TY - JOUR
AB - Abstract The objective of this article is to explore how policy supported instruments aimed to stimulate research-based innovation influence long-term innovation activity in firms with different knowledge bases. In an effort to contribute to the renewal of existing industry, some policies aim to stimulate firms to adopt and apply research-based knowledge in innovation processes. This article includes a qualitative study of a specific ‘R&D brokering policy instrument’ in Norway aimed at increasing R&D-based innovation processes in firms. R&D brokering policy instruments include funding schemes that are designed to foster and transfer technology and knowledge between firms and research communities. The study shows that the absorptive capacity for R&D collaboration varies depending upon the dominant knowledge base of firms. It is well-acknowledged that no policy fits all regions. This study also implicates that no R&D brokering policy instrument fits all industries equally well. 1. Introduction It is widely agreed that innovation activities are a key impetus for economic growth and prosperity and are thus a top priority for both industrial and regional policy development (Isaksen and Trippl 2014; Tödtling and Trippl 2005). The theoretical construct of regional innovation system (RIS), specific firm characteristics (knowledge bases), and related industrial varieties (smart specialization) are the dominant approaches to studying innovation and learning processes in the geography of innovation literature (Asheim and Gertler 2005; Isaksen and Trippl 2014; Martin et al. 2011; Tödtling and Trippl 2005). These approaches allow us to consider regional variations in collaboration and the interdependencies between different actors (such as policy makers, decision-makers, universities, industries) and to take into account regional and industrial specificities for innovation and learning processes (Tödtling and Trippl 2005). RIS is a generic and systemic approach on which much of regional innovation policy builds its actions and implementations on. While practical politics have embraced differentiated regional innovation policy approaches (e.g. Tödtling and Trippl 2005), less attention has been given to how firm-specific features influence the outcome of different policy instruments. Current innovation policies have to a certain degree abandoned the notion of linear innovation processes, acknowledging that innovation is a complex process. Even so, many regional innovation policies still focus on promoting innovation by fostering R&D activities. This paper aims to contribute with insights concerning the influence of R&D brokering policy instruments on innovation activities in firms. The assumption is that firm-specific features influence their absorptive capacity (AC) for R&D collaborations, and thus innovation activities in the long run. The remaining parts of this article are structured as follows. Section 2 presents the particular R&D brokering policy instrument looked at in the study. The purpose of the program is to mobilize various regional actors and institutions to collaborate in developing research-based innovation. Section 3 gives an overview of the theoretical point of departure in this article. The development of innovation policy is provided as background to set the context in which the particular policy instrument is applied. The section also elaborates on the concepts of knowledge bases and AC to provide the background for the later analysis of firms’ AC for R&D collaboration. Knowledge bases are an acknowledged way of conceptualizing firm- and industry-specific variations, and will as such be used to explain the variations in AC and of outcomes in R&D collaboration. Based on the theoretical framework an analytical model is presented as background for the coming discussion of firms’ AC. Section 4 deals with methodology and analysis procedures. Section 5 discusses the main results and implications for new policy development. Finally, some concluding remarks and limitations are presented in Section 6. 2. Policy context The reform of government administration in Norway, which came into effect around 2010, strengthened the regional level and gave the regional administrations new tasks. The effort was part of a plan shared with most European regions, where innovation policies to a larger degree were adapted to regional and local conditions. This transformation of policy adaptation started already in 2007 with the initiation of the Program for Regional Development and Innovation (VRI). VRI is a national and regionally funded research and innovation program, aimed at mobilizing various regional actors and organizations to collaborate on developing research-based innovation1 (see Fig. 1). A primary focus for VRI is to assist in the development of industry–academia collaboration, and to generate a deeper understanding of these arenas throughout Norwegian regions. By targeting the financial and R&D means available to priority areas, VRI’s overall goal is to contribute to long-term, sustainable development, and growth throughout the Norwegian regions.2 Figure 1. View largeDownload slide The VRI funding scheme. Figure 1. View largeDownload slide The VRI funding scheme. The VRI program offers different departures for learning and collaboration: within the firm, among firms in networks, and in different partnerships such as within a triple helix constellation. As such, the program is explicitly founded on the idea of RISs (Fitjar et al. 2016). However, VRI concentrates its efforts on building bridges between knowledge organizations and small- and medium-sized firms through various innovation projects. The program share resemblance to initiatives in other European regions, where R&D and innovation policies are developed to stimulate growth and industrial development. A majority of these efforts consider R&D an input to the development of innovation and change. This line of reasoning is very much in accordance with the linear innovation model, where innovation is viewed as equal to commercialization of science (Jensen et al. 2007). The funding scheme provided by the VRI program, with an overall aim to encourage and increase R&D collaboration, is thus viewed as an R&D brokering policy instrument. As such, it will serve as an adequate policy context to explore different firms’ AC for R&D collaboration. 2.1 Overall aim and research question In light of this, the overall objective of this article is to explore how policy instruments aimed to stimulate research-based innovation influence long-term innovation activities in firms with different knowledge bases. In order to achieve the proposed objective this article sets out to analytically establish the relationship between firms’ knowledge base and their AC for policy supported research-based innovation. Next, by providing empirical illustrations of different capacities for R&D collaboration in firms with different knowledge bases, the article sets out to discuss to what extent and how R&D brokering policy instruments have to be differentiated to fulfil their intended aim. 3. Theoretical framework 3.1 Innovation policy—from national technology push to regionalized R&D pull The approach to innovation policy has changed in accordance with the development of new theoretical frameworks since the 1980s (Isaksen and Nilsson 2013). In the 1980s innovation policy was justified as a policy area by an OECD expert report, identifying science and R&D as vital boosts for economic growth. The report not only suggested there be an increase in R&D investments, but also suggested there be more focus upon the AC for new technology (Lundvall and Borrás 2005). Isaksen and Nilsson (2013) describe this policy intervention as the first generation of innovation policies ‘[…]dominated by a “technology push” approached, inspired by the linear innovation model’ (Isaksen and Nilsson 2013, 1921). In the 1990s, OECD initiated new policy directions, in which innovation policies were interpreted more as a means to foster and enhance learning and innovation processes (Lundvall and Borrás 2005). This second generation of innovation policies was inspired by the concept of ‘systemic failure’ in the ‘system of innovation School’ and gave innovation polices an analytical foundation (Isaksen and Nilsson 2013). Industrial dynamics, the AC of firms, as well as both the demand and supply side were acknowledged as important for successful innovation processes (Lundvall and Borrás 2005). The shift in focus between the first and second generation of innovation policies also included a stronger emphasis on the regions’ role in developing and implementing polices (Isaksen and Nilsson 2013). This ‘regionalization’ of innovation policies builds upon