Elsevier

World Development

Volume 32, Issue 2, February 2004, Pages 305-326
World Development

Industrial Clusters, Knowledge Integration and Performance

https://doi.org/10.1016/j.worlddev.2002.12.001Get rights and content

Abstract

Scholars examining the phenomenon of industrial clusters have begun to regard them as social communities specializing in efficient knowledge creation and transfer, in addition to neo-classical arguments focusing on the advantages of localization. We seek to contribute to this body of work by developing the argument that both the degree of knowledge integration between an industrial cluster’s agents and the scope of their economic activities are key dimensions behind their economic performance. We present a model that incorporates a hypothesized relationship between these three dimensions and argue that a formal test of this hypothesis constitutes a promising area of future empirical research in this field.

Introduction

That economic agents gather together in close geographic proximity and establish relationships with one another in order to better perform certain economic activities is a fact that can be traced back to the earliest urban developments.1 Close-knit geographic clusters have remained a relevant economic phenomenon even at the dawn of the 21st century, nurturing some of the most successful players across a broad array of global industries, including––somewhat paradoxically––the same information technology industries that give us the ability to work and communicate virtually.

Active membership of an industrial cluster during the second half of the 20th century provided one of the best opportunities for small and medium-sized enterprises to survive and stay competitive on a regional, international and even global scale. At the same time, large international, multinational and global companies risked losing entire parts of the value chain in the key areas in which they competed––such as manufacturing, product design and Research and Development (R&D)––to nimble, formidable companies closely clustered in specific geographic locations. Some of these large companies were also able to leverage the enormous potential and capabilities that industrial clusters offer. Typically they did this by locating key company operations in carefully selected industrial clusters around the world, or by using these industrial clusters as critical innovators, e.g., from the R&D, supplier or customer perspective.

In this paper we offer a knowledge-based framework to facilitate understanding of the key factors governing the way a cluster functions and its international success. Although there are many publications on the subject––or perhaps because of this––both the use of the term and the existing explanations of the underlying phenomena of clusters can be rather confusing. As a result, regional policy makers and businessmen alike often find it difficult to address the potential threats as well as the promising opportunities that these clusters provide.

In essence, we argue that two fundamental dimensions will allow both analyst and practitioner to gain a real grasp of the cluster phenomenon. On the one hand, by looking at the nature and quality of a cluster’s underlying social fabric, it is possible to understand its potential for knowledge creation and innovation. On the other hand, by assessing the reach and scope of a cluster’s economic activities, it is possible to understand the forces driving its competitive and business logic. Altogether, social knowledge, economic factors and the forces of business competition provide a fundamental––if not exhaustive––understanding of clusters in a dynamic sense. The implications of this understanding for macro- and micro-economic policy design and implementation cannot be overemphasized. In addition, an understanding of what makes clusters work in practice is also critical for all firms competing in the global arena.

The paper is organized, as follows. In Section 2, we provide a definition of industrial clusters based on a comprehensive review of the major literature sources, from the early 20th century to more recent contributions. Significantly, in our definition of industrial clusters, social and knowledge-based elements are brought together more explicitly than they have in previous discussions.

Building on this definition, we discuss the major characteristics of industrial clusters in detail in 3 Industrial clusters are social entities, 4 Who belongs to industrial clusters?, 5 Why is geographic closeness so important?, 6 How do members of industrial clusters combine?, 7 What binds industrial clusters together?, 8 The global scope of today’s industrial clusters. These include: the social nature of an industrial cluster’s knowledge interactions; the broad diversity of their social fabric––including much more than purely economic agents; the key importance of locally confined relationships and specialized economic linkages for efficient knowledge creation and transfer; the “common glue” that binds industrial clusters together; and the competitive scope of industrial clusters in today’s increasingly interconnected, global milieu.

In Section 9, we introduce a knowledge-based taxonomy of industrial clusters that brings together the main elements examined in our previous discussions. This classification provides a way to assess the degree of knowledge integration of industrial clusters, which is understood to be a critical dimension behind their economic performance. In Section 10, we argue that the scope of competition of industrial clusters constitutes a second critical dimension in understanding their economic performance. Based on mainstream strategic management concepts, we develop a framework to appraise the scope of an industrial cluster’s competitive dynamics from external (market), internal (firm) and social (learning) dimensions.

Finally, in Section 11, we build on the foundations laid in the previous sections in order to postulate the hypothesis that is central to this paper, making a case for the link between knowledge integration, the scope of competition and the economic performance of industrial clusters. We conclude by arguing that a formal test of this hypothesis constitutes a promising, long-overdue area of future empirical research in this field.

Section snippets

What are industrial clusters?

