken, 2010; Boschma et al., 2014; Balland et al., 2015). To facilitate the literature research on proximity, the present authors conducted a systematic overview of a set of scientific publications devoted to the concept using the two most commonly accessed databases –​namely, Web of Science (WoS) and Scopus. The selection of the publications was performed in accordance with the “snowball” procedure. The first stage consisted of selecting publications with the word “proximity” in the topic or title (in the case of WoS) or in the title, abstract, and among the keywords (in the case of Scopus). In the second stage, the set of publications was limited to full-​text reviewed publications and chapters in books.The final stage pruned the set further to include only those publications that fell into specific categories. In this way, close to 4,800 publications were identified in the WoS database from the categories of business, management, and economics, and over 7,300 publications from two additional categories –​namely, geography and sociology. In turn, the number of hits in the Scopus database was even larger, amounting to over 6,300. After adding the social sciences category, this rose to over 20,200 (see Table 3.1). Given the number of publications pertaining to particular categories of proximity, it is clear that geographical proximity had the lead. The extended dataset created on the basis of the WoS database identified 623 publications devoted to geographical proximity; in the set based on the Scopus database, 1,295 publications were found. Social proximity was in second place; third was cognitive proximity. Organizational and institutional proximity were in the bottom two places. In an attempt to observe changes in the popularity of the topic of proximity itself, as well as the popularity of its key dimensions in the scientific analyses at hand, the results of this overview were limited with respect to the time period and the criterion of belonging to specific scientific fields: that is, a time period of the three years between 2019 and 2021, and publications assigned to categories strictly tied to economic sciences. No significant changes were noted for such a limited sample. Geographical proximity remained the most commonly studied dimension of proximity, followed by social proximity, cognitive proximity, organizational proximity, and institutional proximity. It is worth referring to previous analyses in this scope. In 2006, Knoben and Oerlemans (2006) performed a literature review with respect to the issue of proximity (tied to such topics as: innovations, organizations, networks,

newgenrtpdf

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Search criteria

Web of Science Topic or title (all years) Document types: article, book chapter Categories: Business; Management; Economics (2019–​2021) Categories: Business; Management; Economics; Geography; Sociology Scopus Article title, abstract, keywords (all years) Document types: article, book chapter Subject area: Business, management, and accounting; Economics, econometrics, and finance (2019–​2021) Subject area: Business, management, and accounting; Economics, econometrics, and finance; Social sciences

Keyword “Proximity”

“Geographical proximity”

“Social proximity”

“Cognitive proximity”

154,068 125,958 4,792

2,069 1,843 496

570 496 106

145 128 79

81 72 34

94 89 52

1,250 7,360

124 623

33 146

35 92

6 46

16 57

191,498

2,526

655

167

132

105

146,416 6,364

2,161 635

537 127

149 89

117 63

98 52

1,558 20,253

141 1,295

34 306

38 134

13 102

10 93

Source: Authors’ own study based on the WoS and the Scopus databases (the figures quoted are as at June 17, 2022)

“Organizational proximity”

“Institutional proximity”

50  Dimensions of proximity

Table 3.1 The results of the review of scientific publications with the keyword “proximity”

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Dimensions of proximity 51 cooperation between companies) in the ISI and ABI/​Inform databases. The overview encompassed scientific papers published in the years 1984–​2005 (the ISI database) and 1971–​2005 (the ABI/​Inform) database). Although the analyzed texts comprised only a small portion of all existing scientific publications, the determinations of the authors of the study can be deemed interesting, as they pointed to certain trends, which were already visible in analyses at that time. The decidedly most common topic of articles from these databases was geographical proximity, which became the topic of inquiry in 80 cases. The second most common dimension of proximity –​ organizational proximity –​featured in only 13 instances. The remaining aspects of proximity –​cultural, technological, cognitive, institutional, and social –​were much rarer. However, this does not mean that the above-​cited dimensions of proximity exhausted the set of the possible types. Apart from the dimensions already discussed, Zeller (2004) points to several other such dimensions, which the authors of the overview of the ISI and ABI/​Inform databases did not identify –​such as relational proximity, virtual proximity, internal proximity, and external proximity.When set together with the previously presented results, the results of Knoben and Oerlemans’s analysis demonstrate that the issue of geographical proximity remains relevant and is still a topic of theoretical consideration and empirical research. However, at present it is social proximity that is the focus of research, which in Knobens and Oerlemans’s set was in last place. The above analysis is supplemented with an analysis of citation rates, conducted on the same two databases used before (WoS and Scopus) (see Table 3.2). The analysis of citation rates indicates that the earliest publications on proximity (considered in the analysis) appeared in 1999. In first place, with the highest number of citations, was the groundbreaking paper “Proximity and innovation: A critical assessment” by Boschma (2005), which has by far surpassed the other publications devoted to proximity. The second publication on the list, for instance, co-​authored by Torre and Rallet (2005), despite a very high rate, is cited almost four times less frequently. In terms of the popularization of this topic, of note is the large contribution by representatives of the French school of proximity. The chapter continues by focusing on the five dimensions of proximity and how they are understood (established on the basis of the literature review), as well as selected results of empirical research.When choosing the sequence for presenting specific dimensions, the guiding factor was the susceptibility to change displayed by proximity in each of the indicated aspects: from social proximity, relatively the most susceptible to modifications, through organizational, institutional, and cognitive proximity –​all on a similar level with respect to openness to change –​up to geographical proximity, in the case of which introducing changes would require the highest investment of resources.

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52  Dimensions of proximity Table 3.2 Citations of publications concerning the concept of proximity Authors

Publication title

Boschma

Proximity and innovation: A critical assessment Torre & Rallet Proximity and localization Broekel & Knowledge networks in the Boschma Dutch aviation industry: The proximity paradox Torre & Gilly On the analytical dimension of proximity dynamics Balland, Proximity and innovation: Boschma & From statics to dynamics Frenken Boschma & The spatial evolution of Wenting the British automobile industry: Does location matter? Boschma & The spatial evolution of Frenken innovation networks. A proximity perspective Kirat & Lung Innovation and proximity: Territories as loci of collective learning processes Boschma Role of proximity in interaction and performance: Conceptual and empirical challenges Carrincazeaux, Proximity and localisation of Lung & corporate R&D activities Rallet

Year of publication

Number of citations WoS

Scopus

2005

2,954

3367

2005 2012

709 276

824 317

2000

255

307

2015

234

254

2007

224

255

2010

203

283

1999

179

234

2005

93

94

2001

62

73

Source: Authors’ own study based on the WoS and the Scopus databases (the figures quoted are as at June 17, 2022)

Social proximity When it comes to understanding the term “social proximity,” theoreticians and practitioners alike broadly agree on tying it with the term “embeddedness,” thus satisfying the principle that non-​economic factors are crucial for individual or collective entities undertaking economic activities. In this specific case, those non-​economic factors must be, because of the specific nature of the discussed term, limited to a certain subgroup thereof –​that is, to the relationship between entities creating larger wholes. For these relationships to be considered as an indicator of the existence of social proximity, they must be characterized by trust established for at least one of the following reasons: affinity, friendship, or ties based on past personal experiences connecting the analyzed entities (Czakon, 2010; Boschma, 2005a; Heringa et al., 2014; Broekel & Boschma, 2012). To convert these relationships to the group level, social proximity may refer to the degree of overlap between

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Dimensions of proximity 53 networks of relationships of individuals, comprising organizations that are in contact with one another (Boschma et al., 2014), as well as the network of past and present cooperative ties between these organizations. In other words, social proximity may be –​by referring to its existing meaning –​seen as entities remaining in relationships based on affinity (e.g., family members), friendships (e.g., individuals from a circle of friends), or previous experience (e.g., previous and current coworkers, members of the same trade associations). Relationships based on emotions and trust must be tied to entities capable of both –​that is, individual people, not the organizations they comprise. However, the effects of these entities being in social proximity will nevertheless resonate both on specific individuals (directly) and on their aggregates (indirectly). Furthermore, another crucial issue from the perspective of each organization (including, therefore, economic entities) is that the network of social relations can be managed –​that is, one is capable of consciously influencing its shape and development (at least to some degree). This is a crucial tool with a view to coordinating actions within and across organizations, including regulating information flow or maintaining an efficient system of social control. The above-​ mentioned effects of entities being in social proximity typically include improving learning processes (as well as raising innovative potential) (Czakon, 2010; Boschma, 2005a), which may include mechanisms of exchanging tacit knowledge (Boschma, 2005a; Boschma et al., 2014; Doloreux, 2002). Of significance are also those factors which function independently from the processes of learning and exchanging information, such as lowering the risk of opportunistic behaviors (Boschma, 2005a) or lowering the possibility of the emergence of a conflict between cooperating organizations (Boschma et al., 2014). The positive influence of social proximity on results of cooperation between entities has already been broadly described in the literature devoted to the topic of proximity. Thanks to empirical research, it is known that networks of social relationships built on personal acquaintance –​as a result of shared work experience, for instance –​are very important to processes of the mutual exchange of knowledge (Breschi & Lissoni, 2009) or are, at least, an incentive to maintain more frequent contact between organizations hiring individuals in social proximity (Agrawal et al., 2006). From here, there is but a small step to acknowledging that personal interactions are a factor that positively influences the innovative capacity of companies –​as research on Montreal high-​tech companies has shown, representatives of these sectors felt that both of these variables are strongly connected (albeit considerably less so in the smaller bio-​pharmaceutical sector in comparison with the larger aeronautic sector) (Tremblay et al., 2003). A positive influence of social proximity on the innovative capacity of companies was also demonstrated in a study of companies from the Italian high-​tech sector: close relationships with partners (clients, vendors) were conducive to the existence of processes of knowledge creation and transfer (though this effect only existed in companies which were additionally characterized by high absorbency) (Presutti

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54  Dimensions of proximity et al., 2019). A positive relationship was also noted in European studies on the technological development of particular EU regions, where social proximity was considered to be one of the factors determining innovative activities (though the strength of this influence was determined to be moderate) (Paci et al., 2014). In the case of the cooperation of Italian companies with institutions of higher education, social proximity fulfilled the role of one of the determinants of the emergence of cooperative ties of this nature, successfully counteracting the inhibitory tendencies resulting from the large geographical distance, which often characterized the cooperating entities (Guerini et al., 2013). In a similar fashion, an analysis of companies operating within strategic alliances has shown that while social proximity between the engaged entities was conducive to initiating and performing processes of exchanging knowledge between partners, this influence was somewhat limited in comparison to the strength of influence of technological proximity (as one of the types of cognitive proximity) and geographical proximity (Usai et al., 2017). Similar results were obtained in studies on the wine industry in Italy: social ties (based on friendship, personal acquaintance) supported by commercial ties (worker mobility, resource exchange) played a fundamental role in processes of knowledge exchange. However, of particular note is the fact that such exchanges were much easier when the entities established both kinds of ties at the same time, or established commercial ties, than when they were only connected by ties of friendship (Maghssudipour et al., 2020). Generally speaking, however, a decidedly more crucial consequence of the companies’ social proximity was the fact that easier interactions and exchanges of information between entities that knew one another beforehand led to the emergence of self-​renewing processes of cooperation, influencing not just the engaged entities themselves but the entire network in which these entities participated (Usai et al.,2017). However, the embeddedness of economic processes in a dynamically shaped network of social relationships does not lead solely to positive consequences for its entities –​negative effects of binding one’s social relationships with the sphere of economic activities can emerge both in the case of a too weakly developed network of relationships, as well as in the case of too strong a network, exerting too little or too much influence on the actions of its members, respectively. In interesting papers that referenced the principles of grounded theory, Uzzi (1996, 1997) looked into this problem in the New York textile sector and came to the conclusion that, relatively speaking, the most beneficial solution for economic entities would be to establish such a network of relationships, which would not be focused on extremes (that is, with a strong majority of close or distant relationships), but which would integrate both entities that are close to the analyzed entity as well as those that are somewhat socially distant. Nevertheless, pointing to the issue of excessive or insufficient proximity during considerations of social proximity does not mean that the issue only pertains to this dimension of proximity, as it also happens to be true in the case of the remaining dimensions, which will be touched on later and which differ in terms of the

5

Dimensions of proximity 55 consequences of excessive of insufficient distance (cognitive, organizational, institutional, or geographical). The above-​mentioned self-​renewing processes of cooperation visible in some networks of companies –​in accordance with the adopted principle of the dynamic nature of proximity and its particular dimensions –​may lead to the emergence of excessive social proximity and in consequence react too strongly to economic decisions made by entities with cooperative ties. The same process is tied to the tendency of some of the members to engage in cooperation with “friends of friends,” which –​in turn –​influences the growth of the excessive social embeddedness of networks of mutual relationships (Balland et al., 2015). The consequence of such a “deep dive” of a company in its social context is the weakening of its competitive position –​as it is hard to expect that, in dynamically and permanently changing conditions of the environment (both commercial and pertaining to spheres indirectly tied to the economy), limiting oneself to a closed set of relationships will have a net positive result. What is more, staying in such a petrified structure of relationships leads not just to diminishing contacts with other entities but also –​or perhaps even first and foremost –​to doing business in a routine, non-​flexible, predictable way, without much chance for further development (Oerlemans & Meeus, 2005; Uzzi, 1997; Boschma, 2005a; Balland et al., 2015; Pucci et al., 2020). The search for the most beneficial degree of social proximity is therefore crucial for each company. In this context, it is worth noting that a negative effect on the business dealings of a company is observed not only in the case of an excessive level of social proximity (the consequences of which were described above) but also an insufficient level, which in turn inhibits the functioning of the company and its achievement of goals, as it results in a lack of trust in partners and insufficient engagement in joint actions (Uzzi, 1997; Boschma, 2005a). The only efficient solution to this apparent paradox of proximity is to establish such a network of relationships, which will include both ties characterized by considerable social proximity (enabling the company to act in conditions of relatively low uncertainty and high trust) and by ties which are commercial in nature (Uzzi, 1997; Boschma, 2005a), characterized by, admittedly, low social proximity, albeit providing the company with access to new partners, unique opportunities, and cutting-​ edge ideas. This multifaceted nature of the effect of social proximity on the functioning of cluster members seems to be supported by the results of studies held in Great Britain (Herbane, 2019), which analyzed several aspect of the functioning of companies: location, networks, external crisis events, prevention, the formalization of resilience and strategy. The results of the studies have allowed a division of the entities under consideration into clusters of different specificity, considering both their attitude toward social proximity as well as other kinds of proximity. The identified groups were as follows: Attentive Interventionists, Light Planners, Rooted Strategists, and Reliant Neighbors. To focus on social proximity alone at this point, it is

56

56  Dimensions of proximity worth mentioning that the cooperating companies resorted to very diverse strategies with respect to establishing and developing relationships with their partners: Attentive Interventionists and Light Planners did not consider social proximity to be the most significant (which was particularly apparent in the case of the first of the two groups), while Rooted Strategists and Reliant Neighbors considered it to be one of the most important elements (Herbane, 2019). Though social proximity probably should not be seen as a key factor for holding business (its moderate influence on the processes of knowledge exchange or the innovative activities of companies was mentioned earlier), it can considerably facilitate or inhibit the goals set by the company. Social proximity is a peculiar “lubricant,” which –​when applied in moderation –​ efficiently reduces natural friction between entities with natural differences. Furthermore, remaining in social proximity is a key factor for transferring tacit knowledge, often decidedly more crucial than codified knowledge which is usually transferred in parallel. This was also noticed by researchers dealing with the issue of proximity. Social proximity, though previously somewhat dismissed (at least according to a ranking prepared by Knoben and Oerlemans (2006), in which social proximity finished in last place among other dimensions of proximity), in the current study turned out to be the second most prominent dimension, numbers wise, when it comes to the number of published papers. As shown in Table 3.1, the years 2019–​2021 alone saw the publication of over 30 papers (33, according to WoS; 34, according to Scopus) devoted to this issue (limited to economic sciences).

Organizational proximity Social proximity, as discussed above, is sometimes tied to proximity in its organizational dimension. This is particularly the case in the context of considerations pertaining to facilitations and stimulants of cooperation between two or more organizations. Both social and organizational proximity are based on similarities between cooperating entities, though these similarities must be problematized on somewhat different levels of analysis. Social proximity is to some degree tied to the micro level, while organizational proximity has been fully assigned to the macro or, at least, the mezzo level. And though social proximity does not cause large problems when we attempt to capture its essence with the use of scientific definitions, the situation is somewhat more complicated when it comes to organizational proximity, which we are looking at here. Organizational proximity is not as prevalent in academic publications in comparison to geographical or social proximity. In fact, interest in this issue among academics can be described as insignificant. This is made apparent both by the results of the study performed by Knoben and Oerlemans (2006), as well as by the results of the bibliometric analyses here. In the prepared literature review, organizational proximity was outpaced by geographical,

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Dimensions of proximity 57 social, and cognitive proximity. Depending on the database and the criteria involved, it was placed either fifth (WoS) or fourth (Scopus). In the years 2019–​2021, this dimension of proximity featured –​depending on the database –​in six (WoS) or thirteen (Scopus) publications which fell into areas of knowledge strictly tied to management and economy (see Table 3.1). In their analysis of the issue of organizational proximity, Knoben and Oerlemans (2006) have identified the tendency to gravitate toward one of three archetypes (when analyzing later references to organizational proximity, it can be said that these archetypes are still valid). Organizational proximity as a symbol of actors belonging to the same space (including a network) of relationships –​this is how the issue was defined by Boschma (2005a), who decided that organizational proximity refers to the scope of relationships shared within or across organizations, which in turn was tied to the degrees of autonomy and control of these relationships on the part of the organizations. The continuum of observable levels of organizational proximity begins, therefore, from a situation in which there exists the complete lack of organizational proximity (no ties between completely independent entities), through loose ties (weak ties between autonomous entities), up to cases in which we can observe a very strong level of organizational proximity, characteristic of, for example, organizations or their associations structured in an extremely hierarchical way. Organizational proximity as a symbol of facilitations of interactions, manifesting itself as a collection of tacit and explicit rules and regulations, strengthened by a similar system of beliefs combining engaged actors –​this is how the issue has been considered in studies on companies from the Dutch water sector (Heringa et al., 2014), although in this instance the specificity of shared procedures and beliefs was limited to mechanisms responsible for motivating a given collective to action. The main line of division ran between commercial entities (profit-​oriented) and non-​profit entities, which were further divided into four categories of organizations: business, scientific, governmental, and non-​governmental. The division of organizations into commercial and non-​profit was also used in Metcalfe’s (1995) considerations on organizational proximity. Common procedures, modes of thinking and production as factors facilitating the achievement of group goals were also referenced by authors of a diagnosis of the innovative potential of companies from the Montreal region (Tremblay et al., 2003) and representatives from the French school of proximity (Gilly & Torre, 2000; Torre & Rallet, 2005). Gilly and Torre’s work in particular is distinguished by the use of a two-​tiered approach to organizational proximity –​through the lens of separate logics of isolating this dimension of proximity: a logic of belonging (actors belong to the same network of relationships, which means that they enter into common and repeated interactions built on routine and rules and regulations, the latter of which bind all sides) and a logic of similarity (actors are similar to one another in terms of their frames of reference and codified knowledge). This is why the logic of belonging is usually tied to the issue of the efficiency of coordinating activities among the actors, while the logic of

58

58  Dimensions of proximity similarity is rather based on the similarities of the viewpoints and the modes of operation of the network members. Organizational proximity among employees of a multiplant company, identifying with one another, is created thanks to belonging to the same organization and knowledge of its specific procedures (Knoben and Oerlemans, 2006). Such an understanding of organizational proximity was also adopted by Schamp et al. (2004). This category includes, for example, research centers functioning within the same institution of higher education or companies belonging to a parent company. Of interest is the fact that in order to determine that two or more entities are in organizational proximity, it is not necessary for them to remain in a relationship of direct cooperation (Boschma et al., 2014). The directions of thought on organizational proximity discussed here do not exhaust the definitional richness of this theoretical category. However, in order not to broaden the already apparent differences between particular definitions of this dimension of proximity, at this stage it seems worthwhile to underline the significance of those definitional elements, which to the largest degree reflect the specific nature of organizational proximity. This dimension of proximity refers to the correspondence between two or more organizations in terms of both the logic of similarity (e.g., the similarity of internal structures and processes, the degree of mutual interorganizational dependencies) as well as the logic of belonging (sharing a certain relationship space in the form of, e.g., participation in the same higher-​tier organizations). Such a reconstructed definition underlines, on the one hand, the degree of similarity between the structures of two entities (without determining at this point whether or not these entities have cooperative ties) and their degree of connection (by determining the scope of autonomy that they have with one another when they are engaged in a given relationship or, in turn, by determining the level of control that one of the entities has over the other) and, on the other hand, does not abandon the broader analytical perspective, from which it is crucial to determine the scope of how much the relational networks of the researched entity and other entities connected thereto correspond with one another. One of the key tasks set before the organizational dimension of proximity is to reduce uncertainty and minimize the risk of undertaking opportunistic actions by one of the co-​partners (Boschma et al., 2014). This function becomes particularly significant in the case of cooperative activities with a view to exchanging and creating knowledge, as reaping the benefits of intellectual property is easier to achieve and less risky (due to numerous legal imperfections tied to the protection of this kind of resource) than to committing theft of material goods. Such control over the process and effects of the exchange and creation of knowledge may be held by organizational ties between cooperating organization (the creation of a transparent hierarchy within the framework of a single organization or the organizational combination of the structures of previously autonomous structures). Furthermore, organizational proximity facilitates the process of knowledge

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Dimensions of proximity 59 creation and exchange by creating a stable basis for the dissemination of tacit knowledge –​essential for the proper understanding and application of packets of formal (codified) knowledge –​as well as the combination of other, less standard resources (Knoben & Oerlemans, 2006). This attribute is active not just in the area of economy but also science –​this fact seems to be attested by the results of a study of (university-​based) innovation ecosystems in the United States and the European Union (Runiewicz-​Wardyn, 2020). In the study, organizational proximity turned out to be one of the main motors of cooperation within university ecosystems, as well as one of the factors contributing to the creation of social networks within ecosystems and the exchange of knowledge. Another significant role performed by organizational proximity is compensating for the lack of geographical proximity between cooperating entities –​it is as efficient in the process of developing informal relationships as geographical proximity itself (Rallet & Torre, 1999). One example of such organizations are international organizations (e.g., corporations), in which the same procedures and conventions are used (in the form of, for instance, position names or access to an internal company computer network) regardless of the actual location of specific units (Cooke, 2006). In effect, despite the fact that considerable physical distance between employees cannot be said to be ideal when it comes to cooperation, the creation of simple and relatively reliable channels of communication together with very low uncertainty as to the actions of the partner (resulting from membership in the same organization) enable the establishment and development of remote cooperation (Hansen, 2015). Although the arguments mentioned so far may lead to the conclusion that organizational proximity plays a significant role in cooperative ties in the economic realm, it is crucial to describe in detail the conditions under which the role of this dimension of proximity is actually of primary importance, and those under which it becomes less important. This matter was explored in numerous publications devoted to issues dealing with proximity and –​what is relatively uncommon in scientific work in disciplines such as management –​the conclusions show a surprisingly high level of mutual agreement. It can be claimed with much certainty that while organizational proximity pertaining to the relationships of two or more entities has a positive effect on initiating and developing mechanisms of cooperation (particularly those aimed at exchanging knowledge), the strength of this effect can at best be described as moderate (Usai et al., 2017). Of much more interest and significance from the perspective of establishing cooperative ties in the economy is the fact that the existence of organizational proximity between cooperating entities did not translate in any way, or translated in a minor way, to the innovative activities of these entities. Such conclusions were made by researchers performing studies on such diverse units as all regions of the European Union (Paci et al., 2014), the Dutch aviation sector (Broekel & Boschma, 2012), or a European network of genomics organizations (Cassi & Plunket, 2015). Furthermore,

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60  Dimensions of proximity organizational similarities between cooperating partners had a negative effect on achieving the goals of cooperation (specific innovative solutions, academic publications, raising the total turnover of the company, etc.). This negative effect was not visible in the case of the so-​called “soft” (i.e.,) hard to quantify, effects of cooperation. In effect, though most of the cooperation is between entities which are close to one another organizationally, in order to achieve particular goals, it is would be definitely more worthwhile to select partners with considerable organizational proximity (Heringa et al., 2014). Results of other studies also suggest that organizational distance has a positive effect on cooperation aimed at using existing technologies –​ however, it does not play a significant role in cooperative ties aimed at moving technological boundaries (Petruzzelli, 2008). Similarly to other dimensions of proximity, organizational proximity also should achieve a certain optimal level for a given cooperative tie –​ when it is maximized, it can have net negative effects. The desire to make two or more entities as organizationally similar as possible leads to organizational petrification and isolation from the broader surroundings of the cooperating structures, which in turn leads to closing down the processes of exchanging knowledge and other resources in formulaic, routine boundaries (Boschma, 2005a). Such an isolated system of organizationally similar entities would suffer from the lack of new ideas and information (because it would not exchange information from outside the set-​up), which would considerably lower the competitive position of the cooperating parties. This is also the reason for the above-​mentioned inhibitory effect of organizational proximity on the innovative activities of companies. In order to create and implement an innovative solution in any sphere, it is necessary to display considerable flexibility (including organizational flexibility), which, particularly in organizations with a strong hierarchy, is virtually nonexistent. The stronger the relationship of mutual co-​dependence, the less initiatives appear and the smaller the reward (Boschma, 2005a). The solution to this conundrum is the creation of flat, non-​hierarchical networks of cooperation between independent companies, whose actions are co-​coordinated by a clearly distinct central structure (Boschma, 2005a), albeit the coordinating position of this central structure should in no way be tied to formal oversight and power (as this would introduce an openly hierarchical relationship). Rather, it should be the result of its leadership position, willingly accepted by the member entities (akin to accepting the dominant role of some authority). In the conditions of such a constructed loose system of ties between autonomous entities, organizational proximity should reach a sufficient level to reduce the uncertainty and risk of opportunistic actions, and not exceed a level of similarity that would lead to excessive bureaucratization and petrification. The creation of organizational proximity within processes of cooperation or the selection of partners motivated by similarities in structure or co-​ dependence is undoubtedly a crucial element of the conscious management of not only a single company but also a group of companies (i.e., a cluster

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Dimensions of proximity 61 or a cluster initiative). Though the correct selection of partners (organizationally speaking) does not guarantee the achievement of the goals set by the members of a given cooperative structure, failing to undertake such an action will undoubtedly lead to numerous difficulties in the process of achieving goals.

Institutional proximity The placement of the discussion devoted to institutional proximity –​ following right after an analysis of the category of organizational proximity –​ is not accidental and stems from, among other things, the considerable overlapping of the definitions of both these terms. The “organizational structures” referred to in the definitions of organizational proximity, which comprise the sum total of the functional and hierarchical relationships between the elements of the analyzed entities, are a construct which derives directly from the culture of a given organization, including the system of the institutions cooperating within its framework. Some authors go as far as to include within organizational proximity phenomena and processes characteristic for institutional, cultural, and social proximity by pointing to the considerable overlap between specific theoretical categories (Knoben & Oerlemans, 2006). Strong ties between these terms were also mentioned by Boschma (2005a), who also included social proximity in this group. However, it would seem that keeping this category separate allows us to capture different phenomena than those described before, which allows these theoretical efforts to arrive at a satisfactory level of precision and integrity of explanations and descriptions. Institutional proximity was and continues to be one of the least popular dimensions of proximity, which reached the ultimate or penultimate spot in rankings (see Table 3.1). In the three years between 2019 and 2021, 16 publications were found in the WoS database, in which institutional proximity was at least one of the elements (there were ten such publications in the Scopus database). Of note is the fact that, in this instance, the number of hits was close to the results for organizational proximity. The results of the study by Knoben and Oerlemans (2006) mentioned earlier also located institutional proximity in last place among all of the considered dimensions of proximity present in academic publications collected in the ISI an ABI/​ Inform databases –​only three publications were devoted to it specifically. However, if we were to consider –​in accordance with what was discussed above –​that institutional and cultural proximity are in fact theoretical categories which refer to one and the same (or at least a very similar) set of phenomena and processes, when taken together, they would feature in second place, following organizational proximity. In effect, one could adopt the interpretation that while issues pertaining to institutional order or –​more broadly speaking –​cultural order in the organization are not considered to be essential in analyses of proximity in publications, they should not be dismissed out of hand either.

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62  Dimensions of proximity Despite the fact that –​as Knoben and Oerlemans (2006) show, among others –​the understanding of institutional proximity featured quite often in discussions, most of the approaches consistently made use of the term “institution” proposed by North (1990). According to his concept, institutions created by people are limited by developing political, economic, and social interactions. Such limitations may be both informal (e.g., customs, traditions) and formal (constitutions, legal codes, etc.).All of the above constructs pertain to the interactive game between organizations and –​strictly speaking –​comprise both its voiced and unvoiced macro principles, which affect particular actors and their teams (organizations) in the process of agreeing upon their actions (Boschma et al., 2014; Edquist & Johnson, 1997). By referencing the understandings of institutional proximity present in literature, this term may be understood as the degree of overlap between elements of a formalized normative order (legal rules and administrative requirements that are in force in a given area), as well as informal value systems, patterns of thought, and behavior, within which the analyzed entity functions along with related entities. Similarly to all of the other dimensions of proximity discussed above, institutional proximity is a construct with a dynamic nature –​that is, one which with time may manifest itself in various ways depending on its level for a given relationship. It is no accident that Balland, Boschma, and Frenken connected the term “institutional proximity” with the term “habitus” coined by Bourdieu (1986, 1996), as both these theoretical categories stress the often dismissed “permeation” with specific features, characteristic of the place taken by the entity in the social structure (Balland et al., 2015). However, though the term habitus was primarily reserved for individuals undergoing the process of socialization, institutionalization itself –​that is, the integration and internalization of the principles of social life –​mostly pertained to collective entities (though, of course, the “vehicles” of these principles are individuals embedded in specific spheres of social reality). Strengthening the institutional proximity of cooperating entities is a factor which crucially facilitates the management of cooperative ties between co-​partners. The main tool of the institutionalization of this cooperation consists of repeatable acts of exchange between partners. Their repeated nature, together with arriving at goals that are satisfactory to all sides, form the basis for the emergence of a sphere of common goals, principles, and a common code of ethics (Balland et al., 2015), which in turn leads to the creation of a touchstone toward further processes of exchange, which are made all the more easier. However, this process is associated with a risk, which stems from the given cooperative tie reaching an excessive level of institutional proximity –​becoming walled in within routine, known relationships, and activities (particularly when one’s own interests are under threat or when a given entity feels a sense of obligation toward other entities comprising the network) (Grabher, 1993). Such a state of matters weakens the competitive position of the company due to the lack of flexibility required in an innovative economy and makes the creation and practical use of cutting-​edge

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Dimensions of proximity 63 solutions impossible (or, at best, considerably inhibited) (Boschma, 2005a). The remedy to such a state of matters is obvious –​one should attempt to ensure a balance between maintaining the existing institutional structure, within which partners operate and create specific cooperative ties, and opening up this structure to include new elements (both with respect to the sphere of potential co-​partners as well as institutions –​both formal and informal –​that influence the reality in which particular acts of exchange are performed). Boschma points out that an efficient institutional structure must find a balance between three main elements: stability, openness, and flexibility. The drive toward stability will translate into providing the structure with confidence in its further existence and proper operations by reducing uncertainty and the risk of undertaking opportunistic actions. Providing openness will create opportunities for the existence of new elements of the structure. In turn, flexibility will allow for experimentation within such institutions and introducing regulations not used before within the structure, which could translate into raising efficiency (Boschma, 2005a). One negative side of this approach is that the only force capable of shaping reality in its institutional dimension in the scope presented above is the political system itself –​and, as is clear, the political system is burdened with certain features that prevent it from achieving its own optimum state. Institutional proximity fulfills the role of a “bonding agent,” which combines into a relatively cohesive whole the multitude of seemingly distant ideas, values, and norms. In the above-​mentioned study on Italian strategic alliances (Usai et al., 2017), institutional proximity exerted influence (albeit rather limited) on cooperation and exchange between cooperating parties. The significance (albeit not crucial) of institutional proximity is also attested in the study on companies from the Danish clean technology sector, which claim that cooperation between entities within Danish regions was more frequent in the case of culturally similar entities in comparison with actors from neighboring countries. Even larger cultural differences were identified in the case of partnerships with companies located outside of Europe (Hansen, 2015). There was a clear co-​occurrence of institutional and geographical proximity –​the larger the physical distance between partners, the larger the feeling of cultural distance. For this reason, it can be said that geographical proximity and institutional proximity overlap, albeit though the latter does not have the power to substitute the former (Gertler, 2003). Boschma (2005a) identifies an interesting distinction between the ties characterizing institutional and geographical proximity by pointing out that this relationship will be most visible in the case of institutions of an informal nature, because one characteristic feature thereof is that they manifest themselves and are refreshed in day-​to-​day direct contacts between actors (which is easier to achieve in conditions of geographical proximity). Formal institutions (such as the legal system) to a large degree remain independent from individual interpersonal contacts and their scope can therefore encompass a decidedly larger area (e.g., an entire country). However, it should be noted that studies conducted between 2005 and 2007 in the game development sector have

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64  Dimensions of proximity shown that only institutional proximity did not reach statistical significance with respect to the issue of forming and cooperating within the network (Boschma et al., 2014). Embedding two or more organizations in different institutional contexts may be a significant hindrance to introducing mechanisms of cooperation. In such conditions, ensuring organizational or social proximity does not have to lead to the entity engaging in activities such as learning processes (Gertler, 2003). Furthermore, the lack of strong institutions –​that is, the lack of a common axionormative order –​could mean that, in search of guarantees for their actions, the group members may turn to informal contacts and relationships based on trust –​that is, relationships rooted in social proximity (Knack & Keefer, 1997).While such a strategy could prove to be worthwhile in the case of survival in the short term in crisis conditions, it is hard to imagine conducting business in the long term in conditions of institutional uncertainty.

Cognitive proximity Cognitive proximity –​similarly to the institutional proximity just described –​ is present in literature on this topic in two basic forms: as true cognitive proximity and as technological proximity. However, though in the case of institutional and cultural proximity both dimensions were almost interchangeable (the referents of these terms almost overlapped), technological proximity is a term with a narrower scope, usually tied to similarities in differences in resource from the technical databases of cooperating entities (Heringa et al., 2014; Cunningham & Werker, 2012;Tremblay et al., 2003). In other words, this term falls within the understanding of cognitive proximity and is one of its aspects. Cognitive proximity is a term with a decidedly wider scope, which encompasses differences (or the lack thereof) in the entire body of knowledge available to cooperative entities engaged in a given cooperative tie (Nooteboom, 2000). The most general understanding of this term refers to the fact that cognitive proximity is simply the similarity between the processes of perceiving, interpreting, understanding, and evaluating reality (Wuyts et al., 2005). At the same time, it is an essential element of the proper functioning of the processes of communication and the mechanisms of transferring knowledge, as it enables the accurate identification, proper interpretation, and efficient use of new elements belonging to the system of knowledge (Cohen & Levinthal, 1990). However, it is worth adding that some researchers (especially those deriving from the French school of proximity) understood cognitive proximity more in the categories of a group of individuals belonging to the same “community of practice” (which is therefore capable of efficient remote communication); that is, in a way which approaches the definition of organizational proximity (Torre & Rallet, 2005). Relevant literature also contains examples of features that may play the role of indicators of the existence of cognitive proximity. These include patents, which belong to the same classification category, similarities with respect to

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Dimensions of proximity 65 the product offer, similarities with respect to the educational profile of the organizations’ employees (Boschma et al., 2014) or at least a comparable level of education (Kuttim, 2016). With reference to the earlier remarks on cognitive proximity, one may draw the conclusion that this term will refer to the level of overlap of systems of knowledge (both general and related to the technological sphere) of the analyzed entities. The significance of cognitive proximity manifests itself in many ways. However, the key aspect, for which this dimension of proximity refers to, is the process of creating and sharing knowledge. Both the exchange and the creation of knowledge are processes which are not executed in an intellectual void, but are instead based on existing systems of knowledge of cooperating parties. In the process of exchanging knowledge (in other words, de facto in the process of learning), two partly similar, partly distinct systems of knowledge and experience are combined, which, by mutually feeding one another, strive toward –​in the minimal scenario –​mutually supplementing their cognitive structures with elements that were previously lacking and –​in the fullest scenario –​creating new content in response to the posed problem. However, one should bear in mind that when both systems of knowledge are in active cooperation, they do not take in new elements without reflection, but, rather, modify them in a more or less clear way, trying to make them fit into the specific nature of their own systems of knowledge. In other words, learning is an emergent process, whose effects are always larger than the sum of the exchanged elements (Tremblay et al., 2003). In effect, the creation of a “network of knowledge” requires cooperation between a certain group of entities with at least a minimal level of cognitive proximity. Research on innovative ecosystems, focused on universities from the EU and the US, has shown that cognitive proximity (with the participation of organizational proximity) is the main driver of social cooperation in university ecosystems (Runiewicz-​Wardyn, 2020). Without at least a partial overlap of the databases of cooperating parties, it would be extremely hard to constitute relationships aimed at the exchange of experiences and ideas. This is important because of the need to use a common language (e.g., specific specialist terminology). The level of cognitive proximity –​similarly to all other previously described dimensions of proximity –​will change with time and the growing number of “acts” of exchange; that is, with the accumulation of knowledge by particular co-​partners operating within a given cooperative network. Furthermore, such repetition will probably lead to the gradual, increasingly more apparent, equalization of the entities’ cognitive structures, and, what follows, to the gradual erasure of their differences, which was described more broadly in the context of the dynamics of cognitive proximity (Balland et al., 2015). In other words, cognitive proximity is also in danger of being elevated to an excessive level, which prevents the process of knowledge exchange from having a satisfactory outcome.This has been noted by Nooteboom (2000), among others, who stressed the necessity of striking a balance between cognitive distance (because of the availability of new information) and cognitive proximity (because of more efficient

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66  Dimensions of proximity processes of absorbing new ideas) –​as information is equally useless when it is “too new” (the lack of cognitive proximity makes it impossible to understand the information and absorb it into one’s own system of knowledge), as well as when it is no longer new within a given system of knowledge (the lack of suitable cognitive distance eliminates the newness effect). For companies to achieve success, therefore, it is essential for their knowledge bases to be complementary, but not the same (Broekel & Boschma, 2012), as only such a combination guarantees creativity and the efficient growth of the engaged systems of knowledge. Similar conclusions can be drawn from studies on the German R&D sector: cooperation within shared projects was more efficient when the engaged parties remained within a small cognitive distance (or technological distance –​as the authors used both of these terms interchangeably) (Marek et al., 2017). Another reason for keeping a certain cognitive distance between cooperating parties is maintaining the possibility of avoiding “closure” within a limited, known reality –​that is, remaining able to venture outside rigid modes of operation and beyond too well-​known paths of relationships. Such closure may lead companies to refrain from implementing new technologies or exploiting new market opportunities (Boschma, 2005a). By performing a relatively well-​known set of actions, the entity may not feel the need to modify such actions or abandon them in favor of other, perhaps more efficient, ones. This phenomenon is known in literature as “the competence trap” (Levitt & March, 1988). An excessive level of cognitive proximity –​that is, the strict overlapping in the competence sphere –​decidedly raises the risk of uncontrolled flows of units of knowledge, including those that it would be inadvisable to share with other partners (Boschma, 2005a).The gradual dismantling of mental barriers, which accompanies the growth of mutual understanding (and the level of cognitive proximity), breaks down even those barriers, which are essential for the entity, as they secure a competitive position in the market. For this reason, it seems crucial to take action to prevent the excessive growth of cognitive proximity. According to Boschma (2005a), only a base of relatively diverse partners, who are capable of finding common threads of cooperation, guarantees the constitution of cooperative ties that are resilient to the threat resulting from an excessive level of cognitive proximity. Analyses show that this dimension of proximity was not given much attention in the literature. Issues pertaining to competence proximity took the penultimate place in the ranking of the number of hits as a key element of scientific papers published in the ABI/​ Inform databases up to 2005 (Knoben and Oerlemans, 2006). In the prepared comparisons given here (see Table 3.1), cognitive proximity usually features in third place, only slightly ahead of organizational and institutional proximity. Nevertheless, in recent years we can observe a clear trend –​cognitive proximity is becoming more significant. In a set of publications from the years 2019 to 2021, derived from the WoS and Scopus databases, 35 and 38 management and economics publications, respectively, on cognitive proximity were identified –​slightly more than in the case of social proximity.

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Dimensions of proximity 67 Despite the fact that researchers do not undertake studies in this area often, the results of their studies point to the large significance of cognitive proximity with respect to entering into cooperation by companies, the resulting knowledge exchange, and innovative activities –​here, however, the results are not as conclusive. Research on the Dutch water sector has shown that entities with similar knowledge bases more frequently reported achieving positive results in the context of undertaking cooperative activities (Heringa et al., 2014). Similar results were obtained in an analysis of the German patent sphere –​the existence of cognitive proximity was tied to better chances of cooperation leading to good results (Cantner & Meder, 2007). Analogous conclusions were drawn in an analysis of cooperation between parties from the European nanotechnology sector –​joint technological experiences and similar systems of knowledge facilitated cooperation between entities from this sector. What is more significant and corresponds with previous remarks, the best results were achieved in cases where technological proximity was maintained at a moderate level (Cunningham & Werker, 2012). Research on Italian strategic alliances conclusively pointed to technological proximity as a factor which is the most essential in determining the process of exchanging knowledge across organizations –​this effect became stronger with the growing degree of similarity between the production profiles and knowledge bases of the co-​partners (in effect, it was the most apparent in the case of companies from the same sector) (Usai et al., 2017). Furthermore, a positive influence of technological proximity has been noticed for the spillover of knowledge (Aldieri & Vinci, 2016). Research on cooperative ties between wineries in the Montefalco region of Italy allows us to conclude that the presence of “active” cognitive/​technological proximity (active in the sense that it pertains to entities which already cooperate with one another in the economic sphere) is highly significant for the efficiency of the process of exchanging knowledge between cooperating parties (Maghssudipour et al., 2020). However, it is equally crucial that the Italian companies from the wine industry preferred to establish relationships with a view to exchanging knowledge with entities with primarily complementary systems of knowledge (similar enough to understand, but guaranteeing an essential dose of newness). In turn, Norwegian studies suggest that remaining in cognitive/​ technological proximity alone is not enough to motivate a given group of entities to cooperate (Hjertvikrem & Fikjar, 2020). The issue of the effect of cognitive/​ technological proximity on the innovative activities of companies is equally inconclusive. There exist both studies which allow us to draw a conclusion on the significant role of cognitive proximity with regard to innovative activities (Paci et al., 2014), as well as others which point to the lack of an influence of this dimension of proximity on companies’ innovative potential (Broekel & Boschma, 2012). Others, still, stress that the relationship between cognitive distance and innovative activities of the actors engaged in a given relationship take the shape of an upside-​ down U –​that is, both the lack of cognitive proximity and its extremely high level did not bring about large innovation benefits (Cohendet & Llerena,

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68  Dimensions of proximity 1997). It is worth mentioning that cooperation that exploits existing technologies was more efficient in the context of technological proximity, while cooperation aimed at pushing technological boundaries was more beneficial in the presence of technological distance (Petruzzelli, 2008). Another, albeit equally crucial, matter is the relationship between cognitive proximity with geographical proximity, which is described in the next section. As results of the analysis by Paci et al. (2004) on data from EU regions have shown, cognitive and technological proximity were the most important factors influencing the innovative activities undertaken in these regions and this influence was even more visible when the entities under consideration were in geographical proximity to one another.A similar strengthening of the role of cognitive (technological) proximity in the presence of geographical proximity appeared in reference to cooperation between Italian companies with institutions of higher education.The analyzed commercial entities were more likely to cooperate with universities which offered knowledge compatible with their own experience and the more they entered such cooperative relationships, the smaller the physical distance of their seat of residence to the university –​the effect wore off with the growing distance between both cooperating entities (Guerini et al., 2013). Partly overlapping competencies held by companies from the same area (and thus in geographical proximity) triggered a larger capability for absorbing knowledge in these entities and allowed them to more efficiently use the processes of learning (in comparison with companies with similar competencies but different locations) (Boschma 2005a). Given the level of innovation of companies, it is worth noting that in the Dutch aviation sector, relations with partners who are geographically close, albeit who remain at some technological distance, had a positive effect on innovativeness (Broekel & Boschma, 2012). However, an analysis of the US renewable energy sector has shown that though technological proximity was a factor that had a positive effect on the innovativeness of cooperating parties, their geographical distance did not modify this effect in any way. Instead, cultural distance was of much greater mediating importance (Guan & Yan, 2016). To conclude the direct analysis of the relationship between cognitive proximity and geographical proximity, it can be said that geographical proximity overlaps with cognitive proximity only when a given region is highly specialized –​that is, when the given employee competence profile or the geographical conditions of the given region play a key role in determining the possibility of undertaking activities tied to a specific production process or operations tied to offering specific services (Hansen, 2015). On the other hand, cognitive proximity may be treated as a substitute for geographical proximity: compatible knowledge bases of the cooperating parties enable efficient cooperation even when the engaged co-​partners are located far from one another (Hansen, 2015). This matter will be presented in detail below in the section devoted to geographical proximity. The considerations presented above lead to the conclusion that cognitive proximity is one of the key dimensions of the relationship of entities within their cooperative ties. At the same time, they are a guideline (for theoreticians

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Dimensions of proximity 69 and practitioners alike who deal with phenomena tied to the issue of the cooperation of economic entities) as to which aspect of proximity should be accented the most in any processes of creating corporate structures (e.g., clusters). It is necessary for entities creating such structures to be cognitively similar in order to understand one another, but also different enough in this regard to be able to learn from one another (Cunningham & Werker, 2012).

