Paper to be presented at the DRUID Academy Conference 2018at University of Southern Denmark, Odense, Denmark

January 17-19, 2018

Modes of innovation in mature industrial sectors - evidence from upstream petroleum

Erlend Osland Simensen University of Oslo

Centre for Technology, Innovation and Culturee.o.simensen@tik.uio.no

AbstractDespite its importance for global economy, innovation in resource-based industries is an under-studied

phenomenon. This paper analyses the importance of two ideal modes of innovation - STI and DUI - in theNorwegian upstream petroleum industry relative to other sectors. Our results show that there are fluctuations inthe composition of innovation modes across sectors, as expected. Furthermore, regression analyses show that

petroleum-related companies rely significantly more on the DUI mode in order to innovate. The effect of DUI oninnovation performance is stronger for process than product innovations. Our findings highlight the importanceof focussing on policy measures that facilitates DUI mode of innovation. More specifically, it is important toconstruct innovation policies aimed at increasing collaboration, hands-on training and knowledge diffusion

among companies and across industrial sectors.

Jelcodes: O30,-

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Modes of innovation in mature industrial sectors – evidence from upstream petroleum

Erlend Osland Simensen and Taran M. Thune

Centre for Technology, Innovation and Culture (TIC), Department of Social Sciences, University of Oslo, Norway

Abstract Despite its importance for global economy, innovation in resource-based industries is an under-studied phenomenon. This paper analyses the importance of two ideal modes of innovation - STI and DUI - in the Norwegian upstream petroleum industry relative to other sectors. Our results show that there are fluctuations in the composition of innovation modes across sectors, as expected. Furthermore, regression analyses show that petroleum-related companies rely significantly more on the DUI mode in order to innovate. The effect of DUI on innovation performance is stronger for process than product innovations. Our findings highlight the importance of focussing on policy measures that facilitates DUI mode of innovation. More specifically, it is important to construct innovation policies aimed at increasing collaboration, hands-on training and knowledge diffusion among companies and across industrial sectors. Keywords: Innovation modes; STI&DUI; Sectoral Innovation;

1. Introduction Companies’ characteristics and their surroundings influence how they work with innovation. What they produce or sell, who their customers are and which part of the value chain they belong to, are all factors that determine what type of innovation instruments that fit their strategy. For some types of companies, the level of R&D investments, patenting activities or the number of highly educated employees would be greatly important for their ability to develop new products and services. However, for many other firms, implementing and modifying existing technologies and development of knowledge through learning and working with existing solutions would be the normal way to stay innovative and competitive (Jensen, Johnson, Lorenz, & Lundvall, 2007). This latter type of innovation activity, although common, is more difficult to measure empirically, as it is informal and on-going and do not translate easy into specific expenditures or discrete activities that can be easily counted. Partly because of measurement challenges, innovation research has had a biased focus on hi-tech sectors and industries, and has tended to neglect innovation in so-called low-tech sectors and ‘softer’ type of innovations (Martin, 2016; Robertson, Smith, &

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von Tunzelmann, 2009). Lundvall (2007) calls the bias towards science-based indicators in innovation research a “distortion” of the innovation systems approach. He argues that “[t]he fact that science and codified knowledge become increasingly important for more and more firms in different industries – including so-called low-technology ones – does not imply that experience-based learning and tacit knowledge have become less important for innovation” (Lundvall 2007, p.4). Lundvall further stresses that this misinterpretation of innovation systems is behind the so-called “innovation paradoxes” that exist in various countries. Innovation paradoxes usually mean that for some national economies, there is no linkage between innovation performance and economic prosperity (Fagerberg, Mowery, & Verspagen, 2009; Fitjar & Rodríguez-Pose, 2013). The result is that social sciences and innovation studies provide little insight on the many innovations that has had impact on the society, although not captured by conventional innovation parameters. To exemplify why such narrow approaches are insufficient, Ben Martin (2016, p. 9) points to the enormous impact that the shipping container has had on the globalisation and economic growth, without having been subject for one single study in any innovation studies journal. Empirical research on innovation thus need to include complex analyses adapted to changing world where manufacturing has decreasing relative importance for the economy. To address this problem, we argue that it is need for a better understanding of innovation processes and outcomes in mature industrial sectors. These sectors are often classified as low- or medium-tech, but they are still significant, high performing and innovative. We argue that this is due to the mismatch between the low-tech notion and the most used innovation definitions (Mortensen & Bloch, 2005; Schumpeter, 1982). The definition of low- and medium-tech is based on companies’ share of R&D investments. This only captures a fracture of innovative activities in a firm, and innovation scholars should thus not rest on this definition when looking for innovation in firms and industries. Mature sectors are complex structures that often consists of a heterogeneous set of actors (von Tunzelman & Acha, 2006). Defining a mature sector or industry can therefore be a challenging task. This is particularly true for the so-called low- and medium- tech industries (LMT) that: “[…] resist easy classification, precisely because many of them are not distinctive or singular in technological terms” (von Tunzelmann & Acha, 2006, p. 411). Traditional industry codes do not capture the full width of an industry since they often focus on specific parts of the value chain, clearly not fitting an industry or a sector that consists of different types of companies serving different parts of the value chain. In mature industries, the locus of innovation is not necessarily inside the firm, but rather in the intersection between multiple firms that populate the complex value chains. Large, vertically integrated firms were previously common in many mature industries, such as car manufacturing, food production, pharmaceutical industry,