theoretical and empirical contributions acknowledging that location-specific resources stimulate innovation processes, and that diversified policies are needed to address the heterogeneity of European regions (Tödtling and Trippl 2005; Isaksen and Nilsson 2013). However, as supported by recent contributions from Fitjar et al. (2016), and as argued in Section 3.2, practical politics are still strongly focused on research and technology push. 3.2 Innovation policy instruments in general—removing barriers Since the context of policy making is different across regions, it follows that the mix of policy instruments needs to be carefully developed to adjust to the regional context. It also follows that policy instruments need to be adjusted to meet the challenges from industries and firms in a particular region. From a societal perspective it is imperative that industries and firms are innovative. From a firm’s perspective this is easier said than done, at least in times of economic decay and international turbulence. A firm has plausible reasons for not investing in innovation. Innovation projects are often associated with complexity, high risks, high costs, and long time-frames, to mention a few obstacles (Tidd et al. 1997). The overall aim of innovation policies is thus to remove some of the barriers for innovation in regional industries and firms. Policy instruments may thus be defined as ‘[…] a set of techniques by which governmental authorities wield their power in attempting to ensure support and effect (or prevent) social change’ (Borrás and Edquist 2013, 9). There are mainly three overall categories of policy instruments: 1) regulatory, 2) economic and financial instruments, and 3) soft instruments (Borrás and Edquist 2013). It is the second category which is of interest in this article. Economic and financial instruments are often labeled ‘carrots’ in the literature (Borrás and Edquist 2013) and it involves the funding tools and incentives applied to help firms and industries perform better. A distinction can be made between the first and the second generation of economic and financial instruments (Lengrand 2003). The first-generation instruments take a linear approach to the innovation process, whereas innovation is normally seen as the commercialization of R&D results. Such instruments are typically characterized by financial support to firms with activities that involve R&D. The second-generation instruments acknowledge that innovation processes are complex, and that innovation is not an ‘isolated process’. These instruments typically involve financial support for collaborative processes among the actors within, for example, an RIS (Lengrand 2003). The regionalization of innovation policies and the argument that no policy fits all regions (Tödtling and Trippl 2005) is influenced by the theoretical construct of RISs. RIS is a framework for exploring the interaction and collaborations between firms, R&D organizations, and support systems (e.g. policy system), framed in a socio-cultural and institutional environment (Isaksen and Trippl 2014). Different types of RIS would require different types of policies to manage the need for regional restructuring, growth, and development (Fitjar et al. 2016). Moreover, as Borrás and Edquist (2013) argue, the policy instruments chosen are closely related to the activities within innovation systems, and as such the ‘[…] problems to be mitigated by innovation policy [are] closely related to identification of deficiencies or bottlenecks related to these activities’ (Borrás and Edquist 2013, 17). 3.3 Knowledge bases in firms—a key to understanding innovation processes A consequence of the regionalization of innovation policies is a concurrent focus on regional-specific contexts, such as institutional set-ups, in developing and implementing policies and instruments. Furthermore, in the Norwegian context great attention is given to industry-specific challenges and opportunities, more recently made extra relevant by the oil and gas crisis. However, firm-specific features and characteristics are to a lesser extent considered when implementing policies and designing instruments that are, for example, targeting firms to increase their research-based innovation processes. There is a vast strand of literature concerned with different innovation patterns that highlight the heterogeneity of firms and their innovation activities going all the way back to Pavitt’s (1984) three part taxonomy of firms (e.g. Castellacci 2008; Leiponen and Drejer 2007). More recent contributors have further developed the taxonomy and left the dichotomy of tacit versus codified knowledge in favor of more cross-industrial typologies of innovation and learning processes in firms. One way to conceptualize firm- and industry-specific variations is ‘knowledge bases,’ which have been widely embraced by the scientific community of economic geographers. The knowledge base of a firm is the key knowledge needed to pursue innovation processes (Asheim and Gertler 2005); either these processes are organized as projects or in the established organizational structure. Building upon different rationales for knowledge creation, dominance of tacit and codified knowledge, as well as different modes for learning and innovation (Martin 2013), a distinction can be made between three knowledge bases as illustrated in Table 1, respectively, analytical, synthetic, and symbolic knowledge bases (Asheim and Gertler 2005; Asheim et al. 2011; Cooke and Leydesdorff 2006). Related to their structural, relational, and geographical dimensions, knowledge base characteristics are, for example, used by Martin (2012) to explain how different firms innovate, as well as the nature of the different innovation networks involved. Table 1. Characterization of different knowledge bases Knowledge base Analytical Synthetic Symbolic Type of industries (typical examples) Biotechnology, life science, some segments of ICT. Automotive, aviation shipbuilding, process industry. Film, music, television, animation, publishing, gaming industry. Dominant mode of learning Science, technology, and innovation (STI) (formal R&D). Knowledge exchange occurs selectively, e.g. in scientific communities. Codified, specialized learning. Doing, using, and interaction (DUI). Knowledge exchange between supplier–customer and/or participants in community of practice. Learning by solving concrete practical problems (Know-how) DUI. Project-based learning and cooperation. Know-who and know-how. Knowledge exchange within regional milieu. Importance of related cultural knowledge. Knowledge base Analytical Synthetic Symbolic Type of industries (typical examples) Biotechnology, life science, some segments of ICT. Automotive, aviation shipbuilding, process industry. Film, music, television, animation, publishing, gaming industry. Dominant mode of learning Science, technology, and innovation (STI) (formal R&D). Knowledge exchange occurs selectively, e.g. in scientific communities. Codified, specialized learning. Doing, using, and interaction (DUI). Knowledge exchange between supplier–customer and/or participants in community of practice. Learning by solving concrete practical problems (Know-how) DUI. Project-based learning and cooperation. Know-who and know-how. Knowledge exchange within regional milieu. Importance of related cultural knowledge. Authors own table based on Martin (2012, 1422–1427). Table 1. Characterization of different knowledge bases Knowledge base Analytical Synthetic Symbolic Type of industries (typical examples) Biotechnology, life science, some segments of ICT. Automotive, aviation shipbuilding, process industry. Film, music, television, animation, publishing, gaming industry. Dominant mode of learning Science, technology, and innovation (STI) (formal R&D). Knowledge exchange occurs selectively, e.g. in scientific communities. Codified, specialized learning. Doing, using, and interaction (DUI). Knowledge exchange between supplier–customer and/or participants in community of practice. Learning by solving concrete practical problems (Know-how) DUI. Project-based learning and cooperation. Know-who and know-how. Knowledge exchange within regional milieu. Importance of related cultural knowledge. Knowledge base Analytical Synthetic Symbolic Type of industries (typical examples) Biotechnology, life science, some segments of ICT. Automotive, aviation shipbuilding, process industry. Film, music, television, animation, publishing, gaming industry. Dominant mode of learning Science, technology, and innovation (STI) (formal R&D). Knowledge exchange occurs selectively, e.g. in scientific communities. Codified, specialized learning. Doing, using, and interaction (DUI). Knowledge exchange between supplier–customer