During the 1990s the explosion of specialized and popular literature on industrial clusters gave them an unprecedented relevance across a range of areas, including business management and economic, political, public and social policy. There was also a degree of confusion over what the various authors mean––and do not mean––by industrial clusters. Our first consideration therefore is terminology.

It is important to point out from the outset that we are not concerned here with the kinds of

Industrial clusters are social entities

The first thing to note is that our definition of industrial clusters states that it is the nature, quality and strength of a cluster’s underlying social fabric that determines how it integrates existing and new knowledge in order to create superior products and services. In essence, this is what more clearly differentiates industrial clusters from simple geographic agglomerations of economic agents. Gordon and McCann (2000, p. 520) observe:

The strength of [an industrial cluster’s]

Who belongs to industrial clusters?

Our definition of industrial clusters includes a close-knit social community of people and a broad set of economic agents, not just firms. Studies that look at Emilia Romagna’s industrial districts in Italy offer some of the most striking characterizations of cohesive social communities actively underpinning the economic strength of clusters (Becattini, 1990, p. 39):

The most important trait of [an industrial district’s] local community is its relatively homogenous system of values and views,

Why is geographic closeness so important?

Furthermore, our definition of industrial clusters stresses the notion that the members of such a cluster are localized in close proximity within a particular geographic region. We are therefore concerned here with the types of economic advantages strictly stemming from a high degree of geographic concentration among firms. These types of economic advantages have been well described in the classical and neoclassical economic tradition examining industrial complexes (Czamanski & Ablas, 1979;

How do members of industrial clusters combine?

Our definition of industrial clusters highlights that its members work together in related or linked business activities. Indeed, the scale- and knowledge-based advantages generated within an industrial cluster stem from both the number and the nature of the particular linkages between its members. In a well-developed industrial cluster, these linkages can be numerous, unique and specialized to the industrial cluster, including:

  • ––common customers (both firms and individuals);

  • ––common suppliers

What binds industrial clusters together?

Next, our definition stresses that members of an industrial cluster must share and nurture a common stock of product, technology and organizational knowledge. Indeed, some authors have described this critical characteristic of industrial clusters as constituting a “social glue” (Porter, 1998) that binds the cluster together. Others have referred to a “common glue” or an “organizational glue” that socially amalgamates diverse structural agents and integrates key knowledge across cultural,

The global scope of today’s industrial clusters

Finally, our definition stresses that the ultimate goal of industrial clusters is to generate superior products and services that are valuable to customers in the marketplace. There are at least two crucial points to be made about this notion. First, although an industrial cluster might benefit from some protective measures at the outset, in the long-term, selection mechanisms that reflect the dynamics of business competition must be in place (Porter, 1998). Otherwise, an industrial cluster

A knowledge-based classification of industrial clusters

Some of the approaches developed in the past to understand the phenomenon of industrial clusters include templates for applied regional cluster analyses (Feser & Bergman, 2000), descriptive frameworks for strategic and competitive analyses (Carrie, 2000) and empirical classification models (Gordon & McCann, 2000; Porter, 1998). What appears to be a common characteristic of these approaches is that they rely on the notion of economic linkages among a cluster’s economic agents to categorize and

Scope of competition of industrial clusters

As previously mentioned, a second critical dimension for understanding a cluster’s competitive and business logic is the reach and scope of the economic activities carried out by its member firms (Porter, 1998). These activities can be grouped according to three broad factors (Morosini, 1998): (a) those that are largely external to the firm, i.e. customers, product markets and the macro-level demographic, regulatory and legal frameworks governing these customers and markets; (b) factors that

Link between knowledge integration, scope of competition and the performance of clusters

A broad array of existing empirical evidence (some of which is referenced in the previous sections) suggests that both the degree of knowledge integration and the scope of competition are co-evolving factors that are crucial to explain the economic performance of industrial clusters. Although the empirical evidence remains slightly fragmented, it suggests that firms in industrial clusters that present a high degree of knowledge integration and compete globally innovate more, present stronger

Concluding remarks

We have looked at industrial clusters from an economic and a social perspective. The complexity and richness of this phenomenon seems to be as great as its potential to contribute economic value to both the economic agents and the social communities involved. We suggest that by looking at the crucial dimensions of knowledge integration and the scope of (global) competition, much of the underlying functioning fabric of industrial clusters can be captured in ways that are meaningful to both the

Acknowledgements

The author would like to express his thanks to IMD Research Associates Deepak Khandpur and Sophie Linguri for their assistance with this paper. The author also thanks Carlos Alberto Schneider and Marcelo Otte at CERTI Foundation, Florianopolis, Brazil, for their help and support to our field research work.

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