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mining, petroleum sector, etc. However, many of these large firms have increasingly disintegrated and created complex value chains around them. Consequently, also mature industries display networked forms of organising, and not only young, high-tech sectors (see e.g. Powell, Koput, & Smith-Doerr, 1996). This is also relevant for understanding why it is difficult to determine patterns of innovation in mature industries, as innovation occur across firms (Acha, 2002). To understand innovation in mature industries, the interaction between a heterogeneous set of economic actors is more relevant than looking at R&D&I investments within singular firms. With these perspectives in mind, the ambition of this paper is to investigate modes of innovation in mature industries that appears to be both ‘low-tech’, but still highly innovative and productive. We draw upon the literature on modes of innovation (Jensen et al., 2007) – the conventional Science and Technology Innovation mode (STI) and the more informal Doing, Using and Interacting mode of innovation (DUI). We use this dichotomy to empirically test whether firms in one mature industry - upstream petroleum production - rely more on the DUI mode of innovation and whether this explains their relatively high measured level of innovation, compared to other industrial sectors. The paper presents an empirical study that has attempted to overcome the limitations of existing studies by building a sample of firms bottom-up and that way able include the various parts of the upstream petroleum value chain. This includes companies not classified as oil companies, but nonetheless have huge parts of their activities in the oil and gas industry (which is true for many different types of manufacturing and service companies in Norway). By comparing a sample of petroleum-related companies to other Norwegian sectors we were able test the various assumptions about innovation in a mature industry like the upstream oil and gas. Firstly, we showed that this sector is not low- or medium-tech in a national context. Then we have been able to show that this industry is more reliant on DUI mode to innovate. In addition, these companies have a larger focus on service and organisational innovation rather than product innovations. Next, we will start the theoretical section with sectoral systems of innovation as a starting point. From here, we will outline the concept about modes of innovation with a particular focus on mature industries. Last in the literature review, we review existing studies of innovation within the petroleum industry to provide a context for the study. In section three, we present the methodology and data used, as well as the results of the empirical study. Finally, the last section presents a discussion of the findings, implications for theory and policies and suggestions for further research.

2. Literature review and analytical framework Sectoral variations and modes of innovation

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This paper takes a sectoral approach to innovation (Breschi & Malerba, 1997; Malerba, 2005)1. More specifically, we compare how companies belonging to one particular type of industry work with innovation to other sectors. Defining the boundaries of industries or sectors is not always a straightforward procedure. In an increasingly complex and interrelated economy, interaction between companies from different industries and sectors are becoming more important for the economic development. To understand how innovation happens and how it fosters economic growth, it is important to look at both the sectoral differences, but also how sectors and industries rely on each other in order to innovate. A seminal contribution to this literature is “Pavitt’s taxonomy” from 1984. Based on a database of British innovations, Keith Pavitt grouped manufacturing firms into four categories: supplier-dominated, scale-intensive, specialized suppliers and science-based industries. Firms from these different categories vary in several characteristics, but most importantly the way the work with technological change. This matter for our understanding of innovation in companies, and it is essential that researchers consider these differences in analyses of innovation. More recent sectoral taxonomies such as the one presented by Castellacci (2008), adds to Pavitt’s work by including service companies in the taxonomy. What is pertinent to ask is how companies in different sectors work with innovation? One way to investigate this is to look into their preferred modus operandi when they work with innovation. Do firms in certain types of industries work systematically different from other companies? A relevant literature stream to enlighten this topic is the research done on modes of innovation (Jensen et al., 2007). This literature argues that there are two archetypes of innovation; the science, and technology-based innovation mode (STI) and the learning by doing, using and interacting mode (DUI). Both of these modes are highly important in order to innovate. Although Jensen et al.’s seminal study has been important for empirical studies more recently, the concept has longer roots. The concept is tied to the notions of different ways of working with research and learning, and dates back to work from classical economists such as Adam Smith (Lundvall, 2007, p. 7). What characterises these modes, how do they differ across industries and sectors, and how do researchers operationalise them in empirical studies? The STI mode relies upon conventional and explicit knowledge, which is information that actors can share and acquire easily. A typical trait of such knowledge is that it is relatively easy to describe in a written manual and then understood by most readers that are educated within the area. Companies often acquire this type of knowledge by investing in R&D and by having high skilled employees. Since other companies easily can obtain such knowledge, intellectual property rights (e.g. patents) are used to 1Our analytical focus is on the companies, but we acknowledge that the characteristics we identify are highly influenced by the development of the surrounding innovation system. Hence, although this is not an innovation system analysis, we regard the system as an underlying explanation for the results in this analysis. 2Thereasonweonlyuseoneisthattheotherrelevantindicatorsarehighlycorrelatedwitheachother,whichaffectstheoverallmodel.

