Between collaboration and competition: co‐located clusters of different industries in one region—the context of Tuttlingen’s medical engineering and metal processing industries
2022-10-01

This study examines the phenomenon of co-located industrial clusters with different industry cores in one region. A multi-method research design, including semi-structured qualitative interviews and a standardized survey, was chosen. The empirical results show different patterns of collaboration and competition: on the one hand, the hiring of employees, especially in the segment of (highly) skilled labor, is an important concern for firms facing a tight labor market of co-located clusters. Consequently, filling vacant positions is challenging for firms of both industrial clusters, which results in a competitive environment, including labor poaching. On the other hand, the linkages between firms are influenced by the institutional setting, such as changing regulatory environment, leading to more collaboration, even though an intense rivalry between participating firms has existed for decades. Este estudio examina el fenómeno de los conglomerados (clusters) industriales coubicados con diferentes núcleos industriales en una misma región. Se optó por un diseño de investigación multimétodo, que incluyó entrevistas cualitativas semiestructuradas y una encuesta estandarizada. Los resultados empíricos muestran diferentes patrones de colaboración y competencia: por un lado, la contratación de empleados, especialmente en el segmento de mano de obra (altamente) cualificada, es una preocupación importante para las empresas que se enfrentan a un mercado laboral reducido de conglomerados coubicados. En consecuencia, cubrir los puestos vacantes es un reto para las empresas de ambos grupos industriales, lo que da lugar a un entorno competitivo en el que incluso se quitan la mano de obra. Por otro lado, los vínculos entre las empresas se ven influidos por el entorno institucional, como el cambiante entorno normativo, que conduce a una mayor colaboración, aunque durante décadas haya existido una intensa rivalidad entre las empresas participantes. 本稿では、一つの地域に異なる産業の中心地がある産業クラスターが共存する現象について検討する。定性的調査のための半構造化インタビューと標準化サーベイを含むマルチメソッド研究のデザインを採用した。実証的な結果から、コラボレーションと競争はパターンが異なることが示された。特に(高度な)熟練労働の分野の従業員の雇用は、同じ場所に共存するクラスターの、逼迫した労働市場という問題に直面している企業にとって大きな懸念事項である。そのため、両方の産業クラスターの企業は欠員補充が困難であり、結果的に競争環境となり、従業員の引き抜きなどが起こる。その一方で、企業間のつながりは、長年激しい競争関係に同業者間であっても、規制環境の変化などの制度的な設定に影響され、より多くのコラボレーションにつながっている。 Scientific debates about industry clusters have concerned multiple disciplines and become widespread over the last two decades, covering a diverse spectrum of aspects and establishing different theoretical frameworks. Subsequently, the cluster concept has become the panacea for policymakers and regional economic development entities (Bathelt & Zhao, 2016; Sellitto & Luchese, 2018) due to the assumption that activities of complementary and linked firms and their collaboration with universities, network organizations, and further actors creates synergies, increases productivity, and leads to future economic growth (Götz & Jankowska, 2017). Porter's (1998, 2000) common and established definition of clusters being geographic concentrations of interconnected firms and institutions of a certain field, including actors all along the value chain, captured between collaboration and competition, is used as a starting point. So far, much research covering the multifaceted aspects of clusters as location-bound geographic phenomena aims toward understanding the existence of clusters and the development of clusters over time (Götz & Jankowska, 2017). While these evolutionary approaches focus on the development and different phases of cluster evolution, surprisingly little is known about multiple clusters and their interaction in a specific city or region (Bathelt & Zhao, 2016). Based on an extensive literature review, Lu et al. (2018) argue that studying relations between clusters provides a promising research direction. Addressing this research gap, Bathelt and Zhao (2016, 2020) conceptualized multiple co-located clusters and developed four ideal types, analyzing clusters with the same industry core located in one megacity. Building on these aspects, this study analyzes two co-located clusters with different industry cores located in one case study region, focusing on the following research questions: (1) which linkages exist within and across co-located clusters with different industry cores located in the same region and (2) how are different areas of these linkages influenced by forms of collaboration and competition? The Tuttlingen region in the south of Baden–Wuerttemberg (southern Germany) has a long manufacturing tradition and presents an interesting case study since two dominant clusters with different industry cores are located within this region: the medical engineering and the metal processing cluster. Furthermore, the past ten years in particular have been characterized by an increase and differentiation of organizations and change of institutional settings (EU legislation adapting medical device regulations). 