Geographical proximity Geographical proximity is the most often discussed dimension of proximity in relevant academic literature. At the same time, it is the one type of relationship (among not just economic entities but also entities of different kinds) that is not only the hardest to modify but also the most fundamental and the earliest identified. To reference historical voices, the main line of deliberation for both Marshall (1890) and Hearn (1864) focused on the co-​existence of economic entities in a given territory, as well as their actions in conditions characteristic for the given area. In the literature review by Knoben and Oerlemans (2006), this was by far the most popular dimension (80 publications). The same conclusions stem from our own work –​in all of the analyses, geographical proximity turned out to be first on the list in terms of the number of publications devoted to the topic of proximity (see Table 3.1). Also of note are the large differences between particular dimensions in terms of the number of hits: the subject of geographical proximity appeared in 496 (WoS) and 635 (Scopus) works from the fields of management and economy. Social proximity, which was placed second, appeared in 106 and 127 publications, respectively. By limiting the search to the years 2019 to 2021, 124 (WoS) and 141 (Scopus) publications on this dimension were found. The understanding of geographical proximity seems to be relatively unequivocal (Knoben & Oerlemans, 2006). The relatively most popular understanding of this term is the definition by Boschma, who states that it is the physical distance between actors, which can be understood directly (as the distance measured in specific units) or relatively (e.g., as the time required to move from point A to point B) (Boschma et al., 2014; Boschma, 2005a). Torre and Rallet’s approach could be considered as similar. For them, geographical proximity is tantamount to the distance (measured in, for example, kilometers) between units (at different levels of aggregation) in physical space. Another similar approach is that of Gilly and Torre (2000), who tied geographical proximity to the division of the relational space by distance and related it to the location of companies, so that it encompassed the social dimension of economic mechanisms termed “functional distance.” To sum up the theoretical approaches presented above, geographical proximity will be understood as the relationship between the entity, located in a specific point in physical space, and other entities –​crucial from a given point of view –​which remain within a small distance (either physical or temporal).

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70  Dimensions of proximity The above theoretical perspective stresses the proximity relationship between entities in terms of their location, which is crucial to understanding the specific nature of this phenomenon. The significance of the consequences of sharing a single area in physical space for the effects of the operations of economic entities located in a specific place has found a strong presence in the work of multiple authors analyzing the role of location in the economic and commercial sphere. According to Porter (1990, 2008), highly localized processes create and consolidate competitive advantage of both the region itself and specific entities functioning therein. This thought formed the basis for the emergence of numerous territorially oriented theories of innovation (such as the above-​ mentioned “innovation ecosystem” or “regional innovation system”). The significance of geographical proximity in the creation of competitive position was also noted by Jaffe et al. (1993) as well as Audretsch and Feldman (2004). In each of these cases, geographical proximity was indicated as the source of a privileged position for local companies in their access to, creation of, or dissemination of knowledge. Boschma (2005a) tackled this issue in a similar fashion by pointing out that companies located in the proximity of sources of knowledge reap more benefits –​and the larger the number of such knowledge sources in a given area, the larger the potential benefits for local entities. What is more, local companies with more or less similar competencies from a specific area of knowledge would be more capable of absorbing knowledge and learning than companies from outside the area. The smaller the distance between partners, the smaller the costs of exchanging knowledge and information and the more efficient the communication between particular entities (Doloreux, 2002). Furthermore, the process of strengthening trust between partners within a given cooperative tie requires frequent interactions, which is much easier to accomplish when the parties are within a close physical (geographical) distance. Such interactions within a single area are further strengthened when they are anchored in a single system of values and sociocultural norms known to all members (Simmie, 2003). The consequences of occupying a single area by cooperating economic entities are a matter of continued interest to theoreticians and practitioners because it is impossible to ignore the effects of holding operations in specific geographical conditions. From the perspective of this work, it is most essential to focus on the consequences of a shared location (remaining in geographical proximity), a condition that forms the basis for the creation and development of cooperative ties between entities occupying a single area. However, it is also worth noting that the fact that a group of entities occupies a certain space may not always mean that such a situation will only facilitate the processes of establishing and developing cooperation. Analysis of the latest scholarship devoted to this issue shows that, while in most cases in modern studies and analyses, the coexistence of entities in a single area was at least one of the factors which had a positive effect on the undertaken cooperative activities, situations in which geographical proximity fulfilled the role of a sufficient condition for the emergence of an efficient cooperative

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Dimensions of proximity 71 tie. What is more, some of the conclusions pointed to the fact that the small physical distance between economic entities may have no effect whatsoever on their cooperative actions or even inhibit their development. The current analysis has shown that, in the vast majority of the most recent studies, the small physical distance between economic entities (which derive from different sectors of the economy) had a positive influence on their acts of cooperation (oriented to attaining diverse goals) and their effects. Sometimes it was enough for these entities to be within close geographical distance to see positive effects (e.g., Inoue et al., 2017; Marek et al., 2017; Ciobanu, 2016; Benos et al., 2015; Boschma et al., 2015; Wu et al., 2015). However, in most cases in order for geographical proximity to exert a positive effect on the cooperation of the analyzed entities, it was crucial for factors other than geographical proximity to exist (e.g., other dimensions of proximity) (e.g., Geerts et al., 2018; Davids & Frenken, 2018; Ahmad & Hall, 2017; Boschma et al., 2017; Crescenzi et al., 2017; Drejer & Østergaard, 2017; Korbi & Chouki, 2017; Mascia et al., 2017; Aldieri & Vinci, 2016; Kuttim, 2016; Lavigne & Nicet-​Chenaf, 2016; Bahlmann, 2015; Ellwanger & Boschma, 2015; Godart, 2015; Lander, 2015; Levy & Talbot, 2015; Parrino, 2015). Several publications (Ayoubi et al., 2017; Scherrer & Deflorin, 2017; Fontes & Sousa, 2016; Guan & Yan, 2016) underlined the neutral role of geographical proximity for cooperative activities and only one publication reported a clearly negative relationship (Fitjar et al., 2016). In conclusion, it can be said with caution that when economic entities occupy the same space, the effect is more often positive than negative or neutral with respect to their cooperative ties. Representatives of the school of thought underlining the significance of geographical proximity for the functioning of companies (including the establishment of cooperative ties) point to the fact that despite the omnipresent processes of globalization permeating all aspects of life, most contacts still have the form of direct interactions between entities remaining in geographical proximity (Boschma & Wal, 2007; Suire & Vicente, 2009; Hoekman et al., 2010; Boschma et al., 2014). Of significance is the fact that although new communication technologies enable and facilitate the creation and development of remote contacts, they did not succeed in eliminating (at least thus far) the need to strengthen and consolidate relationships by way of personal meetings –​that is, the need for two or more entities to remain, at least for some time, in geographical proximity (Rallet & Torre, 1999). It would even seem that in some cases, the relationship between geographical proximity and something that can be termed virtual proximity is one of the elements which fuel the development of relationships between entities. A study of the Canadian wine sector has shown that there exist companies which much more gladly provide their digital recommendations to other companies in their vicinity (their potential direct competitors) in comparison with companies located far away (Zhu et al., 2020). The significance of geographical proximity for interorganizational cooperation was also confirmed in a study on the European nanotechnology sector –​it turned

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72  Dimensions of proximity out that, statistically speaking, this is the dimension of proximity that is key for the cooperation of European companies from this sector (Cunningham & Werker, 2012). Similar conclusions may be drawn from analyses of groups of cooperating companies (in the form of strategic alliances), within which at least one entity was located in Italy –​though all of the analyzed dimensions of proximity were essential for the interorganizational exchange of knowledge, two of them determined this process with more strength: technological proximity (as an aspect of cognitive proximity) and geographical proximity (Usai et al., 2017). Remaining within close physical distance only brought about positive effects for the spillover of knowledge and the creation of new patents in Japan (Inoue et al., 2017), the USA, and Europe (Aldieri & Vinci, 2016), or cooperation between institutions of higher education and local companies in Estonia (Kuttim, 2016) and China (Lin et al., 2015). Studies on German cooperative networks in the biotechnology sector also pointed to the fact that the lack of local proximity inhibited interregional cooperation (between nodes of the network located in different regions) (Mitze & Strotebeck, 2019). On the other hand, research on companies from the high-​tech sector from the Tiburtina valley in Italy indicated that geographical proximity between companies and their key clients inhibited the results of innovative activities (Presutti et al., 2019). What is even more interesting, this negative influence manifested itself particularly in the case of entities with the largest absorptive potential. Different conclusions may be drawn from research on the Tuscan Life Sciences cluster –​the number of local relationships (between cluster members) turned out to be tied curvilinearly with the efficiency of R&D activities, albeit this interdependence took the shape of an upside-​down U (Pucci et al., 2020).This means that remaining in geographical proximity is beneficial for innovative activities only in specific conditions –​conditions in which the number of close local relations does not dominate over the entire cluster network. On the other hand, scholarship is not lacking in such approaches to geographical proximity, in which this dimension is brushed aside or considered almost fully insignificant in the process of establishing and developing cooperative ties across organizations (particularly in the context of the dynamic development of ICT or the execution of the concept of Industry 4.0, which marginalizes geographical distance in industrial activities). In the context of the complexity of agreements between partners in alliances and clusters in the field of biotechnology, the hypothesis, according to which geographical proximity would influence the character and complexity of the content of the agreement on cooperation, turned out to be false –​the specific nature of cooperative activities (in the formal aspect) was not tied to proximity in the aspect of location (Kim & Globerman, 2020). A study on the food processing sector in Thailand has shown that geographical proximity between companies and a local source of knowledge (universities/​research institutions) had no effect on the introduction of innovative solutions in the scope of the product offer (Tippakoon, 2020). The reason for this state

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Dimensions of proximity 73 of affairs is first and foremost the nature of the companies comprising the food processing sector in Thailand –​these are mostly small companies with limited absorptive capabilities, for which the absorption of advanced knowledge from scientific institutions is very hard or even impossible. Another quite apparent phenomenon is the end of the dominant role of geographical proximity over the remaining dimensions of proximity in processes of shaping a cooperative network –​especially since, despite the fact that in the first stages of the development of the lifecycle of the cluster, geographical proximity is an essential determinant of development; in the case of mature structures, it can have an inhibitory effect and even lead to the dissolution of the cluster itself. The effect of geographical proximity seems to point to the tendency of weakening with the degree of control over the extra-​spatial aspects of proximity (Balland et al., 2015). This tendency becomes all the more apparent in connection with the effects of cooperation between entities –​that is, the creation and implementation of innovations, joint academic publications, financial results (the larger the geographical distance between cooperating parties, the more they mentioned the existence of the above-​mentioned effects of cooperation) (Heringa et al., 2014). In turn, in the context of the relationships between geographical proximity and organizational proximity, Rallet and Torre (1999) came to the conclusion that organizations (including companies) are entities which are in essence non-​geographical –​despite being situated in a specific physical space, they are not defined by territory nor limited by it. However, this viewpoint seems rather too detached from reality: international corporations (which were used by the authors as example organizations independent from their territories), though they retain their organizational unity in various areas, they nonetheless remain objects influenced by their location because of its attribute of a determining geographical environment. This means that each organization, comprising elements located in different points of physical space, is influenced by each of these areas in the scope of, for example, legal/​administrative regulations characteristic of a given territory, taxation, the specific nature of the local labor market, up to attributes so elusive as the mentality of the local community in which part of the organization operates. Another difficulty in the analysis of the role of geographical proximity in the operations of economic entities located in a given area is faced both by researchers of the discussed phenomena as well as by practitioners operating in the economic sector –​namely, the isolation of geographical proximity from among the remaining dimensions of proximity used in the analysis. While geographical isolation may stimulate the remaining dimensions of proximity, it is often substituted by them itself (Boschma, 2005a). This considerably enriches (but also complicates) the picture of the relationships between different proximities. One should bear in mind that the most important feature of geographical proximity rests in the fact that it is a means to an end –​if not a means to achieving all other proximities (Cooke, 2006). In other words, the fact that entities are in close geographical proximity is an important

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74  Dimensions of proximity supplement to the process of constructing and strengthening social, organizational, cognitive, and institutional proximities and their mutual relationships (Boschma, 2005a). Though the existence of geographical proximity is not a necessary condition for the emergence of cooperative ties between economic entities, it may function as a supporting factor for the creation of such relationships in the remaining aspects of proximity –​the effect of physical “vicinity” will always have the practical effect of influencing the emergence of a characteristic “overlay” between the spatial dimension of proximity and other dimensions thereof (Malmberg & Maskell, 2006). This characteristic relationship between spatial proximity and non-​spatial dimensions thereof is a very crucial phenomenon from the perspective of considerations and analysis of the role of geographical proximity in the functioning of enterprises. Here, two main mechanisms can be identified that govern the relationships between the physical dimension of proximity and the remaining dimensions (Hansen, 2015): the “substitution” mechanism (when the non-​spatial forms of proximity can substitute for spatial proximity without sacrificing the quality of the existing cooperative tie or without lowering the chances of developing the ties that are still in the process of being created) and an “overlap” mechanism (when geographical proximity facilitates the emergence and development of non-​spatial forms of proximity). Each of the previously discussed types of proximity may be analyzed in the context of its ties with geographical proximity through the lens of both of these mechanisms. Geographical proximity and social proximity Hansen’s (2015) studies on a sample of Danish companies from the clean-​ tech sector led to the conclusion that common location translated (in this case) to a higher chance of the emergence of cooperative ties based in part on the relationship of trust. This was facilitated by the ease of initiating acts of exchange between entities, low costs of communication, and the considerably reduced anonymity between co-​partners. In effect, geographical and social proximities may both be in a relationship of substitution (social proximity may substitute its geographical counterpart) and overlap. One element that points to the substitution of geographical proximity by social proximity arises from the results of a study by Guerini et al. (2013), who, following an analysis of cases of cooperation between high-​tech companies and universities in Italy, noticed that the chances of establishing such cooperation fell with the growing geographical distance between potential co-​partners.When established between potential partners in advance, social proximity can serve the function of a neutralizer of the negative influence of physical distance. Geographical proximity and organizational proximity The relationship between these two types of proximity cannot be included in the “overlap” category, as geographical proximity is not a catalyst for the processes of the constitution and strengthening of organizational

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Dimensions of proximity 75 proximity –​both in its inter-​and extra-​organizational aspects. Furthermore, when organizational proximity reaches a high level (and that is the pursuit of each organization) –​that is, when the organization has a detailed and precise division of tasks, coordinated by a strong, central unit, and the partners have common experiences (an element of cognitive proximity) –​the need for spatial proximity will diminish (Rallet & Torre, 1999). The exchange of tacit knowledge –​necessary in the case of all cooperative ties –​can then be possible thanks to the temporary geographical proximity, or during periodic direct meetings between individuals engaged in executing specific tasks. References to the meaning of the term “temporary geographical proximity” can also be found in many other articles (e.g., Boschma et al., 2014; Knoben & Oerlemans, 2006). Geographical proximity and institutional proximity The matter of the overlap of both types of proximity was not particularly hard to grasp –​physical proximity ensured by the functioning of entities in the same area had a “commonalizing” function on the nature of the institutions operating in that area, including the creation of an axiomatic order common for all of the entities therein. If not from anywhere else, such a conclusion may be drawn from research on the relationships of high-​ tech clusters with organizations representing institutions of higher education, industry, and the government –​geographical proximity could support the establishment of cooperative ties between these entities despite obvious institutional differences (Ponds et al., 2007). However, the issue of the substitution of geographical proximity by institutional proximity should be seen as leading to rather ambivalent conclusions. On the one hand, it is said that institutional proximity can have an effect remotely –​that is, the physical proximity between entities may be substituted by broadly understood culture (Bradshaw, 2001; Saxenian & Hsu, 2001) (e.g., of a given organization). On the other hand, scholars point to the fact that the lack of institutional proximity is the largest drawback of cooperative ties over a long distance (Gertler, 2003). Hansen (2015) solves this dilemma by pointing out that geographical proximity is necessary to maintain the correct level of institutional proximity over time. For this reason, though one could imagine a situation in which institutional proximity substitutes the physical co-​location of two or more entities, it would have to be limited in time because of the above-​ mentioned “dependence” of institutional proximity on proximity in physical space. In other words, the effect of such a substitution would be decidedly weaker than in the case of the substitution of geographical proximity with social proximity described earlier. Geographical proximity and cognitive proximity This relationship –​similarly to the relationship between geographical proximity and organizational proximity described above –​is characterized by the substitution of geographical proximity by cognitive proximity. This is

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76  Dimensions of proximity mentioned by, for instance, Singh (2005), who points out that geographical proximity is particularly significant in a situation where the establishment of interdisciplinary academic cooperation is accompanied by a low level of cognitive proximity between the potential co-​partners. With a high level of cognitive proximity, the significance of the co-​occurrence of geographical proximity is decidedly smaller. When it comes to the “overlap” relationship between both types of proximity, it should be said that such a relationship does not occur, to all intents and purposes. According to Hansen (2015), there exists a rare case in which it would be correct to speak of the overlap of geographical and cognitive proximity: the example of highly specialized regions. In such areas, strong specialization (the cognitive proximity of entities tied to, for example, the production of specific goods) is partly the result of the specific nature of the given location, and partly itself strengthens its geographical uniqueness in return. Another interesting exception from this principle is one of the conclusions from the above-​mentioned study on the cooperation between Italian high-​tech companies and institutions of higher education (Guerini et al., 2013) –​while these companies gladly cooperated with “cognitively close” universities, the effect was most visible when it was accompanied by geographical proximity of the cooperating parties.With the growing distance between companies and institutions of higher education, the significance of cognitive proximity as a factor determining the constitution of a given cooperative tie became smaller. However, research on the Norwegian marine economy sector has shown that geographical proximity along with technological proximity (a subtype of cognitive proximity) are insufficient for the entities with such connections to establish cooperative ties (Hjertvikrem & Fitjar, 2020). One interesting supplement to these remarks is the matter of establishing the most beneficial level of spatial proximity by cooperating entities. An insufficient level of geographical proximity would mean the lack of a certain group of common conditions, which are decisive with respect to the nature of the undertaken activities. A territory is, one the one hand, a neutral space and, on the other, something which can be termed the “determining geographical environment” (Tremblay et al., 2003). In other words, it is a specific area with resources of a certain quality and quantity. These resources may take the form of both material factors (in the form of, for example, natural resources) or –​or perhaps even first and foremost –​human capital (in the form of, for example, the labor market or networks of relationships among individuals). As studies on Montreal companies from the bio-​ pharmaceutical, telecommunications, and aeronautic sectors have shown, the key factors which were taken into account by entities from these industries in their decision-​making processes on going in a pro-​innovative direction of development were (i) access to a network of information and personal interactions and (ii) the quality of the local resources tied to the broadly understood human capital (Tremblay et al., 2003). In other words, the lack of a common foundation resulting from the nature of the company’s location may have an inhibiting effect on the development of the emerging or

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Dimensions of proximity 77 existing cooperative ties. However, excessive spatial proximity may also have a negative influence on the operations of cooperating parties. As Malmberg and Maskell (1997) point out, a dense local community may, apart from strengthening innovative activities and industrial dynamics, create a situation of “closure.” This happens when the local structures are so focused on a specific type of operation that they become unable to shift to a different developmental path. Boschma (2005a) approached this issue in a similar way by pointing out that the risk of closure is largest in highly specialized regions. The co-​occurrence of geographical proximity and technological proximity (a subtype of cognitive proximity) as a crucial element of knowledge transfer and the generation of innovations was also mentioned in studies on the Chinese steel industry, pertaining to the patent activity of its entities (Zhang et al., 2020). The density of the network (the quantity and proximity of the locations of its entities) to some degree moderates excessive technological proximity (in the context of generating innovations).What is even more crucial, the co-​occurrence of both these proximities is much more important for cooperation with respect to innovation than the existence of one of them. In order to ensure that entities have the optimal level of geographical proximity in the region, it is necessary to undertake actions on two levels –​diversify the local economy and work toward companies from the area establishing supralocal relationships. Diversification of the local economy makes the entities embedded therein more resilient to turbulence in specific sectors and the establishment of cooperative ties of supralocal nature positively stimulates relationships across the region thanks to “refreshing” them with new impulses and ideas (Bathelt, 2005). Regions that may be considered to be successful in terms of their economic activity have a larger number of supraregional relationships than those which are still aspiring to reach this level of success (Boschma, 2005a). It would be best for companies (regardless of their field) if they owned a certain pool of relationships with entities remaining “at arm’s length” (i.e., at some distance). This would allow them to remain vigilant and open to changing market conditions and the networks of close ties rooted in locality. This would translate to lowering transaction costs or improving the processes of learning across organizations (Uzzi, 1997). Despite arguments on the side of the representatives of the school of thought marginalizing the place and role of geographical proximity in the process of establishing and strengthening cooperative ties, it is this precise dimension of proximity which should be deemed essential for the mechanisms of stimulating and coordinating cooperation both among companies and among entities representing every other type of organization.The most crucial argument is as follows: people and their aggregates (e.g., companies) are units which exist in physical space –​that is, take up a specific spot in geographical space. As a consequence, assuming different levels of potential among different areas in space, individual and collective entities operate in different conditions. This means that different types of operations require locations with features that often differ considerably from one another,

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78  Dimensions of proximity which in turn determines the necessity of the rational and conscious choice of the site of operations.

Proximity in theories on industrial clusters Of course, the concepts of proximity described here are not ideas that exist in a void. Not only do they correspond to different concepts of clusters, they also seem to explain well the thoughts behind the visions of cooperation between economic entities located in a given shared area. In reference to Marshall’s concept –​or to Italian industrial districts in particular, which are its extension –​the relationship of these areas with practically all of the dimensions of proximity discussed in this work is clearly visible. Each of these dimensions is to some degree present in particular elements of each industrial district. These elements include: location, economic entities, the labor market, knowledge, and community of norms and principles. The first of these components –​location –​directly evokes the use of geographical proximity. This will be a distinguishing feature both of the individual level (personal contact between single individuals), as well as collective, in reference to the coexistence of social constructs comprising individuals (e.g., companies). The “economic entities” component has the potential to be approached in the context of social, organizational, and cognitive proximities. The relationship between economic entities and social proximity is rooted in the creation and establishment of social relationships between participants in the socioeconomic life of the district. It is very hard to separate these two layers of the functioning of individuals operating in the district –​these are in essence two rails of the same railway track. Economic entities in the aspect of organizational proximity manifest themselves in the sharing of specific production processes, while in the aspect of cognitive proximity it is in the similarity or complementarity of competencies and technologies used in the district. The labor market aspect remains under the influence of social, institutional, and cognitive proximities. With regard to the social aspect, the “labor market” translates to residing and operating on a given terrain of a group of individuals with specific competencies, some of whom entered into personal relationships with one another, regardless of whether in the role of an employer or an employee. Residing in the same place, being raised in the spirit of norms and principles that are important to the community, is conducive to the emergence of institutional proximity, while sharing a strictly defined scope of competencies that members of the labor market have in the district may be treated as an expression of cognitive proximity. The “knowledge” component is a field affected by both cognitive and social proximities –​it is worth bearing in mind that the knowledge characteristic for a given district is carried by individuals and transferred during their interactions. On the one hand, therefore, there must exist a certain cognitive community, in which particular ideas are understood and interpreted in the same way, as well as a certain affinity between the actors in the labor market, who in

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Dimensions of proximity 79 such conditions are more eager to distribute tacit knowledge. The last of the mentioned components –​community of norms and principles –​may be treated as a conclusion of the influence of all of the remaining components. At the same time, its existence is a necessary condition for this influence to appear to begin with. In other words, a certain entry level of the community of norms and principles in the district is necessary as a basis for the establishment of the remaining components, which will in turn strengthen the feeling of cooperation within a specific axionormative order. Relationships with different dimensions of proximity can also be easily observed through the lens of groups of theories of regional development, which underline the significance of knowledge and innovation. The concept of a “learning region” directly corresponds to the cognitive dimension of proximity by highlighting the matter of sharing a scope of knowledge and the skills of entities operating in a single area. This “uniformity” of the area is a nod to the significance of geographical proximity, while the inevitably emerging cooperative relationships between entities at different levels of aggregation can be treated as a reflection of these entities remaining in social proximity (relational aspect) and sometimes organizational proximity (practical aspect). The concept of an “innovative milieu” also pays respect to the presence of proximities: social (by making one of the components of this concept a network of informal social contacts), geographical, institutional (common cultural background), cognitive, and organizational (joint execution of processes of innovation and creation, as well as the distribution of knowledge among engaged partners). Systemic theories of innovation (“national innovation system,” “regional innovation system,” “innovation ecosystem”) underline the significance of geographical, institutional, social, organizational, and cognitive proximities. These types of proximity mostly manifest themselves through the creation of a certain system of relationships among players in a given territory (social proximity and organizational proximity –​the latter often created on the basis of the former), who take part in a game with the same rules for everyone (institutional proximity). During the game, the players create (and make use of) specific knowledge resources (cognitive proximity). All this happens, of course, in a single common area (geographical proximity), though its scope differs depending on the concept (country vs. region). In turn, the “innovation ecosystem” is a concept that is not as strong in terms of accenting the role of geographical and institutional proximities. Nevertheless, it underlines the significance of organizational and social proximities. In the context of the innovation ecosystem, organizational proximity is present in the concept of the division of labor based on specialization, while for social proximity it is in the quite obvious form of relationships between particular elements of the system. Coming back to the concept of the cluster, it is easy to notice the presence of each of the dimensions of proximity in what Porter considers to be a cluster. The idea that a cluster is a geographical gathering of entities of different kinds implies that these entities remain in geographical (locational)

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80  Dimensions of proximity proximity. Participation in a structure of a higher order translates to being subjugated to at least a partial organizational commonality, which means that it creates conditions for the emergence and development of organizational proximity. Furthermore, operating within a single, relatively homogeneous cultural space is tantamount to operating within a single legal system and –​ most probably –​even a single axionormative order, which in turn can be considered as a context that enables the emergence of institutional proximity. The cluster members cooperate, but also compete with one another. They often participate in joint initiatives initiated by the cluster as a whole or by one of its components (the cluster coordinator or one of the cluster members). They are able to enter into social relationships and intensify them until it becomes possible to establish trust between the members –​this is an obvious manifestation of social proximity. In turn, such cooperation leads to the identification of cognitive proximity, which itself is certainly a necessary condition for effective cooperation. On the one hand, therefore, some of the competencies of cooperating parties will become similar to one another and, on the other, others will still be distinct, albeit complementary to those already held. In this context, the flagship synergy effect of cluster cooperation will be a proof for the existence of each of the dimensions of proximity in a specific cluster organization. In order to find publications which would –​on the one hand –​refer to proximity and its particular dimensions and –​on the other –​also include issues tied to the concept of a cluster or a cluster organization in the course of a systematic literature review, we conducted an additional analysis, based on the previously prepared (using the WoS and Scopus databases) sets of academic publications picked on the basis of keywords tied to proximity and its five dimensions. To this end, reference was also made to keywords tied to clusters, namely: “cluster,” “cluster initiative,” and “cluster organization” (each of these words was used independently on the same sets of texts) (see Table 3.3). By limiting the sets to academic papers published in fields which are strictly tied to management and economy, a total of 316 publications were located in the WoS database that pertained both to proximity and the concept of a cluster. The Scopus database featured almost four times as many publications of this sort –​1,292 (the initial number of publications was larger as well). Because of the preponderance of works devoted to geographical proximity, the largest number of works on the concept of a cluster were identified with respect to this specific dimension (54 in WoS and 230 in Scopus). In turn, when it comes to the three least numerous sets selected for cognitive, organizational, and institutional proximities, it was noted the least number of publications which would refer to a “cluster” (11, 5, and 7 in WoS; 65, 46, and 33 in Scopus). Completely different results were obtained with the use of the other two keywords: “cluster initiative” and “cluster organization.” Only four publications pertaining to CIs and three pertaining to COs were found in the WoS database. In turn, there were 19 and 9 such publications, respectively, in the Scopus database. However, upon

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Table 3.3 The results of the review of scientific publications with the keywords “proximity” and “cluster” Search criteria

Web of Science Category: Business; Management; Economics “Cluster” “Cluster initiative” “Cluster organization” “Grounded theory”

“Proximity”

“Geographical “Social proximity” proximity”

“Cognitive proximity”

“Organizational proximity”

“Institutional proximity”

4,792 316 4 3 15

496 54 1 1 1

106 12 0 0 2

79 11 0 0 3

34 5 0 0 1

52 7 0 0 1

6,364

635

127

89

63

52

1,292 19 9 123

230 8 4 9

51 1 1 4

65 1 2 8

46 1 1 5

33 1 0 2

Source: Authors’ own study based on the WoS and the Scopus databases (the figures quoted are as at June 17, 2022)

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Scopus Subject area: Business, management, and accounting; Economics, econometrics, and finance “Cluster” “Cluster initiative” “Cluster organization” “Grounded theory”

Keyword

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82  Dimensions of proximity reading these publications, it was found that most of them were thematically very distant from the explored research field. Furthermore, some of them –​ despite the defined research criteria –​pertained to a cluster.Two publications turned out to be closest in terms of the issue discussed –​both on agricultural cluster in Chile (Geldes et al., 2015, 2017). These publications discuss different dimensions of proximity through the lens of their influence on the development of business cooperation between cluster members. The results presented in the first publication (Geldes et al., 2015) show that cooperation in the analyzed cluster is primarily dependent on social proximity. In turn, the second publication (Geldes et al., 2017) underlines the role of cognitive and organizational proximities in the context of the development of innovations. In the course of the literature review, the initial sets of publications (isolated with the use of the keyword “proximity”) were further limited, this time with the use of a methodological approach. In this case, the focus was on specific publications that referred to the methodology of grounded theory (used in this publication). It turned out that in the set derived from the WoS database, only 15 publications refer to grounded theory (however, none pertain to the concept of a cluster, nor to CI or CO). A much larger number of publications on the combined issues “proximity” and “grounded theory” were found in the Scopus database –​as many as 123. The scarcity of publications combining all three of the analyzed threads (proximity, cluster organization/​cluster initiative, grounded theory) creates ample space for completely pioneering research. On the other hand, it does not allow for the confrontation of the obtained results (and generated concepts) with relevant literature and other approaches that would be at least partly similar.

Conclusion The concept of proximity is a scientifically attractive, albeit little explored, area in management sciences, including in the scope discussed here. The literature review shows that scholars dealing with the issue of proximity mostly referred to its division into five principal dimensions: cognitive, social, organizational, institutional, and geographical. By referring to the classic division of the dimensions of proximity introduced by Boschma, they did not hesitate to underline the significance of just some of its aspects and to consider the others to be of secondary or background importance or omit them altogether. A characteristic feature of each of the proposed classifications of proximity is a certain blurring of the definitional boundaries between each of the terms and the dynamic nature of each of the analyzed dimensions. While one can deal with blurred definitional boundaries by constantly clarifying existing understandings of specific terms used in analyses, respecting assumptions on the dynamic nature of proximity requires more effort: first, acknowledging the necessity of abandoning static explanations of proximity, which weaken its explanatory potential, and, second, implementing in one’s

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Dimensions of proximity 83 research practice an approach that would actually acknowledge the dynamics of each of the constructed dimensions of proximity. Sticking with a static approach would not let theoreticians and practitioners alike get any closer to an answer to the crucial question of whether or not entities establish cooperation with partners that are already “close” to them in one way or another or whether it is the cooperative ties which shape the feeling of “proximity” in its various dimensions (Balland et al., 2015). The static perspective directly states that proximity has primacy over relationships, while the dynamic perspective should ask this question in a somewhat different form –​namely, under which conditions does “proximity” become a source of relationships and under which do relationships themselves create “proximity”? An interesting answer to this issue may be found in a publication by Padgett and Powell (2012, p. 26): “In the short run, actors create relations; in the long run, relations create actors.” On a more detailed level –​that is, one that refers to proximity in its specific aspects –​it can be said that each of the dimensions differs from the others not just with respect to the nature of the relationship it describes but also a certain propensity to change. This means that some of the dimensions of proximity used in the literature are considerably more flexible (they undergo changes easier and more often), while others react to modifications after some time and to a smaller degree. The literature review suggests that the dimension of proximity that is relatively the most open to changes is social proximity (Dosi & Nelson, 1994), while the one that is relatively the most resistant is geographical proximity (Stam, 2007). Furthermore, there is no dimension of proximity that would function in full isolation from the remaining dimensions. This means that the dynamics of a given aspect of proximity are influenced not only by what is happening within that aspect but also everything else that is happening within all of the remaining aspects. This is because dimensions of proximity form a certain structured system, in which each of the elements is tied to the others and changes to one part of the system influence other parts to varying degrees. Generally speaking, the larger the number of dimensions in which entities gain the feeling of proximity, the larger the chance of entering into a relationship of knowledge exchange (Usai et al., 2017), while keeping a certain distance in some aspect of proximity (because of the higher costs of coordination and transaction costs) will be conducive to attaining more specific goals (e.g., in the form of patents) (Heringa et al., 2014). The above-​ described characteristic has its exceptions (e.g., the reluctance of competitors in geographical proximity to engage in processes of exchanging knowledge), which was discussed in parts of the chapter devoted to particular dimensions of proximity. Knowledge of proximity and its dimensions, along with the ability to use it in practice, seems to be, therefore, of extreme significance for the management of both single companies, as well as their groups, operating within higher-​order organizations, of which one group are cluster initiatives. The presented overview of the theoretical concepts of the issue of cooperation between economic entities anchored in a given location lets us formulate a

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84  Dimensions of proximity conclusion with respect to their trouble-​free translation to a language and optics of the use of proximity and its different aspects. It turned out that regardless of whether we analyzed the earliest theories of location, the later visions of industrial districts, or the more modern theories based on knowledge and innovations (including the concept of a cluster), each of these drew upon elements directly tied to the concept of proximity in order to define itself and describe its reality.This is all the more crucial when we consider that without the concept of “proximity,” many elements comprising the definition of subsequent forms of the coexistence of economic entities operating in a given area would remain outside the scope of the influence exerted by the individual managing them. Proximity ensures a certain specification of the features, processes, and mechanisms at the foundation of economic operations, thus facilitating their understanding and raising the possibility of their efficient management.

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Dimensions of proximity 87 Guerini, M., Bonaccorsi, A., Colombo, M. G., & Rossi-​Lamastra, C. (2013). The role of geographical, social and cognitive proximity in collaborations between high-​tech entrepreneurial ventures and universities. 35th DRUID Celebration Conference, June 17–​19, Barcelona, Spain. Hansen, T. (2015). Substitution or overlap? The relations between geographical and non-​spatial proximity dimensions in collaborative innovation projects. Regional Studies, 49(10), 1672–​1684. Hearn, W. E. (1864). Plutology: Or the theory of the efforts to satisfy human wants. London: Macmillan/​Melbourne: George Robertson. Herbane, B. (2019). Rethinking organizational resilience and strategic renewal in SMEs. Entrepreneurship & Regional Development, 31(5–​6), 476–​495. Heringa, P. W., Horlings, E., van der Zouwen, M., van den Besselaar, P., & van Vierssen, W. (2014). How do dimensions of proximity relate to the outcomes of collaboration? A survey of knowledge-​intensive networks in the Dutch water sector. Economics of Innovation and New Technology, 23(7), 689–​716. Hjertvikrem, N., & Fitjar, R. D. (2020). One or all channels for knowledge exchange in clusters? Collaboration, monitoring and recruitment networks in the subsea industry in Rogaland, Norway. Industry and Innovation, 28(2), 182–​200. Hoekman, J., Frenken, K., & Tijssen R. J. (2010). Research collaboration at a distance: Changing spatial patterns of scientific collaboration within Europe. Research Policy, 39(5), 662–​673. Inoue, H., Nakajima, K., & Saito, Y. U. (2017). Localization of knowledge-​creating establishments. Japan and the World Economy, 43, 23–​29. Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 577–​598. Kim, J., & Globerman, S. (2020). Physical distance vs. clustering as influences on contracting complexity for biopharmaceutical alliances. Industry and Innovation, 27(8), 892–​919. Kirat, T., & Lung,Y. (1999). Innovation and proximity: Territories as loci of collective learning processes. European Urban and Regional Studies, 6(1), 27–​38. Klimas, P. (2011). Wymiary bliskości w sieciach innowacji [Dimensions of proximity in innovation networks]. Przegląd Organizacji, 4, 16–​20. Knack, S., & Keefer, P. (1997). Does social capital have an economic payoff? A cross-​ country investigation. The Quarterly Journal of Economics, 112(4), 1251–​1288. Knoben, J., & Oerlemans, L. A. (2006). Proximity and inter-​organizational collaboration: A literature review. International Journal of Management Reviews, 8(2), 71–​89. Korbi, F. B., & Chouki, M. (2017). Knowledge transfer in international asymmetric alliances: The key role of translation, artifacts, and proximity. Journal of Knowledge Management, 21(5), 1272–​1291. Kuttim, M. (2016). The role of spatial and non-​spatial forms of proximity in knowledge transfer: The case of a technical university. European Journal of Innovation Management, 19(4), 468–​491. Lander, B. (2015). Proximity at a distance: The role of institutional and geographical proximities in Vancouver’s infection and immunity research collaborations. Industry and Innovation, 22(7), 575–​596. Lavigne, S., & Nicet-​Chenaf, D. (2016). Out of sight, out of mind: When proximities matter for mutual fund flows. Economic Geography, 92(3), 322–​344. Levitt, B., & March, J. G. (1988). Organizational learning. Annual Review of Sociology, 14(1), 319–​338.

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88  Dimensions of proximity Levy, R., & Talbot, D. (2015). Control by proximity: Evidence from the ‘Aerospace Valley’ competitiveness cluster. Regional Studies, 49(6), 955–​972. Lin, T. C., Kung, S. F., & Wang, H. C. (2015). Effects of firm size and geographical proximity on different models of interaction between university and firm: A case study. Asia Pacific Management Review, 20(2), 90–​99. Maghssudipour, A., Lazzeretti, L., & Capone, F. (2020). The role of multiple ties in knowledge networks: Complementarity in the Montefalco wine cluster. Industrial Marketing Management, 90, 667–​678. Malmberg, A., & Maskell, P. (1997).Towards an explanation of regional specialization and industry agglomeration. European Planning Studies, 5(1), 25–​41. Malmberg, A., & Maskell, P. (2006). Localized learning revisited. Growth and Change, 37(1), 1–​18. Marek, P., Titze, M., Fuhrmeister, C., & Blum, U. (2017). R&D collaborations and the role of proximity. Regional Studies, 51(12), 1761–​1773. Marshall, A. (1890). Principles of economics. London: Macmillan. Mascia, D., Pallotti, F., & Angeli, F. (2017). Don’t stand so close to me: Competitive pressures, proximity and inter-​organizational collaboration. Regional Studies, 51(9), 1348–​1361. Metcalfe, S. (1995). The economic foundations of technology policy: Equilibrium and evolutionary perspectives. In P. Stoneman (Ed.), Handbook of the economics of innovation and technological change (pp. 409–​512). Oxford: Blackwell. Mitze, T., & Strotebeck, F. (2019). Determining factors of interregional research collaboration in Germany’s biotech network: Capacity, proximity, policy? Technovation, 80, 40–​53. Monge, P. R., Rothman, L. W., Eisenberg, E. M., Miller, K. I., & Kirste K. K. (1985). The dynamics of organizational proximity. Management Science, 31(9), 1129–​1141. Nooteboom, B. (2000). Learning and innovation in organizations and economies. Oxford: Oxford University Press. North, D. C. (1990). Institutions, institutional change, and economic performance. Cambridge and New York: Cambridge University Press. Oerlemans, L., & Meeus, M. (2005). Do organizational and spatial proximity impact on firm performance? Regional Studies, 39(1), 89–​104. Paci, R., Marrocu, E., & Usai, S. (2014). The complementary effects of proximity dimensions on knowledge spillovers. Spatial Economic Analysis, 9(1), 9–​30. Padgett, J. F., & Powell, W. W. (2012). The problem of emergence. In J. F. Padgett & W. W. Powell (Eds.), The emergence of organizations and markets (pp. 1–​30). Princeton, NJ: Princeton University Press. Parrino, L. (2015). Coworking: Assessing the role of proximity in knowledge exchange. Knowledge Management Research & Practice, 13(3), 261–​271. Petruzzelli, A. M. (2008). Proximity and knowledge gatekeepers: The case of the Polytechnic University of Turin. Journal of Knowledge Management, 12(5), 34–​51. Petruzzelli, A. M., Albino, V., & Carbonara, N. (2009). External knowledge sources and proximity. Journal of Knowledge Management, 13(5), 301–​318. Ponds, R., Van Oort, F., & Frenken, K. (2007). The geographical and institutional proximity of research collaboration. Papers in Regional Science, 86(3), 423–​443. Porter, M. E. (1990). The competitive advantage of nations. New York: Free Press. Porter, M. E. (2008). On competition. Boston: Harvard Business School Publishing. Presutti, M., Boari, C., Majocchi, A., & Molina-​Morales, X. (2019). Distance to customers, absorptive capacity, and innovation in high-​tech firms: The dark face of geographical proximity. Journal of Small Business Management, 57(2), 343–​361.

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Dimensions of proximity 89 Pucci, T., Brumana, M., Minola, T., & Zanni, L. (2020). Social capital and innovation in a life science cluster: the role of proximity and family involvement. The Journal of Technology Transfer, 45(1), 205–​227. Rallet, A., & Torre, A. (1999). Is geographical proximity necessary in the innovation networks in the era of global economy? GeoJournal, 49(4), 373–​380. Rice, R. E., & Aydin, C. (1991). Attitudes toward new organizational technology: Network proximity as a mechanism for social information processing. Administrative Science Quarterly, 36(2), 219–​44. Runiewicz-​ Wardyn, M. (2020). The role proximity plays in university-​ driven social networks. The case of the US and EU life-​science clusters. Journal of Entrepreneurship, Management and Innovation, 16(3), 167–​196. Saxenian, A., & Hsu J. Y. (2001). The Silicon Valley–​Hsinchu connection: Technical communities and industrial upgrading. Industrial and Corporate Change, 10(4), 893–​920. Schamp, E. W., Rentmeister B., & Lo V. (2004). Dimensions of proximity in knowledge-​based networks: The cases of investment banking and automobile design. European Planning Studies, 12(5), 607–​624. Scherrer, M., & Deflorin P. (2017). Prerequisite for lateral knowledge flow in manufacturing networks. Journal of Manufacturing Technology Management, 28(3), 394–​419. Simmie, J. (2003). Innovation and urban regions as national and international nodes for the transfer and sharing of knowledge. Regional Studies, 37(6–​7), 607–​620. Singh, J. (2005). Collaborative networks as determinants of knowledge diffusion patterns. Management Science, 51(5), 756–​770. Stam, E. (2007). Why butterflies don’t leave: Locational behavior of entrepreneurial firms. Economic Geography, 83(1), 27–​50. Suire, R., & Vicente, J. (2009). Why do some places succeed when others decline? A social interaction model of cluster viability. Journal of Economic Geography, 9(3), 381–​404. Tippakoon, P. (2020). Local vs non-​local sources of knowledge for the low-​tech firms’ product innovation: Evidence from the food-​ processing industry in Thailand. Journal of Asia Business Studies, 14(5), 651–​670. Torre, A., & Gilly, J. P. (2000). On the analytical dimension of proximity dynamics. Regional Studies, 34(2), 169–​180. Torre, A., & Rallet, A. (2005). Proximity and localization. Regional Studies, 39(1), 47–​59. Tremblay D. G., Fontan J. M., Klein J. L., & Rousseau S. (2003). Proximité territoriale et innovation: Une enquête sur la région de Montréal [Territorial proximity and innovation: A survey of the Montreal region]. Revue d’Économie Régionale & Urbaine, 5, 835–​852. Usai, S., Marrocu, E., & Paci, R. (2017). Networks, proximities, and interfirm knowledge exchanges. International Regional Science Review, 40(4), 377–​404. Uzzi, B. (1996). The sources and consequences of embeddedness for the economic performance of organizations: The network effect. American Sociological Review, 61(4), 674–​698. Uzzi, B. (1997). Social structure and competition in interfirm networks:The paradox of embeddedness. Administrative Science Quarterly, 42(1), 35–​67. Wu, A., Wang, C. C., & Li, S. (2015). Geographical knowledge search, internal R&D intensity and product innovation of clustering firms in Zhejiang, China. Papers in Regional Science, 94(3), 553–​572.

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4 Research methodology

This chapter describes the research methodology, first discussing the adopted research paradigm, the applied research strategies, as well as the research process.The following sections examine the research procedure used at the three designed research stages. Each of them is described in the same way: first, the sample selection method is discussed, followed by data collection techniques, and finally the techniques of data analysis and interpretation.The concluding part of the chapter deals with methodological rigor.

Paradigm and research strategy This study applied the interpretative-​symbolic paradigm (Sułkowski, 2012), enriched –​by reference to Charles Sanders Peirce and the philosophy of abduction (Peirce, [1931] 1958) –​with a pragmatic worldview (Creswell, 1998). The aim of developing an original theoretical concept and providing the most probable explanation on the basis of the observed facts related to the development of proximity in COs, along with giving priority to qualitative research, may indicate both the inductive approach (Kotarbiński, 1986; Babbie, 2017) as well as the abductive one (Peirce, [1931] 1958). However, the abductive approach provides more possibilities in the scope of accounting for creativity in the process of formulating research hypotheses in comparison with its inductive counterpart, which is based on formulating generalizations (Davis, 1972; Dubois & Gadde, 2002; Magnani, 2011). For this reason, the present authors adopted the abductive approach, which turned out to be particularly useful in conditions of formulating the concepts of proximity and cooperation –​key from the perspective of this work –​as it allowed for the creative use of existing theoretical categories, their modification (in accordance with observations and reflections), and verification. This is in keeping with the proposals of Peirce ([1931] 1958, p. 7672), who reached the conclusion that a good method of deriving the most reliable hypothesis on the basis of abduction is to go through the following three stages: assumption, deduction, and further testing. Therefore, the study was based on a mixed research strategy: a sequential exploratory scheme, in which the first stage consisted of qualitative research; the second, quantitative research; and the third, qualitative research once again (see Figure 4.1). DOI: 10.4324/9781003194019-4

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92  Research methodology

Stage I qualitative research (grounded theory)

Stage II quantitative research (surveys)

Stage III qualitative research (case studies)

Figure 4.1 Research strategy. Source: Authors’ own elaboration

The qualitative research (stage I) was focused on exploring proximity development in COs. At this stage, the methodology of grounded theory (Glaser & Strauss, 1967) was applied, as it was perfectly suited to generating theoretical concepts (especially in a field that has been scarcely recognized so far), introducing a high methodological regime. Grounded theory is considered to be a very fitting tool to be used in research processes based on induction and abduction, as it provides a great foundation for the processing of obtained empirical data with the use of existing, albeit modified, theoretical concepts, as well as their combinations (Kelle, 1995; Coffey & Atkinson, 1996). The process of creating theories requires additional intellectual work, during which the data obtained in the course of empirical research serve as a touchstone toward the creation of new theoretical proposals and form the basis of new theoretical hypotheses (Coffey & Atkinson, 1996). Qualitative research has led to the emergence of the concept of the development of proximity in COs, while the relationships between the main elements of this concept were described precisely in the form of abductively introduced research hypotheses. The observed relationships became the point of departure toward holding quantitative research (stage II), the goal of which was to test the research hypothesis formulated on the basis of the results of qualitative research. At this stage, surveys (non-​experimental models) were the main research strategy. Finally, the detailed goal of qualitative research (stage III) was to expand control over the research area in order to gain insight into the relationships identified between particular elements of the generated concept of proximity in COs. This also allowed the authors to better understand the significance of specific dimensions of proximity at different stages of the development of cooperation between the entities at hand, as well as the conditions of the transformation of one dimension of proximity into another. At this stage, the main research strategy consisted of case studies, that is, the thorough evaluation of a given slice of reality, The research was designed to consist of three steps, as proposed by Yin (2014). In the first step, the authors defined the objects under study (and made use of the same comparison group as at stages I and II of the research –​that is, COs). In the second step, the authors adopted a specific variation of case analysis (a multiple-​case study). The third step consisted of adopting the perspective of the previously generated

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Research methodology 93 concept of the development of proximity in COs as the main theoretical concept, which created the framework for the analysis. The use of a mixed strategy and triangulation of source data and research methods further helped eliminate (or at least reduce) the weaknesses of both quantitative and qualitative research, and at the same time identify the convergences.

