Absorptive and transformative capacities in nanotechnology innovation systems

Available online at www.sciencedirect.com

J. Eng. Technol. Manage. 24 (2007) 347–365 www.elsevier.com/locate/jengtecman

Absorptive and transformative capacities in nanotechnology innovation systems Krsto Pandza a,*, Robin Holt b,1 a

Leeds University Business School, Maurice Keyworth Building, University of Leeds, LS2 9JT, United Kingdom b Liverpool University School of Management, Chatham Building, University of Liverpool, L69 7ZH, United Kingdom Available online 24 October 2007

Abstract We identify managerial challenges offered by an emergent nanotechnology innovation system in which knowledge is dispersed, asymmetric and contested. We argue the proposed models of knowledge absorption and transformation enhance existing theories of knowledge and emergent technology by recognizing how and why knowledge flows among the actors in emergent technology innovation system. We base our argument on combined research evidence from a Delphi study concerning the future of European manufacturing, from which a selected statement has been analyzed, and from analysis of the NanoManufacturing Institute at Leeds University, UK; specifically its work in building ties between different actors associated with nanotechnology. We conclude our paper with suggestions for future research. # 2007 Elsevier B.V. All rights reserved. JEL classification: O32; O33; M10; L60 Keywords: Nanotechnology; Knowledge; Absorptive capacity; Transformative capacity; Innovation system

1. Introduction Using bottom-up structural arrangements at the atomic and molecular level, scientists and engineers are for the first time engineering materials and devices with dimensions of the order of a billionth of a meter (Poole and Owens, 2003). Emerging from a convergence of quantum physics, molecular biology, computer science, chemistry and engineering (Foster, 2006; Mehta, 2002), nanotechnology has become a general purpose technology (Bresnahan and Trajtenberg, * Corresponding author. Tel.: +44 113 343 4509; fax: +44 113 343 4885. E-mail addresses: [email protected] (K. Pandza), [email protected] (R. Holt). 1 Tel.: +44 151 795 3714; fax: +44 151 795 3001. 0923-4748/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jengtecman.2007.09.007

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1995; Shea, 2005) with applications that have spread through different industries with varying magnitudes of impact on firms and their competitive position. There is a general understanding in the technology innovation literature that changing technology paradigms (Dosi, 1982) and discontinuous episodes in technological progress (Tushman and Anderson, 1986) create environments of uncertainty. Nanotechnology represents one such environment; perhaps one as significant as the development of the computer, though the character and influence of these developments remain nascent. Its being an emerging technology represents a significant opportunity for investigating how firms begin recognizing and organizing knowledge in conditions of uncertainty. We propose to do this by identifying innovation systems as a unit of analysis (Carlsson et al., 2002). A nanotechnology-driven innovation system consists of different actors involved in the creation, diffusion and utilization of knowledge about nanotechnology and its applications. Carlsson et al. (2002) argue that delineating such a technological system and identifying characteristic actors, and the relationships among them, becomes extremely difficult, notably, as in this case, when the knowledge is highly dispersed and extends beyond a particular national innovation system or industrial sector. This dispersion produces uncertainty in knowledge coordination within and among the involved firms, since nobody knows in advance what kind of knowledge integration is needed in which circumstances (Tsoukas, 1996). If this dispersion is also asymmetric then firms face further challenges in trying to develop capabilities (Eisenhardt and Martin, 2000; Verona and Ravasi, 2003) for acquiring knowledge from their environment and transforming the emergent technology into novel domains of application. In this paper we investigate both the nature of knowledge dispersion in nanotechnology innovation systems and the consequences for the different actors that are engaged, or potentially engaged, in the creation, diffusion, utilization and absorption of relevant knowledge. To begin, we adduce evidence from a European Delphi analysis, in which the lead author participated, a component of which considered manufacturing experts’ opinions on the prospects for nanomanufacturing. The findings suggest any technology innovation system associated with nanotechnology will be characterized by dispersed, contested and asymmetric knowledge. We then utilize a number of interviews with a variety of actors in a nanotechnology innovation system in order to identify characteristic firms’ positions and investigate the mechanisms that determine knowledge flows. We argue that for knowledge to flow in such a system actors need capabilities in the form of absorptive (Cohen and Levinthal, 1990) and transformative (Garud and Nayyar, 1994) capacity, whilst recognizing that different actors have different knowledge endowments and different strategic needs in respect of such capacities. In order to depict the nanotechnology innovation system we introduce three characteristic forms of system distinguished by both the actors involved and levels of absorptive and transformative capacity employed. In doing so, we aim to advance the debate about the management of emergent technology, the recent literature on which is largely skewed towards biotechnology (Casper and Murray, 2005; Gilsing and Nooteboom, 2006; Franken et al., 2005). More studies on nanotechnology will, we argue, provide novel insights into technology innovation dynamics. 2. Absorptive and transformative capacity in technology innovation The emergence of radically new technology typically creates performance asymmetries among competing firms. Research has shown that incumbent firms experience a decline in their performance when competing against firms that build their knowledge base around an emergent technology. Reasons identified range from: firms being locked-in to customer expectations that

