Technological Capabilities and Patterns of Innovative ...

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Technological Capabilities and Patterns of Innovative Cooperation of Firms in the UK Regions Simona Iammarino

a c

b

, Mariacristina Piva , Marco Vivarelli

b c d

& Nick Von Tunzelmann

c a

Department of Geography & Environment, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, UK b

Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, I-29122, Piacenza, Italy E-mail: c

Science and Technology Policy Research (SPRU), The Freeman Centre, University of Sussex, Falmer, Brighton, BN1 9QE, UK E-mail: d

Institute for the Study of Labour, Forschunginstitut zur Zukunft der Arbeit (IZA), Schaumburg-Lippe-Str. 5–9, D-53113, Bonn, Germany Version of record first published: 24 May 2012.

To cite this article: Simona Iammarino, Mariacristina Piva, Marco Vivarelli & Nick Von Tunzelmann (2012): Technological Capabilities and Patterns of Innovative Cooperation of Firms in the UK Regions, Regional Studies, 46:10, 1283-1301 To link to this article: http://dx.doi.org/10.1080/00343404.2012.679259

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Regional Studies, Vol. 46.10, pp. 1283–1301, November 2012

Technological Capabilities and Patterns of Innovative Cooperation of Firms in the UK Regions

Downloaded by [Universita Cattolica del Sacro Cuore] at 01:11 26 November 2012

SIMONA IAMMARINO*‡, MARIACRISTINA PIVA†, MARCO VIVARELLI†‡§ and NICK VON TUNZELMANN‡

*Department of Geography & Environment, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, UK. Email: [email protected] †Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, I-29122 Piacenza, Italy. Emails: [email protected] and [email protected] ‡Science and Technology Policy Research (SPRU), The Freeman Centre, University of Sussex, Falmer, Brighton BN1 9QE, UK. Email: [email protected] §Institute for the Study of Labour, Forschunginstitut zur Zukunft der Arbeit (IZA), Schaumburg-Lippe-Str. 5–9, D-53113 Bonn, Germany (Received August 2009: in revised form March 2012) IAMMARINO S., PIVA M., VIVARELLI M. and VON TUNZELMANN N. Technological capabilities and patterns of innovative cooperation of firms in the UK regions, Regional Studies. This paper focuses on the relationship between firms’ technological capabilities and different forms of cooperation for innovation, paying specific attention to the role of regional location of the firm. The findings, based on the Fourth UK Community Innovation Survey (CIS4), show that cooperation within business groups, vertical cooperation with customers and suppliers, and horizontal cooperation with universities turn out to be significantly associated with firms’ technological capabilities. However, it is found that the analysis for the UK as a whole masks stark regional differences in terms of the relationship between local and non-local collaborative linkages and firms’ technological capabilities and competences. Innovative cooperation

Technological capabilities

Community Innovation Survey (CIS)

UK regions

IAMMARINO S., PIVA M., VIVARELLI M. and VON TUNZELMANN N. 英国区域中企业创新合作的技术能力与模式，区域研究。本论文聚焦企业技术能力以及追求创新的不同合作形态之间的关联性，并特别关注企业区域区位的作用。根据英国第四区域创新调查(CIS4)的研究发现，商业团体内部的合作、客户与供货商的垂直合作、以及与大学之间的水平合作，证明与企业的技术能力有着显著的关联性。但研究也发现，将英国视为分析的整体，掩盖了地方和非地方合作关系、以及企业技术能力和胜任性之间存在的显著区域差异。创新合作

技术能力

区域创新调查(CIS)

英国区域

IAMMARINO S., PIVA M., VIVARELLI M. et VON TUNZELMANN N. Les capacités technologiques et la structure des projets de coopération de nature novatrice des entreprises situées dans les régions du Royaume-Uni, Regional Studies. Cet article porte sur le rapport entre les capacités technologiques des entreprises et diverses formes de coopération de nature novatrice, prêtant une attention particulière au rôle de la localisation régionale de l’entreprise. Les résultats, basés sur la quatrième enquête communautaire sur l’innovation (ECI 4) montrent que la coopération au sein des groupes d’entreprises, la coopération verticale avec les clients et les fournisseurs, et la coopération horizontale avec les universités s’avèrent en corrélation étroite avec les capacités technologiques des entreprises. Cependant, il s’avère aussi que l’analyse du Royaume–Uni dans son ensemble apporte des différences régionales frappantes en termes du rapport entre les liens de collaboration locaux et non locaux et les capacités et les compétences technologiques des entreprises. Coopération de nature novatrice Régions du Royaume-Uni

Capacités technologiques

Enquête communautaire sur l’innovation (ECI)

IAMMARINO S., PIVA M., VIVARELLI M. und VON TUNZELMANN N. Technische Fähigkeiten und Muster der innovativen Zusammenarbeit von Firmen in britischen Regionen, Regional Studies. Im Mittelpunkt dieses Beitrags steht die Beziehung zwischen den technischen Fähigkeiten von Firmen und den verschiedenen Formen der Zusammenarbeit zur Innovation unter besonderer Berücksichtigung der Rolle des regionalen Standorts der Firma. Aus den Ergebnissen, die auf dem vierten Community Innovation Survey (CIS4) in Großbritannien beruhen, geht hervor, dass eine Zusammenarbeit innerhalb von 0034-3404 print/1360-0591 online/12/101283-19 © 2012 Regional Studies Association http://www.regionalstudies.org

http://dx.doi.org/10.1080/00343404.2012.679259

Simona Iammarino et al.

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Unternehmensgruppen, eine vertikale Zusammenarbeit mit Kunden und Lieferanten sowie eine horizontale Zusammenarbeit mit Universitäten in signifikantem Ausmaß mit den technischen Fähigkeiten der Firmen zusammenhängen. Allerdings stellen wir fest, dass die Analyse von Großbritannien als Ganzem starke regionale Unterschiede hinsichtlich der Beziehung zwischen lokalen und nicht-lokalen kooperativen Verbindungen und den technischen Fähigkeiten und Kompetenzen der Firmen verdeckt. Innovative Zusammenarbeit

Technische Fähigkeiten

Community Innovation Survey (CIS)

Britische Regionen


IAMMARINO S., PIVA M., VIVARELLI M. y VON TUNZELMANN N. Capacidades y modelos tecnológicos de la cooperación innovadora de empresas en las regiones británicas, Regional Studies. En este artículo analizamos la relación entre las capacidades tecnológicas de las empresas y las diferentes formas de cooperación para la innovación, prestando atención especial al papel de la ubicación regional de las empresas. Los resultados, basados en la cuarta encuesta comunitaria sobre innovación (CIS4) en el Reino Unido, indican que la cooperación entre los grupos comerciales, la cooperación vertical con clientes y proveedores, y la cooperación horizontal con las universidades están relacionadas en gran medida con las capacidades tecnológicas de las empresas. Sin embargo, observamos que el análisis para el Reino Unido en su conjunto oculta fuertes diferencias regionales en lo que se refiere a la relación entre los vínculos colaboradores locales y no locales y las capacidades y competencias tecnológicas de las empresas. Cooperación innovadora