Research stage I Sample selection Qualitative research based on the methodology of grounded theory (stage II) was the most important stage of empirical research. It was performed with a view to obtaining the overall goal indicated at the onset of the work –​that is, the generation of a multidimensional concept of proximity explaining its role in the development of cooperation in COs. In selecting COs for the study, the aim was to ensure the necessary representativeness thanks to the use of the maximum variability and diversity (Flick, 2018). This is in line with the principles of the methodology of grounded theory, within which the discovery of conceptual categories and the relationships between them is examined on the basis of the constant comparative method, which rests in minimizing, as well as maximizing, differences between the comparative groups (Glaser & Strauss, 1967). In the course of selecting COs, the logic of extreme case sampling was adopted, on the assumption that regularities noticed in the cases at the extremes may also be present in the cases between them, which would allow for control over similarities and differences. The differentiating criterion for the studied COs was their sector of operation: the research included two different industries –​ICT and the metal sector. It was assumed that differences between these sectors may translate into the significance of particular dimensions of proximity. This primarily pertains to two dimensions of proximity –​namely, geographical and social proximity –​which form the basis for the development of other dimensions (e.g., competence and organizational proximity). Geographical proximity seems to be less significant when it comes to the ICT sector because of the possibility of operating in the form of virtual project groups. In turn, in the metal sector, the pursuit toward shortening physical distance between particular links in the value chain leads to concentration in a single area of entities representing the same or similar sectors of the economy. In both of the industries selected for the purposes of this research, the basis for the development of social proximity is also different. In the ICT sector, cooperation between entities is mostly project-​based and rests upon multiple, albeit short, relationships between partners. In the metal sector, cooperation within the value chain promotes the establishment of long-​lasting cooperative ties between partners representing different links of the value chain.

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94  Research methodology In turn, one factor that points to the similarity between the researched COs was the national context. All of the COs function in Poland –​that is, within a similar framework that shapes their institutional proximity. The similarity is also the result of the adopted requirement with respect to the adequate level of maturity of the CO (which in turn is tied to organizational proximity), which is influenced by, among other things: the age of the CO (measured in years from the beginning of its operation); its size (measured in the number of its members); and the scope of its activities. For the purposes of the research, it was decided that the COs must have at least five years of operation, at least 20 members, and undertake activities with at least a regional impact, directed both at developing cooperation within, as well as outside, the cluster. Ultimately, four COs were included in the study, on the assumption that for the purposes of the generated theory, it is more important to reach a balance between similarities and differences in the research sample than to have a large number of cases. Two of the studied COs –​Metal Cluster of Lubuskie Province (MCLP) and Metal Working Eastern Cluster (MWEC) –​are from the metal sector. The remaining two –​ Mazovia Cluster ICT (MC ICT) and Interizon: Pomeranian Region ICT Cluster (Interizon) –​are from the ICT sector.The localization of the studied clusters turned out to be another differentiating factor, as all four clusters are located in different parts of Poland (see Table 4.1). The studied COs were launched at a very similar time –​during the course of this study (2016), each functioned on the market for over seven years. All four were established as the result of the strong engagement of companies, which is particularly true for MCLP, which had a bottom-​up genesis, while the remaining three COs could be characterized as “mixed” initiatives, created with the participation of institutions from outside the business world. The main initiators of the COs from the metal sector were institutions from the nongovernmental sector, while in the case of COs from the ICT sector it was institutions from the R&D sector. Another similarity pertained to the scope of influence, which in the case of all four COs could be characterized as regional. In turn, differences were noted with respect to the number of entities comprising the studied COs. COs from the ICT sector turned out to be much larger –​MC ICT and Interizon had 200 and 130 members, respectively, while in the case of MCLP and MWEC from the metal sector, these numbers were just 35 and 78, respectively. In all four COs, the selection of entities for the study was performed in accordance with the principles of theoretical sampling (Glaser & Strauss, 1967). In the first step, the research focused on cluster coordinators –​that is, legal-​organizational entities fulfilling coordinating functions in the cluster organization, followed by the cluster members themselves, selected on the basis of time spent as part of their CO and their level of engagement in actions undertaken within the CO. In order to maximize differences, it was decided to include different companies in the study, regardless of their age, size, operational profile, and competitive position. Institutions from the R&D sector, business environment institutions, and educational institutions were

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Table 4.1 The sample characteristics (stage I) CO name

Sector

Location

Creation date

Metal Cluster of Lubuskie Province (MCLP) Metal Working Eastern Cluster (MWEC) Mazovia Cluster ICT (MC ICT) Interizon: Pomeranian Region ICT Cluster (Interizon)

Metal sector

Poland, Lubusz

2008

Metal sector

Poland, Lublin

ICT, telecommunications ICT, telecommunications

Poland, Masovian Poland, Pomeranian

Territorial scope

Genesis

35

Regional

Bottom-​up

2009

78

Regional

Mixed

2007 2009

200 130

Regional Regional

Mixed Mixed

Research methodology 95

Source: Authors’ own elaboration

Number of members

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96  Research methodology also included in the research sample whenever it turned out in the course of the study that their role in the development of the given CO is significant. The intended state of theoretical saturation was obtained after analyzing a sample of 30 entities: nine from MCLP (one coordinator; four companies; one R&D institution; two educational organizations; one business environment institution), six from MWEC (one coordinator; five companies), six from MC ICT (one coordinator; four companies; one R&D institution), and nine from Interizon (one coordinator; seven companies; one R&D institution). The respondents were individuals representing the coordinators and selected cluster members. In the case of companies, the respondents were for the most part their owners or individuals from the higher echelons of management (that is, CEOs, directors, members of the board).The entities in the study were represented by a single person (except for four cases, in which an entity was represented by two people). In total, 34 people participated in the study (six from MC ICT, ten from Interizon, seven from MWEC, and eleven from MCLP). Data collection techniques The main data collection technique at this stage consisted of interviews. For the purposes of the present research, two types of interviews were used: an in-​depth personal interview with selected respondents and a group interview held in one of the COs (MCLP). Data from individual interviews were collected in February–​April, 2016. A total of 35 interviews were held (in the case of one individual, two interviews were held). The average time of each interview was about 75 minutes. Because of the sequence and the form of the questions, the interviews could be considered as partly standardized and unstructured. They were also partly categorized and concentrated on five thematic areas pertaining to the development of cooperative ties in the studied COs. The first area referred to the general information on the given CO and its members. The later areas pertained to the forms of cooperation undertaken and benefits in the CO, the flow of knowledge and information therein, as well as the level of commitment (see Table 4.2). The group interview was held in one of the COs –​MWEC –​on May 22, 2017, in the place of business of the coordinator (in Lublin, in the Provincial Club of Technology and Rationalization). It lasted about 90 minutes. A total of ten people participated in the discussion (one representative of the coordinator, eight representatives of cluster companies, and one university employee). The goal of the interview was to conduct an initial verification of the developed conceptual assumptions, further enriching the collected research material. With a view to supplementing the interview, an analysis of other available data was performed, which encompassed widely available popular-​ science publications on cluster policy in Poland (including reports from benchmarking studies and inventories of COs) as well as materials on the

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Research methodology 97 Table 4.2 Main interview topics (stage I) Topics

Substantive scope

General information on the CO and membership therein

The process of the formalization of the CO The features and strategy of the CO The specific nature of the member organization in the CO The length of membership in the CO and the motives of joining the CO Developed forms of cooperation within the CO The development of relationships and trust in the CO

Forms of cooperation undertaken in the CO Creating opportunities and receiving benefits in the CO

The benefits of participation in the CO The opportunities created in the CO The key success factors tied to membership in the CO The main problems raised by ties to membership in the CO The flow of knowledge The benefits tied to the flow of knowledge and and information in information in the CO the CO The method of knowledge and information flow in the CO The key success factors tied to creating the conditions for knowledge and information flow in the CO The main barriers to knowledge and information flow in the CO Commitment to The commitment of coordinators to activities undertaken the CO within the CO The commitment of cluster members to activities undertaken within the CO The level of development of the CO in specific areas Source: Authors’ own elaboration

studied COs (such as reports, expert reports, promotional material, developmental strategies). The analysis also encompassed Internet sources (including information available on the private websites of the studied COs as well as on social media portals such as Facebook and LinkedIn) and documents and records created in the course of the ongoing operations of the studied COs (such as rules and regulations, newsletters, task lists, and suchlike). Data analysis and interpretation techniques The collected empirical material was analyzed and interpreted in parts on the basis of theoretical sampling and the constant comparative method. The main techniques of the analysis and interpretation of the collected qualitative data consisted of substantive analysis and coding, which is the fundamental method of conceptualizing data in the methodology of grounded theory. In the first step, open coding was applied, in the result of which they derived the basic codes. Then, on the basis of the constant comparative method,

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98  Research methodology the core categories were derived, which to the fullest degree explained the variables present in the studied area. In the next step, selective coding was applied, which focused on tying specific core categories, referring, on the one hand, to the four identified levels of the development of cluster cooperation and, on the other, to proximity and its main dimensions. The selective collection and analysis of data continued up to the moment of theoretical saturation of the core categories and the full integration of relationships between them, while taking into account their properties. In this way, it was possible to create the conceptual framework of the development of proximity, which was then “superimposed” on the previously generated concept of the trajectory of the development of cooperative ties. This means that the core categories (and their properties) reflecting the derived dimensions of proximity in COs were connected to core categories (and their properties) referring to the previously identified levels of cooperation in COs.

Research stage II Sample selection Qualitative research held at stage II was based on the same population that was selected for the purposes of qualitative research at stage I –​that is, on the group of COs operating in Poland. At this stage, it was also assumed that the research should encompass mature cluster structures in order to capture mechanisms tied to both the development of proximity and the development of cooperative ties.To begin with, the authors focused on COs with the status of Krajowe Klastry Kluczowe (KKK: Key National Clusters). Then, they also considered cluster organizations functioning on the market for at least five years. Furthermore, they strove to achieve an adequate level of diversity with respect to the research sample. For this reason, they concluded that the sample should include at least ten COs, and at the same time a single CO should not comprise more than 10% of the research sample. The decision was also made to limit the research sample to cluster companies, which were made the main object of the study. Ultimately, 400 cluster members from different parts of Poland took part in the study. Three voivodeships were the most represented, in which almost half (46%) of the studied companies were located: Lesser Poland (65 companies), Silesian (63 companies), and Pomeranian (54 companies). Such an overrepresentation is the result of, among other things, the fact that it is these particular voivodeships that are home to half of the 18 Polish Key National Clusters (four in Lesser Poland, three in Silesian, two in Pomeranian). Companies of different ages took part in the study. Only 2% were companies with less than 5 years’ experience. The largest sets were made up of companies that had been operating from between 5 and 20 years (about 37% of the respondents) and from 21 to 30 years (about 33%) –​around 70% in total.The remaining companies (roughly 28%) had been active in the market

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Research methodology 99 for over 30 years, with 8% of them having over 50 years’ experience. When it comes to the length of membership in a CO, 75% of the companies were members for over 5 years and 40% for over 10 years. The research sample also included companies that had been members of a CO for over 15 years (about 8% of the respondents).The majority of the studied entities belonged to the SME sector: these small and medium-​size enterprises made up the two largest sets (over 31% and almost 31% of the respondents, respectively). Micro-​enterprises in the sample amounted to 24% and the remaining 14% were large enterprises. In accordance with the research principles, each of the studied companies was represented by a single respondent with the best possible knowledge of the CO and its relationship with their company. The respondents were mostly individuals from the management –​owners/​co-​owners (33% of the sample), managers or directors (33%), as well as members of the boards of directors (almost 18%). The remainder (around 16%) were specialists and other individuals delegated with responsibility to represent the company in the given CO. Data collection techniques At this stage, the survey research was meant to provide quantitative data, which would be generalized for the entire population of COs in Poland.The principal research was preceded by preliminary research that was conducted in companies with membership in the four COs from the qualitative research sample at stage I. The initial research was carried out between June and July 2017, on a group comprising 132 of the most active companies among all of the entities from the studied COs (38 from MWEC, 13 from MCLP, 45 from MC ICT, and 36 from Interizon).The interviews took the form of an online questionnaire and allowed the researchers to evaluate the designed research tool (its verification and the introduction of small modifications) as well as test the research hypotheses (Lis, 2018). At the stage of principal research, data were collected between October and December 2021 by way of telephone interviews (CATI). The questionnaire consisted of three main parts: the introduction, the substantive part, and a concluding section. The substantive part of the questionnaire contained questions pertaining to the seven variables identified in the created concept of proximity in COs: geographical proximity, competence proximity (in terms of the scope of competence, the level of competence development, and access to knowledge and information), social proximity, commitment, and achieved goals (in different dimensions). In order to research the attitude of cluster members with respect to problems and diagnose the attitudes of individuals representing them within the CO, the authors used measurement tools prepared at the stage of variable operationalization. Furthermore, the fact that the questions maintained the same form (mostly closed) allowed the authors to unify the responses and, in consequence, formulate generalizations. The concluding section contained questions on the particulars of the

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100  Research methodology company (e.g., about the localization, the number of employees, the time in the market, the length of time as members of the cluster initiative). Data analysis and interpretation techniques Statistical analysis of data from the qualitative research was performed in three steps encompassing: descriptive statistical analysis, variable correlation analysis, and structural equation modeling. In the case of descriptive statistical analysis, the objective was to find characteristic points for the studied collective, based on which it would be possible to identity the scope in which these entities shared certain attributes from the slice of reality that had been the subject of the previous qualitative research. Beside percentage frequency distributions, the authors applied one of the central tendency measures –​the mode. The percentage distributions seemed to be the most beneficial means of presenting the obtained results because of the specific nature of the predefined responses to most of the questions in the questionnaire. On this basis, it was natural to make use of the mode –​a measure of the value/​variant of the most common answer. Variable correlation analysis was aimed at finding relationships between selected pairs of variables. The authors applied the Pearson correlation coefficient (Kline, 2015). It was also decided that a given relationship between variables could be considered statistically significant when p ≤ 0.05. In the last step, the authors tested the developed theoretical model using structural equation modeling. The main aim of this step was to confirm the existence of the identified relationships between selected variables, particularly between selected types of proximity, described in the form of logically tied research hypotheses portraying the sequence of analyzed phenomena in a CO. In the study, the authors applied a two-​step Anderson–​Gerbing modeling approach (Anderson & Gerbing, 1988, 1992), the first step of which consists of the creation and evaluation of a measurement model, which then leads to the verification of the adopted latent constructs by explaining correlation formulae between the observable variables describing them. Only then, having tested the significance of the measures of the modeling approach, is it possible to build and evaluate the structural model with a view to investigating the relationship of the influence between the latent variables. At the first stage, in order to test the measurement models, the authors used both exploratory and confirmatory factor analysis. At the second stage, they constructed structural models reflecting the paths of influence between the latent variables, with a view to testing the posed research hypotheses. They conducted a path analysis, evaluated their statistical significance, strength, the type of variable influence (β measure), and the value of the t measure (which should exceed 1.96). This was done in order to either accept or discard a given hypothesis on this basis. For the purpose of this analysis, the authors used the sem and gsem packages from STATA SE 16.

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Research stage III Sample selection At stage III, similarly to stage I, the authors resorted to strategic sampling –​ that is, the meticulous selection of cases in accordance with predefined criteria.With the aim of developing substantive theory for the adopted empirical research area, the authors determined that the research sample should consist of –​as in the case of the previous two stages –​formalized COs. Furthermore, in order to broaden the scope of the developed theory, we decided that the research should encompass COs functioning in different European countries, which would allow us to capture the national context (which significantly influences the functioning of COs), as well as proximity in its institutional and social dimensions. At this stage, the authors also attempted to include very typical cases in the sample, in which the mechanisms of the development of proximity manifest themselves in a similar way to most organizations of this type. The authors also decided that representativeness is not significant at this stage of the study. When deciding on the inclusion of specific entities in the sample, the main aim was the desire to compare such COs which, on the one hand, manifest certain similarities and, on the other, show differences, both of which may influence the significance of specific dimensions of proximity and their mutual relationships. Using such an approach, it would be possible to identify the similarities between different COs, as well as to observe differences between organizations with a very similar profile, which could enrich the developed concept. The authors also assumed that the studied COs should have at least a regional scope of influence and develop diverse forms of cooperation. With the above criteria in mind, the authors included three COs in the study from three different European countries: Slovakia, France, and Bulgaria (see Table 4.3). The selection of European countries is the result of the common framework for examining cluster-​based policy on the supranational (EU), national, and regional levels. Two of the studied COs (Techtera and BFA) operate in the textile industry and one (CKB) in the educational sector in the field of cybersecurity. One of the factors that was decisive with respect to the selection of these industries was the specific nature of the cooperative ties between the entities. In the textile industry, cooperation was aimed at the development of long-​lasting relationships between entities located at different stages of the value chain. In turn, in the educational sector, the focus is both on the development of ongoing cooperation as well as –​because of the additional specificity of the selected CO (ties to the ICT sector) –​the development of short-​ term project-​based business relationships. Each of the studied COs had been established with a strong engagement on the part of business. Their scope of influence can be considered as national.

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102  Research methodology Table 4.3 The sample characteristics (stage III) CO name

Sector

Location

Creation Number of Territorial date members scope

Cluster Education Slovakia 2018 Kybernetickej Bezpečnosti –​ Cybersecurity Cluster (CKB) Techtera Textile France 2005 industry Sdruzhenie Textile Bulgaria 2019 Balgarska industry Modna Asotsiatsia: Bulgarian Fashion Association (BFA)

Genesis

18

National

Bottom-​up

221

National

Bottom-​up

63

National

Bottom-​up

Source: Authors’ own elaboration

Table 4.4 Similarities and differences in the research sample Criteria of similarities and differences

CKB

Sector

x

Localization Age Size

Education Textile industry Western Europe Central and Eastern Europe Mature cluster (over 15 years) Young cluster (less than 5 years) Large cluster (over 100 members) Small or medium-​size cluster (less than 100 members)

x x x

Techtera

BFA

x x

x

x x

x x x

Source: Authors’ own elaboration

Given the adopted criteria of sample selection, the group of studied COs manifested interesting similarities and differences. Apart from the sector of operation, these are: location (Central and Eastern Europe vs. Western Europe), age (mature vs. young cluster structures), and size (large vs. small and medium-​size cluster structures). In this way, the authors obtained three different combinations of COs (Techtera & BFA vs. CKB; CKB & BFA vs. Techtera; CKB & BFA vs. Techtera) (see Table 4.4). With reference to the selection of entities and individuals representing selected cluster organizations, the research focused on the representatives of CO coordinators because of their knowledge of the functioning of the given CO. They also considered the perspectives of the cluster members

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Research methodology 103 themselves, as represented by people from the highest echelons of management. Data collection techniques At the described level, the authors made use of different data collection techniques in line with the adopted case study strategy, within which diverse procedures of data collection are used. For each of the three selected COs, the authors made use of the same set of research tools, which facilitated comparisons between the analyzed organizations as well as enabling the authors to draw general conclusions on the entire group of studied COs. Among the research tools, the most important were interviews with CO coordinators, held in relation to two measurement times. The first stage of research based on interviews with coordinators was conducted between April and June 2022. Its goal was to analyze selected elements of the generated concept of proximity from the perspective of individuals managing COs. At this stage, the interviews were held in written form and were standardized (albeit not structured). The scenario of the interview was designed to include five thematic areas pertaining to geographical, social, competence, and organizational proximity, as well as commitment (see Table 4.5). The adoption of this type of interview allowed the authors to collect comparable data and capture details of the analyzed issues. Furthermore, it allowed the respondents to freely shape their responses and mitigated the issue of the language barrier (the respondents were able to choose a version of the questionnaire in English or their native language). The second stage of the research, the aim of which was to deepen knowledge on selected issues, was carried out between July and September 2022. The authors decided to hold personal, partly standardized, and unstructured interviews. The average time of an interview was about 40 minutes. A supplementary technique to the interviews was a survey questionnaire (the same as at stage II during quantitative research), which allowed the authors to account for the perspective of the cluster members. The selection of the sample served a specific goal –​the research was held in a group comprising 13 companies (7 from CKB; 3 each from Techtera and BFA). Another important source of information were secondary data, which were analyzed in the period between February and August 2022. Their analysis allowed the authors to, on the one hand, consider a much wider context of the functioning of the studied COs and, on the other, obtain additional data on their activities. The analysis encompassed a very diverse set of documents –​first and foremost, material on the COs themselves, including developmental strategies, newsletters, promotional materials, minutes of the meeting, as well as Internet resources (private websites, CO websites in the European Cluster Collaboration Network, social media sites). The authors then analyzed publications on cluster policy in Europe and in the countries of operation of the studied COs, including popular-​science publications, research reports, expert materials, etc.

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104  Research methodology Table 4.5 Main interview topics (stage III) Topics

Substantive scope

Geographical proximity

The geographical scope of the CO The criteria of the selection of cluster members (accounting for geographical proximity) The significance of geographical proximity from the perspective of the development of cooperation in the CO The influence of geographical proximity on other types of proximity in the CO The influence of geographical proximity on the commitment of cluster members to the activities undertaken within the CO

Social proximity The level of development of social proximity in the CO The significance of social proximity from the perspective of the development of cooperation in the CO The role of the coordinator in the development of social proximity Factors hindering the development of social proximity The influence of social proximity on other types of proximity in the CO The influence of social proximity on the commitment of cluster members to the activities undertaken within the CO Competence proximity

The sector scope of the CO The criteria of the selection of cluster members (accounting for the sector) The significance of competence proximity from the perspective of the development of cooperation in the CO The role of the coordinator in the development of competence proximity The functioning of taskgroups within the CO Factors hindering the development of competence proximity The influence of competence proximity on other types of proximity in the CO The influence of competence proximity on the commitment of cluster members to the activities undertaken within the CO

Organizational proximity

The developed forms of cross-​organizational cooperation in the CO The role of the coordinator in the development of organizational proximity The functioning of project groups in the CO Factors hindering the development of organizational proximity The influence of other types of proximity on the development of organizational proximity in the CO

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Research methodology 105 Table 4.5 Cont. Topics

Substantive scope

Commitment

The level of commitment of cluster members to the activities undertaken in the CO The role of the coordinator in raising the commitment of the cluster members Factors hindering the commitment of cluster members in the activities undertaken in the CO The influence of specific types of proximity on the commitment of cluster members The influence of the commitment of cluster members on specific types of proximity in the CO

Source: Authors’ own elaboration

Data analysis and interpretation techniques At this stage, the basic technique of data analysis consisted of qualitative analysis focused on the derived core categories, which created the conceptual framework of the generated concept of proximity in COs. These categories pertain to the identified dimensions of proximity: geographical, competence, organizational, and institutional, as well as another variable –​the commitment of cluster members to the activities undertaken in their COs. To begin with, the collected data were grouped in accordance with the derived categories and analyzed in line with their consistency within these categories. Then, data obtained from different sources for each of the COs were compared, accounting for different measurement times. Finally, in the third step, the authors performed comparative analyses for the three analyzed cases.

Methodological rigor At each of the stages of the research, the authors followed methodological rigor in order to document the reliability and accuracy of their findings and validate the results to the fullest possible degree. Reliability and accuracy were further strengthened by both the appropriate selection of the research sample, as well as by the adoption of correct procedures of data collection and analysis. Furthermore, at stage I, an additional means of achieving methodological rigor consisted of ensuring the compliance of the research procedure with the principles of the methodology of grounded theory. Qualitative research In the course of qualitative research, the authors strove to achieve an adequate level of variability and diversity of the entities comprising the

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106  Research methodology sample. Furthermore, at stage I, two principles set by grounded theory were adopted: the principle of theoretical sampling and the principle of theoretical saturation. At the stage of data collection, the use of interviews allowed the richness of the terms in the studied research area to be captured. In the course thereof, field notes were made with the use of a unified recording and transcription procedure, including the researchers’ own thoughts, including thoughts on the context and course of the conversation. Furthermore, the transcription of each interview was verified by comparing it with the source material. In turn, at the stage of data interpretation and analysis, the constant comparative method and coding (open and selective) facilitated the interpretation of data and enabled the identification of core categories, which formed the framework of the generated concept of proximity in COs. The coding was performed with the utmost precision in order not to distort the definitions of the codes. All this considerably strengthened the reliability and accuracy of the research. To ensure higher accuracy, the authors used data and methodological triangulation.This pertains not only to qualitative research but also to the entire research process itself.Triangulation also enabled comparison with other data and conclusions drawn from specific stages of research. Furthermore, at both stages of the qualitative research, the authors performed in-​depth, repeated analysis of data, accounting for all of the collected cases.They further applied deviant case analysis, which means that the research material was analyzed for long enough to find explanations for all elements, including those which deviated from the observed general tendencies. Quantitative research During quantitative research (stage II), the authors used Cronbach’s α to measure the reliability of the constructs. In turn, validity was analyzed in the context of internal and external validity and the validity of the constructs. Internal validity was based on causal relationships between selected variables, observed during the qualitative research (stage I) and formulated as research hypotheses. External validity referred to the possibility of generalizing results. The authors attempted to strengthen it by ensuring the diversity of the research sample. In the context of the validity of the construct, the authors used exploratory and confirmatory factor analysis (EFA and CFA) to test the developed measurement models. EFA was used to test the designed measurement models and derive factors with the best fit with latent constructs (and best explain variability). The authors also used EFA to test measurement models for the procedure of structural modeling. In order to test the significance of the measurement model in the procedure of structural modeling, they used CFA, Both EFA and CFA were performed with the use of a maximum likelihood estimation. First, the authors evaluated the degree of fit of the model with empirical data by using absolute fit indices and incremental fit indices. Following the goodness-​of-​fit tests, the authors determined the fit of the

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Research methodology 107 applied measurement tools on the basis of the measure and statistical significance of factor loadings and the measure of the average variance extracted (AVE), calculated independently for each latent variable in the model. Quantitative research also made additional efforts tied to validation, which pertained especially to the developed research tool and the method of collecting data. The preliminary research (carried out on a group of 132 members of the four COs, selected for the study at stage I) enabled the authors not only to conduct an initial verification of the observed relationships between data but also to verify and modify the survey questionnaire. At the stage of data collection, validation mostly consisted of verifying the data prepared for analysis. First, the authors verified the validity of coding the data collected in the course of the research. In this way, they were able to correct a small number of errors and obtain results that are congruous with the actual responses of the respondents. Next, the authors verified the completeness of the collected data, as incomplete responses could –​as in the case of errors in the coding –​distort the results obtained.

References Anderson J. C., & Gerbing D. W. (1988). Structural equation modeling in practice: A review and recommended two-​step approach. Psychological Bulletin, 103(3), 411–​423. Anderson J. C., & Gerbing D.W. (1992). Assumptions and comparative strengths of the two-​step approach: Comment on Fornell and Yi. Sociological Methods & Research, 20(3), 321–​333. Babbie, E. (2017). The basics of social research. Boston, MA: Cengage Learning. Coffey, A., & Atkinson, P. (1996). Making sense of qualitative data: Complementary research strategies. London: SAGE. Creswell, J. (1998). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: SAGE. Davis, W. H. (1972). Peirce’s epistemology. The Hague: Martinus Nijhoff. Dubois, A., & Gadde, L. E. (2002). Systematic combining: An abductive approach to case research. Journal of Business Research, 55(7), 553–​560. Flick, U. (2018). Designing qualitative research. SAGE. Glaser, B. G., & Strauss, A. L. (1967). Discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine. Kelle, U. (1995). Theories as heuristic tools in qualitative research. In I. Maso, P. A. Atkinson, S. Delamont, & J. C. Verhoeven (Eds.), Openness in research: The tension between self and other (pp. 33–​50). Assen, The Netherlands:Van Gorcum. Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). New York: Guilford Press. Kotarbiński, T. (1986). Elementy teorii poznania, logiki formalne i metodologii nauk [Elements of the theory of knowledge, formal logic and methodology of science]. Warszawa: PWN. Lis,A.M. (2018). Współpraca w inicjatywach klastrowych: Rola bliskości w rozwoju powiązań kooperacyjnych [Cooperation in cluster initiatives: The role of proximity in the development of cooperative relationships]. Gdansk: Wydawnictwo Politechniki Gdanskiej.

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108  Research methodology Magnani, L. (2011). Abduction, reason and science: Processes of discovery and explanation. New York: Kluwer Academic. Peirce, C. S. ([1931] 1958). The collected papers of Charles Sanders Peirce. Cambridge, MA: Harvard University Press. Sułkowski, Ł. (2012). Epistemologia i metodologia zarządzania [Epistemology and methodology of management]. Warszawa: PWE. Yin, R. K. (2014). Case study research: Design and methods (5th ed.). Thousand Oaks, CA: SAGE.

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5 The role of proximity in the development of cooperation in cluster organizations The results of a qualitative research

This chapter discusses the results of qualitative research based on the grounded theory methodology. It presents the role of proximity in the development of cooperation in cluster organizations. First, the generated conceptual categories of proximity are described and the results of empirical research on the development of proximity in COs are shown. Next, the theoretical concept of proximity in COs (generated from the conducted research) is presented. Individual dimensions of proximity –​the main categories selected in the coding process –​are then used to discuss this concept. As part of the concept of proximity in COs, two ordered sets of research hypotheses are also presented, showing the relationships between proximity and the identified levels of cooperation, as well as between the individual dimensions of proximity.

Conceptual categories This section presents the conceptual categories on which the generated concept of development of proximity in COs is based. All categories used were based on the results of qualitative research conducted as an exploration of the issue addressed even before the stage of the detailed literature analysis (in this particular field). According to the guidelines of grounded theory, it is advantageous –​from the point of view of subsequent empirical results –​ to discover the key categories for the study on one’s own (Glaser & Strauss, 1967), avoiding the possibility of being “inspired” by the results of reflection or research carried out by other researchers. Therefore, individual elements of the generated concept of proximity in COs were not a preconceived idea but represented the best theoretical explanation of the tendencies observed during the qualitative research phase in the analyzed COs. First, the specifics of the area under study were carefully recognized, and only then the first conclusions were formulated about it. The qualitative research serves to analyze what determined the progression through the levels of cooperation in COs. The analysis and interpretation of the data collected showed that a decisive factor was how “close” or rather how “far apart” –​to faithfully reflect the emergence of “proximity” categories in the research –​the partners were located in relation to each DOI: 10.4324/9781003194019-5

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110  The role of proximity:The results of qualitative research other. The similarity between them was another important factor. Thus, the overarching logic guiding the explanation of the development of cooperative relationships in the studied COs was the logic of “distance” and “similarity.” Following the logic of distance, attention was paid to: • • •

the physical (geographic) distance separating the partners; distance in the value chain (the distance between links in the value chain occupied by individual companies); distance in terms of social relationships and interorganizational relationships.

Based on the logic of similarity, further categories were distinguished, relating to: • • • • • • •

industry affiliation; similarity of competence systems (both in terms of scope and level of development); common knowledge systems; similarity of companies in terms of their internal organization (similarity of structures, processes, etc.); operation of businesses under conditions created by the same or very similar normative order and similar cultural patterns of behavior and thinking; similar objectives arising from a common location and a common industry affiliation; common development trajectory (resulting from the coincident competence profiles of the entities and the similarity of the objective framework in which these entities operate).

The turning point at this stage of the research was the observation that the categories that emerged are essentially different dimensions of the same category –​namely, proximity. In line with the principle of continuity, it was decided to refer to the findings of predecessors, assuming that such an approach allows us to extend the state of knowledge and avoid unnecessary repetition. Therefore, it was decided to use the division of proximity already functioning in the literature –​distinguishing geographical, social, organizational and institutional dimensions. However, the results of empirical research showed that, in order to better explain the development of cooperation in the analyzed COs, it would be necessary to differentiate proximity in the cognitive dimension a little more. This is because relations of cooperation between cluster enterprises were established in a different way when the similarity between them lay in the scope of competencies they possessed and differently when it referred to the level of advancement of these competencies. This observation led to the distinction of a completely new dimension of proximity (unprecedented also in the literature) –​namely, “competence proximity,” and its replacement with the category of “cognitive proximity.”

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The role of proximity:The results of qualitative research 111 The justification for the procedure was the unsuitable scope of meaning of cognitive proximity (established on the basis of the literature) in relation to the scope determined by the categories that emerged in the research, which in turn reflected the idea of the competence proximity dimension proposed above. However, in order to properly identify the designatum of the concept of “competence proximity,” it is necessary to discuss at this point the category of “competence” –​for it is in the concept of competence, according to the approach adopted in this book, that the meaning of cognitive proximity is hidden. The issue of competence in social sciences (i.e., interest in the effectiveness of actions taken by individuals in the work environment) is not a new topic but, due to the lack of clear and certain findings in its scope, it is still current. However, the reflection on “competencies” did not appear out of nowhere –​it was preceded by earlier reflections of management theorists and practitioners on the skills of employees and their impact on the effectiveness of activities associated with them. The first publication to directly use the term “competence” to describe these factors was White’s (1959) paper. Another publication considered to be a classic in this area is Lundberg and Wolek’s (1970) book. A special role in anchoring the notion of “skills” in management science and reflections on employees’ competencies was attributed to Taylor’s work (1919), which constituted a cornerstone for the constitution of the so-​called scientific organization of work. Taylor pioneered a methodical, scientific approach to analyzing jobs and working conditions, and identifying the skills critical to the effective execution of a specific manufacturing process. Each of the skills considered important was reduced to a finite number of component activities, the perfect mastery of which by the employee was to translate into increased efficiency of actions based on them. To increase the likelihood of successful completion of the production process,Taylor also postulated that the psychophysical characteristics of a person performing a particular type of work should be identified and clearly defined.Thus, it can be considered that although “competence” began to be addressed explicitly only half a century later, it was in the second decade of the 20th century that Taylor had already identified two of the three components of the concept: “skills” and “abilities.” The emergence of the third and last of the main elements that, along with the notions discussed above, make up the essence of the concept of competence was the result of reflection and research undertaken in the 1960s by Argyle and Kendon (1967) on skilled performance. While noting the key importance of an individual’s knowledge and skills to “skilled performance,” Argyle and Kendon also emphasized that there was an additional factor whose presence determined the quality of the use of that knowledge and skill set within a particular action –​this was to be “motivation.” According to their reflections, each “skilled performance” had to begin with the will to perform it –​that is, with the occurrence of a specific motivation toward making changes to the reality. It was to be followed by an analysis of said reality, supported by knowledge and reasoning processes. Thus, it was possible to decide on the choice

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112  The role of proximity:The results of qualitative research of appropriate means of action for the recognized conditions in which a given action was to be carried out. On this basis, an individual could already proceed to the phase of performing specific activities. In connection with the above, the term “competence” (in the individual context) will be understood as internalized, belonging solely to a particular individual and determining competitive advantage within a structured and dynamic system of resources, including cognitive (knowledge and skills), performance (psychophysical abilities conditioning the quality of application of knowledge and skills to a given sphere of reality), and mental (level of motivation, manifested attitudes) ones, oriented toward effective implementation of goals set in a given, unambiguously and strictly defined sphere of reality. However, the formulation of a definition of “competence” at the individual level does not exhaust the topic of competence in management sciences. The term is also used at the organizational level to describe the strengths of a given supra-​unit structure. Hamel and Prahalad (1998) referred to these organizational characteristics as “identified core competencies,” which are, in fact, a set of individual employee competencies and those characteristics of the analyzed organization that created its competitive advantage to the greatest extent. Thus, in the organizational context, competence will mean a characteristic, and belonging solely to a particular organization, structured and dynamic system of individual competencies of employees determining competitive advantage and all those elements of the functioning of a given collective entity, which to the greatest extent determine its uniqueness and competitiveness (know-​how generated, business strategy and management model applied, as well as characteristic organizational culture complementary with other elements, etc.). All such elements constitute the concept of an “organization’s competence.” Furthermore, their fundamental resistance to being copied by competitors from the sphere of reality to which the analyzed organization is currently assigned makes them important in terms of creating competitive advantage. Relying on the concepts of “competence” defined above (referring to both the individual and supra-​individual level), the similarity of structured and dynamic competence systems of the entities in question can be considered as competence proximity. This understanding of competence proximity makes it possible to relate this concept both to the situation where the level of analysis would be the individual level (the subjects would then be human individuals) and the supra-​individual level (the subjects would then be higher-​level structures, such as companies). An important element of the reflections on competence proximity is a specification of the aspect related to the scope of competence within it. In this approach, competence proximity can be understood as the convergence of the competence profiles of entities and the related similarity of the objective framework in which these entities operate (the situation in the market of raw materials relevant to the industry, the specific nature of suppliers and customers, the legal regulations of the sector, etc.). The dynamic development of specific sectors of the economy, the emergence

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The role of proximity:The results of qualitative research 113 of hypercompetition, sudden and abrupt changes in the rules of the market game, or globalization processes affecting individual sectors may lead to a significant dispersion of the position of enterprises in terms of their competence profiles, or –​more generally speaking –​the objective framework in which these entities operate. However, it should be recognized that they continue to operate in line with a certain common habitus1 that results from the specific nature of a given sector. Another important issue is the necessity of taking into account the definition of “competence proximity” discussed earlier, not only the similarity of entities’ competence scopes (connected, for example, with belonging to the same industry) but also the level of their advancement. This means that although companies may be in a certain proximity to each other, determined by a similar range of competencies, the level of this proximity will not be high if these entities differ in the level of competence (e.g., degree of technological advancement). Thus, it should be noted that as a theoretical construct referring to the similarity of competence systems of entities, both in terms of scope and level of sophistication, competence proximity goes beyond the understanding of cognitive proximity adopted in the literature. The main difference between the designata of the concepts of “cognitive proximity” and “competence proximity” is that the former concerns primarily the convergence of knowledge systems of entities, while the latter focuses on intellectual and competency issues, taking into account a certain community of being determined by the objective framework (e.g., broadly defined financial conditions, industry-​specific relationships with suppliers and customers, etc.). Relying on the above findings, it was decided that the core of the concept of development of proximity in COs would be six central categories, referring directly to the five dimensions of proximity: geographical, competence (in the context of both the scope of competence and the level of competence development), social, organizational, and institutional. Using the conclusions drawn from the qualitative research and critical analysis of the literature, definitions of the different dimensions of proximity adopted in this study were formulated (see Table 5.1). Each distinguished proximity category was described by a set of properties established during the data analysis and interpretation stage. Due to their relevance to the quality of the generated concept, some of these properties were separated from the central categories and formed additional subcategories, complementing the selected proximity dimensions (see Table 5.2). Category 3a, “access to information and knowledge,” supplemented competence proximity in terms of the level of competence development. This means access to a pool of information (made available by the coordinator or other CO members) to which entities are entitled by virtue of their membership in the CO. However, the type of information received depends on the level of advancement of cluster cooperation. Along with moving to higher levels of cooperation, the information obtained in COs becomes more tailored to the members’ needs and expectations, more valuable and

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114  The role of proximity:The results of qualitative research Table 5.1 Conceptualization of the main dimensions of proximity Central categories

Dimensions of proximity

Definition of individual dimensions of proximity

Geographical proximity

Geographical proximity

Relationship according to which an entity located at a particular point in physical space remains at a small (physical or temporal) distance from other entities that are relevant from a given point of view Similarity of structured and dynamic competence systems in terms of the scope of competence

Competence Competence proximity proximity (scope of competence) Competence Similarity of structured and dynamic proximity (level competence systems in terms of the level of competence of competence development development) Social proximity Social proximity Maintaining relationships by entities based on: kinship (e.g., family members), friendship (e.g., persons forming a group of friends), or experience (e.g., former and current colleagues, members of joint industry organizations) Organizational Organizational Similarity of entities in terms of their proximity proximity internal organization (especially in relation to the structure and processes performed) and their level of connection (considering interorganizational dependencies and affiliation to the same higher-​level organizations) Institutional Institutional Functioning of two or more entities in proximity proximity conditions developed by the same or similar normative order –​that is, a collection of formalized legal rules and administrative requirements in force in a specific area, including cultural patterns of behavior and thinking that are key for these entities to function Source: Authors’ own elaboration

unique, and thus requires more and more trust between the giver and receiver of information. Another category, attitudes developed in COs (category 4a), was complementary to social proximity. The reason for distinguishing this category was the observation that the effects of social proximity on each of the four levels of cooperation in COs have a varied impact on the cooperating entities, fostering the development of three attitudes (included in the described subcategory): openness, reciprocity, and vigilance. Openness can be defined as the predisposition of some cluster entities (interconnected by cooperation relationships in the first form) to both relatively easily generate and

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The role of proximity:The results of qualitative research 115 Table 5.2 Central categories in the generated concept of proximity in COs Category number and name Property/​category name Category 1: Geographical proximity

• Distance between cluster members • Distance between coordinator and cluster members • Convergence of goals resulting from common location • Common trajectory of development

Category 2: Competence • Common industry affiliation proximity (scope of • Convergence of goals resulting from common competence) industry affiliation • Scope of competence • Similarity/​homogeneity of competence • Complementarity of competence • Diversity/​heterogeneity of competence • Multisectoral strategy • Specialization strategy • Common trajectory of development Category 3: Competence • Level of competence development proximity (level • Development orientation (including innovation of competence development) development) • Access to information and knowledge • Cooperation with R&D sector Category 3a: Access to information and knowledge

• General (diverse) information • Detailed (selected) information • Information used to identify missing resources • Priority access to key information about the environment • Information reserved for trusted partners • Formal (explicit) and tacit (implicit) knowledge

Category 4: Social proximity

• Relationships developed between selected cluster partners before joining the CO • Establishing contact; overcoming the barrier of anonymity • Developing relationships; overcoming the barrier of mistrust • Developing relationships with entities outside the CO • Development of trust • Verification of trust; reduction of risks associated with cooperation • Continued development of trust to foster further cooperation • Attitudes

Category 4a: Attitudes

• Openness • Reciprocity • Vigilance (continued)

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116  The role of proximity:The results of qualitative research Table 5.2 Cont. Category number and name Property/​category name Category 5: Organizational proximity

• Participation in the same organizations • Organizational maturity • Use of common IT and communication systems in the CO (e.g., communication platform, databases, cooperation management platform) • Integration at the process level: implementation of selected processes together (e.g., common supply, distribution, promotion systems, common quality standards) • Integration at the organizational level: various form of cooperation between cluster entities (e.g., joint projects, conducting joint business, cooperation in the value chain) • Cooperation in the field of innovation • Common trajectory of development

Category 6: Institutional • Functioning as part of similar or the same proximity environmental conditions • Approach to the environment • Involved citizens •  Cooperation with the education sector •  Cooperation with public authorities •  Common trajectory of development Category 6a: Approach to the environment

•  Monitoring changes in the environment •  Adapting to changes in the environment •  Influencing the environment (local/​regional level) •  Influencing the environment (supraregional level)

Source: Authors’ own elaboration

absorb messages containing information important from the point of view of a given entity. Reciprocity should be understood as a standard emerging in some cluster entities (interconnected by cooperation relationships in the second form) of responding to actions taken by other entities with actions generating effects of similar value, or initiating a sequence of such actions with the expectation of obtaining an equivalent return. Reciprocity is associated primarily with mechanisms for complementing the lacking financial resources of cluster partners and, subsequently, with access to information relevant from the point of view of cooperating entities. Finally, vigilance means an attitude developed by some cluster entities of readiness to use opportunities located outside a CO, especially in the aspect of establishing contacts with entities external to a CO but also –​in the second place –​in relation to absorbing new financial resources and information. “Openness” and “reciprocity” may be considered predispositions acquired by entities in terms of their attitudes and behaviors adopted and undertaken in relation to other members of the CO. In turn, “vigilance” is an attitude

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The role of proximity:The results of qualitative research 117 and a determinant of behavior toward other external entities, which are not part of the CO. The last subcategory distinguished under institutional proximity reflects the approach of cluster entities to their environment (category 6a). Cluster companies can manifest a variety of attitudes within the scope of influence on the closer and wider environment, from adopting an extremely passive attitude and treating the conditions of the environment as insignificant, through constant monitoring of changes in the environment and adjusting to them (according to the capabilities of a given entity), to actively influencing decisions and actions taken both at the regional and supraregional level.

