Abstract

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<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>The purpose of this paper is to explain the emergence of collaboration networks in entrepreneurial clusters as determined by the way entrepreneurs exchange knowledge and learn through business transactions needed to implement temporary supply chains in networks of co-located firms.</jats:p>
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<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>A socio-computational approach is adopted to model business transactions and supply chain formation in Marshallian industrial districts (IDs). An agent-based model is presented and used as a virtual lab to test the hypotheses between the firms’ behaviour and the emergence of structural properties at the system level.</jats:p>
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<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>The simulation findings and their validation based on the comparison with a real world cluster show that the topological properties of the emerging network are influenced by the learning strategies and decision-making criteria firms use when choosing partners. With reference to the specific case of Marshallian IDs it is shown that inertial learning based on history and past collaboration represents in the long term a major impediment for the emergence of hubs and of a network topology that is more conducive to innovation and growth.</jats:p>
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<jats:title content-type="abstract-subheading">Research limitations/implications</jats:title>
<jats:p>The paper offers an alternative view of entrepreneurial learning (EL) as opposed to the dominant view in which learning occurs as a result of exceptional circumstances (e.g. failure). The results presented in this work show that adaptive, situated, and day-by-day learning has a profound impact on the performance of entrepreneurial clusters. These results are encouraging to motivate additional research in areas such as in modelling learning or in the application of the proposed approach to the analysis of other types of entrepreneurial ecosystems, such as start-up networks and makers’ communities.</jats:p>
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<jats:title content-type="abstract-subheading">Practical implications</jats:title>
<jats:p>Agent-based model can support policymakers in identifying situated factors that can be leveraged to produce changes at the macro-level through the identification of suitable incentives and social networks re-engineering.</jats:p>
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<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>The paper presents a novel perspective on EL and offers evidence that micro-learning strategies adopted and developed in routine business transactions do have an impact on firms’ performances (survival and growth) as well as on systemic performances related to the creation and diffusion of innovation in firms networks.</jats:p>
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