Technology and innovation management – Shawn Cunningham: Thinking out loud

Exploring the gaps between universities and industries

When working on technological change and the improvement of innovation systems, the topic of the different gaps between universities and industries often comes up. This is true for South Africa, but also for my work in Europe and Asia. The gaps are described differently by development projects, academics and business people, and my job is to usually figure out where the opportunity to close these gaps lies.

In my experience of trying to close some of these gaps, it is important to be as specific as possible about nature and maybe even the effects of these gaps. It is important to go to businesses and to find out what they expect from universities, while also going to academics and researchers and finding out what they expect from the industry. Often, the expectations expressed by these two groups are unreasonable and hard to reconcile. Sometimes people are simply wrong about what they think is needed or should be done.

However, these different expectations are not the biggest obstacle to closing the gap. Often the bigger obstacle is a lack of imagination of what is even possible in terms of cooperation, interaction and information flow. This is made worse by low levels of trust by one side of the other side. Also, stakeholders often have little insight into how others value certain interactions and information flows.

Over the last 14 years I have worked occasionally with a Faculty of Engineering at a University of Technology. In the image below, I share some of the different interaction patterns that we have observed over the years of closing gaps between selected departments or technology centres and industries. I am grateful to Dr SJ Jacobs who agreed that I can share this illustration.

The possible different relations between universities and industries

Of course, some relationships are more important than others. Also, not everybody agrees with the direction of the arrows that I have used in the diagram. I also know that some academics find it hard to believe that they can learn from industry. At the same time, many business people are surprised when they realise that their own companies have learned through the personal relations of their employees with their alma mater.

Even if a certain relationship adds little value in the bigger scheme of things, for the people directly involved it could mean a lot. For instance, for an engineering student to find an industry project that they could work on as a research project is a big deal, even if this may not be so important for a company or even for the academic department involved. In many post-graduate degrees, students are required to work on a real-life project, which often requires a company to give a student access to their facilities, data, senior management or other resources.

To reflect on the relations between a university and a community or a region would require another picture, but I will do that in a next project.

To be transparent, for many years I have used a simpler version of this map that was developed by the late Jorg Meyer-Stamer in the early 2000s. I include the original map below. I think many of my readers might have seen this map in RALIS (Rapid Appraisal of Local Innovation Systems) training course material or RALIS diagnostic processes.

The original map developed by Jorg Meyer-Stamer

While this original map is still useful to explain the different kinds of interactions between the private sector and a university, I found that we need a more detailed diagram if we want to improve relationships, design new services or improve the performance of programmes.

What do you use to map the relations?

What are some of the common myths or gaps that you come across often when you work on this topic?

Some of the challenge prospective clients that reach out to me are grappling with

Due to my research, public speaking and writing my favourite topics I regularly receive requests to help somebody that is grappling with an issue either around meso-organisational change or about technological capability, innovation or disruption.

Usually, after a few emails, we schedule a phone call to discuss their context, their intent and my service offering. Thanks to my journal and reflection processes I can track the original requests and the ensuing correspondence or projects. Over the last six months, I have noticed some patterns that are now repeating. Here are some of the most frequently discussed points. While I can help with some of these, with some I cannot help for various reasons.

Because I have always focused on training other consultants and my own clients, I thought it would be a good idea to share these early observations with you. (Larry, Goran, Bojan, Nik, Albina, Garth, these are for you). To save you all from many emails, I have written 8 blog posts in one!

So here are the emerging patterns of 2019:

I am frequently contacted by organisations or projects that believe that technological change, or preparing for the 4th industrial revolution (4IR) is a project. That there is something that we can do quickly (one of the most popular search terms on this blog site is “formula for 4IR” and “4IR method”. Preparing for technological change, responding to disruptions, or even preparing to disrupt others is a capability that is distributed over companies, public and civil organisations, regions and individuals and over time. It is not a project that ends, it is a capability that must be continuously nurtured. After addressing one threat or challenge and the next two will be on the horizon. While I love training, what these organisations really need are new technology, innovation, change and knowledge management capabilities.
I am asked by development organisations to prepare their target groups or beneficiaries for the 4th industrial revolution by focusing on one threat. For instance by mastering computer-aided design, design thinking, or helping entrepreneur to cope with advances in digitalisation, 3D printing, or master some automation or sensor technologies. However, the reason why so many people lump so many technological advances together under the banner of the 4th industrial revolution is that these technologies are converging, and if they are not yet converging, they are rapidly learning from each other. That means the capabilities are converging or starting to follow similar evolutionary patterns. That also means that very few economic activities are left untouched by changes in other sectors, technologies and markets. Again, this is not about training. It is about competence, leadership, sense-making and innovation. Perhaps it is mostly about learning, relearning and knowing what you have to master next. People also commonly confuse “digitalisation” with writing software, whilst telecommunication costs, insufficient regulatory frameworks for e-commerce, closed government (as opposed to open government) or very fast connectivity and data security are ignored.
People that can quickly master a new domain, like machine learning, big data or concurrent design, will have a distinct advantage in the future. People that are specialised in one skill, especially a vocational skill, may be more vulnerable. But my main point here is that splitting up the technologies is not helpful. Again, the broad technical capability must be fostered. However, in addition to point 1, I want to add that the ability to track, master, integrate and leverage multiple specialised domains continuously over time is very important, even if they do not yet appear to have a relation to your industry, business or organisation.
I am asked to help only the private sector in a country, region or sector. Many organisations believe that the private sector is most vulnerable to disruptions. I believe that many competent firms would be OK, but not all. Uncompetitive companies, un-innovative companies and undermanaged companies are going to be more vulnerable unless the state can afford to protect them and in so doing possibly raising the costs to the society. But what we must not lose focus of is that when one public sector organisation, programme or function fails, the effects could be far-reaching. Take for instance what happens when a local municipality in a developing country is undermanaged. It will affect the whole community. The challenge is that in developing countries the “revolution” or the “disruption” will be about social institutions (local government, universities, technical vocation colleges, schools, or whole governments etc.) that will be caught in a weak position – and unable to catch up or get ahead. So supporting the private sector in a place where many public institutions are failing is just naive. You do not address a market failure by focusing mainly on the private sector, just as you do not address government failure by only working with the government.
This point is an extension of the previous point. Many organisations that approach me want me to help them get the private sector more innovative. But here is the problem. It is not possible to develop a prosperous and successful private sector without the same happening in the public sector and in civil society. Actually, any form of innovation starts with a good basic and often some good higher qualifications. The changes that people can work together in a sophisticated way, without these arrangements being replicated in other sectors are naive. Complex forms of cooperation within an organisation, company, NGO, school or church depends on the ability to work together to solve problems that span over the ability of individuals. This needs trust, and it comes from the broader society and its formal and informal institutions. You cannot develop the private sector in a vacuum. Management teams of companies are not suddenly going to behave in novel arrangements that don’t exist in schools, sports teams, civil organisations, universities or political parties. Maybe it is possible to develop only the private sector in the short term, but for long term economic development, healthy public sector organisations are a pre-condition. The social technologies that enable the private sector to innovate, to combine old and new ideas, to figure out new ways of arranging teams around objectives, problems and opportunities are in most countries developed with the direct or indirect help of the public sector. Often these ideas are first developed around social, political or local problems. The quickest way to instigate innovation is to focus on creativity, better decision-making and increased performance in publicly funded programmes and civil organisations. Do you want to quickly get new forms of dialogue or new technology to spread in a location? Start with the schools, the local theatre, church or community organisation – and watch how fast the private (and hopefully public) sectors will catch on. Often the most adaptive private sector leaders are serving on the boards of the schools, local NGOs, and they take up new ideas very quickly.