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prevent them reacting to disruptive technology (Christensen, 1997); demand conditions (Adner, 2002); an inability to reconfigure the knowledge base due to inertia (Leonard-Bartnon, 1992); managerial cognition unable to make-sense of new technology (Tripsas and Gavetti, 2000); and a mix of economic, organizational and strategic factors (Hill and Rothaermel, 2003). Despite consensus that incumbent firms find it difficult to compete in emergent technology environments, some studies do show how incumbents can constructively respond (Ahuja and Lampert, 2001; Hill and Rothaermel, 2003). Rothaermel and Hill (2005), for example, assert that incumbents’ performance improves if the complementary assets (Teece, 1986) needed to commercialize the new technology are specialized. In such a context, incumbent firms are able to exploit technology innovations that emerged from new and innovative firms. Nicholls-Nixon and Woo (2003) liken this relationship to a setting in which a productive co-existence (in their case, between large pharmaceutical and smaller bio-tech firms) emerges as a consequence of new technology. This co-existence is salient because it shifts the research focus from direct incumbent versus new entrant competition to an awareness of how knowledge flows between firms that cooperate and collaborate within particular innovation systems. Again, research has elaborated on the potential benefits of establishing collaborative multifirm networks in which knowledge easily flows and members of the network strive to continuously innovate (Chesbrough, 2003; Miles et al., 2005). These collaborations are proffered as some form of response to the problems encountered by firms struggling with emergent technologies; affording firms the size and scope to access potentially innovative knowledge without exposing them to the myopia and rigidities typically associated with incumbents (Granovetter, 1985; Gulati and Garguilo, 1999; Dahl and Pederson, 2005; Collins and Hitt, 2006). Networked links to ‘outside’ knowledge lends a sense of identity and substance to productive activities that otherwise are informed only by internal expectation (Menon and Pfeffer, 2003). In creating such productive co-existence, however, firms need to recognize the tendency of networks themselves to develop myopia and inertia if relations are too formal (Almeida et al., 2003) or too tight (Yli-Renko et al., 2001). These studies of emergent technology networks highlight the importance of Cohen and Levinthal’s (1990) notion of absorptive capacity for conceptualizing knowledge flows between the actors constituting any emergent technology innovation system. Cohen and Levinthal (1990) define absorptive capacity as the organizational capability to recognize, value and assimilate external knowledge in order to increase firm innovativeness. In many ways absorptive capacity is echoed by the concept of dynamic capabilities as introduced by Teece et al. (1997), since it is a capacity of adaptation to exogenous technological change (Van den Bosh et al., 2005; Zahra and George, 2002). Studies adopting the notion of absorptive capacity link firms’ innovative capability to: the use of external knowledge sources as sources for new ideas (Laursen and Salter, 2004, 2006); finding partners in new technology ventures (Rothaermel and Thursby, 2005); or the creation of new products (Katila and Ahuja, 2002). The suggestion is that such firms can be understood as interpretative systems (Daft and Weick, 1984; Thomas et al., 1993) that scan the environment and then filter and judge novel technologies so as to guide future action. From this demand side perspective, the suppliers of knowledge are viewed as static owners of relevant knowledge awaiting discovery by active searchers in possession of adequate absorptive capacity. Network studies suggest, however, that suppliers of knowledge are not so mute, nor the dispersed knowledge so easily recognized and readily integrated. This is especially so in emergent technology environments where many firms lack the in-house knowledge and established

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routines necessary for absorptive capacity (Bogner and Barr, 2000) because of a lack of both prior experience (Zollo and Winter, 2002) and a suitably entrepreneurial orientation to knowledge (Wiklund and Shepherd, 2003). Absorptive capacity is, therefore, not solely responsible for knowledge flows among actors in technology innovation system. An important stream of research (Debackere and Veugeles, 2005; Siegel et al., 2004; Vohora et al., 2004; Zucker et al., 2002) shows that, in addition to firms, other actors such as universities behave actively in attracting industrial partners and overcoming scepticism in customer perceptions by helping to broadcast potential applications. This catalysing role also holds true for highly innovative small firms (Hicks and Hegde, 2005) that act as suppliers in markets for technology. Here the actors, such as universities and smaller technology-driven firms, who have established an identity based on their expertise in aspects of an emergent technology, have a need less for absorptive capacity than transformative capacity (Garud and Nayyar, 1994). Where absorptive capacity is concerned with exogenous technological change, transformative capacity describes the capability to constantly redefine a portfolio of product or service opportunities based on knowledge endogenous to the firm. Cattani (2005) argues that this transforming of endogenous knowledge into new domains of application cannot be interpreted simply as knowledge exploration (March, 1991), because there is a significant difference between having a pool of knowledge that is potentially available for other applications and actually being capable of using internal knowledge for novel and unanticipated applications. In such cases, novel applications ultimately trigger novel progressions of knowledge, whilst making the most of existing knowledge. In the context of emergent technology many actors in an innovation system face the challenge of transforming their endogenous knowledge into applications across a myriad of industry contexts. So within emergent technology innovation systems, knowledge flows through the mechanisms of demand-driven absorptive capacity and supply-driven transformative capacity. Both organizational level capabilities determine the characteristic patterns of knowledge flow and the position of actors in the system. In an emergent technology innovation system different actors are differently endowed with these organizational capabilities and, consequently, a network is never complete, it contains missing links that can be considered as structural holes (Burt, 1992) in the knowledge flows. This creates a need for knowledge brokers in form of different intermediate agencies whose role is bridge the holes between the industries, firms and universities (Hargadon and Sutton, 1997), acting almost as architects (Pollock et al., 2004) of the innovation system. To further refine our understanding of such innovation systems in contexts of emerging technology we applied these concepts to empirics arising from our study of actors (firms, universities, knowledge brokers, etc.) working with nanotechnology (or potentially so). Our objective was not only to identify the nascent forms of innovation system present in terms of actors and their respective capabilities, but the associated presence and strength of emerging knowledge flows. That way we could provide a richer sense of how innovation systems do in fact emerge, and also show how, in certain environments such as nanotechnology, there is potential for different system forms. 3. Method In a broad sense, we followed a technological systems approach insofar as: we understood nanotechnology itself (framed through considerations of its commercial application) to be the unit analysis; we investigated how actors involved in commercially using this technology were

