Knowledge Creation, Growth, and Transfer within Industrial Networks of Practices: The Role of Absorptive Capacity and Direct Centrality

Knowledge Creation, Growth, and Transfer within Industrial Networks of Practices: The Role of Absorptive Capacity and Direct Centrality

Lucio Biggiero (University of L'Aquila, Italy & CIRPS, Italy) and Mario Basevi (Italian Institute of Statistics, Italy)
DOI: 10.4018/978-1-4666-9770-6.ch012


In this chapter we test the hypothesis that uneven links distributions and uneven absorptive capacity between an industrial cluster members provide some kind of competitive advantages. Through an agent-based model has been built and calibrated on real data taken from an aerospace industrial cluster, that hypothesis is contrasted against the normal, the uniform, and the U-shaped distribution. The focus of the model is on knowledge variables, agents' learning capacities, and structural variables, like firms size and proximity. Physical production is not considered, excepted for its degree of complexity, which determines also the degree of knowledge complexity. This work shows that, actually, the best performance in terms of cluster knowledge creation, growth and diffusion is obtained when firms connectedness and absorptive capacity are distributed in a scale-free way. More generally, the more unbalanced are these two variables (especially absorptive capacity), the better is knowledge performance. These results are rather robust, and obtained while keeping all other variables very balanced at the beginning of each simulation.
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What appeared more and more evident during the last two decades is that, given its tremendous complexity and methodological heterogeneity, it is very hard to find clear, sound and convergent results from empirical studies on knowledge transfer, industrial clusters and innovation networks, often reciprocally incomparable and theoretically inconclusive. To face with these limitations agent-based simulation modelling (ABSM) are diffusing (Carley, 2009; Davis et al., 2007; Gilbert, 2008; Gilbert & Terna, 2000; Gilbert & Troitzsch, 2005; Harrison et al., 2007; Tesfatsion & Judd, 2006; Uhrmacher & Weyns, 2009), with the hope to build strong and comparable results. When these models are not too abstract, and especially if its parameters are calibrated - inspired by and set up - with real data, results of virtual experiments can dramatically improve and increase our scientific knowledge. This paper adopts this methodological perspective by building KNOWTIC, an agent-based model on knowledge creation, growth, and transfer of industrial clusters (IC).

According to various scholars (Arikan, 2009; Lorenzen & Maskell, 2004; Maskell, 2001; Tallman et al., 2004), the need to build, enhance, and exploit collective knowledge is supposed to be one of IC major drivers. The process of collective knowledge formation occurs through recursive and (mostly) self-organizing mechanisms (Biggiero, 2001, 2006), that is, grounding on largely spontaneous bottom-up forces. After one or more forms of proximity have been established (Boschma, 2005), knowledge creation and transfer (Ernst, 2002) is enabled. Indeed, this picture requires a number of other favourable conditions to occur, and their exploration is just at the beginning. In fact, many IC decline and fail, and many of them loose identity, structure, social cohesion, and competitive advantages. Moreover, nobody knows how many IC could have been formed but never born. In this paper we do not investigate such contextual conditions, and assume that an IC already exists.

The focus is on the way that tacit and explicit knowledge circulates and grows, and how knowledge is employed in collaborative and non-collaborative activities, depending on agents’ collaborative propensity, learning attitude, imitation or innovation choice, research expenditure, geographical proximity, size, and a number of other variables whose discussion is succinctly made in the third section. The core research issue concerns if and how the specific distribution of firms’ absorptive capacity (ABC) and connectedness (Dc) influence an IC knowledge performance. To this aim, a normal distribution of these two variables is benchmarked, ceteris paribus, against a uniform, a U-shaped, and a power-law distribution.

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