In the innovation-driven global economy, the increasing connectivity among diverse stakeholders involved in cross-border innovation networks and the agglomeration of innovation activities in specialized regions have become prevailing paradigms (Balland & Rigby, 2017; Malmberg & Maskell, 1997; Scalera*et al.*, 2018; Turkina & Van Assche, 2018). Facilitated by economies of scale and knowledge spillover effects, interconnected business actors (e.g., original equipment manufacturers (OEMs), suppliers, distributors, and professional service providers) and related institutions (e.g., government authorities, universities, R&D centers, and financial investors) are collaborating with each other and generating densely connected networks in geographically concentrated clusters (Porter, 1998). The formation and evolution of clusters not only enhance the productivity and innovation capabilities of local enterprises but also contribute to the global competitiveness of local industries in subnational regions (Bell & Giuliani, 2007; Bourdin & Nadou, 2018; Turkina*et al.*, 2020; Wang*et al.*, 2023).

Meanwhile, traditional research on clusters has often emphasized the role of close distances among co-located members while overlooking the substantial heterogeneous patterns reflecting how different stakeholders establish networks, as well as the impact of network structure and dynamics, on the innovation performance of embedded economic actors (Balland & Rigby, 2017; Bathelt & Li, 2020; Hassine & Mathieu, 2020; Ter Wal & Boschma, 2011). Without taking the complementary cognitive, organizational, social, and institutional conditions into consideration, geographical co-location-based cluster research faces severe shortcomings (Balland*et al.*, 2015; Boschma, 2005; Lagendijk & Lorentzen, 2007). First, without constructing a well-functioning coordination system among cluster members, the co-location effect may result in knowledge lock-in and relational over-embeddedness in narrowly focused regional networks (Crespo*et al.*, 2014; Uzzi, 1997). Second, geographically distant partners can still form tight and sustainable alliances based on similar knowledge and a social basis, while co-location does not serve as a sufficient condition for autonomous interactions in networks (Boschma, 2005; Giuliani, 2007; Mariotti*et al.*, 2010). Although numerous studies have highlighted the geographical and organizational divergence of cluster configurations, research on the coordination mechanisms contributing to the co-location and network effects within clusters of regional innovation ecosystems remains insufficient (Lagendijk & Lorentzen, 2007; Sultana*et al.*, 2023; Torre, 2014).

Addressing this research gap, this paper explores the geographical and organizational mechanisms that contribute to collaboration and coordination among stakeholders involved in innovation clusters. Grounded on the theoretical works of the French School of Proximity Dynamics (Bourdin & Nadou, 2020; Torre & Gilly, 2000; Zimmermann*et al.*, 2022), we characterize the distinct cluster configurations as (1) Cluster agglomerations in which the physical distances among members are short, whereas the co-located benefits stem from shared costs in infrastructure and knowledge spillovers; (2) Cluster community in which the members build up closely connected networks based on organizational similarities and frequent interactions. We argue that there can be a great variation in territorial governance in terms of the location of members and organizational logic. Moreover, we propose that the institutional intermediaries act as important coordinators that play various roles in cluster territorial governance.

To address the different patterns of territorial governance in both types of clusters, we compared the location of members, organizational logics, and roles of institutional intermediaries in two pharmaceutical clusters in advanced economies: Cité Biotech in Canada and Polepharma in France. As the leading pharmaceutical clusters in their respective host countries, both clusters were founded in the early 2000s as the result of enterprise–government–university collaborations aiming to strengthen regional innovation capabilities and competitiveness. After two decades of development, they have evolved to adopt two different cluster configurations under different territorial governance regimes. Cité Biotech appears to be a cluster agglomeration with a high degree of spatial agglomeration among members in the city of Laval. In contrast, Polepharma is a cluster community with numerous closely connected members in business networks across various French regions. The multi-tier analysis of territorial governance mechanisms revealed that both clusters have a high concentration of local stakeholders in designated regions, whereas the cluster community has more members who reach beyond the boundaries of the focal regions. In terms of organizational logics, members in both clusters reflect a high degree of heterogeneity, whereas frequent interactions between members in cluster communities have created a stronger sense of “logic of belonging.” In both clusters, institutional intermediaries play the crucial role of brokers who initiate new connections and facilitators who diffuse information and knowledge among cluster members. Furthermore, the institutional intermediaries of cluster communities also act as mediators of corporate governance affairs among members, orchestrators of large-scale social events, and innovators who lead new ventures and entrepreneurial projects. Summarizing the key findings of the comparative case study, we conclude this paper with theoretical and practical implications for regional innovation ecosystem configurations and upgrading industrial competitiveness.