and/or participants in community of practice. Learning by solving concrete practical problems (Know-how) DUI. Project-based learning and cooperation. Know-who and know-how. Knowledge exchange within regional milieu. Importance of related cultural knowledge. Authors own table based on Martin (2012, 1422–1427). The knowledge base concepts are ideal based, and one would expect to find combinations of knowledge bases in different firms. Nevertheless, the three-fold distinction of different knowledge bases serves as a point of departure in the analysis in this paper. Because innovation processes in firms are strongly influenced by their dominant knowledge base, it may be a fallacy to ignore this in trying to understand how firms assimilate external knowledge (Asheim et al. 2011; Cohen and Levinthal 1990). The following sections will demonstrate the importance of firms’ knowledge base in terms of their AC for R&D-based collaborations. Drawing on Cohen and Levinthal (1990) the AC for R&D collaboration in firms is defined as ‘the ability of firms to recognize the value of new external information, assimilate it, and apply it…’ (Cohen and Levinthal 1990, 128). In this article AC is understood and used as an extensional definition to explore whether the firms use the external knowledge obtained in the R&D projects to further their collaboration or not, as is explained more in depth in the following sections. 3.4 Firm’s AC and its alignment with business processes in firms Cohen and Levinthal’s (1990) paper on AC has contributed to deepening our knowledge of how firms’ ability to assimilate new knowledge largely is a function of prior knowledge. Further how firms’ innovation performance is path- and history-dependent (Cohen and Levinthal 1990). However, AC is not only about assimilation, but also about exploitation and to what degree different firms are able to use new external knowledge. ‘The general consensus is that AC is a multidimensional construct involving the ability to acquire, assimilate, and exploit knowledge’ (Liao et al. 2003, 66). This multidimensional construct of AC is adopted in this paper and thus needs some elaborations to shed light on the mechanisms at play in firms’ receptiveness to an R&D-based policy financed project. Cohen and Levinthal’s original approach to AC has been used to understand investments in research and development, and has later been elaborated to understand and provide insights of organizational responsiveness and adaption of knowledge. In addition to Cohen and Levinthal’s original features of AC, mainly that of history dependence and domain specificity, Zahra and George (2002) have extended and reconceptualized the concept. They identify four organizational capabilities that build on each other and which are essential for AC, and in turn, organizational change and evolution. The capabilities are distinguished as how firms’ 1) acquire, 2) assimilate, 3) transform, and 4) exploit new knowledge (Zahra and George 2002). Acquisition of knowledge ‘[…] refers to a firm’s capability to identify and acquire externally generated knowledge that is critical to its operation’ (Liao et al. 2003, 66). The components involved in knowledge acquisition are prior investments and knowledge, as well as the intensity, speed, and direction of this acquisition. Assimilation of knowledge involves how firms understand, interpret, and assimilate the external knowledge in learning processes (Zahra and George 2002). Transformation is about internalization, and how the firms ‘[…] refine the routines that facilitate combining existing knowledge and newly acquired and assimilated knowledge.’(Zahra and George 2002). The final distinguished ‘AC capability’, exploitation, is about how a firm is able to incorporate the knowledge in its activities and operations, as well as how firms ‘[…] leverage existing competences or create new ones by incorporating acquired and transformed knowledge’ (Zahra and George 2002, 190). Zahra and George (2002) further differentiate between potential and realized AC. Potential AC captures firm’s acquiring and assimilation of external knowledge. While realized AC is the results of transformation and exploitation. In this sense, one may adhere a firm’s dominating knowledge base as described in Section 3.3 as a predominant condition for how they acquire and assimilate external knowledge. Whereas realized AC by extensional definition is the outcome of the collaborating project in this paper, which also influences long-term innovation activity in firms. The two subsets of AC co-exist, but neither is alone sufficient to improve firm capacities. In order to exploit knowledge, you first need to acquire it. However, acquiring new knowledge is no guarantee for successful exploitation of the knowledge (Zahra and George 2002). Thus, the different components of AC and how the firms go about building AC will influence the firms’ responsiveness to new knowledge, for example, knowledge acquired in collaborating projects initiated by an R&D brokering policy instrument. The surveyed firms in this paper are distinguished by their dominated knowledge base (see Section 3.3) and, thus, they have potential AC dependent upon their learning processes and initial ways of sourcing knowledge (see Table 2). Table 2. Knowledge bases and potential absorptive capacity Knowledge base Analytical Synthetic Symbolic Potential absorptive capacity High with regard to external R&D knowledge. Lower with regard to experience-based knowledge for commercializing research results. Low with regard to R&D-based knowledge. Higher with regard to experience-based knowledge about industrialization of research results. Low with regard to R&D-based knowledge and with regard to industrialization of research results. Knowledge base Analytical Synthetic Symbolic Potential absorptive capacity High with regard to external R&D knowledge. Lower with regard to experience-based knowledge for commercializing research results. Low with regard to R&D-based knowledge. Higher with regard to experience-based knowledge about industrialization of research results. Low with regard to R&D-based knowledge and with regard to industrialization of research results. Authors own table based on Martin (2012), Cohen and Levinthal (1990), and Zahra and George (2002). Table 2. Knowledge bases and potential absorptive capacity Knowledge base Analytical Synthetic Symbolic Potential absorptive capacity High with regard to external R&D knowledge. Lower with regard to experience-based knowledge for commercializing research results. Low with regard to R&D-based knowledge. Higher with regard to experience-based knowledge about industrialization of research results. Low with regard to R&D-based knowledge and with regard to industrialization of research results. Knowledge base Analytical Synthetic Symbolic Potential absorptive capacity High with regard to external R&D knowledge. Lower with regard to experience-based knowledge for commercializing research results. Low with regard to R&D-based knowledge. Higher with regard to experience-based knowledge about industrialization of research results. Low with regard to R&D-based knowledge and with regard to industrialization of research results. Authors own table based on Martin (2012), Cohen and Levinthal (1990), and Zahra and George (2002). Synthetic and symbolic firms lack prior R&D-based knowledge in contrast to analytical firms. Synthetic firms are in general experienced in the practical development of products and services. Symbolic firms, that are often project-based, may also be short of in-house competence in making new products and services from R&D projects. Small analytical firms, however, may lack the experience and competence in commercialization. The different firms’ receptiveness to the external knowledge (Realized AC) is thus dependent upon how well the firms manage to leverage the potential AC and use this to transform and exploit the knowledge to advance the firms’ activities and operation. 