Chapter 7. The next production revolution and institutions for technology diffusion

Shapira Philip
Youtie Jan
Manchester Institute of Innovation Research, Alliance Manchester Business School, University of Manchester School of Public Policy, Georgia Institute of Technology Enterprise Innovation Institute, Georgia Institute of Technology

Institutions for technology diffusion facilitate the spread and use of new knowledge and methods that can assist companies in adopting new manufacturing technologies. Such institutions also help companies to achieve objectives ranging from improved production efficiency to product development, strategic planning, and training. This chapter examines publicly oriented technology diffusion institutions and their rationale, organisation, and services. Case studies of varied approaches are presented, including dedicated field services, technology-oriented business services, applied technology centres, information exchange, and demand-side incentives, and effective practices and operational insights are distilled. Key policy suggestions include the need for greater recognition that strong institutions for technology diffusion, in conjunction with complementary framework measures, are essential for widespread deployment of the next production revolution. Technology diffusion institutions should be encouraged to share and refine their practices, build collaborative partnerships, and address missions of sustainability and responsibility. Particular attention is required to assist small and medium-sized enterprises (SMEs) and to address governmental failures in technology diffusion.



Institutions for technology diffusion will be vital in spreading the use of next-generation production technologies. If institutions and mechanisms for technology diffusion are weak, and firms and industrial systems lag in absorbing and effectively using the new technologies and approaches, then the next production revolution could stall. But these institutions also need to change and innovate to achieve diffusion effectively and responsibly. This chapter examines the nature and role of institutions for technology diffusion. It also explores how these institutions are changing, and may need to change, to respond to and influence the development of next-generation production technologies.

Over the coming decade, major transformations are anticipated in how the world makes and uses manufactured goods and services (Kagermann, Wahlster and Helbig, 2013; Foresight, 2013; Buffington, 2016). The technological drivers of this “next production revolution” include burgeoning developments in information and communications (such as big data, cloud computing, and the Internet of Things [IoT]), the rise of digital and additive (3D) manufacturing, and the emergence of new bio- and nanomaterials that offer novel functionalities (OECD, 2016). Parallel changes are expected in manufacturing business models, with greater openness, flexibility, customisation, user engagement, interaction, and attention to value-added services and sustainability, as well as adjustment in how manufacturing firms are organised, who they employ, and where they are located (OECD, 2010a; Chesbrough, Vanhaverbeke and West, 2014; Wu et al., 2015; Prendeville et al., 2016). For advanced economies, there is the hope that the next production revolution can revitalise older industrial regions and strengthen national industrial competitiveness through “smart” factories with the agility, efficiency, and intelligence to raise productivity and obviate the need for offshoring (Alessi and Gummer, 2014; Brennan et al., 2015; NAE 2015; The White House, 2016). For emerging economies, advances in manufacturing technologies and methods offer fresh opportunities to engage in higher-value and more sustainable production (Birtchnell and Hoyle, 2014; Rauch, Dallasega and Matt, 2016).

In past decades, predictions of major technological transformations in industry have not always been realised (Youtie et al., 2007), as with expectations of automated factories in the 1950s or the spread of molecular machines in the 1980s. Bottlenecks that constrain, or at least slow down, radical technological ideas can include economic viability, financing, market demand, strategic fit, technical readiness and time-to-implementation, the power of incumbent technologies and the appearance of unexpected alternatives. The latest expectations about technological transformation in manufacturing certainly face such issues. Additionally, in the coming period, the promise of the next production revolution could be moderated, if not stymied, if it fails to address a series of fundamental societal and institutional challenges. A number of these challenges are already evident, such as concerns regarding human workers being replaced by robots, fears about autonomous machine decision making, cyber security and data privacy, and public risk and ethical apprehension towards aspects of biological engineering (see Chapter 8, as well as the discussion in OECD [2016]).

The role of technology diffusion institutions is particularly important in enabling SMEs to upgrade and derive benefits from the transformation of manufacturing. Just as the nature of production is evolving, so should the approach to technology diffusion, as diffusion itself becomes more complex, involves more participants, and occurs over accelerated timeframes and greater scales. This rising complexity must lead to an increased emphasis on networked approaches and renewed efforts to anticipate and address issues of governance in institutions which facilitate technology diffusion. If institutions for technology diffusion can adapt and innovate, taking on roles that address societal as well as economic and technological issues, this could positively contribute to the socially responsible implementation of the next production revolution.

This chapter examines institutions for technology diffusion, their rationale, how they are organised, and the services they provide. Existing and new institutions are discussed, with a focus on publicly oriented mechanisms. The discussion builds on a typology of publicly oriented technology diffusion mechanisms. The typology includes dedicated field services, technology-oriented business services, applied technology centres, technology information exchange, demand-based behavioural change, and open knowledge-sharing. Case studies of selected institutions are presented.

Tested approaches to fostering technology diffusion already exist, such as agent-based intervention, brokering, mentoring, collaborative projects, and referral services, which are able to assist firms in adopting and absorbing new manufacturing technologies and methods. These tested approaches continue to have utility and validity. New approaches are also emerging, including open-source knowledge transfer and community building. Some longstanding institutions that adopt a conventional paradigm of established public programmes to provide services to clients are also able to address new production technologies and take on new functions. Alongside these existing models are new institutions for technology diffusion which typically arise out of emerging technologies and which serve as mechanisms for knowledge exchange, experimentation, and application. Both kinds of institution have important and complementary roles.

The chapter concludes with policy recommendations for strengthening institutions of technology diffusion. In this regard, policy making clearly needs to ensure the integration of technology diffusion and its institutions into the implementation of the next production revolution. There is an inescapable tendency to emphasise exciting research advances and the potential of novel technologies. However, major economic and societal value will only occur if these technologies are responsibly designed and deployed together with users and other stakeholders, and if these technologies can be scaled up, diffused, and improved in use. Indeed, advantages will tend to flow to the companies and systems that are most effective in deploying new technologies and business models. Policy makers tend to acknowledge the critical importance of technology diffusion at a high level, but to overlook technology diffusion in the subsequent allocation of attention and resources. It is important to redress this situation.

Programmes to upgrade existing firms (the majority of firms) must be appropriately resourced, alongside programmes to promote advanced technology development and start-up enterprises. Where institutions for technology deployment are weak or non-existent, they should be reformed, or new institutional capabilities created. Experimentation, learning, the development of relevant new skills and business models should also be encouraged in institutions for technology diffusion. Insights from pilot activities should be incorporated into existing and new technology diffusion institutions. Similarly, service practices and approaches should be systematically reviewed to ensure that these are effective and customised for the communities served, to ensure knowledge exchange, and to ensure the scale-up of new approaches as needs evolve. Management mechanisms should be developed to reform (or replace) technology diffusion institutions that are resistant to change. There are practices that policy makers should seek to avoid. Perhaps the first of these relates to the inclination to concentrate attention and resources on policies to back research breakthroughs and exciting laboratory technologies and to overlook, or at least poorly support, the industrial scale-up and diffusion of new technologies. Furthermore, efforts to diffuse new technologies often target predictable early adopters. These adopters tend to be large multinationals, high-technology start-ups, and the small number of companies involved in technology development. Policy attention should not just be placed on these likely early adopters, but also on the much larger number of existing SMEs. Indeed, a substantial part of the success of the next production revolution will depend on take-up by SMEs.

Reflecting on the rationale for policies to support institutions for technology diffusion is also important. Such policies should not be pledged as programmes that can restore lost manufacturing jobs. Technology diffusion institutions can help firms today to adjust their business approaches and to adopt new technologies, products, and strategies. Upgrading the ability of manufacturing communities to absorb next production revolution technologies will take time (five to ten years or more). This means that technology diffusion institutions need to be empowered and resourced to take longer-term perspectives.

The systematic and networked nature of many aspects of the next production revolution demands a high level of co-operation among producers, users, and other actors. Firms, suppliers, users, and intermediary institutions should be included in collaborative strategies for diffusion. Accordingly, technology diffusion institutions, which have often worked at an individual project level, now need to adopt strategies and actions that can work in multi-actor collaborations. They also need to address missions of sustainability and responsible research and innovation.

Finally, it is vital to undertake an ongoing review and analysis of organisational designs and models for technology diffusion under the evolving conditions of the next production revolution. In so doing, evaluation metrics should give more weight to longer-run capability development, rather than short-term incremental outcomes. Sharing good practices is also essential. Policy and management approaches should stimulate technology diffusion institutions to upgrade their current methods and to trial promising new approaches as the innovation landscape evolves.

What are institutions for technology diffusion and what do they do?

While technology is a term that is often associated with machines and devices, our understanding of technology has to be broader, encompassing the organisation and application of knowledge for practical purposes.1 Technology may be embodied, as in machinery, or disembodied, in the form of know-how, methods, and processes. The diffusion of technology can be viewed as the process by which innovations and new technologies disseminate and get taken up.2 Institutions for technology diffusion are intermediaries, structures and routines that facilitate the adoption, spread and use of knowledge, methods, and technical means, ranging from improving the efficiency of existing production facilities and introducing new process technologies to product development, strategic planning and training. A technology diffusion institution may combine a tangible presence (e.g. facilities), capabilities (people, expertise, communications), and partnerships (with technology developers and users) alongside “soft” aspects where tacit knowledge is shared via informal interactions (specialists, other companies).

While an innovation system invariably contains multiple sources of technology diffusion, such as universities, professional societies, and the media, the focus in this chapter is on public or quasi-public institutions, or parts of those institutions, that prioritise technology diffusion roles. These institutions are a significant, although at times undervalued, component of the mix of research and development (R&D), technology, business support, human capital, regulatory and related policies that nations and regions use to foster economic development and innovation. Technology diffusion is differentiated from technology transfer, although these two practices are related and can complement one another. Technology transfer implies the conveying of a technology from its developer to a user, with a prime example (in the public sphere) being the establishment in the United States (under the Bayh-Dole Act of 1980) of university and federal laboratory technology transfer offices to license federally funded intellectual property to companies.3 In this context, technology transfer is the transfer of ownership rights of the outputs from R&D under contractual arrangements between a public organisation and an assignee. Technology diffusion is more embracing in scope and intensity: while it can include technology transfer, it also includes active outreach to firms to assist them to use their existing technologies and processes more efficiently, to provide guidance, expertise and training to upgrade the absorptive capabilities and performance of these firms, and to help diagnose problems and address them, including through applied projects.

Dedicated technology diffusion institutions include industrial extension programmes, technology-oriented business services, applied technology centres, and also university technology transfer offices. Additionally, networks, partnerships, and open-source collaborations are increasingly important in orchestrating technology diffusion. The effectiveness of these institutions depends on the absorption capability of firms for new knowledge and technology services (Cohen and Levinthal, 1990) and to the extent of demand for innovation and new technology (Edler, 2016). Technology diffusion institutions do contribute to efforts to build absorptive capability, e.g. through training, information exchange, and mentoring. Similarly, efforts have been made to facilitate absorption through such mechanisms as innovation vouchers that encourage potential users to engage with knowledge or technology suppliers (OECD, 2010b). In turn, both the development and take-up of new technology is influenced by broader factors in the innovation and policy systems of regions and nations. For example, Hekkert et al. (2007), identify seven interrelated innovation system functions that are critical for understanding the dynamics of technological change: the presence of active entrepreneurship, knowledge development processes, knowledge diffusion networks, search guidance, market formation, financial and human capital resource mobilisation, and orientation to change. Other analysts have highlighted the importance of socio-technical regimes and multi-level frameworks in the elaboration of technological transitions (Geels, 2002).

