ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 17 received: 2020-07-15 DOI 10.19233/ASHS.2021.02 DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES? Tomaž PIPAN University of Ljubljana, Biotechnical Faculty, Department of Landscape Architecture, Jamnikarjeva ulica 101, 1000 Ljubljana, Slovenia e-mail: tomaz.pipan@bf.uni-lj.si ABSTRACT Global industrial restructuring is a main reason for the current state and shape of contemporary European cities. A major problem in peripheral regions in Europe is the loss of the process and assembly segment of industrial production. Two approaches, tourism and high-tech innovation, are at the forefront of city transforma- tion in post-industrial Europe. As tourism is a well-established development driver, we analyse whether digital innovation, a subset of high-tech innovation, can be a parallel pathway for peripheral regions to achieve more resilient and balanced development. This paper describes two digital innovation models, one in London and an- other in Berlin, as city development drivers to speculate on their limits and suitability for developing peripheral regions. As their direct translation is difficult, this paper defines assessment criteria within the triple helix model of innovation and re-casts them through the concept of slow innovation, which is applicable in peripheral regions. We conclude that there are different pathways to achieving suitable digital innovation conditions and that although digital innovation is currently not a major development driver in the periphery, it will be viable if interpreted through peripheral concepts. Keywords: digital innovation, peripheral regions, redevelopment, Berlin digital innovation cluster, London digital innovation cluster INNOVAZIONE DIGITALE: COSA PUÒ IMPARARE LA PERIFERIA DAI CENTRI GLOBALI? SINTESI La ristrutturazione industriale globale è una delle cause principali della condizione e della forma delle città europee contemporanee. Uno dei problemi più rilevanti delle regioni periferiche europee è stata la perdita del segmento processo e assemblaggio (P&A) nella produzione industriale. Due approcci, turismo e innovazione ad alto contenuto tecnologico, giocano un ruolo di primaria importanza per la trasformazione urbana dell’Europa post-industriale. Poichè il turismo è già una prassi consolidata, è utile chiedersi se l’innovazione digitale, un sub-settore dell’innovazione tecnologica, potrebbe rappresentare un percorso parallelo per favorire uno svilup- po più bilanciato e resiliente nelle regioni periferiche. L’articolo descrive due modelli di innovazione digitale, uno a Londra e uno a Berlino, come motori di sviluppo urbano, mettendone in luce limiti e potenzialità per lo sviluppo delle regioni periferiche. Essendo difficile replicare direttamente questi approcci, si utilizza qui la defi- nizione di criteri di valutazione sulla base del modello di innovazione a tripla elica, rielaborandone i contenuti attraverso il concetto di innovazione lenta applicabile nelle regioni periferiche. Le conclusioni illustrano che esistono diversi percorsi verso l’innovazione digitale, e che nonostante quest’ultima non sia il principale motore di sviluppo nelle periferie, è altresì praticabile se reinterpretata attraverso concetti periferici. Parole chiave: Innovazione digitale, regioni periferiche, riqualificazione, Berlin digital innovation cluster, London digital innovation cluster ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 18 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 INTRODUCTION AND RESEARCH QUESTION Due to the occurrence of profound economic change related to the globalisation of production systems around the turn of the millennium, the European Union (EU) and the rest of the Global North saw an increased focus on both tertiary-sector services and the high-tech and innovation segment of industrial production. According to ESPON (2005, 9), ‘manufacturing industries are in decline in most [EU] regions’. A major reason for this was offshoring of the labour-intensive process and assembly (P&A) segment of industrial production (e.g. tailoring, shoe- making and assembly of kitchen appliances, toys and other low-tech products) to developing countries such as China (Lin, 1997; Yeung, 2001; Wu, 2007). Within the EU, the peripheral regions were more de- pendent on the P&A segment than the central areas, such as ‘the European Pentagon’—which is the most urbanized area of the EU, surrounded by Hamburg, Munich, Milano, Paris and London. The economic consequences were more severe in the peripheral regions that were heavily dependent on industry and weaning protective polices. Suitable examples include the Multi Fibre Arrangement and the decline of the apparel industry of northern Italy after its con- clusion (Hadjimichalis, 2006; Micelli & Sacchetti, 2014). Various strategies were proposed to substitute for the P&A loss – ‘jobs were increasingly created in the service sector and in knowledge-intensive profes- sions’ (OECD, 2015, 26). Just as Fordism instigated the rise of the middle class in the 1940s, the intro- duction of service economy ushered the rise of the ‘creative class’ (Florida, 2002), which is constantly engaged in the innovation and production of con- sumer products and services. An important service sector that reinvented itself during this time was tourism, which substantially aided the development of peripheral EU regions through concepts such as industrial culture (Harfst et al., 2018). This was fol- lowed by high-tech innovation—whose conceptual background can be traced back to Marshall’s (1890) industrial agglomerations and the concept of region- related variety of production, which continues into a regional innovation system debate (Doloreux & Gomez, 2017). Creating a viable, innovation-based economy is a long-term effort and requires strategic development that is more readily achievable in cen- tral regions (Doloreux & Gomez, 2017; Benneworth & Hospers, 2007). However, the concept of slow innovation contradicts this belief and presents the conditions under which innovation can flourish in the periphery (Shearmur, 2015). Therefore, it offers a different reading of conditions for innovation and indicates that the dimensions of classical innovation approaches do not apply to the periphery (Eder, 2019; Eder & Trippl, 2019). The reason we deal with digital innovation in peripheral regions is to examine the possibilities of widening the economic base. Ever since the P&A loss and reorientation of services, tourism has been one of the most visible and readily achievable sec- tors. However, heavy reliance on one service sector is strategically problematic, as it creates regions that are non-resilient to change. Marshall (1890), saw variety-poor industrial regions ‘liable to extreme depression, in case of a falling-off in the demand for its produce’ (Marshall, 1890, 157). The same holds for service sectors as well. A timely example is the recent economic problems faced by inner-city areas where a high portion of the economy is based on cultural tourism. The non-existent touristic migra- tion due to the COVID-19 pandemic (Gössling et al., 2021) made these areas virtually empty. There- fore, it is sensible to diversify the economic base, particularly in today’s contemporary times, when the world is so interdependent and thus frequently in crisis. A successful region should, therefore, be based on the understanding of robustness and complementarity of either different industrial pro- ducers or different tertiary sectors that support and complement each other. The diversification towards a knowledge economy, of which innovation is a part, is one such method that complements tourism well. ‘Overall, globalisation and constantly changing markets have meant that knowledge and innovation have become key factors in the sustainable development of any economy’ (Galvao et al., 2019, 813). In parallel with the cohesion policy that strives for equal development of all EU regions, the knowledge economy is a key policy directive of the EU and foresees knowledge production as an important part of the EU economy. Innovation was one of the main development drivers in the Lisbon Strategy and the Europe 2020 policy, implemented through the In- novation Union flagship framework (EC, 2010). This paper deals with the peripheral regions of the EU (ESPON, 2005; Davies & Michie, 2011) and analyses how ‘digital innovation’ (OECD, 2019)—a specific type of innovation—can aid in their transformation. Digital innovation can be exemplified through the new types of companies such as Uber, Netflix, Airbnb and easyJet. These companies rely heavily on digital data and digi- tal infrastructure, and through their operations, radically disrupt the management practices of existing services in the field. In the report Digital Innovation: Seizing Policy Opportunities, OECD (2019) identifies four basic changes in innovation dynamics. (1) The data are becoming key input for innovation products; (2) the products are mainly services; (3) the speed of innovation is increasing because of its non-physical nature and ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 19 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 quick production capability and (4) innovation is ever more collaborative, as it is interdisciplinary and requires varied expertise (OECD, 2019, 26). The conditions regarding the classical innovation concepts of ‘knowledge spillovers’ (Komninos, 2002), ‘buzz’ (Storper & Venables, 2004) and ‘city as place’ (Amin & Thrift, 2002) indicate that digital innovation will favour global hubs such as Berlin and London, where it is indeed an impor- tant part of the economy. This study investigates whether peripheral re- gions represent viable hubs for digital innovation ecosystems despite their less favourable global po- sition, which is a prerequisite for digital innovation. METHOD AND STRUCTURE This study uses a qualitative rather than quan- titative method of investigation. The theme of the study is examined in terms of examples and the conditions under which they are sustained. The study draws upon a ‘thick description’ defined by Geertz (1973), where through the narrative depth of the argument, the concrete world is described and individual examples are examined that seem relevant for the research question. The paper describes two digital innovation models—‘Silicon Roundabout’ (Old Street) in Lon- don and ‘Silicon Allee’ (Torstrasse) in Berlin—as the city development drivers and speculates on their limits and suitability for developing periph- eral regions. As it is difficult to translate the two examples onto the periphery, this paper achieves this by defining the assessment criteria within the triple helix of innovation (Etzkowitz & Leydesdorff, 1995) and recasts them through the concept of slow innovation (Shearmur, 2015), which is applicable in peripheral regions. The two examples are reviewed under these criteria to identify the characteristics that make them successful. This forms the basis for discussion to speculate which characteristics, and to what extent, are reproducible in the peripheral regions or if any characteristic of peripheral regions can substitute them. As the theme of this study is to test the viability of the two models, it is impossible to deal with them through the classical approach of literature review. Thus, when describing the key concepts, we use review articles and key thinkers in parallel with the argument. The case studies are explained through research articles and hard data. As there are limited hard data on the development of regional innovation systems and spin-offs generated by the research community (Kozina & Bole, 2018, 262), we augmented the re- search material with the reports provided by visible Inno-tech companies themselves. Although some bias might exist, a rough estimate in terms of the investment value and direction of the innovation field is possible. In addition, these reports appro- priately describe the topography of innovation in different cities. The data were gathered from sev- eral independent sources, such as the online data provider for start-ups Dealroom.co, the webpage of European Digital City Index 2016, State of EU Tech Report 2019 by the venture capital (VC) firm Atomico, and the Deloitte Tech Hub Potential Index for Germany. The rest of the paper is organised into three sections. First, the main concepts of peripheral regions—digital innovation, triple helix of innova- tion and slow innovation—are reviewed and the assessment criteria are identified. Second, the two models of digital innovation—Silicon Allee and Silicon Roundabout—are reviewed under the iden- tified criteria. Finally, the viability of the identified triple helix requirements is validated through slow innovation characteristics in peripheral regions; if they are not viable, the specific innovation con- cepts of peripheral regions that can substitute them are discussed. DEFINITION OF CONCEPTS AND CRITERIA Peripheral regions In the literature and in policy, peripheral regions in the EU have not been clearly defined (Davies & Michie, 2011). Depending on the country and sector, the dimensions that define peripherality shift with the topic and geographic scale. Davies & Michie identified a set of criteria ranging from poor access to global markets and service centres, through low population density, aging or declin- ing population, to limited access to the services of general interest. ESPON (2005) defines functional urban areas (FUAs) as a measurable agglomeration concept in which urban functions have a critical density. Here, the EU Pentagon, an area surrounded by Hamburg, Munich, Milano, Paris and London, represents ‘the European core with approximately 14% of the EU27 area, 32% of its population and 43% of its GDP’ (ESPON, 2005, 3). The rest is considered periphery. Meanwhile, the concept of developing Metropolitan European Growth Ar- eas (MEGAs) additionally accounts for peripheral centres as important nodes. MEGAs are identified through the following four criteria: economic mass, competitiveness, global connectivity and knowledge basis. These criteria enable us to con- nect the concepts of geography to that of the triple helix of innovation, which has the following three dimensions: presence of industries (corresponding to the economic mass and global connectivity), ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 20 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 collaboration with universities (corresponding to the knowledge basis) and policies to support the connections (corresponding to competitiveness). The peripheral regions referred to here are outside the Pentagon because of the factors such as lower com- petitiveness and human capital, and represent the FUAs around level-4 MEGAs (e.g. Ljubljana, Lodz, Poznan, Riga and Sofia) (ESPON, 2005, 117). These factors are mainly attributed to the lack of direct global connections or to absence of visible seat of multinationals. When talking about periphery, we should not ignore the impor- tance of small- and medium-sized towns (SMESTOs), as they ‘form important hubs and links, especially for rural regions’ (ESDP, 1999). Bole et al. (2016) reported that it is exactly SMESTOs in peripheral regions like the Alps that achieve job density per inhabitant similar to that observed in larger towns, serving as Hinterland’s employ- ment centres. The SMESTO research suggests analysing the networked condition of smaller towns as providers of industry- and service-related activities, research and innovation (ESPON, 2006). Innovation The concept of innovation was developed for global cities, which are augmented by a vast increase in size and perceived as instruments of global capitalism (Sas- sen, 1991). Amin and Thrift (2002) summarised Sassen’s view as ‘centres of global command and control, based on the presence of global corporations, the transnational capitalist class, and labour power from around the world’ (Thrift, 2002, 53). They house the major global corpora- tions, seats of financial institutions and banks which can offer management know-how, global connections to cli- ents and opportunity and venture capital funds, creating a perfect condition for an ‘innovation ecosystem’ (Bassis & Armellini, 2018). Komninos (2002) described an array of services and external support required for such an environment to succeed, from large industrial complexes to innovation support programmes, high-tech education, business services and venture capital funds (Komninos, 2002, 29). Such an environment creates its own internal world, whose important allure for young people is ‘the buzz’ (Storper & Venables, 2004) and the possibility of (fi- nancial) success that is measured through the intellectual property of individual