3.5 The set-up of the collaboration—an analytical framework Exploring the AC for future R&D collaboration in firms requires a theory informed research design, and drawing on the theoretical insights and typologies elaborated above, an analytical model as shown below (Fig. 2) is suggested. Since the potential for how firms learn and absorb knowledge differs, the outcome of the collaborating projects is also expected to differ. Figure 2. View largeDownload slide Analytical model. Figure 2. View largeDownload slide Analytical model. The firms apply for funding through the funding scheme, which has the overall aim to increase research-based innovation in regional industries. The policy instrument targets different industrial sectors, depending upon the priorities made by the governments in charge of the VRI program in different regions. The type of RIS is assumed to influence the regional industrial structure as well as what kind of dominating industries and appurtenant knowledge bases one may expect to find in firms. Firm-specific features of sourcing knowledge, learning, and performing innovation projects are in turn assumed to affect the firms’ potential AC for research-based innovation processes. The potential AC is assumed to affect firms’ long-term cooperation with R&D milieus or their realized AC. Three alternative outcomes are defined: ‘status quo’, ‘increased’, or ‘decreased’. ‘Status quo’ indicates relatively minor changes in how firms relate to R&D collaboration in the future. ‘Increased’ indicates that the projects have stimulated firms to increase their collaboration with external R&D partners. While the latter outcome, ‘Decreased’, is indicating the opposite of increased, pointing to barriers related to transforming and exploiting the external knowledge obtained in firm activities and operations. The geographical locations of firms are not part of the analysis in this article. A choice was made to concentrate on analysing the relationship between firms’ dominant knowledge base and AC for R&D-based collaboration (the shaded areas in Fig. 2). This is not to dismiss the importance of different regional contexts, but rather to suggest that this is relatively well documented in the literature already. Hence, to illustrate how the potential mechanisms and links put forward may work, take the example of a firm with a dominating synthetic knowledge base. Such firms normally source their knowledge for innovation within their value chain. Their point of departure are know-how and practical skills, and their potential AC is based on recombining existing knowledge by doing, using, and interaction (Asheim and Coenen 2005; Martin 2013). It is reasonable to assume that engineering-based firms, with a synthetic knowledge base, have a different starting point in transforming and exploiting research-based knowledge compared to firms with other knowledge bases. Analytical firms, more STI by nature, are expected to have greater potential for absorbing the external R&D knowledge, while firms with a dominating symbolic knowledge base may face some difficulties in transforming and utilizing R&D knowledge in to firm operations (see also Table 2). 4. Methodology Building on theories of innovation and learning processes in firms, a retroductive strategy (Blaikie 2009) was applied to try to establish the existence of structures and features that could explain the relationship between the dominant knowledge base in a firm and an outcome of either, ‘status quo’, ‘increased’ or ‘decreased’ in terms of future external R&D collaboration (see Fig. 2). 4.1 Data collection A total of 22 semi-structured interviews were conducted. The firms were selected according to the following main criteria. The firms are relative small/micro firms in a European setting. The firms have no or only minor experience collaborating with external researchers. All firms have received funding in the period between 2011 and 2015, to perform an innovation project in collaboration with an R&D institution. Of the 22 interviews, 6 of them were conducted with firms from the county of Finnmark, 6 with firms located in Rogaland county, and 10 firms located in the Agder counties. All three regions have been running a VRI program in the period chosen. The firms belong to a variety of industrial sectors, depending on the particular region’s focus. Semi-structured interviews were chosen because they give opportunities for gaining in depth information about the firms and their activities (Kvale and Brinkmann 2014). A qualitative study with semi-structured interviews can provide useful insights to the phenomenon being studied (Welch et al. 2011; Yin 2013). In this case, it can provide insight into whether the firms use the externally funded project to increase their R&D-based innovation activities in terms of future collaboration with R&D partners. The interview guide had the following main topics: Questions about the company and innovation activity, including questions to establish the importance of ‘Scientific-/research-based knowledge’ (STI) versus ‘Experience from past projects and/or regular activity in the firm’ (DUI). Questions about the project, including questions about how the project was established and the role of R&D partners and R&D in general. Project results, including questions about whether the project led to any internal/organizational changes and furthermore whether the firms would consider continuing the collaboration with the same or another R&D partner. Assessment of the VRI project, including questions about how they valued the project in terms of their innovation activity, and more generally whether the instrument was understood as being suited to their current needs, and finally about the suitability of other innovation policy instruments. The interviews lasted between 45 and 60 min. All informants agreed to be recorded and the interviews were later transcribed. 4.2 Analysis procedure Following recommended practices for qualitative data analysis (i.e. Gioia et al. 2013), all transcribed interviews were read trough in an iterative fashion, making notes of first impressions of any trajectories and links between the type of company and continued R&D collaboration. The next procedure involved exporting all the interviews to the NVivo qualitative Research Software. The coding of firms to different ‘knowledge base’ nodes was based on their own reported answers related to what kind of company they are in terms of products and/or services provided, as well as how they perform their innovation activities. The initial coding was later cross-checked and triangulated with generally available company information, as well as the theory of knowledge bases with typical examples of firms within each of the ideal types (see Table 1). The following is an example of coding from the study to illustrate a firm with a synthetic knowledge base: ‘We’re mainly an engineering-based firm. We have a close connection to market needs, and based on the market needs identified we come up with solutions and develop our products. We do this by combining prior experience with the existing products’ (Firm L, Table 3). An example of a firm with an analytical knowledge base can be seen in the answer from this firm when asked ‘Is the knowledge base of your company primarily scientific, engineering or creativity based’? ‘I would say scientific … there is a balance between research institutes, users, customers … I would say 1/3 each’ (Firm C, Table 3). And, lastly, here is an example of a firm in the symbolic knowledge base category: ‘Our primary concern is mediating local culture and nature’ (Firm H, Table 3). The firms were accordingly coded into the three main types of knowledge bases as shown in Table 3. Table 3. Firms’ dominating knowledge base in study Analytical Synthetic Symbolic Firm A (Rogaland) Firm L (Rogaland) Firm G (Finnmark) Firm B (Agder) Firm M (Finnmark) Firm H (Agder) Firm C (Rogaland) Firm N (Finnmark) Firm I (Finnmark) Firm D (Agder) Firm O (Rogaland) Firm J (Agder) Firm E (Agder) Firm P (Agder) Firm K (Agder) Firm F (Agder) Firm Q (Finnmark) Firm R (Finnmark) Firm S (Agder) Firm T (Rogaland) Firm U (Rogaland) Firm V (Agder) Analytical Synthetic Symbolic Firm A (Rogaland) Firm L (Rogaland) Firm G (Finnmark) Firm B (Agder) Firm M (Finnmark) Firm H (Agder) Firm C (Rogaland) Firm N (Finnmark) Firm I (Finnmark) Firm D (Agder) Firm O (Rogaland) Firm J (Agder) Firm E (Agder) Firm P (Agder) Firm K (Agder) Firm F (Agder) Firm Q (Finnmark) Firm R (Finnmark) Firm S (Agder) Firm T (Rogaland) Firm U (Rogaland) Firm V (Agder) Table 3. Firms’ dominating knowledge base in study Analytical Synthetic Symbolic Firm A (Rogaland) Firm L (Rogaland) Firm G (Finnmark) Firm B (Agder) Firm M (Finnmark) Firm H (Agder) Firm C (Rogaland) Firm N (Finnmark) Firm I (Finnmark) Firm D (Agder) Firm O (Rogaland) Firm J (Agder) Firm E (Agder) Firm P (Agder) Firm K (Agder) Firm F (Agder) Firm Q (Finnmark) Firm R (Finnmark) Firm S (Agder) Firm T (Rogaland) Firm U (Rogaland) Firm V (Agder) Analytical Synthetic Symbolic Firm A (Rogaland) Firm L (Rogaland) Firm G (Finnmark) Firm B (Agder) Firm M (Finnmark) Firm H (Agder) Firm C (Rogaland) Firm N (Finnmark) Firm I (Finnmark) Firm D (Agder) Firm O (Rogaland) Firm J (Agder) Firm E (Agder) Firm P (Agder) Firm K (Agder) Firm F (Agder) Firm Q (Finnmark) Firm R (Finnmark) Firm S (Agder) Firm T (Rogaland) Firm U (Rogaland) Firm V (Agder) A majority of the firms interviewed were coded in the ‘Synthetic knowledge base’ node. Considering that a majority of the firms, particularly in Agder and Rogaland, are specialized within the oil and gas sector, which is dominated by engineering-based firm, this is not a surprising result (Hauge et al. 2017). 