Capacidades tecnológicas

Encuesta comunitaria sobre innovación

Regiones británicas

JEL classifications: O30, O31, R11

INTRODUCTION: TECHNOLOGICAL CAPABILITIES, FIRMS AND REGIONS The evolutionary approach to technical change considers technological capabilities as the outcome of in-house technological competences and of complex interactions among individuals, firms, and organizations within a specific socio-economic and institutional environment. Among these interactions, a crucial role is played by the cooperation in research and development (R&D) and innovative activities, where such a cooperation can involve other firms in the same sector, suppliers, customers, consultants, and scientific institutions like universities and public laboratories. A shortcoming of most previous studies that have investigated technological capabilities is that the latter are seen at the same time as inputs and outcomes (among others, WESTPHAL et al., 1990; ROMIJN , 1999; WIGNARAJA , 2002). In this paper it is argued that in order to evaluate firms’ technological capabilities, variables related to outcomes such as the introduction of new products or processes are more appropriate. This distinction comes mainly from the differentiation between competences and capabilities introduced by VON TUNZELMANN and WANG (2003). Whilst competences are understood as enhanced inputs to produce goods and services, capabilities generally involve different forms of learning (MALERBA , 1992) and the accumulation of new knowledge, eventually embodied in new products and new processes. In turn, innovative cooperation should play a crucial role in enhancing firms’ competences and capabilities. In particular, since capabilities are to be taken as the results of adaptive learning processes that are sustained through a variety of external connections and sources of innovation (VON TUNZELMANN and WANG , 2003, 2007), innovative cooperation should be more relevant for firms exhibiting technological capabilities in comparison

with those only showing technological competences or – a fortiori – with those non-innovative firms characterized by neither technological competences nor technological capabilities. Hence, the main hypothesis to be tested in this paper is whether there is a positive relationship between cooperation for innovation and firms’ technological competences and capabilities. The analysis, conducted at the firm level, devotes specific attention to the role of the environment of the firm in the form of its geographical location. In fact, the complex interactions involving cooperation for innovation and the development of technological competences and capabilities are likely to be influenced by the institutional setting, the industrial structure and organization, and the network relationships which are peculiar to regional systems of innovation (for example, COOKE , 1992; BRACZYK et al., 1998; EVANGELISTA et al., 2002). The paper is structured in five sections. The following section summarizes the literature on firm-level technological capabilities, innovation linkages and sources external to the firm, emphasizing the relevance of the regional environment as an appropriate dimension to study such relationships. The third section provides an overview of the data, variable selection and construction, and methodology used in the empirical analysis. The fourth section discusses the results at the firm level, taking into account both the overall national context and the specific regional environments. The fifth section offers some concluding remarks and policy implications of the research here carried out.

PREVIOUS LITERATURE AND HYPOTHESES TO BE TESTED Since NELSON and WINTER ’s seminal An Evolutionary Theory of Economic Change (1982), contributions in the area of firm-specific capabilities have proliferated in


Technological Capabilities and Patterns of Innovative Cooperation of Firms in the UK Regions and around resource-based views, evolutionary economics, the economics and history of technical change, strategic management and, more recently, evolutionary economic geography. A crucial distinction between competences and capabilities has been introduced by VON TUNZELMANN and WANG (2003). Competences are understood as stemming from inputs to produce goods and services; in this sense, they are preset attributes of individuals and firms, with the enhancements typically produced either internally to the firm or by a different organization. For example, one may think of a firm’s endowment of adequate skills as the necessary internal competences to obtain value from R&D and innovation investments (PIVA and VIVARELLI , 2009). By the same token, the recruitment of university graduates or the spin-offs from larger innovative firms may be intended as the necessary internal competences for innovative small and medium-sized enterprises and new technology-based firms that want to obtain values from external spillovers (ACS et al., 1994; AUDRETSCH and VIVARELLI , 1994; ARRIGHETTI and VIVARELLI , 1999). Capabilities instead involve both internal and external learning, and accumulation and integration of new knowledge on the part of the firm. Consequently, capabilities are to be taken as the results of adaptive learning processes that, in their collective dimension, can be highly localized, giving rise to ‘system’ capabilities, that is, referring to a specific spatial and industrial setting such as a regional innovation system (VON TUNZELMANN , 2009b; IAMMARINO and MC CANN , 2012). In other words, while technological competences are prerequisites or resources for innovation activity, technological capabilities correspond to knowledge that, through learning and absorption, is ready to be incorporated into new products and processes. For instance, a pharmaceutical firm endowed with an adequate R&D laboratory and performing research on a new vaccine is a firm with technological competences, while a competitor already testing a new vaccine on patients is a firm with technological capabilities. This distinction in terms of real applicability is substantial, both analytically and empirically, though, in reality, many shades of grey are possible between one category and the other. In fact, over the longer-term, competencies can be transformed into enhanced capabilities, while additional capabilities may also ‘feedback’ into more and deeper needs for strengthening or extending the range of competencies. Thus, a company wishing to update itself rather rapidly in an area of technology in which it has little experience but sees as important for its future might acquire the competencies from outside and seek to mesh them somehow with its existing capabilities. This involves complex learning procedures (for example, ‘learning to learn’) which in the view lie at the heart of successful development processes. Learning is linked to different but specifiable sources of knowledge, which may be internal to

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the firm (for example, generated via production activities) or external (for example, transferred from supplier firms, user firms or general advances in science and technology). Indeed, the main extensions to orthodox static notions of capabilities involve both interactive1 and dynamic capabilities. Formally, the interactive dynamic capabilities of firms represent the extent to which the change in their own capabilities influences or is influenced by the change in the capabilities of other external actors, that is, competitors, clients,2 suppliers, universities and public laboratories, etc. (ZOLLO and WINTER , 2002; VON TUNZELMANN and WANG , 2007; VON TUNZELMANN , 2009a). Within these relationships, a focal role is played by formal cooperation in R&D and other innovation activities. It should be stressed that in practice capabilities can be developed in all functions of the firm and not just in ‘technology’ in the narrow sense, for example, in marketing, in organization, in finance, etc. At the end of the process, the vector of firm’s capabilities drives the emergence of a vector of new processes and new products, where the accumulated knowledge is embodied (VON TUNZELMANN and WANG , 2007, p. 198).3 However, the empirical estimation of such complex learning processes would require panel micro-data with detailed characteristics of firms’ internal routines and practises. Such a difficulty is still rarely overcome given the very limited availability of a longitudinal dimension in firm-level innovation surveys such as the Community Innovation Survey (CIS) used in the present study. Therefore, the simplifying assumption is that of a sequential logic between the categories of competencies and capabilities; in other words, the know-how accumulated through actual learning and experience in the production of outputs has to lie beyond mere ‘potential’ – or sensibility of firms to change, thus competencies; hence, capabilities are associated with ‘realizations’ – or the ability of firms to handle change (VON TUNZELMANN , 2009a).4 Particularly important for the purposes are learning processes external to the firm that need to be based on the firm’s ‘absorptive capacity’ (COHEN and LEVINTHAL , 1989, 1990). In fact, without learning and absorption processes the influence of interactions on the firm’s competences and capabilities may be held back. In this framework, firms can learn horizontally, that is, from spillovers from other producers and competitors as well as from independent research carried out in universities and research institutes, or vertically, by interacting with upstream suppliers and downstream users. In turn, these different channels of interaction can originate those cooperative innovation activities which are detected by the CIS data used in this study. In terms of the level of analysis, the study of technological capabilities at the micro-level sets the firm at the centre of the investigation (BELL , 1984; BELL and PAVITT , 1995; HOBDAY , 1995). By the same token, there is a rather copious empirical literature on the link


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between different forms of cooperation and innovation performance at the firm level (for example, CASSIMAN and VEUGELERS , 2002; VANHAVERBEKE et al., 2002; CRISCUOLO and HASKEL , 2003; BELDERBOS et al., 2004a; PIGA and VIVARELLI , 2004; LAURSEN and SALTER , 2004, 2006; FAEMS et al., 2005). Yet, no study has been carried out on the relationship between different forms of collaborative linkages and different levels of firms’ technological status. To link these two streams of literature is precisely one of the aims of this paper, in which the analysis is conducted at both the national and the regional levels. The significance of the regional dimension of innovation systems has emerged as the logical consequence of the interactive model of innovation,5 which puts the emphasis on the relations with knowledge sources external to the firm. In fact, the previous literature started from the above discussed microeconomic perspective of the firm – for instance, in investigating the vertical relationships in the form of supplier and buyer linkages – then realizing that these relationships (both vertical and horizontal) had their footholds within a regional system of innovation (for example, CAMAGNI , 1991; FRITSCH , 2000; STERNBERG , 2000). Indeed, cooperative relationships may be strongly influenced by spatial proximity mechanisms that favour processes of polarization and cumulativeness (for example, LUNDVALL , 1988; VON HIPPEL , 1988; COOKE et al., 1997). More recently, the ‘open innovation’ model (for example, CHESBROUGH , 2003; LAURSEN and SALTER , 2006) has complemented the innovation system perspective by reinforcing the view that innovative firms draw knowledge from a variety of external sources and linkages, integrating them into their own routines and learning processes, thus achieving advanced technological capabilities. In this approach, the latest applications of the capabilities framework to regional innovation systems have emphasized that regions can be considered as spatial congregations of actors – that is, suppliers, producers, consumers, etc. – each with its own unique level of capabilities (VON TUNZELMANN , 2009b) and all embedded in a specific regional institutional setting. Yet, at the regional level capabilities cannot be considered merely as the sum of individual firm-level capabilities developed in isolation (LALL , 1992; IAMMARINO et al., 2008; VON TUNZELMANN , 2009b); a region embeds many systemic elements external to the firm that influence its technological capabilities and growth (for example, HOWELLS , 1999; COOKE , 2001; EVANGELISTA et al., 2002; IAMMARINO , 2005). Thus, idiosyncratic regional structural and institutional features, networks and cooperative agreements emerge and have an influence on firms’ R&D and innovative competences and capabilities, eventually resulting into a specific regional innovation pattern (KLEINKNECHT and POOT , 1992; COOKE et al., 1997; HASSINK , 1997).