Innovation systems have particular national characteristics and needs and vary (including at regional levels) in the organisation of their functions. Hence, contrasts can be expected in the design and operation of technology diffusion institutions between and within different innovation systems. At the same time, institutions for technology diffusion, depending on such aspects as their leadership, strategy, scale, and relationships, can correspondingly have an influence on innovation system functions. This can occur through providing guidance about new technologies, linking companies with sources of finance for manufacturing modernisation, or signposting new market opportunities for innovative product development. Institutions for technology diffusion can also serve as conveners by connecting individual firms with the sometimes myriad and complex array of programmes and providers within multifaceted innovation systems. The relationships of technology diffusion institutions to their host innovation systems can be continuous, in the sense of pursuing tried and tested approaches to technological upgrading and supporting incremental change. The significance of such a continuous role should not be underestimated: SMEs often move slowly in adopting new technologies and, when they do, a step-by-step approach is appropriate from resource, capability, and risk management perspectives. Yet, institutions for technology diffusion may also have to take on discontinuous strategies, developing innovative mechanisms and approaches that are particularly relevant for deployment of major new technologies, particularly where those technologies also require associated socio-technical system changes. For example, in enabling the deployment of automated factory systems, a new partnership of users, vendors, customers, and intermediary institutions may be needed to facilitate new digital design and data sharing systems, address issues of job restructuring andretraining, and introduce integrated management and inventory arrangements.

Rationales for institutional intervention

The major benefits (as well as the consequences) of technological advancement materialise when those technologies are diffused and applied.4 This is a critical point: policy deliberation on emerging technological transformations often focuses on the future models and exciting innovations in R&D laboratories, and on a handful of promising prototypes. But significant and broad impacts, be they economic, environmental, or societal, will only accrue with diffusion. Moreover, the feasibility and performance characteristics of emerging technologies and associated business models can be advanced where learning from diffusion among users and customers feeds back to developers through iterative design, build and test processes (Fleck, 1997; Govindarajan and Trimble, 2004; Baden-Fuller and Haefliger, 2013).

However, in practice, diffusing technologies is not straightforward: there can be multiple challenges and failures that limit, confound or block the adoption and effective use not only of leading-edge but also current best-practice manufacturing technologies and methods. This in turn can lead to sub-optimal performance in terms of critical manufacturing process variables, such as productivity, quality, yield, waste, energy use, response time, feasible batch size, and costs, and also in terms of capabilities to design and develop innovative products and add value to users and customers (Box 7.1). Sub-optimal manufacturing performance not only impacts individual firms (including ultimately their survival) but also can have adverse effects on industrial supply chains and sectors, regional clusters, and national economic competitiveness, and serve as a constraint on the ability to afford, absorb and deploy new technologies and methods. The challenges and consequences of lags in industrial upgrading are especially evident among existing manufacturers, particularly SMEs (NAPA, 2003; National Academy of Engineering, 2012). For example, results from the US Census of Manufacturers, conducted every five years, suggest that value-added per employee in SMEs (defined as manufacturers with fewer than 500 employees) has generally been 60% of that of their larger counterparts over the 1992-2012 period (US Census Bureau, 2016). The OECD report The Future of Productivity, which compares manufacturers in multiple countries, finds a significant gap in the labour productivity growth of manufacturers between a leading set of frontier firms and the majority of non-frontier firms. Frontier firms, which tend to be larger, more profitable, and younger, enjoyed average annual growth in labour productivity of 3.5% comparedto 0.5% for non-frontier firms between 2001 and 2009 (OECD, 2015). The diffusion of technologies and techniques, which includes enhancing the capacity of firms and their supply chains to re-engineer their systems, can help to raise productivity levels. The McKinsey Global Institute estimates that 55% of potential productivity gains in developed countries comes from catching up to best practice, with 45% coming from pushing the frontier outwards; the potential productivity gain from catching up is even more pronounced in developing and emerging economies (Manyika et al., 2015).

Box 7.1. Lags in technological upgrading in manufacturing

Why is it that more manufacturers do not upgrade their technologies and processes to move closer to performance and productivity frontiers? Explanations conventionally focus on a mix of contributory market, public, and system failures. These include constraints in input and process factors, such as lack of access to capital, skills, knowledge, and management capabilities, as well as by information deficiencies and asymmetries. Enterprises (especially SMEs) frequently lack information, expertise and skills, training, resources, strategy, and the confidence to adopt new technologies. Suppliers and private consultants can experience high transaction costs in trying to diffuse technologies to many small firms. Public institutions, such as universities and national research centres, are often focused on publications, leading-edge technologies in laboratories, and high-technology start-ups; existing SMEs often find such institutions complicated and unwieldy to engage with, notwithstanding their increased efforts to be more business-facing. Finance for scale-up and implementation is not always forthcoming, with the risk that companies will under-invest. Moreover, industrial companies have systems, routines, and attitudes that are already operating and embedded. These existing systems are often resistant to change. This may be due to competency traps, where the company has expertise and experience in its current methods and is reluctant to change even if new methods are superior, or because an industry segment or supply chain is “locked in” to an inferior approach due to network effects or behavioural embedding. Importantly, while the continued use of “less than best practices” can be due to legacies, preferences, and the ongoing influence of past investments, such “sub-optimal” path-dependent practices can still be profitable for the immediate term. Yet, in continuing to use these practices,they can constrain moving to higher levels of performance and longer-run capabilities to be competitive while maintaining good wage levels and working conditions. For example, manufacturers may retain older, less efficient capital equipment, particularly in older plants, because it is cheaper in the short run rather than installing new machinery that would enable greater customisation capabilities, energy savings, and data collection and analysis for process improvement (Hagerty, 2013). These supply-side issues can interact with demand-side constraints, where users in intermediate and end markets are reluctant or slow to deploy the products of innovative new technologies, again for reasons such as network failures (e.g. no critical mass of users), information, capital and other system constraints (Geels, 2002; Edler, 2010, 2016).

Public and system failures that constrain industrial upgrading provide core rationales for supporting institutions and mechanisms for technology diffusion. While certain constraints to upgrading can be alleviated through indirect financial instruments, such as grants, loans, and tax incentives, a central part of the mission of institutions for technology diffusion is the provision of direct guidance and support. This kind of engaged and expert assistance is particularly important in helping to overcome information gaps, breaking down entrenched path-dependent practices, and in assisting firms and value chains to develop upgrading strategies. Support from technology diffusion institutions seeks to guide and support enterprise capabilities and to assist them in justifying and adopting investments in new technology. Technology diffusion institutions can also work with industrial segments or supply chains that are locked in to outmoded approaches and where change requires the stimulation and support of collective action. For example, the maintenance of mechanical processes in manufacturing can be overlooked until there is a breakdown, while companies may avoid making changes in the process because they have stored inventory, which facilitates production continuity but adds cost. Introducing advanced sensors and communications to provide early signals of wear and relay real-time data back to service providers could eliminate this problem, but would require collaboration among machinery makers, users and maintenance service providers to agree on common protocols. A technology diffusion institution could facilitate a solution through a collaborative project and support adoption by lead users, as well as advising on technological options.

In the fast-moving environment of next-generation production technologies, the conventional market failure rationales for institutional intervention are likely to become even more important, as potential users are challenged to sift through burgeoning amounts of information and to support decision making in the context of rapidly changing technologies and requirements for expertise. Additionally, there are also likely to be increasingly strong systemic rationales for supporting not only current institutions for technology diffusion but also for developing new ones that reflect the characteristics of emerging production and technological developments. Technology roadmaps such as Germany’s “Industry 4.0” and the United Kingdom’s Synthetic Biology Roadmap have helped lay out pathways for systemic and pervasive industrial transformations. These scenarios will come to fruition only if diffusion is fully integrated and implemented at scale. However, many existing institutions are geared for the 20th century, when R&D was seen in a linear way, with diffusion tacked on. Intensified considerations of the need for responsibility in innovation and of targeting global challenges raise further system challenges for technology diffusion institutions. In the future, technology diffusion institutions will need more engagement in missions that not only support diffusion to individual firms, but also link to networks of suppliers, users, and customers. These approaches will increasingly need to incorporate mechanisms for the responsible design, integration, and use of emerging technologies.

Types of technology diffusion institutions

In broad terms, as discussed above, institutions for technology diffusion are intermediaries, employing structures and routines that facilitate the adoption, spread and use of knowledge, methods, and technical means. Although institutions for technology diffusion share the general challenge of addressing market, public, and system failures, there are differences in how these institutions are commissioned, organised and operated. These differences reflect not only the mix of specific failures and targets that each institution is tasked to deal with, but also national, regional, and sectoral variations in innovation system landscapes, policies, and practices. Publicly oriented technology diffusion institutions may be managed by, or associated with, universities, government agencies, and non-profit or for-profit organisations. Their missions may be targeted at transferring leading-edge technologies, deploying known methods to new users, or a mix of these approaches. Again, depending on their mission and orientation, technology diffusion institutions may operate, or be linked with, R&D laboratories, demonstration and training facilities, and exchange and meeting spaces. While technology diffusion will be a primary focus, in many cases technology diffusion institutions engage in a range of activities and partnerships to support their mission, including with other organisations involved in innovation, technology, business, and skills development.

Yet, notwithstanding such multiple combinations in their form and function, it is possible to distinguish categories of these institutions by signature elements of their approach to diffusion and by the modes through which they operate. To illustrate the array of technology diffusion institutions, six exemplary types are identified (Table 7.1). These range from dedicated field services and technology-oriented business services that extend expertise, guidance, and other resources to firms, to applied and advanced technology centres which have the capabilities to undertake business-facing R&D. The typology includes knowledge exchange and demand-based instruments that serve as intermediaries and stimulators for technology diffusion, and also open technology mechanisms which represent new, often virtual, ways to link technology development and diffusion. These six categories are not mutually exclusive: for example, field services are offered by some applied technology centres, while advanced technology centres also engage with knowledge transfer networks. Moreover, this typology is not exhaustive – other categories could be incorporated. However, the range of institutional types encompassed makes it possible to demonstrate the variety of approaches to technology diffusion currently in use, to discern insights about effective approaches and practices, and to consider how these institutions are addressing challenges presented by the next production revolution. The next sections of the chapter discuss these key institutional types in further detail.

Table 7.1. Typology of institutions for technology diffusion

Diffusion mechanisms

Operational modes (primary)


Dedicated field services

Diagnostics, guidance, and mentoring

Manufacturing Extension Partnership (US)

Technology-oriented business services

Advice linked with finance

Capacity development

Industrial Research Assistance Program (Canada)

I-Corps (US)

Applied technology centres

Contract research, collaborative applied research, prototyping and standards

Fraunhofer Institutes (Germany)

Manufacturing USA (US)

Kohsetshushi Public Technology Centers (Japan)

Targeted R&D centres

Advanced research on emerging technologies intertwined with commercialisation missions

Campus for Research Excellence and Technological Enterprise (Singapore)

Knowledge exchange and demand-based instruments

Technology community networking

Knowledge transfer incentives

Knowledge Transfer Networks (UK) Innovation Vouchers (multiple countries)

Open technology mechanisms

Shared technology library

Virtual networking

BioBricks/Registry of Standard Biological Parts (US)

Source: Authors’ analysis.