5. Results and discussion3 Departing from the analytical model (Fig. 2), and based on the responses from firms with different knowledge bases, the study shows variations in their AC and, in the end, the outcome of the funding scheme. 5.1 Status quo—firms with an analytical knowledge base In companies with a dominant analytical knowledge base, scientific knowledge is highly imperative, and innovation processes are based on rational processes and formal models. Relatively large companies with this knowledge base typically have their own R&D departments, and small companies are often established based on basic or applied R&D projects involving radical innovation or new inputs within a specific field (Asheim and Coenen 2005; Martin 2013). Knowledge exchange and inputs are usually codified (e.g. scientific principles and methods), and companies with this knowledge base are explorative in nature. Knowledge application may often materialize itself in new firms and spin-offs (Asheim and Coenen 2005).The firms with a dominant analytical knowledge base in this study are relatively small/micro firms that were either established as a spin-off from larger firms, or as a result of previous R&D work at universities within a specific field. The role of R&D and the R&D partners is mainly reported as a mutual learning process, of equal benefit for both the firms and the R&D partners. As one firm reported, ‘… thus, my idea has been mutually competence building’ (Firm E). Another said, referring to the final project report, that ‘… having this kind of report is very important for building the scientific foundation for the whole system we have developed’ (Firm C). In firms with an analytical knowledge base, VRI projects may ease some of the burden of finding appropriate financing and perhaps integrating the R&D activities in the organizational structure, or expand the opportunity to fully exploit the R&D collaboration. As one firm reported, ‘… it’s hard to defend a full-time position within the field of optics. Thus it is important that we have a scientific environment … and I will ensure that they have enough to do’ (Firm E). A policy instrument aiming to stimulate research-based innovation may thus in principal help small firms with an analytical knowledge base to increase their in-house knowledge, leaving room for the more exploitative activities needed for profit. However, even though the process was mainly reported as being mutually beneficial, the firms’ previous experience with R&D also made them somewhat sceptical of the external R&D partners and the outcome. ‘VRI is perhaps not really for us, … I’ve had extensive experience with R&D and various R&D projects …’ and ‘… our experiences are not that good …’ (Firm D). Few of the firms report any internal or organizational changes as a result of the project, though one firm said that ‘… we will use the evaluation to improve the operation … not the program so much. But it will help us …’ (Firm C). While others reported that ‘… I made a simple analysis, and detected possible reasons for some of the mistakes we initially wanted to discover. This was done primarily independently of the [R&D partner]’ (Firm D). At a minimum one would expect these firms to continue their R&D activities based on their point of departure, displaying an outcome of ‘status quo’. Related to whether this particular instrument managed to stimulate the firms to increase their R&D activities, the results are more or less consistent. ‘It has partly pushed us …’ (Firm C) and, as another firm says, ‘We are currently in three EU funded projects …’ (Firm E). ‘So I would say that we have not gained that much related to this project’ (Firm D). These quotes are not really surprising, considering their point of departure for embarking on knowledge and knowledge development. However, all of them were grateful for the funding, as illustrated by one CEO when asked about the funding instruments importance, ‘It has been crucial. Because we wouldn’t have done this project without [the funding]’ (Firm C). Albeit, a quote from another CEO may sum up these firms’ approaches to the policy instruments designed to stimulate R&D collaboration, ‘The brutal reality is that you are looking for funding to develop the firm, and then you’ll find an excuse to apply [the funds] from that particular policy instrument’ but ‘… there is no [amount NOK] project, so we just make something work’ (Firm E). 5.2 Increased collaboration—firms with a synthetic knowledge base By nature, a company with a synthetic knowledge base is more exploitative than companies with an analytical or symbolic knowledge base. Their activities are normally based on know-how and practical skills, whereas much knowledge development occurs in supplier–customer relationships (Asheim and Coenen 2005; Martin 2013). A problem identified by a customer, which the company does not have the resources or funding to solve, can typically initiate a VRI project. Since they are more exploitative in nature, the VRI-funded projects may help the companies turn their focus in a more explorative direction by collaborating with research environments to solve a concrete challenge or problem. One of the firms illustrated this very well by saying that ‘Everyone is keen to get this project moving forward. Even if it is new thinking, it is not new technology. It is all about putting old technology together in different ways to create a new usage for it’ (Firm O). Another firm confirmed the importance of the R&D partner in the project by saying that ‘As it turned out, the researchers were quite right for the job … even though it was us that ran the project’ (Firm P). However, many firms reported a clash in expectations between the firm and the R&D partner, which caused one CEO to sum up the collaboration this way, ‘I had quite clear expectations … I had expected more enthusiasm and that they were better able to operate independently’ (Firm I). Another CEO reflecting on the wrapping up of the project described it this way, ‘The relationship became kind of strained in the end, because they [the R&D partner] were late on the delivery. In corporate life you cannot use the excuse that there was not enough time to finish a project … that is not the business way of doing things’ (Firm P). Another CEO was more explicit stating that, ‘… we are clearly coming from very different cultures …’ (Firm L). Despite the self-reported differences in culture, a majority of the firms with a synthetic knowledge base were positive to the end result of the project, as summed up by one firm saying; ‘In the aftermath of this [the project], we’ve had a very tight and frequent dialogue with the [R&D partner]’ (Firm T). Thus, the project seems to have been a point of departure for these companies to integrate more R&D-based innovation into their existing production/organizational structure, ‘… we are currently organizing future R&D activities in [the firm] more tightly’ (Firm Q). ‘Previously R&D activities were looked upon as something temporary until a specific problem was solved. Today our thinking is that R&D should be an integrated part of our daily work’ (Firm R). As expected and discussed in Section 3, collaboration works very well for companies with a synthetic knowledge base, as one CEO reported; ‘So you can say that the knowledge from the VRI project will be used in new business case projects with the same technology’ (Firm U). In a similar vein, many synthetic firms report internal and organizational changes attributed to the project, and confirm that future and increased collaboration with R&D partners is a real prospect; ‘Yes, absolutely, we are collaborating with other ones’ (Firm L). ‘… there was a lot of learning in the project on how to collaborate with R&D partners, which we have brought with us’ (Firm P). ‘This was only the beginning of a huge project, which has triggered a huge amount of money flowing to R&D. In this coming project a larger [R&D] Institution is involved’ (Firm Q). The outcome for these firms is thus much more consistent with the initial aim of the policy instrument, in terms of the firms consistently reporting an increase in their R&D collaboration. Apparently, the design and construction of the VRI project may be better suited for experience based, synthetic firms, compared to firms with a more scientific knowledge base. Let us see, then, how firms with a symbolic knowledge base responded to the policy instrument. 