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protect it. Hence, STI mode of innovation is relatively easy to measure through conventional innovation parameters such as level of R&D expenditure, number of high skilled personnel and counts of patents. The STI type of innovation has thus traditionally been the basis of country-level innovation input metrics, and has been the established conceptualisation of innovation. However, in innovation research, the doing, using and interacting (DUI) mode of innovation has received increased attention in recent years. As the name implies, this is the type of knowledge creating and sharing that happens in interaction between collaborators and is embedded in everyday practices of firms. In this type of innovation, learning, experience and experimenting are key processes. This mode concerns the use and share of knowledge that is experience-based and tacit in nature (Nonaka & Takeuchi, 1995; Polanyi, 1967). The tacit trait of the DUI mode makes it significantly more difficult to quantify in statistical analyses. This is also apparent in the empirical studies of this mode. While the indicators used to measure STI are many and tangible, the DUI mode has been operationalised in a wide variety of ways. Apanasovich provides an overview of STI and DUI indicators in her review article from 2016 (p. 733). Here she identifies three standard indicators for the STI mode (R&D; trained scientific personnel; cooperation with universities/scientific institutions), but as many as 13 different indicators the DUI mode. The interacting dimension dominates the DUI indicators, where variables about internal and external collaboration and practical education measures are used. Other indicators have been expenditure on marketing related to technological innovation (e.g. market research, preliminary studies, etc.) and expenditure on technological preparation for production (such as design and engineering). The lack of conceptualisation of this mode empirically, makes it somewhat difficult to compare and perform robust analyses. One main literature stream on STI and DUI modes of innovation is to find the answer of the question of what is the “best mode of innovation” for various forms of innovation output. All studies show that having both STI and DUI is positive for the different indicators of innovation abilities of firms. However, there are nuances to these findings. Jensen et al. (2007) found that a combination of the STI and DUI mode of innovation is the most effective mode. Several studies have later validated this finding (Apanasovic, 2016, p. 734). Fitjar & Rodríguez-Pose (2013) found that the right combination depended on the type of innovation - whether it was incremental or radical, or product or process innovation. Collaboration with customers was particularly positive for all product innovation, but collaboration within the conglomerate was positive only for incremental product innovation. Furthermore, they found that the type of DUI interaction mattered for the innovation output. Interaction inside the value chain was generally seen as very beneficial for all types of innovation, whereas more informal collaboration with competitors seemed to be detrimental for innovation. Parrilli and Herras (2016) found that the STI mode was more important for the technological innovation, whereas the non-technological innovations seems to rely more on the DUI- mode.

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Most of the quantitative studies on modes of innovation are single country and cross-sectorial (González-Pernía, Parrilli, & Peña-Legazkue, 2015; Jensen et al., 2007). The focus has been more on the regional rather than the sectoral or industrial characteristics. One paper that regards the differences in modes of innovation between sectors (but still with a regional focus), is a qualitative study by Isaksen and Carlsen (2010). They compared two different industries in two different regions of Norway, and how they contrasted in the use of STI and DUI modes of innovation. The study revealed that the oil-related supplier companies in Stavanger did not collaborate with the local universities, but relied almost exclusively on DUI type of collaborations (i.e. with customers, suppliers and competitors). This was an opposite finding they had from marine biotech cluster in Tromsø and is also the opposite from what we know from extensive studies of the biotech industry in innovation studies (see e.g. Powell, Koput, Smith-Doerr, & Owen-Smith, 1999). Two studies have qualitatively approached how innovation in industries regarded low- and medium-tech makes use of the DUI mode of innovation. A study from 2011 on the Viennese food sector confirmed that these types of companies relied much more on non-formal collaboration links and a dominance of DUI mode of learning in-house (Trippl, 2011). Furthermore, a more recent study by Trott & Simms (2017) showed that packaged food industry in UK rely more on the DUI mode of innovation than STI compared to hi-tech industries. Here, they confirmed the propositions that this sector, as an example of a low-tech sector, was heavily reliant on the DUI mode to succeed in incremental product innovation. It is thus reasonable to expect that the variations of the relation between STI and DUI modes of innovation with innovation output are as heterogeneous as sectors and industries. So-called low- and medium-tech industries, which by definition score low on STI indicators, may have a stronger focus on the DUI mode than the classified hi-tech firms. However, most of these industry-specific approaches to modes of innovation are qualitative and limits to one particular part of low- and medium tech industries. The quantitative analysis of modes of innovation also seem to have a predominantly regional focus and do not address the differences between sectors or industries directly. Against this background, an ambition in this paper is to investigate whether the DUI mode is more important for mature industrial sectors. The sector we study is the upstream petroleum industry. Innovation in the upstream petroleum industry The upstream petroleum industry is a heterogeneous sector of economic activities. It can perhaps be best described as a complex value chain where a variety of firms in multiple industries are involved in activities related to the identification, extraction, production, transport and sales of unrefined hydrocarbons in the form of crude oil and natural gas. Innovation in the upstream petroleum industry is a topic that has received very little attention in the research literature. This is surprising as the industry is both technology intensive and highly productive, but also very significant for the global economy (Acha, 2002; Pinder, 2001)

