Endogenous effects and cluster transition: a conceptual framework for cluster policy

Figures & data

Figure 1. Stylized illustration of a cluster. Source: own illustration.

Figure 2. Stylized illustration of endogenous effects and network transition. Source: own illustration.

Figure 3. Stylized illustration of a cluster in the emerging phase. Source: own illustration.

Figure 4. Stylized illustration of a cluster in the growing phase. Source: own illustration.

Figure 5. Stylized illustration of a cluster in the sustaining phase. Source: own illustration.

Figure 6. Stylized illustration of a cluster in the declining phase. Source: own illustration.

Figure 7. Overview of dominant endogenous effects along the cluster lifecycle. Source: own illustration using the circlize package in R (Gu, Gu, Eils, Schlesner, & Brors, 2014).

Table 1. Overview of relevant interventionist network-related policy approaches and measures.

	Emerging phase	Growing phase	Sustaining phase	Declining phase
Interventionist approach	Intensifying	Intensifying	Restructuring	Restructuring
Cluster policy	Identifying knowledge gatekeepers and increasing the number of actors (nodes) and the odds of tie formation Science and technology parks Supporting knowledge-based start-ups and risk-taking strategies Promoting entrepreneurial culture and entrepreneurial discovery process Increasing multi-connectivity Promoting social interaction at the local level	Minimizing exclusion (avoiding lock-out) Intra-cluster joint research projects Facilitating knowledge codification (e.g. patents)	Increasing structural disassortativity (avoiding lock-in) Non-local research projects Disciplinary temporary proximity (e.g. technical fairs) Increasing the fitness of the actors (nodes) Education and human capital investment National and international exchange programmes	Increasing the structural hierarchy (overcoming lock-in) Non-local research projects Interdisciplinary temporary proximity (e.g. technical fairs) Increasing the number and diversity of actors (nodes) Supporting start-ups in unrelated knowledge bases Attracting skilled labour force and star scientists Promoting experimentation and technological search Promoting entrepreneurial discovery process

Table

	Case study	Method	Approach	Measure	Result
Giuliani (2011)	The wine cluster in Chile	Quantitative (SAOM^a)	Dynamic	Transitivity reciprocity	The mechanisms of transitivity and reciprocity account for the informal hierarchical structure within the cluster.
Stefano and Zaccarin (2013)	Co-authorship and co-invention in the Trieste area (multiplex approach)	Quantitative (ERGM^b)	Static	Transitivity (triangle AAB and K-triangle ABA) and 3-Star	Transitivity plays a crucial role in tie formation, whereas preferential attachment is found to be insignificant.
Ferriani, Fonti, and Corrado (2013)	Italian multimedia cluster in Bologna	Quantitative (SAOM^a)	Dynamic	Social interaction and economic interactions	Social interactions have a relatively stronger impact on the formation of multiplex ties.
Giuliani (2013)	A wine cluster in Chile	Quantitative (SAOM^a)	Dynamic	Cohesion effects (reciprocity and transitivity)	Reciprocity and transitivity contribute to the core-periphery structure of cluster networks, where actors with a higher degree of fitness (accumulated resources) take central positions.
Balland, Belso-Martínez, and Morrison (2015)	The Toy Valley cluster in Spain	Quantitative (SAOM^a)	Dynamic	Embeddedness status proximity^c	Status is a driving force for the formation of inter-firm links in business knowledge networks, and proximity plays a decisive role in the formation of technical knowledge networks. Embeddedness has a similar impact on both networks.
James, Vissers, Larsson, and Dahlström (2015)	Automotive sector in Västra Götaland, Sweden	Qualitative	Dynamic	Network size Composition pace of change	Over time, knowledge anchoring benefits from the self-enforcing local knowledge networks and actors at the peripheral position.
Molina-Morales et al. (2015)	A foodstuffs cluster in the Valencia region, Spain	Quantitative (ERGM^b)	Static	Transitivity Node degree (out- and in-degree) Proximity dimensions^c	The study proves the negative impact of cognitive and institutional proximities on tie formations, whereas social and geographical proximities facilitate the formation of collaborative ties.
Crespo et al. (2016)	Clusters in the European mobile phone industry	Quantitative	Dynamic	Hierarchy assortativity	Hierarchy and assortativity account for different performances of clusters. Clusters can prevent lock-in as long as they sustain a minimum degree of disassortativity.
Lazzeretti and Capone (2016)	The cluster of high technology applied to cultural goods in Tuscany, Italy	Quantitative (SAOM^a)	Dynamic	Proximity dimensions^c	Social and institutional proximity are the main drivers of network change in the early stages. Geographical and cognitive proximities become the most influential factors in forming innovation network over time.
Belso-Martínez et al. (2017)	The foodstuff cluster in Valencia, Spain	Quantitative (ERGM^c)	Static	Transitivity, reciprocity, and proximity dimensions^c	Transitivity, reciprocity, organizational, social and geographical proximity are relevant driving forces of network formation at the advanced stage of the cluster.
Menzel, Feldman, and Broekel (2017)	The Research Triangle region of North Carolina, USA	Quantitative (SAOM^a)	Dynamic	Transitivity, preferential attachment, and assortative mixing	No fundamental change was observed during and after dot-om bubble in terms of explorative or exploitative tie formations.
Juhász and Lengyel (2017)	The printing and paper product cluster in Kecskemét, Hungary.	Quantitative (SAOM^a)	Dynamic	Geographical proximity, cognitive proximity, transitivity, reciprocity, and cyclicity.	Transitivity, geographical proximity, and cognitive proximity joined with having a common third contribute to tie formation whereas cognitive proximity is essential for tie preservation.
Capone and Lazzeretti (2018)	The cluster of high technology applied to cultural goods in Tuscany, Italy	Quantitative (ERGM^b)	Static	Proximity dimensions^c	The findings show that proximity dimensions have various impacts on different modes of ties. Also, social ties have a strong impact on innovative performances.
Giuliani et al. (2018)	The Cordoba cluster in Argentina	Quantitative (SAOM^a)	Dynamic	Social drivers (e.g. transitivity), institutional drivers (e.g. cluster policy), agentic drivers (e.g. gatekeeper/external star)	The underperforming cluster shows a local core-periphery (not fragmented) structure, implying that the cluster has a functioning network where dominant actors turn to external stars instead of diffusing knowledge at the local level.
Tanner (2018)	Danish wind power clusters	Qualitative (innovation biography approach)	Dynamic	Geographical proximity	Geographical proximity is essential for exchange knowledge and idea. Yet, its configuration changes over time.

^aStochastic actor-oriented models.

^bExponential random graph models.

^cProximity dimensions: cognitive proximity, institutional proximity, social proximity, geographical proximity and organizational proximity.

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