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able to explore, identify, absorb and exploit potential opportunities; and we accepted that actors operated from perspectives of imperfect and asymmetric knowledge (Carlsson et al., 2002). We recognize, however, that the nanotechnology system remains nascent. As Carlsson et al. (2002) point out, identifying actors in such technological systems is both critical and often methodologically difficult because of the rapidly changing knowledge and the concomitant ebb and flow of relevant actors. To conceptualize such a system we use the term ‘nanotechnology innovation system’ whose associated actors are defined with reference to their technological problem-solving activities as well as their endeavour in creating capabilities in technology use. To map such a system we first required a breadth of understanding as to the existence of, and prospects for, nanotechnology within a manufacturing setting. Here we used some of the results from a Delphi analysis of European manufacturing experts’, the running and analysis of which involved the lead author as a member of the core research team. Linston and Turoff (1975) define Delphi as a method for structuring group communication processes using expert consultation to deal with a complex problem. Here the Delphi method acts as a formal intervention to integrate knowledge through structured communications that enable an analysis of a shifting and uncertain social reality (Blind et al., 2001). As such, the Delphi analysis helps to inform the development of research models, in this case providing invaluable data concerning the prevalence of knowledge dispersion and asymmetry. The study used was pan-European Delphi study of future manufacturing trends undertaken as part of the foresight project Manufacturing Vision. This project was launched in order to accompany the ongoing policy process of enhancing European competitiveness in manufacturing industries. Its aim was to include views of European manufacturing experts collected through a two-round Delphi survey on a variety of topics from technology via managerial concepts, logistics, product concepts and working conditions. National partners from 22 European nations supported the survey in their countries. A core team of researchers from eight European institutes conceptualized and conducted the survey and analyzed results. Each of them focused on a particular aspect of manufacturing. Their main task was to select manufacturing experts from industry, academia and policy-making institutions. Care was taken to provide the right mix of experts regarding their background, gender, professional position and size of the firm. For this paper, we have isolated the statement covering nanomanufacturing, defined as products that can be manufactured bottom-up through the self-assembly of atoms or molecules. Experts were asked to consider the importance of nanotechnology for the following three categories: its importance to European manufacturing industry (very high, high, low, very low), the time horizon for realization (2005–2010; 2010–2015; 2015–2020; >2020; Never) and the most important barriers to realization (education/qualification; technical feasibility; social acceptability; EU legislation; economic viability; lack of R&D funding). Where the Delphi survey results give us a sense of the nature of the emergent technological environment, the use of interviews, focus groups and non-participant observation at meetings (see Table 1) gave us access to data concerning the recognition and attempted use of nanotechnology knowledge amongst a recently formed group of actors centred around the Leeds NanoManufacturing Institute (NMi), facilitated through the University of Leeds in the UK. The NMi provided us with the opportunity to investigate real-time how actors were starting to build relations (Leonard-Barton, 1990) before the outcomes of these networking activities were known to us (Van de Ven, 1992). To frame this on-going and open investigation we posed the following research questions: What is the nature of the technology being discussed? What networked relations prevail between and within actors creating and using the technology? What knowledge

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Table 1 List of interviews and observations Interviewees

Number

Nanotechnology Program Manager at a global semiconductor manufacturer Research Project manager at a global semiconductor manufacturer Vice President for research at a leading UK nanotechnology company CTO of a nanomaterial corporate venture Sales director of a nanomaterial corporate venture Vice President for manufacturing innovation and excellence at multinational pharmaceutical company Manager for manufacturing innovation and excellence at multinational pharmaceutical company R&D manager at orthopaedics manufacturers Director of Nanomanufacturing Institute at Leeds University Program manager at Nanomanufacturing Institute at Leeds University Associate Director at UK Micro and Nanotechnology Network Program manager at UK Micro and Nanotechnology Network Programme Manager at NanoFactory (regional agency) Nanotechnology consultant

2 1 1 2 1 2 1 1 4 3 1 1 2 1

Observations Nanotechnology away day at Leeds University Meetings between Leeds University researchers and representatives of UK nanomaterial producer

