The partner next door? The effect of micro-geographical proximity on intra-cluster inter-organizational relationships⋆
Introduction
“There is more learning and science within the circumference of ten miles from where we now sit than in all the rest of the world”; this was the affirmation pronounced by Samuel Johnson in London during the first Industrial Revolution. Since then, although globalization and digital communication technologies have significantly facilitated the interactions among geographically distant actors (Broekel and Boschma 2011; Cassi and Plunket 2014; Geldes et al., 2015), geographical proximity has preserved its importance as a catalyst for knowledge transfer, especially when such transfer occurs among partners with different knowledge bases (Broekel and Boshma 2012; Forman and van Zeebroeck 2019). The importance of geographical proximity for the establishment of knowledge transfer relationships is well explained by scholars who study technology clusters that are defined as agglomerations of knowledge-based firms (Storper 1992; Cesaroni and Piccaluga 2003) localized in distinctive regions where technological externalities, low communication costs, and social capital are especially conducive to raise innovation (Antonelli 2000; Kaasa 2009). Nevertheless, these latter studies have implicitly intended the geographical proximity from a macro perspective as a mere co-location of actors in the same institutional borders (i.e., same nation, region or city), while what appears to be a cluster at the macro level may refer to several geographically (and often technologically) distinct clusters at the micro level, which have different social relationships and unique needs (Feldman 2015). This because agglomeration benefits derived from the geographical proximity are not always equally distributed in the cluster (Boix et al., 2015; Andersson et al., 2019) and the quality and quantity of knowledge flows among cluster members are subject to distance decay (Jaffe et al., 1993; Breschi and Lissoni 2009; Bonaccorsi et al., 2014).
Consequently, an important characteristic in a technology cluster is its micro-geography because a specific micro-location inside a cluster enables firms to participate in and benefit from localized and highly specialized knowledge exchanges that only occur through face-to-face interactions and unanticipated encounters (Storper and Venables 2004; Messeni Petruzzelli et al., 2007; Broström 2010; Balland 2012; Cassi and Plunket 2015; Steinmo and Erasmussen 2016; Delgado et al., 2020). These latter considerations are supported by empirical evidences: considering most successful high-tech clusters, such as Silicon Valley in the U.S. or the biotech cluster in Cambridge in the U.K., the main actors are not only located in the same region but often confined to the same street or even the same building (Guzman and Stern 2016). Therefore, it is necessary to adopt a micro-geographical approach to analyse technology clusters to obtain a more realistic picture of the intra-cluster locational (and relational) advantages, which can sometimes “be traced to a very small neighbourhood” (Mudambi et al., 2018). Embracing a micro-geographical perspective implies identifying how close the actors must be to establish a relationship and benefit from the agglomeration economies of a cluster (Lublinski 2003).
Moving from these premises, our paper attempts to “zoom into” a technology cluster to provide more precise indications about the desirable levels of geographical proximity to establish inter-organizational knowledge transfer relationships. In other words, we evaluate whether being geographically located in the same technology cluster (macro-geography) causes actors to be equally likely to establish inter-organizational relationships, or, on the contrary, whether geographical proximity matters at a smaller scale (micro-geography). Accordingly, our paper seeks to answer to the following research question: RQ1) Does the micro-geographical proximity affect the probability of establishing an inter-organizational knowledge transfer relationship among two organizations in the same cluster?
Moreover, the issue of whether the micro-geographical proximity affects the probability of establishing inter-organizational relationships within a cluster is complicated by the fact that innovation-driven processes involve different types of organizations, which leads to the implementation of a broader spectrum of cooperation practices that link firms to firms, universities to firms, and venture capital investors to firms. These organizations vary in terms of the exchanged knowledge and phase of innovation development (Broström 2010; D'Este et al., 2012; Cassi and Plunket 2015; Steinmo and Erasmussen 2016; Crescenzi et al., 2016). The establishment of the different types of intra-cluster relationships can be affected in different manners by the micro-geographical proximity due to the differential role played by proximity-related determinants such as the type of knowledge exchanged, information asymmetries, and trust mechanisms. Our paper aims to coherently explore this latter aspect to answer the following research question: RQ2) Does the effect of micro-geographical proximity vary according to the type of inter-organizational relationship?