In this paper, the regional dimension of the relationship between cooperation for innovation and firms’ technological status will be investigated in two ways. On the one hand, it will be able to distinguish between cooperation with local versus non-local partners; on the other hand, the aggregate empirical analysis for the UK will be split into macro-regions, in search for the possible territorial peculiarities that the literature suggests as very likely. In particular, the main hypothesis that will be tested in this study is that most of the different forms of firms’ cooperation in innovative activities should turn out to be important in affecting competences and capabilities at the national level, but that this might not be the case at the more geographically specific regional scale. For instance, vertical cooperation with customers and suppliers may be confirmed as crucial in affecting the national innovative capabilities, though emerging as significant only in those regions where specific agglomerations of the industrial structure facilitate this kind of cooperative agreements. On the other hand, innovative cooperation with universities and public research laboratories may be important only in those regions where the institutional environment has supported a regional innovation system geared at facilitating ‘technology transfer’ from the scientific institutions towards the market (REVILLA DIEZ , 2000). In addition, while at the national level one would expect both local and distant innovative cooperation linkages to turn out as significant, at the regional level it may well happen that the more internationalized areas (like the capital region) come out as more affected by non-local cooperation, while relatively less open regions might be more dependent on geographically specific cooperation linkages. Furthermore, peculiarities of the UK regional development – where the ‘north–south divide’ is still apparent – have to be taken into account. Over the last forty years, technological and structural changes have affected the UK regions in different ways (ROWTHORN , 2000; ROBSON , 2009). The decline of many traditional sectors and primary industries and the flourishing of new services have had different impacts in the various geographical areas. Regions such as Wales and the North of England badly suffered from the decline of coal mining, iron and steel production and have since been driven towards more service-intensive sectors. In contrast, London and the South of England have benefited from their historically strong specialization in knowledge-intensive financial and business services (ROBSON , 2009). Other geographical areas have been able to maintain manufacturing specializations in traditional sectors (for example, ceramic goods and cutlery in the Midlands regions; DEVEREUX et al., 2004), which, due to agglomeration advantages, are still able to face the global competition. Also, territorial differentiation in the degree of openness and internationalization may contribute to explain the variation in the role of local

Technological Capabilities and Patterns of Innovative Cooperation of Firms in the UK Regions versus non-local cooperation in affecting firms’ technological competencies and capabilities. To sum up, the empirical analysis will test the following hypotheses:


Hypothesis 1: At both the national and the regional levels, firms’ technological competences and capabilities should be positively and significantly affected by a specific firm’s characteristics, such as size, being part of a corporate group, degree of internationalization, being a start-up, and endowment of human capital.6 Hypothesis 2: At both the national and the regional levels, vertical innovative cooperation – that is, that with customers and suppliers – is expected to have a stronger positive impact on revealed technological competences and capabilities than horizontal cooperation with competitors. All other forms of cooperation should have a positive influence. Hypothesis 3: At the regional level, the geographical scope of the different forms of cooperation (that is, local versus non-local) is expected to vary substantially, depending on different regional institutional settings and industrial structures. For example, forms of cooperation such as those with universities and public research institutes should emerge as significant in particular when established locally; while non-local cooperation linkages should in general come out as more important in the most open and globalized regions.

DATA AND METHODOLOGY This paper uses data from the UK Innovation Survey 2005 (as part of the fourth iteration of the wider Community Innovation Survey – CIS4 – covering European Union countries), which refer to the period 2002–2004. The survey sampled over 28 000 UK enterprises with ten or more employees, had a wide sectoral coverage including both manufacturing and service sectors, and was stratified by the Government Office Region in England, along with Scotland, Wales and Northern Ireland. The final representative sample consists of 16 445 firms. The CIS questionnaire draws on a long tradition of research on innovation. Being based on the Oslo Manual, the CIS is specifically directed to investigate the innovative phenomenon at the firm-level, and has been designed to capture the systemic nature of innovation activities between firms and the environment in which they operate (ARCHIBUGI and PIANTA , 1996; EVANGELISTA et al., 2002; ORGANISATION FOR ECONOMIC CO-OPERATION and DEVELOPMENT (OECD), 2005; LAURSEN and SALTER , 2006). As all the available micro-data sources, the CIS has advantages and disadvantages. Among its advantages, beyond the reliability and validity of sampling procedures, the CIS is the sole database providing detailed information about different sources of innovation (including cooperation between actors), different outputs of innovation (product and process innovation), as well as a firm’s characteristics which have to be controlled for in micro-econometric studies. For instance,

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empirical specifications may control for the Schumpeterian hypothesis stating that large firms have an advantage in innovative activities (that is, firm size can be controlled for through the number of employees, scale economies through belonging to a corporate group, etc.). On the other hand, CIS data have also some limitations. First, they exclude micro-firms (with fewer than ten employees) and although the sample is stratified in terms of size classes, respondents are generally biased towards the large scale (see Table 2 where the average firm’s size turns out to be 276 employees). Therefore, results from CIS studies cannot be easily generalized to the peculiar world of small and medium-sized enterprises.7 Second, most information is gathered at the level of the firm and not at the level of the plant; in the case of multi-plant firms, this can be misleading when the analysis is also conducted at the sub-national level (as it is the case in this paper). However, especially when dealing with macroregions, the authors are confident that this bias should be limited; moreover, in studying innovative activities, it can be assumed that most of the R&D and innovative efforts should be conducted at the headquarter premises. Third, the CIS questionnaire is aimed at identifying innovative firms and this may raise an issue of sample bias in the CIS data. Yet, this is not the case in the study where – differently from many of the studies using CIS data – the focus to the innovative firms is not limited, but the non-innovative ones (more than one-third of the observations; Table 1) are also considered. Following the conceptualization discussed in the second section, technological capabilities at the firm level are signalled by the introduction of a product and/or process innovation. In other words, in order to identify firms with technological capabilities in the period of reference of the CIS4, the strict (outputoriented) definition of innovators is used.8 Such a definition (based on Questions 5 and 9 of the UK questionnaire) applies if, during the period 2002–2004, the enterprise introduced a new or significantly improved product (either a good or a service) and/or new or significantly improved processes for the production or supply of its products. In such a case the respondent is here classified as a firm with technological capabilities. If instead the enterprise has invested in innovative inputs (on the basis of Question 13),9 but without achieving any innovative output (new product or new process) in the relevant period, it is classified as a firm with technological competences. Finally, if the enterprise has declared neither innovative output nor investment in innovative inputs, it is classified as a technologically inactive firm in the period analysed by the UK CIS4. Therefore, the dependent variable is a categorical ordered variable which assumes the following values: 0 in the case of a technologically inactive firm; 1 in the


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Table 1. Regional distribution of the categorical dependent variable

Technologically inactive firm (value 0) Firm with technological competences (value 1) Firm with technological capabilities (value 2) Total

UK

Northern England

Midlands

Eastern England

London

Southern England

Wales

Scotland

Northern Ireland

5308 (35.03) 4105 (27.09) 5740 (37.88) 15 153

1214 (34.82) 983 (28.20) 1289 (36.98) 3486

891 (34.67) 675 (26.26) 1004 (39.07) 2570

461 (35.60) 326 (25.17) 508 (39.23) 1295

540 (36.58) 357 (24.19) 579 (39.23) 1476

910 (32.32) 796 (28.28) 1109 (39.40) 2815

348 (34.39) 293 (28.95) 371 (36.66) 1012

425 (36.51) 335 (28.78) 404 (34.71) 1164

519 (38.87) 340 (25.47) 476 (35.66) 1335

Note: Values shown are the number of firms (relative percentages).