1 4

structures exist and how do these influence awareness of the technology and its potential applications? NMi was officially established in 2004 as an interdisciplinary institute to draw on the University of Leeds’ substantial science and engineering base in nanotechnology. The original idea dated long before actual establishment of the Institute, but was brought to life when nanotechnology was identified as one of the University’s strategic research areas. Today NMi links 16 research centres and departments within the University, providing expertise in a range of generic and enabling platforms for nanotechnology research. Due to the close co-operation with the NanoFactory—a regional spin-off of the original idea that links regional universities and interested firms, and through the World University Network, NMi has access to more than 200 researchers at 17 different institutions worldwide. NMi’s operations are run by an experienced CEO, who has held different, senior R&D management positions at a multinational company in the chemical sector. He is assisted by a program manager and an assistant. Some academic members of the steering committee work for NMi on a part time basis. NMi’s strategic and scientific identity is centred on the manufacturability of consumer products enabled by advances in nanomaterials. The initial task is to co-ordinate different scientific domains and engineering expertise in order to obtain research funding, as well as establishing close ties with industrial partners. The emphasis is on long-term research co-operation rather than short-term service co-operation. First contacts, if successful, lead to face-face meetings. Our interviews were semi-structured, and designed around the research questions. Our nonparticipant observations were an all-day event where NMi facilitated discussion about research co-operation, and events where NMi representatives discussed collaboration with their industry counterparts. These events allowed us to identify the different members of the network and to begin to explore how knowledge flowed within and between the respective people and structures using the experiences of competent informants who had begun to develop and maintain contacts with a wide range of actors with interests in nanotechnology.

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From the interviews and events, following a snowball method (Carlsson et al., 2002), we were able to conduct follow-up, unstructured interviews with other network members. These links were further fostered by our contact with the Micro and Nano Technology Network (MNT)—a UK based initiative with a similar role to NMi to facilitate the use of advances in nanotechnology in different industrial sectors. Interview and observation data were held as transcriptions and observational notes that were then analyzed by both authors with respect to identifying: where knowledge was being claimed and by whom; the possible relations by which that knowledge flowed between network actors (potential and actual); and the formal and informal conditions by which potential new knowledge might be created. 4. Findings 4.1. Delphi analysis The Delphi analysis showed a field that is both excited by the potential for innovation using nanotechnology and yet deeply ambiguous and even sceptical. Table 2 shows that the collective degree of expertise amongst the experts for the statement covering nanomanufacturing was below average. It suggests that randomly chosen manufacturing experts feel uncertain about engaging in nanomanufacturing, with a broad range of opinion concerning its relative future importance for European competitiveness. It is indicative that 35% of participants in the Delphi attached a rank of ‘low’ or ‘very low’ to the prospects of using nanotechnology in manufacturing. This is in stark contrast with a recently introduced European Union 7 Framework Programme, in which nanomanufacturing is considered a major priority. It, however, resonates well with the recently published report by Marks and Clerk (Kinsler, 2006), that sees Europe lagging behind Asia and the USA in the annual rate of filling nanotechnology patents. Table 2 also shows that 60.3% of the respondents thought the innovative potential of nanomanufacturing would be realized, but only in a 15-year timeframe. Significantly, the contested nature of nanotechnology knowledge is suggested by that significant minority of experts for whom the commercialization of nanotechnology will never be realized. Equally suggestive is the dispersed and asymmetric nature of the knowledge, revealed in the relatively high proportion who, despite their manufacturing expertise, felt unable to offer an opinion. This asymmetry is further elaborated on in Table 3 cross-referencing the self-assessed expertise of the participants with their responses to questions on the time frame of realization and Table 2 Descriptive statistic for time of the realization and importance for European manufacturing Expert status

N

Very low Low High Very high

1059 507 289 455

Total

2310

a b

% 45.8 21.9 12.5 19.8

100

N = 2115; N varies because of missing data. N = 2281; N varies because of missing data.

Importance for EU manufacturing Very high High Low Very low

a

% 35.4 29.6 20.1 14.9

100

Time of realization <2010 2010–2015 2015–2020 >2020 Never Do not know b

% 2.5 8.2 12.3 37.3 12.0 27.7 100

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Table 3 Time of the realization and importance for European manufacturing considering self-estimated expertise Expert status

Time of the realization

Importance for EU manufacturing

<2010

Very low/low expertise (3, 4)

Very high/ high expertise (1, 2)

Very low/low/ high expertise (2, 3, 4)

Very high expertise (3, 4)

n

n

n

n

%

%

%

%

26

45.6

31

54.4

44

77.2

13

22.8

2010–2015 2015–2020 >2020 Never Do not know

115 154 495 199 527

61.5 55.0 60.3 72.1 83.7

72 126 356 77 103

38.5 45.0 39.7 27.9 16.3

155 218 600 229 554

82.9 77.9 73.1 83.0 87.9

32 62 221 47 76

17.1 22.1 26.9 17.0 12.1

Very high

420

55.0

343

45.0

524

68.7

239

31.3

High Low Very low

419 315 243

66.7 74.1 81.3

209 110 56

33.3 25.9 18.7

526 361 264

83.8 84.9 88.3

102 64 35

16.2 15.1 11.7

Z Mann–Whitney U Time of the realization Importance for EU manufacturing

Asymp. sig. (two-tailed) 3.910 9.183

0.000 0.000

the importance for manufacturing competitiveness. Participants who declared themselves as having high or very high expertise in the area of nanotechnology were more inclined to view nanotechnology as being more important for manufacturing competitiveness and believed nanotechnology would realize benefits sooner than participants who declared themselves as having low or very low expertise. It is, however, interesting that a minority of self-assessed experts in the field of nanotechnology still believed nanotechnology to be of little importance to manufacturing.

Fig. 1. Importance and time of realization for selected statements.

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Table 4 Comparison of experts’ assessments regarding barriers for implementation Barriers

Expert status

Mean

S.D.