start-ups. The proximity and density of global cities are ben- eficial, particularly for the infrastructure and services required by innovation teams. However, these dense sites never operate by themselves. ‘What seems to matter in- stead is the combination of the global corporate reach of some firms and the economy of time that local proximity provides to highly mobile project teams’ (Amin & Thrift, 2002, 66). A global city’s predominant function lies in its density of services and institutions required by start-ups whose workers represent an important part of Florida’s ‘creative class’ (Florida, 2002). Digital innovation In the last 15–20 years, a new type of spatial restructur- ing led by innovation in digital services—termed ‘digital disruption’ (Skog et al., 2018), due to their radical change in the management structure—has emerged. The services of the ‘sharing economy’ (Grabher & van Tuijl, 2020) have radically transformed the organisation of global production networks. This digital restructuring, like the industrial restructuring at the turn of the millennium, has its own set of spatial and economic consequences. Com- panies like Uber, easyJet, Netflix, Amazon, Booking.com and Coursera are changing our way of life and the spatial organization of the territory. Just as easyJet and Booking. com abolished the need for touristic agencies and drove touristisation of city cores through the roof, is Uber replacing the taxi dispatch services and Amazon depart- ment stores. The development of such services requires a particular type of environment, for which Amin and Thrift (2002) saw the rejuvenation of city cores as ‘cities as sites’ (Thrift, 2002, 63). With the reintroduction of digital inno- vation as a development driver, city centres have begun seeing reactivation due to its compact production (Engel et al., 2018; Kozina & Bole, 2018). Digital innovation is a subset of innovation that has very special characteristics and spatial requirements. In the report Digital Innovation: Seizing Policy Op- portunities, OECD (2019) identifies four basic changes in innovation dynamics that are typical for digital in- novation. First, managing and being able to turn data into value is a key prerequisite; hence, the data are becoming key input for innovation. Second, digital innovation is permeating all sectors. Although each sector has different needs, one key feature remains the same: the products are services, which usually replace intermediaries (Uber) or force service providers to adapt. Third, the speed of innovation is increasing because of the quick production capabilities and the non-physical nature of innovation. Last, innovation is ever more collaborative, as it is interdisciplinary and requires varied expertise. Therefore, environments such as accelerators, co-working spaces, collaborative platforms (physical and virtual) and spatial ecosystems (with meeting, business and leisure infrastructure) are ever more important (OECD, 2019, 27–35). Of the four abovementioned concepts, we select the following two for further investigation: • Access to data in terms of policies and infrastruc- ture • Quality of infrastructure for collaboration Slow and peripheral innovation According to the literature, a major obstacle that hinders the implementation of innovation as a develop- ment driver in peripheral regions is their weak linkage to global networks. To address this issue, Shearmur (2015) ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 21 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 posited the concept of slow innovation. They identified three conditions under which innovation can also be implemented in peripheral regions. First, the value of innovation can change slowly or rapidly. Fast innova- tion is market-dependent, such as the fashion or stock market, whereas slow innovation is technical in nature (fabrication process, new materials and new software); even slower is the basic science produced in academic environments. The fast-decaying value of information can only be acted upon in global centres because of the mentioned infrastructure; however, the slow-decaying value can also be acted upon in non-global locations, such as peripheral regions. Second, some types of in- novation are location-specific because knowledge is geographically rooted, replacing ‘buzz and geographic proximity by various social and network proximities’ (Shearmur, 2015, 426). Some examples include natural conditions for wine growing or the law and regulation of a country. Third, the initial stage of innovation, where it is identified, does not require the entire supporting infrastructure. However, Shearmur (2015) conceded that all above-described concepts—the buzz, networks, ac- cess to global markets, proximity and VC—are required in the second stage when innovation ideas need to be commercialized and released in the market (Shearmur, 2015, 425–433). Recently, a body of work on peripheral innovation that challenges the conception of innovation as defined for core regions has emerged. In his literature review, Eder (2019) challenged exactly the prerequisite for geographic proximities and showed how peripheral regions find other approaches for networking ‘via organizational, cognitive, and technological proximity’ and through conferences (Eder, 2019, 121). They continued to identify a suitable integration of a local company into a ‘global pipeline’ to substitute for the local buzz (and lack of knowledge spillovers). Furthermore, similar to Shearmur, they identi- fied the importance of geography-specific knowledge that emerges through practice in specific occupations. To this end, we should add the networked condition of an SMESTO as a dispersed hub for geographic proximities of innovation as another viable peripheral structure that can accelerate innovation. Based on the above, we draw the following important conclusions when considering how the London and Ber- lin models are transferable to peripheral regions. • Innovation based on slow-decaying information in technology and basic science will be more suc- cessful. • Localized and geography-specific knowledge can be a suitable source of innovation. • Locally embedded companies with suitable access to global networks can substitute for geographic proximities. • The local network of small towns can substitute for geographic proximities. Triple helix of innovation The field of innovation study is truly immense. For example, of the various fields that research regional in- novation systems, Doloreux and Gomez (2017) identified eight major streams and conceded that the research is biased towards core regions, missing the ‘approaches that seek to make sense of growth paths in peripheral and rural regions’ (Doloreux & Gomez, 2017, 385). A more approachable concept is the triple helix of innovation, because it connects well with the definition of the EU territory through the FUA and MEGA concepts, as identi- fied above. Etzkowitz and Leydesdorf (1995) introduced the term triple helix of innovation as a new relationship among the university, industry and government. They observed new behavioural patterns in the neo-liberal world of the 1990s, where innovation has become a pervasive eco- nomic driver of the developed North. One key change is that the classical innovation environment of universities, which used to produce basic knowledge, is becoming more application-oriented, connected to the industry and creating marketable products and spin-off companies. In contrast, the industry, with its global networks and market research background, is integrating its R&D departments into the newly established university outlets to augment its product development and marketing. These new dy- namics are being supported by project-oriented govern- ment policies specifically targeting the applied research to achieve market readiness through various incentives. Galvao et al. (2019) identified the diversification of the triple helix concept into quadruple helix (adding the pub- lic dimension) and quintuple helix (adding the public and environment dimensions). As reported by Galvao et al. (2019), both models rely heavily on the first three dimen- sions but add time-sensitive dimensions, which in turn make the models more complicated. They commented that a comprehensive review of these models still lacks in terms of the triple helix, which has become an important economic model of