Table 2. Variables Name Dependent variable Technological status of the firm Technologically inactive firm = 0 Firm with technological competences = 1 Firm with technological capabilities = 2 Independent/control variables Cooperation partners A: Other enterprises LOCAL A: Other enterprises NON-LOCAL B: Suppliers LOCAL B: Suppliers NON-LOCAL C: Clients LOCAL C: Clients NON-LOCAL D: Competitors LOCAL D: Competitors NON-LOCAL E: Consultants LOCAL E: Consultants NON-LOCAL F + G: Universities and public research institutes LOCAL F + G: Universities and public research institutes NON-LOCAL

Nature Categorical ordered (N = 15 153) N = 5308 N = 4105 N = 5740

Mean

Standard deviation

1.028

0.853

0.036 0.059 0.046 0.091 0.050 0.084 0.026 0.051 0.030 0.049 0.040 0.047

0.187 0.235 0.209 0.287 0.217 0.278 0.160 0.220 0.170 0.216 0.195 0.212

4.043 (276.192) 0.358 0.342 0.150 0.127

1.504 (1403.906) 0.479 0.474 0.357 0.230

Dummies

Size: ln(Employment) (number of employees) Group Internationalization Start-up Human capital

Continuous

Sectors Primary sector Engineering-based manufacturing Other manufacturing Construction Retail and distribution Knowledge-intensive services Other services

Dummies

Regions North England (North East, North West, Yorkshire and the Humber) Midlands (East Midlands, West Midlands) Eastern England London South England (South East, South West) Wales Scotland Northern Ireland

Dummies

Dummy Dummy Dummy Continuous

0.015 0.137 0.175 0.093 0.165 0.169 0.246 0.230 0.170 0.085 0.097 0.186 0.067 0.077 0.088


Technological Capabilities and Patterns of Innovative Cooperation of Firms in the UK Regions case of a firm with technological competences; and 2 in the case of a firm with technological capabilities. Table 1 displays the regional distribution of the three categories of firms. It is interesting to note that the shares of firms belonging to each group are rather equally distributed across regions. The patterns of cooperation for innovation – the main interest in analysing the determinants of technological competences and capabilities – are based on Question 18 of the UK questionnaire, which, in line with the Eurostat standardized questionnaire, is devoted to the types of cooperation partners used by the respondent firms, and their location. In fact, cooperation in R&D and innovative activities may imply a variety of different partners ranging from firms within the same corporate group, customers, suppliers, competitors, and institutional partners such as universities and public laboratories (for example, FRITSCH and LUKAS , 2001; MIOTTI and SACHWALD , 2003; BELDERBOS et al., 2004b; PIGA and VIVARELLI , 2004; LAURSEN and SALTER , 2006). Seven types of partner are listed in the CIS questionnaire: (A) other enterprises within the firm’s group; (B) suppliers of equipment, materials, services or software; (C) clients or customers; (D) competitors or other enterprises in the firm’s industry; (E) consultants, commercial laboratories or private R&D institutes; (F) universities or other higher education institutions; and (G) government or public research institutes. Dummies were created for each of the cooperation partners, aggregating universities/other higher education institutions and government/public research institutes into one category.10 Two location levels for each of the six partners were taken into account: local, that is within approximately 100 miles of the surveyed enterprise (as defined in the CIS questionnaire); and nonlocal. The total number of collaboration dummies was thus twelve. In order to have reasonably homogeneous macroregions, the eight UK regions considered were defined following partial aggregations of NUTS-1 (Nomenclature des Unités Territoriales Statistiques) regions: Northern England (North East, North West, Yorkshire and the Humber), Midlands (East Midlands, West Midlands), Eastern England, London, Southern England (South East, South West), Wales, Scotland, and Northern Ireland. The size of the final sample was 15 153 firms, owing to the presence of missing values in the patterns of collaboration. The authors controlled for the geographical representativeness of the final sample, which turned out to be not statistically different from that of the original sample (for the distribution of the observations across the UK macro-regions, see the last panel of Table 2). While the relationship between the different forms of cooperation and the technological status of the firm is at the core of the investigation, other microeconomic

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factors must be considered. In particular, the firmlevel control variables are the following: .

.

.

.

.

Size: firm size in terms of employment (continuous variable). The Schumpeterian notion that large firms are more likely both to undertake and to succeed in innovative activities has constituted a constant theme in the literature (SCHUMPETER , 1943). Such a notion has been initially challenged from a theoretical point of view (ARROW , 1962), and then proposed again in terms of scale and scope economies in R&D investments (COHEN and KLEPPER , 1996). In the last few decades mixed empirical evidence has been found to support the Schumpeterian hypothesis (for example, COHEN and LEVIN , 1989; KLEINKNECHT and REIJNEN , 1991; AUDRETSCH , 1995; BRESCHI et al., 2000). Group: whether the firm is part of a business group (dummy). Various studies have recognized that the group form of organization tends to play an important role in promoting and supporting innovation (for instance, FILATOTCHEV et al., 2003; PIGA and VIVARELLI , 2004). Internationalization: the extent of internationalization of the markets served by the firm, in terms of whether the firm sells products/services outside the national market (dummy). This is based on the premise that global competition can spur innovation, competences and capabilities, while technologically inactive firms are doomed to be excluded from the international arena (for example, ARCHIBUGI and IAMMARINO , 1999; NARULA and ZANFEI , 2003). Start-up: whether the firm was established after 1 January 2000 (dummy). The debate on the new technology-based firms points out how – at least in some sectors – young companies may be at the core of the innovation process (for instance, STOREY and TETHER , 1998; COLOMBO et al., 2004; COLOMBO and GRILLI , 2005). Human capital: firm-specific skills in terms of the proportion of employees educated to degree level or above (continuous variable). Human capital is seen as being complementary to innovation, constituting per se a competence of the firm and generating a super-additive effect in terms of both innovative and economic performance (for example, ACEMOGLU , 1998; MACHIN and VAN REENEN , 1998; PIVA and VIVARELLI , 2004; PIVA et al., 2005).

The analysis also includes regional dummies in the aggregated model, and sectoral dummies in all specifications.11 The list of all variables used and some descriptive statistics are reported in Table 2. As shown, relatively large firms are being dealt with, 36% of which belong to a business group, 34% of which are internationalized, and 15% of which are less than five years old. In accordance with the nature of the dependent variable, ordered logistic regressions were run.12 The

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following specification was tested for the country as a whole: Technological status = a + b1 log(employment) + b2 (group) + b3 (internationalization) + b4 (start − up) + b5 (human capital) + b6 (cooperation dummies) + b7 (sectoral dummies) + b8 (regional dummies) + 1


To explore whether and to what extent the relationship between technological competences and capabilities and cooperation patterns is region-specific, the same model – without the regional dummy vector – was then estimated for each of the eight UK macro-regions.