Development of proximity in cluster organizations –​the results of the empirical research

Development of cooperative relationships

This section discusses the results of the empirical research.The main narrative framework consists of four levels of cooperation in COs distinguished within the previously generated concept of the trajectory of the development of cooperative relationships in COs (Lis, 2018; Lis & Lis, 2021). The beginning is level I (“Integration at the individual level”), followed by level II (“Allocation and integration at the process level”) and level III (“Impact on the environment”), and the most mature is level IV (“Creation and integration at the organization level) (Figure 5.1). Based on the analysis and interpretation of the collected data, individual categories of proximity were assigned to each of the distinguished levels of cooperation. On the basis of the conducted research, it was found that proximity was a factor supporting cluster cooperation at its different levels. However, on each of these levels, different dimensions played the leading

Activities: collective Goals: collective Interests: collective

Level IV: Creation and integration at the organizational level Objective: setting up conditions to create common added value by pooling resources of the cluster entities Level III: Impact on the environment

Activities: collective Goals: collective Interests: individual Activities: collective Goals: individual Interests: individual

Objective: Impact on the external environment of the organization Level II: Allocation and integration at the process level Objective: Facilitating access to the increased pool of resources, increasing the quality of products and services and / or reducing the business costs

Activities: individual Goals: individual Interests: individual

Level I: Integration at the unit level Objective: Creating a base network of relationships among cluster partners

Figure 5.1 Concept of the trajectory of development of cooperative relationships in cluster organizations. Source: Authors’ own elaboration

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118  The role of proximity:The results of qualitative research roles. This distribution of emphasis in the different levels of cooperation on the different dimensions of proximity is included in the empirical material presented here. It indicates both the dimensions of proximity that proved to be important for the achievement of each level of cooperation, together with the main and specific objectives assigned to them (marked as “input” proximity) and those that were identified as a result of activities performed at a given level by cluster entities (“output” proximity). Proximity at cooperation level I “Input” proximity The first, the simplest, but by no means the least important level of cooperation –​“Integration at the unit level” –​was oriented toward creating a certain common ground for the functioning of the entities grouped in the examined COs. Although, as the research shows, all the dimensions of proximity distinguished in the study had an impact on the formation of this common ground, its central and most important aspect was geographical proximity, which was the basis on which the other dimensions of proximity were freely formed.The research shows that physical coexistence in one area definitely enabled the entities grouped there to plan and undertake common activities. Being located close to each other, they knew the strengths and weaknesses of their environment and, at the same time, the maintenance of direct contact with partners did not consume much time or require significant expenses (see Table 5.3, quotations 1–​2). An important aspect of proximity having a strong influence on the formation of a common ground at the first level of cooperation between the studied cluster entities was their competence proximity (scope of competence). However, it is worth adding straight away that at level I this proximity should, as the results of the research indicate, appear primarily in the sense of sectoral similarity of companies forming a particular CO. Knowing the realities of a given industry (including the problems it faces) or using a common language (e.g., technical terminology) are just two examples of factors identified during the research that affect the effective creation of the first, though not yet fully formed, sense of community among at least some of the cluster co-​partners (Table 5.3, quotations 3–​4). Other dimensions of proximity –​namely, social, organizational, and institutional proximity –​also helped to build a common ground for the integration of cluster entities (members of the studied COs) but their role was secondary or even tertiary. The relationship of social proximity characterizing the owners of enterprises entering the analyzed COs certainly facilitated the process of acclimatization of these business entities in the COs. However, it was not a feature that could be considered crucial for the success of cooperation at level I (Table 5.3, quotations 5–​6). The same was true for organizational proximity in the form of participation in the same higher-​level entities (chambers of commerce and crafts, business clubs,

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The role of proximity:The results of qualitative research 119 Table 5.3 Proximity at cooperation level I Dimension of proximity

Selected quotes

Input (at the start) Geographical Distance between coordinator and CO members and proximity between CO members (1) “Logistics for those who are far away is a barrier of some sort. In a regional cluster, the effect of logistics on meetings sees a decline. It is easier for me to travel 2 km than 20 km. It is also easier for me to find my feet and to be active. If I have meetings tomorrow at 6 p.m. in Warsaw and the coordinator calls me to tell me that there is a meeting, I say ‘well, man, you see, I have to drive 2 hours to get to the meeting, spend 2 hours there, and then drive 2 hours back, which is half of my day already.’ There is no impulsiveness in action; there are no actions taken out of necessity, under time pressure, or on impulse.” (C5) (2) “The fact that we are somewhat far away from each other is an awful nuisance. Contact intensity gets worse. Personal contact is the most important thing, but maintaining it is difficult in this case.” (A2) Competence Common ground (knowledge base): common proximity (scope industry affiliation of competence) (3) “It is difficult to measure the knowledge, information, direct contact in this case; there are also elements of environment or industry integration when you need to help each other out. A group of friends who, if necessary, you can approach and talk about common problems is larger. It is also kind of a way to keep many relations.” (B6) (4) “The entire industry should be integrated in a cluster, as is the case for a chamber of commerce. This could include conferences, journals, guest speakers, presentations of companies and so on.” (A2) Social proximity Relationships developed between selected cluster partners before joining the CO (5) “Many managers of companies or people delegated to work together in a cluster know each other because they have already cooperated or studied together some years back. This does not affect the operation of a cluster, but people know what a company or person does.” (D2) (6) “I keep in touch with these owners because we have known each other for years.” (B4) Organizational Participation in the same organizations proximity (7) “It all started with a metal department within a chamber of commerce. We have known each other from the start –​our metal department evolved into a cluster.” (B5) (8) “We are also members of several other associations, business clubs, employers’ forums. That is where we meet with other companies belonging to the cluster.” (A6) (continued)

120

120  The role of proximity:The results of qualitative research Table 5.3 Cont. Dimension of proximity

Selected quotes

Institutional proximity

Functioning as part of similar or the same environmental conditions (9) “We received funding for the cluster from the Regional Operational Programme, but it has already ended. The second funding period is just beginning.” (D9) (10) “There is no money for clusters in our region. The official answer from the marshal was that there would not be any key clusters in the region, so the only thing we can do is to apply for a key cluster in Poland. In this context, we say that this is a loss for the region as we cannot seize the opportunities that we have.” (B3)

Output (at the end) Social proximity Establishing contact, overcoming the barrier of anonymity (11) “I am a person who likes meeting new people and I decided that the best way of establishing new relationships is a cluster.” (A6) (12) “When asked why they joined the cluster, companies sometimes answer that they wanted to meet new people. Despite being involved, I do not really know a lot of people in the industry.” (D2) Attitude: openness (13) “We visit each other. We make study visits to plants. Companies are not reluctant to show their plants and machinery.” (B2) (14) “Everyone shows what they manufacture or what spare capacity they have. As a result, you can complement each other’s needs –​if there are several dozen people who want to show what they have got and are open, they know what is available and can approach someone or be approached.” (A4) Competence Access to general, diverse information in large proximity (level quantities of competence (15) “It is sometimes the case that the more, the merrier. development) At least we know what is going on around the world. As far as the knowledge gained in the cluster is concerned –​it is the kind of knowledge that does not require an in-​depth analysis.” (D5) (16) “Companies go to business clubs and cluster meetings to exchange that general information and to know what is planned and by whom.” (A7) Source: Authors’ own elaboration

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The role of proximity:The results of qualitative research 121 or a CO) (Table 5.3, quotations 7–​8) and institutional proximity, which is understood as functioning within the same region and being subject to the rules established there (such as being able to benefit from regional support programs for COs) (Table 5.3, quotations 9–​10). Each of the dimensions of proximity played a specific role in the process of development of cooperative relationships characteristic for the first level of cooperation, although –​as indicated earlier –​geographical proximity and competence proximity (scope of competence) are of particular importance here. However, as shown in the results of the research, the mere existence of relationships based on proximity of one or the other type would not be sufficient to introduce transformations and modifications in the studied COs. Such modifications would allow them to evolve as well as become more complex and specialized. Through transformations, they could enter into higher levels of development both in terms of cooperation of the entities forming them (especially enterprises) and in other areas of their functioning. For all these changes to occur, it is necessary for entities forming a particular CO to show commitment to cluster matters, especially through active participation in meetings organized in the CO for representatives of the entities forming it and in specific events. “Output” proximity Geographical proximity and competence proximity (scope of competence) between companies in the studied COs should be treated as a kind of starting point from which, after adding an appropriate level of involvement, it is possible to move on to elements constituting the effects of intracluster cooperation at level I. The first such effect, as indicated in the research, was the growth or appearance of social proximity between cluster entities. The emergence of social proximity as a result of the effective integration of the components of COs can be stated when its input level was close to zero –​ that is, if the entities had not previously had relationships with each other based on affinity, kinship, or past shared experiences. The increase in social proximity, on the other hand, concerned those cluster enterprises which, at the time of entering a given cluster organization, were already in proximity relationships with some of the other entities forming a given CO. However, whether this social proximity had just emerged or had grown (at least for some members), it meant first and foremost the breaking down of the barrier of anonymity that was natural for the initial phase of the COs’ existence among the subjects forming them. The results of the research prove that this factor was important for the process of integration in the examined COs, as it allowed a group of unfamiliar people to become a group of people who recognized each other and, at least some of them, intended to establish closer relations (Table 5.3, quotations 11–​12). The gradual increase in social proximity, and thus the establishment of personal relations by the representatives of the entities forming the examined COs, had an impact on the development of an attitude of openness, both

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122  The role of proximity:The results of qualitative research to other people and to new ideas, information, and concepts. This should be seen as a two-​way process. On the one hand, the increasingly strong relationships that existed between the members promoted an increasing openness to sharing information with other participants in the group. On the other hand, such relationships increased members’ awareness of their own needs and limitations, which could be addressed to some extent based on contacts with other cluster partners (Table 5.3, quotations 13–​ 14). According to the results of the research, there is also another positive effect of the entity’s participation in a CO arising from social proximity and the resulting attitude of openness toward other cluster entities. Namely, participation in COs leads to gaining access to a wide and varied pool of information circulating through both official and unofficial channels (Table 5.3, quotations 15–​16). In a broader context, it also translated into an increase in the competence proximity of cluster members (in terms of the level of competence development): that is, mindset, range of interests, and resources of internalized knowledge. The proximity or even identity of patterns of thinking, in turn, translated into even greater openness and the development of a stronger sense of community with the entities co-​creating a given CO. This enabled the creation of a certain loop in which the increasing level of competence proximity (in terms of the competence development level indicated above) existing among some of the cluster enterprises constituted a development stimulus for social proximity, the growth of which facilitated mutual understanding between the entities forming the CO. However, it is worth remembering that in order to successfully create this loop, it is necessary to take into account geographical and competence proximity (in the previously discussed aspect of the scope of competence) among the cluster entities in the initial conditions. Proximity at cooperation level II “Input” proximity At level II, “Allocation and integration at the process level,” as in the case of level I, each of the dimensions of proximity played a specific role in the process of constructing relationships between partners in the examined COs –​ in this case, the dimensions of proximity which turned out to be the most important from the point of view of initiating cooperation at this level can be indicated too. Relatively, the most relevant dimension of proximity for level II was competence proximity (scope of competence), primarily in terms of the homogeneity of the companies forming a CO. This factor was important due to the compatibility of the needs and expectations of the cluster co-​partners. Affiliation to the same industry put the participating companies in a similar position to face similar problems and challenges, which in turn made it easier for the coordinator to make the right decisions regarding, for example, the adjustment of the resources available to cluster members to their most

123

The role of proximity:The results of qualitative research 123 important needs. While at level I the most important task was to create a common ground for the functioning of cluster enterprises, at level II, as indicated by the results of the research, the aim was to search for and highlight the similarities between partners operating within a CO in order to match the CO’s offer to the profile of its members as closely as possible on the basis of such similarities (see Table 5.4, quotations 1–​4). Table 5.4 Proximity at cooperation level II The dimension of proximity

Selected quotes

Input (at the start) Competence Similarity/​homogeneity: based on a similar set of proximity competencies (substitutability) and based on a (scope of different set of competencies (complementarity) competence) (1) “We are supposedly a metal industry but a widely understood one –​aluminum, forges. We are very diverse and deal with various industries such as the construction industry, heating boilers, wires, diamonds, holders, furnaces, and so on. When we asked cluster companies what they needed, each said something different.” (A5) Training (2) “We participated in training sessions as part of the cluster. They were very specialized and positively evaluated by our people. People with a great deal of knowledge participated in them, but they did not say that they knew everything –​ it was the other way around, actually.” (D5) Material resources (3) “If we launched a prototype shop, it would not be for 80 companies but a few, a dozen at most, because not all of them would need it.” (A7) Integration at the process level (4) “In a cluster, it would be easier to adopt some standards and implement them for similar companies. This joint initiative would bring tangible benefits for each company involved. The higher the quality, the more orders the company receives.” (B4) Output (at the end) Competence Access to detailed, customized information (making proximity (level access to missing resources easier) of competence (5) “A cluster is a kind of information source. They have development) mailings to us. If the Mazovia Development Agency were to send some information to all of us, it would not reach us in the same way as a customized, smart industry email message would.” (C5) (6) “I get a lot of knowledge from the coordinator, who performs analyses and knows which projects are worth participating in. Information must be sorted out because everything requires great effort.” (D5) (continued)

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124  The role of proximity:The results of qualitative research Table 5.4 Cont. The dimension of proximity

Selected quotes

Social proximity

Developing relationships, overcoming the barrier of mistrust (7) “At first, everyone was a bit wary of one another. There was some hesitation because, in theory, companies from the metal industry should be in competition. Relationships developed over time, though.” (A5) (8) “Members start to meet, talk, discuss as part of a cluster, developing relationships and building trust, which is very underestimated.” (D1) Attitude: reciprocity (9) “I have been to a few meetings and realized that if you share something with others, then others share something with you and that makes cooperation an entirely different experience.” (D6) (10) “If you help others, then others help you. This approach is a driving force and leads to the development of relationships that make life easier. People get to know each other, and they trust each other more during projects because they can see each other on a daily basis.” (C2) Attitude: openness (11) “If we receive inquiries, we rely on what we have seen and what companies have shown us during visits to their plants. That is when we are observant. The coordinator should know what others have in store and that is why they show us around. They are not reluctant to do so because they have their own interests in mind. The more we know about a company, the easier it is for us to answer certain inquiries from other companies and to refer them to the right companies.” (A5) (12) “A cluster might run a database of some sort, but, naturally, running a permanent database is one thing.You also need data for it as well as involvement, and openness of cluster companies in terms of providing data for the database. And it is a known fact that not all companies want to share what specialists they have or what specialists they need.” (D8) Integration at the process level (13) “A cluster may feature a purchasing platform for some products and services, such as group C production-​related products. Integration may also revolve around the purchase of services, like plant security and training services.” (B4) (14) “At trade fairs, companies participate under the same logo, which is registered. Companies have no objections to this. The cluster brand is recognized well. Nothing happened that would harm this recognition. For example, when we were making cluster calendars, companies wanted us to include their logos in them and give such calendars to their partners –​with both the cluster logo and logo of the cluster companies.” (A5)

Organizational proximity

Source: Authors’ own elaboration

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The role of proximity:The results of qualitative research 125 Although competence proximity (scope of competence) was the front runner among other dimensions of proximity in the context of creating and using the similarity of cluster co-​partners, competence proximity (in the second aspect –​level of competence development) and geographical proximity also played an important role in this process. The similarity between the members of the examined COs (in the aforementioned dimension) allowed coordinators and members involved to adjust resources of this kind to the cognitive structures of participants in a more effective way. This refers, among other things, to creating mechanisms for providing detailed, personalized packages of information for internally homogeneous groups of cluster co-​partners or creating a training plan for them in order to eliminate their identified weaknesses. Geographical proximity was no less important than competence proximity. On the one hand, it acted as a factor encouraging active involvement in particular forms of the COs’ activity while, on the other hand, facilitating the achievement of goals set by companies (and coordinators) at this level of cooperation. In this case, geographical proximity supported primarily the involvement of cluster entities in the activities of task forces created in COs and active participation in training courses conducted in COs. Organizational proximity and institutional proximity provided additional supportive functions for the formation of subgroups of similar entities in COs, although their role in this process was tertiary at best. “Output” proximity The creation of a significant similarity between at least some of the constituent elements of the examined COs provided an incentive to bring particular aspects of the relations between these elements to a higher level. This, in turn, translated into an increase in the effectiveness of their actions, improving the competitive position of both themselves and the COs. As the results of the research show, these positive effects mainly concerned the strengthening of proximity in three dimensions: competence (in terms of the level of competence development), organizational, and social. Providing companies with resources of a similar nature but, most of all, with information on issues common to them (properly selected and personalized, facilitating the identification of missing resources), led to the formation of a specific sense of community and awareness of the convergence of objectives and actions. That was a step toward strengthening the competence proximity (level of competence development) between these entities (see Table 5.4, quotations 5–​6). The research shows that this process was accompanied by an increase in social proximity: the feeling of being understood by similar entities allowed them to develop deeper mutual trust –​barriers of distrust were more easily overcome (see Table 5.4, quotations 7–​8); on this ground, it was also easier for the reciprocity standard to be constituted as the basic guideline adopted by these entities in their contacts with each other (see Table 5.4, quotations 9–​10) and to develop even greater openness (to sharing information) (see Table 5.4, quotations 11–​12).

126

126  The role of proximity:The results of qualitative research However, the increase in competence proximity (level of competence development) (i.e., something that can also be described as “mutual understanding”) and raising the social aspect of proximity to a higher level is also a step toward strengthening organizational proximity. Cluster entities connected by relations characterized by understanding (competence proximity in the discussed aspect) and trust (social proximity) were more willing to engage in activities aimed at integration (at the level of processes), thus bringing the sense of “community” also to the sphere of direct, everyday practice. This mainly referred to the creation of common distribution channels, joint purchasing, as well as the creation and engagement in a unified system of promoting products and services offered by cluster entities (see Table 5.4, quotations 13–​14). Proximity at cooperation level III “Input” proximity The discussed level of cooperation, level III “Impact on the environment,” differs quite significantly from the other levels. While we have already noted levels that I and II of intracluster cooperation (and level IV still to be presented) were oriented on developing relationships between companies forming a particular CO, the currently analyzed type of cooperation moved the emphasis of cooperation outside the CO –​to entities and institutions operating in its environment, whose specific nature could affect both the entire CO and its affiliated entities. According to the results of the research, at level III it was necessary to create (and then make at least some of the cluster co-​partners aware of) a certain convergence of goals set by them. The research shows that this convergence of goals was primarily based on two dimensions of proximity: geographical and competence (scope of competence) proximity. On the one hand, geographical proximity facilitated the involvement of members in any of the cluster activities, but also, on the other hand, it allowed the embeddedness of the entities in a particular place to be used properly: knowledge of local realities or a sense of bonding with the region. In this case, companies cooperating at level III cared not only about their own particular interests but also acted as representatives of a wider community of businesses (which, despite the lack of involvement in the activities undertaken at this level of cooperation, could become beneficiaries of their effects). What is more, they also appeared as members of a particular regional community (the results of their actions had a specific impact on the situation in the region –​usually leading to its improvement compared with the conditions beforehand). An example of such activities and their effects could be the co-​creation of education programs by cluster entities and regional educational institutions, oriented both toward the needs of cluster enterprises (e.g., the creation of classes with specific professional profiles) and the opportunities of the local labor market (see Table 5.5, quotations 1–​2).

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The role of proximity:The results of qualitative research 127 This went hand in hand with the aforementioned competence proximity: the convergent industry profile of cluster enterprises facilitated the identification and articulation of needs, as well as strengthened the cohesion of actions taken to find appropriate responses to these needs (see Table 5.5, quotations 3–​4).The more numerous and homogeneous the group of cluster entities striving for the introduction of specific solutions at the local level, the greater its pressure on regional institutions, and thus the greater the effectiveness of the actions taken. “Output” proximity According to the results of the research, the effects of level III were associated with three dimensions of proximity: institutional, social, and competence (level of competence development). The increase in institutional proximity resulted from the development of more favorable external conditions for the operation of COs and their members. The possibility of modeling the labor market or influencing the local education system are among the possible implications of the pressure exerted by the involved members of the studied COs (see Table 5.5, quotations 5–​6). The need for dialogue or negotiation with representatives of institutions external to the COs encouraged the development of social proximity. This is because the members of these COs and representatives of administrative bodies at different levels ceased to be anonymous to each other and established certain relationships between themselves (to which both parties will be able to refer in the future) (see Table 5.5, quotations 7–​8). The increase in the level of competence development in the entities that decided to bring their cooperative relations to level III was, in turn, associated with the attitude of “vigilance,” which developed primarily in the group of the most committed members, but also among those participants of the studied COs who wanted to effectively implement the goals they set for themselves (see Table 5.5, quotation 9).Vigilance is nothing more than observing the environment in search of new partners for cooperation, but also constant monitoring of the situation in COs and their immediate vicinity (e.g., region), as well as the willingness to take immediate action when the right conditions exist to achieve a specific goal. Such an attitude caused the entities applying it in practice to gain priority in the access to relevant (from their point of view) information on the (near and far) environment –​thus, their knowledge systems became similar, causing the development of competence proximity in the discussed aspect (see Table 5.5, quotations: 10–​11). Proximity at cooperation level IV “Input” proximity The last and highest level of cooperation identified based on the research (level IV –​“Creation and integration at the organizational level”) required

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128  The role of proximity:The results of qualitative research Table 5.5 Proximity at cooperation level III The dimension of proximity

Selected quotes

Input (at the start) Geographical Convergence of goals resulting from common location proximity (1) “A cluster should act as a stimulus to the development of the industry in the region.” (D4) (2) “We realized that we had to do something for the region when it comes to cooperation with companies, such as establish a common lobby. There were several initiatives of this kind, as a result of which the cluster was the initiator of the establishment of the Gorzów Technological Centre of the Science and Industry and the Mechanical Engineering Department at the Jacob of Paradies University as we did not have engineering staff there. All of these initiative stem from the need to do something for the region. We are responsible for supervising occupational education, in particular mechanical education. (B2) Competence Convergence of goals resulting from common industry proximity affiliation (scope of (3) “It is, however, an industry cluster, so it was clear from competence) the beginning that the cluster is intended to solve certain problems of the industry in the region. The core motivation of the people who are still working today was the possibility to build something different, exert influence, learn from others, talk about problems in a constructive way –​harmless for all of us –​as well as build relations with additional entities such as academic institutions and universities.” (D4) (4) “Competitors come together to drive the industry forward and develop joint actions. We try to make bigger things together.” (D1) Output (at the end) Institutional proximity

Social proximity

Approach to the environment: influencing the environment (5) “A cluster is an entity whose opinions are considered when preparing strategic action plans –​ at a regional and industry level. We give advice on reports prepared by local government institutions.” (D2) (6) “Companies meet to try to stimulate the regional policy in terms of, for example, supporting the space sector.” (D1) Developing relationships with external partners (outside the CO) (7) “A cluster brings together both companies and educational institutions. And once a year, we hold a general meeting where we can meet, get to know each other better, have discussions, communicate our troubles, invite others to cooperate, or ask for help.” (B9) (8) “We keep in regular touch with employers and that is how we got to know each other. Be it through internships or discussions, employers belonging to the cluster participate in our conferences twice a year or even in informal talks, where we can share various things and ideas.” (B10)

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The role of proximity:The results of qualitative research 129 Table 5.5 Cont. The dimension of proximity

Selected quotes

Competence proximity (level of competence development)

Attitude: vigilance (9) “If something starts at a certain point in the country or politicians reveal that something would be preferred, you need to keep your finger on the pulse, know this and make the best use of it, and show companies the direction in which such measures will go. Companies with capital and some human potential in terms of innovation need to know this so that they can develop in this direction.” (B2) Access to relevant information (making it easier to identify environmental conditions): access priority (10) “Still, a cluster is a large institution and, as a result, can be among the first to receive materials from, for example, meetings of EU institutions. These materials are not available to everyone. The cluster can then distribute such materials further to its members. If the European Commission meets and presents legal acts, the cluster can dispatch its representatives to participate in such meetings. An individual is not allowed to participate in them.” (C6) (11) “If the deputy minister visits the cluster and provides information on what tender procedures will look like in 6 months, the cluster finds such information very valuable.” (D1)

Source: Authors’ own elaboration

cluster companies to develop relatively the greatest number of proximity dimensions compared to the levels of cooperation discussed earlier.According to the research, the following dimensions of proximity came to the fore in this case: social, competence (both in terms of scope of competence and level of competence development), and organizational proximity. Social proximity at cooperation level IV is an extremely important element as it refers directly to the trust generated between cluster co-​partners (see Table 5.6, quotations 1–​2). The origin of this trust was more than just a positive relation of affection between representatives of two or more cluster entities –​it was based mostly on positive experiences from previous acts of cooperation of the analyzed entities. As the results of the research show, trust at cooperation level IV is a feature of the relationship between those cluster companies that have already had the opportunity to cooperate with each other and whose cooperation was assessed positively by all parties. The research also indicates that cluster companies are more likely to select partners who have proven themselves in the past, rather than those who have failed or remained anonymous until now (this reduces the risk associated with the establishment of cooperation) (see Table 5.6, quotations 3–​4).

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130  The role of proximity:The results of qualitative research Table 5.6 Proximity at cooperation level IV The dimension of proximity

Selected quotes

Input (at the start) Social proximity Development of trust (1) “Looking for a subcontractor is pretty simple because you can do it without a cluster. However, looking for a partner to cooperate with can be difficult because you have to have some trust in the other party –​the partner.” (D7) (2) “Trust is essentially the conviction that we can invest some work in the business in which the partner will not leave us in the lurch. It is the belief that the partner will be loyal, will not disappoint us involvement-​wise, and will live up to their responsibilities. Every case of cooperation provides for this implicit declaration of activity.” (D10) Verification of trust, reduction of risks associated with cooperation (3) “What do I mean by trust? First example: I disclose the information that I am wary of disclosing because it can be used –​I do not know where or against me –​but this is the main problem. Second example: I sign something with someone else, I have to cooperate with this person, but I do not know if the person is cut out to be a partner. There are several partners so one of them will surely fail and so will the project. I will work a lot but to no avail. So, I understand trust as a risk. I am afraid of an unknown partner or a partner that I know but have never worked with. This also goes for the partner that I have worked with, but it was not necessarily a good experience.” (C4) (4) “It is understandable for the project leader or member to try to recommend companies that the leader or member trusts because they have already cooperated with them –​ and it worked out.” (D5) Competence Level of competence development proximity (5) “We are now discussing an e-​commerce project with an (level of entity. The discussion covers what we can do. However, competence this applies to companies whose profile is suitable and have development) good competencies themselves. In this case, you have to know what questions to ask and try to find a common language.” (C3) (6) “People are interested and want to cooperate if they share the same values and knowledge base.” (D4) Development orientation (including innovation development) (7) “It is common knowledge that a company making fences will not make things out of graphene. First, it cannot afford it, and besides, it has no knowledge of it. Unfortunately, this industry required long-​term involvement and work.” (A4)

13

The role of proximity:The results of qualitative research 131 Table 5.6 Cont. The dimension of proximity

Organizational proximity

Competence proximity (scope of competence)

Selected quotes (8) “We wanted to offer companies an opportunity to participate in cool, innovative new things, but their competence did not cut it. Their personnel were not qualified enough; they lacked specialists that would drive complex projects forwards. They tend to start a project only to find that it is too much for them. When we get such companies involved in our projects, their involvement tended to be a source of problems. There is nobody with innovative thinking in these companies.” (B8) Organizational maturity (9) “All companies that showed some promise had been present in the market for 15 or 20 years, and they are the ones that have already got their slice of the pie, have customers, and simply think what more they can do. Indeed, this is a matter of organizational maturity of the company.” (D9) (10) “Companies can cooperate and get involved if their plants are organized and CEOs are owners or majority shareholders with a little more time to spare.” (B2) Complementarity based on competence heterogeneity (11) “It goes without saying that one of the assumptions of a cluster is that is it formed by companies competing in the market. That is why each and every project must deal with areas that are separate for cluster companies. The main job in the cluster of project leaders, task force leaders, or the animator of the main cluster is to find those connections that do not interfere with each other.” (D3) (12) “Synergy is very difficult, to be honest. We cooperate with a similar company that has a similar profile. The cooperation has been going on for years, but it is not an easy one. There are conflicts of interest.” (D6) Complementarity based on a very different set of competencies (multisectoral strategy, specialization strategy) (13) “There is a category of companies that often use electronics and IT in their products. I think that these companies can join a cluster. If they are willing to support a cluster and they can cooperate with other companies by networking them, engaging them in products, or inspiring them to develop innovative products, then why shouldn’t we let them join?” (D4) (14) “Electronics is so complex now, so it is a good thing that there are companies specializing in some parts of it. If we can find a specialized company, the problem we are having will be thoroughly analyzed and solved once and for all.” (D6) (continued)

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132  The role of proximity:The results of qualitative research Table 5.6 Cont. The dimension of proximity

Selected quotes

Output (at the end) Social proximity Continued development of trust to foster further cooperation (15) “We have already successfully cooperated on one project, so our partner trusts us. We might be the only company that the partner would be willing to cooperate with because it knows that all arrangements will be complied with. As a result, when there are some projects, the partner normally gets us involved because it knows that it can rely on us and that if we commit to something, we will do it.” (D5) (16) “We like and trust each other because we did one project together. We have one or two partners –​with the third one being in the process of becoming one –​that we can cooperate with. We just realized that we cannot afford to try other partners out.” (D9) Competence Access to knowledge proximity (17) “When it comes to general information, you come, you (level of observe. When it comes to detailed information, you competence participate in task forces, build trust. Then there are market development) opportunities, trends.You look at these task forces and projects that are available and are discussed, and, as you look at them, you naturally know the direction of ICT and catch up on trends and industry problems. Knowledge is on another level. It shows up in projects.” (D1) (18) “Trust is needed where there is a prototype, where there is something new, or where knowledge is yet to be well protected in formal terms. Trust is key if there is something that is not patented but may become a product in the future.” (D4) Closure in relation to knowledge (19) “Companies do not want to share knowledge. They guard it carefully.” (B4) (20) “If a company has unique know-​how in the form of technology, no company will want to share it. This is beyond discussion for me. I would not want our company to share something that gives us our competitive advantage either.” (D3) Closing the group to others (21) “Looking at task forces as an example, you can see that when a group of entities cooperates and this cooperation becomes more serious at some point, this openness offered by the cluster must come to an end. The group have made it so far that it should become a consortium. This consortium should become independent of the cluster. It would not be fair when several companies have worked on a project for a year, incurred costs, dedicated time and resources to it, and, at some point, someone else comes along and says that they also deserved this because they were in the cluster.” (D2)

13

The role of proximity:The results of qualitative research 133 Table 5.6 Cont. The dimension of proximity

Organizational proximity

Selected quotes (22) “Knowledge is generated within projects. Some documents are open to everyone, while others are open only to persons who were involved in certain projects.” (D5) Advanced forms of cooperation (23) “A typical cluster is established in order to form a common entity, such as a company or a spin-​off.” (B6) (24) “A cluster is not for large companies but for small ones. Let us assume that small companies want to manufacture a carriage. In this case, one company makes a wheel, another a shaft, yet another reins and a seat, with the entire carriage being sold by these companies together.” (B6) Cooperation in the field of innovation (25) “In terms of innovation development, participation in projects is the most important thing for us. This participation allows us to develop new products and improve existing ones on the market.” (D5) (26) “Our consortia and projects mainly deal with innovative activities. Consortia and projects are generally where new technologies are developed, which are then used by companies to develop products.” (D1)

Source: Authors’ own elaboration

No less important than social proximity was the competence proximity (level of competence development) of entities entering level IV of cooperation. Successful partners possessed a similar level of advancement of competence –​as too great a disparity in this area would have an inhibiting effect on the achievement of the planned results (see Table 5.6, quotations 5–​6). Only some of the cluster companies were able to develop cooperation in the field of innovation (joint implementation of innovation processes) –​this type of cooperation required partners to have appropriate resources (in terms of value, originality, difficulty in imitation) and high competence (see Table 5.6, quotations 7–​8). The requirement of competence proximity (in the discussed aspect of competence development level) was accompanied by the criterion of maintaining the relationship of organizational proximity by the entities. Just as in the case of competence proximity there was no room for taking into account different levels of competence –​the requirement to establish organizationally close relations emphasized the need for the cooperating entities to have similar organizational potentials (organizational maturity) (see Table 5.6, quotations 9–​10). Therefore, neither in the case of competence proximity nor with respect to organizational proximity was there any possibility of a situation in which the stronger entities reached out to the

134

134  The role of proximity:The results of qualitative research weaker ones. At level IV of cluster cooperation, the stakes were the highest and the partners expected each other to meet the highest standards in each of the indicated areas. What is characteristic for this level of cooperation is that the dimensions of proximity which played a key role at lower levels were reduced to insignificance: this refers primarily to geographical proximity and competence proximity in terms of the scope of competence, and strictly speaking the homogeneity of entities. The results obtained indicate that, while the level of competencies of companies initiating cooperation at level IV should be similar, the scope of these competencies should differ. Thus, this level of cooperation was not about achieving competence proximity (in terms of the scope of competence), that is, a state of sectoral homogeneity (as was the case at level II of cooperation), but, on the contrary, a state of sectoral heterogeneity. The specific nature of activities undertaken at level IV of cooperation promoted complementary competencies, not identical ones (see Table 5.6, quotations 11–​12). This allowed the cooperating companies to benefit from the positive effects of such phenomena as “structural holes” or “cross-​pollination” occurring between representatives of different industries (multi-​ industry strategy), but also to develop cooperation in the value chain based on the specialization of the partners (see Table 5.6, quotations 13–​14). The research shows that all the above-​mentioned initial criteria necessary for the formation of cooperation at this highest level (IV) were to serve the development of awareness of not only common objectives (as was the case at level III of cooperation) but also of interests of the individual entities involved.The establishment of a community of goals was a complicated task, although it was still easier to accomplish than developing a common trajectory for the interests of different entities. This sphere is, as indicated earlier, hidden deep in the consciousness of individuals and often touches the areas that these individuals are not even aware of. Therefore, it is necessary to match, as closely as possible, the cognitive structures of entities intending to initiate a cooperative relationship at level IV. It is also important to select potential partners in such a way as to provide them with a sense of security based on mutual trust, on the one hand, and on the similarity of potentials (competence and organizational potential), on the other. All that would guarantee the effective performance of tasks entrusted to them. “Output” proximity The research showed that the cooperation of cluster enterprises carried out through the establishment of relationships of a nature consistent with the requirements of level IV significantly influenced the levels of those dimensions of proximity that played the most important role at the stage of initiating these acts of cooperation.Therefore, a strong development stimulus occurred for the spheres defined as social proximity, competence proximity (level of competence development), and organizational proximity.

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The role of proximity:The results of qualitative research 135 In the dimension of social proximity, the effect of level IV was the strengthening of trust between operating entities, allowing these entities to construct future intentions more boldly. Companies aware of the reliability of cooperation with other, previously positively verified entities could begin to express willingness to initiate or enter into projects characterized by greater risk, based on a sense of security resulting from cooperation with proven partners (see Table 5.6, quotations 15–​16). Based on the conducted research, the result of cooperation of enterprises at the highest level was also the development of proximity in the competence dimension (in the aforementioned aspect of competence development level), caused primarily by easier access to knowledge and priority in using that information and knowledge created during the processes of cooperation with cluster partners (see Table 5.6, quotations 17–​18). This represented an unquestionable privilege, given the strong reluctance of companies to share knowledge with other entities (see Table 5.6, quotations 19–​20). However, it should be emphasized that access to knowledge in the studied COs was strictly regulated and reserved for those entities that contributed to its creation (e.g., through their participation on the project team) (see Table 5.6, quotations 21–​22). As in the case of social proximity development, also in the case of increasing competence development, the cooperating cluster enterprises were more willing to participate in subsequent joint activities rather than engage in undertakings with anonymous or barely known entities. Great importance from this point of view was also attached to the increase in proximity in the organizational dimension, achieved, among other things, through the implementation of joint projects or the launch of joint ventures (this category should also include integration in the value chain, which, however, was not achieved in the surveyed COs) (see Table 5.6, quotations 23–​24). In the case of more advanced projects, the collaborating entities jointly led to the development of innovations (see Table 5.6, quotations 25–​ 26). The convergence of the organizational potentials of the cooperating entities should definitely be treated as a catalyst for their further cooperation.

The concept of proximity in cluster organizations The results of the qualitative research underlying the identification of proximity as a theoretical category that accurately describes and explains the trajectory of development of cooperative relationships in the studied COs indicated that different levels of cooperation in COs were characterized by different configurations in which the various dimensions of proximity occurred. In other words, each level of cooperation had a different, unique need for relationships of a specific nature. Moreover, while moving along the outlined trajectory of cooperation development in COs –​moving from level I to level IV –​one can point to the relationships that occur between the dimensions of proximity themselves. Combining the distinguished

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136  The role of proximity:The results of qualitative research levels of cooperation with specific dimensions of proximity expected at the “input” and “output” of a given level and linking the individual dimensions of proximity together allows proximity (and its dimensions) to be analyzed in dynamic terms (which corresponds to the assumptions about the dynamic nature of proximity). It also gave rise to two sets of research hypotheses. The first group of hypotheses reproduced the relationships observed between the variables related to the four levels of cooperation in COs and the variables related to the distinguished dimensions of proximity, taking into account output and input proximity. In turn, the second group of hypotheses describes the relationships that occur between the various dimensions of proximity. Relations between levels of cooperation and dimensions of proximity The first elements of the generated concept of proximity in COs were hypotheses directly referring to the distinguished levels of cooperation of cluster enterprises, characterizing the specific nature of the various stages of development of cooperative relationships in the studied COs, using the category of proximity (relations between levels of cooperation and dimensions of proximity). Four such hypotheses (H.I–​H.IV) are presented below, one for each level of cooperation: H.I: At level I, the most relevant dimensions of proximity are geographical proximity and competence proximity (scope of competence), leading to the development of social proximity, which is the basis for the development of subsequent levels of cooperative relationships in COs. H.II: At level II, the most relevant dimension of proximity is competence proximity (scope of competence), which enables the development of competence proximity (in terms of the level of competence development) and organizational proximity. H.III: At level III, the most relevant dimensions of proximity are geographical proximity and competence proximity (scope of competence), enabling the development of institutional proximity. H.IV: At level IV, the most relevant dimensions of proximity are competence proximity (in terms of both scope of competence and level of competence development) and social proximity, which enable the further development of competence proximity (level of competence development), as well as social and organizational proximity. In addition, the most important dimensions of proximity, established “at the input” and “at the output” of each of the four identified levels of cooperation in COs2, reflecting the relationships summarized in the form of research hypotheses H.I–​H.IV, were distinguished (see Table 5.7). At level I, the initiation of cooperation in COs may be based on various dimensions of proximity between entities that have joined such an organization, but at such an early stage of development of cooperative relationships,

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The role of proximity:The results of qualitative research 137 Table 5.7 Relationships between the levels of cooperation and the dimensions of proximity in COs Levels of cooperation

Proximity: entry

Proximity: exit

Level I “Integration at the unit level”

•​ Geographical proximity •​ Competence proximity (scope of competence) •​ Competence proximity (scope of competence)

•​  Social proximity

Level II “Allocation and integration at the process level” Level III “Impact on the environment” Level IV “Creation and integration at the organizational level”

•​ Geographical proximity •​ Competence proximity (scope of competence) •​  Social proximity •​ Competence proximity (scope of competence) •​ Competence proximity (level of competence development)

•​ Competence proximity (level of competence development) •​ Organizational proximity •​ Institutional proximity •​  Social proximity •​  Competence proximity •​  Organizational proximity

Source: Authors’ own elaboration

geographical proximity and competence proximity (scope of competence) are the most crucial, and are counted among the main determinants of COs. Both of these dimensions of proximity make it easier for cluster entities to engage in various activities undertaken as part of a CO (at the discussed level of cooperation, these are mainly organized meetings and events). All that –​thanks to contacts established with other participants of the group (breaking down the barrier of anonymity) –​leads to the development of social proximity, which is the foundation for building the next levels of cluster cooperation. At level II, the key dimension of proximity is still competence proximity (scope of competence), but in this case it should be based on the highest possible similarity of competencies of the entities forming the CO. For the appropriate formation of cooperative relationships within the discussed level of cooperation, it is necessary to achieve the greatest possible similarity of the companies in terms of their competence scope.Therefore, entities connected by level II relationships should not only belong to the same sector but also have the same (or very similar) competencies.The homogeneity of members’ competencies facilitates the matching of information and other resources provided by the CO to their needs, which in turn further develops competency proximity (in terms of the level of competency development). Level II commitment fosters further development of social proximity within the group (development of relationships and breaking down the barrier of distrust), leading to even greater openness and reciprocity, which –​thanks to the exchange of information and resources thus made possible –​reinforces

138

138  The role of proximity:The results of qualitative research the development of competence proximity (in the discussed aspect of the level of competence development). At level II of cooperation, there is also an increase in organizational proximity, based on competence proximity (in terms of the scope of competence –​homogeneity of partners), enabling integration at the level of processes (e.g., with regard to quality, supply, distribution processes) in cluster entities. At level III of cooperation, just like at the level I, the main role should be attributed to geographical proximity, supported by the competence proximity (scope of competence) of the entities involved. However, at this level of cooperation, there is much less expectation of identity of the competence scopes (compared to level II). Although it is still important to belong to the same sector (which guarantees good knowledge of the field of activity of entities connected by cooperative links of these levels and ensures the existence of a pool of common goals), the scope of competencies of enterprises does not have to be that convergent. While at the first level of development of cooperative relationships the result of combining the geographical and competence (in terms of the scope of competence) dimension of proximity was to be the development of social proximity, at the third level the involvement of cluster entities is primarily oriented toward the development of proximity of an institutional nature. The community of objectives created based on common location and industry affiliation may encourage cluster entities to engage more in activities aimed at achieving collective objectives related to the development of favorable environmental conditions (oriented toward creating more favorable legal and administrative conditions for doing business or adjusting the education profile in the region to the requirements and needs of cluster companies). The involvement of cluster entities at level III of cooperation also influences the development of social proximity, although it is primarily in the context of developing relationships with partners outside the CO. The most mature level of cooperation in COs (level IV) is the result of the development of proximity in the previous three levels of cooperation, primarily in the social dimension (development of trust) and competence dimension (similar level of development of partners’ competence). The third important type of proximity at the level under discussion is competence proximity (scope of competence), but, unlike at level II, it is based on the heterogeneity of the CO members’ competencies. In this case, the main criterion determining the establishment of cooperation between the entities is the complementarity of competence scopes –​that is, the fact that partners have such sets of skills and knowledge systems that would be complementary and enable the creation of common added value. A secondary issue at level IV is whether these matching competencies originate from companies within the same sector or are related to a different sector. Therefore, cluster co-​partners may represent different sectors of the economy (which was unprecedented at previous levels), while the combination of their diverse yet still complementary competencies may generate new knowledge and innovative solutions that constitute a further development of competence

139

The role of proximity:The results of qualitative research 139 proximity (in the context of raising the level of competence development). In this case, access to new knowledge is limited to those members of the group between whom, as a result of the occurrence of the three dimensions of “input” proximity, a certain organizational “community” was developed based on organizational proximity. Such community is manifested, among other things, in engaging in teams oriented toward developing short-​or long-​ term cooperation (participation in project groups and consortia, cooperation in the value chain, the launching of joint ventures). Over the course of subsequent acts of cooperation, further development of trust (as well as its verification) took place, which served to further strengthen proximity in the social dimension. Relations between dimensions of proximity Within the framework of the created concept of proximity, a second group of research hypotheses was formulated, reflecting the complex nature of causal relationships occurring between the various dimensions of proximity in COs: H1: Geographical proximity and competence proximity (scope of competence) are important for the constitution and development of a cluster organization because they have a positive impact on the commitment of cluster members, which –​in turn –​has a positive impact on the development of social proximity and institutional proximity. H2: Social proximity is important for the development of competence proximity (level of competence development) and organizational proximity. Figure 5.2 presents the discussed relations between the different proximity dimensions in COs.

Geographical proximity

Competence proximity (scope of competence)

C O M M I T M E N T

Competence proximity (level of competence development) Social proximity

individual goals Organizational proximity

Institutional proximity

Development of cooperation

Figure 5.2 Links between dimensions of proximity. Source: Authors’ own elaboration

collective goals

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140  The role of proximity:The results of qualitative research Geographical proximity and competence proximity (scope of competence), which are already formed at the first level of cooperation based on the arrangements regarding the geographical scope and composition of the CO, determine the beginning of the development of cooperative relationships in COs. When discussing the relations between the proximity dimensions in hypotheses H1 and H2, the primary role of geographical proximity compared with the other proximity dimensions should be emphasized. Geographical proximity is the most fundamental dimension of proximity because it serves as a foundation on which proximity in the other dimensions is created and developed. If supported by the involvement of at least some cluster members, geographical proximity is the fastest way to create and develop social proximity, which, in turn, is necessary for all cooperation levels to be established (I–​IV). Based on the social dimension of proximity (and also, indirectly, on geographical proximity that acts as a facilitator for further changes), cooperative relationships between entities may evolve to such an extent that proximity in the competence dimension (in terms of development of competence level) and in the organizational dimension will be generated and developed. As cooperation levels gradually go up, the importance of common location in terms of the effects of cooperation undertaken by companies forming the analyzed COs goes down. This means that geographical proximity becomes less relevant and gives way to the other dimensions of proximity (which is particularly evident at level II and IV). Every company is located at a certain point in geophysical space so it can be somewhat affected by features of this space (distribution of natural resources, labor market specificities, industrial traditions, etc.). Hence, the importance of location should not be entirely ignored. It should still be kept in mind that location in terms of both conducting business and developing cooperative relationships with other entities located in geographical proximity should be taken into account. Characterizing competence proximity is slightly more complex. One of the factors that makes it difficult to present the specific features of competence proximity is its two-​dimensionality or, to be more precise, the need to analyze it based on a scope of competence of companies forming the analyzed COs and the level of sophistication relating to the competencies that they have. As in the case of geographical proximity, competence proximity (scope of competence) is crucial at level I of cooperation where proximity in the other dimensions necessary for further development of a CO is formed. However, unlike geographical proximity, the importance of competence proximity at subsequent levels of cluster cooperation does not decline –​the only change applies to requirements for the distribution of competence of cluster partners. The two aforementioned proximity dimensions serve as a basis for the development of social proximity (as cooperation levels increase, relationships between CO members gradually become closer) and institutional proximity (characterizing level III). However, this development is not automatic and does not apply to all members of a given CO. The research results pointed

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The role of proximity:The results of qualitative research 141 to the existence of a factor that serves as an intermediary in the generation of other proximity dimensions (i.e., social, institutional, competence proximity in the aspect of the level of competence, and organizational proximity) between cluster organizations. This factor was involvement. As a result, by staying physically close to the other entities and being involved in first making, then developing, relationships with them, an entity establishes the right foundation for the development of proximity in its subsequent dimensions. The ease of making new relationships and maintaining and developing the existing ones through functioning in a close physical space (i.e., in geographical proximity), supported by a common interface (resulting from competence proximity in terms of scope of competence), translates into the creation and evolution of the social proximity dimension, which may be considered a key factor in the process of establishing cooperative relations in COs. Social proximity is significant at each of the four cooperation levels; in addition, proximity in this dimension has to improve in order to develop relationships between COs. The highest level of cooperation is only reached by those few entities whose social proximity is relatively high and based on a comparatively long-​term relationship built on mutual trust. Institutional proximity, which is generated primarily at level III, is entirely different from all other proximity dimensions. On the one hand, due to the definitional link with the formal and informal system of institutions, it should be considered the background against which all activities are undertaken by entities; on the other, however, the third cooperation level is generally dedicated to all activities of entities whose common objective is to affect the environment in a certain way. That is why this level of development of cooperative relationships is distinguished from the other observed cooperative relations. Even though few COs make a conscious and organized effort aimed at shaping the institutional (and, above all, formal) dimension of reality, their activities still stand out in comparison to cooperative relationships focused on achieving other goals. As already mentioned, level III is more of a branch than another rung of the “ladder” of cooperative relationships that develops in a rather linear fashion. Social proximity supports the development of the last two proximity dimensions –​competence (level of competence development) and organizational dimensions. As in the case of social proximity, competence proximity (here) increases as the level of cooperation goes up as a result of gaining access to various resources (mainly knowledge and information) offered in a given CO. This means that each successive phase of development of cooperative relationships is characterized by partners with increasingly higher levels of competence development. At the last, fourth, level of cooperation, cooperating entities are required to have a high and, at the same time, very similar level of competence to undertake the most complex forms of cooperation (reserved for this level). For the last dimension, which is organizational proximity, there are two main planes in which it manifests itself in the observed reality of the analyzed

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142  The role of proximity:The results of qualitative research COs.These planes only apply to two out of the four cooperation levels –​the second and the fourth one (at the first and third cooperation level, organizational proximity is occasional and does not play a significant role in the process of creating and developing cooperative relationships). At level II, this dimension of proximity is primarily visible in activities aimed at integrating selected processes in cluster companies, whereas at level IV, organizational proximity applies to the most complex forms of cooperation that lead to a gradual organizational integration of cluster partners. The ability to analyze and further examine the relations between individual proximity dimensions in COs is a particularly valuable aspect of the generated theoretical concept. Knowledge of these relations may prove useful in guiding a CO through the individual levels of cooperation and reaching the highest level, which is characterized by the development of the most mature (and thus most desirable) forms of cooperation (based on common goals and interests). A skillful and completely intentional proximity management in COs is, therefore, one of the most significant factors in building competitive advantage based on cooperation in COs.

