Related Variety

Drawing on Malmberg and Power (2006), a ‘true’ cluster can be defined as a spatially bounded agglomeration of related activities, which is based on co-opetition with actors sharing a feeling of belonging. The concept of RV is understood as the relatedness of a knowledge base utilised by different sectors within a region and can indeed be regarded as a critical factor of a cluster’s development (Gaschet et ah, 2017). The concept of RV was initially put forward by Freuken, Van Oort and Verburg (2007). There is a growing perception of regional clusters as an incarnation of specialisation in an array of related industries, not necessarily specialisation in a narrowly defined single industry (Delgado, Porter & Stem, 2016). The importance of RV may also be interpreted as a recognition of a growing tendency to replace the economies of scale with economies of scope, which is happening thanks to the 14.0.

Related variety is a concept known in evolutionary economic geography, which sees relations between knowledge spill-overs and economic growth, development or renewal (Asheim, Boschma & Cooke, 2011; Boschma & Iammarino, 2007). It refers to the variety of industries present, within a given region, that are linked cognitively (Frenken, Van Oort & Verburg, 2007). These are believed to be able to better leverage the potential for learning and growth, and to give impenis to the emergence of new industries (Boschma, 2014). Inter-industry diversity and technological complementarities are crucial for the development of innovations (Mudambi & Swift, 2012). It implies that new, value-creating innovations are more likely to arise at the intersections of technologies, and these advantages are based on the presence of diverse knowiedge bases (Mudambi, Narnia & Santaugelo, 2018).

Related variety means that the region’s different industries have some commonalities enabling knowiedge exchange and spill-overs. RV stands for learning, which is focused on the context-specific intangible assets available in the region. It demonstrates that regional specialisations and knowiedge bases offer opportunities for future diversification, also by linking together different industries or areas of expertise (Boschma, 2014; Frenken, Van Oort & Verburg, 2007).

The advent of computerised manufacturing technologies marks a clear departure from the logic of the previous economic era, in winch the growing complexity of technologies required more specialisation, resulting in modularisation of technologies and fragmentation of value chains (Alcacer et al., 2016; Langlois, 2002). Currently, advanced production technologies allow' the consolidating of subsequent phases of manufacturing processes and more integral product architecture with closer co-ordination of activities (Rezk, Srai & Williamson, 2016).

Related variety might be summarised as dynamic and complementary externalities originating in similar industries. Aarstad, Kvitastein and Jakobsen (2016) define it as the deployment of additional factor inputs, in contrast to specialisation seen as economies of scale, and in terms of local competition. RV can also be associated with technological diversification and knowledge spill-overs, occurring among firms operating in ‘different but related’ sectors (Cainelli & Ganau, 2019). Gaschet et al. (2017) mention the relatedness of knowledge bases, used by different sectors within a region. RV implies the existence of‘knowledge platforms’, which organises the re-combination of technologies in overlapping industries. In this light, the cluster can be seen as a ‘geographic concentration of linked industries’ (Gaschet et al., 2017), and available studies confirm the role of RV as a success driver, particularly for the most significant European clusters.

The extent to which companies are linked to each other improves their innovativeness, whereas the diversity of knowledge distributed among them, offers the variety that strengthens regional resilience (Sedita, de Noni & Pilotti, 2015; Fratesi & Rodriguez-Pose, 2016). RV is rooted in the existing regional knowledge base and encompasses two complementary dimensions of external knowledge flows: ‘cognitive proximity’ and local ‘absorptive capacity’, which might contribute to a region's development (Bramanti, 2016). As neither pure specialisation nor sole diversification can promise regional success, the exploitation of RV has been advocated as the right way forward. Indeed, the concept of RV stresses that knowledge between the actors should not be too dissimilar and implies that sectors are complementary in terms of competences (Bosclmia & Iammarino, 2009).

The concept of RV was introduced in an attempt to resolve an earlier empirical question put forward by Glaeser, Kallal, Scheinkman and Shle- ifer (1992), whether regions benefit most from being specialised or being diversified. This ‘controversy’ is commonly referred to as ‘MAR versus Jacobs’. It depicts the tensions between the Marshall, Arrow and Romer theories, which suggested that spill-overs take place primarily within a single industry, and the Jacobs’ theory (1969, p. 59) stating that ‘the greater the sheer numbers and varieties of divisions of labour already achieved in an economy, the greater the economy’s inherent capacity for adding still more kinds of goods and services’.