This paper is organized as follows. Section 2 reviews the literature on roles and impact of clusters in the dynamism of regional innovation ecosystem evolution. Section 3 investigates the composition of territorial governance mechanisms of innovation clusters in terms of location of members, organizational logic, and roles of institutional intermediaries. Section 4 showcases the comparative case analysis of the territorial governance mechanisms of Cité Biotech in Canada and Polepharma in France as representative examples of cluster agglomerations and cluster community respectively. Section 5 concludes the theoretical contributions in innovation management and regional development, as well as the strategic implications for regional innovation policy that advocates a comprehensive coordination mechanism among varied stakeholders in the innovation clusters.
# Clusters in the regional innovation ecosystem

In an innovation-oriented economy, knowledge-intensive industries drive regional economic growth by leveraging local innovation ecosystem configurations and global innovation network connectivity (Bourdin & Nadou, 2018; Turkina*et al.*, 2020; Wang*et al.*, 2023). Orchestrated by global value chain configurations, key stakeholders in regional innovation activities (e.g., enterprises, government, universities, R&D centers, financial institutions, and civil communities) establish R&D collaborations with external stakeholders through trade and FDI networks, as well as generate and exchange knowledge across geographical boundaries (Gereffi*et al.*, 2005; Scalera*et al.*, 2018; Turkina & Van Assche, 2018). In this process, they embed themselves in the open innovation ecosystem of multi-tier collaborative arrangements and become involved in the value-creation process of innovation activities concentrated in the local milieu (Adner & Kapoor, 2010; Chesbrough, 2003; Sydow & Müller-Seitz, 2020; Wang & Turkina, 2020).

Defined as “the geographic concentrations of interconnected companies and institutions in a particular field” (Porter, 1998), the clusters act as the basic territorial unit in the regional innovation ecosystem (Li & Bathelt, 2018; Sultana*et al.*, 2023; Turkina*et al.*, 2020). The clusters’ configurations highlight three crucial conditions in the establishment of regional innovation ecosystems: (1) the spatial closeness among co-located actors in agglomerated areas, (2) the social ties among interconnected and interdependent actors in business networks, and (3) the coordination of relationships among co-located actors involved in specialized economic activities (Torre, 2014). On the one hand, the co-location effect of the clusters reduces local production costs, as institutions can share infrastructure and facilities, and this effect also facilitates face-to-face knowledge spillovers at the local level (Malmberg & Maskell, 1997; Torre & Gilly, 2000). On the other hand, the network effect contributes to knowledge generation and transfer through the flows of trade, investment, and R&D partnerships among closely connected stakeholders within and beyond the boundaries of clusters (Bathelt & Li, 2020; Lagendijk & Lorentzen, 2007; Turkina & Van Assche, 2018; Turkina*et al.*, 2016; Wang & Turkina, 2020). Combining both effects, the formation of clusters contributes to the efficiency and competitiveness of regional innovation ecosystems in terms of enhancing productivity, reducing transaction costs, acquiring key assets and information, augmenting the workforce, and accessing public goods provided by institutional agencies (Bathelt*et al.*, 2004; Porter, 1998; Wang*et al.*, 2018).