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According to Acha (2002), innovation in upstream petroleum is characterised by dense networks between heterogeneous agents that make up the petroleum innovation system. During the previous three decades, the disintegration of large integrated oil companies, has given way to a more complex innovation system with multiple players and dense networks (Acha, 2002; Bagheri & Di Minin, 2015; Perrons, 2014; Shuen, Feiler, & Teece, 2014). Bagheri & Di Minin (2015) claim that the upstream petroleum industry has undergone a “substantial but silent” change, which has altered power structures and patterns of collaboration between main actors. They argue that these changes are long-term responses to technological developments in the industry, location of assets and political changes towards nationalization of petroleum resources. The change at industry level that they portray is the change from a limited number of large, integrated petroleum companies, towards a growth in a large number of specialized companies that target particular parts of the value chain or particular petroleum regions. The two major kinds of petroleum related companies - the operators who own and operate assets and the service and supplier firms are largely complementary. Oil operators concentrate on identifying and characterizing reservoirs and management of exploration and extraction processes. Service and supply companies deliver engineering services and develop a range of products both in the development of new fields and offer services in fields that are currently producing. Operator companies have developed substantial knowledge in how to identify and manage hydrocarbon reserves. Most of this knowledge is synthetic in character and derive from operational experiences. Knowledge with strategic advantage for operators is not technological applications in themselves, but knowledge of how and where to use new technologies. They act as “systems integrators” and “lead users”. In the oil industry, most new technologies are complex, expensive and only deployed in small quantities. The use of demonstration projects and field-testing to make new technologies “ready” for deployment is necessary. Operators therefore play a role as sponsor, test site and lead user in the innovation system.To fulfil this function “the company not only develops but absorbs and adapts needed technologies that are new not only to the firm but to the system in which it is acting” (Acha 2001, p.81). Service and supplier companies that develop new technologies are therefore highly dependent on the operators to test their technologies and through this, gain access to new markets. According to Acha (2002), the increased tendencies of specialization and collaboration is a result of the technological and economical challenges that have characterized the industry the last decades. Development of new oil fields is a key innovation arena through which these processes occur. Another tool commonly used to reduce risks and share burden of developing and qualifying new technologies is the

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use of joint industry projects (JIPs) as well as collaborative R&D projects for technology projects with more uncertainty and longer time horizons. Perrons (2014) has attempted to address the consequences of the above-mentioned changes for the innovation performance and patterns of collaboration in the innovation system. The study has many interesting and revealing results, particularly pertaining to the division of labor between the players in the industry and the different modes of innovation that coexist in the industry. The study indicates that operators rely on service companies to innovate and mainly deploy or implement innovations developed by its suppliers. Supplier companies develop both new technologies and generate patents. Small supplier companies are most active in development of radical technologies; but overall large service companies develop the majority of new innovations. In general, firms in upstream petroleum do not regard public R&D as an important source of innovation and do not frequently collaborate with public R&D institutions, such as universities. A key issue addressed about the upstream petroleum industry in the innovation literature is how R&D intensive this industry is, and how this has changed over time. As with all engineering-based industries, measuring technology intensity is difficult as both data on the investment side and economic performance are of such a character that investment and deployment of new technologies are likely to be underreported. The general impression of industry experts is that petroleum identification and extraction increasingly is a high technology “play” due to the increasing technological challenges involved in producing hydrocarbons. As seen above, investment in R&D to support development of new technologies are increasingly carried out by suppliers, but often partly funded by the operators. This might lead suppliers to classify such activities as services, rather than R&D. Likewise, the testing and experimentation with new technologies developed by suppliers – i.e. the lead user function - might not be accounted for as R&D by the operators. Since a lot of R&D work is performed in collaboration, it might in fact not be registered by either party. The consequence is that the upstream petroleum sector appears less R&D intensive than it really is. The locus of innovation in this mature and complex industry is clearly the collaborative networks between firms. In this regard, the industry can be seen as a model industry for understanding how collaboration can be a driver of innovation in mature and complex industries. As seen above, research on modes of innovation in low and medium-tech industries has shown that there is a larger emphasis on the DUI mode in these types of industries. We have also seen that the oil and gas industry is regarded low- and medium-tech, despite its complexity and innovativeness. Furthermore, the locus of innovation in the oil and gas industry lies in the network among firms, which is also contributing to an underassessment of the R&D-intensiveness of the industry. According to the author of most thorough study on innovation in this industry: “upstream petroleum is the definitive process-based industry” (Acha and Cusmano,

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2007, p. 6). Since, most technologies developed and deployed is directed at increasing production volumes or efficiency, we assume that a lot of innovation efforts are directed towards process innovations, and not product innovations. Based on these characteristics, we expect that the DUI mode of innovation is more important for innovation performance in upstream petroleum, and particularly for process innovations, as formulated in the two following hypotheses: H1: The DUI mode of innovation is more important for innovation performance in oil and gas related supply companies, compared to other sectors. H2: The DUI mode of innovation is particularly important for successful deployment of process innovations among oil and gas related supply companies. The DUI mode of innovation is conceptualised in many different ways in empirical exercises (Apanasovich, 2016). It is thus important to be more specific of what types of DUI-related parameters that we expect would be more important for innovation in these types of firms. The petroleum suppliers need to adapt their technology to their customers’, i.e. the oil companies, requirements and standards. For the suppliers to be able change or improve a component in an offshore field, the changes must comply with other components and standards. Hence, a strong collaboration with the oil companies is positive for their ability to innovate, relative to other sectors. This applies also for smaller under-suppliers that have to have a sound collaboration with their customers, the larger supplier and service firms. This leads us to the following hypothesis: H3: Particularly important for innovation in the upstream petroleum supplier industry is a close collaboration between suppliers’ and their customers.

