2009 Nanotechnology Skills Foresight Moscow

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Nanotechnology Foresight: How can we explore Employmentand Skills Implications?

Ian Miles, Manchester Institute of Innovation Research([email protected])

Foresight, Roadmaps and Indicators for Nanoindustry International Workshopat NANOFORUM ’09, Moscow, on Oct. 7-8 2009

ABSTRACT

Policymakers responsible for creating the skill base needed for futureeconomic development need to have better understanding of the implicationsof radical technological change. In the long-term, such change can upsetmany of the forecasts based upon standard approaches to modelling

employment and occupational trends. This study reviews some approachesto dealing with major new technologies, and then explores the usefulness of using familiar ideas from innovation research – diffusion and industry life-cycleanalysis, thinking about technological paradigms and trajectories, for thispurpose. Nanotechnology, in part because of its relative infancy, in partbecause of the intersection of numerous lines of knowledge development atthe nano-scale, and in part because of the many contested claims about thescope and speed of its evolution, poses particular challenges. The paper assesses how confident we can be about skill and employment projectionsrelated to nanoindustries - and industrial applications of nanotechnology moregenerally – and proposes some ways in which we could increase our confidence and provide more policy-relevant intelligence.



Nanotechnology Foresight: How can we explore Employmentand Skills Implications?

Ian Miles, Manchester Institute of Innovation Research

1 Introduction: Foresight and ForecastingForecasting the development of major new technologies is notoriously difficult.We may be able to delineate major trajectories, but timescales are often mis-estimated, and which of several competing platforms and standards is likely tosucceed is often determined by nontechnological factors. Outlining theimplications of technological development for skills and training adds a further level of complexity.

One thing that we can be sure of, is that “business as usual” is likely to bedisrupted. Institutions and practices will need to change, the nature andcontent of occupations and skills will evolve. Contemporary Foresightpractice (cf Georghiou et al, 2008) recognises that it is usually inappropriate tofocus on a “most probable” future. When we confront disruptive change,conventional ways of estimating likelihood break down. It is important toexplore alternative futures, to gain a better grasp on the range of contingencies which might plausibly emerge.

Contemporary Foresight also puts a great deal of stress upon a participativeor interactive element. This reflects awareness that future-relevantknowledge and capacity to act are distributed phenomena. They are notentirely decentralised, let alone evenly distributed across societies, of course:each is concentrated in specific locations. Not only is it that knowledge andcapacity to act may be localised in different sites. Different sorts of knowledge and capability are often, also, be concentrated in different ways.Knowledge is fragmented across numerous disciplines, professions,practitioners, and economic agents for example. (Consider knowledge of fundamental sciences, of various fields of technology application, or ethicsand regulatory frames, of markets and marketing, and so on.) Capacity toact varies widely, of course. It is manifested in various spatial levels andsectoral agencies of governments, business enterprise, and civil societynetworks. (Consider access to finance, to civil society, public opinion andmedia, to regulators, for example.) If Foresight is to be well-grounded and tobe effectively acted upon, these conditions have to be recognised: whichmeans establishing frameworks that engage broader communities. Classicaldeskwork forecasting and modelling have their place, but they are only someof the inputs to a wider Foresight process that will need to access knowledgethat is widely distributed.

Few organisations possess sufficient knowledge about the whole range of

significant factors whose interplay shapes the main contours of futurepossibilities. The conventional response – convening an expert panel – can



be invaluable, as long as the panel is constructed so as to capture a sufficientrange of knowledge: this often means going beyond the standard pool of expertise drawn on by an organisation. This conventional response can fail tobe sufficiently persuasive to a wide range of stakeholders, who may dismissrecommendations as flowing from the “usual suspects” with their established

frameworks and sectional interests, too. The implication is twofold: panelsneed to be constituted in appropriate ways, both in terms of perceivedlegitimacy and practical capability; and techniques to engage wider pools of participants in providing information, creating visions, and exploringimplications, should also be implemented. Among the latter techniques areDelphi and related surveys (polling large numbers of people), consultationprocesses and workshops, scenario and other “visioning” workshops, and thelike. These are primarily engagement rather than being mainly disseminationactivities.

As we have pointed out, capacity to act is also distributed across societies.Engagement is valuable for reasons that go beyond enhancing the input of relevant information to the Foresight process, and beyond providing thelegitimacy that comes from a wider (if not actually democratic) sampling of perspectives. It means that the participants in the process will have a fuller understanding of the Foresight exercise, and of the issues that it isaddressing. This means that they should have increased awareness of important facets of these issues, and thus have greater preparedness to dealwith contingencies as they arise. They should also have a better understanding of the perspectives and likely responses of other participants,and thus be more able to explore possible collaboration and cooperation – or sometimes to achieve a better competitive position compared to others.They will, furthermore, be able to act as “carriers” of the messages from theForesight exercise – they will know more about it, and more about the logicand evidence on which the results and prescriptions are based. Compare theextent of learning achieved from taking part in group discussions anddialogues, with having a report explained to you, to simply reading it on your own.

This general philosophy of Foresight was articulated, especially during the1990s, in the course of developing the approach to grapple with questions of Science, Technology and Innovation (STI) policy. Many early Foresight

exercises focused on determining priorities for R&D and related funding, for example (though issues of skill and training often emerged in the course of such exercises). Much subsequent Foresight has had a wider brief, thoughit is no surprise that in an epoch of rapid and disruptive technological change,STI questions have often loomed large. Indeed, this change has been amajor contributor to the widening, in recent years, of much strategic analysisbased largely on conventional forecasting activity to include more Foresightelements. Analysis of skills, training, and employment is no exception here:we see similar grappling with Foresight perspectives in fields as disparate asenvironmental studies, health planning, criminal justice…. Large-scale, fully-fledged Foresight exercises are thus imperative if skills and training agencies

are to confront the challenges of technological change.



The present essay, however, is not about the design of such a Foresightprocess. Rather, it is a more modest exploration of one type of input into sucha process. It is itself deskwork, exploring the potential and limitations of further deskwork for forecasting the skills implications of nanotechnology (SKIN), andconsidering where wider Foresight activities will be essential. This will explore

some approaches to thinking through SKIN, in order to inform subsequentforecasting and the design as well as the content of any Foresight exercise. Itwill draw extensively on innovation studies and what these have to say aboutmajor technological changes. Even so, it will be somewhat schematic,because this is a large and expanding body of literature. The present essayshould be seen as merely a starting point for more detailed analysis.