T

Sig. (two-tailed)

Education

Very low/low expertise Very high/high expertise

0.29 0.34

0.456 0.474

2.117

0.034

Technical feasibility

Very low/low expertise Very high/high expertise

0.69 0.66

0.463 0.474

1.289

0.198

Social acceptability

Very low/low expertise Very high/high expertise

0.12 0.11

0.325 0.314

0.639

0.523

EU legislation

Very low/low expertise Very high/high expertise

0.06 0.07

0.237 0.250

0.643

0.520

Economic viability

Very low/low expertise Very high/high expertise

0.31 0.29

0.462 0.455

0.765

0.444

Lack of R&D funding

Very low/low expertise Very high/high expertise

0.44 0.45

0.497 0.498

0.231

0.818

0: no; 1: yes.

Fig. 1 offers an interesting comparison between nanotechnology and other emerging technologies in manufacturing that were considered in the Delphi survey. Nanomanufacturing is considered as the technology for which the foreseen time of realization is the longest. It has also the highest ‘never’ and ‘I don’t know’ rate. The uncertain nature and standing of the technology also came to the fore when the experts were asked to consider possible barriers to realizing the potential of nanomanufacturing, although in this case there were no significant differences, apart from education, in their opinions as set against their self-assessed expertise (see Table 4). Fig. 2 shows how, from the six identified sources of obstruction, there was an overriding concern with the technical feasibility of the technology, followed by a lack of research investment. Commercial viability and a lack of qualified exponents were also recognized as of

Fig. 2. Main barriers to the realization of nanomanufacturing.

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potential concern, yet the nascent and hence as yet unknown wider social impacts (and any associated legislation) were considered less of a barrier, something potentially anomalous given mounting concerns about the toxicity and invasiveness of nanomaterials. The data obtained from the Delphi study show knowledge to be dispersed amongst manufacturers in Europe and the considered views about its range and potential suggests an asymmetric awareness of possible applications. We now go on to analyze is the form and nature of the actors within this emerging technology field through whose auspices the knowledge associated with nanotechnology might be both further explored and commercially exploited. 4.2. Actors in nanotechnology innovation system Since the emergence and potential of nanotechnology itself has required co-operation between many scientific, engineering and commercial fields it is logical for academic institutions to act as initiating actors in the emergence of a potential innovation system; NMi being a figurative case in point. Following the supply logic we have adopted in understanding this emerging innovation system, here the university actors are accompanied by actors producing nanoequipment and actors offering design and prototyping capabilities for nano-enabled products. We were better able to understand the nature of these related actors by analysing an MNT report (MNT, 2005), showing that amongst a total of 372 firms listed, 66 were producers of nanoequipment or processes, 120 were in fact university spin-offs, and 123 were product and prototype designers without complementary capabilities in high volume manufacturing. Just 11 companies in these three categories employed more than 200 employees, so the majority were small firm suppliers. The actors associated with NMi were of a similar breakdown in both type and size, though these actors were not limited to UK operations. Another, sympathetic set of actors were producers of nanomaterials. These produce nanoscale structures such as nanotubes, nanowires, etc. From secondary data analysis we found these material producers commercializing nanotechnology by developing new applications in polymers, batteries, fuel cells, coatings, etc. Yet we also found from interviews with managers from those actors associated with NMi that although nano-enabled materials were being made commercially available by these actors, the more significant challenge lay with reliable bulk manufacturing for which there was still little in the way of emerging capability. Again, we found that small firms dominated, with interviewees recognizing that often these firms lacked robust, long-term strategic frameworks. Producers of nanomaterials were accompanied by producers of intermediate products. Producers of coatings, fabrics, memory and logic chips, contrast media, optical components, orthopaedic materials, superconducting wire, etc. belonged to the sectors that largely make use of nanotechnology. According to MNT’s data, these producers were mainly small firms exploiting their knowledge base by, above all, creatively searching for potential applications. What was common to all these actors, then, was how scientific and engineering knowledge was intrinsic to the very existence of the actors’ organizational form. This meant for potentially robust and rich relations between them. The actors’ knowledge was endogenous in that its exponents shared a common sympathy in understanding the technology system as a field of knowledge, and were comfortable with creating and using an emerging technology. The high numbers and diversity of active scientists, technologists and nanotech experts meant, in Carlsson et al.’s terms (2002), the network indicated a strong capacity for experimentation. The nanotechnology was a reason for existing as they did; it was an object in which they had invested