I am often asked to assist struggling industries in developing countries to become innovative, competitive or successful. Maybe the companies were successful once, hopefully not too long ago. The challenge with sectoral upgrading is that the prominent companies must either be very competent in market development, or they must have mastery in a technological domain that has a long cycle time still ahead. With one of these two domains mastered product and process innovation is possible, but perhaps not easy. The real challenge is often that in developing countries the business model innovations are the hardest and the cost of failure are also very high. Thus the incentives to try new business arrangements are low. If the companies are not able or willing to rethink or change their business models, then there is very little one can do. The entrepreneurs that will be successful in five years from now have already made decisions to master emerging markets and technologies today, and they have found a way to foster their competence in these domains within their current companies. They have innovated in the business arrangements, enabling them to innovate in products and processes. If there are no companies that are able to do this it is most likely the best idea to rather invest public funds into investment promotion, education, tech transfer and incubation to try and offset the job-losses when the current companies fail.
I am often approached by internationally funded development projects to do something to create employment in a sector or a region in a developing country. The challenge is the sectors, supporting institutions and even the approach (the ideology) is already decided and cannot be changed. Often even a quick analysis and a few phone calls reveal that the development project has read the situation wrong, or they ignored strong messages of resistance because they believe in their ideology. Yet they persist, and now they are not getting the response from the stakeholders. I notice many of TVET and green economy projects that fall in this category. Even if there is great value in what these organisations have to offer, if they are not responding the binding constraints or challenges (the decision points) faced by the entrepreneurs and government officials, their offer will not be taken up. Or it may be taken up but it won’t stick. My approach for the last few years has been to wait for the projects to realise that they will never reach their targets and then to propose that we try some alternatives to see if we can get some impact. Or I simply turn down the request. Development programmes in the education sector are often so stubbornly focused on their own ideas that work in their own context that they are not willing to consider developing country needs.
I am often asked to help manufacturers or development organisations in developing countries to prepare for technological disruption at the technological frontier. That means technologies that are newly emerging. The problem is, most companies in developing countries will not be disrupted by cutting edge technology. They will be disrupted when older technologies reach new levels of efficiency and scale, perhaps in combination with newer technology. That means that an older technology evolves to become available as a utility service or on a pay-per-use basis. That is how the fundamental disruptions occur that completely displaces existing markets and sociotechnical arrangements. An example if PV electricity to homes. In many developing countries a homeowner can now buy panels, inverters, brackets and batteries from hardware retailers (or online). It may be illegal in many countries, but homeowners can take their homes off the grid. If enough homeowners do that, national power utilities may collapse. Perhaps another example is that as developing countries switch to fibre internet connectivity, all the IT companies that used to provide small servers, desktop maintenance, server maintenance, cabling installations, etc are disappearing. They are disappearing because they have not long ago mastered an older technology (shared server-based computing, remote network maintenance) that has recently become a utility-based service.
I am asked by an international development organisation to help with a project aiming to support 25, or 50 women, girls, lecturers, youth or a handful of companies. 25 out of a population of thousands or millions is really depressing. This is not systemic, nor is it sustainable. I cannot get involved in these projects, my conscience will not allow me. If any beneficiary group is so marginalised or excluded that 10, 20, or 50 seems like a good indicator of impact, then we should really be going back to the drawing board about the complexity of the system and our sensitivity to the decision points, the attractors and the boundaries in the system. Most likely we should be targeting changes in mandates, roles and functions of institutions and not be focused on individual beneficiaries. The system must be very dysfunctional (meaning somebody must be benefitting enough to keep it in this state), and focusing on getting a handful of people through the system despite all the resistance or challenges is not systemic. In fact, everybody that is inspired by this handful might suffer severe challenges to follow in their footsteps. In a complex system, fixing a little part and then scaling it up does not change the fundamental working of the system. But let me stop venting now, I am asked frequently enough to talk about the potential of complexity thinking applied to developed. Maybe this deserves a blog post of its own.