2 Innovation Processes and Trajectories

2.1 Innovation Research

Before turning to skills and employment, it is worth introducing a number of observations about major shifts in technology. Studies of the introduction anddiffusion of new technologies demonstrate that these often take considerabletime to diffuse (e.g. David, von Tunzelmann, 1978). It is also clear thatsuccessful innovation will typically require “product champions”, influentialpeople who will continue to support and promote the innovation even whentimes are tough.

There are cases of rapid take-off. As diffusion studies indicate, this is most

likely to happen:• when the new products are highly visible and their features can readily

be appraised;• when the benefits of these features are large and clearly demonstrable;• when their purchase costs are relatively low;• when the efforts in terms of learning to use them effectively are

perceived to be low;• and when the organisational adjustment (change in work, jobs,

routines) is seen as being relatively unproblematic.Often such conditions will only be achieved after a great deal of effort on the

part of suppliers. There may then be a dramatic take-off after a long period of disinterest (other than on the part of a few pioneers). Examples are theextremely fast uptake of microelectronics (NC Machine tools, robots, etc. inmanufacturing; word processors and PCs in office work) in the 1980s, and of the Web and mobile phones in the 1990s. Interestingly, while some of thesenew applications of IT had been widely heralded, the success of cellular telephony - and text messaging (SMS) services within this – came as a greatsurprise to many commentators.

The Web is a good case of a “design paradigm” that established a market –especially once the web browser and search engine services were introduced.

Most observers had anticipated a take-off of Internet use once PCs werewidely available. But until the Web burst onto the scene (following the



innovation of browsers and search engines) there was little sense thatcommon formats and standards were required. In consequence, much effortwas wasted on developing unique and obsolete interfaces when people wereseeking to develop new online services. The Internet had been in use for decades, but it took improved PCs and networks, and visibly useful and user-

friendly services, to promote widespread uptake. This rapid adoption of theInternet and Web services helped underpin the “dot com bubble” of the end of the 1990s. And what does it take for a design paradigm to establish amarket? It needs to be compatible with social practices and available skills, or at least not challenge these too dramatically. (Thus fax was initially preferredto email in part because it could be treated as an extension to secretarialletter-writing: email took off once professionals were themselves used to usingPCs to prepare documents, graphics, and data analyses, and could see theadvantages of electronic transfer of information.) It needs to offer substantialadvantages without too much inconvenience or need for recourse to expertsupport. A critical mass of users may be helpful to establish economies of scale (e.g. costs of equipment and software), network externalities (other users to communicate with, large data resources online), and the conditionsfor the emergence of competing suppliers (driving down costs) andcomplementary products (increasing functionality): a successful designparadigm establishes such a critical mass, while adopters may be reluctant toinvest in non-standard products.

Discussion of diffusion of innovations is inevitably associated with the familiar subject of logistic S-curves as ways of thinking about innovation and diffusionprocesses. Though familiar, the subject is worth revisiting, with the diffusion,product cycle, and industry cycle versions all being relevant to the presentessay. Figure 1 presents the classic S-curve.

In diffusion analysis, the uptake of an innovation is often modelled in terms of S-curves, which highlight several issues. The diffusion of many innovationsfollows the basic pattern, wherein a period of slow uptake is succeeded byone of much more rapid growth, which in turn is succeeded by slower growthas most potential adopters have acquired the new product. The growth of sales may not mirror the growth in the proportion of the population using theproduct, because some products, at least, can be acquired repeatedly –automobiles and TV sets, for example, are not only replaced, but many

households own more than one.



Figure 1 The S-curve

S-curves in diffusion analysis draw attention to the characteristics of early andlate adopters. At the individual level, early adopters are typically more highlyeducated, more affluent, more linked in to mass media, etc.; at the firm levelthey are typically larger, in more metropolitan regions, linked to internationalfirms (The classic reference here is Rogers, 1995) If we consider thediffusion of major new technologies which can be applied to numerous ends,we can also consider there to be a pattern of diffusion across sectors, withsome being early adopters, some later. For example, at sector level earlyadopters tend to be high-tech sectors, and probably those with closer links tothe sectors in which the innovation was first developed and/or applied.

Such S-curves also point to critical uncertainties in forecasting diffusion. For instance, if a take-off is expected, how rapid will it be? how long will it bebefore take-off can be determined to have occurred? And again, where isthere likely to be a ceiling? What is the potential population of adopters, andis this the same thing as the potential scale of incidences of adoption (what isthe relevant population? 1 Even if we are restricted to one application, might

1

We may find that we have mis-specified the population – among consumers, the estimatesmay have missed out children, in the employment data they may miss foreign workers,among firms, the informal sector, and so on.

Early Adopters/ Early Designs

Mass Markets/

Mass Production / New Applications

Take Off/ Design Stabilisation

Market Saturation/ Product Differentiation

Time

Diffusion Level/ProductMaturity



there be more of the products in question diffused than there are individuals? 2

Should we treat different applications involving the same basic technology asdiscrete cases of innovation?)?

In terms of using these approaches for forecasting developments, it is

important to note some deviations from the general picture. While S-curvesoften do prove rather good fits to empirical data, in practice they are ofteninterrupted by such events as wars and economic downturns. A competingtechnology may substitute for a given innovation before it has been adoptedby all of its potential population – some firms have leapfrogged fax and gonedirectly to email, for example.

But S-curves are also used in another – closely related but distinctive –context: the industry life-cycle approach and that of the product life-cycle .The industry life-cycle approach was initially developed in the context of discussions of international trade and the changing international division of labour. It was a tool for understanding the ways in which production in certainindustries, and of certain products, was shifting across the world. (Vernon,1966, etc) The basic idea here is that industries are liable to “mature” over time. The products they create become more standardised, their processesbecome routine, and the production technologies that they require becomingcheaper and/or simplified. They are less dependent on a highly-skilledworkforce.

