Evaluating clusters: Where theory collides with practice

Abstract

Cluster policies are widely used to strengthen regional competitiveness, yet difficult to evaluate. While academic approaches to cluster evaluation are often ignorant of the needs of policy‐makers and practitioners, practitioner‐led approaches often lack structure and rigour. As such, there remain significant gaps between theory and practice. The contribution of this paper is to reflect on a unique methodological approach that has regularly brought together academics, policy‐makers and practitioners from around the world to collectively address the challenges of cluster evaluation. A participatory process of integrating theory and practice highlights the importance of triple helix engagement to co‐design evaluation of what is a triple helix policy proposition.

Network diversity, distance and economic impact in a cluster: visualising linkages and assessing network capital

“…Firms' absorptive capacitytheir ability to exploit external knowledgeis complementary to external knowledge acquisition (Laursen and Salter, 2006;Cohen and Levinthal, 1990;Hansen and Birkinshaw, 2007) and considered to be multi-dimensional given the separate processes it encompasses (Volberda et al, 2010;Ferreras-Mendez et al, 2015). This implies that the underlying relational dynamics of firms engaging in collaboration, irrespective of other benefits, generates benefits from interactions and is worthy of investigation (Smith et al, 2020). It also points to the potential in separating out resources used by firms in investing in the creation and maintenance of networks when assessing their benefits.…”

Cluster policy resilience: new challenges for a mature policy

“…The first is to understand how cluster policy instruments or mixes of instruments influence (or not) the cooperative dynamics within clusters, while the second is to demonstrate the effects of more sophisticated cooperative dynamics on the performance of cluster actors and the region. Yet, there are significant practical difficulties in both measuring the human dimension of cluster dynamics and in isolating their effects on performance (Smith et al, 2018). Uyarra and Ramlogan (2017, p.50) suggest an approach that is more sensitive to policy dynamics and that requires shifting "attention away from heroic attempts at mechanistic evaluation and employ[ing] a mix of approaches more suitable to capturing both network evolution and learning processes in relation to public policy interventions."…”

“…Uyarra and Ramlogan (2017, p.50) suggest an approach that is more sensitive to policy dynamics and that requires shifting "attention away from heroic attempts at mechanistic evaluation and employ[ing] a mix of approaches more suitable to capturing both network evolution and learning processes in relation to public policy interventions." Moreover, Smith et al (2018) illustrate the benefits of integrating different types of knowledge into cluster evaluation dynamics through analysis of a long-term case of academic-policy-practitioner engagement (the cluster policy evaluation working group established by TCI Network). The message is clear that further advances will require a shift in attitude to see cluster evaluation as a strategic intelligence process that integrates knowledge from and generates knowledge for academics, policy-makers and cluster actors alike.…”

“…They argued that the use of clusters in a policy context "should carry a public health warning" (5) and concluded by echoing words from George Santayana, that "fashionable ideas tend to share one thing in common: they all eventually become unfashionable" (30). In the face of such critique, and alongside well-acknowledged difficulties in gathering robust empirical evidence on the effects of cluster policy interventions (Schmiedeberg, 2010;OECD, 2015;Smith et al, 2018;Uyarra and Ramlogan, 2012), cluster policies have moved in and out of fashion in different places at different times.…”

Evidencing the benefits of cluster policies: towards a generalised framework of effects

“…There is also great variety in cluster policies, rendering the generalisation of results and implications difficult and hampering a holistic overview of the complex patterns of cluster policy effects (Cantner et al, 2019). Finally, data issues complicate cluster policy evaluation research, prompting arguments for blending academic expertise and analysis with the real-time and evolving experience of practitioners that are closer to the most relevant data (Smith et al, 2020).…”