Temporary clusters: a new SME clustering management model

Abstract

Purpose This paper aims to explore a new model to manage small and medium enterprise (SME) clustering process that examines the geographical connectivity conditions within the existing theories on agglomeration. The presented work explores the dynamics governing the decisions related to both the duration and frequency of the different forms of these new clusters. Design/methodology/approach A clustering configurator tool is developed to assist managers for the best temporary cluster model. The configurator considers aspects related to the market, industry and classical clustering requirements as well as social capital (SC). Finally, the performance of various temporary clusters under different demand scenarios and operational conditions are studied using numerical simulation. Findings The results examined the performance of the new clusters under various internal and external defining indicators against potential economic growth, technology spillover and the new metric of SC. The results offered interesting observations suggesting various recommendations to promote these new models to SMEs as well as how to better manage them. Research limitations/implications The presented results are understood in the context of the suggested settings of relationships and scoring weights. Practical implications The new form of clusters will help SMEs overcome the feasibility challenge when considering re-locating to existing clusters while reaping many of these clusters benefits. Furthermore, different recommendations for management aimed at enhancing clustering decisions and the efficiency of SMEs in these new setups are presented. Originality/value This paper suggests a new clustering management approach that capitalizes on the temporal domain rather than classical space or the digital clusters domains. Also, a new management concept called dynamic matching is suggested. SC is considered among clustering objectives which was disregarded in similar studies.