Less mature industries need to be based in technically advanced regions,where they can access high skills, technical support, and innovativeequipment and component suppliers. As they mature, however, they canmore easily be located in less-advanced countries: transnational corporationsmay establish offshore production facilities, indigenous firms in developingcountries may emerge as low-cost competitors. This approach was used toaccount for the shift of some established industries out of the more advancedcountries – and to recommend that these countries should increasinglyspecialise in newer high-technology industries. The approach has its critics –how far does it apply to individual products, product classes, firms, industrysectors; how to account for the persistence of many established industries insome advanced countries; how to handle the phenomenon of “dematuration”and shifts away from Fordist production systems? - but again it points to some

underlying dynamics of successful product innovation.Empirical support for the approach is less conclusive than for S-curves indiffusion analysis. In part this is because many industries display continuingchange and innovation, such that important shares of activity can be retainedin the original high-tech countries. “Dematuration” may take place, withtechnological and organisational innovation undermining the comparativeadvantages of low wage economies. For instance, new process technologymay make labour costs less relevant, and new skills may be required in the

2 The number of mobile phones in use in some countries exceeds the population of these

countries; and it is not surprising that there are more phones used in business than there arefirms, nor that there are many more instances of use of common tools than there are of firmsin the user sectors.



labour force. Additionally, closeness to markets, and flexibility of response tomore volatile markets, may be important; some sectors may not migratebecause of political or corporate strategy. The developing countriesthemselves may become pioneers in the sector, rather than imitators of established technology – and there has been little sign that sectors are

moving around the world to increasingly less developed countries. Finally, itis striking how many industrial activities have been offshored, with manyforms contracting out specific assembly and service activities overseas.Some analysts have argued that intensified international division of labour would mean that latecomers would be restricted to less knowledge-intensivecomponents of value chains, and be unable to acquire capabilities to playmuch of an autonomous role in industries because they are only experiencedin working on small fragments of extended production processes.

Still, the basic ideas of the industry cycle approach do have continuingrelevance. There are, and are likely to continue to be, shifts in the sites of production shifts and changes in the international division of labour. Thedynamics of new technologies requires examining more than one country inisolation in terms of the location of production processes as well as the paceof adoption in different regions. Latecomers can take important roles inindustrial production – and in some cases important new products canemerge from them. Pioneers cannot assume that their own products willcontinue to dominate in other regions of the world.

Moving from the industry life-cycle to the product life cycle approach, further points arise that are particularly relevant to considering the dynamics of newtechnologies. This approach draws on both the diffusion and the industrycycle models, together with the accumulated results of many innovationstudies. (see Utterback, 1996 for the Abernathy-Utterback model) The focushere is not so much on how the market or sector evolves, but on the nature of the innovation itself.

Put very schematically, the Product Life Cycle approach suggests that earlyversions of innovations are typically, even is technologically sophisticated,very basic and rudimentary as compared to their later incarnations. The earlyversions of the innovation appeal to relatively few users, not just because themarkets are underdeveloped. Additional factors include the lack of

widespread knowledge of their current and potential capabilities on the user side, and the lack of knowledge on the supplier side of just what capabilitieswill be valued and how they may be used. This underpins the stress placedby many innovation researchers and practitioners on good communicationlinks between users and producers, which informed the development of the“systems of innovation” approach. It also leads to considering that in the earlystage of product development and diffusion there may be poor linkagesbetween the innovators and many potential users – and types of user (i.e.applications of some sorts may be quite well established, while others aremuch less mature or even yet to be created). More generally, theorganisation of upstream supply chains and downstream systems for

distribution and training, are liable to be rudimentary. A successful productlife cycle will see these features changing as product development and



diffusion coevolve. At the early stage of development, complementary goodsand services are not available, or not widely available, The key productsthemselves are liable to be expensive (and often unwieldy and/or unreliable),as compared to later versions of similar products. They early versions of products are liable to require considerable technical skills for their production

and use, while these may become much more routine in later stages. Skillsthat were rare to begin with, too, may have become much moirécommonplace as training agencies and employees themselves see the needfor them.

However, if the products do gain a foothold, then awareness of themincreases, investment in them grows, experience as to how to apply themaccumulates, and their markets expand. This is in line with the diffusionaccount, but crucially we here see that the innovation itself is changing. Theproduct is redesigned so that it becomes more robust, easier to use, capableof more efficient production. The early suppliers who have demonstrated thepotential for a substantial new market are joined by new entrants, who mayoffer new designs for the innovation. Often there is a competition betweendifferent types of design, with one becoming the dominant “design paradigm”to which others should conform. If the early suppliers are innovative smallfirms, large firms are liable to substantially change the nature of thecompetition in the market (perhaps acquiring the small firms in the process);with superior marketing capacities, the large firms may accelerate thediffusion process and hasten the development of a dominant design. Then,as the market grows, the focus of innovation typically moving away from basicproduct/design innovation, toward improving product quality, and then toprocess innovation enabling larger-scale, cheaper production. (Thisresembles the industry cycle analysis.) The product becomes cheaper to use,with more complementary goods and services, more functionality, lessrequirement for high skills in its use. It can be acquired more readily, withreplacement, repair and perhaps second-hand markets. One version of thisaccount describes the product cycle as involving a shift from technology-pushto demand-pull, though this is somewhat oversimplified. But what the productcycle approach does draw attention to is a pair of learning processes. On theone hand, successful innovations themselves evolve through an extendeddevelopment process once they are on the market (not only when they areprecommercial prototypes in R&D laboratories), as suppliers learn what users

require. On the other hand, users learn about the product and about how touse it effectively. The latter part of the user learning process may be veryprotracted – for example, it is believed that the “productivity paradox” of newIT was only resolved once users had learned how to apply their large ITinvestments effectively. (Joergenson et al, 2008) There is a relatedmessage here: since users play an important role in shaping innovations, it isunwise to assume that inventors and developers of innovative products havea clear view of how these products will be used.

This leads us beyond the literature on S-curves, towards those onorganisational learning, on technological configuration, and on “reinvention”.

Adopters and creators of complementary innovations may effectively modifythe innovation – for example, by finding ways of using it, and areas of



application, which were not anticipated by the inventor or even the by asupplier years after the first introduction of the new product. While by theearly 1980s PC manufacturers were already becoming aware that consumer markets were developing for computers on which to play videogames, for example (which would have surprised computer pioneers!), few would have

anticipated the emergence of large scale music and video downloads, of Web2.0 social networking sites, and so on.

What is also implicit in these approaches is that the social and technologicalnetworks of which the economy is comprised themselves can evolve duringthe innovation process, with changing linkages between producers and usersof innovations, with changing intermediaries (including regulatory and trainingorganisations) and so on. The social networks that are required for successful diffusion of an innovation may themselves take time to develop,and they may be stimulated or impeded by policy action.

2.2 Fundamental Innovations: Beyond S-curves

It has been necessary to introduce the various themes presented above inorder to provide a reasonably firm ground in innovation analysis for thediscussion of nanotechnology. It would be too easy otherwise to be carriedaway by the hyperbole that has accompanied the recognition of the potentialsof doing things at nano-scales.