Conclusion The combination of the results of the empirical research (covered in this chapter) with the current state of knowledge about proximity and individual dimensions thereof (see Chapter 3) serves as an appropriate basis for discussion. It will focus on similar elements from both of the previously presented paths of considerations. Also, it will cover those parts of the study that significantly distinguish the conclusions of the empirical part from the reflections based on the analysis of the related literature. Individual dimensions of the discussion will be developed as a result of distinguishing major thematic areas. Those areas include a list of proximity dimensions, relationships between individual proximity dimensions and the dynamics of proximity dimensions and, finally, the importance of proximity in the establishment and development of cooperation (cooperative relationships) in these types of organizations. Similarities and differences between the literature and the assumptions and results of the study will be identified for each of the aforementioned areas. The list of categories used to develop individual proximity dimensions results from the interpretation based on the analysis of the data collected at the qualitative research phase and the logic of distance and similarity applied. The resultant set of categories points to the well-​grounded concept of proximity by Boschma (2004, 2005a, 2005b) because these categories proved to be largely consistent with the basic dimensions of proximity proposed by Boschma: geographical, social, organizational, and institutional.The only significant difference was a disparate perspective on cognitive proximity applied in this book, which is the fifth dimension proposed by Boschma. The results of the qualitative research pointed to the need to focus on the competence of entities cooperating in COs rather than on the broadly and generally

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The role of proximity:The results of qualitative research 143 understood “knowledge system.”The overlap between the categories derived from the study and the proximity dimensions described in the subject literature is in favor of both the validity of the interpretation used and Boschma’s proximity concept itself, which is supported by the results of independent research. It should be noted that although used relatively most frequently, the proximity dimensions prepared by Boschma are not exhaustive with respect to the set of all dimensions available in the literature: apart from theoretical categories, the meanings of which overlap with the categories already mentioned (e.g., technological dimension as a subdimension of the cognitive proximity dimension) at least partially, the literature also includes terms that transcend the established pattern –​for example, virtual proximity, internal proximity, external proximity (Zeller, 2004), generational proximity, or status proximity (Godart, 2015). The introduction of categories constituting individual proximity dimensions from the qualitative research made it possible to avoid the tendency of using the term “proximity” primarily for studies and considerations focused on issues of corporate innovation or issues of knowledge transfer, which is observed in the literature. An attempt to develop the theoretical path, which would be best suited to the issues analyzed in this book –​that is, the creation and development of cooperative relationships between entities forming COs –​resulted in a detailed picture of the analyzed sphere of reality. The literature analysis revealed a relatively narrower viewpoint adopted by a large proportion of authors who use “proximity” as a category useful to describe or explain the complexity of a given area of social life. The proximity dimensions identified as a result of empirical research were used to develop a concept of the development of proximity in COs and to formulate –​as part of this concept –​two separate sets of research hypotheses pointing to the significance of individual proximity dimensions at four separate levels of cluster cooperation (hypotheses: H.I–​H.IV) and to relationships between selected dimensions (hypotheses: H1–​ H2). The consideration of as many as five proximity dimensions in the concept and linking it with the elements of the concept of the trajectory of development of cooperative relations in COs provide for the development of a broad approach to the analyzed issue. A comprehensive approach of this type is not common in the literature; scholars often use certain proximity dimensions to study a specific issue and analyze a specific part of reality such as geographical, organizational, and technological proximity (Korbi & Chouki, 2017), technological, geographical, and cultural proximity (Guan & Yan, 2016), or geographical, organizational, and social proximity (Heringa et al., 2014). Nevertheless, classic proximity dimensions are applied in papers dedicated to Danish clean-​technology sectors (Hansen, 2015), cooperation in terms of innovative solutions for people with type 2 diabetes in North America and Europe (Hardeman et al., 2015), or the innovation activity (including collaboration with other entities) of Norwegian companies (Fitjar et al., 2016). However, it should be added that the use of the five proximity dimensions (in the form proposed by Boschma or a similar one) in studies and analyses

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144  The role of proximity:The results of qualitative research is relatively less frequent than the selection of only several such dimensions for analysis. The proposed concept of proximity development in COs also takes into account the dynamics of proximity in each of the dimensions. From the point of view of this publication, it is interesting not only to identify relationships between proximity dimensions and a specific area of reality or individual proximity dimensions but also to define the conditions under which proximity may develop or slowly fade away and disappear. The considerations given in this chapter that link the distinguished cooperation levels in the analyzed COs with specific proximity dimensions necessary “at the start” (“input” proximity) and created or evolving “at the end” (“output” proximity) of each of these levels serves as an example of tackling the dynamic aspect of proximity. The selection of COs and the companies forming them as objects whose functioning was used as an example to observe and establish the presence of relationships characterized by proximity (in its different dimensions) was also very important in terms of the significance of the created concept of proximity development. This issue appears to be relevant because few papers discussing different proximity dimensions within COs (see Chapter 3) were identified among the publications relating to the proximity of entities operating in the economic sector. Scientific papers linking the issue of proximity with the concept of clusters were primarily identified in the databases (Web of Science Core Collection and Scopus). The papers from this group that are worth mentioning include a paper on the Japanese automotive cluster (focusing, however, only on geographical proximity) (Yamada & Kawakami, 2015); a paper on the French Aerospace Valley (taking into account geographical and organizational proximity) (Levy & Talbot, 2015); and a paper on the Amsterdam IT and new media cluster (only directly referring to geographical proximity) (Bahlmann, 2015). The use of four COs representing very diverse industries (ICT and metal) and the development of the concept of proximity development referring to the five dimensions of proximity (based on research within these COs) is a significant contribution to the existing scientific output concerning the analyzed issues. In addition, the currently widely discussed issue of clustering, in the extremely rarely addressed aspect of COs, is linked with the issue of establishing and developing cooperative relationships. The latter is presented in the form of a cohesive theoretical concept and discussed with the use of a theoretical category of proximity. All that contributes to the fact that the present work has no equivalent in the literature on the subject. As the subject literature lacks similar publications linking the concept of proximity with the concept of the CO, it is impossible to find scientific papers that address the three aforementioned topics (COs, development of cooperation, and proximity) at the same time. A similar shortage was identified for papers tackling issues related to proximity in connection with the CO concept and grounded theory methodology, which was used in this work to collect, analyze, and interpret data.

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Notes 1 Habitus is a concept derived from the theory of the French sociologist Pierre Bourdieu (1986, 1996), and it denotes a certain common set of knowledge and competencies of entities anchored in a specific area of reality (in this case –​a specific sector). These are necessary for the entity’s proper and effective functioning in this sphere (Lis & Lis, 2014). 2 In this case, it was limited to identifying only those dimensions of proximity that are central to each level, omitting other dimensions of proximity that appear at each level. An additional dimension of proximity appearing “at the input” of level I may be social proximity (e.g., if members knew each other before joining the CO), organizational (e.g., if members participated in the same industry organizations prior to joining the CO), or institutional (in the case of level IV, organizational proximity). In turn, “at the output” of each level (I–​IV), there is social and competence proximity (in terms of the level of competence development).

References Argyle, M., & Kendon, A. (1967). The experimental analysis of social performance. Advances in Experimental Social Psychology, 3, 55–​98. Bahlmann, M. D. (2015). Finding value in geographic diversity through prior experience and knowledge integration: A study of ventures’ innovative performance. Industrial and Corporate Change, 25(4), 573–​589. Boschma, R. (2004). Proximité et innovation [Proximity and innovation]. Économie Rurale, 280(1), 8–​24. Boschma, R. (2005a). Proximity and innovation: A critical assessment. Regional Studies, 39(1), 61–​74. Boschma, R. (2005b). Role of proximity in interaction and performance: Conceptual and empirical challenges. Regional Studies, 39(1), 41–​45. Bourdieu, P. (1986).The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–​258). New York: Greenwood. Bourdieu, P. (1996). Physical space, social space and habitus. Vilhelm Aubert Memorial Lecture, Report, 10, 87–​101. Fitjar, R. D., Huber, F., & Rodríguez-​Pose,A. (2016). Not too close, not too far:Testing the Goldilocks principle of ‘optimal’ distance in innovation networks. Industry and Innovation, 23(6), 465–​487. Glaser, B. G., & Strauss, A. L. (1967). Discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine. Godart, F. C. (2015).Trend networks: Multidimensional proximity and the formation of aesthetic choices in the creative economy. Regional Studies, 49(6), 973–​984. Guan, J. C., & Yan,Y. (2016). Technological proximity and recombinative innovation in the alternative energy field. Research Policy, 45(7), 1460–​1473. Hamel, G., & Prahalad, C. K. (1998). Strategic intent. Boston, MA: Harvard Business Press. Hansen, T. (2015). Substitution or overlap? The relations between geographical and non-​spatial proximity dimensions in collaborative innovation projects. Regional Studies, 49(10), 1672–​1684. Hardeman, S., Frenken, K., Nomaler, Ö., & Ter Wal, A. L. (2015). Characterizing and comparing innovation systems by different ‘modes’ of knowledge production: A proximity approach. Science and Public Policy, 42(4), 530–​548.

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146  The role of proximity:The results of qualitative research Heringa, P. W., Horlings, E., van der Zouwen, M., van den Besselaar, P., & van Vierssen, W. (2014). How do dimensions of proximity relate to the outcomes of collaboration? A survey of knowledge-​intensive networks in the Dutch water sector. Economics of Innovation and New Technology, 23(7), 689–​716. Korbi, F. B., & Chouki, M. (2017). Knowledge transfer in international asymmetric alliances: The key role of translation, artifacts, and proximity. Journal of Knowledge Management, 21(5), 1272–​1291. Levy, R., & Talbot, D. (2015). Control by proximity: Evidence from the ‘Aerospace Valley’ competitiveness cluster. Regional Studies, 49(6), 955–​972. Lis, A. M. (2018). Współpraca w inicjatywach klastrowych. Rola bliskości w rozwoju powiązań kooperacyjnych [Cooperation in cluster initiatives: the role of proximity in the development of cooperative relationships]. Gdansk: Wydawnictwo Politechniki Gdanskiej. Lis, A. M., & Lis, A. (2014). Zarządzanie kapitałami w klastrach: Kapitał społeczny, kulturowy, ekonomiczny i symboliczny w strukturach klastrowych [Capital management in clusters: Social, cultural, economic and symbolic capital in cluster structures]. Warszawa: Difin. Lis, A. M., & Lis, A. (2021). The cluster organization: Analyzing the development of cooperative relationships. London and New York: Routledge. Lundberg, C. C., & Wolek, F. W. (1970). Changing executive style: A model for professional development. Philadelphia, PA.: Department of Industry, Wharton School of Finance & Commerce, University of Pennsylvania. Taylor, F. W. (1919). The principles of scientific management. New York, London: Harper & Brothers. White, R. W. (1959). Motivation reconsidered: The concept of competence. Psychological Review, 66(5), 297–​333. Yamada, E., & Kawakami, T. (2015). Assessing dynamic externalities from a cluster perspective: The case of the motor metropolis in Japan. The Annals of Regional Science, 54(1), 269–​298. Zeller, C. (2004). North Atlantic innovative relations of Swiss pharmaceuticals and the proximities with regional biotech arenas. Economic Geography, 80(1), 83–​111.

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6 The role of proximity in the development of cooperation in cluster organizations The results of quantitative research

This chapter discusses the results of quantitative research on the development of proximity in COs. The first part operationalizes all of the most important variables of the generated concept of proximity. Then, it presents descriptive statistics pertaining to the level of the development of proximity in COs, conceptualized in different dimensions, beginning with geographical proximity, through competence and social proximity, and ending with organizational proximity. The descriptions also considered the level of commitment of cluster members to cluster-​related activities. The final part of the chapter showcases research models reflecting selected relationships between particular dimensions of proximity in COs, described in the form of three research hypotheses. This pertains especially to relationships identified between four of the distinguished dimensions of proximity, which occur when the COs shift to higher levels of cooperation.

Variable operationalization The quantitative research was based on variables identified at the stage of qualitative research and pertaining to the level of development of proximity in COs. The analysis of the development of proximity was based on variables assigned to the main dimensions of proximity distinguished in the formulated theoretical concept, namely: geographical proximity [GP], social proximity [SP], organizational proximity [OP], and competence proximity, considered in three contexts as scope of competence [CPs], level of competence development [CPl], and access to information and knowledge [CPik]. The research also considered variables pertaining to the level of commitment [C]‌. All of the variables in the study have the form of latent variables, which considerably hinders their operational definition because they are purely theoretical constructs of an abstract nature. For this reason, they cannot be directly observed nor measured. Latent variables integrate different manifest (observable) variables. The properties of the latent variables may be inferred on the basis of measurements of the observable variables which describe them. DOI: 10.4324/9781003194019-6

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148  The role of proximity:The results of quantitative research The observable variables were constructed in the form of a list of statements pertaining to specific dimensions of a given latent variable –​each variable was matched with the appropriate set of statements, which were intended to reproduce all of its actual dimensions as accurately as possible (see Table 6.1). A rating scale (a five-​point Likert scale) was used to measure all variables, which allowed the authors to measure the intensity of a given property. Table 6.1 Operationalization Symbol

Statements

Geographical proximity [GP] GP1 Our company is located quite close to the cluster coordinator GP2 Usually, it doesn’t take me much time to get to my cluster coordinator GP3 Our company is located near most of the other companies in the cluster Competence proximity: scope of competence [CPs] CPs1 Our company cooperates with cluster companies that have the same or very similar competence (belong to the same industry; have a similar business profile) CPs2 Our company cooperates with cluster companies that are different from our field of expertise (they belong to the same industry and their competencies are complementary to ours) CPs3 Our company cooperates with cluster companies that have a completely different set of competencies (they belong to other industries) Competence proximity: level of competence development [CPl] CPl1 Our company cooperates with cluster companies whose level of development (technology, knowledge, quality of staff) is higher or much higher than our company CPl2 Our company cooperates with cluster companies whose level of development (technology, knowledge, quality of staff) is similar to our company CPl3 Our company cooperates with those cluster enterprises whose level of development is lower or much lower than our company Competence proximity: access to information and knowledge [CPik] CPik1 One of the effects of joining the cluster is that my company has gained access to a wide variety of information (albeit general information) CPik2 One of the effects of joining the cluster is that my company has gained access to selected information, fully tailored to the profile and needs of my business CPik3 One of the effects of joining the cluster is that my company has gained priority in receiving important information about changes in the external environment CPik4 One of the effects of joining the cluster is that my company is less worried about sharing certain confidential information with selected cluster companies CPik5 One of the effects of joining the cluster is that my company, together with other selected cluster companies, takes part in processes of creating new knowledge (through working groups, project groups, etc.)

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The role of proximity:The results of quantitative research 149 Table 6.1 Cont. Symbol

Statements

Social proximity [SP] SP1 I try to take advantage of every opportunity to make contact with unknown or poorly known members of the cluster SP2 I foster relationships with cluster partners and try to improve them SP3 Thanks to participation in the cluster, we have been able to build trust in relationships with some of the cluster companies SP4 With respect to cooperation, I try to choose only those companies with which my company has had a positive experience of cooperation SP5 I foster relationships with regional partners (public authorities, schools, universities) and try to improve them Organizational proximity [OP] OP1 Access to a wide pool of resources (both tangible and intangible), provided both by the cluster and by the cluster companies OP2 Improvements in the quality of products and/​or services and/​or reduction of business costs (e.g., through shared group purchases, promotion systems, distribution channels, etc.) OP3 Development of cooperation with other cluster entities –​ implementation of joint projects, development of common products/​ services, setting up joint businesses, etc. Commitment [C]‌ C1 Systematic participation in regular meetings organized within the cluster C2 Participation in additional events organized by the cluster (e.g., fairs, conferences, integration meetings) C3 Participation in working groups within the cluster, focused on the achievement of specific objectives C4 Participation in training organized in the cluster C5 Cooperation with other cluster companies aimed at creating more favorable legal and administrative conditions for our businesses C6 Cooperation with other cluster companies aimed at better matching the educational profile of the region (at different levels of education) to the needs of the cluster companies C7 Participation in project-​oriented groups (participation in working groups oriented towards the implementation of joint projects) C8 Participation in teams oriented toward the development of permanent cooperation between cluster companies (e.g., within the value chain) Source: Authors’ own elaboration

The measurement of geographical proximity [GP] takes into account three variables determining the placement of a given CO vis-​à-​vis the coordinator (physical distance and the time required to close it), as well as the remaining COs (physical distance). Competence proximity in the aspect of the scope of competence [CPs] was measured with the use of three observable variables, which pertained to the scope of competence of cluster partners (differentiation between (i) the same, (ii) relatively close, albeit different, and (iii) a completely different set of

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150  The role of proximity:The results of quantitative research competencies). Competence proximity in the aspect of competence development [CPl] was measured in a similar fashion and was based on the comparison between the levels of the development of the competencies of cluster partners. The third variable in this group –​competence proximity in terms of access to information and knowledge [CPik] –​consisted of five observable variables, among which each corresponded to one of the distinguished categories of information and knowledge obtained thanks to participation in COs and assigned to particular levels of cooperation. For the purposes of the research at hand, social proximity [SP] was measured on the basis of five observable variables referring to the methods of building relationships in COs, which took into account five types of relations: (i) making contacts (breaking the barrier of anonymity); (ii) developing relationships (breaking the barrier of the lack of trust); (iii) building trust; (iv) verifying trust on the basis of the experience of cooperation; and (v) developing relationships with regional partners. The measurement of organizational proximity [OP] focused on three selected areas of cooperation within COs: (i) use of tangible and intangible resources; (ii) improving product/​service quality and/​or reducing costs; and (iii) the development of cooperation with other cluster entities in the scope of carrying out joint projects and developing joint products/​services and launching joint commercial operations. The last variable in the research is the commitment of COs to undertaking cluster activities [C]‌. This variable was measured on the basis of eight observable variables, which correspond to particular levels of cooperation in the CO (for each level of development, two of the most characteristic forms of commitment were selected).

The level of proximity development Geographical proximity For the purposes of this research, geographical proximity was included in the first block of statements. Each of the statements from this block pertained to the issue of geographical proximity in a different way: both in terms of subjective feelings with regard to the distance between the location of the given company and the “seat” of the CO (considered to be the headquarters of the CO coordinator) [GP1] and the majority of the other cluster companies [GP3], plus the subjective feeling of the time needed to reach the seat of the CO (the coordinator) [GP2]. The results relating to the distance between the locations of cluster companies and the seats of their COs (that is, where the CO coordinator resides) allowed the authors to estimate the percentage of companies declaring that they are relatively local to the seat of the CO (among the population of cluster companies participating in the research): positive answers were given by over 60% of the respondents from the companies in the study (the sum of the answers “Somewhat agree” and “Completely agree”). In turn, over

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The role of proximity:The results of quantitative research 151 25% of the companies decided that their location did not allow them to claim to be relatively close to the seat of their CO (the sum of the answers “Somewhat disagree” and “Completely disagree”). Of note is the fact that over 15% of the companies found themselves unable to clearly express their feelings with regard to the distance between their location and the seat of their CO. The topic of the perceived distance between a given company’s place of business and the seat of it’s CO [GP1] is tied to the topic of the estimated period of time necessary to travel that distance [GP2]. In this case, the percentages are similar to those presented in the discussion on GP1. As in the previous statement, here about 25% of the companies selected an answer suggesting a relatively large distance between them and the seat of their CO –​that is, selected the answers “Somewhat disagree” or “Completely disagree” when they were prompted to evaluate the statement “Usually, it doesn’t take me a lot of time to get to my cluster coordinator.” However, there is a noticeable difference in the percentages of responses pointing to the small geographical distance between their companies and others in their clusters: just under 54% of the respondents agreed with the statement in GP2, while in the case of GP1 (“Our company is located quite close to the cluster coordinator”), this percentage exceeded 60%. One could assume, therefore, that the declaration of the relative geographical proximity between the company and its CO addressed in statement GP1 will have its natural consequence in the form of the declaration of the relatively short time to move from one place to the other. However, it turned out that numerous companies that confirmed their close geographical proximity to the seat of their CO nevertheless did not declare the answers “Somewhat agree” nor “Completely agree” in the context of the shortness of travel time between both places. The larger number of such companies with a non-​uniform profile of answers can be found in the group of answers “Hard to say” –​20% of responses, which was close to a 5% rise in comparison to the percentage of undecided responses to GP1. The issue was different –​though still in keeping with the above-​mentioned tendency –​in the case of the proximity of the studied companies and the majority of the remaining cluster companies (belonging to the same CO as the studied company) [GP3]. The largest number of companies continued to select the answers “Somewhat agree” or “Completely agree” in response to the statement “Our company is located near most of the other companies in the cluster.” However, this percentage is noticeably lower in comparison with the two statements (GP1 and GP2) discussed above and did not exceed 40%. Just under 33% of the analyzed cluster companies responded in the negative to the statement, while almost 28% found themselves unable to provide an unequivocal response. Of most significance is the relatively large number of companies which did not provide an unequivocal response; one of the reasons behind this may rest in the lack of capability to correctly assess the distance from other entities comprising the given CO. However, it would seem that the key role here is played by relatively low knowledge of

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152  The role of proximity:The results of quantitative research representatives of the studied companies in their cluster partners. This conclusion is all the more probable in comparison with the previous attempt at explaining the relatively large percentage of “Hard to say” responses, when we consider that in a large part of Polish clusters there is a noticeable variation in the level of commitment of the cluster members –​only a small minority of the cluster members display a high level of commitment, including having broad knowledge of their cluster partners. Competence proximity Another interesting issue arising from the described research on cluster companies was the search for characteristic features of the so-​called competence proximity. Because of its multivariate nature, in the present study this term encompassed three groups of statements, though each of the groups referred to a different aspect of competence proximity: (i) the scope of competencies shared by cluster companies [CPs]; (ii) the level of development of the competencies of cluster companies [CPl]; and (iii) access to information and knowledge [CPik]. The first of the threads devoted to competence proximity [CPs] consisted of statements attempting to diagnose the convergence of the scopes of competencies of cluster companies with the competence of those cluster partners with whom those companies cooperated, with a view to: • •

•

estimating the percentage of companies which focus on cooperation with companies with the same or a very similar scope of competence (belonging to the same industry, having a similar business profile) [CPs1]; estimating the percentage of companies cooperating with those cluster partners that had different, though complementary, scopes of competencies (e.g., companies from the same industry, but with different locations on the value chain) [CPs2]; estimating the percentage of companies cooperating with cluster companies characterized by a completely different scope of competence (belonging to, for example, different industries, non-​complementary with regard to the specific nature of the business operations of the studied companies) [CPs3].

Analysis of the responses obtained with regard to these aspects of competence proximity in the area of the scope of competence allowed the authors to claim that the relatively highest number of positive responses (the answers “Somewhat agree” and “Completely agree”) appeared in relation to cooperation with cluster companies with the same or a very similar scope of competence [CPs1] –​over 50% of the studied companies declared cooperation with cluster companies with such a profile. Only just over 28% of the respondents (the sum of the answers “Somewhat disagree” and “Completely disagree”) reported the opposite experience, while 21% of the analyzed cluster companies found themselves unable to unequivocally respond to this statement.

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The role of proximity:The results of quantitative research 153 It can be assumed that the main reason behind an unequivocal answer was the lack of awareness of the actual competencies of the companies’ partners, with whom the respondents entered into cooperation; it is conceivable that the respondents simply did not pay attention to conscious reflection on the competencies of their cooperating partners. The percentage of answers was somewhat different in the case of the companies cooperating with those cluster partners who had scopes of competencies that differed from their own, though they were complementary [CPs2]. In comparison with the previous statement, a drop in the percentage of positive responses manifested itself, along with a rise in the percentage of negative responses; almost 35% of the studied companies responded “Somewhat agree” and “Completely agree,” while over 36% said “Somewhat disagree” and “Completely disagree.” It is clear, therefore, that the analyzed cluster companies were somewhat reluctant to establish cooperative links with those cluster partners whose scope of competence differed from their own –​even though the statement directly pointed to the fact that, while different, the scope of competence nonetheless formed a complementary relationship with the set of competencies of the respondents. There was a somewhat higher –​in comparison with the previous statement [CPs1] –​ percentage of companies which found themselves unable to unequivocally state their attitude toward entering into cooperation with partners that had a different, albeit complementary, scope of competence –​such a mindset was reported by 29% of the respondents. This tendency, namely the drop in responses confirming the establishment of cooperation with other cluster companies with a different scope of competence and the subsequent rise in negative answers, could also be observed in the case of statements referring to cooperation with those cluster companies that had a completely different scope of competence from those of the studied companies (e.g., representing other, non-​ complementary industries). Just over 25% of the studied companies declared cooperation with such entities, while over half of the cluster companies under analysis answered negatively (“Somewhat disagree” and “Completely disagree”). Under 25% of the cluster companies found themselves unable to provide an unequivocal answer to this question. To conclude the issue of competence proximity in the aspect of the preferred scope of competence of cluster members cooperating with the studied companies, it can be said that the studied cluster companies were more likely to declare initiating cooperation with partners having a similar scope of competence than with cluster companies that had a different scope of competence. The dominant value for each of the above-​ mentioned statements is somewhat symbolic: the most common answer to statement CPs1 was “Somewhat agree” (31%); statement CPs2 –​“Hard to say” (29%); and statement CPs3 –​“Somewhat disagree” (32%). In effect, it can be said that the closer the perceived scope of competence of the cluster partners to the one maintained by the studied company, the larger the chance of maintaining a cooperative relationship between the entities. This points to

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154  The role of proximity:The results of quantitative research the significance of competence proximity (in the aspect of the scope of competence) for the process of establishing and maintaining a cooperative relationship within COs. To gain another perspective on competence proximity, the authors analyzed responses to the group of statements pertaining to the level of development of the competencies of cluster companies [CPl], with which the studied companies entered into cooperative relationships. From the perspective of this part of the study, the goal was to: • • •

estimate the percentage of companies that focus on cooperation with companies that have a higher or much higher level of development than themselves [CPl1]; estimate the percentage of companies that focus on cooperation with companies that have a similar level of development to themselves [CPl2]; estimate the percentage of companies that focus on cooperation with companies that have a lower or much lower level of development than themselves. [CPl3].

To start with the first of these cases –​namely cooperation with cluster partners that had a higher or a much higher level of development [CPl1] –​38% of the studied companies had established such cooperation in the past or remain in such a relationship at present. Just over 34% of the respondents did not establish cooperation with a CO company that had a higher or a much higher level of development, while 28% could not provide an unequivocal answer. The situation is different in the case of cooperation with cluster companies of a similar level of development to that of the studied companies [CPl2]. In this instance, over 51% of the studied companies (the sum of the answers “Somewhat agree” and “Completely agree”) confirmed cooperating with such companies in the past or at present. However, 20% of the respondents did not have such experiences, while almost 29% selected the answer “Hard to say.” When it comes to the last possibility –​cooperation with cluster companies that had a lower or much lower level of development than that of the studied companies [CPl3] –​it is worth pointing out that 34% of the studied companies declared having had such experiences. However, the largest percentage –​38% –​had not established such cooperation to date. The percentage of companies which could not provide an unequivocal answer was similar to that of the previous two statements on this aspect of competence proximity (28%). To conclude this dimension of reflections on competence proximity, it could be said that, relatively, the largest group of the studied companies chose to cooperate with those partners among cluster organizations with a similar level of development. Such a path of cooperation was chosen by half of the studied cluster companies. Establishing cooperation with companies that had a higher or a much higher level of development was less popular, as only 38%

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The role of proximity:The results of quantitative research 155 of the respondents gave positive responses. The least popular choice were cluster partners with a lower or a much lower level of development, though, at 34%, the percentage of the cluster companies declaring such cooperation was not all that much lower than for cooperation with companies that had a higher level of development. From the perspective of the operations of COs, therefore, one should keep in mind that the correct selection of the members of the cluster structure may be of extreme significance in terms of the efficiency of the initiatives undertaken within them. The results of the study seem to suggest that one of the possible scenarios of the development of COs that should be taken into consideration is one where the vast majority of the cluster members have a similar level of development (though not necessarily the same scope of competence –​a distinction that was described earlier in relation to statements CPs1–​CPs3), while the strongest root of the CO will consist of a dozen or so strong players, which apart from reaping the benefits of cooperation with equally strong partners will find numerous paths of cooperation with a large number of companies with a somewhat lower level of development, albeit with the potential to develop further. One obvious consequence of such a perspective on constructing a cluster organization is the need for a thorough evaluation of the developmental potential of the cluster members. Only those candidates who pass such an audit should be admitted. The last of the topics related to competence proximity deals with the statement of access to knowledge and information [CPik] resulting from membership in a given CO. In this part of the study, the authors established how participation in a CO influenced access to knowledge and information within the CO. This element should be treated as extremely significant from the perspective of both the functioning of COs, as well as the actual practical manifestation of competence proximity. On the one hand, competence proximity is a condition, the fulfillment of which facilitates information and knowledge flow in the CO, but, on the other, it is also the effect of this flow, which means that each act of sharing resources of this kind will itself stimulate the growth of competence proximity among the members of a given CO. Competence proximity in the aspect of access to information and knowledge was diagnosed by having the companies respond to five statements –​all referring to knowledge and information with different degrees of significance for the studied companies.The list of statements began with an attempt to establish the percentage of cluster companies which, thanks to their membership in an CO, gained broad access to numerous diverse, though general, sources of information [CPik1]. The subsequent statements raised the importance and significance of the information obtained by the studied companies –​this is how it looked in the case of establishing the percentage of companies fully tailored to the profile and needs of their business [CPik2]. In the next stage, the authors established the percentage of companies which noticed that membership in their COs led to gaining priority in receiving important information on the sociopolitical environment, as well as the

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156  The role of proximity:The results of quantitative research conditions of conducting business in the region and the country [CPik3]. At the same time, statement CPik3 was the last in which a unidirectional flow of information was considered –​from the CO and its members to the studied companies. The two remaining elements of this part of the questionnaire –​which the authors intended to reflect the relatively high level of competence proximity in the discussed aspect –​pertained to the reverse direction of information flow (namely, from the studied cluster company to its partners) [CPik4] and the joint creation of new knowledge [CPik5]. Statement CPik4 attempted to establish the percentage of cluster companies that, owing to their membership in a CO, decided to take the risk of sharing certain confidential information with selected cluster partners, while CPik 5 assessed the percentage of cluster companies that, based on membership in their CO, participated with other (selected) cluster companies in processes of creating new knowledge (e.g., through working groups, project groups, and so on). As one could suspect, the most often achieved advantage of joining a CO –​in the aspect of access to information and knowledge –​was gaining access to broad and diverse, albeit general, sources of information [CPik1]. Such an effect of participation in a CO was indicated by over 61% of the studied companies. The popularity of access to such sources of information is understandable –​this is a resource that is easy to generate within the CO and which can be propagated through such avenues as emails overseen by the cluster coordinator or exchanges of information by representatives of cluster companies during regular meetings within the CO. On the other hand, less than 15% of cluster companies declared that they did not perceive any benefits in this area. What was quite unexpected was that almost 25% of cluster companies could not provide an unequivocal answer as to whether or not their membership in a CO translated into these benefits. Also of note is the fact that this percentage is similar (with minor differences) to the case of the other statements from this group. A somewhat smaller percentage of companies noticed effects in the form of gaining access to a select pool of information fully tailored to their operational profile and needs [CPik2]: positive responses (“Somewhat agree” and “Completely agree”) were given by 57% of the studied companies.A negative response (the sum of the responses “Somewhat disagree” and “Completely disagree”) was indicated by almost 22% of the studied companies –​almost as many as those unable to provide a specific response to this statement. Another topic in the analysis of competence proximity in the aspect of access to information and knowledge was to establish whether or not membership in a given CO translated into gaining privileged access to information on the sociopolitical environment and the conditions of doing business in the region and the country [CPik3]. Just over 42% of the studied companies provided a positive answer (sum of the responses “Somewhat agree” and “Completely agree”), while over 33% of them gave the opposite response (with answers “Somewhat disagree” and “Completely disagree”). The percentage of respondents giving an unequivocal answer (“Hard to

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The role of proximity:The results of quantitative research 157 say”) remained similar to analogous percentages that could be observed in the earlier statements from this group and was 23%. While in the case of the previous statements the percentage of cluster companies providing positive answers on reaping the benefits of membership in a CO specified in a given statement was higher than the percentage of companies with a negative answer, in the case of statement CPik4 –​“One of the effects of joining the cluster is that my company is less worried about sharing certain confidential information with selected cluster companies” –​the situation was the opposite. Just over 25% of the studied companies declared that this effect is visible, while as many as 48% of the respondents selected one of the negative answers (“Somewhat disagree” or “Completely disagree”). Over 26% of the cluster companies were unable to unequivocally say if they experienced the described effect. This may seem surprising, as economic entities are known to distinguish between information that they would like to share with their partners from information they would prefer to keep to themselves. Perhaps, in the course of cooperation within the companies’ COs, it was hard to distinguish whether or not the material shared with the cluster partners should be shared with entities outside of the CO. The last statement from the group of elements assessing competence proximity in the aspect of access to information and knowledge was introduced to determine the percentage of cluster companies cooperating (in the past or at present) with their cluster partners in processes aimed at creating new knowledge (e.g., working groups, project groups, and so on) [CPik5]. It turned out that almost 45% of the cluster companies under consideration did have such experiences, while just over 35% of respondents did not and 20% could not provide an unequivocal answer. However, it should be said that this theoretically most advanced stage in the creation and manifestation of competence proximity in the aspect of access to knowledge and information was reached by a considerable number of companies. It is also worth reiterating that lower percentages of positive responses were obtained with reference to, for instance, sharing part of the companies’ confidential information with their partners from the CO [CPik4] or gaining privileged access to essential information on the sociopolitical environment and the conditions of doing business in the region and the country [CPik3]. This could point to the existence of certain specific mental barriers in the consciousness of part of the cluster companies, which seem to jealously protect their secrets and safeguard their position in the hierarchy of cluster-​related access to the pool of the most essential information. Social proximity Discussions of geographical proximity and competence proximity (the latter discussed in its three main aspects) are followed by social proximity. This element was divided in the questionnaire into five main spheres –​each pertaining to a slightly different form of social coexistence within the CO. The study focused on:

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158  The role of proximity:The results of quantitative research • • • • •

taking advantage of every opportunity to make contact with unknown or poorly known members of the cluster [SP1]; fostering relationships with cluster partners and trying to improve them on an ongoing basis [SP2]; building trust in relationships with some cluster companies [SP3]; with a view to establishing cooperative associations, choosing only those entities with which the studied companies had had previous positive experiences [SP4]; fostering relationships with regional partners (from outside the CO) –​ such as public authorities, schools, and universities –​with a view to their ongoing improvement [SP5].

The first conclusion that can be drawn from the results of the study in this area is that differences in the percentages of companies confirming that they had undertaken the actions described in each statement and the percentages of companies which denied seeing such effects are similar for each of the dimensions described in the list. What follows is a detailed analysis of the results for each of the selected aspects of social proximity. The first of the dimensions of social proximity (taking advantage of every opportunity to make contact with unknown or poorly known members of the cluster [SP1]) is a good indicator of the tendency, which could be observed in the case of the remaining statements from this area –​namely, that positive responses dominate throughout. In the case of statement SP1, a positive answer (sum of the responses “Somewhat agree” and “Completely agree”) was given by over 55% of the studied cluster organizations. On the other hand, over 17% responded negatively (sum of the responses “Somewhat disagree” and “Completely disagree”) with respect to undertaking such action during their membership in the CO. Over 27% of the studied companies could not provide an unequivocal answer. The second aspect of social proximity pertained to fostering relationships with other cluster member and trying to improve them on an ongoing basis [SP2]. As in the case of the previous statement, there was a significant majority of positive responses versus negative –​almost 66% of the studied companies agreed with the statement while just over 13% disagreed. In comparison with the previous statement [SP1], there was a somewhat smaller percentage of companies which found themselves unable to provide an unequivocal answer: 22%. A comparable distribution of answers could be observed in the case of statement SP3, which pertained to building trust in relationships with some cluster companies (achieved through the participation of the given company in its CO). Almost 62% of the studied companies provided a positive answer with respect to undertaking such actions (the sum of the responses “Somewhat agree” and “Completely agree”), while under 16% of the respondents provided a negative answer (the sum of the responses “Somewhat disagree” and “Completely disagree”). Almost 23% of the studied cluster companies gave an unequivocal answer.

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The role of proximity:The results of quantitative research 159 Diagnosing social proximity continued by asking cluster companies about whether they chose only those companies with which they had had previous positive experiences when seeking to establish cooperative links [SP4]. This produced similar responses to the case of the previous statement, albeit with a smaller proportion of companies confirming that they had undertaken such actions –​just over 51%.The number of negative responses was comparably higher –​over 27% of cluster companies reported no such experiences. Almost 22% of the respondents were unable to provide an unequivocal answer. The largest difference between the numbers of positive and negative responses was noted in the case of the statement SP5, which asked about the issue of fostering relationships with regional partners (from outside the CO), such as public authorities, schools, universities, and so on. Over 71% of cluster companies declared that they paid attention to maintaining high-​ quality relations with their regional partners. This is by far the highest positive result among the tested dimensions of social proximity. Just over 12% of the studied companies gave the opposite answer, which turned out to be the lowest negative result in the entire dimension of social proximity. Similarly, the percentage of unequivocal answers was the lowest among this group, amounting to just over 15%. From a holistic perspective on social proximity, reconstructed based on the declarations made by the studied cluster companies, it can be said that the issue of building, maintaining, and improving relationships between cluster entities and other players from the sociopolitical realm (that is, both cluster entities and external entities) was significant for most of the respondents. Despite some differences in the percentages of positive responses provided in the context of particular aspects of social proximity highlighted in the study, there was a significant lead among companies approving of relationship-​ building activities over those cluster members who –​for some reason –​did not engage in the process of building and developing relationships with their cluster partners. Organizational proximity The process of creating and developing relationships may at some point develop into more lasting relationships of an institutional nature. In such a case, cluster partners may decide to align some elements of the way they function –​such actions would point to the emergence of so-​called organizational proximity. In the present study, organizational proximity was tied to three main areas of joint activity: • •

access to a wide pool of resources (both tangible and intangible), provided both by the cluster and by the cluster companies [OP1]; improving the quality of products and/​or services and/​or the reduction of business costs (e.g., through shared group purchases, promotion systems, distribution channels, and the like) [OP2];

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160  The role of proximity:The results of quantitative research •

development of cooperation with other cluster entities –​implementation of joint projects, development of common products/​services, setting up joint business, etc. [OP3].

Analysis of responses obtained for specific areas of joint activities seems to lead to the conclusion that a relatively small share of cluster companies gain benefits from undertaking joint activities with their cluster partners.When it comes to the topic of having access to a wide pool of tangible and intangible resources provided both by the cluster and by the cluster companies [OP1], just over 33% of the studied companies declared benefiting from it (the sum of the responses “Somewhat agree” and “Completely agree”). The opposite response was given by over 42% of the studied companies (the sum of the responses “Somewhat disagree” and “Completely disagree”). Just over 24% were unable to provide an unequivocal response. The share of cluster companies that observed a rise in the quality of their products and/​or services and/​or a reduction of business costs (e.g., through shared group purchases, promotion systems, distribution channels, etc.) [OP2] turned out to be even less significant. A positive effect in this area was reported by just over 23% of the studied companies, while 53% reported that they “completely” or “somewhat” did not undertake such actions. Almost 25% of the analyzed companies selected the answer “Hard to say.” The last of the metrics of organizational proximity highlighted in the study –​the possibility of developing cooperative ties to other cluster entities with a view to implementing joint projects, developing common products/​ services, setting up joint businesses, etc. [OP3] –​paradoxically had relatively the greatest share of companies declaring that they had undertaken such activities (over 37%) and the lowest share of companies that had not undertaken activities in this area (under 40%). In comparison with the previously described aspects of organizational proximity, the percentage of the studied companies which found themselves unable to provide an unequivocal answer remained unchanged (amounting to just over 23% of the cluster organizations studied). To summarize the findings on the dimension of organizational proximity, which manifests itself in the actions described in the three questions posed, it can first be said that the studied cluster companies were somewhat reluctant to combine selected aspects of their operations. What is surprising, however, is that the relatively highest positive response was observed with regard to the aspect of organizational proximity that –​in the authors’ view –​was the most labor and resource intensive, and required the largest degree of trust among the cluster partners: namely, cooperation with other cluster entities to implement joint projects, develop common products/​services, set up joint businesses, etc. It can only be assumed that the popularity of this specific indicator of organizational proximity was caused by the desire to engage in those activities from which the return on investment would be the most significant from the perspective of operating economic entities. The undertaking of actions in the two remaining highlighted aspects of organizational

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The role of proximity:The results of quantitative research 161 proximity in this case could be treated as a certain “half measure” and for this reason could have generated a somewhat lower degree of interest. Commitment Apart from the diagnosis of proximity in the geographical, competence, social, and organizational dimensions, another important element of the present study was to ascertain the nature and intensity of the commitment of cluster companies to the diverse operations undertaken within cluster organizations. To this end, as many as eight different aspects of this commitment were distinguished –​the studied cluster companies were asked to address all of the activities and their own approach (on a five-​point scale, with answers ranging from “Hard to say” up to “We commit often” and “We are fully committed”).The authors attempted to evaluate commitment itself by asking the respondents to address their approach to the following activities undertaken within their COs: • • • • • • • •

systematic participation in regular meetings organized within the cluster [C1]; participation in additional events organized by the cluster (e.g., fairs, conferences, integration meetings) [C2]; participation in working groups within the cluster, focused on the achievement of specific objectives (e.g., introducing group sales, the selection of a joint service provider, etc.) [C3]; participation in training organized in the cluster [C4]; cooperation with other cluster companies, aimed at creating more favorable legal and administrative conditions for their businesses [C5]; cooperation with other cluster companies, aimed at better matching the educational profile of the region (at different levels of education) to the requirements of the cluster companies [C6]; participation in project-​oriented groups and consortia with a view to implementing joint projects [C7], participation in teams oriented toward the development of permanent cooperation between cluster companies (e.g., division of activities within the value chain –​in processes aimed at improving production/​ services, launching joint operations, etc.) [C8].