reference. Based on the above, we can set the following three criteria to validate how digital innovation responds to classical innovation requirements: • Presence of global corporations • Connection of applied university research to industry • Government policies supporting start-ups and SMEs DESCRIPTION AND COMPARISON OF INNOVATION MODELS In this chapter, we analyse the two models through which digital innovation is implemented and compare them: ‘Silicon Allee’ (Torstrasse) in Berlin and ‘Silicon Roundabout’ (Old Street) in London. London and Berlin were the top two innovation hubs in the invested capital ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 22 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 London € 1.159.895.296 € 2.152.693.654 € 2.078.340.109 € 4.654.469.384 € 3.725.425.360 € 4.901.708.793 Berlin € 1.337.544.063 € 1.492.381.767 € 909.010.657 € 1.618.044.981 € 1.613.698.883 € 3.582.321.675 Paris € 603.279.862 € 955.183.542 € 1.094.278.015 € 1.563.720.777 € 1.900.571.891 € 2.732.823.460 Stockholm € 323.734.111 € 654.829.020 € 286.044.088 € 392.611.808 € 465.413.721 € 1.930.293.383 Munich € 145.690.905 € 198.200.906 € 221.865.903 € 251.712.270 € 419.428.539 € 1.105.409.190 Tel Aviv-Yafo € 325.481.804 € 210.073.624 € 506.481.811 € 212.978.170 € 617.181.802 € 889.616.339 Amsterdam € 400.798.633 € 186.280.903 € 171.368.361 € 375.272.269 € 374.361.812 € 576.870.628 Barcelona € 113.691.561 € 253.668.844 € 299.010.028 € 482.986.626 € 789.247.629 € 555.264.575 Madrid € 84.059.539 € 103.068.056 € 87.119.411 € 244.608.361 € 312.298.632 € 454.528.951 Cambridge € 53.538.735 € 32.413.293 € 169.562.599 € 138.706.179 € 304.065.813 € 356.325.734 London € 1.159.895.296 € 2.152.693.654 € 2.078.340.109 € 4.654.469.384 € 3.725.425.360 € 4.901.708.793 Berlin € 1.337.544.063 € 1.492.381.767 € 909.010.657 € 1.618.044.981 € 1.613.698.883 € 3.582.321.675 Paris € 603.279.862 € 955.183.542 € 1.094.278.015 € 1.563.720.777 € 1.900.571.891 € 2.732.823.460 Stockholm € 323.734.111 € 654.829.020 € 286.044.088 € 392.611.808 € 465.413.721 € 1.930.293.383 Munich € 145.690.905 € 198.200.906 € 221.865.903 € 251.712.270 € 419.428.539 € 1.105.409.190 Tel Avi -Yafo € 325.481.804 € 210.073.624 € 506.481.811 € 212.978.170 € 617.181.802 € 889.616.339 Amsterdam € 400.798.633 € 186.280.903 € 171.368.361 € 375.272.269 € 374.361.812 € 576.870.628 Barcelona € 113.691.561 € 253.668.844 € 299.010.028 € 482.986.626 € 789.247.629 € 555.264.575 Madrid € 84.059.539 € 103.068.056 € 87.119.411 € 244.608.361 € 312.298.632 € 454.528.951 Cambridge € 53.538.735 € 32.413.293 € 169.562.599 € 138.706.179 € 304.065.813 € 356.325.734 London € 1.159.895.296 € 2.152.693.654 € 2.078.340.109 € 4.654.469.384 € 3.725.425.360 € 4.901.708.793 Berlin € 1.337.544.063 € 1.492.381.767 € 909.010.657 € 1.618.044.981 € 1.613.698.883 € 3.582.321.675 Paris € 603.279.862 € 955.183.542 € 1.094.278.015 € 1.563.720.777 € 1.900.571.891 € 2.732.823.460 Stockholm € 323.734.111 € 654.829.020 € 286.044.088 € 392.611.808 € 465.413.721 € 1.930.293.383 Munich € 145.690.905 € 198.200.906 € 221.865.903 € 251.712.270 € 419.428.539 € 1.105.409.190 Tel Avi -Yafo € 325.481.804 € 210.073.624 € 506.481.811 € 212.978.170 € 617.181.802 € 889.616.339 Amsterdam € 400.798.633 € 186.280.903 € 171.368.361 € 375.272.269 € 374.361.812 € 576.870.628 Barcelona € 113.691.561 € 253.668.844 € 299.010.028 € 482.986.626 € 789.247.629 € 555.264.575 Madrid € 84.059.539 € 103.068.056 € 87.119.411 € 244.608.361 € 312.298.632 € 454.528.951 Cambridge € 53.538.735 € 32.413.293 € 169.562.599 € 138.706.179 € 304.065.813 € 356.325.734 London € 1.159.895.296 € 2.152.693.654 € 2.078.340.109 € 4.654.469.384 € 3.725.425.360 € 4.901.708.793 Berlin € 1.337.544.063 € 1.492.381.767 € 909.010.657 € 1.618.044.981 € 1.613.698.883 € 3.582.321.675 Paris € 603.279.862 € 955.183.542 € 1.094.278.015 € 1.563.720.777 € 1.900.571.891 € 2.732.823.460 Stockh lm € 323.734.111 € 654.829.020 € 286.044.088 € 392.611.808 € 465.413.721 € 1.930.293.383 Munich € 145.690.905 € 198.200.906 € 221.865.903 € 251.712.270 € 419.428.539 € 1.105.409.190 Tel Aviv-Yafo € 325.481.804 € 210.073.624 € 506.481.811 € 212.978.170 € 617.181.802 € 889.616.339 Amsterdam € 400.798.633 € 186.280.903 € 171.368.361 € 375.272.269 € 374.361.812 € 576.870.628 Barcelona € 113.691.561 € 253.668.844 € 299.010.028 € 482.986.626 € 789.247.629 € 555.264.575 Madri € 84.059.539 € 103.068.056 € 87.119.411 € 244.608.361 € 312.298.632 € 454.528.951 Cambridge € 53.538.735 € 32.413.293 € 169.562.599 € 138.706.179 € 304.065.813 € 356.325.734 Lond € 1.159.895.296 € 2.152.693.654 € 2.078.340.109 € 4.654.469.384 € 3.725.425.360 € 4.901.708.793 Berlin € 1.337.544.063 € 1.492.381.767 € 909.010.657 € 1.618.044.981 € 1.613.698.883 € 3.582.321.675 Paris € 603.279.862 € 955.183.542 € 1.094.278.015 € 1.563.720.777 € 1.900.571.891 € 2.732.823.460 Stockholm € 323.734.111 € 654.829.020 € 286.044.088 € 392.611.808 € 465.413.721 € 1.930.293.383 Munich € 145.690.905 € 198.200.906 € 221.865.903 € 251.712.270 € 419.428.539 € 1.105.409.190 Tel Aviv-Yafo € 325.481.804 € 210.073.624 € 506.481.811 € 212.978.170 € 617.181.802 € 889.616.339 Amsterda € 400.798.633 € 186.280.903 € 171.368.361 € 375.272.269 € 374.361.812 € 576.870.628 Barcelona € 113.691.561 € 253.668.844 € 299.010.028 € 482.986.626 € 789.247.629 € 555.264.575 Madrid € 84.059.539 € 103.068.056 € 87.119.411 € 244.608.361 € 312.298.632 € 454.528.951 Cambridge € 53.538.735 € 32.413.293 € 169.562.599 € 138.706.179 € 304.065.813 € 356.325.734 L don € 1.159.895.296 € 2.152.693.654 € 2.078.340.1 9 € 4.654.469.384 € 3.725.425.360 € 4.901.708.793 Berlin € 1.337.544.063 € 1.492.381.767 € 909. 10.657 € .618.044.981 € 1.613.698.88 € 3.582.321.675 Paris € 603.279.862 € 9 5.183.542 € 1.094.278.015 € 1.563.720. 77 € .900.571.891 € 2.732.823.460 St ckholm € 323.734.111 € 654.829.020 € 286.044.088 € 392.611. 08 € 465.413.721 € 1.930.29 .383 Munich € 145.690.905 € 1 8.200.906 € 221.865.903 € 25 .712.270 € 419.428.53 € .105.409.190 Tel Aviv-Yafo € 325.481.804 € 210.073.624 € 506.481. 1 € 212.978.170 € 617.181.802 € 889.616. 39 Amsterdam € 400.798.633 € 186.280.903 € 171.368.361 € 375. 72.269 € 374.361.812 € 576.870.628 Barcelona € 113.691.561 € 253.668.844 € 299.010.028 € 482.986. 26 € 789.247.62 € 555.264.575 Madrid € 84.059.539 € 1 3.068.056 € 87.119.411 € 244.608.361 € 312.298.63 € 454.528.951 Cambridge € 53.538.735 € 32.413.293 € 169.562.599 € 138.706.179 € 304.065.813 € 356.325.734 Table 1: Top investment hubs in Europe by VC investment. London and Berlin have been holding the first two places for the last five years, with London clearly ahead but Berlin gaining fast (Dealroom.co, 2020). Capital invested into start-ups and innvoation (Source dealroom.co, accessed July 2020) Naziv 2014 2015 2016 2017 2018 2019 n n € 1.1 9.8 5.2 6 € 2.152 93 54 € 2.078.3 0.1 4.654.469 384 € 725.425.360 € 4 9 1 708.793 li € 1.3 7.544.0 € 1.492 381 67 € 909.01 .657 1.618.044 981 € 613.698.88 € 3 5 2 21.675 is € 603 79 6 € 955. 83.5 2 € 1.094.2 8.0 1.563.720 77 € 1 900.571.89 € 2 32 23.460 ckholm € 323 734 11 € 654. 9.0 0 € 286.04 .08 € 392.611.808 465.413.721 1 9 0 93.383 ich € 145 690 0 € 198. 0.9 6 € 221.86 .903 € 251.712. 70 419.428.539 1 10 409.190 l viv-Yafo € 325 81 0 € 210. 73.6 4 € 506.48 .81 € 212.978. 70 6 7.181.802 € 8 9. 16.339 terdam € 400 798 33 € 186. 80.9 3 € 171.36 .36 € 375.272. 69 74.361.812 € 5 .870.628 lona € 113 91 6 € 253. 68.8 4 € 299.01 .028 € 482.986. 26 789.247.629 € 555. 64.575 rid € 84. 59.5 9 € 103. 8.0 6 € 87.11 .411 € 244.608.361 3 2.298.632 € 454.528.951 ridge € 53. 38.7 5 € 32.4 3.29 € 169.5 .59 € 138.706.179 04.065.813 € 5 . 