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personal and emotional as well as scientific effort and was in this sense a continual prompt for their creativity and identity. In our case of NMi this sense of commitment and enthusiasm was reflected in the network meetings in which actors’ representatives continually discussed nanotechnology in terms of its potential rather than just its existing capacity. The conversations were of an easy, free flowing nature using technological language which most seemed comfortable using. In these discussions, commercialization was a future-oriented prospect as much as it was an issue of operationally configuring known technology. Watching from the edges of this innovation system were intermediate producers identified as sectors and firms that lacked the knowledge base to absorb the advances in nanotechnology, but who might commercially benefit from doing so. These actors were not members of the NMi network as such, nor were they typically listed in data bases held by MNT, but were mentioned as potential members of the nanotechnology innovation system. In the words of one nanoentrepreneur we interviewed, they were actors (typically firms) that had not yet realized the commercial benefits from ‘‘thinking small’’. Rather than simply wait for such realization to happen, many interviewees were arguing for an active supply role amongst network actors in order to catalyze knowledge creation and use amongst other potential nanotechnology suppliers. Also on the edges of the innovation system, but in some cases further out still, were producers of consumer products such as cars, computers, clothing, aerospace, electronics, pharmaceuticals, food, plastic containers, appliances, etc. These we divided into those sectors and actors already making use of nanotechnology, such as computer, electronics, chemical and pharmaceutical industries, and those not using it. In summary, we managed to interview at least one representative from each above mentioned actors (universities and scientists); nanoequipment producers and designers and nanomaterial producers; nanomaterial users (actual and potential); and the most characteristic qualitative data are presented in Table 5. The analysis of qualitative data shows that both absorptive and transformative capacity emerged as important themes. The need for research collaboration was often mentioned, yet interestingly usually by actors’ representatives that already used nanotechnology. Some interviewees expressed the need for intensive scanning of the environment in order to recognize the opportunities with the potential for contributing to innovative potential. The need for transformative capacity was characteristically recognized by new firms building their identity on nanotechnology. The vice-president for research at a fast growing UK nanotechnology firm, for example, argued that finding ‘‘enthusiastic lead customers’’ was one of the cornerstones of their business model because only these customers can bear the uncertainty of developing and introducing a new nano-enabled product. Moreover, such customer types contribute in-depth knowledge about particular domains of application—the knowledge usually lacking by a firm supplying new knowledge. The firms with endogenous knowledge about nanotechnology also expressed difficulties emerging due to the knowledge asymmetries. Some, especially small, firms struggled to absorb advances in nanotechnology even when approached by firms that offered potentially relevant solutions. Here there was an identified role for emergent knowledge brokers in nanotechnology innovation systems. These actors, established to enhance knowledge flows among other actors in nanotechnology innovation system, tend to increase awareness about actors and sectors not using the nanotechnology. By doing so they support the transformative capacity of suppliers of new knowledge, yet whether they succeed in really increasing the absorptive capacity of the demand side remained undemonstrated.

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Table 5 Examples of interviewees’ description of nanotechnology innovation system Interviewees

Descriptions

Nanotechnology Program Manager

We work in collaboration with Universities and research Consortia to meet challenges related to producing nanoelectronic devices beyond the 22 nm technology node Our business model is build on identifying interesting research opportunities, assuring a robust IP protection and pitching the idea to an enthusiastic lead customer Often other SMEs need something on a small scale, but even this can be too expensive (row material). Moreover, we have so different knowledge. Often we are too far for them. We of course do not want to say too much, but if you do not say enough. . . It is very difficult to anticipate things, because there are so many different contexts and technologies. We need to know our costumers business and know their language I think it will be the same as with biotechnology. Pharmaceuticals will by IP and will not do it themselves. We will involve new firms in the supply chain and incrementally build capabilities The companies that are already in nanotechnology and are attracted by it have had a knowledge base; they had scientists and engineers that were in contact with scientific developments. Those experts within the organizations are seeing trends, interesting peaces of research, there are in the contacts with the development particular in material sciences. The knowledge base at the companies is very important to understand the potentials of nanotechnology. It is different for different fields and different for different players within the field There is surprisingly a lot of ignorance about nanotechnology. There are many SMEs out there, who act as suppliers and could make use of nanotechnology, especially nanomaterials, but they remain unaware I think that awareness about nanotechnology is largely dispersed. Variety of sectors has introduced nano-enabled products. Some of them maybe being unaware that components they assembly are nano-enabled. It is true; however, that many of them have not used nanotechnology for innovations. We are planning industrial awareness packs for marine, wind power, textile, aerospace, food & packaging, special chemical and automotive sectors One of the problems with nanotechnology is that it makes it possible to develop new ways of doing things. This often makes it harder for those familiar only with the ‘‘traditional’’ engineering techniques. . . They are all experts in their own field, but they lack the broad application knowledge to engage with the potential technology users

Vice President for research

CTO at a corporate venture

Sales director at a corporate venture Vice President for manufacturing innovation Director of Nanomanufacturing Institute

Associate Director at Nanotechnology Network Program manager at Nanotechnology Network

Nanotechnology consultant

5. Models of knowledge absorption and transformation in nanotechnology innovation system The Delphi analysis showed nanotechnology to be an emerging knowledge field in which relations with the objects of technology are often dispersed, asymmetric and contested. The exploratory interviews with different actors in the innovation system indicated their different characteristic positions in the innovation system with regard to knowledge endowments about emerging technology, existing levels of absorptive and transformative capacity, as well as their strategic need to develop both capacities in the future (see Table 6). In our study we have identified four characteristic actors in the nanotechnology innovation system; namely established firms in which nanotechnology was historically less relevant; new

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Table 6 Characteristic actors in knowledge absorption and transformation at nanotechnology innovation system Characteristic actors

Established firms Drivers Active observers Passive Novel firms Universities Knowledge brokers