5.3 Decreased—firms with a symbolic knowledge base While synthetic and analytical knowledge bases have been discussed in the literature for a decade, more recently introduced is the symbolic knowledge base characterizing industries within, for example, media, film, publishing, and culture production (Martin and Moodysson 2011). Activities within these companies are often organized in short-term projects as well as ‘short-lived’ organizations, as in the example of film production. Firms with a symbolic knowledge base have more temporary networks involving a large number of actors, often highly geographically concentrated (Martin 2013). These variations in innovation networks suggest distinctive variations in available resources (small communities, highly selective, or fluctuating networks), project creations, knowledge exchanges, relationships, and interaction modes. All of which one may assume will lead to a slightly different outcome of the VRI-funding scheme compared with the previous two discussed groups of firms. The comments of one CEO were indicative of these types of firms’ attitude towards R&D, as she said, ‘I gladly admit that I thought “how on earth can you research this”? I then realized that I had to change my way of thinking’ (Firm J). Another CEO put it this way, ‘I thought it was super exciting to meet up with the R&D partners …’ (Firm K). Of the five firms coded with a symbolic knowledge base, one firm stands out. The reason for this is that its services are developed and offered primarily based on R&D reports about, for example, customer needs. As such, this firm is somewhat more experienced with R&D compared with the remaining four. Thus, the impact of the project it reported is also greater compared with the other firms. As the CEO of this particular firm describes it, ‘The project resulted in enormous internal changes. We have reorganized the whole firm, fired all the employees and established a new company’ (Firm G). The others were more reserved in their answers, as illustrated by Firm I, ‘It would have been fun to continue … but there is no time, I do not have the capacity to go on’. Others described the prospect of continuing or integrating more R&D into their company in the following way, ‘… right now I feel that we are in a phase where we should go back to square one, but it is on my to-do-list [about R&D collaboration)’ (Firm J). ‘We want to apply for another project and do more development … but you know …’ (Firm K). Their intentions are clearly positive, but ‘Business as usual is a barrier to development’ (Firm J). The last quote points at something extremely important and substantially emergent for many of these firms; that is the ‘day to day challenge’ of surviving. ‘In our line of business, the margins are extremely tight. We strive to keep our head above water to keep these margins’ (Firm J). Another barrier to development and innovation is the struggle to find personnel, or competent employees. ‘It is really a barrier to find the right people, even if they are out there’ (Firm K). However, as this is quite pressing for firms with a symbolic knowledge base, it is also apparent to other micro firms with other knowledge bases, as illustrated by an analytical firm; ‘Much of the time that could have been spent on development and innovation is instead used to get new customers’ (Firm D). However, as most symbolic firms build on know-who knowledge in their innovation process, the process of building capacity for new ways of doing business may be even more apparent as a barrier for these types of firms. A reflection from one of the firms confirm this, ‘What makes the process stop is lack of time and resources. The business is too dependent upon me as a person’ (Firm H). Know-who knowledge may not as easily transfer between projects and firms and they may encounter little in terms of the expected outcome of increasing R&D collaboration. Or, in the case of one of the firms in this category, when the changes happen they are quite dramatic (Ref. Firm G). The VRI instrument is designed to minimize the risk of exploring new lines of business and knowledge development. In theory, it should suit firms with a symbolic knowledge base very well. It should allow them to explore possible new organizational paths—for example, further codifying the ‘Know-who’ knowledge. However, the reported experiences deviate somewhat from this expectation. Although they would never have started such a project without the funding, their capacity for absorbing or sourcing the knowledge into new projects and/or further collaborations seems somewhat limited. 5.4 Implications for policy development The empirical study indicates a pattern. Firms’ AC for research-based collaboration depends on their dominant knowledge base. Firms characterized by an analytical knowledge base reports a status quo in their R&D-based activities. It is quite dubious if any increased activity or collaboration can be ascribed to the specific policy instrument. For firms with a synthetic knowledge base, the outcome of the instrument is more consistent with the overall aim in the funding scheme of increasing research-based collaboration. Finally, firms with a symbolic knowledge base stand out as being little affected by the policy instruments. The outcome seems to be minor in terms of increasing R&D collaboration. Innovation policy instruments targeting firms with a symbolic knowledge base, as it has also been argued in other studies, have little or a minor ‘effect’. For firms that strive to explain their innovation activity, the challenge of policy learning is even more immediate, ‘It is really hard to really describe what we are doing in terms of meeting the requirements from the support system’ (Firm H). Moreover, as another firm with a symbolic knowledge base explained about their encounter with a public funding administration, ‘… their reaction is ‘we do not know how to characterize this activity, it is not something we are familiar with’, and then they end up rejecting the application’ (Firm K). In her study of innovation policies and their relevance for the tourism sector, Hjalager (2012) finds that policies are relatively inadequate and ineffective. Hjalager (2012) concludes that the effectiveness of innovation policies requires, ‘[…] governance structures and frameworks that are supplied with a built-in policy learning ability that can lead to continuous development, adaption and legitimation, of future innovation policies’ (351–352). Innovation may be unknown and unfamiliar, but the purpose of innovation policy instruments is to ease the barriers encountered by industries and firms in their pursuit of innovation, development, and growth. The answers to the question of what these barriers constitute of are quite consistent among all the firms in the study, and reflect the comments by this CEO: ‘The barriers to innovation are first and foremost tied to financial capacity—to gain the financial muscles to go all the way. And then there’s recruiting and finding the right competence’ (Firm M). This overall barrier of funding led one of the other CEOs to reflect, ‘So what strikes me is, I’m meeting a long queue of smart people asking for money, and of course we all know there is a certain amount of money available from the authorities’ (Firm A). Knowledge about barriers to innovation, how firms initially source their knowledge or organize their development projects, is imperative for new policy development. Thus, the knowledge and competences of the administrative unit in charge of developing innovation policies is central (Fitjar 2016). Recent literature brings awareness of the fact that regional stakeholders and policy makers struggle to break out from the trajectories of policy making and regional development (Coenen et al. 2015). This is even evident when they are fully aware of the present challenges and the unsuitability of a ‘boxes to tick’ approach to granting funding for new innovation projects. But ‘[…]moving from theory to policy practice’ (Lagendijk 2011, 604) may be a strenuous affair. The public administration aspires for that money to result in something. However, this is not how it works in reality. For us it was just another 200,000 in a larger soup [of money]. Unfortunately, a majority of policy instruments are designed in such a way that you can receive funding for everything, except for what you actually need it for (Firm E). I have a dream that these public agencies are forced to think in new and different ways, only then can we start to innovate together (Firm E). The results of this explorative study, showing a pattern and relationship between firms’ dominant knowledge base and their AC for R&D collaboration (see also Table 2), have a number of implications for policy development. In aiming for long-term research-based innovation activities in firms, it would be a