Dedicated field services

Dedicated field services work with SMEs to help them adopt modern, proven manufacturing technologies and techniques using industry-experienced specialists, commonly in an engineering domain. The services are usually organised in a decentralised manner, with specialists working at the manufacturing site on projects aimed at solving the company’s problems and needs. Dedicated field services offer varied forms of assistance, including help with quality systems, lean manufacturing, energy conservation, environmental protection, health and safety, computer systems and software applications, and product development and marketing. These services usually offer assessment of the company in question, the development of an in-depth project, and the tailoring of relevant training. Dedicated field services can provide services in-house and/or refer companies to other providers including private consultants, government programmes, human resource development organisations, and applied research equipment and facilities centres. Operational funds for these services are often based on a mix of client fees and core public support (Shapira et al., 2015).

The US Manufacturing Extension Partnership (MEP) is a dedicated field service programme targeting SMEs. Established in 1989, the MEP evolved throughout the 1990s into a national network of centres in each US state plus Puerto Rico, with each centre often having field offices at different locations around the state depending on the size of the state. Some centres are organised as private non-profit entities, some as non-instructional units of universities, and others as state government programmes. The National Institute of Standards and Technology (NIST), under the US Department of Commerce, administers the MEP. NIST has provided one-third of the funding for these centres matched in a 3:1 ratio with non-federal sources. The federal contribution to the MEP budget was about USD 130 million in fiscal year 2016. In January 2017, the American Innovation and Competitiveness Act changed the federal share of MEP funding to 50%.5 The centres provide a pragmatic set of services related to process improvement, product development, marketing, training, and sustainability services such as energy conservation and environmental management. Most centres also connect manufacturing SMEs with other private and public assistance sources. Governance is based on a co-operative agreement between NIST and the individual centre. National and centre-level advisory boards also operate, comprised primarily of SMEs. The MEP programme serves 7 000 to 8 000 SMEs nationally through around 12 000 projects. There is an extensive evaluation process composed of customer and activity reporting, independent client surveys, annual reporting, review by expert panels, and special studies, measuring attributable cost savings, sales, capital investment, jobs, productivity and other economic impacts.

Technology-oriented business services

Technology-oriented business services are services designed to help start-ups and small firms by melding business assistance with financial support. They address weaknesses in the links between business technology upgrading efforts and financial capital. Two programmes that exemplify this category are Canada’s Industrial Research Assistance Program and the US Innovation Corps (I-Corps).

The Industrial Research Assistance Program (IRAP) was established in the early 1960s by the National Research Council (NRC) of Canada. IRAP is centrally co-ordinated with a decentralised network of field offices and is administered by the NRC (Shapira et al., 2015). The programme uses former executives to work with companies, offers funding for applied R&D projects to SME clients through non-repayable contributions, and collaborates with partner organisations to provide services to entrepreneurs. The programme operates offices at its own and partner organisations in five regions, with most of the offices concentrated in the provinces of Quebec and Ontario. Nearly half of IRAP’s annual budget of around USD 90 million supports the advisory services and the rest is used to deliver applied R&D funding. The programme does not charge companies for services. It engages in ongoing relationships with a portfolio of client firms. IRAP also provides funding to public sector organisations in remote locations to help them to provide services. Roughly 10 000 firms a year are served of which one-third typically receive non-repayable contributions. Eligible companies are SMEs in product-oriented categories, primarily in information and communication technologies (ICTs), materials and manufacturing, construction, agriculture and food, energy and environment, and life sciences. Customers receiving non-repayable contributions must complete status reports and project and impact assessments. In addition, the programme is subject to a legislatively mandated external assessment every five years.

I-Corps is a programme started in 2011 by the US National Science Foundation (NSF) to accelerate start-up activity from science-based research. I-Corps is based on the “Lean LaunchPad” curriculum at Stanford University, developed by Steve Blank (2013). The idea behind I-Corps is to train teams, comprised of an NSF principal investigator, an entrepreneurial lead (typically a student or postdoctoral researcher) and a mentor. The programme employs lean customer discovery techniques – these are systematic methods to understand what customers most value and to test products or services that best address their needs. The training uses Alexander Osterwalder’s Business Model Canvas (Osterwalder and Pigneur, 2010), requiring teams to develop a hypothesised business model for one or more applications of their research. Teams are required to leave the laboratory and talk to roughly 100 potential customers and partners about their proposed product or service in the context of the hypothesised business model, making changes (also known as pivots) to this business model in response to the feedback they receive. After a three-day “boot camp” (an intensive short course), possible modifications following the feedback are considered. Each team then makes a choice as to whether or not to pursue the application as a start-up or as a licensing opportunity, known as the “go-no go” decision. Anecdotal evidence from early I-Corps cohorts indicated that a lack of supporting services and infrastructure at their home universities limited cohorts’ success. This led NSF to create an ecosystem around I-Corps of what are termed “nodes” and “sites”. Nodes are regionally distributed locations at universities that provide training, while sites provide entrepreneurship and commercialisation support at the university to I-Corps teams, often on their home campusto enable team formation. VentureWell, originally known as the National Collegiate Inventors and Innovators Alliance (NCIIA), operates a National Innovation Network, managing a database of I-Corps activity, engaging in community building, and performing ongoing evaluations of the programme. There are no comparison group studies of I-Corps, but initial assessments report a three‐fold increase in familiarity with the business model canvas after the training. The NSF budget for I-Corps was USD 30 million in fiscal year 2016 (US NSF, 2016). Team awards of USD 50 000 cover expenses associated with training and customer discovery. Sites receive up to USD 100 000 for three years. Nodes receive USD 2 million to USD 4 million over a three-year period to provide training. Interest in I-Corps has spread to other US federal agencies including the National Institutes of Health, the Department of Energy, the Department of Defense, the Department of Homeland Security, and the Small Business Administration. Similar lean customer discovery methods have spread globally, e.g. to SynbiCITE in the United Kingdom, which uses these methods to accelerate the commercialisation of synthetic biology applications (SynbiCITE, 2016).

Applied technology centres

Applied technology centres conduct contract R&D for companies, state and local governments, and other types of organisations. Applied technology centres can be part of larger comprehensive organisations. A prominent example is the Fraunhofer Society – a private non-profit network of about 60 research institutes in Germany that carry out contract research for the government (at national and state levels) and business organisations (Fraunhofer, 2016). Established in 1949, the Fraunhofer Society falls under the German Ministry of Education and Research but largely manages its own operations. Each Fraunhofer institute specialises in a particular technology or sector. The institutes use a mix of in-house researchers and students to perform their research. Services include joint pre-competitive research, bilateral applied research with individual firms, prototyping, and pre-production and co-operative technology transfer arrangements. Fraunhofer services tend to be large, highly customised, high-value projects. One-third of the Fraunhofer budget comes from core institutional sources in amounts exceeding USD 700 million. The remainder of the budget derives from work with private industry and public sector agencies. Fraunhofer institutes can provide services to clients outside their regions and there are institutes located in the United States under the aegis of Fraunhofer USA. The institutes produce publications, patents, research contracts, licences, and start-up companies. Other groupings of applied technology centres include TNO in the Netherlands, the GTS Institutes in Denmark, SINTEF in Norway, and Technalia in Spain (Solberg et al., 2012; Shapira et al., 2015).

Manufacturing USA, formerly known as the National Network for Manufacturing Innovation, is an initiative to develop a Fraunhofer-like system in the United States focused on applied research and the commercialisation of key manufacturing technologies (US NNMI, 2016). The network is comprised of institutes anchored by a private non-profit organisation. The first institute, dealing with 3-D printing, was founded in Youngstown, Ohio in 2012. Core funding for the institutes comes from various agencies, depending on the mission of the institute, including the Department of Defense, the Department of Energy, the Department of Commerce, the National Aeronautics and Space Administration, and NSF. Core multi-year funding of some USD 60 million is matched by a mix of sources, including memberships for multinational and smaller private sector companies, universities, and non-profit organisations. Membership benefits include royalty-free access to intellectual property (IP) depending on the organisation’s membership fee level, participation in institute R&D projects, and influence on the research agenda of the institute. Many of the institutes deal with significant standards issues, particularly in efforts to manufacture products involving complex systems. For example, one of the projects of the Digital Manufacturing and Design Innovation Institute is to develop a digital manufacturing commons for data sharing, analysis, modelling, tooling, and building. Being able to draw on the expertise of a membership consortia facilitates this institute’s ability to marshal input and participation from the relevant community (DMC, 2016).

Targeted R&D centres

While applied technology development centres focus on business-oriented projects, driven by high levels of business engagement and governance, targeted R&D centres are driven largely by researchers themselves with a mission of leading-edge emerging technology research combined with a mandate to generate economic impact. Increasingly, such centres are also tasked with tackling societal challenges through the development and diffusion of their targeted technologies. Targeted R&D centres are typically located at universities, with significant support for their research themes provided by government research sponsors pursuing policies to advance specific emerging technologies that are anticipated to have significant economic and societal impacts. There has been increased and more explicit consideration of such economic and societal aspects in several recent flagship R&D initiatives in emerging technology domains. Among national examples, NSF has sponsored 17 Nanoscale Science and Engineering Centres in the United States, each in specific areas of emerging nanotechnology, under a framework (the 21st Century Nanotechnology Research and Development Act6) that emphasised not only advanced research but also the incorporation of new technologies into products, the development of skills and tools, and responsible development (Fisher and Mahajan, 2006; Rogers, Youtie and Kay, 2012). In the United Kingdom, six Synthetic Biology Research Centres have been sponsored under a national programme to advance research capacity, foster industrial linkages and the commercialisation of synthetic biology, develop training, and promote attention to responsible research and innovation (UK Synthetic Biology Roadmap Coordination Group, 2012; Shapira and Gök, 2015).

Targeted R&D centres can involve multiple universities and other stakeholders, and multi-institutional approaches often involve international relationships. Singapore’s Campus for Research Excellence and Technological Enterprise (CREATE) is one such approach that combines targeted research and commercialisation with international partnerships.7 Developed by the Singaporean government, CREATE uses research partnerships between Singapore’s two major research universities and ten prestigious research universities in the United States, Europe, the Middle East, and Asia to develop new production technologies linked to the city-state’s major societal challenges (CREATE, 2016). CREATE began in 2006 under the auspices of the National Research Foundation of Singapore. CREATE established a USD 250 million research facility adjacent to the National University of Singapore (NUS) University Town (UTown) campus in support of this research. In addition, each foreign partner university received roughly USD 100 million to perform the applied research, 75% to 80% of which is allotted to the Singaporean university partner – typically NUS or Nanyang Technological University (NTU) – for researchers and equipment. The research university arrangements have been structured to last for a five-year period, renewable for an additional five years.

The programme originally designated ten foreign universities as collaborators: the Swiss Federal Institute of Technology, Zurich; the Massachusetts Institute of Technology (MIT); the Technical University of Munich; the Hebrew University of Jerusalem; Ben-Gurion University; the University of California, Berkeley; Peking University; Shanghai Jiao Tong University; and Cambridge University. Each research project involves two senior investigators: one usually from NTU or NUS (although other Singaporean universities may participate) and the other from the foreign partner university. The projects fall into four interdisciplinary areas: human systems (such as tropical and infectious diseases), environmental systems (such as water management), urban systems (such as driverless vehicles), and energy systems (such as building efficiency). The programme uses several mechanisms to ensure that commercialisation is established in Singapore. Each partner university must establish a Singaporean limited liability company to receive and manage the research funds. All foreign university research centre directors must be based in, or spend the majority of their time in Singapore, while all senior investigators of the partner universities must adhere to a one-year residency requirement in which at least six consecutive months are spent in Singapore. Additionally, the Singapore Technology Licensing Office manages all IP. Management of the programme involves key performance indicators mutually agreed to by the National Research Foundation and the foreign university.