Knowledge position

Endogenous Exogenous Exogenous Endogenous Endogenous Endogenous/exogenous

Absorptive capacity

Transformative capacity

Current situation

Strategic importance

Current situation

Strategic importance

High Medium Low High Low-medium Medium

High

Low-medium Low Low Low-medium Low Medium

Medium

Medium Medium High

High Medium-high High

firms that base their identity almost solely on nanotechnology; universities involved in scientific and technological research that drives developments in nanotechnology; and finally knowledge brokers endeavouring to enhance knowledge flows among the existent and potential members of the nanotechnology innovation system. 5.1. Established firms Established firms were further divided into drivers, active observers and passive actors. For those actors called ‘drivers’, knowledge about nanotechnology was already endogenous and represented part of the firm’s core knowledge. These firms (e.g. semiconductor firms) were also in possession of high absorptive capacity, since their existing knowledge and asset base allowed them to aggressively acquire external knowledge in order to sustain and extend their knowledge trajectories. This, however, implied they were less concerned with how to use this knowledge in new domains. This suggests that transformative capacity within these ‘driver’ actors was less developed or less strategically important so long as the emergent technology promised to be competency-enhancing and did not possess a disruptive threat. Actors termed ‘active observers’ were those firms in which knowledge of emergent technology was exogenous. They were not actively using this knowledge for current innovation, yet were aware of potential implications and, therefore, actively observed recent developments in the area of nanotechnology (e.g. pharmaceutical firms). They were aware that they had to stay alert to, and remain confident in, the potential of the technology, allowing them to appreciate appropriate opportunities. Actors termed ‘passive’ were viewed as potential members of the innovation system. They lacked the absorptive capacity to utilize developments in nanotechnology and remained in a position of strategic challenge when it came to increasing their knowledge to a level that would at least enable them to scan the environment. 5.2. New firms Novel firms that based their existence on nanotechnology represented, together with the universities, the supply side of the nanotechnology innovation system. Their absorptive capacity was high, because their engagement with nanotechnology allowed them to closely follow relevant developments. Moreover, they very often acted as entrepreneurs in bringing novel developments to the market. This implies it was unlikely that their absorptive capacity

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needed any significant increase to achieve competitive advantage. They were however, challenged when developing a transformative capacity in finding applications across a variety of different domains; a challenge intensified in the context of asymmetric knowledge dispersion. 5.3. Universities Universities traditionally play an important catalysing role in the development of emergent technology, and nanotechnology was not an exception. It is difficult to incorporate concepts such as absorptive and transformative capacity in the context of universities; yet they are nevertheless valuable as ways of conceptualizing the challenges faced by universities when attempting to integrate knowledge across different disciplines. Research groups within universities are usually notoriously mono-disciplinary and absorbing knowledge over the disciplinary borders remained a significant managerial task for the nanotechnology enterprise associated with NMi. It was also suggested that the universities involved would also have to work harder at actively transforming their knowledge into the markets and not remain passive, short term service providers. 5.4. Knowledge brokers Finally, knowledge brokers had an important role in sustaining and facilitating knowledge flows, especially where the knowledge flowed between universities, novel firms and active observers and passive actors. Strategically they faced the challenge of having to develop high absorptive capacity to efficiently co-ordinate the dispersed knowledge that emerged from the innovation system. Their transformative capacity also needed to be high, especially when dealing with passive actors. This analysis of the different actors, their knowledge endowments, and their current levels of absorptive and transformative capacity coupled to their strategic importance, allows us to model three characteristic states of a nanotechnology innovation system (see Fig. 3). 5.5. Thick innovation systems The thick innovation system consists of actors with endogenous knowledge about nanotechnology. Here knowledge asymmetry and dispersion exist, but come to compliment one another in ways that resolve any contested views concerning commercial viability. The various experts, sharing similar awareness of the existing technology, are able to realize a level of partnership that would tackle existing problems in the knowledge field and begin to build capability in line with customer requirements. This creates productive coexistence. From a strategic perspective, actors within such a thick innovation system strive to align their technology trajectories in order to sustain the performance of their existing product concepts and enhance the existing knowledge base. Yet it is likely in this context that whilst innovation is sustainable and potentially fast in pace, it is limited in its discontinuous potential. The typically larger downstream actors will tend to use their well-developed absorptive capacity for guided scanning of the environment to select technology that enables them to sustain the incrementalism of their own technology development, whilst at the same time hedging against any technology that increases uncertainty for their own strategic identity.

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Fig. 3. Characteristic models of knowledge absorption and transformation.

5.6. Thin innovation systems The notion of incrementalism in technology innovation is also central for the thin innovation system. Here the actors we identified as ‘drivers’ are replaced with ‘active observers’. It is a characteristic of this state of the nanotechnology innovation system that novel firms strive to transform their knowledge for the benefit of active observers. In doing this they are being supported by knowledge brokers, yet their knowledge remains unabsorbed by active observers, or at least not utilized for innovative products or services. Even if novel products or services do incorporate some nano-enabled solutions these are more likely to be incremental improvements of existent ones. The active observers in thin innovation systems still face the challenge of distinguishing opportunities by being active in networked relations with knowledge suppliers in order to configure the feasibility and viability of the technology. The observers are well advised,