fallacy not to consider this pattern in new policy approaches. The perception firms have of policy instruments in general, as illustrated above, may be interpreted as a message to policy makers to break out of old trajectories in policy making—trajectories that have persisted for decades, treating all firms as being equally able to absorb external R&D knowledge, irrespective of their knowledge base. The path-dependent trajectory in policy making is also visible by the design of policy instruments that are still advocating a relatively linear innovation approach. In a similar vein, we should take into consideration that although the overall goal of increasing research-based innovation is fair and understandable in the Norwegian context, we need more differentiated policy instruments to make the outcome positive for all types of firms. In this perspective, the distinction between potential AC and realized AC can shed light on how to address key constraints for R&D collaboration in firms. Firms’ potential AC is conditioned by their initial knowledge base and the question is whether policy instruments can remedy barriers for realized AC. However, new policy development would need to take into consideration all aspects of AC (acquisition, assimilation, transformation, and exploitation) as well as the interdependency between the potential and realized AC in firms (see Table 4). Zahra and George (2002) propose that the acquisition and assimilation of knowledge in firms are affected by the diversity of external knowledge, while transformation and exploitation capacitates are improved by the similarity of external knowledge (Zahra and George 2002). To increase symbolic firms’ absorptive capacities, we may need research-based innovation models that include clusters of firms collaborating with R&D communities (cf. table 2). As they normally source their knowledge through short-term projects and through cooperation within a regional milieu, cluster constellations may work to strengthening their ability to both assimilate (potential AC) and exploit external knowledge (realized AC). One way forward for policy making would thus be to stimulate such cluster constellations in order to build firm capacities, for instance for research-based innovation. Table 4. Differentiated policy instruments Dominating knowledge base in firms Differentiated policy instruments Analytical Building capacity for more exploitative activities to remedy lack of experience with commercialization. Synthetic Building capacity for more explorative activities within firms to remedy lack of prior knowledge with R&D-based knowledge. Symbolic Combining both exploitative and explorative activities in networks, to remedy lack of both prior R&D knowledge and lack of in-house competence in exploiting external knowledge. Dominating knowledge base in firms Differentiated policy instruments Analytical Building capacity for more exploitative activities to remedy lack of experience with commercialization. Synthetic Building capacity for more explorative activities within firms to remedy lack of prior knowledge with R&D-based knowledge. Symbolic Combining both exploitative and explorative activities in networks, to remedy lack of both prior R&D knowledge and lack of in-house competence in exploiting external knowledge. Table 4. Differentiated policy instruments Dominating knowledge base in firms Differentiated policy instruments Analytical Building capacity for more exploitative activities to remedy lack of experience with commercialization. Synthetic Building capacity for more explorative activities within firms to remedy lack of prior knowledge with R&D-based knowledge. Symbolic Combining both exploitative and explorative activities in networks, to remedy lack of both prior R&D knowledge and lack of in-house competence in exploiting external knowledge. Dominating knowledge base in firms Differentiated policy instruments Analytical Building capacity for more exploitative activities to remedy lack of experience with commercialization. Synthetic Building capacity for more explorative activities within firms to remedy lack of prior knowledge with R&D-based knowledge. Symbolic Combining both exploitative and explorative activities in networks, to remedy lack of both prior R&D knowledge and lack of in-house competence in exploiting external knowledge. ‘Firms that can utilize research partnerships […] can increase their AC’ (Lau and Lo 2015, 100). Due to their STI nature, analytical firms are well prepared for utilizing R&D-based knowledge (cf. Table 2), but may benefit from more exploitative influence in order to stimulate the balancing act of both exploration and exploitation. This study’s result of ‘status quo’ in terms of R&D collaboration may indicate that these firms struggle to internalize new external knowledge in the organizational structure. Analytical firms are often characterized by knowledge application materializing itself into new firms and spin-offs (Asheim and Coenen 2005). A new organization or firm is often established to pursue knowledge acquired and covers the need for extensional funding. Policy instruments enabling these types of firms to pursue the transformation and utilization of new knowledge within an existing organizational structure would benefit long-term innovation activity. An R&D brokering policy instrument may as such not be the priority choice for these firms. The balancing act between core competencies and developing new ones is essentially different in companies with a synthetic knowledge base compared with more analytical firms. This feature has an essential impact on the capability building in these firms, and as they are more exploitative by nature, they would benefit from policy instruments that stimulate more explorative activities, such as an R&D brokering policy instruments. This is also confirmed by the study in this paper. 6. Conclusion The aim of this article was to explore how R&D brokering policy instruments influence long-term innovation activity in firms with different knowledge bases. The objective was to establish a relationship between firms’ knowledge base and their AC for policy supported research-based innovation. The qualitative explorative study showed a pattern indicating that firms’ AC for future R&D collaboration is dependent on their dominant knowledge base. This may indicate that the way they absorb external R&D knowledge influences their innovation activities in the long run. The role of research activity is quite intensive in innovation processes in firms with an analytical knowledge base. How much of this intensive effort the particular funding instrument can take credit for is quite ambiguous. It is probably not that much. To increase their innovation activity, more effort is needed to help these firms build capacity for more exploitative activities (cf. Table 2). In firms with synthetic knowledge bases, the study shows that the instrument stimulated further collaboration with knowledge organizations, even though many reported ‘a clash of cultures’ as being characteristic of their experiences. These firms may be highly stimulated to increase their innovation activities by R&D brokering policy instruments. For firms with a symbolic knowledge base, experiences involving something totally different in terms of methods, focus, and scope were reported. However, few of them report having any future capacity for collaborating with R&D organizations. These firms may benefit greatly from efforts to stimulate both explorative and exploitative activities. Today, however, the concurrent innovation policy paradigm fails to fit many symbolic firms. Hence, the overall argument in this article is that in addition to different types of RIS requiring different types of policies, different types of firms require different types of policy instruments to stimulate innovation processes. Drawing on current geography of innovation literature, the results of the explorative study are as expected for firms with synthetic and analytical knowledge bases. However, failing to acknowledge these firms’ specific features in new policy approaches may be a fallacy, and efforts are needed to bridge the gap between knowledge developed in the literature and knowledge adopted in policy development. This article is an effort to do so. We may also need to rethink how we stimulate research-based innovation. Both synthetic and symbolic firms lack prior R&D-based knowledge, and symbolic firms may also lack in-house competence of how to exploit new external knowledge. Small analytical firms often lack the experience and competence of transforming and exploiting external R&D-based knowledge into new services and products. The persistent innovation model, employed in the VRI programme, argues