3. Methodology, data and results 3.1 Data

Since the oil and gas industry is diverse and spans an extensive range of industry codes, there is no direct way to define this industry. This is particularly valid for the companies that supply the oil operators with equipment and technology. These are the most numerous types of companies in the industry and serve different roles in the oil and gas innovation system. In order to overcome this challenge we sampled the companies bottom-up. We have included companies that are members of oil related industry associations, and our sample thus consists of the companies that regard themselves as oil-related supplier companies. By setting such a strict definition of what a supplier firms is, we have made a sample of the most important supplier firms, but we do not claim to cover all companies in the sector. Our sample included 617 firms, which is significantly lower than estimates that has aimed at describing the whole industry (Blomgren et al., 2015; Rystad Energy, 2014). We have thus rather aimed at making a sample applicable for statistical analysis of these archetypes of companies. The pragmatic reason for this approach is that making a database that

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covers all oil-related companies is very difficult, and including all companies that are have some oil and gas related activities could harm our analysis for the two following reasons. Firstly, deliveries to oil and gas projects fluctuates with the conjunctures in the industry, hence companies that one year had large deliveries to the oil and gas industry, could the next year have nothing. Secondly, companies that only have part of their income in the oil and gas industry (some definitions use 20% of the turnover), could be detrimental for our analysis in that we pollute the sample with companies that mainly work towards other sectors. In addition, the aim of this study is not to delineate the Norwegian oil and gas industry, hence making a core sample would be more beneficial for our type of analysis.

To analyse the impact of DUI and STI modes of innovation across sectors in Norway, and with a specific focus on the oil-related industry, we combined the Norwegian CIS survey from 2012 with our database of supplier and service companies. That way we were able to identify the core of oil-related supplier companies and how they answered in the CIS survey. This also allowed us to combine the extensive qualitative data in the SIVAC database with the biannual innovation and R&D survey. When matching the datasets we ended up with a sample of 329 oil and gas - related supplier companies. This is an acceptable coverage of our total population of 617 companies in total. It could nevertheless raise some issues about the significance level of the SIVAC results, as well as generalisability the results has for the all oil related petroleum companies. However, as the results indicate, we are able to achieve significant results when using only this sample. Large firms, that is, larger than 10 employees, have to answer the CIS survey each year. This allows us to draw some conclusions on behalf of the largest and most central Norwegian oil and gas related supplier companies. The sample consists of a diverse set of companies, with 85 different NACE codes present, see the distribution on the sector level in figure 1. Mining and quarrying, the only sector with directly petroleum-related NACE codes, make up only 16% of the sample, whereas manufacturing firms make up 50% of all the companies.

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Figure 1. NACE composition of the 329 supplier related companies with match in CIS

3.2 Variables and descriptive statistics As reviewed in the theory section, STI indicators of innovation are usually quite consistent, whereas DUI indicators varies between studies (Apanasovich, 2016). The CIS survey includes many variables on reasons to (or not to) innovate, innovation inputs and processes, outcomes as well as many items on the nature of innovation collaboration. For the STI dimension, we have used the logarithmic function of the R&D intensity in the companies2. For the DUI variable, we have first made a composite variable of several dimensions (see table 1). Then we tested the individual effects of these items on the next model (see part 3.4).The “interacting” part of DUI is the easiest to find fitting variables for in the CIS dataset. Here we have applied four variables about whether or not the company has collaborated within the company or with non-scientific partners in order to innovate (Fitjar & Rodríguez-Pose, 2013). In addition to these items, we have added two more items relevant for the DUI mode of innovation. The fifth variable, whether or not the company has “bought external knowledge for development of new products and/or processes, outside R&D”, relates to a measure of non-R&D expenditure for knowledge about new products or processes, connected to the DUI-mode (Apanasovich, 2016, p. 733). The sixth 2Thereasonweonlyuseoneisthattheotherrelevantindicatorsarehighlycorrelatedwitheachother,whichaffectstheoverallmodel.

MINING AND QUARRYING

16 %

MANUFACTURING 50 %

WATER SUPPLY 0 %

CONSTRUCTION 2 %

WHOLESALE 4 %

TRANSPORTING AND STORAGE

6 %

ICT 4 %

FINANCIAL AND INSURANCE ACTIVTIES

0 %

PROFESSIONAL, SCIENTIFIC AND

TECHNICAL ACTIVITIES

18 %

SECURITY AND INVESTIGATION

ACTIVITIES 0 %

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variable is whether the company has been involved in “competence building directly affiliated with the development and/or introduction of new or improved products or processes”. This relates to staff training of non-R&D related processes increasing internal knowledge flow, and hence is an important part of the DUI mode. We have also used three different measures for innovation output as the dependent variables. The first is total turnover stemming from new or improved goods for the market or the company. Hence, this variable applies a broad definition of innovation. The two others dependent variables used are the dichotomous variables of whether or not the company has introduced product innovations or service innovations between 2010 and 20123. See table 1. Table 1. Variables. Explanatory variable Explanation STI Log of total R&D intensity. DUI Whether the company has collaborated with (0-4):

1) own company 2) customers 3) suppliers 4) competitors In addition, the two items: “competence building directly affiliated with the development and/or introduction of new or improved products or processes” [rtr] “Bought external knowledge for development of new products and/or processes, except R&D”. [roek] DUI dimension takes the values 0-6. Chronbach’s alpha: 0,793.

