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首页> 外文期刊>International journal of entrepreneurial behaviour & research >Learning by failure vs learning by habits: Entrepreneurial learning micro-strategies as determinants of the emergence of co-located entrepreneurial networks
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Learning by failure vs learning by habits: Entrepreneurial learning micro-strategies as determinants of the emergence of co-located entrepreneurial networks

机译:从失败中学习与从习惯中学习:创业学习微观策略是共同定位创业网络出现的决定因素

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摘要

Purpose - 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. Design/methodology/approach - 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. Findings - 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 decisionmaking 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. Research limitations/implications - 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. Practical implications - 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. Originality/value - 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.
机译:目的-本文的目的是解释企业家集群中协作网络的出现,这是由企业家交流知识和通过在共同定位的公司的网络中实施临时供应链所需的业务交易学习的方式决定的。设计/方法/方法-采用社会计算方法来模拟马绍尔工业区(ID)中的业务交易和供应链形成。提出了基于代理的模型,并将其用作虚拟实验室,以测试企业行为与系统级别结构性属性之间的假设。结果-仿真结果以及基于与真实世界集群的比较所进行的验证表明,新兴网络的拓扑属性受公司选择合作伙伴时所采用的学习策略和决策标准的影响。关于Marshallian ID的具体情况,表明基于历史和过去合作的惯性学习从长远来看代表了枢纽和网络拓扑出现的主要障碍,更有利于创新和增长。研究局限性/含义-本文提供了一种关于企业家学习(EL)的替代观点,而不是那种在特殊情况下(例如失败)导致学习的主流观点。这项工作提出的结果表明,适应性学习,定位学习和日常学习对创业集群的绩效产生了深远的影响。这些结果鼓励在诸如建模学习或将拟议方法应用于其他类型的企业家生态系统(例如,创业网络和制造商社区)的分析方面激发更多的研究。实际意义-基于主体的模型可以支持政策制定者确定环境因素,从而可以通过确定适当的激励措施和重新设计社交网络来在宏观层面上产生变化。原创性/价值-本文提出了关于EL的新颖观点,并提供了证据,证明在日常业务交易中采用和开发的微学习策略确实对公司的绩效(生存和增长)以及与创新相关的系统绩效产生影响和企业网络中创新的扩散。

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