Knowledge exchange and demand-based instruments

Increasing attention has been paid in recent years to the development of intermediary institutions to facilitate technology diffusion processes. There has also been an expansion in the role of demand-side instruments that can incentivise firms to initiate interactions with technology diffusion intermediaries and with sources of technology on the supply side. Such “boundary-spanning” mechanisms are recognised as being vital to the iterative brokering, mediation, and diffusion of knowledge about new technologies and methods among and between firms and research and technology organisations (Aldrich and Herker, 1977; Tushman, 1977; Kaufmann and Tödtling, 2001; Virani and Pratt, 2016). There are many varieties of these mechanisms, with the growth not only of technology transfer networks but also knowledge exchange and co-production networks, and the increasingly inventive use of behaviour-oriented incentives to encourage firms to cross boundaries to learn about new approaches.

Explicit public policies to foster networks among firms to advance innovation through information exchange and collaboration began in Italy in the 1970s, extending to Denmark in the 1980s, and to many other OECD countries in subsequent years (Cunningham and Ramlogan, 2016). A current example is the United Kingdom’s Knowledge Transfer Network (KTN), a mechanism funded by the publicly sponsored innovation agency Innovate UK. KTN supports networks of companies, universities, investors, non-profits and other interested actors to exchange and collaborate in targeted areas of technology. Specialist KTN staff serve as catalysts for network activities, which currently cover activities in 16 key sectors including biotechnology, creative industries and the digital economy, manufacturing, materials, and sustainability and the circular economy. The KTN also brings together about 20 special sectoral interest groups, on such topics as the flexible manufacturing, energy-efficient computing, robotics, and synthetic biology.8 The KTN delivers its activities through facilitating online exchanges among network members, organising open events, collaborating with knowledge centres (e.g. in materials chemistry or process innovation), facilitating access to funding competitions, and organising taskforces and roadmaps. In 2015-16 KTN reported more than 77 000 members, over 400 events involving in excess of 20 000 participants, more than 120 roadmaps and analyses, many thousands of individual meetings, and assistance with 455 funding proposals and about 120 funding events. The annual budget of the KTN is about GBP 15.8 million.

Knowledge exchange networks typically bring together participating members within and across a value chain of interest, e.g. developers and potential users of an emerging technology, or firms and technology organisations engaged in a particular sectoral supply chain. Such networks can be national or international, but they can also have regional dimensions. Where there is a strong regional dimension to technological knowledge exchange, networks may merge with, or evolve into or from, cluster initiatives that broadly link agglomerations of firms and other innovation system actors within particular geographical localities. In Germany, more than 450 regional cluster networks have been identified, many sponsored by federal and state government alongside other privately led clusters (Clusterplatform Deutschland, 2017). Active technological sectors (with the number of regional clusters as of January 2017) include the environment (79), energy (69), information and communications (69), production (66), materials (48), the automotive industry (41), biotechnology (41), and electrical engineering, measurement, and sensors (34).9 These cluster networks support firms to engage with other firms and institutions in technology development and diffusion activities and to link these to new product development, marketing, and internationalisation strategies. One of the spurs to the development of this dense and diverse array of exchange networks was the Kompetenznetze (Competence Networks) programme, established in the mid-2000s by the then Federal Ministry of Economics and Technology. This programme encouraged the formation and recognition of more than 100 regional networks, including in life sciences, food processing, medicine, renewable energy and information technology, in locations throughout Germany (BMWi, 2010). In 2012, this programme was amalgamated into the German cluster platform jointly sponsoredby the Federal Ministry for Economic Affairs and Energy (BMWi) and the Federal Ministry of Education and Research (BMBF). As part of this platform, BMWi sponsors a “go‐cluster” programme that provides for a cluster certification procedure, access to public funding, participation in cross-network activities, and guidance from an external support agency. There are about 100 designated go-clusters in Germany, involving some 13 000 members including 8 500 companies, mostly SMEs, as well as universities, Fraunhofer Institutes, other non-university research and technology centres, and business organisations. A recent evaluation finds that linking together these complementary resources with firms through regional network clusters has encouraged the generation and implementation of innovations (Ekert, Schüren and Bode, 2016).

In these and similar networks, key practices include shared industrial, technological, or regional interests, open membership, core capabilities to foster information exchange, collaborative activities, and projects, a business orientation towards translating and deploying future-oriented technologies, and effective governance and management (see also BMWi, 2010; Cunningham and Ramlogan, 2016). Knowledge exchange networks can also take on functions related to co-production, where companies working often with technology institutions jointly undertake development projects and pool design, production, training, and marketing tasks typically among a spatially proximate cluster of network members. Smart city and industry networking programmes have been initiated in several European countries and elsewhere. One example is Brainport Eindhoven in the Netherlands where leadership, increased knowledge exchange and new co-production interactions have stimulated revival in an old industrial port region (Horlings, 2014). An initiative sponsored by two United Kingdom research councils is exploring opportunities for “redistributed manufacturing” where advanced manufacturing technologies deployed in localised and clusters of flexible firms can offer a competitive and sustainable edge over conventional globalised supply chains (Pearson, Noble and Hawkins, 2013). Promising cases are being explored by these networks, including in health care and medical products, and in consumer goods and big data, while other networks are examining the potential gains to redistributed manufacturing of 3D printing and makerspaces (Freeman, McMahon and Godfrey, 2016; Moreno and Charnley, 2016; Zaki et al., 2017).

A complementary mechanism to these knowledge exchange intermediaries and initiatives is the use of incentives to foster demand-side interest and stimulate new boundary-crossing relationships that can accelerate technology diffusion among firms, especially SMEs. Such incentives can take the form of innovation vouchers that SMEs can use to purchase time and other assistance from research and technology institutions and other vendors of business assistance. These combine matchmaking with modest financial incentives to stimulate interest, demand, and behaviour change in enterprises to encourage interaction with universities, research organisations, specialised consultants, and other sources of technology and knowledge (Bakhshi et al., 2015). Innovation vouchers are promoted at both national and regional government levels. Innovation vouchers have been sponsored in the Netherlands, Ireland and the United Kingdom (among more than 20 European countries underwriting innovation voucher schemes) as well as in Australia, Canada, the People’s Republic of China (hereafter “China”), India, Singapore, and the United States (DG ENTR-Unit D2, 2009; Langhorn, 2014; CORFO, 2016). The value of the incentive is usually small (ranging from about EUR 3 000 to under EUR 10 000) and may require a cash or in-kind match from the SME. The incentive is not meant to subsidise the cost of a major project: it generally can support just a few days of time. Rather, the voucher is designed as a behavioural inducement to encourage SMEs to talk with people in other organisations to explore new technological and business options, to undertake initial work, and to scope follow-on steps and project options. Innovation voucher schemes may be targeted to eligible SMEs in certain sectors (e.g. in manufacturing or advanced services), emphasise particular technologies (including ICT), or aim to foster links with specific domains of academicor private sector expertise. Although vouchers by themselves are not sufficient to completely alter how SMEs approach innovation, available evaluations (including with randomised control) suggest that innovation vouchers encourage firms to initiate new relationships and projects (Cornet, Vroomen and van der Steef, 2006; OECD 2010; Sala, Landoni and Verganti, 2015; Bakhshi et al., 2015). Good practices associated with innovation voucher schemes include effective public management, well-organised brokering to link firms with sources of expertise, minimal administrative burden on participants, suitable marketing, and capabilities to initiate follow-on projects (OECD, 2010b).

Innovation vouchers are one example of the demand-side instruments that can be used to foster the diffusion of technology. Other instruments include the targeted use of public procurement, tax incentives and other subsidies to lower the cost of new technologies for users, awareness raising, training, the fostering of interactions between users and producers, and regulation that supports the deployment of new technologies (Blind, Petersen and Riillo, 2016; Edler, 2016; Uyarra 2016). Some of these are “soft” mechanisms that use indirect, informational, or behavioural approaches, while others involve direct financial support. If policy seeks to accelerate the diffusion of the technologies associated with the next production revolution, it is likely that such demand-side approaches will need to be increasingly integrated into, as well as delivered alongside, the activities of institutions for technology diffusion.

Open technology mechanisms

Open-source methods of diffusion of new production technologies have emerged in recent years, mirroring the rise of open-source developments in the software industry. An example is the BioBricks Foundation, which was founded in 2006 by Stanford University professor Drew Endy.10 A private non-profit foundation that is pioneering open-source models of technology transfer in synthetic biology, BioBricks seeks to overcome the danger that this emerging field will be dominated by IP protection and secrecy, which could then hold back application, diffusion, and further development. BioBricks has created several programmes to foster open innovation in the field of synthetic biology. OpenWetWare is a wiki application that began in 2007 for sharing information among laboratories around the world about protocols and courses and other information relevant to the synthetic biology community. OpenWetWare has more than 20 000 users. Following a foundational synthetic biology conference held at MIT in 2004, BioBricks sponsors a global synthetic biology conference (SBx.0) for community building, alternating between locations in the United States, Europe, and Asia. The most recent was SB6.0 held at Imperial College in the United Kingdom in 2013 (with more than 700 attendees). SB7.0 is in Singapore in 2017. In 2008, BioBricks began a “request for comment” process that has led to a technical standards framework for standard biological parts to enable these parts to “fit” with one another as they are exchanged. BioBricks has developed a voluntary researcher agreement – the BioBricks Public Agreement – to set out the conditions under which users could employ biological parts. Since 2015, the organisation has promulgated an Open Materials Transfer Agreement (OpenMTA) to address weaknesses in current materials exchange agreements such as limits on commercial participation.Also underway is, a peer-to-peer information technology platform aimed at enabling the monitoring and exchange of biomaterials.

The BioBricks Foundation was funded with small grants from the US NSF and National Institutes of Health until 2015, when it received a three-year USD 3.9 million grant from the Helmsley Charitable Trust’s Biomedical Research Infrastructure Program to create (Helmsley Charitable Trust, 2016). This funding enabled BioBricks to hire a managing director, a legal and technology transfer director and a software specialist, while enabling it to subcontract software development to Stanford University. BioBricks is starting a membership programme to provide long-term funding.

BioBricks also has a relationship with the International Genetically Engineered Machine (iGEM) Foundation and the International Open Facility Advancing Biotechnology (BIOFAB). iGEM began as an MIT class in 2003 taught by Endy and other colleagues and was designed to teach students to develop biological devices. Randy Rettberg, then an MIT researcher and one of the initial BioBricks board members, spun off iGEM as a separate foundation and now serves as its president. iGEM runs the iGEM Competition for student-based teams to develop devices from standard biological parts (and which also requires consideration of risk and societal implications). IGEM also operates the Registry of Standard Biological Parts, which is used by the student competitors and others to advance innovation in synthetic biology, and the Labs Program, which provides access to biological parts to students outside of the competition. In 2015, the iGEM Competition had more than 5 000 participants in more than 200 international teams. BIOFAB is a production facility developed to design and produce more curated, higher-quality standard biological parts for public sharing. BIOFAB is sponsored by the US NSF and is a partnership of BioBricks, the Synthetic Biology Engineering Research Center at Berkeley, and Lawrence Berkeley National Laboratory. The biological parts are available in a “library” to academic groups and companies, leveraging the BioBricks public agreements to specify the terms for use of these biological parts.

In addition to multiple open-source software platforms, other open technology mechanisms have been formed in such areas as robotics, manufacturing hardware, and operational standards for automation in industry. As technologies and production systems become increasingly sophisticated, integrated, and data-driven, such mechanisms are likely to become increasingly important to foster large-scale co-ordination among multiple organisations. Additionally, open technology mechanisms offer flexible and relatively low-cost pathways for both new entrants and incumbents seeking to scale-up and diffuse emerging technologies.