The common ground between the various perspectives examined is theanticipation of continuing change and growing capabilities. Whether what wewill witness will be a technological revolution, or a loosely connected series of radical changes in technology system 3 across a number of industries(manufacturing, health, etc.), there are still liable to be substantial implications3 Freeman (1974 – see also Freeman and Louca, 2002; Freeman and Perez,1988) noted thatthe significance of innovations in industrial processes varies considerably. Some newprocess technologies are relatively minor modifications of familiar devices used in specificapplications . These usually require little retraining and reorganisation of activities, and theymay only reduce product costs or increase quality by a fractional extent. At the other extreme, some new process technologies represent dramatic changes: they can be used to

modify the methods of production in many sectors, and are liable to promote considerablerethinking of work organisation and even of the nature of the end-products of user organisations. Freeman thus proposed a typology of innovations, which are, in terms of increasing significance:

• Incremental Innovations. These occur more or less continuously, including inlater stages of the product life- cycle; they usually involve small modifications with onlyminor implications for training, working practices, organisation; they often derive frominventions and improvements originated by engineers, workers, or user suggestions.• Radical Innovations. These involve more substantial change, which may lead tonew training, new organisation of work and organisation practices - one "rule of thumb"for distinguishing radical from incremental innovations is whether new manuals havebeen written. Radical innovations frequently derive from formal R&D activities, usually onthe part of the supplier; they may well involve product innovation as well as changes in

production processes and organisational arrangements, and thus they may create newmarkets or extend old ones - setting off new product cycles, or "dematuring" establishedproducts.





millennia human beings have been producing and applying “new” materials,and many materials that are only familiar in recent decades - for exampleplastics – actually have a relatively long history. There are extremelyintriguing and completely novel (or unknown) substances that have recentlybeen developed – for example, the fullerenes, carbon nanotubes, and

metcars. NT means new approaches to Advanced Materials (AM) - not just aparticular class of new material, nor even application of a particular techniqueor set of instruments to producing materials. NT knowledge, as applied ineconomic activities at present, is largely a matter of applying a range of newprocesses to be applied to the production, shaping, and configuration of materials, so that effectively the properties of AM or of familiar materialsprocessed in new ways (e.g. “chips” with circuitry engraved upon them) can bedefined at very great detail, at nano- and indeed atomic or molecular levels. 4

Mike Roco (2007) of the US National Nanotechnology Initiative, argues thatthere will be four generations of NT development, as depicted in Figure 2:• First generation, turn of the century – “passive nanostructures” andis typically used to tailor macroscale properties and functions.. These arematerials designed to perform one task, whose specific behaviour is stable intime. Examples include: nanostructured coatings, dispersion of nanoparticles,and bulk materials - nanostructured metals, polymers, and ceramics.• A second phase, beginning in the early years of this century - “activenanostructures” for producing mechanical, electronic, magnetic, photonic,biological, and other effects. These are typically integrated into microscaledevices and systems. Examples include new transistors, components of nanoelectronics beyond CMOS, amplifiers, targeted drugs and chemicals,

actuators, artificial “muscles”, and adaptive structures• A third generation, beginning in the second decade of this century -“systems of nanosystems with three-dimensional nanosystems”,constructed with thousands of interacting components, by means of varioussyntheses and assembling techniques such as bio-assembling; robotics withemerging behaviour, and evolutionary approaches. A key challengeassociated with construction of such systems is enabling networking to takeplace at the nanoscale, and designing hierarchical architectures.. Rocomentions some research areas that will underpin this: heterogeneousnanostructures and supramolecular system engineering. This includesdirected multiscale self assembling, artificial tissues and sensorial systems,

quantum interactions within nanoscale systems, processing of informationusing photons or electron spin, assemblies of nanoscale electromechanicalsystems (NEMS) and converging technologies (nano-bio-info-cogno) platformsintegrated from the nanoscale.• Fourth generation - “heterogeneous molecular nanosystems” .These are seen as integrated nanosystems, with hierarchical systems withinsystems (functioning much like the cells of complex organisms). eachmolecule in the nanosystem has a specific structure and plays a different role.Molecules will be used as devices: their engineered structures andarchitectures will permit fundamentally new functions. This will lead to

4A helpful discussion of this point in the context of AM is provided by Cohendetet al (1991 ).



nanoscale machines, nanosystem biology for healthcare, and new human-machine interfaces at the tissue and nervous system levels, for example.

Figure 2 displays Roco’s estimates of the developments of NT markets.

Figure 2 Roco’s forecast of generations of Nanotechnology

Source: Roco (2007) andhttp://crnano.typepad.com/crnblog/2006/03/new_risks_new_f.html accessed 27/03/09

http://crnano.typepad.com/crnblog/2006/03/new_risks_new_f.html

http://crnano.typepad.com/crnblog/2006/03/new_risks_new_f.html



Figure 3 Forecast of Market size of Nanotechnology

Source: Roco (2007)

There does not seem to be one “heartland” NT, in the way that microelectronicshas been for information technology, or gene sequencing/manipulation for biotechnology. It is possible that one may emerge, that one of the existingtechniques will prove to be the basis for numerous rapid developments. The

diversity of approaches to NT - from microelectronics, materials science,biotechnology, pharmacology, etc. – may make this unlikely. However, the ITexample is one where, despite microelectronics being an obvious case of aheartland technology, there are numerous related technologies, somecomplementary (e.g. software) and some more independent (e.g. optronics andphotonics). There is limited conceptual and empirical analysis to tell us how“tight” the cluster of core underpinning technologies in a technological revolutionneeds to be.

In any case, nano-transformation is a set of technologies with extremely wideapplicability. For example, take the AM case. Barker 5 indicates the potentially

great pervasiveness of new materials technology by noting that the materialssectors of industrial economies may well account for 5-10% of output, and thatmaterials can account for as much as 60% of the cost of manufactures. AMcurrently constitute a small proportion of the total materials markets - and thus theAM revolution, if such a thing exists, has a long way to go. But their role in manystrategic applications (aerospace, etc.) is already substantial. Barker identifies aseries of characteristics that distinguish new from traditional materials technology:these are summarised in Table 1, and are clearly of such importance to modernindustry as to require close attention by producers and users of materials of manykinds. The new properties that AM possess may well create opportunities for newproducts and processes.

5 Barker (1990)



3.2 Innovation and Diffusion

The usual S-curve of diffusion was originally introduced to describe thepattern of uptake of a particular artefact or process across individuals,regions, or enterprises. Using it to describe patterns of uptake across sectorsis an obvious step, and one that is useful for thinking about skills andemployment implications of a new technology. A problem with SKIN,however, is that NT is now just one technology, and is potentially somethingthat can be embodied in many types of application and artefact. It will bemore difficult to assess (and predict) diffusion trends for NT than it was for,say, microelectronics – where surveys did enquire into the share of productsand processes employing microprocessors, for example, or produced or controlled by robotics.