1 Yet so far, only Tuttlingen's medical engineering industry has been the focus of economic geography research (Halder, 2006; Sautter, 2005; Staber & Sautter, 2011). Neither the metal processing cluster itself nor the aspects of collaboration and competition within and across the co-located clusters has been taken into account so far. Therefore, this study adds another theoretical perspective to the case study region and analyzes Tuttlingen's metal processing and medical engineering sectors as two co-located clusters in a unified framework. Co-located clusters across all development stages could benefit from the findings, especially in terms of resilience and beneficial cluster development. A multi-method research design with a qualitative focus has been applied in order to gain empirical insights of the two co-located clusters in the case study region. The primary data contains explorative semi-structured interviews with medical engineering and metal processing industry firms as well as various organizations (associations, network organizations, vocational schools, the University of Applied Science). In addition, a standardized survey (n = 179) was conducted, which provides supplementary insights into firm characteristics and development, such as employment and skill shortages, research and innovation, cooperation and collaboration as well as locational factors. The paper is structured as follows. After this general introduction, the relevant theoretical aspects are given in Section 2. This is followed by a presentation of the methodological aspects, such as data collection and evaluation in Section 3. Based on this, Section 4 presents the case study region and introduces previous publications concerning Tuttlingen. Section 5 presents the results regarding collaboration and competition between the co-located clusters focusing on the following different areas: labor market, linkages with vocational schools and universities, linkages between firms, and participation in network organizations. Finally, Section 6 summarizes the central aspects and suggests directions for further research, while the potential policy implications and options for future research are discussed. While dominant approaches are more focused on the evolution of clusters, surprisingly little is known about multiple clusters and their interaction in a specific city or region (Bathelt & Zhao, 2016). Such studies often focus on cross-cluster linkages in different regions or countries (Bathelt & Zhao, 2020). The following sections take a closer look at the phenomenon of clusters in general, the relationships between them—such as collaboration and competition—as well as the co-location of clusters and closely related aspects. Porter's (1998, 2000) definition of clusters being geographic concentrations of interconnected firms and institutions of a certain field, including actors all along the value chain, captured between collaboration and competition, is used. Despite the establishment of further theoretical perspectives, such as knowledge-based (Bahlmann & Huysman, 2008; Bathelt, 2008) and multidimensional cluster approaches (Bathelt, 2004), which significantly advanced the cluster concept by adding knowledge and knowledge flows within clusters and beyond, key aspects of the definition (such as the importance of geographical proximity, sectoral concentration, and/or social factors) remain upheld to this day (Frankowska, 2020). Resulting characteristics attributed to clusters are sectoral and/or geographical concentration (one region, one industry); division of labor and cooperation between entities within the region; specialization of firms and the labor force (Sellitto & Luchese, 2018) by building on a shared institutional setting, such as social factors and trust (Frankowska, 2020; Götz & Jankowska, 2017; Sarturi et al., 2016); and encouraged actors engaging in knowledge exchange (Terstriep & Lüthje, 2018). These characteristics differentiate emerging agglomerations from fully functional clusters (Lazzeretti et al., 2019; Stephens & Sandberg, 2020). Only firms located in a certain region and therefore included within such a cluster are able to capitalize on these advantages (Capó-Vicedo et al., 2008; Sarturi et al., 2016), since physical proximity and interaction are basic requirements (Beugelsdijk et al., 2010; Felzensztein et al., 2018; Götz & Jankowska, 2017; Kogut & Zander, 1992; Mudambi et al., 2017). These advantages of geographical proximity are intensified by further cognitive, organizational, social, and institutional forms of proximity (Bathelt & Zhao, 2020; Boschma, 2005), ideally leading to shared visions and norms, increasing trust between actors and increased formation of knowledge networks (Balland et al., 2015). Such advantages are synergies, knowledge spillovers, and increased innovative activity, resulting in higher productivity (Götz & Jankowska, 2017). However, excessive proximity can also hinder the development of a cluster as too