Dependent variables Innovation turnover 2012 Log of total turnover stemming from new or improved goods, both for the market

and the firm (radical and incremental innovation) Product innovation Whether the company has introduced product innovation by new or better goods in

2010-2012. Service/organisational innovation Whether the company has introduced product innovation by new or better services in

2010-2012. Control variables Size Log of total amount of employees. Economic regions Dummies of each economic region (two digit level) in Norway as defined by SSB

(SSB, 2002).

3 Here we have also applied lagged variables to the analyses. That is, we have used 2014 variables as dependent and 2012 variables independent. Many of the results that follow are confirmed with lagged variables; however, by crossing variables, we lose quite an amount of variables (since not that many companies have answered both surveys). Therefore, for the sake of the consistency of the analyses, we have decided to use single year variables.

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Table 2. Sample size for the sectors, as well as means and standard deviations of variables in the models across sectors Dependent variables Independent variables Control

Sector N Inno_2012 Product innovation

Service innovation DUI STI LOG EMPL

Petroleum supplier companies

329 4.890 (7.370)

0.252 (0.435)

0.125 (0.330)

0.915 (1.528)

.0712 (0.523)

4.753 (1.340)

All companies

6266 2.744 (5.661)

0.149 (0.356)

0.0764 (0.266)

0.493 (1.142)

0.0531 (0.339)

3.547 (1.209)

Manufacturing C

2016 3.733 (6.413)

0.257 (0.437)

0.0278 (0.164)

0.650 (1.313)

0.0354 (0.220)

3.516 (1.198)

Financial and insurance activities K

259 1.244 (4.157)

0.0193 (0.139)

0.112 (0.316)

0.305 (0.974)

0.0228 (0.2889)

3.824 (1.369)

Professional, scientific and technical activities M

682 3.134 (5.820)

0.138 (.0344)

0.133 (0.340)

0.573 (1.174)

0.209 (0.821)

3.109 (1.089)

Constructing F

694 0.313 (2.114)

0.0159 (0.125)

0.0115 (0.107)

0.130 (0.620)

0.00075 (0.00643)

3.915 (0.876)

Mining and quarrying B

226 2.046 (5.285)

0.0929 (0.291)

0.0619 (0.242)

0.597 (1.266)

0.0580 (0.445)

3.971 (1.610)

When simply comparing mean scores of the independent and dependent variables some interesting differences occur. There are rather large fluctuations among the different sectors (one level NACE), manufacturing firms score high on product innovation, and scientific and technical activities have high on the service innovation. The Norwegian oil-related supplier companies score relatively high on all measurements of innovation applied in this database. However, there are large differences in sample size and standard deviations and the oil and gas related companies are relatively bigger in size, which can explain some of their increased STI activity and innovation performance. In order to investigate these mechanisms and differences further, we can perform regressions for each group and then compare them. This allows us to see if there are variations between sectors on what mode of innovation that is important for innovation activity. Moreover, we are also able to control for the firm size in each group. 3.3 Regression results across different sectors

In order to test how STI and DUI modes of innovation affects innovation across different sectors in Norway, we built a simple regression model with the STI and DUI indicators as explanatory variables. We ran this model on three different dependent variables from the CIS data and compared the results across industries. We first tested this in an OLS model where the dependent variable was total income stemming from innovation. Here, we applied a broad definition of innovation where both turnover from innovations that were new to market and new to firm were included. Results from the OLS model are in table 3. Secondly, we used the dichotomous variables on

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product and service/organisational innovation as dependent variables in two Logit regression models (see 3.4). In the latter we included interaction terms between the petroleum suppliers and the STI and DUI explanatory variables, see table 4.

Table 3. OLS regressions across industries

Petro suppliers

All companies Manufacturing Finance and

insurance Scientific services Construction Mining and

quarrying R&D intensity

0.333(0.648)

0.471*(0.189)

1.121*(0.561)

-0.0332(0.934)

-0.165(0.248)

90.29***(11.67)

-0.291(1.038)

DUI scale 2.799***(0.237)

2.494***(0.0565)

2.449***(0.0995)

3.171***(0.274)

2.438***(0.175)

0.904***(0.122)

1.763***(0.295)

Employees (log)

0.286(0.279)

-0.0431(0.0542)

0.460***(0.110)

0.200(0.187)

0.211(0.191)

-0.0310(0.0895)

0.191(0.250)

Economic region Controlled Controlled Controlled Controlled Controlled Controlled Controlled

_cons -1.255(3.663)

-1.118(2.028)

0.660(0.835)

-0.631(2.527)

-4.772(2.601)

0.139(1.187)

-1.649(4.876)

Observations 323 6158 2001 209 679 692 213 Adjusted R2 0.348 0.258 0.291 0.424 0.241 0.163 0.175 Standardized beta coefficients; Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

For the overall results of the OLS model, it is apparent that STI and DUI modes of innovation are significant and positively related to all the three dependent variables for innovation. This holds for nearly all industries we tested this in, a result that is in line with previous research on the topic (Apanasovich, 2016; Fitjar & Rodríguez-Pose, 2013; Jensen et al., 2007). Although the main results share similar directions across industries, there are variations in magnitudes. For the oil-related suppliers, the effect of DUI mode of innovation seems to be somewhat stronger than for much of the other sectors in Norway, the only exception being the finance and insurance sector. R&D investments have no significant effects with innovation output in the oil and gas sector, according to this model. A compelling result is the strong effect that R&D investments have on innovation in the construction sector. Here, there is very little R&D activity and the ones that invest in R&D have a much higher tendency to have turnover from new or improved goods. To shed light on these findings, we are in need of a more refined analysis with interaction variables and individual effects.