Even a cursory analysis of the results obtained for these dimensions of commitment allows the authors to claim that the majority of the studied cluster companies either did not engage at all in the activities highlighted by the researchers or did so sporadically. And though specific percentages of answers pointing to a large degree of the cluster companies’ passivity in this area differed depending on the types of activities mentioned in each of the statements, for each of the dimensions of commitment, declarations of inaction were the most common.While the reluctance to engage in activities with a large degree of complexity could be easily explained by pointing to

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162  The role of proximity:The results of quantitative research the necessity of devoting considerable resources on the part of the studied companies, the lack of the will to take part in activities requiring practically no effort on their part may only be explained by pointing to their low level of social capital –​that is, their culturally determined distrust toward others participating in social life (in the nomenclature related to the key term from this publication –​namely, “proximity” –​this could be conceptualized as the desire to maintain a relatively large distance in social relations between individual and collective entities). The first of the dimensions of commitment that was used in the present study was tied to the systematic participation in regular meetings organized within the CO [C1]. Full or at least partial commitment to this type of activity was reported by only 27% of the studied organizations. It should be added that this was also the highest percentage of responses pointing to strong commitment to other activities –​in each of the remaining cases, the participation of the cluster companies in the given activity was lower than in the case of C1. It should also be stressed that the number of companies with weak commitment or none at all was twice as large (almost 52%), while 20% of the companies could not provide an unequivocal answer (i.e., they selected the response “Hard to say”). The situation was somewhat worse in the case of statement C2 pertaining to participation in additional activities organized by the CO (e.g. fairs, conferences, integration meetings). While the share of responses with the lowest level of commitment (the answers “We do not commit at all” and “We commit sporadically”) was lower than in the case of the previous statement (51%), the participation of companies with the strongest commitment (the answers: “We commit often” and “We are fully committed”) was also lower –​by 2 percentage points (25%). Close to 25% of the respondents selected the answer “Hard to say.” Another dimension of commitment –​participation in working groups within the cluster, focused on the achievement of specific objectives (e.g., introducing group sales, the selection of a joint service providers, etc.) [C3] –​was relatively the least positive in its outlook (considering all of the aspects of commitment in the study): the largest number of “uncommitted” companies was observed in this area, accompanied by the smallest number of “committed” companies. When it comes to companies that declared either no commitment at all to the above-​mentioned activities or declared sporadic commitment, this group amounted to 70% of all respondents. On the flip side, companies which committed often or fully comprised less than 14% of the total number of respondents. The number of companies that selected the answer “Hard to say” was also quite low (17%) in comparison with other aspects of commitment mentioned in the study. The commitment of the studied cluster companies to training organized by the cluster [C4] looked a bit better (though the results still point to the very low commitment of cluster companies to activities undertaken within their clusters). Just over 58% of companies did not participate at all in training or participated sporadically. Just over 22% declared that they

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The role of proximity:The results of quantitative research 163 participate often or fully, while 20% of the respondents were unable to provide an unequivocal answer. A similar level of commitment as for the case of statement C4 was observed with regard to cooperation with other cluster companies aimed at creating more favorable legal and administrative conditions for their businesses [C5]. Just over 59% of the studied companies declared a lack of commitment in this regard or declared “sporadic” commitment, while under 22% pointed to being committed “often” or “fully.” As in the case of the previous statement, 20% of the respondents selected the answer “Hard to say.” The aspect of cooperation with other cluster companies aimed at better matching the educational profile of the region (at different levels of education) to the needs of the cluster companies [C6] did not stand out considerably when compared to the previously discussed forms of commitment of cluster companies to activities undertaken within their COs. Over 61% of the respondents did not engage in such activities at all or committed only sporadically. In turn, the percentage of companies which declared engaging in such activities was 22%. A lower number of companies in comparison with the previous dimensions of commitment –​below 17% –​could not provide an unequivocal answer. Similar percentages were observed in the case of activities related to participation in project-​ oriented groups and consortia with a view to implementing joint projects [C7]. The group of uncommitted companies included almost 61% of the respondents, while the group of committed companies made up 20% of the respondents. Almost 19% of the studied companies found themselves unable to provide an unequivocal answer. The last of the dimensions of commitment of cluster companies to activities undertaken within clusters –​namely, participation in teams oriented toward the development of permanent cooperation between cluster companies (e.g., division of activities within the value chain –​in processes aimed at improving production/​services, launching joint operations, etc.) [C8] –​ is at the same time one of those areas in which the commitment of the respondents was the poorest. Almost 70% of the studied cluster companies did not engage at all in these activities or committed only sporadically. Just over 17% of the respondents mentioned engaging fully or at least often, while about 13% could not provide an unequivocal answer. Detailed analysis of the responses on the level of commitment of cluster companies to activities undertaken within their COs does not change in any way the reflections formulated in the introduction: the cluster companies participating in the study mostly avoided undertaking efforts aimed even at shallow commitment to the simplest, least demanding activities. Only from a dozen or so to a maximum of 27% cluster entities (depending on the activity) reported ongoing, high readiness to participate in joint initiatives within the CO. Of note is the characteristic, relatively stable percentage of cluster members who attempted to remain on the margins of not only events undertaken within their COs but also –​perhaps –​even the margins of the

164

164  The role of proximity:The results of quantitative research study itself.The group of undecided entities, which found themselves unable to provide unequivocal answers as to their attitude and actions (or which decided to conceal their actual attitude/​experience from the researchers), varied from a dozen or so to over 23% of the studied companies. One can make the assumption that at least part of that group consisted of uncommitted entities, which for one reason or another concealed their passivity with regard to activities undertaken within their COs (as one should not assume that this group of “undecided” entities included any “committed” companies –​there is no reason to suspect that companies would depreciate their own decisions and actions).

Testing research hypotheses The study considered two research hypotheses formulated in the course of a previously developed concept of proximity in COs: H1: Geographical proximity and competence proximity (scope of competence) are important for the constitution and development of a cluster organization because they have a positive impact on the commitment of cluster members, which, in turn, has a positive impact on the development of social proximity and institutional proximity. H2: Social proximity is important for the development of competence proximity (level of competence development) and organizational proximity. The hypotheses tested below introduced certain modifications of the above statements. Hypothesis H1 tested focuses only on two dimensions of proximity: namely, geographical proximity and social proximity, excepting competence proximity (scope of competence) and institutional proximity. In turn, hypothesis H2 takes into account competence proximity in the context of access to information and knowledge. Hypothesis H2′ is an extension of hypothesis H2 and additionally includes the commitment variable. Table 6.2 introduces the content of the three tested hypotheses and their components. In order to test the research hypotheses H1, H2, and H2′, five latent variables were selected: geographical proximity [GP], competence proximity (access to information and knowledge) [CPik], social proximity [SP], organizational proximity [OP], and commitment [C]‌. The final selection of the observable variables describing the assumed latent variables was influenced by exploratory factor analysis. In the case of social proximity, the authors decided –​for conceptual reasons –​to eliminate the observable variable SP5 (though its subtraction from the model did not change the assumptions of the theoretical concept).The relationships between the hypotheses (resulting from the content of hypotheses H1, H2, and H2′) were reflected in models 1, 2, and 2’ (see Figure 6.1).

165

The role of proximity:The results of quantitative research 165 Table 6.2 The texted hypotheses Model 1 H1: Geographical proximity has a positive impact on the commitment of cluster members, which, in turn, has a positive impact on the development of social proximity

H1.1: Geographical proximity has a positive impact on the commitment of cluster members H1.2: The commitment of cluster members has a positive impact on the development of social proximity

Model 2 H2: Social proximity has a positive impact on the development of both competence proximity and organizational proximity

H2.1: Social proximity has a positive impact on the development of competence proximity (access to information and knowledge) H2.2: Social proximity has a positive impact on the development of organizational proximity

Model 2′ H2′: Social proximity has a positive H2′.1: Social proximity has a positive impact on the commitment of impact on the commitment of cluster cluster members, which, in turn, members has a positive impact on the H2′.2: The commitment of cluster development of both competence members has a positive impact on the proximity and organizational development of competence proximity proximity (access to information and knowledge) H2′.3: The commitment of cluster members has a positive impact on the development of organizational proximity Source: Authors’ own elaboration

Model 2’

Model 1 H1.1

H2’.2

Commitment [C]

H2’.3

H2’.1

H1.2 Geographical proximity [GP]

H2.1

Social proximity [SP] H2.2

Figure 6.1 Conceptual models. Source: Authors’ own elaboration

Model 2 Competence proximity [CPik]

Organizational proximity [OP]

16

166  The role of proximity:The results of quantitative research Model 1 reflects the dependencies between geographical proximity, the commitment of cluster companies, and the development of social proximity in the CO, described in the first research hypothesis H1 and its supplementary component hypotheses H1.1–​H1.2. Model 1 makes use of three latent constructs and 15 observable variables. The measurement of geographical proximity included three observable variables, which were set on the level of variable operationalization (GP1–​GP3). Social proximity was limited to four among five of the observable variables defined herein (SP1–​ SP4). The commitment variable was measured on the basis of the previously defined observable variables, corresponding to the distinguished forms of commitment on each level of cooperation (C1–​C8). Model 2 maps relationships between three dimensions of proximity: namely, social, competence, and organizational proximity. It corresponds to the second posed research hypothesis H2, which is subdivided into the hypotheses H2.1–​H2.2. Model 2 includes three latent constructs and 12 observable variables. Social proximity was measured on the basis of the same factors as in the case of Model 1 (SP1–​SP4).Variables pertaining to competence proximity (access to information and knowledge) (CPik1–​CPik5) and organizational proximity (OP1–​OP3) were adopted in the model without alteration. Model 2′ is an extension of Model 2, which apart from three dimensions of proximity includes the commitment of the cluster members, described in hypothesis H2′ and its components H2′.1–​H2′.3. This model is predicated on four latent constructs and 20 observable variables. To measure particular variables, the same factors were used as in the case of Models 1 (social proximity, commitment) and 2 (social, competence, and organizational proximity). The testing of all three theoretical models (1, 2, and 2’) with the use of structural equation modeling was performed in two stages. In the first stage, confirmatory factor analysis (CFA) was performed, supplemented with exploratory factor analysis (EFA) –​both on the basis of maximum likelihood estimation. Having tested the validity of the measures of the measurement model, the authors constructed structural models and analyzed the relationship paths present in each of the models. Model 1 –​testing hypothesis H1 Exploratory factor analysis performed with the use of maximum likelihood estimation confirmed a very large fit of observable variables to latent constructs (the KMO sampling adequacy measure equaled 0.850; a significant result of Bartlett’s test of sphericity points to the presence of latent variables, p < 0.001). Confirmatory factor analysis was performed –​just as in the case of EFA –​on the basis of maximum likelihood estimation. Figure 6.2 depicts the analyzed Model 1. On the basis of CFA, it was confirmed that Model 1 reached the goodness of fit recommended in literature. The χ2 value was 220.243, while RMSEA

167

The role of proximity:The results of quantitative research 167

?1

GP1

?2

GP2

?3

GP3

SP1

?12

SP2

?13

SP3

?14

SP4

?15

SP

GP

C

C1

C2

C3

C4

C5

C6

?4

?5

?6

?7

?8

?9

C7

C8

?10

?11

Figure 6.2 Model 1. Source: Authors’ own elaboration

Table 6.3 Confirmatory factor analysis results for Model 1 N

χ2

p-​value RMSEA pclose AIC

400 220.243 0.0000 0.059

BIC

CFI

TLI

SRMR CD

0.027 17609.150 17800.740 0.927 0.916 0.047

0.989

Source: Authors’ own elaboration

was 0.059.The incremental fit indices exceeded 0.9: the CFI value amounted to 0.927, while TLI was 0.916. Table 6.3 presents the values of selected metrics obtained through CFA. Validity tests of the proposed model were also successful (see Table 6.4): almost all of the standardized factor loadings exceeded the acceptable 0.5 level (with the exception of variable SP4, for which the loading value was approximately 0.36). For each of the latent variables, values of Cronbach’s α reliability coefficient exceeded the minimum value of 0.6, while in the case of variables GP and C these values exceeded well over 0.7, which speaks to the high validity of the scale. Positive results of the performed evaluation of the measurement model allowed for its use in the next stage –​namely, structural modeling, during which the relationships between latent variables were evaluated. On this basis, it became possible to test the research hypotheses posed in the conceptual model. Based on the structural modeling process, the authors determined that the model does not, however, fulfill the expected quality criteria, which makes it impossible to determine causal relationships between the analyzed variables.

168

168  The role of proximity:The results of quantitative research Table 6.4 Standardized parameter estimates for Model 1 Variables GP C

SP

GP1 GP2 GP3 C1 C2 C3 C4 C5 C6 C7 C8 SP1 SP2 SP3 SP4

Cronbach’s α

Factor loading

p-​value

0.75

0.808 0.824 0.498 0.693 0.697 0.703 0.551 0.704 0.583 0.704 0.678 0.552 0.673 0.632 0.357 0.198 0.282 0.560

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000

0.86

0.63

Cov(GP, SP) Cov(GP, C) Cov(SP, C) Source: Authors’ own elaboration

Nevertheless, thanks to the analysis, it is possible to determine co-​ occurrence correlations between particular variables. The obtained results of variable correlation analysis did not provide a sufficient basis for rejecting hypothesis H1.1, as a statistically significant positive correlation was confirmed between geographical proximity and the commitment displayed with regard to various cluster activities. In addition, the obtained results did not provide a sufficient basis for rejecting hypothesis H1.2. As the results of the correlation analysis between commitment and social proximity show, there exists a statistically significant positive correlation. Model 2 –​testing hypothesis H2 In the case of Model 2, exploratory factor analysis also confirmed high goodness of fit of the constructs (the KMO sampling adequacy measure equaled 0.817; a significant result of Bartlett’s test of sphericity points to the presence of latent variables, p < 0.001). In the case of Model 2, confirmatory factor analysis was performed –​Figure 6.3 includes the variables and their correlations as described in Model 2. As a result of the analysis, it was confirmed that the values of all of the indicators under analysis fall within the recommended ranges: the χ2 value was 179.49, while the RMSEA was 0.079 (which falls within the acceptable range, albeit close to the upper acceptable limit for a good fit). The CFI and TLI values almost approached the minimum value of 0.9 assumed for

169

The role of proximity:The results of quantitative research 169

?1

?2

?3

SP1

?9

SP2

?10

SP3

?11

SP4

?12

OP1

OP2

SP

OP

OP3 CPik

CPik1

CPik2

CPik3

CPik4

CPik5

?4

?5

?6

?7

?8

Figure 6.3 Model 2. Source: Authors’ own elaboration

Table 6.5 Confirmatory factor analysis results for Model 2 N

χ2

p-​value RMSEA pclose AIC

400 179.490 0.0000 0.079

BIC

CFI

TLI

SRMR CD

0.000 14442.004 14597.671 0.898 0.892 0.054

0.980

Source: Authors’ own elaboration

models with a good fit (CFI =​0.898, TLI =​0.892). Table 6.5 presents the values of selected metrics obtained through CFA. In the next step, the authors performed validity tests for all of the constructs used in the proposed model (see Table 6.6). For Model 2, just like in the case of Model 1, with the exception of the variable SP4, the standardized factor loadings for the variables exceeded the acceptable 0.5 level. All of the loadings were also statistically significant (p ≤ 0.001). Somewhat poorer results were obtained for scale validity analysis –​Cronbach’s α reliability coefficient exceeded 0.7 for the variable CPik, while in the case of the variables SP and OP it was 0.63 and 0.55, respectively. As a result of structural modeling, it turned out that Model 2 also does not fulfill the expected quality criteria. Therefore, for further analysis the authors limited themselves to analyzing correlations between the variables SP, CPik, and OP. The obtained result of the variable correlation analysis did not provide a sufficient basis for rejecting either of the two hypotheses comprising hypothesis H2. A statistically significant correlation between social proximity and competence proximity in the aspect of access to information and knowledge

170

170  The role of proximity:The results of quantitative research Table 6.6 Standardized parameter estimates for Model 2 Variables SP

CPik

OP

SP1 SP2 SP3 SP4 CPik1 CPik2 CPik3 CPik4 CPik5 OP1 OP2 OP3

Cronbach’s α

Factor loading

p-​value

0.63

0.575 0.659 0.646 0.307 0.614 0.697 0.595 0.570 0.503 0.602 0.691 0.407 0.529 0.714 0.529

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.73

0.55

Cov(OP, CPik) Cov(SP, CPik) Cov(SP, OP) Source: Authors’ own elaboration

in the CO supported hypothesis H2.1. Furthermore, the relationship identified between the SP and OP points to the existence of a correlation between the development of social and organizational proximity (H2.2). Model 2′ –​testing hypothesis H2′ In the case of Model 2′, the fit of observable variables to latent constructs based on EFA turned out to be very high (the KMO sampling adequacy measure equaled 0.884; a significant result of Bartlett’s test of sphericity points to the presence of latent variables, p < 0.001). Figure 6.4 presents variables and their relationships for Model 2′, which were obtained through CFA. The CFA pointed to an acceptable fit of Model 2′. The χ2 value was 519.636; CFI, 0.896; TLI, 0.893; while the RMSEA was 0.074 (see Table 6.7). Model 2′ included the same variables as in the case of the previous two models (SP and C from Model 1, and SP, OP, and CPik from Model 2). For this reason, standardized factor loadings of the observable variables were on the same acceptable levels as in the case of Models 1 and 2. The same values of Cronbach’s α reliability coefficient were obtained for specific latent variables (see Table 6.8). Variable correlation analysis for this model again did not provide a sufficient basis for rejecting any of the hypotheses formulated as part of hypothesis H2′. The authors determined the existence of a statistically significant positive correlation between social proximity and the commitment of cluster companies (which supports hypothesis H2′.1). A relationship was identified between commitment and proximity in the competence aspect by way

17

The role of proximity:The results of quantitative research 171

?9

OP1

?10

OP2

?11

OP3

?12

?13

?14

?15

?16

CPik1

CPik2

CPik3

CPik4

CPik5

SP1

?17

SP2

?18

SP3

?19

SP4

?20

CPik

OP

SP

C

C1

?1

C2

C3

?2

C4

?3

?4

C5

?5

C6

?6

C7

?7

C8

?8

Figure 6.4 Model 2′. Source: Authors’ own elaboration

Table 6.7 Confirmatory factor analysis results for Model 2′ N

χ2

p-​value RMSEA pclose AIC

400 519.636 0.000 0.074

BIC

CFI

TLI

SRMR CD

0.000 23533.172 23796.609 0.896 0.893 0.053

0.989

Source: Authors’ own elaboration

of better access to diverse sources of information and knowledge (which supports hypothesis H2′.2), as well as between commitment and organizational proximity (which supports hypothesis H2′.3).

Conclusion The study paints a rather unfavorable picture of the functioning of the studied COs. The attitude of the cluster companies could be described as passive –​first and foremost in the scope of cooperating with other cluster entities. The vanguard for such a discomforting picture is the results of studies in the area of geographical proximity.What is mostly of concern is not the percentage of companies reporting the long or short distance from their places of business to the seat of the CO (the location of the coordinator) or to most of their cluster partners (as this is something that the companies have no control over) but the relatively large share of companies which found themselves unable to either specify such distances or the time required to arrive at

172

172  The role of proximity:The results of quantitative research Table 6.8 Standardized parameter estimates for Model 2′ Variables SP

CPik

C

OP

SP1 SP2 SP3 SP4 CPik1 CPik2 CPik3 CPik4 CPik5 C1 C2 C3 C4 C5 C6 C7 C8 OP1 OP2 OP3

Cov(OP, C) Cov(OP, CPik) Cov(OP, SP) Cov(C, CPik) Cov(C, SP) Cov(CPik, SP)

Cronbach’s α

Factor loading

p-​value

0.63

0.565 0.660 0.652 0.307 0.620 0.689 0.574 0.568 0.528 0.692 0.717 0.699 0.556 0.703 0.583 0.684 0.680 0.502 0.686 0.510 0.694 0.519 0.552 0.598 0.563 0.719

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.73

0.86

0.55

Source: Authors’ own elaboration

the seat of the CO. Such an attitude speaks to the rather low level of interest among a large section of the cluster members regarding the character of the CO and its other members. One topic highlighting a certain passivity among the studied companies was the analysis of references to indicators of competence proximity in its various aspects. The respondents who were the most likely to enter into cooperation with those among their cluster partners were the ones who represented the same industry and had a similar scope of competence. In the case of companies with diverging scopes of competencies, the situation was far worse, not to mention the entities with entirely different scopes of competencies. Given the level of development of the competencies of cluster partners, it is clear that the studied companies focused on establishing cooperation with those cluster members at a similar level of development. Of note is the characteristic distribution of the opinions of the studied companies regarding access to knowledge and information: in accordance with predictions, the largest percentage of cluster companies reported that they

173

The role of proximity:The results of quantitative research 173 obtained broad access to diverse, albeit general, sources to information. With the growing degree of “exclusivity” in access to information and knowledge, the share of companies declaring access (also in accordance with predictions) grew smaller, though this tendency was broken when it came to the last dimension –​namely, the creation of new knowledge in cooperation with cluster partners. It appears that this element may be connected to the equally surprising large share of cluster companies in the most advanced of the aspects of organizational proximity –​that is, the possibility to cooperate with other cluster companies in the scope of the implementation of joint projects, the development of common products/​services, setting up joint business, and so on. Similarly to that case, also in the context of the discussed dimension of competence proximity, one can assume that the probable motive behind this sudden growth of interest in relatively advanced activities was the desire to gain valuable benefits of such cooperation.What is more, the goals set before the entities start cooperating –​both in terms of creating new knowledge as well as implementing joint projects –​could have been similar and allowed for their achievement by way of a single act of cooperation. The situation was more positive in the area of social proximity. Regardless of the dimension, from 50% to 70% of the respondents declared undertaking activities characteristic of the given indicator. In turn, the very low level of joint activities in terms of organizational proximity points to weak cooperation in the studied COs. Only a small percentage of cluster companies participated in the forms of organizational cooperation specified in the study. Unexpectedly, it turned out that the relatively most popular was ascribed to the form of organizational “communalization,” which –​in the authors’ opinion –​required the most serious investment (in terms of different types of resources: from time to trust itself). A possible explanation for this was included in the text on competence proximity in the aspect of access to information and knowledge. The passive stance of cluster companies was the most visible in the statement pertaining to their commitment to the activities of the CO. It turned out that the studied companies exhibited low levels of activity on each of the levels of cluster cooperation despite the fact that, among the COs’ stated aims, cooperation was practically free from limitations associated with geographical distance. Depending on the indicator, between 50% and 70% of the analyzed cluster companies did not exhibit considerable commitment (or declared no commitment whatsoever). The present study also allowed the authors to put to the test the hypotheses H1, H2, and H2′, formulated based on previously held qualitative research. All three conceptual models –​Model 1 (based on hypothesis H1), Model 2 (based on hypothesis H2), and Model 3 (based on hypothesis H2′) –​turned out to be true under the conditions of the study. However, as the result of structural modeling it transpired that not one of the three models fulfills the expected quality criteria. For this reason, while testing the research hypotheses, the authors limited themselves to confirmatory factor analysis of variable correlations.

174

174  The role of proximity:The results of quantitative research Using the results of the study, the authors determined the existence of statistically significant positive correlations between (i) geographical proximity and commitment, and (ii) commitment and social proximity (Model 1). This means that as geographical proximity and commitment increases, one may also expect the development of relationships between cluster partners.The opposite is also true –​the larger the geographical distance between members may be tied to a drop in commitment on the part of cluster entities and a withering of relationships between them. Statistically significant positive correlations were also identified between (iii) social proximity and competence proximity (in the aspect of access to information and knowledge), and (iv) social proximity and organizational proximity (Model 2). The obtained results allow the conclusion to be drawn that with the development of relationships between cluster partners there is a growth in the similarities between their competence systems through the facilitated access to knowledge and information in the CO (competence proximity), as well as the level of mutual interorganizational relationships and the potential to develop cooperation (organizational proximity). The commitment of cluster companies, correlated with each of the above-​mentioned variables –​that is, social, competence, and organizational proximity (Model 2′) –​was identified as a crucial factor in the analyzed network of relationships.

175

7 Application of the generated concept of proximity to selected cluster organizations in Europe

In this chapter, three case studies covering selected European COs are presented: Techtera (France), Cluster Kybernetickej Bezpečnosti (Slovakia), and the Bulgarian Fashion Association (Bulgaria). They focused on applying the generated concept of proximity in COs.This made it possible, on the one hand, to analyze the development of the described COs using the category of proximity, and, on the other hand, to empirically test the usefulness of the developed concept in the same comparison groups operating in different industries and locations. Each case study followed the same pattern: geographical proximity was discussed first, followed by social, competence and organizational proximity, and finally the involvement of members in cluster activities. The final section of the chapter is a cross-​sectional discussion of the level of development of each of the proximity dimensions identified, as determined by the conducted research.

The case of Techtera General information Founded in 2005,Techtera is a CO operating in the textile industry in France and enjoying the status of a French innovation cluster. Techtera has been awarded the “Cluster Management Excellence Label Gold” certificate, issued at European level as part of the European Cluster Excellence Initiative, an initiative under the aegis of the European Commission. The Gold Label acknowledges the cluster’s high level of excellence in the management of its activities and confirms its commitment to continuous improvement. Techtera is the first European cluster operating in the textile industry to achieve the Gold Label –​currently only 69 out of 2,000 European clusters is certified at such a level. Currently (2022),Techtera employs 14 people but, in addition, part of the Techtera management structure is made up of members of cluster entities and independent experts.Thus, the final number of people involved in the processes conducted by Techtera is much higher. Techtera is an organization focused on undertaking comprehensive innovation activities in the area of textiles. The ambition of Techtera and its members is to set new standards for the textile industry in France, Europe, DOI: 10.4324/9781003194019-7

176

176  The concept of proximity in selected European cluster organizations and across the world.Techtera’s 221 member entities are active in a wide variety of fields, and the combination of their competencies makes it possible to cover innovative activities in areas such as the cultivation of plants of value to the textile industry, the preparation and spinning of textile fibers, the production and weaving of textiles, knitwear, carpets and rugs, cordage, ropes, twines, etc. Furthermore, Techtera and its member companies are making an effort to find new applications for textile products and services offered by cluster companies in areas such as healthcare and hygiene, fire protection, aeronautics, smart materials, and Industry 4.0, among others. To achieve these goals,Techtera manages a network of companies, research laboratories, technical centers, universities, and colleges of other types (grandes ecoles) to stimulate innovation in the sector. Techtera acquires and implements R&D projects at both regional and national, as well as European and even global level. Techtera organizes and leads workshops and working groups to design textile solutions to meet the technological and economic challenges of the textile sector. It supports collaborative approaches, and does so from the very inception of the project idea through to the marketing of the products and services produced. The specifics of Techtera’s operation are well illustrated by its division into three main axes of action: (i) smart, high-​performance materials; (ii) the circular economy and the resource economy; and (iii) the industry of the future and new business models. Each of these axes is made up of specific themes, the use of which allows Techtera to support the activities of its members at each stage of the projects they are engaged with. Axis 1 –​smart, high-​performance materials –​is a highly competitive area, particularly at the global level. Hence, Techtera makes great efforts to pursue innovative projects related to themes such as: (i) high-​performance textile materials and systems (development of new materials and industrial processes for related technologies); (ii) textiles and composites (simulation of properties, industrialization/​automation of processes, biomaterials, recycling processes, composite sensors); (iii) additive manufacturing (with emphasis on the production of multifunctional, configurable materials); and (iv) intelligent (smart) textiles and textile systems (able to respond to external stimuli) of different natures (e.g., passive–​active). The nature of textiles makes them ideal candidates for the future development of flexible sensors, actuators, and other flexible energy sources. Axis 2 –​the circular economy and the resource economy –​is also composed of four sub-​themes: (i) bio-​based and alternative materials; (ii) textile recycling (based on technological development, but also on flow management: securing waste, selective sorting, chemical, mechanical, and thermomechanical recycling processes, recycling of composites, etc.); (iii) eco-​efficient processes (strongly linked to the idea of sustainable development and the need to focus on such technologies for the production and disposal of materials that have the potential to contribute to environmental protection, on the one hand, and, on the other hand, to the strengthening of

17

The concept of proximity in selected European cluster organizations 177 competitiveness by enriching the value chain with new links); and (iv) short circuits (the greatest possible compatibility of the product/​service with the potential of the place/​mode of production –​minimizing resource expenditure in the broadest sense). Axis 3 –​the industry of the future and new business models –​is the starting point for establishing a presence in the field of Industry 4.0 and Internet of Things. Ubiquitous digitalization means a whole range of new opportunities to take advantage of both in terms of production, distribution, and recycling or disposal of materials, but above all in finding new applications for smart solutions in textiles. These trends can manifest under three main sub-​themes: (i) vertical integration of the industry (related, for example, to flows within the existing value chain and the use of digitalization to optimize the processes that make up this chain); (ii) horizontal integration of industry (extended factory) –​moving toward the “extended factory,” specializing in individual subindustries and creating coherent, unambiguous, and facilitating conditions for them to flourish; and (iii) “servitization” –​ shifting the emphasis from product supply to service supply. This is a huge opportunity to be taken advantage of in connection with smart materials and the trend toward digitalization and the collection of large amounts of data on how certain goods are used. Each of these axes is realized through activities undertaken as part of the so-​called Clubs –​that is, groups of entities with competencies in a specific area and an interest in putting them into practice (in the form of initiating new projects or joining, as consortium members, initiatives established outside Techtera). The Clubs are made up of entities with different characteristics –​ companies, research institutions, schools with profiles that coincide with the theme of the Club –​and their meetings take place four to six times a year. The members of the various working groups get to know each other during study visits and meetings, and the trust developed during these events is translated into joint innovation projects. The above-​average high standard of projects carried out by Techtera’s members is reflected in the trust placed in Techtera and its members by numerous financial organizations. It is safe to say that a cluster project (with the Techtera “label”) has a shorter and easier path to obtaining funding for its planned activities. A not inconsiderable role in this process is played by the cluster’s Innovation Commission (composed of independent experts), which reviews each project before possibly granting it approval (the consequence of which is precisely obtaining the Techtera “label”). The activities mentioned so far are just a small part of the entire spectrum of activities undertaken by Techtera for its members and for the idea of setting standards for the European and global textile industry. Listing and discussing all of them would take up the vast majority of the chapter, so it is worth restricting the focus to the substance of this book –​that is, Techtera’s opinion on the relevance of the different types of proximity discussed here and the involvement in the CO’s activities.

178

178  The concept of proximity in selected European cluster organizations Geographical proximity When discussing the issue of proximity and its impact on various spheres of functioning in a CO, it is worth starting with geographical proximity because location in a single physical space immediately brings with it certain specific consequences for the entities operating in it. Techtera –​although now a CO with national reach –​has strong links to the Auvergne-​Rhône-​ Alpe region. In the early days of Techtera, its members were all based in the region, but it soon became apparent that the interest in joining Techtera was much broader. Since Techtera has never decided that the location of the admitted members would be limited, it also includes entities from regions of France (and even from abroad) other than the home region of the CO. According to the coordinator, it is true that there is a greater sense of community among entities from the Auvergne-​Rhône-​Alpe region –​due to a common cultural and historical background –​but this does not have a negative impact on contacts with cluster entities based outside the region. The coordinator also emphasizes that, although it is easier to build relationships between entities located closer to Techtera’s headquarters and it is also easier to initiate and develop cooperation in such conditions, one’s goals can also be achieved when a cluster member is located at a considerable distance from the coordinator’s headquarters (and from most of the members). “Location remains the way to involve members in the cluster’s activities, but it is primarily the themes and the way of working that allow members to be involved, even at a distance, even despite not sharing a common geographical space.” It just requires adjusting the forms of contact and a little more time. However, there is no denying that, for example, the process of learning from one another among cluster members or exchanging knowledge related to the achievements of entities grouped in a Techtera campus are easier when they take place among entities located close to each other and near Techtera’s headquarters. As the coordinator mentioned: “Proximity [geographical] generally facilitates the exchange of information because it makes it more natural –​instinctive. It also creates a cultural and historical reference point that facilitates exchange. Groupings of entities working on similar issues facilitate exchanges, which is why Techtera is located on a campus with several important entities involved in training and research in the field of textiles.” Techtera’s authorities strive to ensure that this common cluster space continues to grow and inspire and facilitate innovation projects –​to this end, a special technical hall, with equipment that will serve all members of the CO, is also being built on the Techtera campus. To summarize –​geographical proximity plays an important role in the operation of Techtera: it makes it easier to establish personal contacts between cluster members and thus acts as a facilitating factor for knowledge transfer between them and the establishment of cooperation; in short, being geographically closer to one another has been a factor that improved the involvement of members in the various activities undertaken within Techtera. It is worth adding, however, that, in the coordinator’s view, both

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The concept of proximity in selected European cluster organizations 179 the overall engagement and the substantive cooperation between the entities could run smoothly and without disruption even when they were separated by a considerable physical distance. Social proximity Location in one area is a good foundation for developing relationships based on direct contact between cluster members. It is therefore a good opportunity to reflect here on social proximity –​a theoretical category directly related to the existence and positive value of interpersonal contacts based on trust. In Techtera, one can see trends comparable to those found in other COs as well –​what makes Techtera different from other COs is the way in which these trends are put into practice (taking advantage of the good elements and reducing the negative ones). One such natural tendency is the uneven intensity of close relationships between cluster members. This means that in Techtera it is possible to identify both a group of entities connected by deeper ties, based on mutual liking and/​or trust and/​or a positive evaluation of previous experiences of contact with others, and a group of entities remaining at a greater distance from one another. This also means varied activity within Techtera, with some member entities having a whole list of successful projects behind them while others avoid joint efforts. In general, however, the majority of members exhibit openness to interacting with cluster partners; as the coordinator pointed out, this is because of the very nature of the cluster and functioning within it. It is worth emphasizing that there are constant efforts in Techtera to animate cooperation between cluster entities, and that these efforts are initiated by Techtera itself (or, more strictly speaking, by the coordinator and specific individuals managing particular aspects of Techtera’s functioning). What is important –​and at the same time what distinguishes Techtera from most other COs –​is that some of the efforts to stimulate the sphere of mutual contacts within Techtera are focused on the very fact of establishing relationships and making people aware of the benefits of undertaking cooperation with cluster partners (in the form of, for example, the so-​called cooperation workshops), and some are aimed at finding solutions to existing problems or identifying new opportunities for the emergence of innovations within the cluster (both product and process innovations as well as technological and social innovations). Besides, “the animation of the network [within Techtera] creates a framework of trust for the exchange of information between members –​for some activities, non-​disclosure agreements are signed, for ­example –​while Techtera proposes activities and events to facilitate access to knowledge and information, such as expert interventions, monitoring, animations concerning technological or market topics. The trust that comes from being a part of Techtera facilitates access to knowledge and information.” Considering all the above, it is worth outlining the thinking of Techtera’s management regarding the problems arising from the natural diversity of the

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180  The concept of proximity in selected European cluster organizations profiles of the entities that make up the CO. It should be borne in mind that the member entities of each CO –​including Techtera –​represent different places in a single value chain (which facilitates cooperation between entities from different links), as well as entities with similar characteristics within a specific link in the value chain (which facilitates the adoption of competitive attitudes), and organizations that escape the framework of such categorization (they have broader characteristics, such as universities, research institutes, or represent an industry that has not yet been used in any of the value chains operating in the cluster). The very presentation of these three main axes for the division of member entities leads one to reflect on the need to find the optimal treatment for entities in each of the identified subgroups. “For most members, the problems are different because if they do not occupy the same place in the value chain, their realities are different (markets, materials processed, trade, needs), and if they do occupy the same link in the value chain, they may be competitors, making cooperation difficult. In both cases, a balance needs to be found between sharing information and protecting each other’s knowledge and intellectual property. The challenge is to find a balance that satisfies as many members as possible and encourages the development of relationships between them. Especially since Techtera is a competitiveness cluster, an innovation cluster whose main mission is to generate collaborative research projects between its members, and therefore innovative projects where the economic stakes and intellectual property stakes can be high.” For example, the above-​mentioned Clubs –​groups connected by one of the three main thematic axes of Techtera –​provide a platform for the formation of a sense of community and at the same time for the generation of new, competitive solutions. Among the activities intended for stimulating social proximity by Techtera, offering companies a presence at major fairs dedicated to the textile and composite sector and their applications markets can be highlighted. Techtera organizes collective pavilions, stands/​forums to promote the French textile offer and creates a collective dynamic through targeted communication activities and networking events. Organizational support saves companies time, and the services offered before the event allow participants to capitalize on their participation both at their own stand and by being present at Techtera’s promotional stand. Events organized or co-​organized by Techtera play an important role in this process: for example, the business convention Textival! –​an event dedicated to professionals in the textile and flexible materials sector, organized every two years in Lyon in partnership with Unitex. With over 400 companies, 600 principals, and 4,500 meetings organized, Textival! is an opportunity to attract future business partners in one day through pre-​planned, targeted meetings. A similar role is played by joint foreign missions organized to discover new markets and new technologies. Missions facilitate and accelerate networking, network development, technological and commercial observation –​all through visits to industrial facilities, research laboratories, or targeted events.

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The concept of proximity in selected European cluster organizations 181 Countries that have hosted Techtera’s representatives include Japan, Korea, Taiwan, Germany, the United States, and Israel. Social proximity is an important factor in cluster activities. The declarations of the Techtera cluster’s coordinator indicate that the managers of this CO are aware of the need to invest time and effort in building bonds between cluster members –​and these are both bonds of a personal nature, based on individual likes, and business relationships strengthened by positive experiences of successful cooperation.The coordinator was also aware of the diverse needs of the members, depending on their specific characteristics –​ hence the multiple paths that Techtera offered to member entities on the way to reducing distances of a social nature. Competence proximity Another type of proximity analyzed in the study on the COs was competence proximity. This concept refers to a situation in which at least some cluster entities feel that the range and level of competencies they possess are similar to those of some other members. The observation of competence proximity in at least some of the cluster entities may be treated as a good sign for the effectiveness of activities based on the cooperation of its members. This is because it facilitates the transfer of knowledge between these entities and reduces the time necessary for them to adapt to each other and to the constantly changing conditions of conducting business activity. Techtera has taken into account the need to ensure an appropriate level of competence compatibility and dynamization –​that is, to provide incentives for the development of existing or the acquisition of new skills and/​or knowledge. The process of selecting new members for Techtera is a natural one –​Techtera’s focus on the broadly understood textile industry (taking into account not only the industries/​branches that make up the full value chain related to the production of specific materials but also complementary industries/​branches) acts to attract entities with these characteristics. “The companies represent the entire value chain, from chemistry to the finished product. They are complementary and target different markets (transport, sports, construction, agriculture, health, and so on).” Moreover, the high status that Techtera has achieved in the European market means that many of the member entities (and new candidates) are companies that want to achieve not only market success but also a sense of empowerment in terms of setting new trends in the industry. The innovative nature of Techtera is perfectly conducive to the realization of motivations of this nature. Techtera’s coordinator pointed out that, due to the high diversity of the members (different characteristics –​different place in the value chain, different level of competencies), heterogeneity of needs related to access to specific knowledge and/​or skills can be observed in Techtera, and thus it is difficult to meet the needs of all members in the same way. And while Techtera’s management was aware of the aforementioned diversity, plans

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182  The concept of proximity in selected European cluster organizations to, for example, improve specific competencies were still implemented at a more general level. Thus, the process of enhancing competencies in Techtera was uneven: training or workshops were attended by representatives of entities genuinely interested in development in the area covered by the training topics, and the level of sophistication of a specific training/ workshop may not have been suitable for some participants (such an event may have “knocked down open doors,” i.e., focused on areas that were already familiar to some participants, or may have been too advanced for those who had not previously mastered the basics of a specific subject).There could also be a situation in which it would be possible to identify such a group of member entities that do not participate in any of the training/​ workshops due to lack of interest in their subject matter or simply different motivations behind them joining the cluster. “Businesses will not necessarily seek to develop their skills: their involvement may come from a desire to network, to expand their knowledge of the industry, to create connections, to meet partners, to gain opportunities in the market.” The process of improving the competencies of cluster entities is an important element in Techtera’s management. In view of the ambition to forge new paths in the textile and fabric industry, and therefore the de facto necessity to constantly seek innovative solutions in all aspects of related economic life, supplementing and updating one’s own knowledge, acquiring new skills or improving those already possessed has become an essential element of the activities initiated by Techtera’s authorities. “Techtera must offer its members activities that can help them gain information and learn about specific topics, allowing cooperation to develop in the textile sector. For example, Techtera organizes workshops on technology topics, with experts, several times a year, concerning emerging new ideas that allow members to learn more about a particular technology and plan activities that link the textile industry to that technology. Techtera also organizes for its members four ‘Clubs,’ which take the form of cyclical meetings (four times a year) on a specific topic (recycling and circular economy, smart textiles and clothing, Industry 4.0), with the same set of participants. These Clubs enable information acquisition, learning and exchange on these topics in the long term [as discussed in more detail earlier in this chapter]. Members of Techtera include not only companies, but also universities, schools, research centers, and training organizations. These participants are involved in Techtera’s activities and contribute to the development of knowledge and skills in the sector.” In addition to the activities undertaken in the Clubs, workshops, and training sessions, Techtera aims to put the acquired competencies into practice quickly –​as working groups were set up at Techtera as a kind of extension of the workshop/​training groups. The aim of these working groups was to generate innovative solutions for the textile industry concerning the issues mentioned in previous training courses and workshops. Techtera’s coordinator emphasized that each member had exactly the same access to knowledge and information (in the case of some initiatives, however, access was strictly limited to those involved in such an initiative

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The concept of proximity in selected European cluster organizations 183 and governed by specific confidentiality agreements). What was to differentiate members of Techtera in the first place, however, was the willingness to use Techtera’s information resources and the time that individual members were willing to devote to such activities. An example of an egalitarian solution regarding access to knowledge and information for Techtera members can be found in the CART’TEX textile industry knowledge base created in 2011, where several hundred different solutions from the area of know-​how from more than 80 companies in the industry can be found. This is not only an element conducive to building competence proximity (using or being inspired by already existing solutions) but also to stimulating social proximity (encouraging contact with the creators of specific know-​how). Techtera placed great emphasis on creating competence proximity among its member entities. Techtera’s managers did not use this specific term to describe the activities they undertake. However, the initiation of training, workshops, working groups, and the creation of the CART’TEX database –​ for use by members of the CO –​allows this conclusion to be drawn.The role of competence proximity in Techtera is all the more important as Techtera’s orientation toward generating innovative solutions for the industry has required, requires, and will continue to require the continuous development of the competencies of those involved in the process. Organizational proximity The activities undertaken by the COs are also characterized –​in addition to all the elements mentioned so far –​by their potential to bring together member entities into more sustainable aggregates. This characteristic means that in the case of certain acts or processes of cooperation of cluster entities with one another or with entities from outside a given CO, their relationships with one another do not cease when the intended effect of this cooperation is achieved, but stabilize and consolidate, connecting the entities in a new, more permanent way. Often a situation arises in which some cluster entities share some aspect of their functioning, seeing it as an opportunity to, for example, reduce operating costs or improve certain processes. This is considered to constitute the category of “organizational proximity.” Techtera places great emphasis on incentivizing cooperation –​both for intracluster cooperation and when cooperating with entities that are not a part of Techtera. “Techtera proposes a number of joint activities to enliven the cluster’s network of companies. Supporting joint R&D projects is probably one of the most important activities, as it aims to bring together organizations with complementary capabilities to achieve a common goal such as creating a new product or new process. Our process for supporting joint projects allows us to both identify opportunities and needs, and put in place measures to respond to them by collaborating.” Importantly, Techtera seeks to support both ad hoc, short-​term cooperation processes and cooperative long-​term projects. Obtaining official cluster support (e.g., in the form of project funding) is preceded by a careful review by the so-​called

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184  The concept of proximity in selected European cluster organizations Innovation Commission operating within Techtera and composed of independent experts. This formula –​evaluating, advising, and awarding the Techtera “label” to only the most ambitious projects –​quickly translated into a high reputation for the developed solutions (products, services, technologies, etc.), and thus significantly raised (and continues to raise) Techtera’s prestige. The scale and importance of this course of action is also evidenced by the fact that the implementation of innovation projects is carried out by a mix of different entities –​universities, research institutions, and laboratories take part in them in addition to enterprises, and the implementation time of such projects can reache even several years. More lasting relationships result from the positive experiences of such project cooperation: some of the entities involved continue their joint activities even after the official end of the project in question. In 2016 alone, 16 projects received the Techtera mark (label), while more than 200 initiatives have already enjoyed the support of this CO since Techtera’s inception.The Clubs mentioned earlier –​ groups of members united by an interest in one of the three main thematic axes –​played a significant role in the process of generating new project ideas and subsequently supporting the implementation of projects approved by Techtera. Regular meetings of Club members, sharing knowledge and ideas with one another (in an atmosphere of trust and often contracted confidentiality), are a great breeding ground for generating new, often ground-​ breaking, ideas and supporting the process of putting them into practice. The working groups mentioned in the section on competence proximity are also one of the complements to this way of stimulating cooperation and increasing organizational proximity between Techtera members. Although they have been in operation for a shorter period of time and are an aftermath and extension of the topics raised in the numerous training courses and workshops, they also allow those involved to pursue ideas that are relevant to them. These ideas, however, usually relate to more specific, narrower issues than the Clubs’ projects. Techtera’s coordinator did not identify the names of the companies that stand out the most on the cluster cooperation map. He did, however, provide an interesting general characterization of the cluster entities that, in his opinion, could deserve such status: “In terms of proximity to the plant, most of these companies [most actively cooperating] are located near Techtera’s offices. Most of them have been involved with Techtera for several years, or even since the cluster’s inception. The industrial sector is not necessarily a factor determining their importance (even if a strong link to textiles is essential) because, although most of them are involved in textiles, others are linked to chemicals or textile applications sectors.” According to the coordinator, setting up cooperation in Techtera is, on the one hand, crucial and, on the other hand, extremely difficult to do. This is because it is necessary for a group of factors to occur simultaneously. The factors are: (i) a service offered by Techtera that coincides with the expectations and needs of its members; (ii) themes of cooperation that are attractive to a certain group of entities associated within the cluster; and (iii)

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The concept of proximity in selected European cluster organizations 185 a relatively high level of commitment from those Techtera members who are seriously considering implementing a particular project. Synchronizing the occurrence of these factors is already quite a challenge for cluster authorities, but it is not the only adversity they may face when trying to animate cluster cooperation. According to the coordinator, a considerable obstacle to the establishment of cooperative relationships in Techtera is the necessity to establish unambiguous rules of cooperation that are acceptable to all participants in a given project, especially with regard to the intellectual property concerning the generated solutions, the confidentiality of the results, and taking into account the competition (in the sense, for example, of a fair distribution of benefits resulting from the commercialization of the results of such a project). What is also interesting is what, in the coordinator’s opinion, determines the development of cooperation between cluster entities –​this factor is “them complementing each other. The diversity of markets, technologies, materials, skills, and products in the textile industry means that cooperation between its players will collectively allow them to improve their competitiveness and capacity to innovate. And it is Techtera’s responsibility to find ways to establish collaborative relationships that take advantage of this complementarity.” It is therefore clear that, from the coordinator’s point of view, the key to success in the area of cooperation is to ensure that the profiles of the constituent entities of the COs adequately complement one another. This advice seems to be of critical importance both for the day-​to-​day operation of COs and for the authorities (central and local government) creating the conditions for conducting business activities in the areas assigned to them. Organizational proximity in Techtera emerges in two phases: in the first phase it is the result of effective actions of Techtera’s management encouraging cluster entities to establish cooperation with each other; the second phase is most often the aftermath of positive experiences from cooperation that took place in the first phase –​on the basis of these experiences, some entities decide to continue cooperative activities with existing partners, but in other thematic areas. In each case, however, it is Techtera (or, more precisely, the good conditions it creates for establishing collaborations and commercializing their results) that acts as fertile ground for specific project activities. Commitment The characteristics and actions taken by Techtera in connection with the types of proximity highlighted in the study do not exhaust the pool of themes raised in the study. An important conclusion of the path dedicated to the different types of proximity is the theme focused on the issue of the involvement of cluster entities in the initiatives that Techtera’s authorities offer to encourage members to take a proactive stance. Already at the very beginning of discussing the conclusions of this part of the study, it is worth pointing out that even in such a developed CO

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186  The concept of proximity in selected European cluster organizations with a European reputation, there is a tendency that characterizes the vast majority of COs (regardless of whether they have their roots in bottom-​up movements or top-​down decisions) –​the level of involvement of individual member entities in cluster activities is very different and closely dependent on a whole range of different factors. These factors can be divided –​according to the Techtera coordinator’s account –​into three main groups: Techtera-​ dependent, cluster company-​dependent, and external. “[Involvement] varies from company to company, topics covered, and services offered, which can change from year to year. It depends on elements internal to Techtera (for example, the services offered), factors internal to the companies (current problems, available resources), and factors of an external nature (for example, the COVID-​19 pandemic has changed the level of involvement of companies because previous problems have been turned upside down). The involvement of companies has been more or less important in the life of Techtera, but there has always been a certain group of members –​not always the same ones –​who have been particularly involved and have used Techtera’s services intensively.”The extent of the involvement was different: some entities chose to use as many of the services offered by Techtera as possible (provided that these services met the needs and expectations of the entity in question) –​for instance, support in initiating and conducting R&D projects, support for the entry and/​or presence of the enterprise in question on the international market, assistance in networking, etc. Others, on the other hand, focused on a small number of selected areas of Techtera’s activity –​for instance, the ability to obtain and use specialist knowledge. Some of the most committed entities took their cluster activity to a higher level and became involved in the cluster’s management processes –​representatives of some of them ended up on Techtera’s Management Board or as part of its Executive Committee. Techtera is very committed to inducing involvement of its members. In order to achieve this, it is necessary to identify the group and individual needs of the members and for Techtera to prepare a portfolio of services/​ activities that –​because of their knowledge –​will be the most suitable for the cluster entities. An indication of how seriously Techtera’s authorities take this area of operation can be seen in the fact that Techtera’s administration constantly monitors the activities of each member organization by collecting their opinions, asking for evaluations, and identifying their needs. All these activities support the management process in Techtera and allow the planning of such activities that have the potential to translate into the success of the projects in which the cluster entities choose to participate. The main obstacles that Techtera has to overcome on its way to effective engagement of its constituent entities seem to be, first and foremost, the time that cluster entities have to be willing and able to dedicate to such involvement and the collective approach to tasks that is preferred in Techtera –​not every member is immediately ready to act as part of larger wholes (even as a member of a specific working group). Coexistence in a CO sometimes requires the rebuilding of the existing mentality of entrepreneurs and other types of organizations –​many of which are used to acting individually

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The concept of proximity in selected European cluster organizations 187 (except for the need to cooperate with, for example, raw material suppliers, customers, etc.). According to the coordinator, what determines the willingness of a particular cluster entity to get involved in activities undertaken by and within Techtera to the greatest extent is the occurrence of the following factors: “complementarity with other member companies, the nature of the activities offered by the cluster, the ability of the cluster to support the entity in matters of innovation and market access, the benefits that the entity may gain from using the network approach.” Among the entities that chose to get seriously involved in cluster matters, there were some additional benefits beyond those resulting from, for example, a successfully implemented innovation project. The first such benefit was the development of relationships with cluster partners –​not only in the sense of including new entities in one’s own network of relationships but, above all, also in the context of consolidating and strengthening relationships with those organizations with which an entity has worked in the past. This benefit was largely based on gaining positive experience from the cooperation, but was often also reinforced by the resulting trust, and even individual friendship, between representatives of the cooperating organizations. The second of the non-​obvious benefits resulting from the involvement of entities in activities undertaken in Techtera was the strengthening of their potential in terms of the ability to generate innovation. According to the coordinator, this was because of the emphasis that Techtera put on collective action. Under such conditions, it was easy for knowledge, skills, and resource exchange to occur between those involved in achieving a common goal and the resulting increase in the competence potential of the cooperating organizations did not disappear after the end of, for example, a project. A third benefit of engagement was being able to more effectively find the information and knowledge of interest to the entity. This benefit does not mean better access –​because access was the same for all Techtera members –​ but, precisely, more effectively gather the information –​that is, allocating a certain amount of time to successfully find the necessary threads (which the uninvolved entities could not do because of their lack of involvement). The actions taken by Techtera demonstrated the relevance that involvement had for the managers of this CO. Without involved members, Techtera would become apathetic and it would be impossible for it to carry out even the simplest projects effectively. However, the willingness to be a pioneer in its field, blazing trails in the textile and fabric industry, provided the right motivation for both Techtera’s authorities (properly incentivizing cluster entities to undertake joint activities) and the members themselves (naturally attracted primarily to dynamic organizations wishing to make an active impact). Techtera is a true leader in its field and an example to follow in the area of CO creation and management. It is a cluster structure in which geographical proximity matters, but its absence does not hinder cluster entities from achieving their goals. It is also an organization in which the

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188  The concept of proximity in selected European cluster organizations diversity of competencies is a huge advantage, driving both the activities attributed to competence proximity and strongly influencing the willingness to get involved in cluster projects (broadly defined). And, finally, it is a place where the established relationships (social proximity) are relatively easily transformed into more permanent relationships, often making some aspect of the operation of the organizations joined by them common for them (organizational proximity).