much similarity in competences can result in duplication and cause more competition, leading to greater difficulties in collaboration and cooperation (Lis, 2019) or even lock-ins (Balland et al., 2015; Grabher, 1993; Hassink, 2010). While linkages within clusters have already been examined to a large extent and cross-cluster linkages so far have mainly focused on connections between agglomerations and clusters of different regions (Bathelt & Zhao, 2016), cross-cluster linkages within one region are yet to be analyzed. Framing different industry clusters located in the same regions and examining the relationship between both, intense collaboration as well as distinct competition are expected, as discussed in the context of (un)related variety and regional economic growth (Frenken et al., 2007). Even if these clusters address different markets or cooperate with different suppliers (Lu et al., 2013), because they are located in the same region, they require access to similar sets of infrastructure and further localized resources, such as vocational schools, universities, and network organizations as well as the local labor market itself (Bathelt & Zhao, 2016). Therefore, the coexistence of clusters of different industries can trigger innovation and economic growth due to the creation of specialized suppliers, knowledge spillovers, a shared labor pool, and different underlying knowledge bases (Boschma, 2018; Frenken et al., 2007; Lu & Cao, 2012). These effects are described as the benefits of localization economies (Marshallian externalities) if firms are from the same industry. Urbanization economies attribute economic advantages to the size of the respective agglomeration and Jacobs's externalities are built on innovations arising from the combination of knowledge from different industries (Frenken et al., 2007). Considering these potential positive and negative outcomes of co-location, the underlying relationships of collaboration and competition between firms and across clusters may influence the described infrastructure. The establishment of new academic courses or vocational programs, government funding, or labor pooling as well as labor poaching within the local labor market are potential outcomes of collaboration and competition. In general, both cooperation and competition are needed for business relationships to be efficient (Bengtsson & Kock, 2014; Mudambi et al., 2017). In particular, clusters are characterized by competition as well as cooperation and collaboration, simultaneously balancing one another (Felzensztein et al., 2018; Götz & Jankowska, 2017; Matuschewski, 2006). As put by Thomson and Perry, (2006, p. 23) "most scholars would agree that cooperation and collaboration differ in terms of their depth of interaction, integration, commitment, and complexity, with cooperation falling at the low end of the continuum and collaboration at the high end." The process of collaboration is perceived as more complex and abstract than cooperation, while cooperation focuses more on specific desired outcomes and occurs as an early, integrated part of the collaboration process (Kusa et al., 2019; Miles et al., 2006). From a network perspective, both the process of collaboration as well as cooperation build on the underlying aspects of relationship-building, trust, and knowledge-sharing (Keane & Costin, 2019), regardless of the partnering entity. Collaboration with universities, vocational schools, and firms enables access to tacit knowledge as well as (unpublished) codified knowledge, helping firms build on the latest research findings (Parmentola et al., 2021). Notably in recent years, collaborations between universities and firms have intensified, as collaborative research by academia and industry is believed to be a relevant source of innovation (Parmentola et al., 2021). Yet existing scientific evidence is ambiguous. As described by Götz and Jankowska (2017), clusters facilitate knowledge creation but this is due to competition between firms rather than collaboration and cooperation (Malmberg & Power, 2005). Rivalry between firms and labor market mobility among employees in combination with knowledge spillovers could be a more important advantage than inter-firm collaboration or specific inter-firm cooperation projects (Götz & Jankowska, 2017). If firms maintain a balance between competition and cooperation, the scientific literature also refers to this phenomenon as coopetition (Bengtsson & Kock, 2014; Felzensztein et al., 2018). In coopetition, competitors join forces in certain business areas, while remaining competitive in others (Gnyawali et al., 2006; Sellitto & Luchese, 2018). However, due to the vagueness of the term coopetition, it is not used in this paper. Instead the analyses mainly relies on the term collaboration. Overall, cooperation and collaboration are often used interchangeably (Kusa et al., 2019; Thomson & Perry, 2006). While the emergence, mechanisms, evolution, and characteristics of clusters have been studied extensively, hardly anything is known about multiple