Institutions for technology diffusion: Trajectories of change and challenges

Institutions for technology diffusion operate broadly across the innovation systems landscape, with varied targets and diverse organisational forms and functions, as the examples above illustrate. Some institutions for technology diffusion are long-established and deeply embedded in their respective innovation systems, while others are evolving or newly emerging. In the context of the next revolution in production, new institutions will be needed to creatively promote knowledge exchange, organisational change, capacity development, and demand for technology diffusion in emerging technological areas and in new business models. At the same time, it is also important for established institutions for technology diffusion to upgrade and orient their approaches to address the specific challenges and opportunities presented by next-generation technologies.

Institutions for technology diffusion are part of larger systems of innovation; how technology diffusion institutions contribute to, relate with, and leverage their larger systems will depend on the structures and policies enacted in host environments. At a broad policy and system level, a range of relevant factors influence the performance of technology diffusion institutions. These include policies and practices for R&D, university-industry collaboration, finance for business investment, skills, labour markets, infrastructure, IP, trade, fiscal, and macroeconomic policy, as well as the level of system attention to technology diffusion policies (OECD, 1998; Bozeman, 2000; OECD, 2015; Kochenkova, Grimaldi and Munari, 2016; Caiazza and Volpe, 2017). An essential task, through attention to policy mix (Flanagan, Uyarra and Laranja, 2011), is the co-ordination of innovation system framework policies and indirect mechanisms with policies towards institutions for technology diffusion. In particular, both broader frameworks and specific policies should encourage meso- and micro-level strategies that can ensure the effective design and operation of technology diffusion institutions. Here, a series of good practices have been identified. These are raised in the case examples and also further discussed in other studies (see e.g. Shapira et al. [2015]).

An essential good practice is an organisational setting which supports capable management of institutions for technology diffusion. As discussed, the organisational settings for publicly-oriented institutions for technology diffusion can include universities, technology centres, economic development and government agencies, and non-profit corporations. Organisational settings vary according to the innovation systems landscape in different countries, and developed systems can have multiple organisational arrangements that may be centralised or decentralised. The key point, however, is to ensure that whatever organisational arrangements are employed, they enable effective operations, which also means that there should be arrangements for both internal and external performance reviews that can prompt adjustments in services and management, and where necessary, modifications to the organisational setting. Other relevant meso- and micro-level practices for technology diffusion institutions include an explicit client base of firms (which could be broadly across sectors or targeted to specific industries), sufficient programme scale to reach significant numbers of firms within this base, and a structured approach to services to optimise available resources. Additional good practices for technology diffusion institutions include the use of personnel with industrial experience, links to other facilities and service partners, and a base of core funding to ensure stability. In developed industrial and technological ecosystems, there may be multiple institutions for technology diffusion with distinct missions. Across this system landscape, capabilities are needed to upgrade existing firms (typically SMEs) to current levels of technological modernisation, as well as to enhance leading-edge technological capabilities in existing and new firms where that is appropriate, and to work with firms through individual, group, and network modes (Park, 1999;Shapira et al., 2015).

It is critical that institutions for technology diffusion, and their host innovation systems, establish approaches that match current good practices. If there is currently an effective base for technology diffusion, institutional adaptations and innovations to diffuse emerging technologies can build on this. If the current base is weak, renewed efforts to upgrade and initiate technology diffusion institutions may be required. In either case, institutions for technology diffusion will need to take on board what is known about effective policies and practices. They will also have to adapt and innovate to reflect characteristics that may be accentuated in, if not drive, the next revolution in production. This will help to ensure that institutions for technology diffusion are fit for purpose, as the technological, industrial, and governance context for their operations changes with the next revolution in production.

As noted in the opening parts of this chapter, key features of the next production revolution include the transformative role of ICT, the rise of digital manufacturing, far-reaching changes in materials and economic foundations, and the emergence of new business models with a greater emphasis on user engagement, sustainability, and responsible innovation (OECD, 2016). Of course, attempts to foresee future developments in technology, business, and policy inevitably have to be qualified, as the next revolution in production could unfold in multiple ways. With that caveat in mind, and building on case examples discussed in this chapter and on broad insights from the literature, eight key aspects of technological, economic, and policy change are identified that are intrinsic to the next revolution in production. These should be considered by technology diffusion institutions and policy makers (Table 7.2). These aspects of change, with examples, are discussed below.

Table 7.2. Technological, economic and policy changes associated with the next production revolution and implications for technology diffusion institutions

Change aspect

Implications for technology diffusion institutions


Integrate diffusion of digital technologies in all aspects (including design, materials, production, products, communication, services).

Iterative and rapid emergence of new technologies and business models

Mobilise capabilities for rapid and customised responses. Adapt and innovate organisational business models to reflect new needs and opportunities.

Move from project models and formal planning approaches to flexible methods, more group assists, greater sharing.

New capability requirements

Build up capabilities of firms and local innovation ecosystems for technology absorption.

Enhance capabilities of institutions in emerging technologies and their integration.

Increased role for collaborative technology partnerships

Bring together multiple actors, including universities, research centres, and private sector organisations to collectively address research translation, scale-up and technology deployment.

Global rise of new knowledge clusters

Develop boundary-crossing and international linkages and partnerships.

Vital importance of sustainability

Embed longer-term considerations of environmental sustainability in technology approaches.

Growing attention to responsible research and innovation

Embed attention to responsible research and innovation in technology approaches.

Catalytic roles for policy and government

Leverage policy and government support through catalytic roles, partnerships and demand-side stimulation.

Source: Authors’ analysis.

  • Digitalisation. Digital information technologies will be at the core of future technology development and adoption. Analogous to the rise of open sharing of research articles and data is the emergence of libraries promoting sharing of technology building blocks. An example already highlighted is BioBricks, which promotes an open-source standard first developed at MIT to enable sharing and enhance usage of synthetic biology parts through the Registry of Standard Biological Parts. The registry is populated primarily with submissions from the iGEM Competition, although a more specialist and higher-quality public library, BIOFAB, has also been established. These open-source mechanisms exist against a backdrop of traditional proprietary biotechnology approaches. Likewise, the Digital Manufacturing and Design Innovation Institute, part of Manufacturing USA, is using a digital commons approach for development of manufacturing software tools. These examples highlight the burgeoning roles of institutions for technology diffusion not only in diffusing and helping to integrate digital technologies into individual manufacturing companies, but also in developing new collaborative and virtual models across industry sectors and networks to accelerate the use of innovative digital approaches.

  • Iterative and rapid emergence of new technologies and business models. Institutions for technology diffusion have conventionally adopted linear, project-based models of interacting with companies, often based on formal planning approaches and systematised procedures. While such approaches are likely to continue as a standard for working with enterprises, it is anticipated that the next revolution in production will stimulate, and require, institutions for technology diffusion to increasingly take on more flexible, discovery-based, approaches. A related implication is the need to mobilise capabilities for rapid and customised responses, to increase the pace and relevance of technology diffusion approaches. Signals are already apparent that institutions for technology diffusion are grasping these challenges, with the growing role of flexible and customised methods, more group assists, and greater emphasis on collaborative iteration. An example is the I-Corps programme, established by the US NSF and now increasingly disseminated by other agencies and organisations. I-Corps accelerates the commercialisation of science-intensive research using training influenced by “lean customer discovery” and “business model canvas” concepts. Teams of researchers and budding entrepreneurs are encouraged by programme mentors to undertake ongoing and reflexive interactions with customers and partners. These flexible interactions encourage the early reshaping of technological and business models to meet market demands and opportunities (Weilerstein, 2014). BioBricks, in encouraging collaborative exchange, learning and sharing in its community, is also fostering an iterative approach.

  • New capability requirements. An essential feature of the next production revolution is not just the emergence of new technologies and business models, but also the convergence and integration of these technologies and business models. For example, digital and physical technologies will increasingly be amalgamated, e.g. in the software engineering of new biomaterials, while fusions of design, manufacturing, logistics and services are expected. Producers will need to acquire new skills in emerging technologies and in the systematic integration of these technologies, as well as in such areas as industrial networking and co-production. In turn, technology diffusion institutions will need to enhance expertise in emerging technology domains and their integration, and pursue strategies that will assist in upgrading the absorptive capabilities of firms and their industrial ecosystems to engage with the next production revolution. Technology diffusion institutions can address these capability challenges in several ways. In the US MEP initiative, attention is paid to internal staff training and human resource planning, with centres also employing flexible arrangements to use third-party service providers who can be varied as technical needs change. In Germany, the Fraunhofer Society and its institutes offer a range of advanced training programmes to business including in new technologies. Fraunhofer and its institutes also transfer knowledge through extensive collaborations with companies, and supports its researchers to spin out and set up their own companies. Additionally, the close relationships of Fraunhofer institutes with universities facilitates the engagement of early-career researchers in projects with companies: this brings new technical skills into projects and subsequently aids diffusion as these early-career researchers gain expertise in industrial applications and subsequently take up positionswith companies. The US I-Corps Sites programme offers universities a new structured and supported method for research commercialisation: this franchising model represents a further way of scaling up capabilities for new approaches to technology diffusion.

  • Increased role for collaborative technology partnerships. Collaborative partnerships (in the context of technology diffusion) involve the bringing together of multiple actors, including universities, research centres, and private sector organisations, to collectively address tasks of applied research, translation and technology deployment. While such partnerships have a long tradition, next-generation production technologies have stimulated the rise of new partnerships that cross sectoral boundaries and which are designed to address the “scale-up” gap between research and commercial production. These technology partnerships also provide opportunities for tacit as well as formal knowledge exchange, the pooling of capabilities and specialties, agreement on common protocols, and leveraging sponsorship sources through calls for private funds to match public funding. An example is Manufacturing USA, which uses private non-profit organisations as the hub of a network of company and university organisations to develop standards and prototypes in areas such as additive manufacturing and digital manufacturing and design. Partnerships typically have a specific emerging technology focus, as in the case of Manufacturing USA which in early 2017 links 14 manufacturing innovation institutes, with each institute formed from multiple public and private sector partners and concentrating on a particular advanced technology.11 Similarly, in the United Kingdom, a network of 11 Catapult Centres established through Innovate UK seeks to transform leading-edge research into new products and services, again with each centre composed of multiple partners from the university, public, and business sectors and combining a mix of public and private revenues.12 Collaborative technology partnershipsmay be organised through national networks and initiatives, but they often have important regional dimensions and may involve international partners and partnerships (as discussed below). The next production revolution will probably demand the greater use of collaborative partnerships for technology diffusion to systematically address the translation of complex emerging technologies and to draw on both public and private resources.

  • Global rise of new knowledge clusters. The next production revolution will mobilise a range of clusters of knowledge and innovation around the world. These include locations in developed economies in Europe, North America, and East Asia, but also in rapidly emerging economies. In China, new initiatives are underway to upgrade manufacturing, with a focus on innovation and advanced manufacturing technologies.13 In multiple city-regions in China, including in Beijing, Shanghai, and Shenzhen, there are substantial clusters of leading-edge research, companies and entrepreneurs actively engaged in innovative manufacturing approaches (Bound et al., 2016; Saunders and Kingsley, 2016). Dynamic regional innovation clusters have grown or are emerging in India, Brazil, and other parts of the world (Dutta and Lanvin, 2013; Engel, 2014). Institutions for technology diffusion have generally operated within national and regional jurisdictions, although some institutions have established international locations. Fraunhofer Institutes have developed locations outside of Germany, in part to offer services to German-managed global supply chains but also to access specialised technological expertise in other countries. In addition to operating branded centres abroad, another strategy is the involvement of international organisations and companies not as clients but as partners in technological collaboration and diffusion. For example, the United Kingdom’s Cell and Gene Therapy Catapult has reached an agreement with the Kanagawa Prefecture of Japan to foster the application and commercialisation of regenerative medicine and cell therapy in both Japan and the United Kingdom, which includes facilitating market access for British firms in Japan, and for Japanese firms in Britain and Europe.14 As discussed earlier inthe chapter, Singapore’s CREATE programme partners with domestic and foreign universities to facilitate research capacity development and commercialisation in applied areas that address societal challenges facing the city-state such as urban congestion, tropical diseases, and access to energy sources. As knowledge and innovation clusters rise around the world, institutions for technology diffusion will increasingly need to find ways to access and engage in knowledge and technological exchange with counterparts in these locations.