The dynamics of the co-location and network effects in clusters reveal the functions of a regional innovation ecosystem and how it contributes to the region’s competitiveness on a global scale. In this regard, the French School of Proximity Dynamics developed a theory on how physical distance as well as social and institutional embeddedness affect the configurations of regional innovation ecosystems (Torre & Gilly, 2000; Torre & Rallet, 2005; Zimmermann*et al.*, 2022). Drawing on the geographical and organizational characteristics of regional innovation ecosystems, Torre and Rallet (2005) identified two principal mechanisms contributing to cluster formation: (1) geographical proximity, which refers to the physical closeness or short distances between members within a cluster; and (2) organized proximity, which is defined as the relational closeness among members that provides the ability to facilitate frequent interactions, thereby enabling the creation of common patterns and identities among cluster members, irrespective of their geographical distance (Torre, 2014).

Intuitively, the spatial agglomeration of economic actors in clusters reflects their geographical proximity to the region’s innovation ecosystem. Based on a systematic literature review of the research on the co-location effect, Torre and Gilly (2000) attributed three groups of determinants of geographical proximity to the performance of the regional innovation ecosystem: (1) the specificity of human capital endowed in localized production systems, (2) the relational coordination of internal and external collaboration in local production activities, and (3) knowledge spillovers from local science–industry relations. In economic terms, co-location with similar and related companies reduces production costs and enhances the operational efficiency of embedded members who share infrastructure and reduced transportation costs (Bathelt*et al.*, 2004; Malmberg & Maskell, 1997; Torre & Gilly, 2000). Moreover, the close geographical distance and familiar social context contribute to the common knowledge base and industrial capability building, and they foster a consensus of social norms and values among co-located members (Li & Bathelt, 2018; Phelps, 2010; Sultana*et al.*, 2023; Turkina*et al.*, 2020). Nonetheless, geographic proximity does not necessarily guarantee actual interaction and subsequent knowledge spillovers without considering the organizational and social contexts (Bell & Giuliani, 2007; Mariotti*et al.*, 2010; Shearmur*et al.*, 2023). The effective functioning of local innovation ecosystems is largely influenced by coherent and sustainable collaboration among stakeholders in clusters (Adner & Kapoor, 2010; Cohendet*et al.*, 2010; Doloreux & Turkina, 2023; Sultana*et al.*, 2023). In cluster communities with high degree of organized community, the members can build up long-term interactions based on trust and commitment (Balland*et al.*, 2015; Bathelt*et al.*, 2004; Uzzi, 1997). Cross-regional cluster community formation can be instigated by common events, competitions, and projects coordinated by institutional intermediaries regardless of the heterogeneous geographical locations (Doloreux & Turkina, 2023; Grandadam*et al.*, 2013; Sultana*et al.*, 2023). In this sense, building closely connected networks and maintaining their connectivity through frequent interactions among co-located members become crucial conditions in facilitating knowledge acquisition and application dynamics in innovation ecosystems (Chesbrough, 2003; Lagendijk & Lorentzen, 2007; Sydow & Müller-Seitz, 2020).
# Territorial governance of innovation clusters

Considering the limitations of geographical proximity in explaining the social interactions among cluster members, numerous studies call for a more comprehensive approach that underlines the conditions that contribute to the formation of dynamic and effective clusters (Boschma, 2005; Giuliani, 2007; Mariotti*et al.*, 2010). In this regard, the French School of Proximity Dynamics developed the paradigm of the territorial governance mechanism that integrates both geographical and organized proximity under the coordination of institutional agencies (Bourdin & Nadou, 2020; Niang*et al.*, 2022; Torre, 2014; Torre & Rallet, 2005).