3.4 Detailed model with interaction variables

It is important to note that sample sizes are small and that the standard errors are rather large. It is thus difficult to say whether the observed differences between the sectors are significant or not. In order to investigate this we introduce interaction variables with the STI variable and all the individual DUI-related variables. Moreover, we ran these explanatory variables in two additional models: one for product innovation as dependent variable and another for service innovation. This way we are able to observe if the differences hold for both types of innovation and for the different types of the DUI mode. There are two main reasons for including the extra regressions. Firstly, innovation is multifaceted and we suspect due to the

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literature on O&G companies and the LMT sector that our sample of companies put more emphasis on service innovation than on product innovation (hypothesis 2). Secondly, since innovation is a phenomenon that can be interpreted in many different ways, using several dependent survey data variables strengthens the findings if the results are consistent. See table 4 for the general model.

Table 4. Detailed models with each item. OLS with income from innovations as dependent variable and two Logit regressions for product and service/organisational innovation. Innovation broad

OLS (1)

Product innovation Logit

(2)

Service innovation Logit

(3) R&D intensity 0.451*

(0.181) 0.294**

(0.0898) 0.300**

(0.0985) Internal collaboration 1.072**

(0.353) 0.205

(0.178) 0.244

(0.202) Collaboration with suppliers 0.878*

(0.343) 0.260

(0.174) 0.0532 (0.205)

Collaboration with clients/customers

3.399*** (0.335)

1.278*** (0.165)

0.629** (0.195)

Collaboration with competitors -0.977*

(0.380) -0.455* (0.196)

-0.000677 (0.211)

Competence building 6.424***

(0.193) 2.019*** (0.0980)

1.970*** (0.123)

External knowledge bought outside R&D

1.620*** (0.262)

0.314* (0.125)

0.339* (0.141)

Log employees (control) -0.115*

(0.0516) -0.138*** (0.0352)

-0.161*** (0.0451)

Economic region (control) Controlled Controlled Controlled _cons 1.617***

(0.189) -2.049*** (0.126)

-2.759*** (0.163)

N 6158 6158 6158 adj. R2 /Pseudo R2 0.324 0.200 0.190 Standard errors in parentheses ^ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

The strength of this model is the introduction of the interaction variables between our two explanatory variables and the oil and gas related supplier companies. This model allows us to observe whether the differences between the petroleum-related suppliers compared to the rest of the companies are significant. The main results are consistent over the three models and confirms our OLS results from table 3. However, it also relieves some interesting nuances. The main pattern for all sectors, that STI and DUI modes of innovation are important for innovation output, is confirmed also for the two additional dependent variables. However, collaboration with competitors negatively correlated with innovation output, a finding that is consistent with Fitjar and Rodriguez (2013). To investigate the differences for the supplier companies, we can have a look at the interaction terms between these variables and the petroleum suppliers. See table 5.

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Table 5. Interaction terms with the petroleum supplier companies

Interaction terms with supplier firms Innovation broad OLS

Product innovation Logit

Service innovation Logit

R&D intensity * Petroleum suppliers 0,254 (0.0531)

0,272 (0.303)

-0,176 (0.356)

Internal collaboration * Petroleum suppliers 2.602*** (0.775)

-0,19 (0.387)

1.227*** (0.379)

Collaboration with suppliers * Petroleum suppliers 1.308^ (0.777)

-0,381 (0.365)

0,467 (0.372)

Collaboration with clients/customers * Petroleum suppliers

1.884** (0.725)

-0,341 (0.343)

0.964** (0.348)

Collaboration with competitors * Petroleum suppliers

3.053* (1.217)

-0,024 (0.578)

0,393 (0.576)

Competence building * Petroleum suppliers 1.336** (0.496)

0,302 (0.226)

-0,339 (0.240)

External knowledge bought outside R&D * Petroleum suppliers

2.642*** (0.706)

0.642^ (0.344)

-0,255 (0.350)

When it comes to the particularities of the oil and gas related companies, there are no indications that the oil and gas related supplier companies are significantly different when it comes to the importance of R&D investments on innovation. This holds true for all three dependent variables. On the DUI mode, however, (almost) all DUI variables are positive and significant. The effect seems to be most consistent and strongest for internal collaboration and collaboration with clients/customers. These two results are confirmed only for service innovation, but not for product innovation (H3). This result indicates that it is not necessarily product innovation that is the main output of collaborative efforts in the oil and gas industry. Lastly, petroleum-related companies do also seem to have higher effects of collaboration with competitors, competence building and purchase of external knowledge outside of R&D. These results are not confirmed by the Logit models, but seem to have only a consistent effect on innovation measured in monetary terms.