co-located clusters in one city or region, either theoretically and empirically (Bathelt & Zhao, 2020; Turkina et al., 2020). Yet, the majority of densely populated urban areas show diverse economic structures with a multitude of smaller or larger agglomerations of firms across different industries rather than just one dominant industry. Considering previous findings, co-located clusters are likely to be connected to each other, sharing the labor force (labor pooling), knowledge, intermediate goods, institutional settings, and/or organizational structures (Bathelt & Zhao, 2016, 2020; Lu et al., 2018; Turkina et al., 2020), as co-location enables collective problem-solving due to these elements (Felzensztein et al., 2018). Since 2016, this research gap has been addressed by a handful of studies, covering the research areas of business studies, finance, and economics as well as economic geography. Turkina et al. (2020) describe co-location as a promising step in the right direction since the existence of co-agglomerations are acknowledged and cluster relations are focused on. Bathelt and Zhao (2016) focus on biomedical clusters with one industry core across Bejing and thereby show that these clusters of the same industry are strongly linked with each other via cross-cluster linkages. 2 Building on these findings, Bathelt and Zhao (2020) propose a framework by which co-located clusters can be categorized based on their intensity and type of cross-cluster linkages. Lu et al. (2016) link the previously mentioned cluster-life cycle debate with clusters' co-location effects by identifying different effects between neighboring clusters or industries depending on different life phases. While emerging clusters show negative effects on total factor productivity of other co-located industries or clusters, mature clusters have positive effects. Following Delgado (2020) and Frenken et al. (2015), such positive effects can be the co-location of innovation and production for industry clusters, especially for those with a higher knowledge intensity. These positive effects are caused by attracting resources and attention from other local industries and clusters due to the new economic power of the emerging cluster. Focusing more on the linkages themselves, Turkina et al. (2020) show that connections between clusters are not uniform or evenly spread across the cluster itself, since the firms, organizations, or actors within are mostly specialized one way or the other. This leads to connections between firms from neighboring clusters with technological similarities and common business contexts, thereby fostering linkages across clusters (Turkina et al., 2020). Regarding the distribution of co-located clusters, Liu et al. (2021) find an industrial divide: the city level shows patterns of co-location of ICT technologies, while less urban areas tend to be associated with production industries. Building on Bathelt and Zhao (2016, 2020) and Turkina et al. (2020), in this study co-located clusters of different industries are defined as economic actors, which are part of an own as well as of an overlapping organizational field, located in the same region. They are characterized by a combination of linkages within and across the clusters, covering the areas of labor market and production, as well as research and innovation. Such linkages within the same cluster and across co-located clusters can take different forms (Bathelt & Zhao, 2020; Malmberg & Maskell, 2002), influenced by collaboration and competition. Bathelt and Zhao, (2016, 2020) expect such learning linkages and knowledge flows to exist not just within industry clusters but across neighboring co-located clusters as well. They identify four ideal-type configurations of co-located clusters with the same industry core—(1) collaborating clusters, (2) competing clusters, (3) one single large cluster, and (4) no cluster at all—while highlighting that empirical cases cannot be expected to fit any one of these types exactly(2020). Collaborating clusters generate relationships that include labor market, production, and research linkages or form a heterogeneous linkage pattern. Therefore, firms can utilize advantages based on the different forms of proximity (cognitive, organizational, social, and institutional). A higher density of linkages within the cluster and a significantly lower density of linkages across/between the clusters is expected (Bathelt & Zhao, 2020, p. 316). A second configuration of co-located clusters are competing clusters. In this type, there can be labor market competition, competition for government funding or regional investments as well as competition for commercial spaces or customers if there is little product differentiation. Missing collaborative linkages and no shared knowledge would lead to cluster-specific knowledge ecologies, separate local buzz, and no pipelines across the competing clusters (Bathelt & Zhao, 2020, pp. 317–318). Building on the previous type of collaborating clusters, deepening and extending the already existing linkages