Table 2 presents one listing of applications of NT that can fairly readily betranslated into sectoral areas of application. An effort to fit a prospectivechronology to NT applications was made in a study for Texas technicalcolleges by Vanston and Elliot (2003). Their overview is presented in Table 3.A recent and more sectoralised forecast from Harper (2008) features inFigure 3. . The trends in this forecast seem to refer to end-uses, at least to alarge extent. Thus, electronics does not appear as an application area in itsown right - though nanoscale engineering is already a reality in this sector.Notable is the expected shift from high-tech military/aerospace applications tothose focusing on social concerns such as health and nutrition.



Table 1 Characteristics of New Materials Technology 6

6 Based on Barker, 1990, op cit.

• Information Content R&D, Processing and design expertise are muchhigher proportion of total costs, energy and raw materials lower.

• Complexity Greater control of materials microstructure, so AM oftenconstituted by a series of phases yielding a desired microstructure with specificproperties. Requires multidisciplinary knowledge inputs.

• Integration of Function Packing of more performance characteristics intosmaller areas and volumes, reduced steps in manufacturing process.

• Added Value High unit prices related to information content and level of processing required.

• Variety Broad and diverse range of materials, reflecting variety of manufacturing methods and raw material inputs, and amount of scientific and

engineering knowledge, now available; so scope for more customisation to user requirements.

• Market Size Already having impact on nearly all sectors of manufacturingindustry, especially high-tech sectors; likely to have multiplier effect across wholeeconomy.

• Market Growth Whereas many traditional materials have mature or saturated markets, AM display rapid growth.

• Life Cycle Apparently short, reflecting increased competition amongcontinually evolving materials, and shorter life cycles of products in which used.



Table 2 Applications of Nanotechnology

1 MEDICINE1.1 Diagnostics; 1.2 Drug delivery; 1.3 Tissue engineering2 CHEMISTRY AND ENVIRONMENT2.1 Catalysis; 2.2 Filtration3 ENERGY3.1 Reduction of energy consumption; 3.2 Increasing the efficiency of energyproduction; 3.3 The use of more environmentally friendly energy systems; 3.4Recycling of batteries4 INFORMATION AND COMMUNICATION4.1 Memory Storage; 4.2 Novel semiconductor devices; 4.3 Novel optoelectronicdevices;; 4.4 Displays; 4.5 Quantum computers5 HEAVY INDUSTRY5.1 Aerospace; 5.2 Construction; 5.3 Refineries; 5.4 Vehicle manufacturers6 CONSUMER GOODS6.1 Foods; 6.2 Household; 6.3 Optics; 6.4 Textiles; 6.5 Cosmetics

Source: Wikipedia “List of nanotechnology applications” athttp://en.wikipedia.org/wiki/List_of_nanotechnology_applications accessed

27/03/09

Note: this is simply an illustrative list. The author is aware of NT applicationsbeing developed across a much wider span of industries – for example, nano-sensors for agriculture and environmental use – and of many moreapplications within the sectors listed above (e.g. nanosystems for fuel cellsand batteries).

http://en.wikipedia.org/wiki/List_of_nanotechnology_applications%20accessed%2027/03/09






More elaborate forecasting of skill requirements will need to have moredetailed and better-grounded estimates of NT uptake in various applicationareas and sectors. Probably the best step would be to identify a key set of NT

applications and then seek to estimate thee extent to which they will be takenup, at what speeds, in different sectors, countries, types of firm, etc.

Table 3 Vanston and Elliot’s Forecast of Commercial Realisation of NT

Short-term (0-3 years) Mid-term (3-5 years) Long-term (5+ years)Modest Commercial Opportunity

Instrumentation, tools andcomputer simulation

Instrumentation, tools Instrumentation, tools

Nanomaterials (metal andceramic nanopowders,fullerenes, carbonnanotubes)



Important Commercial Opportunity - Life sciences (diagnostics) Life sciences (diagnostics,

screening and taggingtechnologies)

- Electronics/IT/optical devices “Smart” nanomaterials

- Computer simulation -Large Commercial Opportunity

- Life sciences (drug delivery) Life sciences (drug delivery,design and development)

- Electronics/IT (data storage,microprocessors)

Electronics/IT/optical devices(data storage, memory,optical devices, molecular and quantum computing)

NEMS(nanoelectromechanicalsystems)

Source: based on Vanston, J and L Elliot (2003)



Figure 3 A Forecast of NT Markets

Source: Harper (2009)

In the absence of solid data, or even of expert judgements that allow for sufficiently detailed analysis, all we can do at this point is to present aschematic depiction of the sorts of development that are liable to occur, if NTproves to be a generic technology, or set of technologies, with applicationsacross many domains. This future seems entirely plausible, given the strikingwealth of applications that is already emerging from first generation NT. ThusFigure 4 outlines the likely emergence of multiple diffusion curves, as newnano-tools are developed – applications of various kinds, adopted acrosswider or narrower sets of user sectors. It does not attempt to fit exact timelines or specify the lead and lagging user sectors – this is a matter for detailedexamination by experts (though it is possible to make some generalisations

about the types of firm and sector that are liable to be lead users). We arealways liable to be surprised as products find applications well away fromthose considered by the pioneering inventors and innovators. We are alsolikely to find that the take-off of some diffusion curves is slower thananticipated, as regulatory, technical or skills problems kick in. This is probablyone reason for many forecasts of rapid generational change alreadyappearing premature. (See NIA, 2007, for a striking and more realistic view of generational change.)



4 Innovation and the Demand for Skills

4.1 Nano-skills? What do we know about the skill requirements associated with technologicalchange? Clearly the picture will vary across the different sorts of innovationdiscussed earlier, so let us focus on major technological changes, affectingmany sectors and processes, such as NT is believed to be.

The frameworks set out earlier give us some basic perspectives on how thenature of key technologies typically changes (for instance, toward morestandard and usable designs) as the market develops (typically an S-curve of diffusion and a proliferation of applications). These frameworks do not tell usdirectly about the speed of development in any given area – though we shouldcertainly beware the trajectory of hype! – but they do provide someimplications for skills and related institutional issues. We could for example,anticipate a long-term growth (and for a long period accelerating growth) inthe requirements for specialist knowledge of the fundamentals of the newtechnology – in R&D and in emerging applications. Accompanying this, atfirst with some lag but ultimately outpacing it by many times, will berequirements for skills in the applications of the new technologies, fusing

Figure 4 Diffusion Curve of new Core Technologies

Nicheapplications

Multiple Applications:

successivemass

markets

Trigger and enabling applications

Ongoing QualitativeChange

Time – may be DECADES

Adoption of different

applications in various

firms,sectors,

etc.

