The competitive advantage of geographical clusters as complex adaptive systems: an exploratory study based on case studies and network analysis
department of Mechanical and management engineering - Polite
department of Mechanical and management engineering -Politecnico di Bari
department of Mechanical and management engineering -Politecnico di Bari
Last modified: September 5, 2006
This paper presents an exploratory study on the sources of competitive advantage of GCs, that is a relevant topic in the referred literature (e.g. Porter, 1998). The latter, in fact, has focused much attention on the reasons explaining the GC competitive success, such as: the flexible specialization conceptualized by Piore and Sabel (1984); the localized external economies concept anticipated by Marshall (1920) and further formalized by Becattini (1990) and Krugman (1991); the industrial atmosphere notion conceived by Marshall (1919); and the innovative milieux notion developed by the GREMI (see, for instance, Maillat et al., 1995).
These studies have identified the main critical factors governing the success of GC firms. These can be traced back to the following features that successful GCs possess: the physical and cultural proximity of many small and medium sized firms; the division of labor among firms; the presence within the area of complementary competencies and skills; the high degree of specialization of both firms and workforce; the existence of a dense network of inter-firm relationships where firms co-operate and compete at the same time; the presence of a dense network of social relationships mainly based on face-to-face contacts; and the easy and fast circulation of knowledge and information in the area.
These features, which assure the competitive advantage of GCs when the competitive context is characterized by increasing and not particularly sophisticated demand, seem to be insufficient to guarantee the GC success in the current competitive scenario much more dynamic, unpredictable, and instable. In such a context many GCs are undergoing a decline phase.
As a result, the attention of scholars and policy-makers has been shifted and is now much more oriented to develop theories on GC survival in the new competitive scenario by looking for new sources of competitive advantage for GCs (Baptista, 2000; Sull 2003). With this regard, recent studies have pointed out that changes in the GC organizational structure and in their strategies are necessary to guarantee the GC competitiveness. For example, some GCs have internationalized their production system by delocalizing their production process in foreign countries, so determining profound changes in the GC structure (Corò and Rullani, 1998; Biggiero, 2002). Some GCs have introduced new innovation strategies much more focused on developing radical innovations by making alliances with universities and research centers (Belussi and Arcangeli, 1998; Carbonara, 2004; Corò and Grandinetti, 1999). Therefore, these studies suggest how GCs have to modify for surviving. This means that the competitive advantage of GCs is associated to the posses of a new set of features. Taking things to extreme, GCs possessing these features are competitive and survive, the others not.
However this approach, that is consistent with the traditional studies on GCs, presents some limitations. In fact, it adopts a static perspective aimed at identifying a set of features explaining GC competitive advantage in a given particular context. In this way every time the competitive scenario changes, it is necessary to identify a new set of features.
Complexity science is a theoretical approaches may help scholars overcome these limitations. In fact it investigates properties and behaviors of complex adaptive systems (CASs) and aims to explain how heterogeneous agents1 “self-organize” to create new structures in interactive systems, with the goal of understanding how such structures emerge and develop (Casti, 1994,1997; Coveney and Highfield, 1995; Holland, 1995, 1998; Johnson, 2001).
By adopting the complexity science approach, the GC competitive advantage is not the result of a set of pre-defined features characterizing GCs, but it is the result of dynamic processes of adaptability and evolution of GCs with the external environment. The competitive advantage resides in the GC capabilities of adaptability and evolution with the external environment. The result of this evolutionary process is not known a priori, but spontaneously emerges from the interactions among the system components and between them and the environment. This view is consistent with the recent studies on strategic management about new dynamic sources of competitive advantage based on resources, capabilities, and knowledge (Teece et al., 1997).
In this paper GCs are considered as a complex adaptive systems (CASs) (Gell-Mann,1994; Dooley, 1997), given that they exhibit different properties of CAS, such as the existence of different agents (e.g. firms and institutions), the non-linearity, different types of interactions among agents and between agents and the environment, distributed decision making, decentralized information flows, and adaptive capacity (Albino et al., 2005).
Thus complexity science is specifically used to develop three theoretical propositions concerning the GC adaptive capacity. First, we identify three main properties of CASs that affect the adaptive capacity, namely the interconnectivity, the heterogeneity, and the level of control, and define how the value of these properties influence the adaptive capacity. Then, we associate these properties with specific GC characteristics so obtaining the key conditions of GCs that give them the adaptive capacity so assuring their competitive advantage.
To test these theoretical propositions, a multiple case study on two Italian industrial districts1 is carried out. These case studies use a structured questionnaire for collecting data. The data aimed at identifying the network of inter-firm relationships in each industrial district, the adaptive capacity, and competitive performance of each industrial district. Then, we build the network of each industrial district using UCINET software (Borgatti et al., 2002) and we measure the level of heterogeneity, interconnectivity, and control of the network by applying the network analysis technique. Finally, we verify the propositions examining whether the hypothesized values of heterogeneity, interconnectivity, and control assure higher adaptive capacity and competitive performance.
1The Italian industrial districts are characterized by the agglomeration of small- and medium-sized firms integrated through a complex network of buyer-supplier relationships managed by both cooperative and competitive policies. Italian industrial districts are considered a specific kind of geographical cluster (Markusen, 1996).
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