These are just some thoughts about the challenges that some organisations are grappling with when they reach out to me. These are some of the common objections that many clients are challenged by based on my writing, teaching or speaking. Perhaps these are also the reasons why some clients decide to appoint somebody else or to never reach out to me in the first place. But these are also the points that keep me awake at night, the recurring themes that come up even when I am trying to walk the dog.

Let me know if any you’ve also had these conversations, or whether your organisation, funder or clients are stuck on the same issues. If there is sufficient interest in any of these points then we can perhaps think of how to explore these deeper, or perhaps we can even get together to brainstorm these.

Identifying the meso organisations that strengthen technological capability

This article is from the Mesopartner Annual Reflection 2019 (Cunningham, 2019). In this article, I explore how one could go about to discover the network of meso organisations in a country that helps the private and the public sector to strengthen technological capability.

During the past year, Mesopartner has been working with the Trade and Industrial Policy Strategies (TIPS) and the Department of Trade and Industry (the dti) in South Africa to develop a strategy to identify and respond to discontinuous technological change (see Article 11 in this Annual Reflection). As part of this research, we developed an approach to map the technological landscape of meso organisations that can assist South African enterprises and government programmes to adapt to technological change.

First, we developed a framework to identify meso organisations and functions. Various typologies were evaluated that could be used to classify, measure and manage the performance of those organisations involved in technology dissemination or building technological capability. We started with four typologies of public technology diffusion proposed by the OECD (1997) that are based on operational focus:

Supply-driven: programmes to transfer and commercialise technology from government research programmes to private enterprise, both high-tech and low-tech. It also involves education, skills development and standards.
Demand-driven: these initiatives start with a diagnosis or the perspective of enterprises and aim to respond to the challenges or opportunities faced by private enterprises. These could be aimed at plugging specific performance, technology and capability gaps in the enterprises and are often focused on smaller businesses.
Network-based: these are often sub-national or regional, and are aimed at creating or strengthening bridging effects, inter-firm partnerships in promoting information flows, and the diffusion of technology. Examples are cluster promotion, strengthening of industry or business associations, and fostering collaboration around skills development, research and development, or the development of shared infrastructure.
Technological capability dialogue, adaptation and socio-technical infrastructure building: these intentional initiatives are aimed at working on a system-wide level to upgrade the technology diffusion capability of the national system of innovation within the context of global and regional economic and technological change and opportunities. This is often in the form of dialogue and reflection about why certain initiatives are not yielding the expected results, or why certain industries are not striving to increase their innovation, use of technology or competitiveness. An example is the effort by several government departments to collaborate in a national digitalisation strategy, or the effort around the mining and ocean economy in South Africa in the past few years.

Some of these organisations are created to enable international trade. An example is the South African National Accreditation System (SANAS) and other organisations involved in South Africa’s technical infrastructure. Other domestic organisations could be created to support a shift in the economy through a supply-side focus, such as the National Cleaner Production Centre, which provides technical support and training to the manufacturing sector. Programmes and functions established through industrial, innovation, education or technology policies should also be assessed as part of the framework.

As we started identifying and mapping the meso organisations, we realised that two critical types of actors were not captured by the typology we created:

Private actors that provide public goods or mixed goods, such as technology demonstration, training and the provision of technology modules in open-source formats. For instance, Siemens in South Africa provides demonstration facilities and accredited technical training courses to the public.
Intermediaries or facilitators in the system that broker relationships between different meso organisations and other actors. They may do this as part of another mandate, or they may be set up for this purpose. For instance, in South Africa, there is a huge education crises. A range of non-governmental organisations has emerged that provide important services to the marketplace and the public sector. Many of these organisations conduct research, provide lecturer training, develop training content in open-source format, mobilise public and private stakeholders into collaborative projects and provide public information on shortcomings in the education system. These organisations are critical to overcome coordination failures and to strengthen information flows between different actors in different spheres of society. However, in a typical meso mapping exercise these, organisations could be overlooked or ignored because the public sector or development cooperation partners may see them as interfering in functions that should be provided by the public sector.

International organisations, consultancies and programmes should also be considered in this framework. For instance, as part of executing its commission with various clients, Mesopartner often plays an intermediary role connecting various meso organisations, policymakers, researchers and leading firms to strengthen dialogue or joint decision making, or supporting collaboration. Other organisations that advise industries and governments and create publicly accessible advisory content should also be included.

A challenge that many developing countries face is that meso organisations have to work hard at creating capabilities that should have already existed five years ago, while trying to keep abreast of new international and domestic shifts that require new management capabilities, human resources, technologies and strategies. Not only the private sector can be overwhelmed or paralysed by competing technological choices, but public sector management can suffer the same symptoms. This means that in the framework provision should be made to differentiate between basic (or fundamental) offerings and future-oriented or more advanced offerings. This is not an additional kind of organisation, but it could be different functions provided by the same organisations

While some organisations may be more important for improving the productivity and competitiveness of incumbent firms, others may be more relevant for lowering entry barriers to new start-ups and investors. Even if new start-ups lack market access or technological experience, in a dynamic environment their different knowledge and unique technological capability may put them at less of a disadvantage than the incumbents.

Some meso organisations may be hard to classify because they offer diverse services to different beneficiaries. For instance, universities often play an essential role in lowering the costs of gaining access to new knowledge, codified knowledge and research. At the same time, a university may offer industry access to scarce equipment on a pay-per-use basis, while a university laboratory may offer certification or analytical services to another research group. Or a research programme based at a university may be a sophisticated client to a private enterprise that specialises in advanced equipment, while the same enterprise may be dependent on post-graduate students from the university. Some of these relationships and interdependencies are impossible to map without deep insight into how knowledge, technological ideas and people flow between organisations in the public and the private sectors. Yet it is possible for the same organisation to show up in different typologies, in different markets served, or in multiple roles.