  • Vital importance of sustainability. Sustainability is an increasingly important feature of next-generation production. Indeed, many aspects of next-generation production, such as the deployment of biomaterials to replace petrochemicals, the use of nanotechnologies in more efficient renewable energy solutions, and the development of redistributed manufacturing approaches that reduce transportation requirements, promise greener and more sustainable processes and products. These should contribute to addressing global challenges related to the environment, energy, and greenhouse emissions. Yet, in addition to addressing global goals, attention to sustainability can return benefits directly to companies and consumers, through reducing waste and materials usage, lowering life-cycle costs, and prompting process and product innovation. In the United States, the MEP offers services to assess energy usage and put forth recommendations for reduced consumption, to identify process improvement opportunities, and to assist with compliance with environmental regulations. This assistance includes compliance with energy standards such as the energy management standard ISO 50001 and environmental management standards in the 14000 series. At a wider level, the United Kingdom’s Energy Systems Catapult helps firms to develop and capture commercial opportunities across the energy system.15 Attention to environmental sustainability is likely to be a growing feature of technology diffusion activities in the next production revolution.16

  • Growing attention to responsible research and innovation. Responsible research and innovation aims to anticipate the societal, ethical, and legal, as well as the environmental, health and safety implications of new science and technology. It also seek to avoid or modulate adverse effects, and foster inclusive approaches to, and outcomes from, research and innovation (EU, 2012; Owen, Stilgoe and Macnaghten, 2012). Particularly in Europe and the United States, attention to processes of responsible research and innovation, especially in emerging technologies such as nanotechnology, synthetic biology, and digital technologies, artificial intelligence and automation, has increased in recent years (Owen, Bessant and Heintz, 2013; McBride and Stahl, 2014; Gregorowius and Deplazes-Zemp, 2016; Michelson, 2016). Such technologies comprise core technologies of the next production revolution. Technology diffusion institutions, alongside other public and private actors involved in the next production revolution, will need to embed and operationalise processes of responsible research and innovation in their activities. While this involves awareness of relevant laws, regulations, and protocols, including at international and national levels, responsible research and innovation is much more than a compliance task. It involves (to draw on a United Kingdom framework) processes of anticipation – of potential economic, social, and environmental impacts; reflection – on implications, motivations, uncertainties and dilemmas; engagement – opening up deliberation and dialogue; and action – to influence the research and innovation process.17 Attention to responsible research and innovation in the UK Synthetic Biology Roadmap, and its subsequent embedding in the United Kingdom’s Synthetic Biology Research Centres, offers an implementationexample. The critical issues that arise as responsible research and innovation is operationalised will depend on the technology. For example, in deploying new information technologies in companies and in networks of producers and users, there will need to be attention to data protection, privacy, and security. New medical technologies may raise complex ethical issues. Life cycle and environmental factors need particular consideration in the application of new renewable energy technologies. However, across the range of technologies involved in the next production revolution, all raise economic, social, and environmental issues in one form or another. One common concern is about equity considerations in dealing with displacement effects of emerging technologies on segments of the population that might lose their jobs or see their jobs transformed. At broad levels, institutions of technology diffusion have an important role in working with technology developers, policy makers, companies, publics, and others in their communities to encourage early consideration of responsibility in research, design and initial development. Additionally, in the deployment of specific technologies, institutions for technology diffusion should build specific steps and actions into projects and service plans for responsible innovation to ensure attention to any adverse effects and seek to avoid or mitigate potential difficulties. An example of how this might be done for the I-Corps programme raises a set of public value questions that teams can ask as part of their discovery process (Youtie and Shapira, 2016).

  • Evolving roles for government. It is common to look to government, usually the national government, as the director, manager and source of funding for institutions for technology diffusion. This role for government endures, since without public intervention and funding, market failures will lead to under-investment in technology diffusion, as discussed earlier in this chapter. In particular, in national innovation systems where institutions for technology diffusion are currently weak or disorganised, direct public intervention and sponsorship is likely to be necessary to promote upgrading and effective service delivery. Yet, building on a broader movement that began some years ago to deliver public policy through other mechanisms, such as public-private partnerships, the next production revolution will raise needs and opportunities to further evolve government roles in fostering technology diffusion. Policies to support technology diffusion as part of the next production revolution will involve the fostering of roles and agents that serve as catalysts, brokers, and stimulators to engage other public and private organisations to collaborate and leverage resources and to join together to pursue pathways for new technology adoption and responsible innovation. Manufacturing USA, as already mentioned, represents an approach in which non-profit organisations in the United States must partner with private industry, universities, and other non-profits through membership arrangements. This helps to secure funding, but most importantly brings together the portfolio of expertise and capabilities required for advanced technology scale-up and deployment. Attention to spanning the policy mix in the diffusion of next-generation technologies will demand the greater engagement of actors that can help to address financial, information security, regulatory, environmental, human resource and societal requirements. Pilotingcreative new approaches and institutions for technology diffusion, especially in key emerging technologies, will be vital, as will a willingness to experiment and foster discovery and iteration of new methods. Additionally, policy will surely place greater emphasis on the demand side, while also addressing societal and global challenges. Greater roles in technology diffusion and its co-ordination at regional and city levels will emerge, especially with enhanced efforts to channel the next production revolution in ways that redistribute manufacturing towards promoting local revitalisation and sustainability.

Overall, as production technologies transform, existing approaches to technology diffusion will need to be improved, and new diffusion models will be have to be fashioned, to facilitate efficient, effective and equitable deployment. New models are probably going to be more collaborative and open, with more diverse funding and creative approaches to building capacity and diffusing technology. These approaches can be adopted both by existing and new institutions. Innovation systems that have the foresight, flexibility, and drive to more rapidly enhance and refine their institutions for technology diffusion will be more likely to gain a competitive edge from the next production revolution. Systems that have weak or lagging institutions for diffusion could well be at a disadvantage, irrespective of their strengths in basic science.

However, challenges are evident for the diffusion of new production technologies and for the development of next-generation institutions for technology diffusion. Promising new technologies and models will come up against incumbent approaches deeply embedded in existing industrial facilities and ecosystems. For example, the fully integrated and automated factories proposed in the 1980s were not realised to the extent predicted due in part to the difficulty of incorporating existing supply chains and because of shortened product life cycles. Introducing new ways to integrate and diffuse technology can take time, patience and the ability to experiment. Yet many governments want visible results quickly, without risk. Additionally, while new production technologies are frequently promoted for their public value and their ability to address societal challenges, the funding and evaluation models under which many public technology diffusion institutions work lead to prioritising client counts and fee revenues rather than public values per se. There can be a focus on disseminating the latest advanced technology, when many enterprises and users lack absorptive capabilities for highly sophisticated methods. Such cases warrant pragmatic approaches to technology diffusion, coupled with long-term relationships that can build capabilities for more advanced strategies. Path dependencies in technology diffusion institutions themselves may also present roadblocks, leading to failure to upgrade expertise, services, and business models. Concerns over governmental accountability, combined with ongoing public austerity in many economies, could likewise mean that current institutions will be reluctant to risk change. This could slow the emergence of better institutions for technology diffusion.

Moreover, while effective institutions for technology diffusion are vital for deployment of the next revolution in production, especially for SMEs, these institutions cannot do everything. The scale, scope and quality of the diffusion of the next revolution in production also depend on national and regional innovation system frameworks. Elements involved here include provision for upgrading finance, infrastructure and education, including vocational training.

Policy recommendations

As discussed throughout this chapter, policy making needs to ensure the integration of technology diffusion and its institutions into the design and implementation of the next production revolution. While there is an inescapable emphasis on the exciting research advances and potential of the latest round of innovative new technologies, major economic and societal value will only be obtained if these technologies are responsibly designed and deployed in conjunction with users and other stakeholders, and if these technologies can be scaled up, diffused, and improved in use. Upgrading and reshaping the capabilities of technology diffusion institutions and integrating these institutions into next production revolution strategies are essential steps. Specific policy recommendations that would help to achieve these objectives are presented in Box 7.2.

Box 7.2. Policy recommendations: Institutions for technology diffusion in the next production revolution
  • Recognise that effective institutions are essential for the widespread deployment of the next production revolution. Where such institutions exist, their role and mission must be integrated into next production revolution strategies. Where they are weak or non-existent, new institutional capabilities should be formed or created. The emergence of new institutions for technology diffusion should be nourished, experimentation and learning supported, and the development of relevant new skills and business models enhanced.

  • Refine and share effective practices for technology diffusion. Institutions for technology diffusion should be encouraged to systematically review their service practices and approaches, to ensure that these practices are effective and customised for the communities they serve, to trial and scale up new approaches as needs evolve, and to exchange knowledge about practices. This requires policy attention to strategy, resourcing, operational support of management, personnel training, assessment and evaluation, and knowledge exchange.

  • Build collaborative understanding and joint action in the deployment of the next production revolution. Next-generation production involves change in firms, but also necessitates engagement and co-ordination in value chains, sectors, and clusters. This is more than a technical mission. There is a need to engage firms, suppliers, users, and intermediary institutions in collaborative strategies to leverage the system and network attributes associated with the next production revolution. These collaborations will need to span regional, national, and international boundaries.

  • Ensure complementary innovation system framework policies, indirect measures, and demand-side incentives to embed and amplify the effects of institutions for technology diffusion. It is vital to give attention to issues of policy mix and to organisational linkages to ensure that research and technology development are joined with diffusion, and that technology diffusion is integrated with related policies (including for finance, infrastructure, skills development, and procurement).

  • Address missions of sustainability and responsible research and innovation in the design and deployment of the next production revolution. Attention to economic, societal, and environmental considerations has to be integrated into the policies of institutions for technology diffusion. This will involve engagement with clients, stakeholders and publics, as well as greater use of foresight and anticipatory approaches.

  • Ensure the needs of SMEs are addressed in technology diffusion. While institutions for technology diffusion work with many types of firms, particular attention is needed to help SMEs navigate the next production revolution. A foundational task is to build the absorptive and transformational capacities of SMEs to engage with, and benefit from, the next production revolution.

  • Address governmental failures in technology diffusion interventions. Ensure that programmes working to upgrade existing firms (the majority of firms) are appropriately resourced, in addition to programmes that support advanced technology development and start-up enterprises. Develop management mechanisms to reform (or replace) technology diffusion institutions that are path-dependent and resistant to change. Ensure that government evaluation measures give more weight to longer-run capability development, rather than short-term incremental outcomes. Promote design/build/test approaches that can experiment with new models of technology diffusion, and encourage the incorporation of insights from these pilots into existing and new technology diffusion institutions.

As a corollary to the recommendations detailed above, with their focus on strengthening the roles and alignment of technology diffusion institutions in the transformation of manufacturing, there are also practices that policy makers should seek to avoid. Perhaps the first of these relates to the inclination to concentrate attention and resources on policies to back research breakthroughs and exciting laboratory technologies and to overlook, or at least poorly support, the industrial scale-up and diffusion of new technologies.