TECHNOLOGICAL CAPABILITIES AND COOPERATION PATTERNS: RESULTS Table 3 shows the results for the aggregate model – that is, for the UK as a whole. First of all, it has to be highlighted that the results are especially driven by the category of firms with technological capabilities.13 On the other hand, the few peculiar features that characterize the group of firms with technological competences with respect to the other two firm groups support the conjecture that these firms represent somehow an intermediate innovative behaviour (also D’ESTE et al., 2011). This sits comfortably with the choice of distinguishing the three categories of firms according to their technological status. In line with the theoretical expectations discussed above and Hypothesis 1, all the variables related to firm characteristics are highly significant at the 1% level (with the exception of start-up, with a level of significance of 5%), indicating a positive impact on the likelihood of firms to be classified as firms with technological competencies and capabilities. Likewise, the sectoral dummies indicate that the industrial structure matters in affecting the innovative status of the firm; indeed, all the sectoral dummies turn out to be highly significant. Moreover, and not surprisingly, the impact is greatest in engineering-based manufacturing, which is the chosen reference category. As far as the independent variables concerning cooperation partners are concerned, as expected (Hypothesis 2) linkages with other enterprises within the group, suppliers, clients, and public research and higher education institutions are all positive and highly significant at both local and non-local levels. Consistently with the previous literature, both the dummy for belonging to a group and the two dummies indicating cooperation with firms within the same business group turn out to be statistically significant at the 1% level, pointing to the strategic role of group relationships in enhancing the technological status of the individual firms involved. Interestingly enough, the enforcement role of cooperation appears more

obvious for firms in the same business group and also located at a short distance.14 Geographical distance instead does not seem to play a dominant role in the vertical cooperative links, where actually the coefficients related to non-local suppliers and customers give results that are higher in magnitude than those related to local partners. This outcome can be related to the highly globalized nature of the UK economy as a whole, especially when – as in this case – both manufacturing and services (including finance) are taken into account. However, the vertical relationships come out as highly significant in all four cases. By contrast, horizontal cooperative links with competitors and consultants do not seem to have a significant influence in enhancing the probability that a firm achieves either technological competences or capabilities. In other words, while innovation seems to be reinforced by collaborations along the supply chain, once attention is turned to the horizontal dimension, rivalry seems to dominate. This outcome is generally in line with Hypothesis 2, stating the critical importance of vertical innovative linkages.15 Cooperation with universities and public research institutes turns out to be significant and positively affected by the close proximity of the involved partners. This is not surprising, due to the localized nature of labour markets and the fact that the university–firm innovative link often occurs through the hiring of graduates and postgraduates by local firms. Moreover, as stated by KOSCHATZKY and STERNBERG (2000): extraregional linkages are more important for pure market relationships whereas spatial proximity matters when tacit knowledge must be transferred – and this is the case with innovation linkages with research institutions …. (p. 489)

On the whole, at the national level Hypothesis 2 turns out to be confirmed by the empirical analysis, with the only qualification to be made on the horizontal forms of cooperation (competitors and consultants) which emerge as unimportant in fostering both innovative competences and capabilities among UK firms. Moreover, on aggregate, only some forms of innovative cooperation appear to have a stronger impact when they are locally established, namely those that are intra-group and those with universities and public research institutes. The analysis at the national level also highlights the relevance of the firm’s environment – in terms of its regional location – in influencing its technological status. The regional dummies in the aggregate regression are in fact jointly significant at a 1% level, but with rather pronounced variations in terms of sign, magnitude and significance of the coefficients. This result provides further justification of the choice to investigate the regional location, which may have an important role in moulding technological firms’ competences and capabilities.


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Table 3. Determinants of firms’ capabilities, UK with regional dummies Ordered logistic regression. Categorical ordered dependent variable: 0 = technologically inactive firm; 1 = firm with technological competences; and 2 = firm with technological capabilities (1) ln(Employment) Group Internationalization Start-up


Human capital Cut-off_1 Cut-off_2

0.12*** (9.60) 0.23*** (6.27) 0.56*** (14.59) 0.10** (2.26) 1.41*** (16.99) 0.01 (0.12) 1.36*** (15.81)

(2) Sectoral dummies

Yes***

Primary sector

–1.06*** (–7.67) –

Engineering-based manufacturing Other manufacturing Construction Retail and distribution Knowledge-intensive services Other services

Cooperation partners for innovation A: Other enterprises within group LOCAL A: Other enterprises within group NON-LOCAL B: Suppliers LOCAL B: Suppliers NON-LOCAL C: Clients LOCAL C: Clients NON-LOCAL D: Competitors LOCAL D: Competitors NON-LOCAL E: Consultants LOCAL E: Consultants NON-LOCAL F + G: Universities and public research institutes LOCAL F + G: Universities and public research institutes NON-LOCAL Likelihood ratio (LR) χ2(d.f.) Pseudo-R2 Number of observations

0.40*** (2.90) 0.32*** (2.62) 0.44*** (3.43) 1.16*** (10.81) 0.44*** (3.38) 0.69*** (6.16) –0.27* (–1.69) 0.05 (0.38) 0.14 (0.92) –0.02 (–0.12) 0.46*** (3.47) 0.34** (2.43)

–0.01 (–0.20) –0.97*** (–13.87) –0.82*** (–13.54) –0.28*** (–4.37) –0.72*** (–12.66)

Regional dummies

Yes***

North England

0.11 (1.63) 0.14* (1.95) 0.09 (1.12) –0.16** (–2.11) 0.20*** (2.88) 0.13 (1.54) –

Midlands Eastern England London South England Wales Scotland Northern Ireland

0.14* (1.80)

χ2(30) 3854*** 0.12 15 153

Notes: z-statistics are given in parentheses: *Statistically significant at the 10% level; **statistically significant at the 5% level; and ***statistically significant at the 1% level. Column (1): control and cooperation regressors; column (2): seven sectoral dummies (Department for Innovation, University and Skills (DIUS) [now Department for Business, Innovation and Skills (BIS)] sectoral classification – engineering-based manufacturing is the reference case) and eight regional dummies (NUTS-1 [Nomenclature des Unités Territoriales Statistiques] regional aggregations – Scotland is the reference case). Yes*** for sectoral and regional dummies reporting that they are, respectively, jointly significant at the 1% level.

Table 4 presents the estimates for the individual UK regions.16 As can be seen, in general Hypothesis 1 is also confirmed at the regional level. In fact, a firm’s specific characteristics turn out to be positive and significant in twenty-nine cases out of forty, while the sectoral dummies are significant in thirty-five cases out of forty-eight (and jointly significant in all the eight regional regressions).

Turning one’s attention to the cooperation variables, Table 4 shows remarkable differences according to the specific regional context. These differences concern both the magnitude and the significance of the different cooperation linkages and their geographical scope. Hypothesis 2 is generally confirmed, as vertical innovative cooperation has a stronger positive impact on technological competences and capabilities in most regions,


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whilst horizontal cooperation with competitors turns out to be mostly insignificant with a few exceptions. Also, Hypothesis 3 is broadly supported by the estimates as regional variation emerges with reference to the geographical scope of cooperation linkages. Specific results coming out from the individual macro-regions deserve further investigation in order to qualify better both Hypotheses 2 and 3. For instance, London emerges as a peculiar case in terms of the relationships between cooperation for innovation and firms’ technological status. The only type of cooperative linkage that turns out to be significant is that with suppliers at both local and, even more, nonlocal levels. This would suggest that a firm’s location in the metropolitan area does not tend to entail a strong relationship between collaborative linkages and its technological capabilities (for similar results on firms’ capabilities in city-regions, see SCHIENSTOCK , 2009). Indeed, the London metropolitan area has to be understood more as an ‘accumulation node’ of global economic and financial transactions, rather than a selfcontained regional economic and innovation system.17 Therefore, the complex structure of the capital city and its articulated relationships with both other regions in the UK and the rest of the world suggest that London’s boundaries per se do not set up significant interactions for local firms in terms of either markets or social organizations (BUDD , 2006), but rather comprise an array of control and management nodes for global transactions and businesses (NEWMAN and THORNLEY , 2005). Considering Southern England, the impact of collaborative relations on firms’ capabilities is much more evident than for London, though the strongest of these links are again mostly non-local. The only notable exception is collaboration with local clients, which strongly increases the probability of the firm being classified as having technological capabilities. The peculiar strongly positive effect of horizontal collaboration with non-local competitors may be explained by the ‘M4 [motorway] corridor effect’ that could reflect national and international strategic technological alliances among large and/or multinational enterprises headquartered outside the region. More generally, and in contrast with the national result, both London and Southern England show a positive impact of horizontal cooperation with non-local (Hypothesis 3) competitors (Hypothesis 2), possibly driven by their extreme degree of internationalization which generates positive spillovers from open competition. In the Eastern England region, the likelihood that firms display technological competencies and capabilities is positively influenced by collaborations with non-local suppliers and with both local and non-local customers. This latter effect may be justified by the presence of the ‘Cambridge cluster’, whose successful innovative performance is, however, counterbalanced by some lagging behind the rest of the region (GRAY et al., 2006).