25.734 Zurich 41109088 33621206 28961815 215942419 170744087 340768720 North West England 45753804 50896770 145927880 515004806 75121675 335765662 Copenhagen 73769990 164682869 92543328 88041768 282674994 335664046 Victoria State 120000 28945451 9090909 45229090 179625667 323806415 Helsinki 39807357 78483632 91244088 110758540 253621723 290380297 Utrecht 17100000 73070000 29610000 39136363 12432272 250708018 Dubai 72727272 18181816 339590904 207572724 29909088 216427266 South East England 150327463 89624729 298777038 220866434 310227743 213124226 Manchester 11605818 24534422 84584486 271065243 41461781 196806452 Hamburg 91668180 111465907 201363636 302922727 450539088 182593693 Atlanta 0 178186289 55749998 66309090 106927270 174163931 Vilnius 4805363 29187270 1381818 13454544 55224272 152351222 Scotland 138188756 46041314 267387944 110966646 116136726 151646929 Dublin 141543597 138595353 365748485 196941818 262741815 115574191 Milan 11399816 28118178 36420909 68529090 57909544 115441688 Edinburgh 84985667 27340002 180615144 38759374 61329526 111611272 Warsaw 3400636 17001132 32569089 26068180 17401815 109843303 Oxford 39452017 12530725 171354836 93092380 222938180 103249263 Lausanne 2137120 39763635 35771178 54549088 115645149 100870844 Rotterdam 2974090 1499090 145010000 32581818 9895000 93737736 Wales 24671400 34038952 25715018 29646912 83160000 93345202 Lyon 30440180 18420000 37500000 20880000 107464272 92399998 Marseille 7909090 1700000 8100000 26500000 34800000 89600000 North East England 57200419 116651609 76186566 97250000 249162726 86936670 Cardiff 14880000 6657000 15273818 17928000 14700000 85900567 Seoul 3999999 33111140 27090908 55964244 33090905 85281816 Vienna 19695452 64749998 52388181 77786362 71854545 81450000 Lisbon 2607725 2942107 8045453 7090908 24427271 78727271 Yorkshire and the Humber 29831878 29582085 47527111 51442253 104840362 75981350 Odense 2518181 1333333 7968164 3306060 67114665 72203633 Tallinn 9844999 42845108 25875453 17580363 186529086 69419086 Leiden 8200000 0 300000 15472727 41300000 66000000 Delft 2200000 0 13250000 3450000 16181818 64640908 Oslo 16009090 510011 49152253 49663537 95089811 64187316 Aarhus 272727 3513333 7800000 4830033 48900000 59000000 Northern Ireland 3901817 5099062 16370000 14382385 22215347 56792486 Ghent 2000000 0 7945454 11000000 0 56650000 Espoo 20454544 12253725 19360000 44709090 62985090 54396988 South West England 132542842 126670636 151606407 237426943 289160600 52704107 Eindhoven 31067726 6322727 33563635 60094181 9412000 51012426 Nantes 2181818 25709090 17610000 54270000 14968181 46818181 Budapest 6227725 17457724 10212181 14062362 70792452 46067617 Moscow 39470710 103847270 53081817 544529084 81169621 42909088 Bristol 50189664 20717054 56976000 134534056 253239489 41414292 Kraków 13694543 32405453 40125452 20699089 1742090 36204543 Bordeaux 995454 1042272 3800000 35577272 20900000 32200000 Frankfurt 18181817 13800000 20500000 30300000 31522726 28390908 Valencia 3354545 2035909 1852000 4599540 4160999 27798691 Lille 15000000 13995454 16890909 19100000 19650000 27390909 Cologne 13849999 29340907 33627272 36000000 80903634 24290000 Gothenburg 8850054 4909090 10300027 23185599 48248824 22501949 ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 23 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 Sheffield 3979998 1191102 5721563 11008981 5280000 22403635 Antwerp 0 15109089 23280000 19900000 3930000 21381817 Porto Metropolitan Area 1400000 1854544 28403636 2264545 7459000 21369943 Rennes 0 4000000 4900000 21366666 17400000 21270000 Toulouse 1181818 26715000 3600000 29850000 20530000 21000000 Belfast 3118181 4500072 5430000 14298385 4474544 20792486 Reading 18599997 5168556 13454544 43805200 7090909 19596056 Düsseldorf 14545454 22849090 0 18400000 28000000 18500000 Newcastle upon Tyne 10931709 2701320 33963439 3120000 37197272 18023633 Malmö 3768464 48484022 20433844 58377337 43376434 17978744 Riga 4538326 3185454 7324090 5036362 6480907 17654277 East Midlands 35609054 30425089 46302545 127334000 19977360 16373137 West Midlands 15167038 17678921 86232726 35632581 47351817 15962726 Kiev 11999998 2563634 4763591 656363 11429090 15587269 Strasbourg 1000000 2900000 6700000 1000000 2910000 15000000 Brighton 21390000 33107999 9923636 3245466 47528727 14738909 Dundee 4545454 1552800 0 2400000 0 13861817 Birmingham 5863150 5078921 13323636 14112581 21373636 13020000 Sofia 4500800 10705957 10999976 10051256 12654545 12880502 Nice 4300000 448572 5200000 4900000 8500000 11738090 Nottingham 35365454 21183272 31638545 10680000 16977360 10722886 Glasgow 11553090 5960432 76332800 22739272 38450000 10111358 Norwich 272727 7846188 0 199440 12640181 9151319 Bratislava 967181 5609090 3772727 0 12300000 8500000 Liverpool 14015892 3600000 1740000 12216000 13165454 6731989 Rome 5947126 4089999 7147272 5689000 5720000 6678758 Brussels 1600000 25000000 3740400 2000000 2127272 6618180 Prague 6590908 7788090 645453 0 2000000 6207953 Zagreb 4160909 36363 2500000 0 2074544 5836238 Leeds 1451880 20583855 12108000 9000000 72464362 5697090 Athens 200000 295454 1650000 1800000 21131817 5614042 Istanbul 2727271 14492268 13599997 24754542 22115450 5427269 Oulu 10529543 10094544 20370000 33599999 43086725 4620241 Wrocław 0 518181 1039089 5729545 2227272 3840908 Bucharest 0 1454545 750000 34652727 139535909 3670000 Redruth 0 0 0 0 1200000 3491094 The Hague 0 418181818 11250000 500000 3400000 3350000 Dortmund 3000000 10272727 181818 1090909 4000000 2000000 Minsk 18181 0 0 7713635 14999997 1727271 Bournemouth 240000 0 0 456000 0 1700000 Middlesbrough 0 144000 0 0 3600000 1636363 Leicester 0 2423636 14640000 60000000 0 1553888 Stuttgart 0 1000000 1487272 0 6500000 1000000 Poznań 350000 540909 11000000 8409088 670000 763908 Southampton 1636363 0 240000 8308000 6817200 720000 Newcastle-under-Lyme 0 0 0 0 0 563636 Braga 0 390000 400000 300000 227272 500000 Belgrade 368181 330000 1699999 1437801 2732000 499999 Cork 1550000 1090909 2700000 2200000 7100000 263636 Gdynia 0 300000 464000 240909 1090909 227272 Groningen 0 11272727 10580000 5015000 500000 200000 Luxembourg City 2272727 9090909 19581818 31900000 14545454 0 Geneva 1000000 0 48443181 0 0 0 Sarajevo 0 0 0 0 0 0 Brno 100000 100000 1200000 0 0 0 Nijmegen 0 9580454 0 0 50400000 0 Exeter 12816000 7392000 8760000 410052 10200000 0 Hull 0 607128 0 420000 240000 0 Ipswich 400800 0 0 1200000 0 0 Plymouth 2727272 6363636 1818181 0 0 0 Sunderland 1256590 10909090 24772727 300000 8640000 0 Truro 0 0 538428 0 0 0 Worcester 600000 0 0 0 19200000 0 Malvern 0 0 0 0 0 0 Gdańsk 360000 1522909 130000 700000 666157 0 Bialystok 0 0 0 0 0 0 ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 24 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 in 2019 (Tab. 1), and represent the first two choices of start-up founders in terms of location (Atomico, 2019, 206). However, their digital innovation environments are entirely different. Silicon Allee Unlike the rest of Germany, Berlin’s core economy has always been that of services rather than industry. Histori- cally, the creative sector and urban culture have always been strong economic and selling points. The environ- ment and allure of the city form a significant basis for an innovation environment. Even though Munich is a more mature and established tech-hub in Germany (Deloitte, 2018), Berlin is more dynamic with the highest growth (13%) (Börsch, 2019) and highest investment in start-ups (Atomico, 2019). Moreover, the local innovation environ- ment benefits hugely from Berlin’s established academic environment, as it boasts the highest number of students in science, technology, engineering and mathematics (STEM) in Germany (Börsch, 2019; Deloitte, 2018). In addition to world-class universities, Berlin’s innovation environment also benefits from the suitable institutional support re- ceived in the form of grants by the Investitionsbank Berlin, which ‘actively contributes towards developing Berlin as a hub for business and industry’ (IBB, n.d.). Finally, its innovation ecology is heavily supported by German cor- porations through the creation of their own VC funds and accelerators, such as DB StartupXpress (Deutche Bahn; dbmindbox.com); the famous Beyond1435 (beyond1435. com), which is a collaboration of LBA Group, Bombardier Transportation, Deutsche Bahn AG, Siemens AG, Swiss Federal Railways and TUI Group (Hatzfeld, 2017), or the Bayer CoLaborator with state-of-the-art research facilities and infrastructure (colaborator.bayer.com). EDCi (2016) cited startuphubs.eu, which reported 171,000 start-ups employing 667,000 people in 2016. The example of Berlin represents a unicum on the world stage in terms of its buzz, and therefore, one of the most desirable locations for the young creative class. Its colourful history has always attracted entrepreneurial people, artists and creatives. Its post-World War II crisis and the subsequent transformation of East Berlin created unique conditions economically and spatially. Aban- doned housing, industrial estates and dysfunctional economy were the preconditions for bottom-up inter- ventions that created a lively non-institutional scene of urban squats, avant-garde artists and unfettered culture. Such an urban environment created