Furthermore, the diffusion of new technologies will not be accomplished only by strengthening technology transfer from universities, which tends to be focused on early-stage science. Similarly, it cannot be accomplished by turning to general business assistance programmes that provide tax breaks, loans, or conventional strategic planning services. The diffusion of technologies requires effective intermediary mechanisms of human interaction and the exchange of tacit knowledge. Moreover, while electronic communication and web-based resources are now indispensable aids, technology diffusion cannot be accomplished solely by posting assessment tools or briefing documents on the Internet: it requires experienced specialists with the knowledge and relational skills to understand problems and develop customised solutions.

Perhaps the most common pathway taken with diffusion of new technologies is to target them to likely early adopters. These adopters tend to be large multinationals, high-technology start-ups, and the small number of companies dedicated to the development of technologies. Policy attention should be placed not only on these early adopters, but also on the much larger number of existing SMEs. Not all SMEs can or will seek to modernise, but there are many SMEs that technology diffusion institutions can prompt and support to adopt new manufacturing technologies and approaches. A substantial part of the success of the next production revolution will depend on take-up by SMEs, and this will have leveraging effects on supply chains and regional clusters where SMEs predominate.

The stated rationale for policies to support institutions for technology diffusion is also important. In particular, such policies should not be pledged as programmes that can restore lost manufacturing jobs or rapidly revive old industrial regions. Technology diffusion institutions can help firms today to adjust their business approaches and to adopt new technologies, products, and business strategies. This can help individual firms to stay in business and strengthen their abilities to offer good jobs (although new technologies may result in shifts in the profile of jobs and their tasks). It is likely that the major positive effects of technology diffusion institutions on upgrading the capabilities and performance of manufacturing communities to absorb next production revolution technologies will take time to materialise (five to ten years or more). Institutions and firms need time to build deep relationships and undertake collaborations. Indeed, the full outcomes of technology diffusion interactions, if they are substantial, will take significant efforts over many years to appear. This means that technology diffusion institutions need to be empowered and resourced to take longer-term perspectives. While it is desirable that services and programmes have flexibility, instability or short-term perspectives for the institutions themselves is not likely to support effective practice.

This chapter has argued that effective institutions for technology diffusion are essential for the widespread deployment of the next production revolution. The policy system tends to acknowledge this point at a high level, but then overlook technology diffusion in the subsequent allocation of attention and resources. It is important to redress this situation. Advantages will tend to flow to the companies and systems that are most effective in deploying the technologies and business models of the next production revolution. This chapter has also reinforced the need for complementary innovation system framework policies, indirect measures, and demand-side incentives to embed and leverage institutions for technology diffusion.

Building collaborative understanding and joint actions to deploy the next production revolution will also be an important task and challenge for institutions for technology diffusion. The systematic and networked nature of many aspects of the next production revolution demands a high level of co-operation among producers, users, and other actors. Technology diffusion institutions, which have often worked at an individual project level, now need to increasingly adopt strategies and actions that can work in multi-actor collaborations. These institutions need to span boundaries, be they at regional, national, or international levels, to access knowledge and forge new joint actions. Moreover, these institutions need to address missions of sustainability and responsible research and innovation in helping to deploy the next production revolution.

The adoption of new technologies and business models will probably be more prevalent and faster among larger companies, with an important role for disruptive start-up firms. However, a core mission of a system of technology diffusion is to ensure that existing SMEs are involved, that strategies and services are appropriate and affordable, and that more of these firms are encouraged to upgrade their absorptive and transformational capabilities.

There is a need to address governmental failures in technology diffusion interventions. These concern the attention and resources allocated to technology diffusion, the need to ensure that evaluation systems are appropriately focused on longer-term rather than short-run measures, and the importance of piloting creative and experimental approaches and then building insights from these efforts into existing and new institutions for technology diffusion.

Finally, it is also vital to undertake ongoing review and analysis of effective organisational designs and new models for technology diffusion under the evolving conditions of the next production revolution. Yet, this involves more than undertaking assessment and evaluation, and sharing good practices, although these are all important. More fundamentally, there is a need for policy and management approaches that will stimulate technology diffusion institutions to upgrade their current methods and to trial promising new approaches, to embed innovative technologies and responsible methods into their own operations, and to enhance client and user absorptive capabilities.


Alessi, C. and C. Gummer (2014), “Germany bets on ‘smart factories’ to keep its manufacturing edge”, Wall Street Journal, 26 October,

Aldrich, H. and D. Herker (1977), “Boundary spanning roles and organization structure”, Academy of Management Review, Vol. 2, No. 2, pp. 217-230,

Baden-Fuller, C. and S. Haefliger (2013), “Business models and technological innovation”, Long Range Planning, Vol. 46, Issue 6, pp. 419-426,

Bakhshi, H. et al. (2015), “Assessing an experimental approach to industrial policy evaluation: Applying RCT+ to the case of Creative Credits”, Research Policy, Vol. 44, Issue 8, pp. 1462-1472,

Birtchnell, T. and W. Hoyle (2014), 3D Printing for Development in the Global South: The 3D4D Challenge, Palgrave Macmillan, Basingstoke.

Blank, S. (2013), “Why the lean start-up changes everything”, Harvard Business Review, Vol. 91, No. 5, pp. 63-72.

Blind, K., S.S. Petersen and C.A.F. Riillo (2016), “The impact of standards and regulation on innovation in uncertain markets”, Research Policy, Vol. 46, Issue 1,

BMWi (German Federal Ministry of Economics and Technology) (2010), “Cluster management excellence. Volume II: Sustainability and effectiveness of clusters and networks”, Kompetenznetze Deutschland Initiative, Federal Ministry of Economics and Technology, Berlin.

Bostrom, N. (2014), Superintelligence: Paths, Dangers, Strategies, Oxford University Press, Oxford.

Bound, K. et al. (2014), China’s Absorptive State: Research, Innovation and the Prospects for China-UK Collaboration, Nesta, London, (accessed 12 January 2017).

Bozeman, B. (2000), “Technology transfer and public policy: A review of research and theory”, Research Policy, Vol. 29, No. 4-5, pp. 627-655,

Brennan, L. et al. (2015), “Manufacturing in the world: Where next?”, International Journal of Operations and Production Management, Vol. 35, No. 9, pp. 1253-1274,

Buffington, J. (2016), Frictionless Markets: The 21st Century Supply Chain, Springer International Publishing Cham, Switzerland.

Caiazza, R. and T. Volpe (2017), “Innovation and its diffusion: process, actors and actions”, Technology Analysis & Strategic Management, Vol. 29, No. 2, pp. 181-189,

Chesbrough, H., W. Vanhaverbeke and J. West (eds.) (2014), New Frontiers in Open Innovation, Oxford University Press, Oxford.

Cohen, W.M. and D.A. Levinthal (1990), “Absorptive capacity: A new perspective on learning and innovation”, Administrative Science Quarterly, Vol. 35, No. 1, pp. 128-152,

CORFO (Corporación de Fomento de la Producción de Chile) (2016), “Voucher de Innovación”, [Innovation voucher], webpage, Corporación de Fomento de la Producción de Chile, Santiago, Chile, (accessed 12 January 2017).

Cornet, M., B. Vroomen and M. van der Steef (2006), “Do innovation vouchers help SMEs to cross the bridge towards science?”, CPB Discussion Paper 58, CPB Netherlands Bureau for Economic Policy Analysis, The Hague, (accessed 12 January 2017).

CREATE (2016), “Campus for Research Excellence and Technological Enterprise (CREATE)”, webpage, National Research Foundation, (accessed 27 October 2016).

Cunningham, P. and R. Ramlogan (2016), “The impact of innovation networks”, in J. Edler, P. Cunningham, A. Gök and P. Shapira, (eds.), Handbook of Innovation Policy Impact, Edward Elgar, Cheltenham.

DG ENTR-Unit D2 (2009), Availability and Focus on Innovation Voucher Schemes in European Regions, Directorate-General for Enterprise and Industry, European Commission, Brussels, (accessed 12 January 2017).

DMC (Digital Manufacturing Commons) (2016), “Digital Manufacturing Commons”, website,, (accessed 27 October 2016).

Dutta, S. and B. Lanvin (eds.) (2013), The Global Innovation Index 2013: The Local Dynamics of Innovation, Cornell University, INSEAD and WIPO, Ithaca, Fontainebleau and Geneva.

Edler, J. (2016), “The impact of policy measures to stimulate private demand for innovation”, in J. Edler, P. Cunningham, A. Gök and, and P. Shapira, (eds.), Handbook of Innovation Policy Impact, Edward Elgar, Cheltenham.

Edler, J. (2010), “Demand-based innovation policy”, in R.E. Smits, S. Kuhlmann and P. Shapira, (eds.), The Theory and Practice of Innovation Policy: An International Research Handbook, Edward Elgar, Cheltenham.

Ekert, D., V. Schüren and A. Bode (2016), “Evaluation des Programms go-cluster des Bundesministeriums für Wirtschaft und Energie (BMWi), InterVal, Berlin”, [Evaluation of the go-cluster program of the Bundesministerium für Wirtschaft und Energie (BMWi), InterVal, Berlin], pdf?__blob=publicationFile&v=3 (accessed 12 January 2017).

Engel, J. (ed.) (2014), Global Clusters of Innovation: Entrepreneurial Engines of Economic Growth Around the World, Edward Elgar, Cheltenham.

EU (European Union) (2012), “Responsible research and innovation: Europe’s ability to respond to societal challenges”, Publications Office, European Commission, Luxembourg, (accessed 12 January 2017).

Everett, R.M. (2003), Diffusion of Innovations, Fifth Edition, Free Press, New York.

Fisher, E. and R.L. Mahajan (2006), “Contradictory intent? US federal legislation on integrating societal concerns into nanotechnology research and development”, Science and Public Policy, Vol. 33, No. 1, pp. 5-16,

Flanagan, K., E. Uyarra and M. Laranja (2011), “Reconceptualising the ‘policy mix’ for innovation”, Research Policy, Vol. 40, No. 5, pp. 702-713,

Fleck, J. (1997), “Contingent knowledge and technology development”, Technology Analysis & Strategic Management, Vol. 9, No. 4, pp. 383-398,

Foresight (2013), The Future of Manufacturing: A New Era of Opportunity and Challenge for the UK, Government Office for Science, London.

Fraunhofer (2016), “Fraunhofer-Gesellschaft – About Fraunhofer”, webpage,, (accessed 27 October 2016).

Freeman, R., C. McMahon and P. Godfrey, (2016), “Design of an integrated assessment of re-distributed manufacturing for the sustainable, resilient city”, in R. Setchi et al. (eds.), Sustainable Design and Manufacturing 2016, Smart Innovation, Systems and Technologies, Vol. 52, pp. 601-612, Springer, Heidelberg,

Geels, F.W. (2002), “Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study”, Research Policy, Vol. 31, pp. 1257-1274,

Geroski, P. (2000), “Models of technology diffusion”, Research Policy, Vol. 29, No. 4-5, pp. 603-625,

Govindarajan, V. and C. Trimble (2004), “Strategic Innovation and the Science of Learning”, MIT Sloan Management Review, Vol. 45, No. 2, pp. 67-75.

Gregorowius, D. and A. Deplazes-Zemp (2016), “Societal impact of synthetic biology: Responsible research and innovation (RRI)”, Essays in Biochemistry, Vol. 60, No. 4, pp. 371-379,

Hagerty, J. (2013), “Forget revolution. More like renovation. At many US manufacturing plants, the winds of change have barely caused a ripple”, Wall Street Journal, 10 June, (accessed 27 October 2016).