that when a firm is coupled with an R&D organization it increases research-based innovation activity. This model is, however, not viable in the long term. As many of the policy instruments available in Norway consider R&D to be an important impetus for innovation, a lesson drawn from this study is that we need more differentiated working methods in new policy approaches. There is a need for policy makers to dismiss the idea that all policy instruments targeting different firms will work equally well in terms of the outcome for all firms. Because, as previous research have confirmed, prior knowledge matters for the development of AC in all its complexity and highly impact the innovation performance of firms (Cohen and Levinthal 1990). This study is relatively small in both focus and scope, and more research is needed to explore the influence of firms’ differentiated knowledge bases on the outcome of innovation policy instruments in general. Moreover, since the knowledge base typology is ideal based one may expect to find combinations of knowledge bases in firms. How the efforts to stimulate research-based collaboration and innovation processes will turn out for these firms has yet to be explored. Finally, more research is needed in terms of deepening our knowledge about R&D’s role in different firms with different knowledge bases. Footnotes 1 The Research Council of Norway Url: http://www.forskningsradet.no/prognett-vri/Forside/1224529235249. 2 www.forskningsradet.no/vri. 3 Note that all quotes are the author’s own translation. Acknowledgements This research is in part funded by the Research Council of Norway in the Programme for Regional R&D and Innovation (VRI) project number 233788/O50. The interviews were in part conducted by colleagues at University of Stavanger and The Research Institute Alta Norut. I am grateful for valuable comments from the two reviewers, and feedback given at paper presentation at the 11th RIP Conference in Cardiff 3 and 4 November 2016. The usual disclaimer applies. References Asheim B. , Coenen L. ( 2005 ) ‘ Knowledge Bases and Regional Innovation Systems: Comparing Nordic Clusters ’, Research Policy , 34 / 8 : 1173 – 90 . Google Scholar CrossRef Search ADS Asheim B. , Gertler M. ( 2005 ) ‘The Geography of Innovation’. In: Fagerberg J , Mowery DC , Nelson RRs (eds) The Oxford Handbook of Innovation , pp. 291 – 317 . New York : Oxford University Press . Asheim B. , Boschma R. , Cooke P. ( 2011 ) ‘ Constructing Regional Advantage: Platform Policies Based on Related Variety and Differentiated Knowledge Bases ’, Regional Studies , 45 / 7 : 893 – 904 . Google Scholar CrossRef Search ADS Blaikie N. ( 2009 ) Designing Social Research . Cambridge : Polity Press . Borrás S. , Edquist C. ( 2013 ) ‘ The Choice of Innovation Policy Instruments ’, Technological Forecasting and Social Change , 80 / 8 : 1513 – 22 . Google Scholar CrossRef Search ADS Castellacci F. ( 2008 ) ‘ Technological Paradigms, Regimes and Trajectories: Manufacturing and Service Industries in a New Taxonomy of Sectoral Patterns of Innovation ’, Research Policy , 37 / 6 : 978 – 94 . Google Scholar CrossRef Search ADS Coenen L. , Moodysson J. , Martin H. ( 2015 ) ‘ Path Renewal in Old Industrial Regions: Possibilities and Limitations for Regional Innovation Policy ’, Regional Studies , 49 / 5 : 850 – 65 . Google Scholar CrossRef Search ADS Cohen W. M. , Levinthal D. A. ( 1990 ) ‘ Absorptive Capacity: A New Perspective on Learning and Innovation ’, Administrative Science Quarterly , 35 / 1 : 128 – 52 . Google Scholar CrossRef Search ADS Cooke P. , Leydesdorff L. ( 2006 ) ‘ Regional Development in the Knowledge-Based Economy: The Construction of Advantage ’, The Journal of Technology Transfer , 31 / 1 : 5 – 15 . Google Scholar CrossRef Search ADS Fitjar R. D. , Isaksen A. , Knudsen J. P. ( 2016 ) Politikk for Innovative Regioner . Oslo : Cappelen Damm Akademisk . Gioia D. A. , Corley K. G. , Hamilton A. L. ( 2013 ) ‘ Seeking Qualitative Rigor in Inductive Research Notes on the Gioia Methodology ’, Organizational Research Methods , 16 / 1 : 15 – 31 . Google Scholar CrossRef Search ADS Hauge E. S. , Kyllingstad N. , Maehle N. et al. ( 2017 ) ‘ Developing Cross-industry Innovation Capability: Regional Drivers and Indicators within Firms ’, European Planning Studies , 25 / 3 : 388 – 405 . Google Scholar CrossRef Search ADS Hjalager A.-M. ( 2012 ) ‘ Innovation Policies for Tourism ’, International Journal of Tourism Policy , 4 / 4 : 336 – 55 . Google Scholar CrossRef Search ADS Isaksen A. , Nilsson M. ( 2013 ) ‘ Combined Innovation Policy: Linking Scientific and Practical Knowledge in Innovation Systems ’, European Planning Studies , 21 / 12 : 1919 – 36 . Google Scholar CrossRef Search ADS Isaksen A. , Trippl M. ( 2014 ) ‘Regional Industrial Path Development in Different Regional Innovation Systems: a Conceptual Analysis’. Lund University, CIRCLE-Center for Innovation, Research and Competences in the Learning Economy.’ Jensen M. B. , Johnson B. , Lorenz E. et al. ( 2007 ) ‘ Forms of Knowledge and Modes of Innovation ’, Research Policy , 36 / 5 : 680 – 93 . Google Scholar CrossRef Search ADS Kvale S. , Brinkmann S. ( 2014 ) Den Kvalitativa Forskningsintervjun . Aalborg : Studentlitteratur . Lagendijk A. ( 2011 ) ‘Regional Innovation Policy between Theory and Practice’. In: Cooke Ps (ed.) Handbook of Regional Innovation and Growth , pp. 597 – 608 . UK : Edward Elgar Publishing Limited . Lau A. K. , Lo W. ( 2015 ) ‘ Regional Innovation System, Absorptive Capacity and Innovation Performance: An Empirical Study ’, Technological Forecasting and Social Change , 92 : 99 – 114 . Google Scholar CrossRef Search ADS Leiponen A. , Drejer I. ( 2007 ) ‘ What Exactly Are Technological Regimes?: Intra-industry Heterogeneity in the Organization of Innovation Activities ’, Research Policy , 36 / 8 : 1221 – 38 . Google Scholar CrossRef Search ADS Lengrand L. ( 2003 ) Innovation Tomorrow: Innovation Policy and the Regulatory Framework: Making Innovation an Integral Part of the Broader Structural Agenda . Luxembourg : Office for Official Publications of the European Communities . Liao J. , Welsch H. , Stoica M. ( 2003 ) ‘ Organizational Absorptive Capacity and Responsiveness: an Empirical Investigation of growth-oriented SMEs ’, Entrepreneurship Theory and Practice , 28 / 1 : 63 – 85 . Google Scholar CrossRef Search ADS Lundvall B.-Å. , Borrás S. ( 2005 ) ‘Science, Technology and Innovation Policy’. In: Fagerberg J , Mowery DC , Nelson RRs (eds) The Oxford Handbook of Innovation , pp. 599 – 631 . New York : Oxford University Press . Martin R. ( 2012 ) Knowledge Bases and the Geography of Innovation . Lund : Media-Tryck, Lund University . Martin R. ( 2013 ) ‘ Differentiated Knowledge Bases and the Nature of Innovation Networks ’, European Planning Studies , 21 / 9 : 1418 – 36 . Google Scholar CrossRef Search ADS Martin R. , Moodysson J. ( 2011 ) ‘ Innovation in Symbolic Industries: the Geography and Organization of Knowledge Sourcing ’, European Planning Studies , 19 / 7 : 1183 – 203 . Google Scholar CrossRef Search ADS Martin R. , Moodysson J. , Zukauskaite E. ( 2011 ) ‘ Regional Innovation Policy Beyond “Best Practice”: Lessons from Sweden ’, Journal of the Knowledge Economy , 2 / 4 : 550 – 68 . Google Scholar CrossRef Search ADS Pavitt K. ( 1984 ) ‘ Sectoral Patterns of Technical Change: Towards a Taxonomy and a Theory ’, Research Policy , 13 / 6 : 343 – 73 . Google Scholar CrossRef Search ADS Tidd J. , Bessant J. , Pavitt K. ( 1997 ) Managing Innovation: Integrating Technological, Market and Organizational Change . Chichester : Wiley . Tödtling F. , Trippl M. ( 2005 ) ‘ One Size Fits All?: Towards a Differentiated Regional Innovation Policy Approach ’, Research Policy , 34 / 8 : 1203 – 19 . Google Scholar CrossRef Search ADS Welch C. , Piekkari R. , Plakoyiannaki E. et al. ( 2011 ) ‘ Theorising from Case Studies: Towards a Pluralist Future for International Business Research ’, Journal of International Business Studies , 42 / 5 : 740 – 62 . Google Scholar CrossRef Search ADS Yin R. K. ( 2013 ). Case Study Research: Design and Methods . London : Sage publications . Zahra S. A. , George G. ( 2002 ) ‘ Absorptive Capacity: a Review, Reconceptualization, and Extension ’, Academy of Management Review , 27 / 2 : 185 – 203 . Google Scholar CrossRef Search ADS © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
TI - Firms’ Absorptive Capacity for Research-Based Collaboration—an Analysis of a Norwegian R&D Brokering Policy Program
JO - Science and Public Policy
DO - 10.1093/scipol/scx081
DA - 2018-08-01
UR - https://www.deepdyve.com/lp/oxford-university-press/firms-absorptive-capacity-for-research-based-collaboration-an-analysis-DjdMEt2FTL
SP - 533
EP - 542
VL - 45
IS - 4
DP - DeepDyve
ER -