SuccessiveNew Technologies

with improved characteristics,diffusing more

rapidly

Cascading Applications,new platformsand potentials

(ceilings of curves may

differ)

Common Platforms



domain-related skills with good understanding of the technology. Withanother lag, many more workers will be affected in the sense that they willneed less specialised knowledge in the technology, though possibly morepractical experience will be required about use of the technology in specificcircumstances. Typically, we would anticipate the formation of new

professional classifications, qualifications, accreditation, and associations andnetworks over this period; the reconfiguration of training courses and possiblesome new institutions.

This might be a process unfolding over a few decades, or over many.Predicting the actual pace of NT advance is bound to be difficult. Somerecent commentators are anticipating major market take-off in the next fewyears, for example. Despite the current economic gloom, Tim Harper (of theconsultancy CMP Cientifica) anticipates " a big take-off in 2011, and by 2012the industry is really going to be booming" (Guardian (London) 26/03/09,Technology Supplement p1). Nanotechnology pharmaceutical and healthcareproducts are forecast to be worth $3.2tn by 2012, with military-use nanotechproducts taking 14% of the total market ($40bn). NT products for the motor industry are put at 4%, those for foodstuffs at up to 2%, and other productsdesigned addressing pollution will also be significant. China’s role in NT willbe comparable with those of the EU and US. In the same newspaper report,Richard Appelbaum (Center for Nanotechnology in Society, University of California) considers that by 2014, some 15% of manufacturing output willconsist of NT products. Such estimates run into all sorts of definitionalproblems – are we talking nanomaterials, systems, or things that happen tohave been themselves shaped by NT devices? But it is striking that after some years where the hype bubble around NT seemed to have burst, it isonce again being promoted enthusiastically in various media outlets.

When it comes to discussing SKIN, the situation is somewhat less clear.Some NT proponents are happy enough to stress the potential for jobcreation. Thus Roco (2007), citing input from industry and academic experts,projected that $1 trillion in products incorporating nanotechnology and about 2million jobs worldwide will be affected by nanotechnology by 2015…Extrapolating from information technology, where for every worker another 2.5

jobs are created in related areas, nanotechnology has the potential to create 7million jobs overall by 2015 in the global market. Indeed, the first generation of nanostructured metals, polymers, and ceramics have already entered thecommercial marketplace.” (The word “affected” seems rather slippery here.)

But there are few studies that examine more specific skill requirements,especially if we are looking for studies involving substantial research intooccupational trends, job announcements, or even industry opinion. 7 Only acouple of such studies have been located while preparing this paper.

One such is by Vanston and Elliot (2003), prepared to inform technicalcolleges. They stress the uncertainties of NT development, though much of the report considers forecasts that are in general rather optimistic. Because7

Roco (2007) noted that “ Small Times reported 1,455 U.S. nanotechnology companies inMarch 2005 with roughly half being small businesses, and 23,000 new jobs were created insmall startup ‘nano’ companies”.



of the uncertainties, basic skills that could be applied in various related fieldsare emphasised. Colleges were recommended to prepare programmes (in acoordinated way) but not expect to deliver them until demand becomes moreclear – which could be the case very rapidly, once take-off is achieved.. Asalready mentioned, the development of commercial applications across

various fields are liable to follow a sequence, as depicted in Table YYY. Thusit is possible to speculate about some patterns of emerging SKIN. The areawith most graduate employment opportunities in the near future isnanomaterials : technicians’ jobs include production supervision, qualitycontrol, equipment calibration and maintenance, user education, respondingto customer requests. . One area that is strategically important but mayinvolve relatively few (very high skill) jobs is instrumentation, tools andcomputer simulation . Longer term prospects are bright in electronic,optical and information applications , though limited immediate employmentwill be created: skills may be analogous to those in computers andmicroelectronics. Life science will require higher level skills than those of technicians, and this may be a bottleneck; NT may impact health professionsin general; NT applications in environmental areas will require dataprocessing, equipment operation and repair, and administrative positions.This is a promising approach – SKIN analysis will be much enhanced byreliable appraisal of the sectoral diffusion of NT – though in practice the areasexamined seem fairly restricted.

The report presents a series of speculative job descriptions with notes aboutskill requirements – for example a medical technologist to perform clinicallaboratory tests and procedures using NT, with appropriate advancedtechnical skills. Other positions include mechanical and electronicstechnicians, machinist, mechanical designer, and so on.

In one very interesting study – which could well do with an update - Hendry(1999) considered three new technology industries – optoelectronics,biotechnology, and advanced materials. Interviews and literature reviewswere used to explore skill needs in these three areas, in the UK. Advancedmaterials (AM) is the closest to our field of NT, but since it does notspecifically focus on the nano-level, a range of other materials activities areinvolved (and of course, non-materials focused NT is not examined).Advanced materials were defined as polymers, ceramics, and high-

performance metals, and composites and laminates of these. The materialsarea is well-established in the UK, with, for example, an Institute of Materials – optoelectronics on the other hand was highly fragmented. Hendry notedthat the take-up of AM was slower than expected, with low-cost high-valueproducts dominating, mainly produced by SMEs as larger firms had withdrawnfrom the area at the time of writing. Fairly rapid growth was largely beingdriven by demand from aerospace and automotive industries.