a unique urban buzz that is difficult to recreate institutionally and is perfect for the creative class. Areas such as Kreuzberg and Neuköln, with numerous hipster cafes and night clubs; Mitte, with its chick boutiques, and the rugged Friedrichshain highlight the intriguing urban culture of Berlin. From cult night clubs, such as the Berghain across the cultural melting pots of Tacheles and Kopi art squats, to more institutionalised and development- oriented Holzmarkt, Berlin represents an immense resource for young creatives and a fertile ground for innovation (Phillips, 2016; Moeller, 2016). The creative class started to see opportunities in these areas in the 1990s. The start of digital innovation in Berlin can be pinned to 1999 when the Samwer brothers sold their internet start-up Alando, modelled after eBay, to eBay for 34 million USD (Moeller, 2016; Olarinoye, 2020). One of the main start-up clusters is located around Torstrasse at the edge of Prenzlauer Berg. In popular culture, this area has been nicknamed Silicon Allee with innovation con- notations. Support and co-working hubs, such as Silicon Allee Campus and Google-backed Factory, are located here, both of which provide an ecosystem of services and infrastructure for start-ups (EDCi, 2016). In popular cul- ture, the term Silicon Allee is frequently used (Freedman, 2020; Hanford, 2019; Adams, 2016); however, Phillips (2016) called it a misnomer as it is difficult to pin down the production of digital innovation in one part of Berlin. Moeller (2016) conceptualised four digital innovation clusters based on the classical requirements for digital in- novation: urban and cultural amenities for young creative class and rent prices and available housing stock for re- appropriation. Their study results revealed two main clus- ters: the above-mentioned area of Silicon Allee focusing around Torrstrasse and the fashionable Kreuz-Köln, west of Görlitzer Park. Furthermore, they identified two additional clusters: around Boxhagener Platz and the Kürfürsterdamm area in West Berlin (Moeller, 2016, 19, Figure 4). A combination of Berlin’s fertile environment of ameni- ties and nightlife with the institutional backing of global German companies and STEM students from universities is a potent mix of conditions for digital innovation to prosper. Silicon Roundabout A complete opposite in terms of structure, funding and institutional support to Berlin’s innovation environ- ment is London. If Berlin represents an institutionally well-backed approach, London’s laissez-faire tradition led to an organically grown innovation environment. A major benefit to the start-up economy is the UK’s low- est administrative barriers to entrepreneurship globally (OECD, 2013, 37, Figure 11). Throughout the metrics in the Atomico (2019) report, London ranks first amongst the European innovation hubs. The huge pull of London is supported by its neo-liberal diversified economy based on Thatcher’s deregulation of the banking sector, which provides a strong footing to fin-tech start-ups (digital solu- tions for the financial sector). EDCi (2016) cited Startup Hubs Europe, which reports 275,000 companies employ- ing approximately 1.5 million people. One of the most recognized clusters with a complete ecosystem for start-ups is the Old Street area, popularly called the Silicon Roundabout, and after its success, re- branded into Tech City (Nathan et al., 2019). Old Streets’ morphogenesis is based on economic decline and spatial ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 25 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 degradation. It is layered upon the deindustrialization process of the 1980s, when vacant halls created a fertile ground for lively business services, loft renovations and a beginning of the creative, new media and art scene, which came to full fruition in the 1990s (Nathan & Vandore, 2014). This gave the area its feel and ‘buzz’—a perfect breeding ground for networking, knowledge spillovers and young people interested in experiencing urban life and creating a ‘bounded milieu’ of the innovation ecosystem (Nathan & Vandore, 2014, 2292). A suitable microcosm under this effect is the Shoreditch area with numerous cafes, bars, fixie bike shops, barber shops and an organic market—all fuelled by the local creative economy of information and communication technology (ICT) start-ups and seed funds investing heavily in innovation. Even though London is home to the world’s top uni- versities, such as Imperial College and UCL, there is an extremely high cost of ICT talent, which indicates a weak connection between the university environment and STEM jobs in London (Nathan & Vandore, 2014; EDCi, 2016). Additionally, ‘many of the “traditional” key actors in high- tech clusters, such as major employers, activist universities, or “Triple Helix” activity are absent’ (Nathan & Vandore, 2014, 2295). The authors also reported that compared to the US, UK VCs are considerably more cautious and that local entrepreneurs lack seniority and experience. Despite these detriments, London is still the foremost digital inno- vation hotspot in Europe. Its status as a global metropolis compensates through the sheer force of global connection volume that translates into opportunity. Comparison of the two models Both London and Berlin represent the two most important hubs for digital innovation in Europe. They have built upon a vibrant creative community and urban culture developed in both cities. Lively and rich art and night scene on one hand and good connections to capital and corporations on the other form a fertile ground for the creative class, which searches for big city experience and a productive environment for progressive start-ups. However, the similarities end there. The London model is based solely on the neo-liberal tradition and the laissez-faire market, which can only exist in global city centres of command and control. It is an example of regional geography where trans-national corporations, primarily in banking (connected to other global hubs), sustain opportunities and the innovation environment itself. Even though Silicon Roundabout ecology is not connected to London’s university sector, it is the most successful innovation cluster in Europe. In terms of the triple helix concept, the question is how the London example measures up against the three identified criteria. Clearly, the industry supports well and gives opportunities to spin-offs and start-ups; however, as indicated by Nathan and Vandore (2014), these do not connect well with the university environment. In terms of policy support, liberal policies regarding taxation and non-bureaucratic procedures for company creation contribute substantially to the innovation environment as such. However, subsequent attempts at managing and supporting digital innovation by the government of David Cameron have been criticised (Nathan, 2011). This suggests that the policy segment is important in countries where such policies are more constrictive. Moreover, not all dimensions of the triple helix need to be equally represented for digital innovation to be viable. In terms of digital innovation requirements, London meets all criteria. It has a great collaboration environment with co-working spaces, accelerator environments and whole innovation ecosystems, such as the Shoreditch area. Data access is one of the most relaxed in Europe, and with its succession from the EU, this can be further improved (however to the detriment of final users). In contrast, Berlin is an example where the local insti- tutions and German corporations provide strong support, primarily due to the long tradition of industries and their loyalty. However, their regional affiliation needs to be envisioned in global terms, which means that Berlin’s in- novation ecology cannot exist without the global connec- tion of its corporations. It is, as Micelli and Sachetti (2014) illustrated through an industrial model, a bounded local geography where small producers can only exist if there is a globally connected company as an ‘interface between the local territory and global market’ (Micelli & Sachetti, 2014, 85). Through the same mechanism, Bayern or Sie- mens in Berlin are obliged to support the local innovation environment if they want to continue producing value. In terms of the triple helix conditions, Berlin’s digital innovation has a different