The case of Cluster Kybernetickej Bezpečnosti General information Cluster Kybernetickej Bezpečnosti –​Cybersecurity Cluster (CKB) is a CO operating in the Slovak Republic. It was founded in 2018 as an association of legal entities dealing with legal, procedural, and technical issues related to the comprehensive protection of information security and data assets. The impulse to create a CO in this particular industry was the lack of a professional association that could provide bodies interested in cybersecurity with access to experts –​that is, proven knowledge and skills in the field of digital data security.The CKB is part of the Union of Slovak Clusters, is certified by the European Secretariat for Cluster Analysis (ESCA), and is also present on the European Cluster Collaboration Platform (ECCP).The CKB comprised 16 members in 2022. The guiding principle of the CKB’s mission, included in its strategy, is to focus on raising the awareness of both state authorities (central –​national –​ and local government) and the business community of the importance of cybersecurity in the day-​to-​day operation of their institutions. The CKB’s intentions apply to all sectors of the economy –​the reason for this is that nowadays very dynamic digitalization is taking place in every industry and, as a result, the threat of unauthorized use of acquired, processed, and archived data is constantly increasing. The goal of the CKB is to offer assistance in implementing solutions for enhanced digital security to all organizations willing to collaborate in this area. In addition, the CKB aims to engage and promote cybersecurity education in secondary schools and universities, and contribute to innovative efforts to raise educational standards and awareness of cybersecurity in the Slovak Republic. The CKB makes efforts both to initiate cooperation between its own constituent entities and to emphasize the need for networking and cross-​sectoral cooperation. In this way, it assists in the process of solving societal challenges, strives to create a more transparent environment, and enables smaller market players to interact with organizations with a wealth of experience. Given the continuous cooperation between the business community, the scientific and research base, public authorities, municipalities, and the non-​profit sector, the CKB supports through innovation not only the development of one region but also of the whole of Slovakia. Moreover, the CKB has already started to realize its ambition to gradually integrate digital security

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The concept of proximity in selected European cluster organizations 189 cluster communities operating outside of Slovakia –​primarily those from the so-​called Visegrad Group countries (in addition to Slovakia, it includes the Czech Republic, Hungary, and Poland). A large part of the CKB’s activities is of an educational nature and involves popularization: the CKB organizes workshops, conferences, and webinars to which both public and private institutions (of various levels and nature) are invited. As it is apparent that there are significant differences in the needs of entities associated with organizations belonging to different sectors, the CKB seeks to develop a personalized approach that takes into account the different legal, technical, and substantive matters. Steps have been taken which will result in the preparation of reports on, among other things, the banking sector, transport, public administration, health care, and many others –​each of these areas will be refined with the participation of organizations active in these spheres (most likely the cooperation will take the form of workshops held in person). The CKB created the Cybersecurity Centre of Excellence to support information security education. In cooperation with cluster member the Secondary Vocational School of Electrical Engineering in Liptovský Hrádok, this pilot project was implemented as a demonstration of innovative training of secondary vocational school students in cybersecurity. The center uses state-​of-​the-​art solutions that are also used to monitor critical infrastructure in the Slovak Republic. The CKB is also developing cooperation with universities.Together with representatives of the Faculty of Security Engineering at the University of Žilina, the CKB’s members prepared documents so that the faculty could be accredited as a unit providing innovative cyber security education in accordance with the Cybersecurity Act. The CKB’s members recognize the need to support activities conducted with cluster partners: this includes the area of joint communication as well as working in joint meetings. There is a strong emphasis on the continuous development of cluster entities –​training organized for members plus joint trips (including abroad) to cybersecurity management/​monitoring centers and events to exchange knowledge and experience between representatives of the cybersecurity industry from different countries are intended to meet this need. Participation in working groups organized and led by the Ministry of Investments, Regional Development and Informatization of the Slovak Republic is also planned. The organs of the CKB are the General Assembly and the President. The General Assembly, as the highest body, approves fundamental decisions and, among other things, makes decisions regarding the admission of new members. The President has primarily managerial and organizational functions. Practical collaboration between the CKB’s members usually takes place through the formation of micro-​teams, which facilitates the satisfaction of specific client needs by making use of the members’ diverse specializations. In addition to regular general meetings, members may initiate additional face-​to-​face and teleconference meetings as required, inviting a wider or narrower range of interested partners to participate. Maintaining constant,

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190  The concept of proximity in selected European cluster organizations direct communication between cluster members allows rapid response to customer requests. Funding for the CKB’s activities is provided by registration fees and annual membership fees, the amount of which is agreed annually at the General Assembly. Geographical proximity As with the Techtera cluster, it seems sensible to start with the issue of geographical proximity when discussing the findings concerning the CKB. Geographical proximity is independent of the subsequent activities of the cluster entity in question and, in some limited way, is indicative of at least one of the many factors behind the decision to join a particular CO. It is worth pointing out at the outset that –​according to the CKB’s coordinator –​the physical distance separating the individual members from the coordinator’s headquarters and from each other was neither taken into account in the context of agreeing to new members joining, nor is it a factor inhibiting (or accelerating) the development of cooperation in the CKB. The reason for this, according to the coordinator, is that the COVID-​19 pandemic and the associated restrictions on face-​to-​face contact that emerged in most of the world have forced people (including the business community) to adopt virtual space as a fully fledged substitute for face-​to-​face meetings. “Over the past two years, not only in the cluster but also in other institutions, there has been a significant shift in the communication channels toward an online environment. We have been contacting, and continue to communicate with, individual cluster members via online streaming, video conferences, phone calls, and emails.” It may be that this opinion of the coordinator is strongly influenced by the nature of the industry in which the CKB is active (the ICT sector in the broadest sense), where long-​distance communication was already common even before the SARS-​CoV-​2 pandemic broke out –​since projects carried out by entities operating in the ICT industry can also be implemented quite efficiently through indirect communication. The CKB’s coordinator emphasized that shifting the burden of keeping in touch and conducting meetings in the digital sphere allows entities (both the CKB’s partners and representatives of organizations from outside the CKB) to meet in greater numbers. If such meetings were to take place in person, then many potential participants might choose not to attend due to other work commitments or the inconvenience of traveling to the venue. Online meetings enable schedules to be more flexible and allow people to take part in more activities than would be possible with face-​to-​face meetings. An additional advantage of events conducted over the Internet is the possibility of reviewing the prepared materials (multimedia presentations, video recordings, etc.) and contacting selected meeting participants. As the CKB’s member entities represent slightly different paths within the general theme of cybersecurity (e.g., focusing on technical, legal, or procedural issues) and audiences from outside the CKB may come from different industries, regions, or levels of government, online meetings offer a better chance

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The concept of proximity in selected European cluster organizations 191 to prepare and implement meetings that group organizations interested in a specific topic (in other words, online avenues provide more flexibility). In summary: geographical proximity is not seen as a necessary factor to achieve the CKB’s members’ goals. The specific nature of the industry in which the CKB operates and the timing of the pandemic, which has changed communication channels from face-​to-​face to indirect mode, seem to now allow the constituent entities of the CKB to achieve their planned objectives. Social proximity Interestingly, however, despite the preference for indirect forms of communication between the CKB’s members (telephone, Internet), the coordinator speaks of positivity (even friendship) between cluster partners. These declarations should be treated with caution, though, because indirect communication (via ICT) does not facilitate the establishment and development of personal relationships between representatives of cluster entities; it only helps them to maintain cooperative ties during times when personal contact would be impossible (Lis & Lis, 2019). The short period of operation of the CKB prior to the outbreak of the COVID-​19 pandemic, and, hence, the relatively short time that its members had to get to know each other and establish personal relationships based on mutual understanding, allows us to assume that, although it is possible that individual friendships may have emerged during this time, it is unlikely that such relationships were established between all member entities. It therefore seems safer to accept the thesis of a general “positivity” that can be observed –​according to the coordinator –​between members. Other areas of the CKB’s operation also seem to strongly benefit from the very positive relationships between members –​ for example, the development of professional competencies: “The relations between cluster members are excellent; therefore, the transfer of professional competencies between individual cluster members is at a high and professional level.” This also includes access to (professional) knowledge and information, and the development of cooperation between cluster partners. The only obstacle to developing these relationships was said to be the excessive workload experienced by the organizations affiliated with the CKB. The CKB’s coordinator has made various efforts to stimulate the development of relationships between the cluster’s member organizations –​for example, organizing or participating in industry workshops and conferences, and offering training for members. A large proportion of these activities took place online, mainly because of the restrictions that Slovakia’s central administration (as well as those of other countries –​which was of relevance for international events) put in place to combat the pandemic. Although the coordinator strongly emphasized the positive nature of the members’ relationships with each other when reflecting on the involvement of cluster entities in activities initiated and co-​conducted by the CKB, he also pointed out that the key element in this area is the individual willingness to participate in specific activities and projects. It seems, therefore, that –​as

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192  The concept of proximity in selected European cluster organizations was the case for Techtera –​the key to stimulating interest, and thus involvement, of members in cluster activities should be the creation of an appropriate offer, profiled to the needs and expectations of member organizations. To sum up: based on the data obtained, one gets the impression that there was complete harmony and positivity in the area of communication between the CKB’s members even though most of the interactions took place via the Internet. The CKB’s members were keen to share their professional competencies and knowledge among themselves to the extent of covering professional interests. The only element hindering greater involvement of members in cluster activities was –​despite the claimed excellent atmosphere prevailing in the CKB –​the heavy workload experienced by representatives of member entities. Competence proximity The relatively small number of the CKB’s members did not prevent some differentiation in their competence profiles –​it therefore had some impact on the constitution of competence proximity in terms of the range of knowledge and skills held by these entities. According to the coordinator’s account, member entities represented one of two specializations: (i) legal –​which includes all those organizations that analyze cybersecurity issues from the point of view of formal records and administrative requirements and have the ability to create, for example, the documents necessary for submission in public procurement application processes; and (ii) technical –​which focuses on practical solutions for data security, management of information systems, network management, and responses to conscious or unconscious breaches of security of specific data. It is unclear whether the profile of companies wishing to join the CKB influenced the decision to accept them, or whether there was an unspoken assumption that entities that are in some way related to cybersecurity will probably apply to join the CKB –​the coordinator’s statement on this matter is enigmatic: “Partly yes, but not necessarily.” On the other hand, the coordinator’s declaration was clear regarding the actions he was taking to develop the relationships and the competencies of the CKB’s member organizations. The establishment of the Cyber Security Excellence Centre in 2022 –​which serves an educational function for young people as well as operating as a kind of forum for the exchange of information and knowledge for the CKB’s members –​was done with this objective in mind. A similar role was played by the participation of the CKB’s members in various events, such as conferences, webinars, and workshops –​each of which was an opportunity not only to educate the organization and those from outside the CKB but also to share experiences within the cluster and learn from each other.That said, the coordinator dismissed the thesis that the different range and varying levels of competencies influenced the development of cooperation in the CKB (and it is difficult to say clearly whether this is about participation in joint projects or also about such basic activities as meetings and exchange of experiences).

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The concept of proximity in selected European cluster organizations 193 Problematic –​according to the coordinator –​is the fact that in Slovakia there is still little awareness of the importance of clusters and COs for the development of the country’s economy. This makes it difficult to get a message across to potential audiences and does not help to make it credible in their eyes. “Cluster organizations in the Slovak Republic are still not seen as fully fledged partners, consisting of organizations with experts and know-​how. Together with the Union of Slovak Clusters, as representatives of individual Slovak clusters, we strive to strengthen the position of clusters in Slovakia as important partners in various fields.” Excessive workload as a factor inhibiting involvement in various aspects of the CKB’s operations was observed again in the case of “bridging of the gap” between competencies. Competence proximity in the CKB is a rather fuzzy topic. On the one hand, the CKB is made up of organizations with different profiles, albeit with the common denominator of “data security,” and these organizations cooperate with each other during various events, but, on the other hand, this cooperation does not run very deep because the CKB lacks, for example, smaller working groups or project teams. It also seems that attracting new members with specific profiles is not a central issue for the coordinator. On the positive side, the CKB is building digital competencies and awareness of the importance of cybersecurity among representatives of various social groups, from state administration to secondary school students (the recently commissioned Cyber Security Excellence Centre is particularly useful to the latter group). Organizational proximity The CKB does not seem particularly interested in developing in the area of organizational proximity. No formal working groups have been established in the CKB to date. Cooperation of the CKB members in different fields (legal, process, technical) means there are smaller subgroups, but they are informal in nature, formed based on the similarity of their members’ interests/​their main activity. There have been no cases of short-​or long-​term cooperation between cluster companies or collaboration with other COs. There have also been no new entities such as start-​ups and spin-​offs emerging. The only element that makes the organizational aspects of the CKB’s constituent entities joint to some extent is the Cyber Security Excellence Centre mentioned earlier. According to the coordinator, this is an example of the generation of common products and services by the CKB’s members: “[the CKB’s most important result is] the Excellence Centre, as the core activity of our cluster is education.We focus on the exchange of information, knowledge, and know-​how among cluster members, so that they can then pass on their experiences and lessons learned further, in accordance with the Cybersecurity Act, and thus [influence] the increase in public awareness of cybersecurity. In cooperation with the Secondary Vocational School, a seminar for fourth-​year students of the Secondary Vocational School of Electrical Engineering in Liptovský Hrádok was held in September 2021.

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194  The concept of proximity in selected European cluster organizations A cybersecurity course was also launched for the fourth grade. The aim of the students’ training is to provide not only theoretical knowledge of the law and processes but also practical experience in monitoring cybersecurity. Students can try out solutions for security incidents in the Open Training Lab, where professional technology related to the field of cybersecurity monitoring is used.” The CKB’s coordinator was unable to identify the organizations most relevant to stimulating cooperation within the CKB –​rather, he focused on the positive relationships between the individual members. The coordinator himself has so far not taken initiatives to develop cooperation between member entities. The only project the CKB was involved in was carried out together with the Slovak Ministry of Economy –​for the duration of the project (until December 2023), the CKB will receive support for cybersecurity education. When asked about the factors that determine the development of cooperation between cluster entities to the greatest extent, the coordinator mentioned sectoral affiliation and the realization of jointly agreed goals. However, he stated that currently the achievement of these goals is hampered by the state administration, which, in his opinion, does not pay due attention to the COs in Slovakia and underestimates the potential these organizations have to influence the national economy. An additional factor hindering cluster entities from engaging more strongly with the issues and activities undertaken by the CKB has been and continues to be, as mentioned many times already, the heavy workload of members working in areas other than the CKB (i.e., the day-​to-​day running of their own companies and organizations). Organizational proximity in the CKB turned out to be underdeveloped. Member entities tended not to go beyond participation in selected conferences or webinars, there was a lack of cooperation in smaller task groups, and no solutions that could be called joint products or services were developed. The only outcome that qualifies as an element of organizational proximity building is the Cyber Security Excellence Centre, where member entities have been given a forum in which to share experiences and turn them into educational material for young Internet users. Commitment The concluding topic for the description of the results of research conducted on the CKB is the involvement of members in activities initiated and co-​led by the CKB as an overarching institution or a certain group of member entities (as a joint project). According to the coordinator’s account, as time went on, the involvement of the members increased –​this was mainly manifested in participating in training, providing individual support to other cluster entities (primarily in their day-​to-​day operations), and participating in project activities (primarily related to the creation and operation of the Cyber Security Excellence Centre). The specific characteristics of the CKB made the time of the pandemic, or, more precisely, its beginning,

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The concept of proximity in selected European cluster organizations 195 a particularly “fruitful” period for itself and its members’ activities: “In our cybersecurity and information security sector, the change was noticeable due to the COVID-​19 pandemic, as a result of which the education sector in the Slovak Republic was put to a severe test. The level of digitalization was relatively low and schools were forced to switch to remote learning, which caused serious problems for many parties. Demand in the IT sector (for products and services) has increased significantly overall. In a very short time, not only private sector organizations but also public administration has had to cope with changes in service delivery and adapt to working in an online environment. In this respect, the frequency of communication between cluster members has also increased, resulting in a deeper and better relationship [between them].” The elements that determine the involvement of member entities (with a particular focus on businesses) in the CKB’s activities to the greatest extent are, according to the coordinator, factors such as: developed relationships and industry connections (i.e., de facto, key elements for social and competence proximity), as well as a good common theme that will foster involvement. For its part, however, the CKB did not take measures aimed at increasing members’ involvement in its activities. Even so, at the time of conducting the study, the coordinator declared that he would like to encourage more member entities to present their knowledge and skills in a forum larger than just the cluster (e.g., during industry meetings). Another path is to look for potential new members who could provide the CKB with a strong stimulus to enliven cooperation and bring ideas for new initiatives. Further development of relationships between members could, according to the coordinator, increase the share of joint activities undertaken by cluster entities. The level of competence also seems to matter in terms of increasing the level of involvement (higher competence =​more involvement). Involvement may, in turn, prove to be a vital element in gaining better access to knowledge and information in the CKB. The involvement of cluster entities in the CKB’s activities underwent a boom when the COVID-​19 pandemic broke out. Virtually all institutions and organizations were then faced with the need to make radical changes in the ways they operated and move from working offline (face-​to-​face) to an online mode. This, in turn, has led to an increased interest in the topic of online data security and resulted in an increased demand for the services of specialists in this field.This event seems to have given the CKB more energy and mobilized some of its members to get more involved in cluster activities. However, it seems that the level of involvement of cluster entities at the time of conducting the study was not in line with the expectations of the coordinator, who was looking for candidates for new members in the hope of undertaking new initiatives and starting cooperation. At the same time, he was unable to identify the factors preventing members from becoming more active. The CKB is a new and relatively small CO focused on issues of data and information security in the broadest sense. It is primarily educational

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196  The concept of proximity in selected European cluster organizations in nature, which is reflected strongly in the activities it undertakes –​mainly conference presentations and webinars introducing participants to the arcana of online information security management and incident response. On the one hand, the coordinator emphasized the good atmosphere in the CKB and the amicable relations between members. On the other hand, the factors considered to be characteristic of the presence of competence and organizational proximity could be observed to a minor degree. The level of involvement of the members in the CKB’s activities seems to be sufficient to sustain this CO, but too low to think about developing or having a significant impact on the public administration and its decisions. It is worth bearing in mind, however, that the described CO has a history of only four years, half of which was during the pandemic, with the associated restrictions. Although, on the one hand, the restrictions connected with the pandemic have strengthened interest in the topics covered by the CKB, they have, on the other hand, effectively prevented many of the plans of the described CO from being implemented.

The case of the Bulgarian Fashion Association General information The Bulgarian Fashion Association (BFA) is a CO comprising entities from the textile, clothing, and IT industries, focused on jointly achieving goals in the area of sustainable development, innovation, digitalization, and the circular economy. The main reason why the BFA was established was to support the fashion industry in Bulgaria and to introduce its products as competitive goods on the European market. This is to be achieved in a way that ensures that the affiliated entities operate according to the principles of a circular economy and sustainable development and that the very process of creating new products is permanently embedded in innovative thinking and closely linked to the sphere of research and development. The BFA is a very new organization –​it was founded in September 2019 and currently has 63 members. One of the founding organizations was Fashion.bg.Ltd, a company dynamically operating at the intersection of fashion and IT, initiating the creation of an online community of Bulgarian textile and clothing manufacturers and fashion brands. The BFA is active in the areas of textile production, garment manufacturing, leather products manufacturing, lingerie manufacturing, and the manufacturing of other accessories. The textile and clothing path goes hand in hand with the path of broadly understood “digitalization” and the integration of the textile and clothing industry into the “Industry 4.0” economy. The range of services provided to its members by the BFA covers four main areas: (i) internationalization support –​support is primarily provided in the form of finding and creating business opportunities in third-​country markets: “They want business and therefore internationalization, that is, business-​to-​business events and support in reaching new markets, and they

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The concept of proximity in selected European cluster organizations 197 expect you to provide them with information and the opportunity to travel to attend trade shows, conferences, and so on –​all that [could be] needed for [cluster members] to find new customers”; (ii) facilitating cooperation between members (within the BFA) –​undertaking activities oriented toward connecting BFA entities into functional, larger wholes (dedicated to the realization of a specific goal); (iii) facilitating cooperation with entities from outside the BFA –​this element resonates in combination with point (i), namely, internationalization –​the main expectation of the members being BFA assistance to establish new business connections with companies and business environment organizations outside this CO; and (iv) communication (circulation of information within the BFA, obtaining valuable information from outside, and an appropriate communication strategy when dealing with entities from outside the cluster). The BFA is made up of a diverse group of entities –​as the BFA’s members include fashion production companies, designers, textile companies, organizations focused on networking, universities, research centers, as well as companies bringing fashion and its derivatives into the digital world (the IT industry). This mix of profiles allows the BFA to carry out a variety of activities: from connecting companies with R&D institutions in the process of finding innovative solutions, connecting cluster companies with other organizations as part of creating new business opportunities, to implementing digital solutions in selected links of identified value chains. Each of these elements is carried out within the context of pursuing sustainable development and a circular economy (in which cooperation between cluster entities and R&D institutions –​whose potential and nature predisposes them to developing innovative solutions –​is particularly important). The BFA is involved in several projects, among which one of the most important for the BFA is The CLOTH Project (CLuster Alliance fOr the Transition to green and digital fasHion). The CLOTH project’s premise coincides with the general goals of the BFA –​the aim being to integrate the European fashion market and put it on a “green, smart, and competitive” course by creating new cross-​sector alliances, including between COs operating in other countries. The project is intended to contribute to increasing competitiveness in the sector (and the growth of the sector itself, internationally), taking into account the impact of the decisions made on environmental conditions and the social consequences. The project is being carried out in an international consortium comprising entities from five European countries: Bulgaria, Denmark, France, Spain, and Romania. Consortium members pursue specific objectives under one of three groups: Fashion and Textile, Circular Economy, and Creative Industry and Digital. Another project –​to some extent related to CLOTH –​is the FASCINATE (Sustainable Fashion Alliance for International Markets) project. This is a project that aims at internationalizing European clusters, grouping together small and medium-​ size companies in the textile and fashion industries, staying on course to make the idea of sustainability and the circular economy more practical (the collaboration also includes the shoe industry). The project partners, from

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198  The concept of proximity in selected European cluster organizations Bulgaria, Denmark, Spain, and Portugal, are striving to create and promote new value chains connected to the European continent, built cross-​sectorally and utilizing the potential of textile/​fashion, footwear, and new technology companies. The FASCINATE project should culminate in the creation of a joint internationalization strategy for those European brands and companies that have embodied the slogan “sustainable fashion.” Geographical proximity The BFA is a CO bringing together entities from across Bulgaria. This means that the building blocks of geographical proximity are rather difficult to discern, and thus –​in all likelihood –​the BFA could not enjoy many of the benefits of locating the majority of its members in a small area (in addition, an area where the coordinator’s headquarters would be located, or at least close to it). However, the lack of a clear geographical concentration seems deliberate on the part of the coordinator –​the geographical location of the candidates wishing to join the BFA was not taken into account in the decision-​making process for admission to the cluster. The distances of 350–​ 400 km from the coordinator’s headquarters and their role in the process of cooperation development in the cluster were described by the coordinator as a “minor impediment” to building cooperative relationships between cluster entities. According to the coordinator, there was “good communication via email” with members located far from the BFA’s headquarters. Not sharing the same space was, in the coordinator’s opinion, supposed to have little impact on the development of relationships between cluster partners, while in general it was not supposed to interfere with the development of their competencies, the development of cooperation, or with their access to knowledge and information in the cluster. The latter was to be provided by a newsletter sent out to all the BFA’s members. It also seems that, in terms of building the involvement of cluster entities in the activities undertaken by the BFA, geographical proximity has not been recognized as an important element of the process.The key, according to the coordinator, is the simple interest in a particular topic and the importance of the event that would catch the attention of the cluster partners: “If a company wants to participate, the location does not matter. And if it is a small event that does not really matter to them, they will not be willing to travel.” Thus, the bigger the event and the greater its relevance to the objectives of the cluster entity in question, the greater the chance of prompting the involvement of that entity. These reflections seem to indicate that geographical proximity is of little importance to the BFA’s coordinator. Social proximity The BFA also seems to assign a low level of importance to social proximity in terms of the functioning of this CO. The description of the relationships

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The concept of proximity in selected European cluster organizations 199 within the BFA can initially be reduced to a general statement: “Some cluster members cooperate with each other; others maintain relationships only with the cluster.” And although this description fits the situation in any CO –​since it is always possible to distinguish between a group of the BFA’s members who are looking for an opportunity to do something together with cluster partners and a group of those members who, apart from the mere fact of becoming a member of that CO, do not undertake any activities with cluster partners –​the direction taken from the rest of the coordinator’s statements regarding social proximity makes it safe to assume that social relations between the BFA’s members are not a key element for this CO. One indication for this conclusion is the belief of the BFA’s authorities that there are absolutely no barriers to establishing relationships between cluster partners. Meanwhile, the long distances separating the BFA’s members from one another (naturally hindering face-​to-​face contact and therefore the establishment and development of relationships between them) and the treatment of email as a remedy for the consequences of remaining at a great geographical distance are themselves, in the authors’ view, inhibitors of relationship development in the CO. Also, in the sphere of translating relationships into the development of members’ competencies, the coordinator doubted the existence of such a mechanism –​he only saw the opportunity for a simple exchange of knowledge and success stories. The BFA’s authorities were unable to address the potential connection between relationships in the CO and the development of cooperation among its members –​despite this being a key aspect of the functioning of any CO, the coordinator declared a lack of knowledge in this area. Instead, he attributed great importance to the impact of the relationship between cluster members and their access to knowledge and information –​it can therefore be assumed that the better the relationship between partners, the better, in the coordinator’s opinion, their access to information and knowledge. To some extent, the influence of relationships could also be observed in the area of the members’ involvement in the BFA’s activities –​ here, however, the coordinator referred not so much to his knowledge of the BFA as to trends in human psychology: “I think that from a psychological perspective there probably is some influence, because people usually look at each other.” It seems, therefore, that this influence was mainly limited to copying certain behaviors from other members and/​or the willingness to fulfill the associated expectations of the group. A final argument that demonstrates the relatively low presence of factors related to social proximity is the range of activities that the coordinator has undertaken in the interests of developing relationships in the BFA –​only the example of organizing trade missions for members was mentioned, which, according to the coordinator, “is a very effective way to develop relationships between cluster members.” Social proximity in the BFA has not had much stimulation so far. The lack of focus on relationship building results in a small number of bonding

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200  The concept of proximity in selected European cluster organizations activities that the analyzed CO has to offer. If we add to this the relatively large distances separating the individual cluster members (often several hundred kilometres), it is clear that social proximity in the BFA will be difficult to reinforce. Competence proximity Slightly more attention from the BFA’s authorities was given to the elements related to the sphere of competence proximity. According to the coordinator’s statement, the industry affiliation of candidates for future members of the BFA was a factor taken into account in the decision-​making process for acceptance or rejection –​organizations aspiring to become the BFA’s members should operate in the area of fashion as it is broadly understood. “Our cluster members operate in the fashion industry. Most of them represent the same links in the value chain, but we also have some members from different parts of the value chain. However, they all operate in the same industry.” The BFA has tried to undertake some activities oriented toward both developing the competencies of representatives of individual cluster entities and providing better access to knowledge and information –​in both cases the same activities were chosen: organizing training sessions, seminars, and foreign missions. Essentially, it could be concluded that the BFA is consciously trying to create and implement a policy of strengthening factors related to competence proximity. At the same time, however, the declared absence of any barriers in the process of competence development in the BFA and in access to knowledge and information, as well as the lack of resonance between industry affiliation/​scope and level of competence development and the development of relationships in the BFA, incline one to treat such claims with great caution. This is because it is difficult to believe that the BFA is an ideal cluster structure in which there would in fact be no barriers in the areas mentioned above, and it is even more difficult to agree with the nonexistence of a desire to benefit from the experience and resources of partners from the same link in the value chain and/​or to build a common value chain with organizations representing its different links. The above is, after all, the essence of cluster cooperation. There were no project/​task groups active in the BFA (which is a consequence of the previously described state of affairs), which should be considered as another argument indicating that the competence proximity building is at an early stage. The coordinator also did not recognize the potential of the scope and level of competence being translated into the development of cooperation between members or their level of involvement in the activities undertaken in the BFA. Competence proximity in the BFA exists in a basic form: entities representing the fashion industry are incorporated into the BFA, and training, seminars, and trade missions outside Bulgaria are organized. However, there is still a lack of cooperation determined by similarities or differences in the scope and level of competence.

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The concept of proximity in selected European cluster organizations 201 Organizational proximity The low level of commoditization of activities in the BFA may also be evidenced by the issues related to organizational proximity because it was in this area that the BFA showed the least activity –​in comparison to the activities characteristic of the other proximity types. During the period of operation of the BFA, neither any form of short-​term cooperation (e.g., participation in joint projects) between cluster entities nor any long-​term cooperation (e.g., in the form of creating a new value chain) has been developed. The coordinator may have qualified the cases of exchange of market information, plus the success stories and experiences shared between some of the BFA member companies as a manifestation of long-​ term cooperation, but in terms of the category of “organizational proximity,” this kind of activity cannot be taken into account here. The BFA has also failed to develop any joint products or services, just as there have been no joint spin-​off or start-​up initiatives, or even smaller working groups dedicated to finding specific solutions. So far, in the BFA, no measures oriented toward the initiation and development of cooperation between cluster entities have been taken. At the same time, the BFA’s authorities declared the absence of any barriers negatively affecting the development of cooperation in this CO. They were also unable to identify among their members the entities that would be or had the opportunity to become the most important cooperation partners for the BFA. In summary: the factors related to the development of organizational proximity are not present in the BFA. The reason for this may be the short period of operation of the analyzed CO (less than three years at the time of the study). In addition, a focus on the ad hoc functioning of the CO was observed in the BFA, while strategic, long-​term issues were somewhat less important. Commitment The topic of the involvement of cluster entities in cluster activities concluding the description of each CO is a consequence of the choices and actions taken by the CO’s authorities during its operation. When analyzing this aspect of the cluster reality, it is worth starting with a general assessment of the involvement of the BFA’s members in its activities, and this involvement seems to depend –​according to the coordinator –​on the compatibility of the activity in which a member would take part with its needs and on the industry affiliation (compatibility). If these conditions are met, one can speak of high involvement. It also seems that with the passage of time, a higher level of involvement could be observed in the BFA. However, it is difficult to say what this tendency was caused by (the coordinator did not take any special measures oriented toward stimulating involvement, although –​to complete the picture –​he also did not see any barriers inhibiting members’ involvement). It can be assumed that simply getting used to how the CO

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202  The concept of proximity in selected European cluster organizations operates played a role in this process, which for some entities led to a desire to become more involved. The research on the BFA shows that involvement has been shown to play an important role in the processes of relationship development (social proximity), competence development and improved access to knowledge and information (competence proximity), and collaboration development (organizational proximity). Taking into account all the aspects of involvement in the BFA already mentioned, it can be concluded that this area of cluster life has also not yet managed to develop sufficiently. Cluster entities only selected some of the activities offered by the BFA and did not have the opportunity to become more active. However, it seems that this attitude is due to the general attitude of the coordinator and cluster entities, which mainly expect the BFA to help them gain access to foreign markets (which the BFA tries to provide). The BFA is a very new CO, bringing together entities operating in the fashion industry as it is broadly understood. Based on the coordinator’s account, it appears that there are no significant barriers to the development of proximity between the entities in the discussed dimensions and related involvement (only in the case of competence development did the coordinator point to the lack of time as a barrier to members’ development in this sphere). However, it is also a CO toward which its members had limited expectations. As indicated by the coordinator, the main need reported by cluster entities was the need for “internationalization,” a term understood as the process of making access to foreign markets easier for cluster members. However, no emphasis on building relationships between members, nor on developing their competencies or stimulating cooperation, was observed. Perhaps this was the reason for the relatively low activity of this CO in the analyzed areas.

Development of proximity in the analyzed COs Geographical proximity When analyzing the answers given by coordinators of individual COs participating in the study, there is an interesting shift in their approach to the issue of cluster entities staying in relative spatial proximity (i.e., close) to each other and to the coordinator’s headquarters. Techtera, which is the longest-​ established CO, started out as an association of entities from a single region of France (AuvergneRhône-​Alpe) and has developed into a national and even international structure. Although Techtera did not lay down the criterion of being based in a specific region as a prerequisite for new member candidates, the coordinator repeatedly emphasized in the study that remaining geographically close to each other facilitates basically all aspects of Techtera’s functioning –​that is, the development of relationships between members, the initiation of and successful cooperation between members, the development of competencies

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The concept of proximity in selected European cluster organizations 203 (including the exchange of knowledge and experience), as well as involvement in cluster activities. However, the coordinator allowed the possibility of conducting effective activities also in the case of entities significantly distant from the coordinator’s headquarters and the other cluster partners. The remedy for this situation was to be the use of electronic media for communication between the involved institutions. The CKB –​with a short history of only four years –​did not take the location of potential members into account when assessing candidate profiles. One of the reasons for this was pointed out explicitly by the coordinator himself, noting that “Slovakia is a small country,” so the issue of geographical proximity is solved almost by definition. A second reason for this attitude toward potential cluster members may be the abrupt shift from face-​to-​face to IP-​to-​IP communication –​that is, from direct contact to contact via electronic media. This trend became most apparent during the COVID-​19 pandemic, when the vast majority of organizations around the world were forced to limit their business activities in the traditional form and move internal and external communication to the Internet. In the CKB this was simple and easier to do than for most because the CKB is made up of entities operating in the ICT industry. The situation in the BFA was completely different: the coordinator stated that they did take into account the location of the entity aspiring to become a member of the BFA; however, this criterion covered the whole country (and the BFA’s members also include entities from outside Bulgaria). It is therefore difficult to create a cluster of entities with the characteristics of Porter’s industrial cluster in such a situation. The coordinator also expressed the opinion that not being in physical proximity to one another had only a minor impact on the development of relationships in the BFA, and he did not expect it to have any impact at all on the issues of developing cooperation, access to knowledge and information, and raising the level and expanding the scope of competencies. This fact also seemed to be irrelevant to the question of members’ involvement in cluster activities –​according to the coordinator, the decisive factor here was the interest in the topic of a particular event/​activity and its relevance to the entity in question. The change mentioned in the first paragraph of this summary refers to the discernible loss of importance of the spatial clustering factor for the nature of the existence and operation of COs. An element that until recently appeared to be one of the most crucial in terms of cluster attributes is now being marginalized. This transformation was greatly influenced by the increasingly widespread use of electronic media for business and educational activities. Although this trend had been noticeable for a long time, the outbreak of the SARS-​CoV-​2 pandemic greatly accelerated the process. Of course, one should not draw sweeping, general conclusions based on qualitative studies of the three selected COs. Instead, the trend could be considered as potentially interesting and additional research dedicated to the topic could be conducted. It is possible that it will be necessary to redefine what has so far been regarded as industrial clusters and related COs, and thus the ways

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204  The concept of proximity in selected European cluster organizations in which they are created and managed (both from the point of view of practitioners –​coordinators –​and from the point of view of institutions creating a legal and administrative framework for cluster activity) will also have to change. Social proximity Looking at the opinions and experiences of the coordinators of the studied COs and focusing on the factors associated with social proximity, it can be concluded that their statements in this regard seem to be somewhat related to how they perceived the factors connected with geographical proximity. It is also connected to a great extent with the types of proximity discussed later –​especially organizational proximity, with which the “cooperation” factor of typical economic life is associated. Efforts to stimulate the establishment and development of relationships between cluster members can be observed in Techtera. This is not a simple matter, because the larger the organization (in terms of the number of members and range of operations), the greater the inertia –​this also applies to COs. Techtera was the largest of the studied COs (with more than 200 members), but it was also an organization strongly concerned with creating opportunities for the development of relationships between representatives of cluster entities. In addition to the numerous –​and quite traditional –​ways of bringing cluster partners together (training sessions, workshops, conferences, trade fairs, exhibitions, foreign missions, etc.), Techtera relies on the conscious use of the diversity of member entity profiles naturally present in COs (different places in the industry value chain, different sizes of the entities, different character of operations –​e.g., university, company, business environment institution, etc.). The effect of this approach –​strongly influencing the factors connected, according to the authors, with other types of proximity –​ was consciously directing the life of the CO in such a way that subgroups were formed within it. Members of subgroups united by a common goal met relatively more often than was the case for general meetings/​events involving all members. Moreover, Techtera has created a kind of campus in close proximity to the coordinator’s headquarters, where a large part of the cluster initiatives are concentrated. Direct contact is conducive to the development of relationships; however, the entities benefiting from it will primarily be those located close to the coordinator’s headquarters (importance of geographical proximity). Techtera’s coordinator pointed to the important role that relationship development was expected to play both in stimulating cooperation and involvement of members and in developing their competencies, as well as providing access to knowledge and information. The CKB has not had much time to form a network of relationships between its members. Founded in 2018, just two years later it was faced with the need to adjust its operations to the restrictions related to the coronavirus pandemic. However, the dozen or so members that make up this CO did not have much trouble transferring their activities to the electronic

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The concept of proximity in selected European cluster organizations 205 arena –​after all, basically all of them were active in the ICT industry. The coordinator repeatedly emphasized the good atmosphere between cluster entities. Among the activities that were expected to translate into improved relationships in the CKB, the coordinator mentioned the various training, conferences, and webinars that cluster members participated in. It is worth mentioning here, however, that most of them took place in the presence of representatives of other institutions, as the events were educational and aimed at different audiences. One can therefore get the impression that the socialization of the CKB members took place incidentally and was a kind of side-​effect of activities aimed at achieving other goals. The coordinator expressed his opinion on the great importance of developing relationships in the CKB to increase access to knowledge and information, raise competencies, and build cooperation between cluster members. In terms of involvement, interest in a specific activity in which a given entity would be involved was the crucial aspect. In the BFA, the issue of relationships seemed to remain in the background. The CO in question was relatively new –​it has existed for less than three years at the time of the study –​and most of its time in operation was during the period of the pandemic and the associated restrictions. An indirect form of contact between cluster partners (using Internet tools) prevailed, while the only activities undertaken by the coordinator with the aim of, among other things, improving relations between members was the organization of foreign trade missions. At the same time, the coordinator did not see the possibility of the translation of these relationships into the development of cooperation, nor the impact of this sphere on the process of the development of competence of cluster entities. However, he admits that the relationships are significant to some extent for the members to get involved in cluster activities and stated that the relationships are important for gaining better access to information and knowledge. The issue of factors related to social proximity turned out, on the one hand, to clearly differentiate the studied COs and, on the other hand, to align with the opinions of the coordinators concerning geographical proximity. Techtera, with the most accentuated elements of geographical proximity, is also the organization with the relatively most dynamic network of relationships (compared to the other two COs). Although it adopted indirect forms of communication (via online tools), it also emphasized that a common cultural background (the same region) makes it easier for cluster members to understand each other, and created conditions in which cluster entities would have a chance to establish and develop (both personal and business) relationships with each other. In the CKB and the BFA, on the other hand, things were different. Both COs were much younger than Techtera and half or more of the time they were in operation was during the period of pandemic restrictions. The pandemic has forced a shift in the balance of communication toward indirect communication, primarily using the Internet. For the CKB, it was basically like sanctioning the activities it already mastered and implemented (given that the CKB operates in the

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206  The concept of proximity in selected European cluster organizations ICT industry). Social proximity was not central in the CKB, but at the same time it does not seem to have been pushed to the margins. In contrast, in the BFA, where there was no emphasis on building proximity in terms of common location, there were also no initiatives dedicated explicitly and only to the development of relationships between cluster partners. All of these arguments seem to make another conclusion plausible. Although it is not of a general nature, one factor could be considered an interesting topic for future research: the lack of geographical proximity in COs does not seem to be conducive to the later formation of social proximity. This conclusion may be of relevance to the development of cooperation in a cluster –​it is an aspect that will be summarized a little later. Competence proximity The similarity in terms of the knowledge and skills possessed and the complementarity of the competence profiles of individual CO members seems to create a path parallel to the one outlined above –​that is, certain impact of geographical proximity on the number and intensity of relationships formed between cluster entities.This does not imply a complete isolation of competence proximity from geographical and social proximity, but, rather, it being connected to them with fewer links than in the case of a direct relationship between geographical and social proximity. In Techtera, there was a very strong emphasis on the development of members’ competencies. The primary reason for adopting such a policy towards members was Techtera’s ambition –​successfully realized –​to become first a European and then a global leader in setting new trends for the wider fashion industry.This meant –​and still means, as Techtera assiduously pursues its goal –​that there had to be a clear focus on generating innovative solutions for all aspects of the fashion industry. However, this required well-​prepared staff and constant updating of existing competencies. In addition to ensuring a broad representation of entities from different parts of the value chains created within the fashion industry (which ensured complementarity of competencies),Techtera constantly organized activities with the main aim of directly raising the level of competence in a given area (conferences, training courses, study visits, workshops with experts) or stimulating creativity and the creation of innovative solutions in specifically established task groups (short term) or the so-​called Clubs (long term). Additionally, working in groups united by the desire to achieve a common goal also translated into an increase in the level of competence of the members who formed them. The coordinator saw the positive impact that having a similar or complementary range of competencies had on the development of relationships and collaborative processes in Techtera. In the case of competence and involvement, no such link was found. In the CKB, the issue of competencies and their development was somewhat different. The competence profiles of the cluster entities were essentially divided into two areas: entities that were specialized in the legal aspects