4. Discussion and implications In this paper, we have explored the variations in innovation modes among sectors in Norway. More specifically, we have investigated the oil and gas related supplier companies’ composition of innovation modes and compared them to the other sectors in Norway. Our model confirms that both STI and DUI modes of innovations are positive for innovative output, which holds for all indicators of innovation tested. We also show that there are fluctuations across sectors for the relative importance of these modes on innovation. We confirm that the enhanced importance of the DUI mode in the oil and gas related industry is significant (H1), also on some variables for service innovation (H2). We were also able to shed light on which parts of the DUI mode that this holds for. Collaboration with customers were more important for the suppliers (H3). In fact, most indicators of DUI seemed to be more important for supplier

17

companies. The strongest effects were the customers and internal collaboration since they also showed an enhanced effect for service innovation. Our findings contribute to the modes of innovation literature by showing quantitatively that a particular type of sector characterised as non-high-tech has a significant larger benefit from the DUI mode of innovation than other sectors. It is the first paper, to the authors of this paper’s knowledge, to quantitatively test the differences of the STI and DUI modes’ impact on innovation output across sectors. This way we have been able to confirm previous case studies of other low- or medium-tech industries (Isaksen & Karlsen, 2010; Trippl, 2011; Trott & Simms, 2017). By applying a unique dataset of petroleum-related suppliers, we have been able to show that this sector indeed is more dependent upon experience-based knowledge and collaboration compared to other companies. We overcome the weaknesses of anonymous CIS data by sampling our data bottom-up and then applying micro level data from the survey. Our study is thus a contribution to understanding poorly defined sectors, such as many low- and medium-tech sectors, better. Our composition of oil-related supplier companies also confirms that using NACE codes to define such an industry is rather inadequate. It is reasonable to assume that this result is transferable to other mature industries in other countries. These are industries that draw on various forms of knowledge and consist of companies spanning many industry classifications, either as customers or as suppliers. We have used the notion of low- and medium-tech sectors throughout in this paper, but we will argue that such a distinction is rather inadequate for a good understanding of innovation and economic growth. Rather, we should apply the initial meaning of innovation, and innovation scholars should pursue studies of sectors that have complex learning habits. This requires a better conceptualisation of the DUI mode, in particular for use in statistical analyses. That way, it would be possible to construct more convincing arguments of the existence and importance of these types of industries. Moreover, to better be able to compare results across national, sectorial and technological boundaries. Since innovation research has been biased towards investigating hi-tech industries such as the biotech industry, the knowledge about these sectors is relatively scarce. This has led policy makers to believe that STI stimulating activities are most important to foster innovation. In this paper, we have shown that there most likely are significant and fundamental differences between sectors and industries on how they work with innovation. This underpins the mantra that one size does not fit all in innovation policies. If it is so that this bias has led to neglecting of these parts of the economy, this is not trivial. Only 3% of all value added stems from high-tech industries (von Tunzelmann & Acha, 2006, p. 407), basing policies on only these types of industries would therefore be detrimental for the economy and economic growth. These are central themes that innovation studies since its beginning has had ambitions to explain.

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Our findings have thus some important implications for policies on the national, regional and sectoral level. As has been stressed by many innovation researchers in recent years (Lundvall, 2007; Robertson et al., 2009), innovation policies should be more than only stimulation of R&D in the high technology sectors. Hence, our findings highlights the importance for policy makers to focus on measures that facilitates DUI mode of innovation. Innovation policies aimed at increasing collaboration, hands-on training and knowledge diffusion among companies and sectors are highly due. Bengt-Åke Lundvall’s point that there exist no general laws of innovation systems across nations is also highly relevant for sectoral systems:

The idea that the aim of innovation research is to end up with general laws that can be applied equally in all national systems is mistaken. There are certain activities that can be linked to innovation and that link innovation to economic growth in all systems. But the mechanisms differ across different national systems. This is why theoretical work on national innovation systems cannot dispense from historical analysis.” (Lundvall, 2007, p. 24)

While this paper has novel findings about the innovation modes, it does not come without weaknesses. Firstly, we have only used data from Norway, a country with a rather particular industry structure. It would be interesting to see research that includes other countries and industries, as well as comparisons between countries and industries in different national innovation systems. Secondly, a larger dataset would have enabled us to use more advanced regression methodologies such as hierarchical models. Thirdly, a better definition and operationalisation of the DUI mode of innovation would make it easier to compare results across industries and with previous findings. This could be done by forming a definition with the use of the existing CIS variables, or by introducing new DUI-specific variables in this European-wide survey. Although a country-specific study, we regard the Norwegian petroleum innovation system as a good example of an innovation system in a complex, mature and well-functioning sector. Hence, this analysis will have relevance for other mature sectors also outside of Norway. In addition, it shows that managing natural resources is a possible path to development also for less developed economies (Andersen et al., 2015). The so-called low- and medium-tech industries still constitute the majority of the world economy (von Tunzelmann & Acha, 2006), and should therefore receive a more systematic treatment than what has been the case so far. References: Acha,V.L.(2002).Framingthepastandfuture:thedevelopmentand

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