disposition. First, there is a wealthy presence of global corporations; second, these corporations are well connected to the universities, cre- ating incubators and appropriate STEM student supply. Third, although companies are subjected to more stringent polices and administration requirements, suitable policy incentives are provided by Berlin banks (IBB, n.d.) and the municipality through active development policies, such as the ‘be Berlin’ campaign (Phillips, 2016). In terms of digital innovation prerequisites, further improvement is required. There is a general concern that the EU General Data Protection Regulation (GDPR) will impede digital innovation (Martin et al., 2019). On the other hand, Berlin contains numerous co-working and accelerator spaces that support collaborative work; furthermore, its social and cultural infrastructure for young creatives is unrivalled. DISCUSSION: DIGITAL INNOVATION IN PERIPHERY After reviewing the case studies through the identi- fied criteria, we analyse whether the models can be transferred to peripheral regions. For this purpose, we will confront the conclusions obtained from the tipple helix review with slow innovation requirements. Wherever we ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 26 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 identify problems, other qualities of peripheral innova- tion will be discussed. Discussion of the two models has already shown that different pathways to achieving digital innovation in cities are possible. Triple helix and peripheral innovation For the triple helix requirement, no particular con- stellation of drivers and local conditions is preferred; however, all three (global industries, applied research in universities and supportive policies) need to be present. In London, the university segment is lacking, but the open market policies are much more favourable to start-ups and SMEs. The latter is heavily taxed in Berlin, but indus- trial support in the form of infrastructure and government loans, as well as an appropriate STEM student market, will fill this gap. In both cases, global networks and fertile innovation ecosystems with suitable amenities and infra- structure are well developed. In the following paragraphs, we discuss each criterion in more detail. Global industries’ criteria seem the most problematic for peripheral regions to achieve adequately. Background research on the development and structure of the EU (ESPON, 2005) has shown a significant lack of peripheral regions in the global reach of their companies. City cores in such regions neither readily house a lively banking sector like London nor feature a pool of native industrial giants like Berlin. However, slow innovation suggests implementing the first stage of innovation (idea creation), which is less dependent on the global infrastructure. In addition, new research focusing specifically on innovation in peripheral regions (Eder & Trippl, 2019) criticises the classical innova- tion concepts, as they were developed for core regions. They suggest a set of responses available to peripheral regions, such as establishing branch offices in global centres or branding techniques for ‘soft locational factors’ emphasizing the natural and other qualities of the environment ‘far from urban congestion’ (Eder & Trippl, 2019, 1515). In addition, the SMESTO literature puts the network of small towns on par with larger towns in terms of job density per inhabitant (Bole et al., 2016), where this network can be considered a ‘bounded milieu’ for innovation. Finally, taking the example of Berlin’s locally imbedded companies in connection to Micelli and Sacchetti’s (2014) concept of a company as an interface between local producers and the global market, the possibilities for digital innovation in peripheral regions can be significantly expanded. We can adduce a few exam- ples from the local environment, such as the pharmaceutical company Lek in Ljubljana, the avionics company Pipistrel in Ajdovščina or the diversified technology company Kolektor in Idrija. All the above-mentioned companies can act as interfaces to the global market for the local network of slow innovation that is bound within the SMESTO network. In addition to these regional solutions, level-4 MEGAs exhibit strong cultural tourism, and the environment of their city cores offers new research potential into tourism and innova- tion infrastructure synergies. In terms of the second criterion of connecting industry to marketable university research, the situation is rather positive. Barra et al. (2019) reported that the second- tier universities, rather than the first tier, produce more marketable knowledge spillovers useful to the industry. This, coupled with the slow innovation concept to focus on technology and basic knowledge of research, yields a strong foundation for applied research in such regions in the segment of digital innovation. However, Bonac- corsi (2017) cautioned that only supporting institutional innovation in universities is problematic, as it yields lim- ited success. Marques et al. (2019) indicated that in less-developed regions, universities can play a catalytic role in development of the region, but only with well- established innovation departments. However, the case studies related to the triple helix requirement indicate that London compensates in this segment with its global pull. The unique regional knowledge embedded locally and supported by slow innovation can be one such com- pensation in peripheral regions. Last, all MEAG-3 and MEGA-4 cities have well-established universities and re- search institutes, such as the local environment example of Chemical Institute in Ljubljana and the Institut Jozef Stefan focusing on applied physics and mathematics. In terms of the triple helix criterion—the support of government policies—Kozina & Bole (2018) indicated that strategic policies can help steer the spatial patterns of innovation in a favourable direction. This is supported in the two case studies where policy played a vital role. In London’s example, the liberal policies with low barriers to entrepreneurship (e.g. low VAT taxes and the simplic- ity of creating a company) are beneficial. However, in Germany, due to the more stringent economic policies, the environment compensates with suitable incentives for SMEs and start-ups, as well as a well-established support environment provided by the local industry. These exam- ples suggest that in peripheral regions, policymakers need to recognise the importance of local producers and create tailored policies to that effect. Adapting local level policies is easier than national level. In contrast, the problem of constricting policies regarding data, such as the EU’s newly adopted GDPR, can be problematic for digital innovation. Martin et al. (2019) reported that the GDPR is a two-edged sword for data-dependant start-ups. On one hand, it sup- ports innovation in technologies for GDPR compliance or exploitation and suggests a higher possibility for the end-user to buy European digital products, due to security assurance. On the other hand, for the start-ups themselves, there might be a significant product abandonment, espe- cially in the segment where data are used without clear benefit to the end-user. Even if the effect is mixed, the GDPR places additional administrative barriers on the ‘lean start-up’, on which the majority of digital innovation is based. Peripheral regions might have an upper hand due to favourable EU cohesion policies, which can enable local actors to ‘exploit different innovation benefits encountered in peripheral regions’ (Eder & Trippl, 2019, 1526). ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 27 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 CONCLUSION As part of global restructuring at the turn of the millen- nium, when a significant part of the P&A segment moved to developing countries, the peripheral regions in the EU sought strategies that could better support their trans- formation. To consider the diversification of economic dependencies, we reviewed the possibility of digital in- novation as an additional driver for these regions. This paper presents two global examples of London and Berlin and their digital innovation ecosystems as case studies. As a direct translation of these models to periph- eral regions is not possible, it is achieved through the triple helix of innovation concept, which is interpreted through the slow innovation principles. In classical literature of regional innovation systems, innovation as such follows the Marshallian concept of industrial agglomerations; however, unlike traditional industries in the secondary sector, which require large areas to operate, digital innovation is compact, and thus, a great mechanism for densification and diversification. Global cities yield suitable evidence for micro-clustering in the digital sector, as the need for communication, connection and collaboration is very important (Nathan & Vandore, 2014). In addition, the classical literature proposes innovation requirements, such as connection to global markets, buzz, knowledge spillovers, managerial know-how and VC, all of which are scarce in peripheral regions. Digital innovation further requires suitable ac- cess to data related to policies and infrastructure, as well as suitable infrastructure for collaboration. The digital innovation examples of London and Berlin are unique; thus, they cannot be directly replicated in peripheral regions. There are two main problems: lack of global industry with access to global markets and lack of the ‘buzz’ generated in these unique locations, which contributes to the much-needed knowledge spillovers that foster innovation. However, a review of the two mod- els provides concrete conclusions for peripheral regions. The two examples show that there are various path- ways to digital innovation; if one segment is strong, the other can be compensated. Berlin achieves this through suitable industrial presence of locally embedded native corporations, collaborative infrastructure and strong con- nection to the university environment, where the policy aspect is quite constricted. In contrast, London has a weak connection to the university environment, which is compensated through liberal economic policies. The classical RIS literature lists the presence of global corporations as the most difficult triple helix require- ment for peripheral regions. Here, geographic proximity favours the key innovation concepts, such as knowledge spillovers, urban buzz, VC and access to global markets. Both studied examples are well embedded in the world economy with numerous global corporations. However, innovation in peripheral regions works differently, and geographic proximities and global access can be achieved in different ways, such as branch offices, through a net- worked concept of SMESTO, or through an imbedded local company with global connections. In contrast, peripheral innovation substitutes some concepts. For example, the concept of urban buzz is replaced by the quality of a good natural environment, quietness and nature, which carry more value in contemporary times. Otherwise, research into touristic infrastructure to carry some functions of central cities can be investigated. In terms of the connection of the applied university research to industry, the conditions are rather beneficial for peripheral regions, as their centres house second- tier universities, which more readily create marketable innovation than the first-tier ones. The niche market for these regions is in digital innovation, which takes local knowledge as the basis and focuses on technical or basic knowledge rather than knowledge that is fashionable and whose value can quickly decline. In terms of government policies supporting start-ups and SMEs, EU-based innovation firms will always be at a disadvantage. However, the arguments of slow innovation suggest that peripheral regions have a specific advantage in nimble and quickly tailored local policies. If a regional or local administration recognises the significance of a local company, they can tailor the policies to suit their needs. In terms of a specific digital innovation policy, a major hurdle is the GDPR. In conclusion, we can speculate on the initial ques- tion. Do peripheral regions represent viable hubs for digital innovation ecosystems despite their less favour- able global position? The proposed substitution of core innovation concepts with slow innovation concepts makes digital innova- tion viable in peripheral regions. The SMESTO concept can even be considered a networked condition for the innovation ecosystem. There is no reason why digital in- novation cannot represent a parallel development driver to approaches such as cultural tourism and industrial culture. However, it will never represent a core activity of peripheral regions; it is difficult to imagine concepts such as quiet and beautiful nature replacing the allure of a vibrant city, or an annual institutionalised network- ing conference replacing knowledge spillovers in global centres overflowing with young talent. The density of connections to the global markets is a difficult resource to substitute in the innovation environment. However, digital innovation can and should augment peripheral regions to diversify their economic base, especially under the persistent global crisis. ANNALES · Ser. hist. sociol. · 31 · 2021 · 1 28 Tomaž PIPAN: DIGITAL INNOVATION: WHAT CAN PERIPHERY LEARN FROM GLOBAL CENTRES?, 17–30 DIGITALNA INOVACIJA: KAJ SE LAHKO OBROBJE NAUČI OD GLOBALNIH CENTROV? Tomaž PIPAN Univerza v Ljubljani, Biotehniška fakulteta, Oddelek za krajinsko arhitekturo, Jamnikarjeva ulica 101, 1000 Ljubljana, Slovenija e-mail: tomaz.pipan@bf.uni-lj.si POVZETEK Globalno industrijsko prestrukturiranje je eden glavnih razlogov za stanje in obliko sodobnih evropskih mest. Ena največjih težav obrobnih regij Evrope je izguba industrijskega segmenta proizvodnje in izdelave (angl. process and assembly). Dva pristopa, turizem in visokotehnološke inovacije sta v ospredju prenove v postindustrijski Evropi. Ker je turizem dobro uveljavljeno gonilo razvoja, se sprašujemo ali je digitalna inovacija, kot podskupina visokotehnoloških inovacij, lahko vzporedno gonilo za doseganje odpornejšega in bolj uravnovešenega razvoja obrobnih regij. Članek opisuje dva modela digitalne inovacije londonskega in berlinskega, z namenom razumevanja njune primernosti za razvoj obrobnih regij. Ker so modeli globalnih mest stežka uporabni za obrobja, članek ugotavlja njuno uporabnost skozi koncept trojne vijačnice inovacij (angl. triple helix of innovation), ki govori o povezavi akademskega raziskovanja, produkcijske zmogljivosti in globalne vpetosti industrije ter podpornih politik. Dodatno članek naslavlja vprašanje inovacije v luči koncepta t.i. počasne inovacije (angl. slow innovation), ki je primernejši za obrobne regije. Prvi sklep članka ugotavlja, da je pogoje za digitalno inovacijo moč doseči z različnimi razmerji elementov trojne vijačnice. Berlinski digitalni grozd je dobro podprt z velikim številom študentov na področju informatike ter odličnim podpornim okoljem nemških korporacij, a ima visoke davke in administrativno zahtevno okolje za nova podjetja. Na drugi strani ima londonski digitalni grozd šibko vez z akademijo, ki jo kompenzira z liberalno tržnimi politikami Velike Britanije in močno globalno vpetostjo Londona. Drugi sklep članka pa ugotavlja, da čeravno digitalna inovacija ni primarno gonilo razvoja obrobnih regij, je lahko pomembno gonilo, v kolikor se jo razume skozi koncept počasne inovacije. Tako je problem šibke globalne vpeto- sti obrobnih regij lahko naslovljen z dinamiko mreže malih in srednjih mest ali s pomembnim lokalnim podjetjem, ki ima dobro globalno povezanost. Aplikativno akademsko raziskovanje je v obrobnih regijah bolje zastopano kot v centralnih, saj so prav univerze na obrobju dosti bolj aktivne na področju patentov. Obenem je za počasno inovacijo pomembno osredotočanje na lokalno specifično inovacijo na področju tehničnih ali bazičnih znanj in ne na znanje katerega vrednost lahko hitro upade. Kar se tiče politik imajo lahko obrobne regije posebno prednost v gibčnih in hitro prilagodljivih lokalnih politikah. Če regionalna ali lokalna uprava prepozna pomen lokalnega podjetja, lahko politike prilagodi dejanskim potrebam. Predlaga- na zamenjava temeljnih inovacijskih konceptov s počasnimi inovacijskimi koncepti omogoča razumevanje digitalne inovacije v obrobnih regijah. Digitalna inovacija bi morala zavzeti večjo vlogo v razvoju obrobnih regij, s čimer bi regija zagotovila večjo razpršenost gospodarstva, zlasti v času, ko so krize in ekonomska nihanja postali stalnica. 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