As far as vertical cooperation is concerned, the results for the Midlands are similar to those for Eastern England, though with more significant linkages with clients and an additional positive impact of local suppliers on firms’ technological capabilities (though significant at the 10% level). A peculiar feature of the Midlands is the positive effect on firms’ capabilities of linkages with universities and public research located outside the region. Indeed, in recent decades the Midlands – characterized until the 1970s and 1980s as the Fordist heartland of the country, particularly for automotive and metal manufacturing, and by coalbased industry – have gone through a period of post-industrial economic restructuring which, more recently, has sparked local innovation potential and connectivity among firms (ADVANTAGE WEST MIDLANDS (AWM), 2004; GREEN and BERKELEY , 2006; HARDILL et al., 2006).18 Northern England turns out to be the UK region where the association between the independent variables here considered and firms’ technological status is the most striking. For instance, the start-up variable is strongly significant and positively affects the probability of firms having technological competencies and capabilities, driving the effect at the country level at large. This might be interpreted in terms of a process of gradual replacement of declining and mature industries and shifts of the regional industrial structure towards more advanced manufacturing and service sectors (TOMANEY , 2009). This seems to be further supported by the positive and significant sign of cooperative linkages with both local and non-local private research and consultants, which again determines the result at the national level. Remarkably, the North is also the only region where the probability of being a firm with technological capabilities is strongly increased by collaborations with local public research institutes and universities, once more driving the result for the UK as a whole. Such a result, in line with the expectation stated in Hypothesis 3, may be interpreted in the light of what was labelled as ‘a type of evolutionary learning process within the policies pursued by the government since 1997’ (GOODCHILD and HICKMAN , 2006, p. 123), that eventually led to the ‘Northern Way’ strategy implemented for the three Northern English regions (North East, North West, and Yorkshire and the Humber) in 2004. Although the assessment of the ‘Northern Way’ has proved rather controversial (for example, GOODCHILD and HICKMAN , 2006; GONZÁLEZ , 2006), the efforts of regional development agencies (RDAs) in strengthening multilevel intraregional coordination for economic and social development, with a particularly strong emphasis on the local knowledge base and on the integration of innovation, research, and education and training, have been largely acknowledged: the remarkable concentration of highrank universities in the region (for example, Manchester, Newcastle, Leeds, Sheffield, etc.) has acted as one


Ordered logistic regression: regional analysis. Categorical ordered dependent variable: 0 = technologically inactive firm; 1 = firm with technological competences; and 2 = firm with technological capabilities.

ln(Employment) Group Internationalization Start-up Human capital Cut-off_1 Cut-off_2 Cooperation partners for innovation A: Other enterprises LOCAL A: Other enterprises NON-LOCAL B: Suppliers LOCAL B: Suppliers NON-LOCAL C: Clients LOCAL C: Clients NON-LOCAL D: Competitors LOCAL

(1) Northern England

(2) Midlands

(3) Eastern England

(4) London

(5) Southern England

(6) Wales

(7) Scotland

(8) Northern Ireland

0.12*** (4.62) 0.22*** (2.79) 0.61*** (7.34) 0.26*** (2.88) 1.89*** (9.26) 0.13 (0.97) 1.54*** (11.58)

0.15*** (4.82) 0.20** (2.18) 0.56*** (5.84) 0.04 (0.39) 1.58*** (7.02) –0.02 (–1.08) 1.31*** (8.73)

0.16*** (3.55) 0.26** (1.99) 0.66*** (4.90) 0.08 (0.49) 1.76*** (5.62) 0.12 (0.22) 1.46*** (6.59)

0.10*** (2.98) 0.45*** (4.05) 0.32** (2.77) 0.26* (1.73) 1.06*** (5.79) –0.52* (–1.92) 0.65** (2.40)

0.11*** (4.27) 0.13 (1.52) 0.61*** (6.61) –0.07 (–0.62) 1.41*** (7.26) –0.26* (–1.85) 1.19*** (7.93)

0.08 (1.34) 0.21 (1.37) 0.88*** (5.43) 0.03 (0.16) 0.82** (2.36) –0.37* (–1.37) 1.13*** (4.03)

0.07 (1.48) 0.27** (2.01) 0.75*** (5.35) 0.17 (1.00) 0.93*** (3.26) –0.23 (–1.00) 1.21*** (5.04)

0.14** (2.53) 0.21 (1.56) 0.39*** (3.23) 0.07 (0.44) 1.80*** (5.44) –0.08 (–0.30) 1.26*** (4.53)

0.22 (0.73) 0.41* (1.67) 0.67** (2.45) 0.83*** (3.76) –0.10 (–0.36) 0.56** (2.49) –0.02 (–0.07)

0.13 (0.39) 0.55 (1.47) 0.63* (1.77) 1.51*** (4.36) 1.00*** (2.93) 1.37*** (4.36) –0.39 (–0.98)

0.43 (0.87) –0.20 (–0.46) 0.65 (1.51) 1.52*** (3.91) 0.99** (2.14) 0.81** (2.07) –0.16 (–0.26)

0.32 (0.75) 0.03 (0.11) 0.78** (2.05) 1.40*** (4.59) 0.09 (0.21) 0.44 (1.36) –0.39 (–0.83)

0.30 (0.89) 1.11*** (3.61) –0.44 (–1.47) 1.23*** (5.19) 0.82*** (2.58) 0.27 (1.00) –0.60 (–1.36)

0.89* (1.73) –0.15 (–0.28) 0.91* (1.68) 1.01** (2.25) 0.37 (0.73) 0.16 (0.33) –0.58 (–1.01)

1.35*** (2.92) –0.24 (–0.49) 0.66 (1.53) 0.79** (1.99) 0.10 (0.22) 0.96** (2.41) –0.40 (–0.75)

1.03 (1.60) 0.82 (0.88) 0.31 (0.62) 1.71*** (3.27) 0.90* (1.72) 2.24*** (2.96) –0.70 (–0.11)

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(Continued )


Table 4. Determinants of firms’ capabilities, regions of the UK

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D: Competitors NON-LOCAL E: Consultants LOCAL E: Consultants NON-LOCAL F + G: Universities and public research institutes LOCAL F + G: Universities and public research institutes NON-LOCAL Sectoral dummies Primary sector Engineering-based manufacturing Other manufacturing Construction Retail and distribution Knowledge-intensive services Other services Likelihood ratio (LR) χ2(23) Pseudo-R2 Number of observations

(1) Northern England

(2) Midlands

(3) Eastern England

(4) London

(5) Southern England

(6) Wales

(7) Scotland

0.20 (0.76) 0.65** (1.97) 0.61** (2.04) 0.81*** (3.17) 0.08 (0.30)

–0.99*** (–2.88) –0.08 (–0.20) –0.75** (–2.06) 0.04 (0.11) 0.91** (2.50)

–0.71 (–1.60) –0.45 (–0.77) 0.20 (0.39) 0.62 (1.16) 0.47 (1.04)

0.70* (1.69) –0.22 (–0.52) –0.29 (–0.68) 0.10 (0.22) 0.24 (0.59)

1.03*** (3.30) 0.73* (1.78) –0.24 (–0.74) 0.47 (1.29) 0.26 (0.75)

0.48 (0.99) 0.49 (0.76) 0.38 (0.70) –0.03 (–0.08) 0.66 (1.10)

–0.56 (–1.16) –0.08 (–0.17) 0.36 (0.66) 0.56 (1.19) 0.50 (1.04)

–0.69 (–0.96) –0.92 (–1.38) 1.02 (1.04) 0.54 (1.03) –1.65* (–1.77)

Yes*** –1.13*** (–3.20) –

Yes*** –0.84** (–2.10) –

Yes*** –0.73 (–1.46) –

Yes*** –1.84*** (–3.76) –

Yes*** –0.70* (–1.89) –

Yes*** –0.97 (–1.60) –

Yes*** –1.23*** (–3.91) –

Yes*** –1.29*** (–3.17) –

1.18 (1.57) –0.66*** (–4.67) –0.65*** (–5.23) –0.19 (–1.44) –0.48*** (–4.22)