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and will also be experimenting with nanotechnology, enabling them to identify complimentary resources and strategically appropriate technological innovations. 5.7. Interrupted innovation systems The interrupted innovation system poses the biggest challenge for creating and enhancing innovation, because knowledge flows are potentially broken, bottlenecked or altered, irrespective of the openness and potential of the technology being used or engaged with. In this form of the system knowledge remains asymmetric, dispersed and contested. It is also not yet complementary. This creates obstacles for any knowledge flow as well as for innovation. This innovation system is sharply divided on the demand-side, represented by passive actors, and on the supply side consisting of novel firms, knowledge brokers and universities. The demand side struggles largely because it lacks the knowledge necessary to absorb the emergent technology. The supply side, on the other hand, finds it difficult to creatively search for possible applications. The novel firms’ and universities’ transformative capacity cannot off-set the lack of absorptive capacity amongst the passive actors. Although, knowledge brokers strive to support novel firms’ transformative capacities by trying to raise awareness among passive actors, their real challenge may be in assisting passive actors in the development of absorptive capacity whose absence typically results from an historical lack of endogenous knowledge. 6. Directions for future research and conclusions This paper reports combined evidence from a forecasting exercise and qualitative investigation in order to provide emerging insights into a nascent nanotechnology innovation system. The highly explorative nature of the paper is an actual invitation for more studies that may increase our knowledge of the structure and dynamics of emergent technology systems in general, and nanotechnology innovation systems in particular. A variety of phenomena could be explored and a different research design applied. Here, a careful theory-driven case study design involving the identification of innovation networks which represent our three models as closely as possible and which study both the capabilities employed for facilitating knowledge flows and the factors that impede or foster knowledge absorption and transformation, would appear to be viable strategy. The three models suggest the nanotechnology innovation system to be far from unitary. Knowledge is heavily dispersed and held asymmetrically. Moreover, the nature of the technology is such that potential applications and associated problems appear limitless. In such an environment we suggest there will be many more actors involved (or potentially so) than those we have so far identified. These may include shareholders, venture capitalists, patent lawyers and government regulators. So future research might also usefully identify such further actors and their possible role in, and influence over, the innovation system. Our research also only covers a specific period of time. Further research would be welcome that looked more closely at the dynamics by which each innovation system emerged and potentially changed, from interrupted to thin and then thick, or even back to interrupted. Such a processual perspective would enrich the insight into how and why the knowledge flows in the ways it does, and how such flow might change the nature of the knowledge itself. Qualitative case research always raises the issue of generalizability and so further empirical research is required to validate the concepts and the models presented in this paper. We have emphasized the importance of absorptive and transformative capacity for knowledge flow in a

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type of emergent technology innovation system. So it would be important to examine the relationships between different levels of absorptive and transformative capacity and knowledge flows. Different contextual and firm-specific factors and their influence on relationships between absorptive and/or transformative and knowledge flows could be examined. Such factors might be identified on inter or intra-firm levels. Acknowledgements This research is supported with a Marie Curie Intra-European grant (MEIF-CT-2005-023268) to the first author and benefited from the Specific Support Action project (6 Framework Programme), Manufacturing visions—integrating diverse perspectives into pan-European foresight. Contract No. NMP2-CT-2003-507139, Specific Support Action at 6 Framework Program, Project coordinator: Fraunhofer ISI. References Adner, R., 2002. When are technologies disruptive? A demand-based view of the emergence of competition. Strategic Management Journal 23, 667–688. Ahuja, G., Lampert, C.M., 2001. Entrepreneurship in the large corporation: a longitudinal study of how established firms create breakthrough inventions. Strategic Management Journal 22, 521–543. Almeida, P., Dokko, G., Rosenkopf, L., 2003. Start-up size and the mechanisms of external learning: increasing opportunity and decreasing ability? Research Policy 32, 301–315. Blind, K., Cuhls, K., Grupp, H., 2001. Personal attitudes in the assessment of the future of science and technology: a factor analysis approach. Technological Forecasting & Social Change 68 (2), 131–149. Bogner, W.C., Barr, P.S., 2000. Making sense in hypercompetitive environments: a cognitive explanation for the persistence of high velocity competition. Organisation Science 11, 212–216. Bresnahan, T.F., Trajtenberg, M., 1995. General purpose technologies—engines of growth? Journal of Econometrics 65, 83–108. Burt, R.S., 1992. Structural Holes. Harvard University Press, Cambridge, MA. Carlsson, B., Jacobsson, S., Holme´n, M., Rickne, A., 2002. Innovation systems: analytical and methodological issues. Research Policy 31, 233–245. Casper, S., Murray, F., 2005. Careers and clusters: analyzing the career network dynamic of biotechnology clusters. Journal of Engineering and Technology Management 22 (1/2), 51–74. Cattani, G., 2005. Preadaptation, firm heterogeneity, and technological performance: a study on the evolution of fibre optics. Organization Science 16 (6), 563–580. Chesbrough, H., 2003. Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston. Christensen, C.M., 1997. The Innovator’s Dilemma. Harvard Business School Press, Boston, Massachusetts. Cohen, W.M., Levinthal, D.A., 1990. Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly 35, 128–152. Collins, J.D., Hitt, M.A., 2006. Leveraging tacit knowledge in alliances: the importance of using relational capabilities to build and leverage relational capital. Journal of Engineering and Technology Management 23, 147–167. Daft, R.L., Weick, K.E., 1984. Toward a model of organizations as interpretations systems. Academy of Management Review 9, 284–295. Dahl, M., Pederson, C., 2005. Social networks in the R&D process: the case of the wireless communication industry around Aalbirg, Denmark. Journal of Engineering and Technology Management 22 (1/2), 75–92. Debackere, K., Veugeles, R., 2005. The role of academic technology transfer organisations in improving industry science links. Research Policy 34, 321–342. Dosi, G., 1982. Technological pardigms and technological trajectories. Research Policy 11, 147–162. Eisenhardt, K.M., Martin, J.A., 2000. Dynamic capabilities: What are they. Strategic Management Journal 21 (Special Issue), 1105–1121. Foster, L.E., 2006. Nanotechnology: Science, Innovation, and Opportunity. Pearson Education, NJ.

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