We examine the concept of knowledge externalities, namely the benefits that co-located firms receive in terms of knowledge, focusing on the role of interactive learning processes and adopting the single firm perspective, whereas in literature their role has mainly been analyzed adopting the system perspective and focusing on knowledge spillovers. The geographical clustering process is studied as an emerging property of a system made up of independent firms making location choices. The aim of the paper is to analyze how the firm heterogeneity affect the geographical clustering process. In fact, so far literature and empirical evidence do not provide a conclusive answer to this regard. To pursue our aim, an agent-based model of geographical clustering is developed, based on knowledge externalities produced thanks to learning by imitation and learning by interaction and a simulation analysis is then carried out. The main result is that the heterogeneity reduces the willingness of firms to geographically cluster and enhance the development of knowledge.
Teams are framed as individuals embedded in hierarchical and knowledge networks, who interact among each other with the aim of accomplishing a common task. Social interactions are the means through which team members exert their mutual social influence, change opinions, and converge to a common understanding. In this paper, we investigate how the density and connectivity of the team knowledge network and the team organizational structure relate to team performance. The latter is measured in terms of level of agreement among the team members (consensus outcome). We first develop a theoretical model grounded on social influence theory and then a computational model based on the Ising approach. Successively, we carry out a broad simulation analysis in environments characterized by different levels of uncertainty. Results show that high-density values of the team knowledge network are beneficial in the majority of cases, but may become detrimental, when the uncertainty of the environment is low, the team knowledge network exhibits a random connectivity, and the team organizational structure is characterized by high centralization of the authority and a strong leadership behavior. We also find that scale-free connectivity of the team knowledge network hinders the achievement of consensus, compared to the random connectivity case. Based on the simulation results, we finally identify the best organizational structure that should be adopted to improve the consensus outcome.
Previous literature has emphasized that developing trust among supply chain (SC) firms is a critical element in achieving SC effectiveness. Since developing trust is an expensive task, however, making an informed decision whether to invest or not in trust requires careful assessment of trust benefits. Therefore, we advance a simulation-based methodology to quantify performance improvements associated with trust in SCs. We develop an NK simulation model of a generic SC that captures the SC dynamics under two alternative scenarios, characterized by the presence and absence of trust respectively. A procedure is then illustrated to quantify the benefits of trust in the SC. We also apply our proposed methodology to a real-world SC. Results show that, when trust is pervasive across the SC, performance increases at both the levels of the overall SC and its leading firm.