–0.02 (–0.18) –0.98*** (–5.95) –0.82** (–5.79) –0.32** (–2.17) –0.81*** (–6.14)

0.06 (0.30) –1.16*** (–4.49) –0.71*** (–3.45) –0.40* (–1.80) –0.89*** (–4.45)

–0.70** (–2.40) –1.60*** (–4.99) –1.40*** (–4.92) –1.04*** (–3.80) –1.32*** (–4.91)

0.08 (0.55) –1.06*** (–6.21) –0.73*** (–5.09) –0.24* (–1.65) –0.71** (–5.23)

0.02 (0.08) –1.33*** (–5.02) –1.02*** (–4.43) –0.14 (–0.58) –0.80*** (–3.76)

–0.22 (–0.99) –0.87*** (–3.52) –0.74*** (–3.19) –0.20 (–0.87) –0.74*** (–3.52)

–0.19 (–0.90) –0.97*** (–4.22) –1.07*** (–5.18) 0.16 (0.61) –0.74*** (–3.45)

891*** 0.12 3486

700*** 0.13 2570

418*** 0.15 1295

323*** 0.10 1476

801*** 0.13 2815

298*** 0.14 1012

293*** 0.12 1164

341*** 0.12 1335

Notes: z-statistics are given in parentheses: *Statistically significant at the 10% level; **statistically significant at the 5% level; and ***statistically significant at the 1% level. Seven sectoral dummies are included (Department for Innovation, University and Skills (DIUS) [now Department for Business, Innovation and Skills (BIS)] sectoral classification). Yes*** for sectoral dummies reporting that they are jointly significant at the 1% level.

(8) Northern Ireland



Table 4. Continued


Technological Capabilities and Patterns of Innovative Cooperation of Firms in the UK Regions of the main pillars of such strategies (for example, BYRNE and BENNEWORTH , 2006; GORE and JONES , 2006; WILSON and BAKER , 2006). Indeed, in the Northern regions of England universities have been actively involved in regionally based initiatives aimed at spurring interactions with business activities and science policies directed to face the changes in the external competitive environment: regional higher education consortia, such as One Northeast in Newcastle, the Yorkshire and Humberside Universities Association, the North West Universities Association, and Higher Education North West – which started their activities in the 1990s – have played a crucial role in opening new opportunities to establish closer links among regional actors (for example, CHARLES and BENNEWORTH , 2001; CHARLES , 2003). A relatively strong presence of multinationals and a high degree of openness19 – as also highlighted by the magnitude of the coefficients of the Internationalization variable – underlie the patterns of collaboration for innovation in the regions of Wales (COOKE et al., 1994; COOKE et al., 1998; ARNDT and STERNBERG , 2000; DE LAURENTIS , 2006) and Scotland (RAINES et al., 2001; DE LAURENTIS , 2006). In Wales, only the cooperation with non-local suppliers influences the likelihood that firms are in the category of those with technological capabilities. Similarly to Wales, and in accordance with Hypothesis 3, Scotland shows a positive impact of cooperation with extra-local suppliers and clients, possibly due to the corporate vertical integration of the multinational enterprises located in the region (TUROK , 1997; RAINES et al., 2001). In the case of Northern Ireland’s vertical cooperative linkages, showing strongly positive coefficients for collaborations with non-local suppliers and customers, the result is not surprising due to the strong economic integration of Northern Ireland with the other UK regions. On the whole, Northern England and the Midlands turn out to be the regions where cooperation is associated the most with technological competencies and capabilities (they display the highest number of significant coefficients),20 while the globalized London metropolitan area together with the more peripheral Wales, Scotland and Northern Ireland are the regions where only few forms of (vertical) cooperation significantly affect firms’ technological status.

CONCLUSIONS The aim of this paper has been to investigate the relationship between different forms of collaborative linkages and firms’ technological competences and capabilities, considering in particular the role of the environment of the firm in the form of its regional location. On the one hand, the results confirm that a specific firm’s characteristics (such as size, belonging to a corporate group, international openness, etc.) and sectoral

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aspects do matter in affecting the technological status of the firm. More interestingly for the purpose of this paper, innovative cooperative agreements play a relevant role in enhancing firms’ technological competences and, even more, capabilities. Among the different forms of cooperation, in line with expectations, the most important turn out to be the vertical links with suppliers and customers. On the other hand, the regional findings indicate that highly significant results obtained for the UK as a whole actually mask considerable differences among the regions. In particular, the findings show remarkable regional specificities in terms of the association between collaborative patterns and technological capabilities at the firm level. For instance, UK regions such as the Midlands and, even more, Northern England, show the greatest evidence of utilizing a richer variety of collaborative linkages at the firm level to restructure their regional systems of innovation and enhance their technological capabilities. On the contrary, the highly internationalized metropolitan region of London displays a weak association between cooperative patterns and the technological status of firms located there. By the same token, local networking is also less crucial in less central – but highly opened – regions such as Scotland, Wales and Northern Ireland. While the results – based on a specific dataset such as CIS, with its own advantages and disadvantages as discussed in the third section – cannot be generalized without caveat, still some interesting policy implications can be put forward. The main suggestion for regional policy is that the scope for interaction varies greatly at the sub-national scale and in some regions is potentially huge, provided that both private and public resources are devoted to identifying and supporting the most effective linkages for the observed region. In other words, managing regional interactions and cooperation in order to enhance firms’ technological capabilities is not a free lunch. In particular, regional policy should start from the distinction drawn between technological competences and capabilities, being aware that simply marshalling the resources – that is, increasing innovation inputs – cannot be enough, and finally recognizing that fuelling the link between cooperative networking and firms’ capabilities is a policy target that has to be tailored to the specific features of a given regional economic and innovation system. On the whole, taking into account the economic and social structures of the different UK regions, policies that proved to be successful in one region have not automatically been effective in other contexts; this calls for targeted innovation policies, bearing in mind the geographical and sectoral structures which characterize the potential beneficiaries. Until 2010, RDAs in many UK regions, also peripheral, proved quite successful in helping create employment, regenerate or upgrade urban and rural areas, and strengthen collaborative


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networks between private and public sectors. The most recent literature has pointed out the risks involved in the scrapping of RDAs and of the overall UK regional architecture and their replacement with local enterprise partnerships (LEPs) – private-led local authority– business bodies for local economic development. Institutional and governance weakness, extreme localism, excessive fragmentation of territorial units for policy implementation, and a dramatic reduction of resources are among the feared risks (for example, BENTLEY et al., 2010; LIDDLE , 2011; WARD , 2011), which may eventually lead to anti-regionalist and re-centralization tendencies and seriously jeopardize UK regional

development and imbalances in the current climate of worldwide economic recession. Acknowledgements – The authors gratefully acknowledge funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under Grant Agreement Number 216813. The authors would like to thank two anonymous referees for their extremely valuable and extended suggestions on how to improve the paper; and are also grateful for comments on earlier versions of the paper from the partners of the Intangible Assets and Regional Economic Growth (IAREG) Project. All errors and omissions remain the authors’ own.

APPENDIX A Table A1. Sectoral distribution of the categorical dependent variable

Firm technological inactive (value 0) Firm with technological competences (value 1) Firm with technological capabilities (value 2) Total

Primary sector

Engineering-based manufacturing

Other manufacturing

102 (45.13) 69 (30.53) 55 (24.34) 226

433 (20.89) 586 (28.27) 1054 (50.84) 2073

597 (22.50) 778 (29.33) 1278 (48.17) 2653

Construction

Retail and distribution

Knowledgeintensive services

Other services

724 (51.28) 444 (31.44) 244 (17.28) 1412

1154 (46.18) 620 (24.81) 725 (29.01) 2499

672 (26.20) 572 (22.30) 1321 (51.50) 2565

1626 (43.65) 1036 (27.81) 1063 (28.54) 3725

Note: Values are given as the number of firms (relative percentage).

