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Purpose

This study aims to investigate the dynamic nature of local economies through the lens of cluster life cycle (CLC) theory. The authors offer an original perspective on the comparison of the mature Old World wine industry and the growing New World one, viewed through the lens of the CLC aiming to understand the specific stage of cluster development for these regions. The authors aim to define the CLC dynamics of two world-renowned wine clusters, symbolizing these old and new world wine industries: Napa and Bordeaux.

Design/methodology/approach

The research adopts a case study approach, specifically analyzing the Bordeaux and Napa Valley wine clusters, using a CLC identification framework. This study integrates recent conceptual advances from evolutionary and institutional economic geography, incorporating a dynamic “path” approach to cluster development stages.

Findings

In examining Bordeaux, despite indications of maturity in the wine industry, the cluster displays signs of transformation, marked by reorganization, diversification and adaptation to new conditions, such as enotourism and technological clusters. In contrast, Napa Valley, while well established, exhibits characteristics of sustainment with stabilized business dynamics.

Research limitations/implications

This study acknowledges its exploratory nature and the need for future empirical studies on various clusters to fully validate and refine the CLC identification framework.

Practical implications

The proposed CLC identification framework serves as a valuable tool for policymakers and companies by facilitating precise identification of cluster development stages. This tool enables stakeholders to better understand and address the specific needs and characteristics of clusters at different stages, enhancing the effectiveness of targeted interventions and support measures.

Originality/value

This research advances CLC theory by introducing the development trajectories of the Napa and Bordeaux clusters, analyzed independently of their dominant industries, using the CLC path identification model. This approach provides a fresh perspective on cluster evolution, enriching the theoretical framework with insights that transcend traditional industry-specific analysis. By enhancing the precision and relevance of cluster stage identification, this research offers a valuable guide for policymakers and cluster stakeholders, enabling them to better understand the dynamics of cluster development and intervene effectively at critical stages.

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