Hekkert, M.P. et al. (2007), “Functions of innovation systems: A new approach for analysing technological change”, Technological Forecasting and Social Change, Vol. 74, No. 4, pp. 413-432,

Helmsley Charitable Trust (2016), “The BioBricks Foundation”, webpage, (accessed 27 October 2016).

Horlings, L.G. (2014), “Leadership, governance and knowledge in an enterprising place: The case of Brainport Eindhoven in the Netherlands”, in L. Pugalis and J. Liddle (eds.), Enterprising Places: Leadership and Governance Networks, Emerald Group Publishing Limited, Bingley, pp. 149-175.

Kagermann, H., W. Wahlster and J. Helbig (eds.) (2013), Recommendations for Implementing the Strategic Initiative Industrie 4.0: Final Report of the Industrie 4.0 Working Group, sponsored by the Federal Ministry of Education and Research of Germany, nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_ accessible.pdf.

Kaufmann, A. and F. Tödtling (2001), “Science-industry interaction in the process of innovation: The importance of boundary-crossing between systems”, Research Policy, Vol. 30, No. 5, pp. 791-804,

Kochenkova, A., R. Grimaldi and F.J. Munari (2016), “Public policy measures in support of knowledge transfer activities: A review of academic literature”, Journal of Technology Transfer, Vol. 41, No. 3, pp. 407-429,

Langhorn, K. (2014), “Encouraging entrepreneurship with innovation vouchers: Recent experience, lessons, and research directions”, Canadian Public Administration, Vol. 57, Issue 2, pp. 318-326,

Manyika, J. et al. (2015), Global Growth: Can Productivity Save the Day in an Aging World?, McKinsey Global Institute, San Francisco.

Michelson, E. (2016), Assessing the Societal Implications of Emerging Technologies. Anticipatory Governance in Practice, Routledge, Abingdon.

McBride, N. and B. Stahl (2014), “Developing responsible research and innovation for robotics”, Proceedings of the IEEE 2014 International Symposium on Ethics in Engineering, Science, and Technology (ETHICS ’14), 27, IEEE Press, Piscataway, NJ, (accessed 12 January 2017).

Moreno, M. and F. Charnley (2016), “Can re-distributed manufacturing and digital intelligence enable a regenerative economy? An integrative literature review”, in R. Setchi et al. (eds.), Sustainable Design and Manufacturing 2016. Smart Innovation, Systems and Technologies, Vol. 52, pp. 563-575, Springer, Heidelberg,

NAE (National Academy of Engineering) (2015), Making Value for America: Embracing the Future of Manufacturing, Technology, and Work, Committee on Foundational Best Practices for Making Value for America, National Academy of Engineering, National Academies Press, Washington, DC.

NAPA (National Academy of Public Administration) (2003), The National Institute of Standards and Technology’s Manufacturing Extension Partnership Report 1: Re-examining the Core Premise of the MEP Program, National Academy of Public Administration, Washington, DC.

NAE (National Academy of Engineering) (2012), “Making things: 21st century manufacturing & design”, report of a Symposium of the National Academy of Engineering, National Academies Press.

OECD (Organisation for Economic Co-operation and Development) (2016), “The next production revolution: An interim project report”, internal document, OECD, Paris.

OECD (2015), The Future of Productivity, OECD Publishing, Paris,

OECD (2010a), Eco-innovation in Industry: Enabling Green Growth, OECD Publishing, Paris,

OECD (2010b), “Innovation vouchers”, OECD, Paris,

OECD (1998), Technology, Productivity and Job Creation: Best Practices 1998 Edition, OECD Publishing, Paris,

Osterwalder, A. and Y. Pigneur (2010), Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, John Wiley & Sons, Hoboken, NJ.

Owen, R., J. Bessant, and M. Heintz (eds.) (2013), Responsible Innovation: Managing the Emergence of Science and Innovation in Society, John Wiley & Sons, Chichester.

Park, Y.-T. (1999), “Technology diffusion policy: A review and classification of policy practices”, Technology in Society, Vol. 21, No 3, pp. 275-286,

Pearson, H., G. Noble and J. Hawkins (2013), Workshop on Re-distributed Manufacturing. Technical Report, Engineering and Physical Sciences Research Council, Swindon, United Kingdom,

Prendeville, S. et al. (2016), “Makespaces: From redistributed manufacturing to a circular economy”, in R. Setchi et al. (eds.), Sustainable Design and Manufacturing 2016: Smart Innovation, Systems and Technologies, Vol. 52, Springer International Publishing, Cham, Switzerland.

Rauch, E., P. Dallasega and D.T. Matt (2016), “Sustainable production in emerging markets through Distributed Manufacturing Systems (DMS)”, Journal of Cleaner Production, Vol. 135, No. 1, pp. 127-138,

Rogers, J.D., J. Youtie and L. Kay (2012), “Program-level assessment of research centers: Contribution of Nanoscale Science and Engineering Centers to US Nanotechnology National Initiative goals”, Research Evaluation, Vol. 21, No. 5, pp. 368-380,

Sala, A., P. Landoni and R. Verganti (2015), “Small and medium enterprises collaborations with knowledge intensive services: An explorative analysis of the impact of innovation vouchers”, R&D Management, Vol. 46, Issue S1, pp. 291-302,

Saunders, T. and J. Kingsley (2016), Made in China: Makerspaces and the search for mass innovation, Nesta, London, (accessed 12 January 2017).

Shapira, P. et al. (2015), Institutions for Technology Diffusion, Inter-American Development Bank, Washington, DC, (accessed 12 January 2017).

Shapira, P. and A. Gök (2015), “UK Synthetic Biology Centres tasked with addressing public concerns”, The Guardian, 30 January, (accessed 12 January 2017).

Solberg, E. et al. (2012), Markets for Applied Research: A Comparative Analysis of R&D Systems in Five Countries, Rapport 46/2010, Nordic Institute for Studies in Innovation and Research (NIFU), Oslo.

State Council (2015), 中国制造 2025 (Made in China 2025), State Council, Beijing, People’s Republic of China, afa32da17b40cc8e5b66.pdf (accessed 12 January 2017).

Stilgoe, J., R. Owen and P. Macnaghten (2013), “Developing a framework for responsible innovation”, Research Policy, Vol. 42, No. 9, pp. 1568-1580,

Stoneman, P. and G. Battisti (2010), “The diffusion of new technology”, in B.H. Hall and N. Rosenberg (eds.), Handbook of the Economics of Innovation, Vol. 2, pp. 733-760.

SynbiCITE (2016), “SynbiCITE”, corporate website, (accessed 27 October 2016).

Teece, D.J. (1986), “Profiting from technological innovation”, Research Policy, Vol. 15, No. 6, pp. 285-305,

The White House (2016), “President Obama announces winner of new Smart Manufacturing Innovation Institute and New Manufacturing Hub competitions”, Fact Sheet, June 20, Executive Office of the President, White House Office, Washington, DC,

Tushman, M. (1977), “Special boundary roles in the innovation process”, Administrative Science Quarterly, Vol. 22, No. 4, pp. 587-605,

UK Synthetic Biology Roadmap Coordination Group (2012), A Synthetic Biology Roadmap for the UK, Technology Strategy Board, Swindon, United Kingdom, (accessed 12 January 2017).

US Census Bureau (2016), “Economic Census”, Government Printing Office, Washington, DC,

US Government Accountability Office (2011), NIST Manufacturing Extension Partnership Program Cost Share, GAO-11-437R, Government Printing, Washington, DC.

US NNMI (United States National Network for Manufacturing Innovation) (2016), “Manufacturing USA – The national network for manufacturing innovation”, website, (accessed 27 October 2016).

US NSF (United States National Science Foundation) (2016), NSF Budget Requests to Congress and Annual Appropriations, National Science Foundation, Government Printing Office, Washington, DC, (accessed 27 October 2016).

Uyarra, E. (2016), “The impact of public procurement of innovation”, in J. Edler et al. (eds.), Handbook of Innovation Policy Impact, Edward Elgar, Cheltenham.

Virani, T.E. and A.C. Pratt (2016), “Intermediaries and the knowledge exchange process: The case of the creative industries and higher education”, in R. Comunian and A. Gilmore (eds.), Higher Education and the Creative Economy: Beyond the Campus, Routledge, Abingdon, pp. 41-58.

Weilerstein, P. (2014), “NCIIA: Students as the vanguard in a geographically dispersed approach to stimulating science and technology innovation”, in J. Engel (ed.), Global Clusters of Innovation: Entrepreneurial Engines of Economic Growth around the World, pp. 359-377, Edward Elgar, Cheltenham.

Wu, D. et al. (2015), “Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation”, Computer-Aided Design, Vol. 59, No. , pp. 1-14, 2014.07.006.

Youtie, J. et al. (2007), “A brief history of the future of manufacturing: U.S. manufacturing technology forecasts in retrospective, 1950-present”, International Journal of Foresight and Innovation Policy, Vol. 3, No. 3, pp. 311-331.

Youtie, J. and P. Shapira, (2016), “Exploring public values implications of the I-Corps program”, Journal of Technology Transfer, pp. 1-15,

Zaki, M. et al. (2017), “The role of big data to facilitate redistributed manufacturing using a co-creation lens: Patterns from consumer goods”, 50th CIRP Conference on Manufacturing Systems, Procedia CIRP.


← 1. It should be noted that this understanding of technology assumes that it is specifically or at least ultimately under human command. There are, however, long-running debates about technological control (see e.g. Winner, 1997), with increasing concerns more recently about autonomous technologies (Bostrom, 2014).

← 2. For further exposition of technology diffusion and related concepts of knowledge and innovation diffusion, see e.g. Geroski (2000), Everett (2003), and Stoneman and Battisti (2010).

← 3. Bayh-Dole Patent and Trademark Amendments Act of 1980, Pub. L. No. 96-517 (12 December 1980), 35 USC §§200-12.

← 4. Multiple factors are posited to understand how certain new technological designs become dominant, particularly where there is rivalry and competition, including appropriability regimes and complementary assets (see e.g. Teece, 1986). Nonetheless, as a rule, diffusion is essential to securing returns (especially spillovers and societal as well as private returns) to the prevailing technology.

← 5. The American Innovation and Competitiveness Act of 2017, Pub. L. 114-329 (6 January 2017).

← 6. 21st Century Nanotechnology Research and Development Act of 2003, Pub. L. 108-153 (3 December 2003), 15 USC 7501.

← 7. This section also draws on insights gained through interviews in Singapore by J. Youtie in March 2016.

← 8. For information on the KTN see

← 9. Information available at Form.html? (accessed 12 January 2017).

← 10. Based on an interview with the senior counsel and director of BioBricks on 29 August 2016 and a review of the BioBricks website, retrieved from

← 11. (accessed 12 January 2017).

← 12. (accessed 12 January 2017).

← 13. China has announced a national programme to upgrade manufacturing “Made in China 2025” (State Council, 2015). See also (accessed 12 January 2017).

← 14. Cell Therapy Catapult signs Memorandum of Understanding with Kanagawa Prefecture, Japan. (accessed 12 January 2017).

← 15. (accessed 12 January 2017).

← 16. Broader definitions of sustainability encompass environmental, economic, and social sustainability. While this section focuses on environmental sustainability,topics related to economic and social sustainability are discussed under the heading of responsible research and innovation.

← 17. Engineering and Physical Sciences Research Council, Framework for Responsible Innovation, (accessed 12 January 2017).