By definition, territorial governance is defined as “the set of processes and mechanisms through which different parties or actors of various natures contribute to working out—sometimes through discussion, and sometimes through conflict—common projects for the future development of the territories” (Torre, 2014). While geographical and organized proximities explain the natural and inherent characteristics of clusters, territorial governance explains how these characteristics are managed, configured, and developed over time through deliberate coordination of the institutional intermediaries (Bourdin & Nadou, 2020; Niang*et al.*, 2022; Torre, 2014; Torre & Rallet, 2005). Focusing on regions as the central element in integrating innovation ecosystems, territorial governance functions as an institutional approach to coordinating sectoral strategies, establishing interorganizational networks, and balancing the demands and interests of different levels. In this sense, cluster territorial governance addresses the roles of physical distance (geographical proximity) and social embeddedness (social proximity) in the configurations of regional innovation ecosystem, thereafter, facilitates the interactions closely located and connected stakeholders in clusters (Torre, 2014; Torre & Rallet, 2005). In assessing the mechanisms governing the formation of clusters based on geographical and organized proximity, we aimed to create a holistic framework that includes the three principal components in the territorial governance of clusters in the regional context based on the research of the French School of Proximity Dynamics.

## Location of members

The location of cluster members is a fundamental element considered by cluster territorial governance. On the one hand, triggered by the co-location effect on cost sharing and knowledge spillovers, local firms and other related stakeholders gather together in geographically bounded areas. Thereafter, these co-located stakeholders form cluster agglomerations with a high degree of geographical proximity and industrial specialization (Jaffe*et al.*, 1993; Malmberg & Maskell, 1997; Torre & Gilly, 2000). On the other hand, coordinated by global value chain mechanisms, specialized regions become interconnected in the international innovation network, whereas stakeholders in local cluster agglomerations can establish collaborative linkages with global partners through cross-border pipelines of trade and FDI flows (Bathelt*et al.*, 2004; Li & Bathelt, 2018; Turkina & Van Assche, 2018; Wang*et al.*, 2023).

Comparing the locations of members in both types of clusters, we determined that there is a higher tendency toward local concentration in geographical proximity-based cluster agglomeration. Meanwhile, the locations of members in the organized proximity-based cluster community are more diversified. Compared to the cluster agglomeration with a strong local territorial focus, the cluster community also includes trans-local collaborations with other national and international stakeholders outside of the cluster. As clusters become embedded in global networks, new knowledge is generated, transferred, and absorbed at both the local and international levels (Bathelt & Li, 2020; Lagendijk & Lorentzen, 2007; Turkina & Van Assche, 2018; Turkina*et al.*, 2016). However, the degree to which the range of members of cluster communities overlaps that of cluster agglomerations has yet to be investigated based on the organizational logic of territorial governance and the roles of various institutional intermediaries.

## Organizational logic

Considering the social context of cluster formation, Torre and Rallet (2005) proposed two organizational logics, namely, logic of similarity and logic of belonging as fundamental elements that foster organized proximity among cluster members.

The logic of similarity denotes the tendency of cluster members who are “alike” or share similar systems of representation tend to become close to each other (Torre & Rallet, 2005). The homophily effect of “like associates with like” drives cluster members with similar knowledge bases, industrial capabilities, and social and social identities to form close network connections (Bell & Giuliani, 2007; Grimbert*et al.*, 2023; McPherson*et al.*, 2001). Grounded on shared knowledge bases and organizational capabilities, organizations of similar industrial expertise collaborate in co-production, co-management, and joint ventures alliances and forms horizontally integrated clusters as consequence (Phelps, 2010; Turkina & Van Assche, 2018; Turkina*et al.*, 2016; Wang*et al.*, 2018).