APPENDIX B Table B1. Multinomial logistic regression (N = 15 153), UK with regional dummies Base: 0 = Technologically inactive firm ln(Employment) Group Internationalization Start-up Human capital Constant A: Other enterprises LOCAL A: Other enterprises NON-LOCAL B: Suppliers LOCAL B: Suppliers NON-LOCAL C: Clients LOCAL C: Clients NON-LOCAL

1 = Firms with technological competences

2 = Firms with technological capabilities

0.06*** (3.37) 0.18*** (3.58) 0.30*** (5.48) 0.06 (0.95) 1.28*** (10.25) –0.27** (–2.35) 0.01 (0.03) 0.54** (1.96) 0.94*** (4.16) 0.93*** (4.27) 0.40* (1.78) 0.37 (1.62)

0.16*** (9.75) 0.31*** (6.30) 0.73*** (14.26) 0.13** (2.20) 1.95*** (16.41) –0.94*** (–7.94) 0.41* (1.71) 0.72*** (2.75) 0.95*** (4.30) 1.76*** (8.68) 0.66*** (3.01) 0.99*** (4.57) (Continued )


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Table B1. Continued 1 = Firms with technological competences

2 = Firms with technological capabilities

–0.01 (–0.05) 0.24 (0.93) –0.52* (–1.94) 1.16*** (3.18) 0.85*** (3.23) 0.16 (0.54)

–0.35 (–1.31) 0.23 (0.92) –0.14 (–0.55) 0.93*** (2.60) 1.01*** (3.91) 0.53* (1.90)

–0.74*** (–4.25) –

–1.49*** (–7.51) –

0.01 (0.11) –0.63*** (–6.83) –0.83*** (–9.93) –0.63*** (–6.94) –0.65*** (–8.21)

–0.01 (–0.12) –1.46*** (–14.02) –1.07*** (–12.93) –0.43*** (–5.06) –0.97*** (–12.31)

0.03 (0.33) –0.05 (–0.53) –0.10 (–0.98) –0.29*** (–2.80) 0.13 (1.44) 0.08 (0.72) –

0.17* (1.85) 0.21** (2.17) 0.15 (1.34) –0.19* (–1.77) 0.28*** (2.98) 0.21* (1.80) –

D: Competitors LOCAL D: Competitors NON-LOCAL E: Consultants LOCAL E: Consultants NON-LOCAL F + G: Universities and public research institutes LOCAL


F + G: Universities and public research institutes NON-LOCAL Sectoral dummies Primary sector Engineering-based manufacturing Other manufacturing Construction Retail and distribution Knowledge-intensive Other services Regional dummies North England Midlands Eastern England London South England Wales Scotland

–0.10 (–0.98)

Northern Ireland Likelihood ratio (LR) χ2(d.f.) Pseudo-R2

0.24** (2.23)

χ2(60) 4063*** 0.12

Notes: z-statistics are given in parentheses: *Statistically significant at the 10% level; **statistically significant at the 5% level; and ***statistically significant at the 1% level. Engineering-based manufacturing is the reference case for sectoral dummies; Scotland is the reference case for regional dummies.

NOTES 1. The category of ‘learning by interacting’ from upstream suppliers or downstream users was developed by LUNDVALL (1992) in his analysis of the Danish system of innovation. 2. In fact, the understanding and the articulation of needs and wants by customers entail a complex learning process, while the spectrum of wants (preference order) constantly evolves over time (WITT , 2001). This is particularly true when consumers are intermediate consumers, that is,

themselves producers further down the supply chain (ROSENBERG , 1976; VON HIPPEL , 1988). 3. Needless to say, the entire process is driven by the search for profitability; in the context of ‘dynamic competition’, this will call for a constantly changing response by the firms, or what TEECE et al. (1997) refer to as their ‘path’. 4. The authors are grateful to an anonymous referee for inviting them to discuss the complementary relationship between competencies and capabilities over time. 5. As opposed to a crude ‘linear model’ (KLINE and ROSENBERG , 1986).


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6. These firm-specific effects – already singled out as crucial by the microeconomic literature on cooperation in R&D and innovation – are not at the core of this study; however, they will be briefly discussed in the following (third) section. 7. This marks a significant difference between CIS data and those collected within the European Regional Innovation Survey (ERIS) research project that were instead biased in favour of small and medium-sized enterprises (KOSCHATZKY and STERNBERG , 2000, p. 492). 8. As illustrated by D’ESTE et al. (2011), there are several reasons why the use of such a definition is appropriate. First, it helps towards separating invention from innovation by requiring new products and processes to be of economic value, as shown by the commercialization requirement (that is, introduction to market). Second, it is consistent with the standard definition of innovation provided by the Oslo Manual (OECD, 2005). Third, it helps in separating the firm’s efforts in innovative activities (as measured by its investment in R&D-related activities) from the outputs of those activities (as reflected by the market introduction of new products). Thus, it is congruent with the distinction between competences and capabilities adopted in this paper. 9. Question 13 in the UK CIS questionnaire asks whether, in the reference period, the firm engaged in any of the following seven innovation activities: (1) intramural R&D; (2) acquisition of R&D; (3) acquisition of machinery, equipment and software to produce new or significantly improved products; (4) acquisition of external knowledge (for example, licensing of patents); (5) training of personnel for the development or introduction of innovations; (6) expenditure on design functions for the development of new or improved products or processes; and (7) expenditures on activities for the market preparation and the introduction of new or significantly improved products (including market research and launch advertising). 10. The coefficients were largely similar between these two categories when (in calculations not reported here) they were estimated separately. 11. The authors followed the clustering criteria used by the former Department for Innovation, Universities and Skills (DIUS), now the Department for Business, Innovation and Skills (BIS): Primary sector; Engineering-based manufacturing; Other manufacturing; Construction; Retail and distribution; Knowledge-intensive services; and Other services. The sectoral distribution of the three categories of firms is reported in Appendix A. 12. Taking into account that the dependent variable is an ordered one ranging from the technologically inactive firms to the firms showing technological capabilities, the option was for the ordered logistic model. However, multinomial logistic regressions were also run with the category of technologically inactive firms as the reference (Category 0). The results from the multinomial, reported in Appendix B for the UK as a whole, and the estimated predicted probabilities of both the

13.

14. 15.

16.

17.

18.

19. 20.

multinomial and the ordered logistic models, supported the choice of the latter, as the probability distribution between the two estimation methods is not substantially different. Furthermore, a Brant test to verify the parallel regression assumption (also called the proportional odds assumption) was performed after the ordered model. The test compares slope coefficients of the J – 1 binary logits implied by the ordered regression model. This test can only be computed if all the independent variables in the ordered model are retained in all the implied binary models. For this reason, it was not possible to compute the test in all the regional models, nevertheless – where feasible – the test provided evidence that the parallel regression assumption has not been violated. In fact, as shown in Tables 3 and 4, the cut-off as between technologically inactive firms and those with competences (enhanced inputs but lacking technological outputs) is generally not statistically significant in the ordered logistic regressions for the country as a whole and for any region in the regional estimations. Equivalent results hold in the multinomial logit regressions (see Appendix B), where the coefficients for firms with technological competences (Category 1) are in general smaller and/or with lower significance levels than those for firms with technological capabilities (Category 2). The corresponding coefficients are 0.40 and 0.32 for local and non-local firms, respectively. This outcome is common to other studies such as that by KOSCHATZKY (2000), using data collected in the German region of Baden and the French region of Alsace within the European Regional Innovation Survey (ERIS) research project. In that study, the vertical innovative cooperations with customers and suppliers emerged as highly significant in both regions, while horizontal cooperations between companies turned out to be much less frequent and relevant. A Chow-like test (GREENE and HENSHER , 2010) was performed in order to verify further the opportunity of running the model for each region separately versus the estimate of the pooled UK model. The null hypothesis is that pooling/homogeneity is appropriate: the likelihood ratio test is 249 346 (with 22 degrees of freedom), thus leading to the rejection of Hypothesis 0 at the 99% level of confidence. In line with the taxonomy proposed by KOSCHATZKY and STERNBERG (2000, p. 498), London may well be considered a ‘global hub’. This path of evolution of the regional industrial structure may also underlie the negative and significant effect on firms’ capabilities of linkages with competitors and consultants external to the region, indicating that the firms located in the Midlands are particularly struggling in the international technological race. Both towards the other UK regions and towards the international markets. Interestingly, for Northern England the coefficients are the highest of all regions for both Categories 1 and 2 in the multinomial logit.

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