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The concept of proximity in selected European cluster organizations 207 of cybersecurity assurance and entities specialized in its technical aspects, threat prevention, and response to incidents that had already occurred. However, the CKB’s members did not operate within any additional project/​ task subgroups. They pursued their goals and objectives primarily through training courses, conferences, and webinars aimed at different audiences.This also provided an opportunity to improve their competencies by drawing on the knowledge and experience of other participants (including cluster partners). The educational nature of the CKB is also highlighted by the Cyber Security Excellence Centre it has created. It is a place that, in addition to its purely educational value for young people, also provides an opportunity for cluster members to use it as a forum for the exchange of ideas and experiences. However, the coordinator did not see the potential of common competencies or their complementarity to translate into either the development of relationships in the CKB or the development of cooperation. The BFA brings together entities from the fashion industry, mostly from Bulgaria. Most of these entities occupy the same place in the value chain, although the BFA members also include entities whose scope of competencies can be considered complementary to the majority. The BFA declared that it cares about the development of its members’ competencies, and training courses, participation in seminars, and foreign economic missions were highlighted among the tools it uses to achieve this goal. However, there were no task/​project subgroups in the BFA, and the coordinator did not see the potential of the issue of members’ competencies translating into the sphere of relationships between them, into the sphere of cooperation, or into their involvement in cluster activities. Factors considered to be characteristic of competence proximity were observed to a greater or lesser extent in each of the studied COs. However, what differed between the studied organizations was an appreciation of the impact that the common or complementary competencies of cluster members could have on other functional areas of the CO –​development of relationships, cooperation, or involvement in cluster activities. Techtera showed a very high awareness of the existence of such links, while the CKB and the BFA did not recognize such trends. This may have been a consequence of the different approaches these COs took toward factors related to other types of proximity. However, answering this question would require conducting more research. Organizational proximity In terms of the functioning of the CO, the key factor is the cooperation of the member entities, which can be fruitful both for all members involved and for the CO as a whole. And it is precisely the cooperation of CO members and the possible commonality of at least some spheres of their functioning that is the main focus of interest in the organizational proximity pathway. Techtera put in intense efforts to stimulate its member entities to cooperate.Virtually all measures taken by Techtera’s authorities were intended

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208  The concept of proximity in selected European cluster organizations to directly or indirectly improve the conditions for cooperation with cluster partners. A perfect example is exemplified by the Clubs operated by Techtera. These have been mentioned several times already and comprise entities with different characteristics, but united by a desire to develop new solutions in the sphere covered by the Club’s themes. Techtera offered specialist support, financial support, and –​in cases of work on exceptionally important innovative solutions –​also an official Techtera “label.” In addition to cooperation in the so-​called Clubs, there were also task groups operating in Techtera. What differentiated them from the Clubs was that, in contrast to the long-​ term cooperation processes in the Clubs, the cooperation in the task groups was confined to a shorter time period. Achieving the results of one project, however, did not mean the end of the collaboration. It was not uncommon for partners to decide to cooperate further, either to build on previously achieved results or to develop a new solution for a different problem. The coordinator pointed out that the factor that primarily influences the development of cooperation in Techtera is the complementary competencies of its members (i.e., complementary scopes of knowledge and skills possessed by cluster partners). In the CKB, the issue of cooperation was different. The research shows that the CKB has not yet formed any subgroups or task groups, and the cooperation of its members was limited to participation in joint events related to cybersecurity. The Cyber Security Excellence Centre deserves a mention here. Although not directly, it fulfilled the function of focusing the attention and activities of the cluster partners. The coordinator pointed to industry affiliation and the desire to pursue common goals as the main determinants for the development of cooperation in the CKB. It seems that the nature of the CKB –​operating in the ICT industry and making extensive use of indirect, virtual, digital communication mechanisms –​and the lack of geographical proximity had some consequences for the sphere of cooperation in the organization. The BFA was the organization with relatively the least visible elements responsible for organizational proximity: no forms of short-​or long-​term cooperation have been developed, no joint products or services have been created, and no measures have been taken to stimulate cooperative activity among members. The reason for this may be the early stage of development this CO is currently at (the BFA was established in 2019). This may also be influenced by the nature of the members’ objectives, which do not require the establishment of direct cooperation with partners, or by problems arising from the high dispersion of cluster entities. At the same time, the coordinator expressed the view that the factor that would be most decisive for the development of cooperation in the BFA would be the relationships between members. Organizational proximity in the studied COs was present with varying intensity. What is puzzling is the reason why two of the three studied COs expend so little effort on establishing and developing cooperation between

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The concept of proximity in selected European cluster organizations 209 their member entities. After all, it is difficult to think about achieving the synergy effect (crucial for COs and in some way defining their identity in contrast to other structures operating in the economic arena) without developed cooperation between partners. These reflections thus indicate potential directions for further research. Commitment Although commitment in cluster activities is not an element that fits into any particular dimension of proximity, it is a factor that is largely responsible for the development of cooperation in a CO. Involvement has the power to influence the factors connected with some of the specified proximity dimensions but also shows sensitivity to how things are in the individual spheres of proximity. It is also worth emphasizing that although the studied COs also differ in terms of the level of involvement of their members, a natural tendency –​also observed in the analyzed COs –​is the presence of both highly involved and passive entities among their members. Even Techtera did not escape this trend, although in this particular CO the problem of its members’ passivity was marginal. It is worth remembering that Techtera is an organization in which not only the needs and expectations of the members but also their satisfaction with them have been monitored continuously and it is an ongoing process. The purpose of this was to prepare and, if necessary, modify the range of services Techtera provided to its members. According to Techtera’s coordinator, the key factors for inducing high member involvement were complementarity with other cluster partners, the nature of activities offered by Techtera to its members, the ability of Techtera to support members in creating innovative solutions, and facilitating market access. These were real expectations of the cluster entities, which Techtera’s authorities tried to satisfy as much as possible. The high level of importance that Techtera’s authorities attached to the involvement of their members also stemmed from a certain perception of the links between the various elements that make up Techtera. This is because the coordinator believed that involvement had a positive impact on the development of members’ competencies, their access to knowledge and information, plus the development of cooperation and of a network of relationships. Therefore, all the efforts to strengthen the involvement of members in the broadly understood life of the CO were understandable. The CKB lacked not only an administrative position responsible for monitoring members but also measures to stimulate the involvement of cluster entities. The coordinator pointed out that involvement has increased over time; however, given the outbreak of the pandemic, which has naturally prompted many organizations and institutions to take an interest in Internet safety issues, this increase in activity seems natural and not related to the nature of the operation of COs. Members were involved in events organized or co-​organized by the CKB, participated in training courses, as well as in

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210  The concept of proximity in selected European cluster organizations project implementation activities (including the establishment of the Cyber Security Excellence Centre). The coordinator also mentioned that he was looking for new members who could bring “a boost to cooperation and new initiatives.” The factors most influencing the development of involvement in the CKB were, in the coordinator’s opinion, developed relationships between members (i.e., the essence of social proximity), industry connections (strongly connected with competence proximity and similarity or complementarity of members’ knowledge and skills), and a so-​called common theme stimulating at least some cluster partners. It is worth noting that most of the factors contributing to this involvement also appeared in the case of the Techtera cluster. The BFA, like the CKB, did not have an administrative position dedicated to analyzing the needs, expectations, and satisfaction of its members.According to the coordinator, the factor most strongly influencing the strengthening of involvement was industry affiliation, while involvement itself seemed to have positive effects both in terms of competence development and access to knowledge and information, as well as the development of relationships between members and the establishment of cooperation. The BFA’s authorities did not see any barriers to members’ involvement in cluster activities; it was also noted that cluster partners showed a strong involvement in those events and activities that best met their needs. It is evident that the latter element –​the need to connect the needs of the members with the specific nature of the activities in which they were to participate –​was indicated in each of the studied COs, so it should be considered (despite the small number of organizations studied) as a factor worthy of closer observation and verification. The question of involvement of cluster members in cluster activities differentiates, like all other studied categories, the analyzed COs. Techtera seemed to approach this issue the most fully, while the CKB and the BFA, although aware of the positive consequences of high involvement of constituent entities, did not choose to send strong impulses to stimulate this functional sphere of the CO. A characteristic feature of all the studied COs (although one may get the impression that this tendency is common to all existing COs and, looking more broadly, to structures formed by the merger of relatively independent parts) was the involvement of only a certain proportion of the constituent entities. It therefore seems all the more important to identify all the factors influencing the stimulation of involvement in the activities of a specific superstructure. However, this should be the subject of a completely separate study.

Conclusion The conducted study enabled the collection of materials illustrating the types of proximity discussed in this book and the associated involvement in an interesting way. Three COs from three different countries were

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The concept of proximity in selected European cluster organizations 211 selected for the study: a CO from France (textiles industry), from Slovakia (IT industry –​cybersecurity), and from Bulgaria (fashion industry). The COs taking part in the study have also existed for different periods of time. Techtera had been in existence the longest, while the CKB and the BFA were very recent creations, operating for half or more of their time during pandemic restrictions. Certainly, this factor should be taken into account when interpreting the obtained results. And although the identified functional features of the studied COs in the areas focused on and summarized in the following paragraphs cannot be considered characteristic of a wider group of COs, it is nevertheless worth studying and exploring at least some of them in detail. Geographical proximity appears to retain its relevance to the effective functioning of the COs, although its importance seems to be slowly decreasing. On the one hand, this trend is influenced by the direction of technological and social change (increased use of digital technologies in economic life) and, on the other hand, it has been reinforced by the need to cope with the restrictions on human contact introduced during the SARS-​ CoV-​2 pandemic. In Techtera, whose core (coordinator and some of the key constituent entities) was located in the same region, it was possible to see greater effectiveness in building a network of relationships between members, which in turn seemed to positively stimulate cluster partners to turn personal relationships into participation in joint projects (i.e., de facto, a factor from the area of organizational proximity). It was supported by Techtera’s policy, according to which the functioning of this CO was divided into various thematic levels. In this way, conditions were created for a wider group of entities to become involved than if only one such level existed in the CO. (It is worth remembering that the coordinators of the studied COs themselves recognized the mobilizing power of offering cluster entities events and activities in line with their expectations and/​or interests.) Competence proximity appeared repeatedly in the study as a factor stimulating involvement of cluster members. It seems that sharing the same range of competencies or levels of mastery was as important as bringing together partners characterized by complementary sets of knowledge and skills in the CO. It essentially depended on the nature of the CO, the objectives it set, but also the motivation of its members, which of these competence options would resonate better in a particular cluster environment. Involvement is the “fuel” for a CO and its members.Without it, no cluster structure would be able to achieve the key purpose for which it is usually established, that is, synergy. It is a category that pervades all the mentioned types of proximity and, by its manifestation, seems to support their development (the exception being geographical proximity, which, although it can foster increased involvement of cluster members, no longer has the possibility of having a feedback effect). On the other hand, the occurrence of factors attributed to certain types of proximity may mobilize member

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212  The concept of proximity in selected European cluster organizations entities toward greater involvement, which –​in turn –​may translate into further development of a given dimension of proximity.

Reference Lis,A., & Lis,A. (2019).To meet or to connect? Face-​to-​face contacts vs ICT in cluster organisations. Engineering Management in Production and Services, 11, 103–​117.

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8 Conclusions

Final remarks The main aim of this book was the formulation of a multidimensional concept of proximity, which would explain its role in the development of cooperative ties in COs. The concept of proximity, generated with the use of the methodology of grounded theory, has been referred to the previously constructed concept of the trajectory of the development of cooperative relationships in COs (Lis, 2018; Lis & Lis, 2021). In effect, different elements of both concepts were successfully tied. As a result, it has been established that different dimensions of proximity strongly affect all of the distinguished levels of cooperation in COs. Links between the various dimensions of proximity were also identified. It was discovered that the development of cooperative relationships in COs is determined by geographical and competence proximity (the latter in the aspect of the scope of competence), on the basis of which –​at the higher levels –​social and institutional proximity are developed, followed by organizational and competence proximity (the latter in the aspect of the level of competence development). Some of these relationships were tested in quantitative studies. Furthermore, the usefulness of the constructed concept of proximity has been tested empirically in selected COs from different European countries. In the case studies provided, the development of cooperative relationships in COs was described through the lens of the development of proximity (and its specific dimensions).

Theoretical and practical contributions This work introduces new elements to the existing system of knowledge by filling a cognitive and methodological gap in the literature devoted to the concept of an industrial cluster. In its epistemological aspect, the new elements take the form of a generated theoretical concept pertaining to the development of proximity in COs, including the identification and evaluation of the significance of particular dimensions thereof at each of the levels of cooperation in a CO, the identification of the dynamics of proximity, as well as the relationships between its particular dimensions. The methodological value of this work rests in the use of an inductive–​abductive approach DOI: 10.4324/9781003194019-8

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214 Conclusions and the methodology of grounded theory in the analysis of the development of proximity in COs. Furthermore, a methodology of the measurement of proximity has been developed with the use of a quantitative method, which is accurate enough that it could be used as a template for other kinds of studies.The work also proposed a method of operationalizing proximity, theoretical constructs for the measurement of proximity in COs, in addition to methods of testing observable relationships between particular dimensions of proximity. Finally, the authors tested conceptual models reflecting the identified links between specific dimensions of proximity. The results of the research will contribute to the state-​ of-​ the-​ art findings in the clustering literature, since they expose a wider view of cooperation developed in geographical proximity in COs by using the “proximity” category. The study combines several areas: namely, industrial concentration and cooperation studies, the concept of the industrial cluster, and the concept of proximity. It reflects both the perspective of organizations at a higher level of aggregation (clusters and COs) and the perspective of organizations as components of higher-​level organizations (cluster members). Furthermore, the study builds on and contributes to the development of the network approach by exposing the relationships among companies remaining in different dependencies (Cooke & Morgan, 1993; Johanson & Mattson, 1993; Czakon, 2012; Ujwary-​Gil, 2020), the sectoral approach and industrial organizations (Porter, 1985), and the resource approach plus the resource-​based view (Wernerfelt, 1984; Mahoney & Pandian, 1992; Barney, 1991). The work also has considerable practical value. The inclusion of the proximity category in the trajectory of the development of cooperative relationships in COs not only provided rich study material of theoretical value but also strictly tied this material with the realm of praxis. This means that the observations may be used in the management of cooperation of real-​life operating COs. The understanding of relationships between the development of proximity and the level of advancement of cooperation in COs by pointing to the significance of particular dimensions of proximity at each of the derived levels of cluster cooperation is of major importance primarily for entities which are directly engaged in COs –​coordinators and facilitators, as well as the cluster members themselves. Knowledge of the development of proximity may be useful for both groups in the course of designing the CO, as well as in the course of later actions aimed at its development. Identification of the significance of geographical and competence proximity may help in establishing adequate barriers to entry into the CO (by defining the criteria of member selection) and forming the organizational structure (by creating smaller sets of entities with a certain similarity). In turn, the possibility of proximity affecting the shaping of subsequent levels of cooperation and the method of transforming its specific dimensions may be considered when defining the development strategy, as well as when initiating diverse activities and engaging therein the members of the CO.

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Limitations and further research The research underlying this publication is –​like in the case of all research processes –​subject to specific limitations, which can be divided into four main threads (sometimes comprising more than one internal path). These threads are as follows: the specific nature of the methodology of grounded theory, subjectivity, data staticity, and a problem with the representativeness of the sample. The methodology of grounded theory, adopted in the first, foundational stage of research, has further set the direction thereof. Its use at the initial stage was justified because the goal of this methodological approach is the support and facilitation of formulating theoretical concepts (including researching those used at subsequent stages of the research). However, as in the case of all methodological approaches, this one also has certain weak points –​in the case of this research, one such weak point was the postulate on the necessity of ignoring the state-​of-​the-​art findings in the study area (Glaser & Strauss, 1967). This requirement has not been fully satisfied because of the second limitation from the above list (subjectivity, which will be discussed below), though the desire to satisfy this concept resulted in, for example, an attempt to minimize the effect of existing concepts on the process of creating the so-​called core categories –​a touchstone for the emerging concept of proximity. This excessive knowledge has been treated as a handicap in the process of the emergence and construction of terms on the basis of the collected empirical material (Kelle, 1995). The second of the limitations pertained to subjectivity, a category which is the richest in internal threads. The first of these, one aspect of subjectivity, was the selection of the specific research problem, research procedure, and relevant literature. Despite aiming for objectivity in the research process, one should remember that even choices in such elementary matters as those mentioned above are reflections of the subjectivity of the researcher, and as such may be treated as limitations. The second internal thread within the limitation of subjectivity was that one of the principles of grounded theory was respected only partially –​namely, to approach the study area with a “clean slate,” without predefined assumptions resulting from having certain knowledge of the research problem to be addressed. The third internal thread within the subjectivity limitation pertains to the selection of methods of obtaining and interpreting data –​for example, developing qualitative questionnaires aimed at asking the respondents to express their opinions freely or constructing specific coding procedures, which assume interpretation (i.e., a subjective process) of the obtained results by the researchers. However, an attempt was made to nullify this problem by tailoring the research process to the remaining rigorous principles characteristic of grounded theory. The fourth internal thread within the subjectivity limitation concerned tying subjectivity with the stage of quantitative research, which –​as is the case with qualitative research –​was based on the subjective opinions of the respondents, expressed with the help of the designed measurement tools.

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216 Conclusions The developed measurement scales have allowed the authors to study individual attitudes, as well as the approach of organizations to specific areas. However, the point-​based interpretation of specific questions in the questionnaire was based on the subjective evaluation of respondents.The problem of subjectivity in the research has been partly solved by using a mixed strategy and a logic of triangulating the source data and research methods. The performed quantitative research as a supplement of qualitative research was intended to eradicate the weaknesses of subjectivity, and also the low representativeness of qualitative research. The third limitation was tied to the static nature of the obtained data (the research made use of a single measurement time). There is nothing negative about data staticity in and of itself; however, given that the concept of proximity, essential for this work, is dynamic in nature (as it deals with the temporal development of specific dimensions of proximity and the transformation of some dimensions into others), this fact should be treated as a limitation. One attempt at lowering the significance of this limitation was to construct some questions in the adopted research tools in such a way, as to force respondents to recall past events/​states (trigger retrospections). The last of the main limitations of the research pertained to the matter of the representativeness of the research sample, or, more precisely, the lack of representativeness in the statistical sense –​that is, at a level which would enable us to draw conclusions about the entire population based on the sample to hand. The authors attempted to minimize the effect of this limitation by selecting individuals for the sample in a way that would ensure that it was adequately heterogeneous (diverse and variable). This, in turn, enabled the authors to make assumptions about the broader usability of the identified relationships (which was further evaluated at the stage of quantitative research on a larger sample of Polish COs, as well as a sample of COs from other European countries). Of minor concern is the operationalization of variables at the stage of quantitative research, during which original measurement tools were designed, based on the results of the qualitative research from stage I. When evaluating the relevant scholarship, no constructs were found that would be congruous (to a satisfactory degree) with the designed conceptual definitions of specific types of proximity. A similar status to the one above (of minor concern, not a definite limitation) can be ascribed to concerns on the results obtained at the stage of testing the research hypotheses. Though the preliminary research positively verified the designed conceptual models (they had a good fit and the defined variables turned out to be reliable) (Lis, 2018), at the stage of the principal research, the authors did not manage to achieve comparable results. However, it should be noted that in the case of the principal research, the sample size was larger and –​first and foremost –​more diverse: extended with new comparison groups (other COs functioning in Poland), which may have influenced the results. The constructed conceptual models were the direct result of observations and reflections made during the extended

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Conclusions 217 qualitative research held in specific COs, while the preliminary research held on the same group confirmed the identified relationships. These limitations and concerns do not, however, negate the cognitive value of the work. One should bear in mind that each scientific process has its limitations, the awareness of which allows the authors to minimize their influence on the accuracy and reliability of the end result. Furthermore, such an awareness may serve other researchers as a signpost pointing in the direction of conducting the same or similar research in such a way as to remove the above limitations. What is more, as Glaser and Strauss (1967) stressed, a generated theory cannot be treated as an end result –​one should strive to expand it and test it. In effect, the research presented in this book should be treated as the preliminary stage of a longer process, in which the concept of proximity and its ties with the issue of the development of cooperation in COs will be developed further and in more detail. The authors believe that the natural extension and continuation of research on this topic should take the form of a longitudinal study (carried out over a longer time period and with at least two measurement times), conducted on a larger sample than the present one and with a broader consideration of European COs. Further qualitative studies could serve as an additional verification of the correctness and completeness of the theoretical categories derived in the present study, while the later quantitative research would form the basis for additional retesting of the previously posed research hypotheses. When planning research in the thematic scope of this book, one should perhaps consider the inclusion in the sample of entities that could become new comparison groups –​that is, entities which are not themselves COs but which share certain features with COs, a higher level of aggregation (an entity comprised of other entities, e.g., companies), focused on cooperation, with a high role of geographical proximity (local concentrations). Such entities could include technology parks, business incubators, and other business associations. Extending the research with new attempts at comparison would undoubtedly positively influence the growth of the universal nature of the still-​developed concept of proximity and the relationship between different types of proximity and the sphere of the development of cooperation in organizations of a higher order.

References Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–​120. Cooke, P., & Morgan, K. (1993). The network paradigm: New departures in corporate and regional development. Environment and Planning D: Society and Space, 11(5), 543–​564. Czakon, W. (2012). Sieci w zarządzaniu strategicznym [Networks in strategic management]. Warszawa: Wolters Kluwer. Glaser, B. G., & Strauss, A. L. (1967). Discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine.

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218 Conclusions Johanson, J., & Mattsson L. G. (1993). Internationalization of industrial systems: A network approach. In P. J. Buckley & P. Ghauri (Eds.), The internationalization of the firm. A reader. London: Academic Press. Kelle, U. (1995). Theories as heuristic tools in qualitative research. In I. Maso, P. A. Atkinson, S. Delamont, & J. C. Verhoeven (Eds.), Openness in research: The tension between self and other (pp. 33–​50). Assen, Netherlands: Van Gorcum. Lis, A. M. (2018). Współpraca w inicjatywach klastrowych. Rola bliskości w rozwoju powiązań kooperacyjnych [Cooperation in cluster initiatives: the role of proximity in the development of cooperative relationships]. Gdansk: Wydawnictwo Politechniki Gdanskiej. Lis, A. M., & Lis, A. (2021). The cluster organization: Analyzing the development of cooperative relationships. Abingdon and New York: Routledge. Mahoney, J.T., & Pandian J. R. (1992).The resource-​based view within the conversation of strategic management. Strategic Management Journal, 13(5), 363–​380. Porter, M. E. (1985). The competitive advantage: Creating and sustaining superior performance. New York: Free Press. Ujwary-​Gil, A. (2020). Organizational network analysis: Auditing intangible resources. Abingdon and New York: Routledge. Wernerfelt, B. (1984). A resource-​based view of the firm. Strategic Management Journal, 5(2), 171–​180.

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Index

Note: Page numbers in italics indicate figures, bold numbers indicate tables, on the corresponding pages. abductive approach 91 access to information and knowledge 113–​114, 115, 122, 148, 155–​157 age of COs researched 98–​99 Aguilera, A. 48 Anderson-​Gerbing modeling approach 100 Argyle, M. 112 attitudes developed in COs 114, 115, 116–​117, 121–​122 Audretsch, D.B. 70 balance: cognitive proximity 65–​66; institutional proximity 63 Balland, P.A. 52, 62 Baron, R.A. 29 Becattini, G. 14, 15, 16 Bellandi, M. 18–​19 belonging: logic of 57–​58; sense of, innovative milieu and 22 BFA see Bulgarian Fashion Association (BFA) Boschma, R. 2, 49, 51, 52, 57, 61, 62, 63, 66, 69, 70, 77, 142–​143 boundaries of industrial clusters 30 Bourdieu, Pierre 62, 145n1 Broekel, T. 52 Brusco, S. 13, 14 Bulgarian Fashion Association (BFA): activities of 196–​198; commitment 201–​202, 210; competence proximity 200, 207; creation of 196; geographical proximity 198, 203; goals of 196; organizational proximity 201, 208; sample characteristics 102; social proximity 198–​200, 205, 206 business ecosystems 27–​28

Camagni, R. 22, 27 Canadian wine sector 71 Capecchi,V. 17 Capello, R. 20 Carrincazeaux, C. 52 case studies: selection of 93–​94, 95, 96, 98–​99, 101–​103, 102, 144; see also Bulgarian Fashion Association (BFA); Kybernetickej Bezpečnosti -​ Cybersecurity Cluster (CKB); Techtera change, firms in industrial districts and 15 Chinese steel industry 77 circular economy, Techtera and 176–​177 citation rates of proximity 51, 52 CKB see Kybernetickej Bezpečnosti -​ Cybersecurity Cluster (CKB) climate, significance of 9–​10 closure 77 cluster, use of concept 1 cluster organizations: age of COs researched 98–​99; benefits of cooperation within 34; clusters and 33–​34; creation of COs studied 94; defined 33; development of proximity in 3; selection of for research 93–​94, 95, 96, 98–​99, 101–​103, 102, 144; size of COs researched 99; as under-​researched 1; see also development of cooperation in COs; levels of cooperation development in COs cognitive proximity: competence proximity and 110–​113; cooperation 65, 67; defined 64–​65; excessive 65–​66; geographical proximity and 68, 75–​76; indicators of 64–​65; industrial clusters and 78–​79, 80; innovation 67–​68; knowledge 64, 65, 67; literature search

20

220 Index on 49, 51, 66; systemic nature of innovation 79; technological proximity and 64, 67 commitment: Bulgarian Fashion Association (BFA) 201–​202; to COs as interview topic 105; importance of 211–​212; Kybernetickej Bezpečnosti -​ Cybersecurity Cluster (CKB) 193–​196, 209–​210; quantitative research 149, 150, 161–​164; Techtera 185–​188, 209 communication technologies, geographical proximity and 71 competence proximity: access to information and knowledge 148, 155–​157; Bulgarian Fashion Association (BFA) 200, 207; cognitive proximity and 110–​113; conceptualization of 114; importance of 211; interview topics 104; Kybernetickej Bezpečnosti -​ Cybersecurity Cluster (CKB) 192–​193, 206–​207; level I cooperation development 118, 119, 120, 136–​137; level II cooperation development 122–​123, 123, 125, 137–​138; level III cooperation development 127, 128, 129, 138; level IV cooperation development 130–​131, 132, 133–​134, 135, 138–​139; level of 113, 114, 115, 121, 125, 129, 133–​134, 136–​137, 148, 154–​155; meaning of 110–​112; properties of 115; quantitative research 148, 149–​150, 152–​157; relations with other dimensions 140; scope of competence 112–​113, 114, 115, 118, 119, 121, 122, 123, 128, 137–​138, 148, 149–​150, 152–​154; Techtera 181–​183, 206 competitive advantage: of clusters 31–​32; geographical proximity 70 complementary industries, presence of 10–​11 constant comparative method 93, 97–​98 Cooke, P. 25 cooperation: cognitive proximity 65, 67; development of cooperative relationships 3; geographical and social proximity 74; geographical proximity 70–​73, 77–​78; institutional proximity 62–​64; organizational proximity 58–​60; social proximity 53–​56; see also development of cooperation in COs; levels of cooperation development in COs court patronage 10 cultural proximity, literature search on 51

culture, innovation systems and 24 Cybersecurity Cluster (CKB) see Kybernetickej Bezpečnosti -​ Cybersecurity Cluster (CKB) Danish clean technology sector 63, 74 data analysis: stage I 97–​98; stage II 100; stage III 105 data collection: stage I 96–​97, 97; stage II 99–​100; stage III 103, 104–​105; staticity of the data 216 dependent subcontractor model in Italy 13–​14 descriptive statistical analysis 100 de Vasconcelos Gomes, L.A. 28, 38n13 development of cooperation in COs: conceptual categories 109–​117, 114, 115–​116, 142–​143; see also levels of cooperation development in COs Diamond Theory of National Advantage 30, 31 dimensions of proximity: conceptualization of 114; cultural proximity, literature search on 51; as interconnected system 2; introduction of 2; level I cooperation development 118, 119–​120, 121–​122, 136–​137; level II cooperation development 122–​123, 123–​124, 125–​126, 137–​138; level III cooperation development 126–​127, 128–​129, 138; level IV cooperation development 127, 129, 133–​135, 138–​139; models reflecting relations between 164–​171, 165, 165, 167, 167, 168, 169, 169, 170, 171, 171; properties of 115–​116; relations between 139, 139–​142, 144; relations with levels of cooperation development 136–​139, 139; see also cognitive proximity; external proximity; institutional proximity; internal proximity; social proximity; technological proximity; virtual proximity distance, logic of 110 diversification of local economies 77 Doloreux, D. 25 Dutch aviation sector 68 Dutch water sector 57, 67 economic structure, institutions and 24–​25 ecosystems, innovation 27–​29 Edquist, C. 22, 24 educational sector: cooperation in 101; see also Kybernetickej Bezpečnosti -​ Cybersecurity Cluster (CKB)

21

Index 221 embeddedness: RIS and 26–​27; social proximity 52 employees: of an area, character of 9; district 17 Enright, M.J. 34, 35 environment: approach to of COs 116, 117; level III of cooperation development 126–​127, 128–​129; for RIS 25–​26 European Union (EU): cluster policy 1; nanotechnology sector 67, 71–​72; policy as driving clusters 32–​33; technological development of regions 54 excessive proximity: cognitive proximity 65–​66; geographical proximity 77; institutional 62–​63; organizational 60; social 55 external proximity 51 extreme case sampling 93 Farinelli, F. 34 Feldman, M.P. 70 final firms 17 Florida, R. 21 Freeman, C. 22 French school of proximity 48, 57, 64 Frenken, K. 52, 62 geographical boundaries of industrial clusters 30 geographical proximity: benefits 35–​36; benefits of clusters 31; Bulgarian Fashion Association (BFA) 198, 203; closure 77; cognitive proximity and 68, 75–​76; communication technologies 71; competitive advantage 70; conceptualization of 114; cooperation 70–​73, 77–​78; defined 69–​70; distance between companies 151–​152; distance of organizations 178–​179; distance to location of CO 150–​151; diversification of local economies 77; excessive 77; industrial clusters and 78, 79; influence of physical space 73; innovation 70, 72; innovative milieu 22; institutional proximity and 63, 75; interview topics 104; knowledge and 70; Kybernetickej Bezpečnosti -​ Cybersecurity Cluster (CKB) 190–​191, 203; level I cooperation development 118, 119, 136–​137; level II cooperation development 125; level III of cooperation development 126, 128, 138; level of 76–​77; literature

search on 49, 51, 69; loss in importance of 203–​204, 211; online meetings 190–​191; organizational proximity and 73, 74–​75; organizational proximity as compensation for lack of 59; and other dimensions of proximity 73–​78; overlap mechanism 74; prominence of 69; properties of 115; quantitative research 148, 149, 150–​152; relations with other dimensions 140; social proximity and 74; substitution mechanism 74; systemic nature of innovation 79; technological proximity and 68, 72, 77; Techtera 178–​179, 202–​203, 211; trust 70; virtual proximity and 71 German R&D sector 66, 67 Gilly, J.-​P. 57, 69 Glaser, B.G. 217 Goglio, S. 16 Grabher G. 26–​27 Granovetter, M. 27 Great Britain, clusters within social proximity 55–​56 grounded theory 92, 93, 97, 109, 215 Grzeszczak, J. 26 Guerini, M. 54, 74 habitus 62, 145n1 Hamel, G. 112 Hansen, T. 74, 75, 76 Hearn, W.E. 8–​9, 12, 36n1 Heringa, P.W. 57 human economic activity in industrial production, decrease in 12 ICT sector, selection of COs for research 93 industrial clusters 29–​35; literature search on proximity 80, 81, 82; proximity and 78–​80, 81, 82 industrial districts: components of 17; definitions 16, 36n1; features defining 17–​18; Hearn on 8–​9; Italian 12–​19, 16, 35–​36; Marshallian 9–​12, 35–​36 information, access to 113–​114, 115, 122, 148, 155–​157; see also knowledge innovation: cognitive proximity 67–​68; geographical proximity 70, 72; industrial clusters 31; learning and 20; organizational proximity 59, 60; Techtera 176 innovation ecosystems 27–​29 innovation systems 22–​27 innovative milieu 21–​22, 79

2

222 Index input proximity 118, 119–​120, 122–​123, 123, 125, 126–​127, 128, 129, 130–​131, 133–​134 institutional environment for innovation 24 institutionalization 62 institutional proximity: balance in 63; as bonding agent 63–​64; conceptualization of 114; cooperation 62–​64; defined 62; dynamic nature of 62; excessive 62–​63; geographical proximity and 63, 75; habitus and 62; industrial clusters and 78, 79, 80; level I cooperation development 120; level III of cooperation development 127, 128, 138; literature search on 49, 51, 61–​62; organizational proximity and 61; properties of 116; relations with other dimensions 140–​141; systemic nature of innovation 79 insufficient proximity 55 interactive chain-​linked model of innovation 23–​24 Interizon: Pomeranian Region ICT Cluster 94, 95 internal proximity 51 international organizations, organizational proximity and 59 Internet of Things (IoT), Techtera and 177 interpretation techniques: stage I 97–​98; stage II 100; stage III 105 interpretative-​symbolic paradigm 91 interviews with COs 96, 97, 103, 104–​105; telephone 99–​100 involvement see commitment Italy: companies and higher education cooperation 54, 68, 76; dependent subcontractor model 13–​14; high-​tech sector, Tiburtina valley 72; industrial districts 12–​19, 16, 35–​36; social proximity in high-​tech sector 53; strategic alliances 67; traditional artisan model 13; Tuscan Life Sciences cluster 72; wine industry 54, 67 Jaffe, A.B. 70 Jing, Z. 29 Johnson, B. 24 Kendon, A. 112 Kirat, T. 52 Klimas, P. 48 Kline, S.J. 23 Knoben, J. 49, 51, 56–​57, 61, 62, 69

knowledge: access to 113–​114, 115, 122, 148, 155–​157; cognitive proximity 64, 65, 67; as fundamental resource 20; geographical proximity and 70; industrial districts, firms in 15; learning regions 21; organizational proximity and exchange of 58–​59; social proximity and exchange of 53; technological and geographical proximity and 77 Kybernetickej Bezpečnosti -​ Cybersecurity Cluster (CKB): activities of 188–​190; commitment 193–​196, 209–​210; competence proximity 192–​193, 206–​207; creation of 188; General Assembly of 189; geographical proximity 190–​191, 203; goal of 188; organizational proximity 193–​194, 208; President of 189; sample characteristics 102; social proximity 191–​192, 204–​206 labor market, specialized, in industrial clusters 31 land, significance of 9–​10 latent clusters 34 latent variables 147 learning: importance of 20; innovation and 20; learning regions 21; regions 79 learning economy 37n6 learning processes, social proximity and 53 Lethias,V. 48 levels of cooperation development in COs: competence proximity 118, 119, 120, 122–​123, 123, 125, 127, 128, 129, 130–​131, 132, 133–​134, 135, 137–​139; development of cooperation and 117; geographical proximity 118, 119, 125, 126, 128, 138; institutional proximity 120, 127, 128, 138; level I, proximity and 118–​122, 119–​120, 136–​137; level II, proximity and 122–​126, 123–​124, 137–​138; level III, proximity and 126–​127, 128–​129, 138; level IV, proximity and 127, 129, 130–​133, 133–​135, 138–​139; level IV cooperation development 138–​139; organizational proximity 118, 119, 121, 124, 126, 131, 133, 135, 138, 139; proximity and 117–​118; relations with proximity dimensions 136–​139, 137; social proximity 118, 119, 120, 121–​122, 124, 125, 127, 128, 129, 130, 132, 135, 137–​138 Lis, A. 6

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Index 223 Lis, A.M. 6 literature search on proximity 49–​51, 50, 52, 144; clusters and 80, 81, 82; cognitive proximity 49, 51, 66; cultural proximity 51; geographical proximity 49, 51, 69; institutional proximity 49, 51, 61–​62; organizational proximity 49, 51, 56–​57; social proximity 49, 51, 56; technological proximity 51 location proximity: innovative milieu 22; see also geographical proximity logics of belonging and similarity 57–​58 logics of distance and similarity 110 Lundberg, C.C. 112 Lundvall, B.A. 22, 24–​25 Lung,Y. 52 Maghssudipour, A. 54 Malmberg, A. 26, 77 management of social relations 53 Mark II of industrial district model 15 Mark I of industrial district model 14–​15 Marshall, A. 9–​12, 35–​36 Marshallian industrial districts 9–​12, 35–​36 Martin, R. 32, 37n4 Maskell, P. 25, 77 Mazovia Cluster ICT (MC ICT) 94 meetings, online 190–​191 Metal Cluster of Lubuskie Province (MCLP) 94, 95, 96, 99 metal sector, selection of COs for research 93 Metal Working Eastern Cluster (MWEC) 94, 95, 96, 99 Metcalfe, S. 22, 57 Moore, J.F. 27–​28 Mytelka, L.K. 34 Nambisan, S. 29 national business systems (NBSs) 23, 37n10 national context, selection of COs and 94, 101–​102, 102 national innovation systems (NIS) 23–​25 national production systems (NPSs) 23, 37n9 natural ecosystems 27 neighbouring industries, presence of 10–​11 networking 26–​27 New York textile sector 54 non-​market contacts 24 Nooteboom, B. 65

North, D.C. 62 Norwegian marine economy sector 76 Oerlemans, L.A. 49, 51, 56–​57, 61, 62, 69 online meetings 190–​191 openness 114, 116, 121–​122 organizational proximity: Bulgarian Fashion Association (BFA) 201, 208; compensation for lack of geographical proximity 59; conceptualization of 114; conditions when important/​ not important 59–​60; cooperation 58–​60; defined 57–​58; excessive 60; geographical proximity and 74–​75; industrial clusters and 78, 79, 80; innovation 59, 60; institutional proximity and 61; international organizations 59; interview topics 104; knowledge, creation and exchange of 58–​59; Kybernetickej Bezpečnosti -​ Cybersecurity Cluster (CKB) 193–​194, 208; level I cooperation development 118, 119, 121; level II cooperation development 124, 126, 138; level IV of cooperation development 131, 133, 133–​134, 135, 139; levels of 57; literature search on 49, 51, 56–​57; logics of belonging and similarity 57–​58; multiplant companies 58; properties of 116; quantitative research 149, 150, 159–​161; relations with other dimensions 141–​142; social proximity and 56; systemic nature of innovation 79; Techtera 183–​185, 207–​208 Ottati, G.D. 16, 17 output proximity 120, 121–​122, 123–​124, 125–​126, 127, 128–​129, 132–​133, 134–​135 overlap mechanism: cognitive and geographical proximity 76; geographical proximity 74; institutional and geographical proximity 75; organizational and geographical proximity 74–​75; social and geographical proximity 74 Paci, R. 68 Padgett, J.F. 83 paradigm, interpretative-​symbolic 91 Parto, S. 25 Peirce, Charles Sanders 91 Perroux, F. 37n5 phase firms 17 physical distance see geographical proximity polarization theories, clusters and 37n5

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224 Index policy as driving clusters 32–​33, 34–​35 Porter, M.E. 1, 29–​33, 70, 79 potential clusters 34 Powell, W.W. 83 Prahalad, C.K. 112 Presutti, M. 53 production costs 8–​9 proximity: blurring of definitional boundaries 82–​83; change, openness to of dimensions 83; complexity of subject 47; cultural proximity, literature search on 51; development of in COs 3; development of term 48; French school 48; growth in use of term 48; industrial clusters and 78–​80, 81, 82; as interconnected system 2; introduction of dimensions to 2; literature search 49–​51, 50, 52; as not homogenous term 48–​49; research into to date 1–​2; scientific research and 47–​48; see also cognitive proximity; external proximity; institutional proximity; internal proximity; social proximity; technological proximity; virtual proximity qualitative research 105–​106; conceptual categories 109–​117, 114, 115–​116, 142–​143; stage I 92; stage III 92–​93; see also levels of cooperation development in COs quantitative research 105–​107; commitment 149, 150, 161–​164; competence proximity 148, 149–​150, 152–​157; dimension relations, models reflecting 164–​171, 165, 165, 167, 167, 168, 169, 169, 170, 171, 171; geographical proximity 148, 149, 150–​152; organizational proximity 149, 150, 159–​161; research hypotheses 164–​171, 165, 165, 167, 167, 168, 169, 169, 170, 171, 171; social proximity 149, 150, 157–​159; stage II 92; variable operationalization 147–​150, 148–​149, 216 Rallet, A. 48, 51, 52, 64, 69 reciprocity 116 regional competitiveness 37n4 regional development theories: industrial clusters 29–​35; innovation ecosystems 27–​29; innovation systems 22–​27; innovative milieu 21–​22; knowledge as fundamental resource 20; learning, importance of 20; learning regions 21

regional innovation systems (RIS) 25–​27 relational proximity 51 relationships see social proximity renewable energy sector in the US 68 research: abductive approach 91; aim 3; data analysis 97–​98, 100, 105; data collection 96–​97, 97, 99–​100, 103, 104–​105; development of cooperative relationships 3; further 217; grounded theory 92; hypotheses 136, 139, 143, 164–​171, 165, 165, 167, 167, 168, 169, 169, 170, 171, 171; interpretation techniques 97–​98, 100, 105; interpretative-​symbolic paradigm 91; limitations of 215–​217; literature overview 4; methodology 92–​93, 105–​107; mixed strategy for 91, 91; qualitative research 92–​93, 105–​106; quantitative research 92, 105–​107; questions 4; rigor of methodology 105–​107; sample selection 93–​94, 95, 96, 98–​99, 101–​103, 102, 144; stage I 93–​94, 95, 96–​98, 97; stage II 98–​100; stage III 101–​105, 102, 104–​105; steps in 92–​93; subjectivity 215–​216; see also literature search on proximity restructuring, firms in industrial districts and 15 Rosenberg, N. 23 Rosenfeld, S.A. 34 Rothwell, R. 20 sample selection: representativeness 216; stage I 93–​94, 95, 96; stage II 98–​99; stage III 101–​103, 102 Sdruzhenie Balgarska Modna Asotsiatsia see Bulgarian Fashion Association (BFA) secondary data 103 Sforzi, F. 16 similarity, logic of 57–​58, 110 size of COs researched 99 size of industrial plants in Italy 12–​13 social contacts, innovative milieu and 22 social proximity: attitudes developed in COs 114, 116–​117; Bulgarian Fashion Association (BFA) 198–​200, 205, 206; clusters within 55–​56; conceptualization of 114; cooperation 53–​56; embeddedness 52, 54; excessive 55; geographical proximity and 74; industrial clusters and 78, 79, 80; innovation and 53–​54; insufficient 55; interview topics 104; knowledge exchange 53; Kybernetickej

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Index 225 Bezpečnosti -​Cybersecurity Cluster (CKB) 191–​192, 204–​206; learning processes 53; level I cooperation development 118, 119, 120, 121–​122; level II cooperation development 124, 125, 137–​138; level III cooperation development 127, 128, 138; level IV cooperation development 129, 130, 132, 135; literature search on 49, 51, 56; as lubricant 56; management of social relations 53; negative effects 54–​55; organizational proximity and 56; properties of 115; quantitative research 149, 150, 157–​159; relationships 52–​53; relations with other dimensions 140–​141; systemic nature of innovation 79; Techtera 179–​181, 204, 205; trust 52, 130, 132, 135; vigilance 116–​117, 127 spatial proximity see geographical proximity spontaneous clusters 34–​35 stage I of research: data analysis 97–​98; data collection 96–​97, 97; interpretation techniques 97–​98; sample selection 93–​94, 95, 96 stage II of research: data analysis 100; data collection 99–​100; interpretation techniques 100; sample selection 98–​99 stage III of research: data analysis 105; data collection 103, 104–​105; interpretation techniques 105; sample selection 101–​103, 102 Storper, M. 26 Strauss, A.L. 217 structural equation modeling 100 subjectivity 215–​216 substitution mechanism: cognitive and geographical proximity 75–​76; geographical proximity 74; institutional and geographical proximity 75; social and geographical proximity 74 suitability of an area for an industry 9 Sunley, P. 32 synergy effect 31 systemic nature of innovation 22–​27, 79 technological proximity: cognitive proximity and 64, 67; geographical proximity and 68, 72, 77; literature search on 51

Techtera: activities of 175–​178; axes of action 176–​178; circular economy 176–​177; Clubs and projects 177; Cluster Management Excellence Label Gold 175; commitment 185–​188, 209; competence proximity 181–​183, 206; focus and ambition of 175–​176; geographical proximity 178–​179, 202–​203, 211; innovative materials 176; Internet of Things (IoT) 177; organizational proximity 183–​185, 207–​208; sample characteristics 102; social proximity 179–​181, 204, 205 telephone interviews with COs 99–​100 textile industry: cooperation in 101; see also Bulgarian Fashion Association (BFA); Techtera Thailand, food processing sector in 72–​73 theoretical sampling 94–​95, 97 theories on clusters 1 Torre, A. 51, 52, 57, 64, 69 traditional artisan model in Italy 13 Tremblay, D.G. 53 trust: geographical proximity 70; social proximity 52, 130, 132, 135, 179 Tuscan Life Sciences cluster 72 two-​step Anderson-​Gerbing modeling approach 100 typologies of clusters 32 United States renewable energy sector 68 untraded interdependencies 26 Usai, S. 54 Uzzi, B. 54 variable correlation analysis 100 variable operationalization 147–​150, 148–​149, 216 vigilance 116–​117, 127 virtual proximity 51; geographical proximity and 71 Wenting, R. 52 White, R.W. 112 Whitley, R. 37n10 Wiig, H. 27 Wolek, F.W. 112 working clusters 34 Xiong-​Jian, L. 29 Zeller. C. 51

26