The logic of belonging represents the tendency of forming close relationships among cluster members that frequently interact with each other under similar rules and behavioral routines (Torre & Rallet, 2005). Whereas the logic of similarity is grounded in homogeneity among cluster members, the logic of belonging enables cluster members in different locations and with different levels of expertise to come into contact and build linkages together. To overcome the barriers of heterogeneous knowledge, these members must build mutual trust, a shared vision, and commitment through long-term repetitive interactions (Balland*et al.*, 2015; Grimbert*et al.*, 2023; Phelps, 2010; Uzzi, 1997). To measure the degree of belonging among cluster members, we refer to the frequency of network events among cluster members as proxy. The term “network event” represents the formal and informal occasions that the network members meet and interact (e.g. conferences and workshops, professional meetings, joint projects, afterwork parties, club memberships) (Borgatti & Halgin, 2011; Breiger, 1974). Based on the sociomatrix of member participation in network events, we can further generate the 2-mode member-by-event networks, and estimate the frequency of interactions based on the tendency of co-participation or co-membership in network events (Borgatti & Everett, 1997; McPherson*et al.*, 2001). For the embedded members, frequent co-participation and co-memberships in formal and informal social events generates strong social ties (Borgatti & Halgin, 2011). The frequent participation in network events shift the cluster members towards favorable brokerage positions in the networks, which further enhance economic performance and social influence (Doloreux & Turkina, 2023; Torre, 2014). At the whole cluster level, the occurrence of events generates the “temporary cluster” for a large number of participants intensively interact in a short time and spatial span. Participation in events enables members to form common cognition and amplify their long-term connectivity across long geographical distance after the event (Li & Bathelt, 2017; Maskell*et al.*, 2006; Torre, 2008).

Logic of similarity and logic of belonging are two complementary factors that contribute to the organized proximity among cluster members (Torre, 2014; Torre & Rallet, 2005). On the other hand, the contribution of logic of similarity is based on the “shared vision” that shapes the common value, routines and patterns grounded on the ex-ante common knowledge base and organizational capabilities. Contrarily, the contribution of logic of belonging is based on the “shared identity” that sustains the interest of members in ex-post social interactions and network building (Grimbert*et al.*, 2023). In our study, we measure the degree logic of similarity by the ex-ante heterogeneity of industrial sectors of members and measure the degree of logic of belonging by the ex-post frequency of network events organized by the cluster agency.

## Roles of institutional intermediaries

Tackling the complexities arising from the varied nature of members in both cluster agglomerations and communities, the territorial governance mechanism elaborates on the diversity of organizational categories and patterns of collaboration among heterogeneous stakeholders. Specifically, it addresses how the public and private sectors interact in innovation activities, as well as how government authorities, enterprises, and other social forces are incorporated into the regional innovation ecosystem (Niang*et al.*, 2022; Stead, 2013; Torre, 2014). The institutional intermediaries in cluster territorial governance can be categorized as government authorities and non-government agencies (Bourdin & Nadou, 2020; Faludi, 2012; Niang*et al.*, 2022; Torre, 2014). As the administrators of laws and regulations, as well as the innovation policymakers, government authorities are responsible for multi-level territorial governance decision-making that engages a wide range of stakeholders (citizens, communities, businesses, and social groups) (Cohendet*et al.*, 2010; Faludi, 2012; Stead, 2013). Furthermore, by ensuring spatial and cognitive proximity between actors through trust and commitment, governmental authorities mobilize territorial resources in favor of local anchorage and coordinate the supply chains as direct stakeholders (Bourdin & Nadou, 2020). Meanwhile, non-government agencies emerge as influential forces that (1) develop skills and competencies, (2) provide strategy resources, (3) coordinate networks, and (4) animate activities in the territorial governance of innovation clusters (Caloffi*et al.*, 2023; Doloreux & Shearmur, 2009; Doloreux & Turkina, 2023).

To coordinate the complex relationships between these stakeholders, institutional intermediaries are crucial middle-ground actors in cluster territorial governance (Bathelt & Cohendet, 2014; Cohendet*et al.*, 2010; Cohendet*et al.*, 2021; Grandadam*et al.*, 2013). Their roles include improvising meetings among various stakeholders in launching new innovation ventures, facilitating knowledge sharing through network pipelines, and fostering collaborations among various stakeholders in innovation activities (Belso-Martinez*et al.*, 2018; Bourdin & Nadou, 2018; Speldekamp*et al.*, 2022).