Next year we will have to try and figure out how to map these organisations without making it overly complicated and difficult to use, maintain and adapt.

Notes:

This article is an output of our Mesopartner research theme on technological change and the changing role of the meso landscape. For more information on this theme or to become involved head over to the Mesopartner Research Theme page.

To stay abreast of my research, please sign up for my personal newsletter here.

Dr Shawn Cunningham

Sources

CUNNINGHAM, S. 2019. Identifying the Meso organisations that strengthen technological capability. Mesopartner Annual Reflection 2019

OECD. 1997. Diffusing technology to industry: government policies and programmes.

Meso Institutions as enablers of Self-Discovery, Increasing Learning and resilience

Updated on 18 April 2020, originally published 15 October 2019

In every economy there are organisations that emerge to address all kinds of market, structural and organisational failures. We call these organisations meso organisations – they perform meso functions aimed at improving the economic performance and prosperity of the micro-level. While some meso functions may be more concerned with creating a regulatory framework and others with education or technological services, in essence all meso functions are about disseminating knowledge between economic actors.

Diversity (or variety) of options is a prerequisite for evolution to work. In natural evolution, variety is created by random mutations in DNA, while variations in the economy are created through an ongoing process of self-discovery at different levels, involving different segments of society (Hausmann and Rodrik, 2003). Rodrik (2000) states that this process can be called a meta-institution. He argues that if it is democratic and participatory, this kind of arrangement typically results in higher-quality growth. This discovery process draws heavily on the ability of groups of organised people in business, government and civil society to conduct a process of combining existing ideas with new ideas in novel designs. It involves both reflecting on the status quo and imagining alternative arrangements.

Nelson (2003:20) stresses that “some of our most difficult problems involve discovering, inventing and developing the social technologies needed to make new physical technologies effective”. The more distributed this kind of search is, the better the variety created and the stronger the resilience of the system becomes.

Businesses that are able to generate or recognise modules that work better and that can be repeated elsewhere by drawing on their past experiences have a huge advantage over businesses that are not able to do so (Dosi and Nelson, 2010; Beinhocker, 2006; Nelson and Winter, 1982). Schumpeter already argued some time ago that innovation consists of “the carrying out of new combinations”, with many of these combinations depending on past knowledge or understanding of physical, social or economic properties (Schumpeter, 1934:65-66). Dosi and Nelson (2010:103) argue that the ability of firms to learn, adapt and innovate is generally highly heterogeneous, idiosyncratic and unevenly spread.

Not all the knowledge needed to conduct ongoing discovery processes is available within a single individual or organisation. Hence social infrastructure, technology, education and business networks are essential in connecting organisations into broader networks of knowledge (Hidalgo, 2015). This is where the diversity, adaptability and resilience of the network of meso organisations and their functions play a critical role.

The factors within firms and beyond firms, including the landscape of meso organisations collectively describe the technological capability of an industry, a country or a sub-national region. The dynamic of how these factors influence each other is the essence of the innovation system of a country, an industry (sector) or a location. The innovation system is not so much concerned with the presence of any given organisations as it is with their ability to network and cooperate in disseminating and adapting knowledge.

Now, to connect this concept of technological capability, it’s back to the meso organisations. Meso organisations and their functions are critical in disseminating technological knowledge in a society, an industry or a region. The process by which these organisations emerge and adjust is unique and depends on the context. I am genuinely intrigued by how these institutions emerge, adapt and change over time to form modern organisations that can respond to, anticipate and adjust to structural change and patterns of economic underperformance in the economy.

Sources

BEINHOCKER, E.D. 2006. The origin of wealth: evolution, complexity, and the radical remaking of economics. Boston, MA: Harvard Business School Press.

DOSI, G. and NELSON, R.R. 2010. Technical change and industrial dynamics as evolutionary processes. In Handbook of the Economics of Innovation. Bronwyn, H.H. and Nathan, R. (Eds.), Amsterdam: North-Holland, pp. 51-127.

HAUSMANN, R. and RODRIK, D. 2003. Economic development as self-discovery. Journal of Development Economics, Vol. 72(2) pp. 603-633.

HIDALGO, C.S.A. 2015. Why information grows: the evolution of order, from atoms to economies. New York: Basic Books.

NELSON, R.R. 2003. Physical and social technologies and their evolution. Piza, Italy: Laboratory of Economics and Management, Sant’Anna School of Advanced Studies.