may lead to the emergence of the third type, a single large cluster and cause former separated cluster dynamics to vanish (Bathelt & Zhao, 2020, pp. 318–319). However, a multitude of firms might be located within the same city or regional context but be predominantly self-sufficient with neither specific knowledge ecology nor shared knowledge linkages. Even sharing the same industrial background, in this fourth type no cluster exists or develops (Bathelt & Zhao, 2020, p. 319). In this empirical analysis, four different domains of cluster linkages between the neighboring co-located medical engineering and metal processing cluster in the Tuttlingen region were examined. After reviewing both the standardized survey results and the interview data, these domains emerged as most prominent. For each domain, it was distinguished whether the cluster-type was formed by collaboration or competition between the participating firms since similarities regarding the type of collaborating as well as the type of competing clusters are expected. A multi-method research design semi-structured and a standardized survey, data labor market and was chosen. the case study region, the of Tuttlingen as well as the city of Tuttlingen itself and the neighboring The data are based on a study in the of a for regional by et al. (2018) and not to in with Bathelt and Zhao (2016, 2020). The primary data semi-structured and interviews with medical engineering and metal processing industry firms as well as various organizations (associations, network organizations, vocational schools, and the University of Applied which were in two phases. The interview focused on the research and the standardized firm The second interview 2018) focused on a understanding of specific aspects. qualitative interviews were qualitative 2010). The interview were with the of a local and a in the interview was and The interview were based on a which was developed the In the second survey specific findings from the standardized survey were it was to the results of both used. The characteristics are described A standardized survey into different sections was examined a different of the activities in more supplementary insights into firm characteristics and development, such as employment and skill shortages, research and innovation, cooperation and collaboration, and locational factors. The and were based on previous and standardized such as the by the for by the for by the of and the Furthermore, findings from the of the interviews and the scientific literature were After a with firms, the firms were by address data from the of and and the of A total of firms and organizations in the survey, leading to a of The survey of all the included and the survey can be in the and the firms included in the are located in the city of Tuttlingen. The remaining are spread over the remaining of the of Tuttlingen. of the firms were and the other of the firms are than years, and one is than of the firms are by the or by of the for of the firms, the are located within the Tuttlingen are located in while are based of the firms are part of the two co-located are metal processing firms, and are medical engineering The Tuttlingen of industrial development to the to which the local predominantly in the 2007). it is strongly associated with large of manufacturing and is therefore described as the Tuttlingen The region was already characterized by agglomerations of firms and production at the of While the production of medical and were the stages industrial development, medical metal and and engineering the core industries in the medical engineering cluster is to as the medical and is as This and industrial development not to the clusters' especially since the clusters are and not by one cluster However, since an by network organizations can be due to the of the Section The existing scientific literature concerning Tuttlingen focuses mainly on the medical engineering cluster. other clusters located in Tuttlingen have been examined in are the metal processing or focuses on a cluster perspective, which is more concerned with along the to and related forms of within the medical engineering cluster. and and activities within the medical engineering cluster and identify factors in regional clusters in Staber and clusters, focusing on manufacturing in the and medical engineering production in Tuttlingen. theoretical framework is based on the cluster and the cluster-life cycle and examines the influence of on cluster development. neither an of the case study region the co-location of clusters nor the of industries medical engineering production has been given the increasing of the co-located clusters and the regulatory new insights to be The medical engineering industry has a long tradition in the Tuttlingen region. the to at the end of the firms to in the of In order to the increasing and established production of in while competitors to on a less was the firm of more than in including at in Tuttlingen 2019; et al., 2018). 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