Based on a systematic review of the research on territorial intermediation in the regional innovation ecosystem, Bourdin*et al.* (2020) summarized five categories of roles played by institutional intermediaries: (1) Brokers: institutional intermediaries improvise initial contact and create connections among cluster members by identifying common interests and promoting partnerships (e.g., joint ventures, collaborative projects, and strategic alliances) among cluster members. (2) Facilitators: institutional intermediaries coordinate the exchange of information, best practices, and technological know-how among cluster members. (3) Mediators: institutional intermediaries bridge the gaps in understanding among stakeholders and create cohesive collaboration networks based on trust and commitment. (4) Orchestrators: institutional intermediaries foster large-scale collaboration at the social level by organizing events and providing platforms for stakeholders. (5) Innovators: institutional intermediaries mobilize key resources in innovation (financial support, infrastructure, and skilled workforce) and lead new ventures in innovation projects that enhance the overall innovation capacity of the cluster and the global competitiveness of the regional economy.
# Discussion and conclusion

Coordinating interorganizational collaboration in innovation clusters has become a crucial contributing factor in the formation of regional innovation ecosystems and innovation-oriented economic growth. Addressing this context, this paper scrutinizes the territorial governance mechanisms of two distinctive cluster configurations: the geographical proximity-based cluster agglomeration and the organized proximity-based cluster community. Specifically, we selected two leading pharmaceutical clusters in advanced economies as examples, namely Cité Biotech in Canada and Polepharma in France, and compared their geographical and organizational features.

We found similarities and differences in the territorial governance of the cluster agglomeration and the cluster community (see Table 3).

First, we explained the importance of territorial governance in the dynamics of innovation clusters in the regional economy. We argue that, effective territorial governance serves as a crucial condition in the establishment of collaborative platforms, the construction of innovation infrastructure, the organization of entrepreneurial events or projects, and the coordination of relationships among public, private, and civil society actors in the regional ecosystem (Adner & Kapoor, 2010; Doloreux & Turkina, 2023; Grandadam*et al.*, 2013; Sultana*et al.*, 2023; Torre & Rallet, 2005). Studying the mechanisms and evolution of territorial governance of innovation clusters reveals how the co-location and network effects jointly drive the competitiveness upgrading of the regional economy (Bourdin & Nadou, 2020; Niang*et al.*, 2022; Turkina*et al.*, 2020; Wang*et al.*, 2023).

<table><caption>Table 3 — Territorial governance of cluster agglomeration and cluster community</caption><tr><td>CriteriaMeasurementCluster agglomerationCluster communityProximity
						baseGeographical proximityOrganized proximityExample caseCité Biotech
						(Canada)Polepharma (France)Location of membersLocal members inside the focal
						regionsHighMediumNational members outside the focal
						regionsMediumHighInternational pipelinesLowHighOrganizational logicLogic of
						similarityLowLowLogic of belongingLowHighRoles of institutional
						intermediariesBrokerMediumHighFacilitatorMediumHighMediatorLowMediumOrchestratorLowHighInnovatorLowHigh</td></tr></table>

Second, we established a comprehensive territorial governance framework for innovation clusters and compared the geographical and organizational features of two distinctive cluster configurations based on the comparative case study. Currently, both the geographical proximity-based cluster agglomeration and the organized proximity-based cluster community exhibit a high degree of spatial concentration among cluster members in focal regions. Meanwhile, the composition of these members exhibits a high degree of heterogeneity in vertical integration rather than following the “logic of similarity” homophily effect in horizontal integration (Li & Bathelt, 2018; Turkina & Van Assche, 2018; Wang*et al.*, 2018). Compared to the geographical proximity-based cluster agglomeration, the organized proximity-based cluster community features a broader range of external pipelines that extend toward other regions in the country and abroad. Additionally, to stimulate a stronger “logic of belonging” in the networks, the governance body of the cluster community tends to organize more frequent and diverse cluster events than the cluster agglomeration. Thereafter, we clarified how the dynamics of geographical and organized proximity shape different patterns of cluster territorial governance (Lagendijk & Lorentzen, 2007; Niang*et al.*, 2022; Torre, 2014; Torre & Gilly, 2000).