Hendry, (1999, p6) noted three key sets of AM skills:

“(i) fundamental understanding of the (specific)materials concerned, with skills in synthesis, design, processing,and fabrication;



(ii) supporting infrastructure (generic) technologiessuch as ultra-precise measurement and testing techniques,modelling and simulation;

(iii) project management skills and appropriate

organisation to carry out concurrent engineering, in which thedesign of a product is done in close conjunction with the design of the manufacturing process, and customers and suppliers arebrought into the design process early on, in order to meet ever-decreasing product development cycles. “

Studies of skill requirements for materials engineers had stressed theuncertainty involved in developing AM applications. This uncertainty calls for close collaboration with supplier firms, the research infrastructure, andcustomers, underpinning the need for “soft skills”. Soft skills are described byHendry as encompassing: creativity, problem solving, proactivity,communication skills, business awareness, and the ability to use andintegrate other disciplines. The variety of evolving applications calls for thislatter, interdisciplinary or interprofessional ability. In the IT world we areincreasingly hearing discussion of the “T-shaped” individual, who combinesdeep specialist knowledge of one area with awareness of the terminology,principles, and issues in other areas of business and/or technology. A similar appraisal emerges here. Hendry quotes Kaounides (1995) as describingmaterials science as a

“multi-disciplinary science requiring inputs from solid-statephysics, chemistry, metallurgy, ceramics, composites, surfaceand interface sciences, mathematics, computer science,

metrology and engineering…. rigid separation of the differentdisciplines is becoming inappropriate … barriers or boundariesbetween them are beginning to erode. The examination of elementary particles, atoms and molecules cuts acrossmaterials whatever their origin....” (Kaounides, 1995:15)

Such discipline-spanning knowledge and skills are often noted in connectionwith innovative industries and sectors. This is one reason why Hendryconcluded that the issue of skill shortages is rather complex. Often the rightskills might exist, but not necessarily the optimum combinations of skills.

Abicht (2009) presents results from a survey of 178 German nano-businesses. Finding that over half of the employees of these companies holdUniversity degrees, he recognizes that this implies that the firms are highlyresearch-intensive character. (The smaller the company, the higher theproportion of employees with such degrees.) The remaining employees areskilled workers (20%), master craftsmen and technicians (10%), with justunder 10% in administrative or unskilled jobs. In terms of expectations, thefirms thought that the share of skilled workers would rise with the shift fromresearch/development to production/service. A significant growth in jobnumbers is projected, with personnel in small and medium-sizednanocompanies growing from 27,300 employees in 2008 to 43,200 by 2013.(Most of this growth is expected to be in the next two years!) Half of thecompanies intend to meet their demand for further education through external



educational institutions. Abicht argues that cooperation with universities andR&D institutes is an essential means of knowledge transfer for nanotechnology companies, because of the need for interdisciplinarity. Butthe companies reported that few knowledge providers have successfullyadapted to the demands from this new industry. It was suggested that

providers of further education have to respond in particular to therequirements of smaller companies.

Training and other courses have difficulties in determining how to combinemore fundamental and more specific knowledge of the technologies andapplications involved, and combining these with skills in project managementand collaboration. The academic community may stress inter-disciplinaryknowledge, while industry seeks to deploy application-specific knowledge(and complains about the limited development of skills specific to particular subsectors). Training bodies need information on the extent to which aparticular depth of knowledge in each of the three sets of skills will be requiredby employees at different levels. (Hendry noted, for instance, the importanceof intermediate skills at technician level.) Industrial success will require skillsat all levels involved, and to the extent that there is a problem, it seems to beone of achieving an optimum balance of skills.

4.2 Skills and Product Cycles

The studies of nano-skills that have so far been generated are necessarilyproviding us with snapshots of SKIN at an early stage of industrial

development. The skill requirements that we see now may provide onlylimited insight into future quantities and configurations.

Here we can recall the discussion of product cycles. Tether et al (2005)discuss skill requirements over three phases of the life cycle. (1) In the early,‘fluid stage’ of the product or industry, the key skills are those of entrepreneurs (often combined with scientific or technical specialists, andskills in marketing). Production skills tend to be more general and adaptable,rather than specific and rigid, as production workers adapt to rapidly changingtechnologies and demand. (2) In a later ‘transitional stage’, with its shift fromproduct to process innovation, management skills become more functional

and ‘scientific’, whilst those of the workforce become more specific, with amore precise division of labour. New specialist equipment may at firstaugment the skills of skilled workers. But over the course of the maturephase of the industry the labour force becomes increasingly deskilled asequipment becomes familiar and routine. (3) In the last phase of the productcycle, production is increasingly ‘offshored’, to countries with lower costs of production (especially labour) – this offshoring pattern was classic for manufacturing industries, and seems to apply to some elements of modernservices, too, as new IT can be used to link service suppliers and users.Some high value added knowledge intensive activities – design, ongoing R&D(e.g. for product differentiation and new products), marketing, and strategicmanagement) – may well remain based in the home country. The skillsinvolved are managerial command & control skills and for a small “elite”, while



the bulk of the workforce has with low or unspecific skills (we would add, salesand logistics to this).

This approach has considerable virtues, but when we are considering a set of products such as those of nanotechnology – where the products of today’s

nano-industries may well be critical inputs for a wide range of user industries – it makes sense to differentiate between the industries innovating corenanotechnologies, and those that are making use of these in applications.These latter “user” industries may themselves often be innovators, creatingnew applications and nano-enabled products and processes. Tether’saccount is mainly focused on the core nanotechnology innovators and thoseof the user industries that are themselves highly innovative users of nano-capabilities.

Jones et al (2007) went further in explicating the skills required to driveinnovation through the various steps of the emergence of a new product or process. The innovator needs skills for:

• Sourcing and selection of ideas – skills requirements here areconnected centrally with the identification, collection and filtering of ideas for innovation . Innovation managers (and employees) will ideallyhave an awareness of existing sources for innovation both within andoutside their organisations, and an ability to ‘scan the horizon’ for - anddevelop relationships that will lead to - new sources of ideas andstimuli for innovation. An ability to interpret data (from market,consumer and competitor research etc.) and to evaluate the viability of innovation ideas is also crucial. Knowledge of and an ability to apply

relevant IP protection mechanisms constitutes a further important skill.Once an innovation idea is selected for possible progression todevelopment stage, skills in arguing for its viability and potential value

– often in the face of strong competition from competing projects –become paramount.

• Development of innovation ideas – upon securing financial supportfor progression of an innovation idea, attention is directed to thepracticalities of development. Here, skills connected with theassemblage of development teams, allocation and management of budgets and resources, generation of appropriate spaces andconditions for experimentation, sourcing and specifying complementary

inputs, and establishing networks and partnerships are called into play.In the development of new artefacts/technologies, the sourcing of technical and design skills is often a central concern.

• Testing, Stabilisation and Commercialisation – a key skill at the‘stabilisation’ stage is evaluation of risks and benefits of continuedexperimentation. Cost effective innovation requires an ability torecognise the optimal point at which to call a halt to prototyping and thecomparison of competing alternatives. It also requires a goodknowledge of the preferences and requirements of the intendeduser/consumer base and an understanding of the ways in (and extentto) which an innovative product or process will meet with anticipatedneeds. An understanding of the ability of potential users to derivebenefits from an innovation (i.e., their ‘absorptive capacity’) is also



necessary. Stabilisation and commercialisation requires that aninnovating company has the skills in place to ensure reproducibility of an artefact or service at an acceptable cost and price (technical,engineering, design and marketing skills are often at the fore here).Commercialisation also requires that attention is afforded to ‘capturing

value’ from an innovation – here, skills associated with managing riskand deriving appropriate roll-out strategies are foregrounded.