NELSON, R.R. and WINTER, S.G. 1982. An evolutionary theory of economic change. Cambridge, MA: Belknap Press of Harvard University Press.

RODRIK, D. 2000. Institutions for high-quality growth: What they are and how to acquire them. Studies in Comparative International Development, Vol. 35(3) pp. 3-31.

SCHUMPETER, J. 1934. The theory of economic development. Harvard, MA: Harvard University Press.

The evolution of technologies, industries and regions

In the earlier research on technological evolution in the 1970-1995 period, attention was mainly paid to either a whole economy or a single sector or technological paradigm. It is broadly understood from this research that different industries and technologies evolve at different rates. This means that over time, some industries may be more important than others, or at least, some may be accelerating while others may be stagnant or declining. In recent research by Saviotti and Pyka (2013), the emergence of new technologies and industries (and the goods and services that they provide) is seen as offsetting the diminishing returns that are innate in the development of existing technologies. Nelson (2015) argues that this is a reason why absorption and further development of these technologies are necessary to maintain economic development.

In enabling technological evolution in countries, a whole range of actors play a part. Individuals and informal networks, to large and small firms all play a role. However, for the last century, most technological advancements have been supported by scientists, the academia and professional societies and a range of supporting meso organisations. In Europe, professional associations often play an important role in the deepening and dissemination of technological knowledge.

I want to come back to the meso organisations mentioned in the earlier paragraph. Meso organisations or functions are created in response to structural issues like market failures, sometimes government failures or persistent patterns of underperformance in the economy. These meso functions are critical in supporting economic actors to discover what is possible in a given economic context, to assist stakeholders to overcome coordination failures, and to provide critical public goods (such as scarce or expensive technological infrastructure, demonstration facilities, testing facilities, public research, and so on).

The meso functions enable a society, industry or even the public sector to discover and absorb new ideas, they enable learning by doing, they encourage the adaptation and dissemination of new knowledge or technologies, and they connect different stakeholders to overcome coordination and search failures. These meso functions are a critical ingredient in the local innovation system as they extend the technological capability of a given sector, industry, market or region in a country.

You would have noticed that I have not yet mentioned universities and public research efforts. This is simply because I have written about them so often as they form a critical part of the local innovation system. I sometimes even think that the higher education sector receives too much attention. Yet, education from basic schooling to higher education plays a critical role. For me, a university is an important meso organisation, and research centres, technology extension centres and laboratories that provides testing facilities are all important meso functions or maybe even meso organisations hosted by a larger organisation.

The importance of the higher education sector in the technological infrastructure varies for different parts of the economy. Nelson contends that scientific and technological research and teaching, especially the more applied fields, provide a base of knowledge that is accessible to all technically sophisticated individuals and firms working to advance technology in a field (Nelson, 2015). However, different fields also depend, to different extents, on scientific and formal research and technology support. Therefore, measuring journal articles and research outputs as a contribution to the national innovation system or as a proxy for technological capability will always paint only a partial picture. It really also depends on the pace of change and scientific advancement that is taking place in a region, a technological domain or an industry.

Furthermore, different industries depend, to different extents, on government support and incentives. In some fields public support is crucial, and in other cases, provides little incentive or value. In many cases innovations preceded science, and continued development is only possible due to the iteration between researchers and enterprises. Nelson continues that the kinds of firms that do most of the innovating differ – in some fields this tends to be large, established firms while in others it is smaller firms or new start-ups (Nelson, 2015).

Nelson draws an important conclusion that has really shaped my own thinking. Nelson states that there is no single set of policies that are applicable to all technologies and industries. What will be effective in some fields will not be in others. For instance, small business promotion in some sectors in one country could work, but it could be ineffective in another country.

In South Africa, with its very high coordination costs and high compliance costs, smaller enterprises in the manufacturing sector are at a huge disadvantage. The distance to sophisticated buyers and the challenges with exports compounds the difficulty for smaller enterprises to compete globally from the local base.

Nelson is also known for his writing on the importance of a wide range of social institutions, both formal (for example a cluster development organisation) and informal (the trust networks between members of the clusters). He refers to these social institutions as social technologies, and he argues that they co-evolve with physical technologies to enable economic development. These social institutions range from central banks to a diverse range of firms, but importantly include other forms of organisations such as scientific and technological societies, universities, government agencies and even capital markets. These institutions are the focus of the discipline of innovation systems.