RV seems to be closely associated with the concept of smart specialisation (or 3S smart specialisation strategy—Koschatzky, Kroll, Schnabl & Stahlecker,

2017). As proposed by McCann & Ortega-Argiles (2014), smart specialisation, while being an explicit focus of regional policy, nevertheless, draws oil the relatedness concept. It argues that diversifying into related industries improves the robustness and resilience of the local economy. Consequently, the economic gr owth of the region can be stimulated by the technological diversification of its embedded industries (Elekes. Bosclmia, & Lengyel 2019). Smart specialisation strategies are often perceived as recycled cluster strategies (Val- daliso, Magro, Navarro, Jose Arangureu & Wilson, 2014), and recently, have become more critically regarded (Hassink & Gong. 2019).

Based on the analysis of Norwegian regions, Aarstad, Kvitastein and Jakobsen (2016) showed that RV is a positive driver of enterprise innovation, whereas the opposite can be said about unrelated variety. Thus, regions representing simultaneously high levels of RV and low levels of unrelated variety> can optimise enterprise performance. Also, results obtained by Gaschet et al. (2017) confirm the role played by RV in the suc- development of European photonics clusters. As neither too much specialisation nor too much diversification is good, the concept of variety seems to offer a promising middle ground. Whereas, the unrelated variety>, understood as a pool of assets accumulated in a particular region, can be perceived as a static category stressing the separateness; the RV, drawing on Jacob’s externalities, should be explained as a dynamic concept emphasising complementarities.

The measure of RV is usually based on the hierarchical structure of official industry classifications (e.g. NACE). It is assumed that the more digits two industries share hi the formal industrial classification, the more closely related they must be. Thus, classification-based relateduess is usually presented by reference to the number of initial digits these industries have in common. There are alternative measures, which include, for instance, information about the export portfolio, firm offer portfolio or shared inputs. These are much less common, but there is a growing awareness that frequently used relatedness measures, based on industry classification, might be not reflecting all relatedness ties (Firgo & Mayerhofer, 2018; Kuusk & Martynovich, 2018). RV builds on the application of relatedness linkages, which as proved by Kuusk and Martynovich (2018), could change over time and have a ‘best before date’. The findings obtained can support previous claims stressing the dynamic nature of relatedness and give rise to the doubts of how accurately we can capture it. Issues such as stability, age or symmetry of these ties are additional aspects of RV, which deserve attention along with its dynamic. These dimensions seem to be captured in this volume by reference to the blending processes.

Fitjar and Timmermans (2019) demonstrate that relatedness comes with some costs, hi terms of increased labour market competition. Based on the case of the Norwegian petroleum industry, they proved that the risk of deskilling hi related industries might outweigh potential knowledge spill-over benefits from their relatedness to the petroleum industry. Consequently, relatedness seems to have its losers and winners.

Vicente (2018) argues that the growing need for more cross-industrial approach requires more co-operation between actors with heterogeneous knowledge bases and the creation of markets spanning the standard industrial classifications. Agahist this background, clusters may represent, not only the answer to the multi-faceted need for new knowledge combinations, but also offer a smoother transition to new transversal innovation systems (Cooke, 2012). The need to focus on RV, rather than purely sectoral narrow specialisation in cluster exploration, may also embody the fact that agglomeration forces may operate at the capability level, rather than at the industry level (Buciuni & Pisano, 2015). The proper assessment of relatedness is complicated by the increasing relevance of transversal technologies, i.e. technologies that are developed and applied in slightly different sectors (Giannini, Iacobucci & Perugini, 2019). Technological relatedness guarantees, on the one hand, a level of similarity, which enables the exchange of knowledge and efficient learning, but on the other—a certain cognitive distance allowing for new knowledge combinations and innovations (Dolise, Fomahl & Vehrke, 2018). It can lead to some path dependencies in technology development but should prevent a dangerous lock-in in the long run (Hassink, 2016).

To stimulate cluster heterogeneity and to avoid the risk of becoming too narrowly focused, its thematic boundary should be opened and related knowledge from inside, added. Alternatively, the geographic borders should become permeable, allowing the sourcing of knowledge from different locations (Fomahl & Hassink, 2017); it includes going international. International expansion offers access to complementary assets and enables the establishment of new market relationships and sourcing information, which may not be available in the domestic market (Morisson, Rabel- lotti & Zirulia, 2013). Thus, it can enrich the cluster’s knowledge base and make it more heterogeneous (Bathelt, Malmberg & Masked, 2004). The inflow of mixed knowledge, in turn, can lead to a revitalisation and renewal of the cluster, acting as a positive shock. Internationalisation can thus be considered as a channel to increase heterogeneity and infuse new ideas into a region. As a result, this volume also investigates the concept of cluster expansion, not only understood as internationalisation (Osa- renkhoe & Fjellstrom, 2017; Islankina & Thumer, 2018), but in terms of stretching processes.

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