• Implementation and diffusion – marketisation, implementation anddiffusion are frequently understood to be connected intimately withproject management and technology transfer skills. Beyond these,skills in managing and coordinating value and supply-chainrelationships, and in evaluating innovation practice and performanceare crucial. Reflexivity is becoming an increasingly importantcomponent of innovation practise as firms recognise that collection andevaluation of data (i.e., knowledge management and intelligencegeneration) can result in the development of improved innovationprocesses.

These are pointers to the skills that are required for major innovations –though in general terms, the implications for nanotechnology development areeasy to deduce. But it is apparent that the quantitatively most significantSKIN emerge as these major technologies are employed. For example,current occupational data suggest that around half of Europe’s workforce areusing PCs and a third are using the Internet, which means that at least basicskills in a range of standard computer applications (word processing,spreadsheets, databases, browsers and search engines, etc.) are being

utilised. These jobs have been transformed – while some clerical jobs havediminished substantially. These are wide-ranging changes, even if the newskills are ones that most professionals would regard as fairly basic – and thatare becoming part of basic secondary education in most advanced countries.In contrast, more advanced IT skills, and the sorts of technical work that gowith these, are much more restricted: only a few percent of Europe’sworkforce are IT professionals, and only half of these are actually located in ITsectors rather than user sectors. 8

So we can also see user industries SKIN as evolving through the productcycle. Figure 5 presents a highly schematic illustration of how this might be

represented, with the lead users being very much user-innovators, requiringhigh levels of understanding of the nano-tools as well as of their ownapplication domains, and with users at later stages of the product cyclebecoming progressively more numerous, and requiring quantitatively moreskill in applying these tools in the particular domains.

8 CEPIS (2006) estimated that 4.2 million IT practitioners within the EU, while approximately180 million people are using IT at work.



This is highly schematic, and it will be necessary to differentiate betweennumerous occupational positions – researchers, other engineers, technicalsupport workers, production staff, managers, and so on, in order to map theentire range of affected occupations. Manufacturing and other applicationsectors will be supported by knowledge-intensive business services (KIBS),and by the supply of trained staff and knowledge inputs from education,training and research organisations. .But the point of the earlier discussion of multiple product cycles is that we are liable to see a series of S-curves asspecific NT applications are developed and adopted in specific industrysectors. As these unfold, so requirements will be experienced through the(changing) occupational structures of these sectors, with variouscombinations of the skill classes emerging in producer and user sectors.Figure 6 combines Figures 4 and 5, to illustrate this.

The content will vary across sectors and applications in relatively predictableways, but much of the balance across the three types of skill will be shapedby work organisation strategies – including those involving outsourcing andoffshoring. New technologies alone do not dictate skill requirements andwork organisation.

Figure 5 Product Cycle: Skills and Employment

Time

Diffusionover

populationof users

New Technology Users

New Technology ProducersA) Entrepreneurial.

Adaptive workforce.Scientific expertise.

D) User-innovators.

B) Efficientproduction & supplychain management.

More routineworkforce.

E) Fusion of domainand new technology

knowledge.New workforce skill

requirements.

C) Marketing skills.Outsourcing.

New development.

F) New users: as E)(skill requirements

reduced?)Established users:

new productdevelopment skills

Major employment

implications – new jobs, jobloss

Moderate employment implications –new jobs



Conclusions

A longer-term examination of SINT requires that we see them in terms of acascade of product cycle and diffusion curves. A succession of job creationand skill requirements are liable to emerge, as the core nanotechnologies areenhanced by complementary technologies and by integration into variousapplication areas, and as dominant designs and common platforms andstandards are established. The larger employment effects are liable to lie inthese application areas, though this is not to underestimate the potentialsignificance of the heartland industries (consider the importance of IT firms intoday’s world). Skill requirements evolve as successive applicationsemerge; it is plausible that applications will be further combined intoinnovations that themselves form the basis for new products and industries.

“Calibrating” this approach requires estimates of the speed of uptake and theextent to which transformative processes are themselves transformed throughNT. In addition to the estimation of which industries and applications take off at which speeds, it will be necessary to confront the arguments about howrevolutionary NT will be – will it essentially be an extension of existingnanoengineering techniques, or embody the more radical visions of molecular engineering and “bottom up” NT. Disputes about these perspectives becomerather important for forecasting beyond the medium term (5-15 years) – and

Figure 6 Diffusion of Multiple Applications of new CoreTechnologies, and Skill Implications

Niche applications

Multiple Applications:

successivemass markets

Trigger and

enabling applications

Ongoing QualitativeChange

Time

Adoptionof different

applications in various

firms,sectors,

etc.

SuccessiveNew Technologies

with improved characteristics,

diffusing more rapidly

Cascading Applications,

new platformsand potentials

(ceilings of curves may

differ)

A

D E F

CB

A

D E F

CB S k i l l a

n d e m

p l o y m

e n t i m

p l i c a t i o n

s

c a s c

a d e a s

a p p l i c a

t i o n s m

u l t i p l y

A

D E F

CB

A

D E F

CB



even within it. Scenario analysis may be one response to such disputes, withalternative scenarios reflecting the realisation of one or other viewpoint..

But equally important for medium and shorter-term analysis is a dimensionthat this essay has largely neglected – the issue of international development

and diffusion of technology. Countries and regions also differ in terms of these features, and one of the most significant tendencies in globaldevelopment has been the rapid “catch-up” of emerging economies in IT.Huge investments in R&D in biotechnology and NT are apparent in severalemerging economies, leading some commentators to anticipate that these willpose challenges to the advanced industrial economies – they may be“latecomers” in many fields, but are at the forefront of several areas of newtechnology. It remains to be seen how the balance between regulatory andentrepreneurial cultures, and the linkages between various elements of national innovation systems, leads to these challenges being realised rapidly.

With such complex issues in play, it makes little sense to pin one’s hopes onthe accuracy of a single forecast. Again, we suggest that it is mostappropriate to combine scenario analysis, in which some plausiblealternatives are explored systematically, with horizon-scanning and “weaksignal” methods, to provide early warning of how far the key elements of various scenarios are being manifest.

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2009 Nanotechnology Skills Foresight Moscow