Third, we distinguished between the roles played by institutional intermediaries in the territorial governance of both cluster configurations. We found that institutional intermediaries in the geographical proximity-based cluster agglomeration provide case-by-case supportive services as brokers and facilitators but do not enforce direct business mandates and engage in key decision-making in large-scale collaborations as mediators, orchestrators, and innovators. In contrast, the institutional intermediaries of the organized proximity-based cluster communities have a stronger tendency to engage in the corporate governance and business negotiations of cluster members as mediators. Subsequently, the cluster community has a higher tendency to attract a larger group of varied stakeholders under the proactive initiation and orchestration of cross-regional innovation collaborations.

In conclusion, this research addressed the ongoing global tendency to change the territorial governance of innovation clusters as a means of enhancing the global competitiveness of regional industries (Bourdin & Nadou, 2018; Turkina*et al.*, 2020; Wang*et al.*, 2023). Entrepreneurs and policymakers should not merely understand cluster formation as a simple process of geographical agglomeration among co-located stakeholders. Complementarily, they need to investigate the comprehensive features of territorial governance in terms of cluster member locations, organizational logics, and the roles of institutional intermediaries in the dynamic evolution of clusters (Bourdin & Nadou, 2020; Niang*et al.*, 2022; Torre, 2014; Wang*et al.*, 2023). In geographical proximity-based cluster agglomerations, the co-location effect can trigger positive spillovers but can also lead to knowledge “lock-in” (Crespo*et al.*, 2014; Uzzi, 1997). In response, enterprises in cluster agglomerations should proactively reach out and engage in large-scale transregional innovation collaborations and frequently participate in cluster events within and beyond regional boundaries to create a stronger sense of network belonging (Bourdin*et al.*, 2020; Grandadam*et al.*, 2013; Torre & Rallet, 2005). In contrast, enterprises within organized proximity-based cluster communities benefit from the mediation, orchestration, and innovation functions of institutional intermediaries; thereafter, they should leverage network connectivity as a strategic resource for innovation and growth. By participating in large-scale cluster events and innovation projects, members of a cluster come into contact with diverse stakeholders in the regional innovation ecosystem, thereafter achieving network-based competitiveness owing to the extended range of linkages and an advantageous network position (Li & Bathelt, 2018; Phelps, 2010; Wang*et al.*, 2023; Wang*et al.*, 2018). To strengthen cluster cohesion and promote a culture of innovation and collaboration, policymakers of institutional intermediaries should actively initiate and coordinate collaborations among various stakeholders in large-scale cluster events and innovation projects. The effective coordination of cluster community collaborations could further enhance knowledge generation and transfer, as well as stimulate innovation-oriented growth.

Considering the exploring nature of the study, we acknowledge the limitations of this descriptive comparative study. In the absence of economic and innovation performance data, this study does not provide an evaluation of the productivity or the efficiency of innovation clusters. Neither have we argued about which territorial governance configuration serves the goals and contexts better than the other one. Meanwhile, the representativeness of selected cases, exogenous factors (e.g., different national institutional contexts and global pharmaceutical industrial positions in Canada and France), and the applicability of the territorial governance framework in different industries and countries are all limitations. Additionally, due to the different policies of communication between two cluster agencies, there may exist bias in observing the frequency of network events. Thereafter, the measurement of organizational logics can be further elaborated based on comparable third-party datasets. For the future studies, we call for a better understanding on how the configurations of territorial governance in innovation clusters contribute to the economic growth and industrial competitiveness of the local areas. Subsequently, entrepreneurs and policymakers can further leverage territorial governance policy to extend regional innovation networks and enhance the common vision and identity through institutional intermediation in the innovation cluster configurations.

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