Nelson emphasises that “that when a potentially new technology emerges, new institutions often are needed to develop it, and invest in and operate effectively the economic practices based on it”.

Nelson acknowledges it is not an easy task, as it is hard to predict which emerging fields of promising new technologies are going to be important in driving economic progress in the future, and which will have a modest impact. The policies to create or reform institutions need to be adaptive and flexible. Arthur (2009:186) confirms the view of Nelson and argues that “We cannot tell in advance which phenomena will be discovered and converted into the basis of new technologies. Nor can we predict which combinations will be created.”

That brings me back to my intent with this post. When we look at technological disruption and change, it is very easy to get caught up in the potential or risks of any given technology. But we must not take our eye of the informal and formal institutions, market systems, regulations and technological domain specific organisations that are needed to make a new technology viable. At the same time, we also have to figure out how to gracefully exit older technologies and how to either shut down or transform public organisations that once had a critical role in supporting those industries and technologies.

Again, I repeat, the so-called fourth industrial revolution is going to be more disruptive at the level of institutions and social arrangements than it will be disruptive for the enterprises that are competing at the technological frontier.

In South Africa, we have a triple-challenge.

1 – Our institutions change very slowly, and we have huge social tensions about how to allocate resources and wealth in the economy. Our local municipalities and local economic development activities are ineffective (with some exceptions in some of the larger metros). Yet, local authorities have hardly any influence over the quality and effectiveness of national meso programmes that are supposed to enable economic change.

2 – This is compounded by a largely uncompetitive economy with lots of market concentration. The regulatory burden in the economy keeps a lot of potential entrepreneurs employed in the corporate and the public sectors.

3 – Our discussions in South Africa about technological change, technological capability and the promotion of the innovation system is dominated by a linear logic of science leading to technology leading to innovation (the so-called STI approach). There is not enough attention being paid to the eco-system of organisations, technology extension agencies that can help enterprises master new technological domains, reduce coordination costs, the so-called Do, Use, Integrate (DUI) kind of innovation. On that point, we also have very few (if any) technological organisations tasked with transforming or upgrading whole sectors or regions in the country from a technological perspective. Everything is aimed at one enterprise at a time.

My research agenda:

This is what my research is about at the moment. I am working with a team from TIPS and the dti (South African Department of Trade and Industry) to strengthen the visibility of this technological meso network, while also strengthening the public sectors ability to spot technological disruptions and to be more pro-active.

Please sign up below if you want to stay informed of our progress as I will not be able to share all of our learning in the public space all the time.

Sources:

Arthur, W.B. 2009. The nature of technology : what it is and how it evolves. New York: Free Press.

Nelson, R.R. 2015. Understanding long-run economic development as an evolutionary process. Economia Politica,Vol. 32(1) pp. 11-29.

Saviotti, P.P. and Pyka, A. 2013. The co-evolution of innovation, demand and growth. Economics of Innovation & New Technology, Vol. 225 pp. 461-482.

Using the S-Curve to identify potential disruptions

This is a continuation of my blog posts based on my research into how technological disruptions and change occurs.

A widely publicised model is the S-curve model that enables the evolution of the performance of a technology (Foster, 1986a; Foster, 1986b). In management of technology textbooks, this model is used to make predictions about the evolution of the rate of technological change, to detect possible technological disruptions, or to determine the limits of a particular technology.

In the S-curve model[1], the Y-Axis tracks the performance of a specific technology, while the X-Axis shows effort measured in R&D investment and resources aimed at technology development (see Figure 4).

s-curve

Figure 4: A technology S-curve

Source: Author, based on work by Foster and Christensen

In the beginning of the development cycle of a specific technology, it takes a lot of effort to get performance increases out of a technology (blue line in Figure 4). This phase is often characterised by many different competitors with many different approaches to solving a given technological problem. This is often followed by an exponential improvement curve where the effort pays off with large increases in performance. Typically, improvements to the performance of the technology at this point in time are driven by several incumbent competitors, with many companies if their technologies are not chosen. After a while, the performance increases for every unit of investment (effort) starts to taper and return on investment diminishes. This is where incumbent firms are most vulnerable, as they try to squeeze as much profit from their existing technologies without looking for new investment opportunities even though they are almost completely dominating the market. New entrants find it very difficult to challenge the incumbents in the existing market place, because the incumbents have established a brand reputation, distribution networks and supporting systems.