To answer to our research questions, we take empirical evidence from the biopharma cluster in the Greater Boston Area (GBA) by analysing the role of micro-geographical proximity in explaining the probability of tie formation among clusters’ members considering three types of inter-organizational relationships: venture capital (VC) deals, joint R&D partnerships and intellectual property (IP) transfer agreements. To build our sample, first, we identified different types of organizations (firms, universities and VC investors) located in the GBA and belonging to the biopharma industry. Then, we mapped the realized inter-organizational collaborative ties among these organizations occurred during the 2012–2017 period by distinguishing among VC deals, R&D alliances and IP transfer agreements (i.e., 277 realized ties, including 175 VC deals, 56 R&D alliances and 46 IP transfer agreements). Using the dyad as the unit of analysis (Diestre and Rajagopalan 2012), we built three subsamples for each deal type, which consider both realized and potential but unrealized ties. Then, we ran probit models, where the dependent variable was the probability that two organizations of the dyad have established a collaborative tie.
Our findings show that micro-geographical proximity is positively related to the establishment of IP transfer agreements and VC deals within the GBA biopharma cluster, while no significant association with R&D alliances is observed. These results suggest the importance of geographical proximity at a micro scale for specific cooperation dynamics such as VC deals and IP transfer agreements. This result emphasizes the importance of adopting a micro-geographical perspective to highlight this “neighbourhood effect”, which would not be possible when considering geographical proximity at the macro level.
We believe that this work contributes to the literature on technology clusters because it reveals the reasons behind the establishment of different types of knowledge transfer relationships among cluster members and emphasizes the role of micro-geographical proximity, which was almost neglected by previous studies. From an empirical standpoint, most empirical studies on technology clusters tend to consider geography in terms of the general co-localization of partners within the same institutional borders (at the national or regional level) and overlook the implications derived from its operationalization at smaller scales from a micro perspective. This study reduces the ambiguity in the notion of geographical proximity by demonstrating the presence of intra-cluster locational advantages for the establishment of certain types of inter-organizational relationships between two organizations. Moreover, our study contributes to the literature on innovation collaborations and open innovation, which often studies the geographical variables by simply distinguishing between local and international cooperation neglecting the effect of geographical proximity at the micro level (Kapetaniou and Lee 2019).
The paper is structured as follows. Section 2 reviews the literature on clusters and the role of geographical proximity in inter-organizational collaborations. Section 3 develops the research hypotheses. Section 4 presents the empirical setting, the data sources and the methodology. Section 5 reports the results of the econometric estimates. Section 6 compares our findings with those of related studies, while section 7 concludes the paper by discussing directions for future research and policy implications.
Section snippets
Theoretical background
The clustering of firms in a geographical area has been the object of growing attention in the economic analysis from different scientific fields, such as regional economics and strategic management. Porter (1998) defines clusters as geographic concentrations of interconnected firms, suppliers, service providers, and associated institutions in a particular industry, where the firms both compete and cooperate with one another. Then, co-localization, competition, and cooperation are considered
Geographical proximity and inter-organizational cooperation: research hypotheses
To understand the effect of micro-geographical proximity on the establishment of inter-organizational relationships within a technology cluster, the different natures of such relationships must be considered. The relationships within a technology cluster are indeed characterized by different cooperation practices depending on the resources that must be transferred during the innovation process (e.g., financial capital, knowledge, and human resources) and the organizations in the process (e.g.,
The biopharma cluster in the GBA
To test our hypotheses, this work analyses the case of the biopharma cluster in the GBA (MA, USA) for the period 2012–2017. Due to its high-ranking position in the U.S. Biotech Clusters rankings (JJL U.S. Life Science, 2016), the biopharma cluster in the GBA is considered a successful case study. The biotech cluster in the GBA is, along with that of Silicon Valley, one of the oldest, best-known and most successful high-tech clusters in the U.S. Moreover, it is, together with San Francisco, one
Main results
We first performed a univariate analysis to verify whether there are statistically significant differences in the average distance in km when considering realized and potential unrealized ties for the three types of inter-organizational ties considered in this study. The results from this analysis are shown in Table 4.
The results from Table 4 suggest that micro-geographical proximity is positively associated with the formation of VC deals and IP transfer agreements. The average geographical
Discussion
This paper aims to explore the role of micro-geographical proximity in shaping the formation of (multiple types of) inter-organizational relationships within a technology cluster. The results from our empirical analysis suggest that micro-geographical proximity is relevant to the formation of VC deals and IP transfer agreements, while we do not find any significant association with the formation of R&D alliances.
In particular, our findings show that the micro-geographical proximity is
Conclusions
The importance of the geographical proximity for the establishment of knowledge transfer relationships is well explained by scholars who study technology clusters, but the latter studies analyse it at the macro-level as the general co-localization of partners within the same institutional borders and overlook the implications that derive from its operationalization in terms of the geographical distance on smaller scales. However, if we consider well-known technology clusters, it is easy to
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This research has been funded by the "Department of Excellence" grant from the italian Ministry of University and Research obtained by the Department of Management and Quantitative Studies (DISAQ) of the Parthenope University of Naples.