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Knowledge Creation, Growth, and Transfer within Industrial Networks of Practices: The Role of Absorptive Capacity and Direct Centrality
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Author(s): Lucio Biggiero (University of L'Aquila, Italy & CIRPS, Italy)and Mario Basevi (Italian Institute of Statistics, Italy)
Copyright: 2016
Pages: 35
Source title:
Relational Methodologies and Epistemology in Economics and Management Sciences
Source Author(s)/Editor(s): Lucio Biggiero (Università dell’ Aquila, Italy), Pier Paolo Angelini (Interuniversity Research Centre for Sustainable Development (CIRPS), Italy), Mario Basevi (Italian National Institute of Statistics, Italy), Nunzia Carbonara (Politecnico di Bari, Italy), Antonio Mastrogiorgio (Interuniversity Research Centre for Sustainable Development (CIRPS), Italy), Eliano Pessa (University of Pavia, Italy), Enrico Sevi (Università dell’ Aquila, Italy)and Marco Valente (Università dell’ Aquila, Italy)
DOI: 10.4018/978-1-4666-9770-6.ch012
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Abstract
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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