Clayton Christensen (2000) explains that incumbent firms are often overconfident about the value of their existing technologies and tend to ignore potential new technological approaches. New entrants that are using a different technology aimed at a different market segment might be entering a new steep S-curve (the red line in Figure 4).

The new technology is usually at a lower level of performance than the original technology, and targeting a small, not-so-profitable niche that the incumbent firms are not willing to fight for (as they are benefitting from the scale of their current customer base). The niche market provides the new technology space to innovate in ways to increase in performance, and at some point, the graphs may intersect. This is where whole industries or technologies can be disrupted, as existing customers switch to a new technology that is in an upward performance curve. Christensen and Raynor (2003) explain that incumbents are very often “relieved” to exit small, low-margin markets, and so they constantly upgrade towards higher-margin or higher-volume target markets. This leaves small niche markets for new entrants where demands are not being met. These niche buyers and the new entrants often work together through several development iterations together, until the performance curve of the new technology crosses the incumbent technology in the broader market.

Christensen argues that whether a technology is disruptive or not depends less on how radical it is, but more on its specific effect on the S-curve. If a new development improves performance of an existing technology, then the incumbents are preserved and tend to benefit most as this improvement often suits its current scale of operations. If a technology creates a new S-curve, then it may disrupt existing technology at some point, leading to a disruptive change in industry structure. This implies that radical or incremental performance improvements in most cases benefits incumbents, while disruptive innovation challenges industry structures. In an interesting twist, Christensen argues that incumbents are not ignorant of new technologies and underserved markets. He argues that they are the victims of their own success in making decisions that leverages existing knowledge, networks, markets and capabilities. Ironically, customers may actually communicate that they prefer incremental improvements on existing technologies rather than adjusting to disruptive technology. It is not only the innovator that faces risk and uncertainty, buyers also try to avoid making decisions about technologies that are only emerging, or where performance, results and requirements are vague or uncertain. Decision-making in research and development may also be biased towards the most likely-to-succeed ideas that directs resources away from tinkering or experimenting with fundamentally different ideas.

Existing companies may be able to spot an emerging technology or group of technologies with a potential to disrupt their current market. However, it may still be very difficult to decide when to switch more resources to completely new technologies that may also require different business structures, culture, market and supplier relations (thus switching resources from the blue line to the red line in Figure 4). The performance of the technology is born from the strategy of firms and how they allocate resources to product, process and business model innovation. One way that governments can reduce the costs of incumbents and new innovators to confront, investigate and test new technologies is through technology demonstration and applied technology research, where companies can visit, use or test technologies hosted by public universities. Because companies know that their competitors might be investigating the feasibility of trying a new technology, they themselves are more likely to invest in new skills, in trying the new technology or exploring how this new technology could result in new markets, business models and capabilities.

Gathering all the information that is necessary to construct an S-curve requires time and can be costly. It is especially difficult to figure out which performance criteria and measures of effort to use to construct the graph. However, when a portfolio of technologies is tracked this way it shows not only inflection points, but when certain technologies may outperform existing dominant technologies. A key question that must be answered in constructing this model is whether to track performance change at the level of components (modules), sub-systems or architectures. Furthermore, even if the performance lines cross, incumbents may not switch if their sunk investments are too high, or the learning cost of the new technology is too high. That is why newer companies are needed in the economy, as they might have lower sunk investments and more to gain from higher performance. Over time, resources shift from the old technology to the new, but only if the new technology is accepted and is disseminated sufficiently.

A critique of the S-curve model is that while the graph makes sense, it is often hard to construct and project into the future. It often makes sense ex-post to explain why a given technology outperformed a previous dominant technology. Also, a weakness of the narrow focus on technological performance disconnects the technology from the broader technological and social context, such as the organisation capacity and supporting networks and infrastructure that is required to make a given technology work.

Notes:

[1] It is called an S-curve because when the results are graphically illustrated the curve that is usually obtained is a sinusoidal line that resembles an S.

Sources

CHRISTENSEN, C.M. 2000. The innovator’s dilemma: when new technologies cause great firms to fail. 1st Ed. New York, NY: HarperBusiness.

CHRISTENSEN, C.M. and RAYNOR, M.E. 2003. The innovator’s solution: creating and sustaining successful growth. Boston, Mass.: Harvard Business School Press.

FOSTER, R. 1986a. Innovation: the Attackers Advantage. New York: Summit Books.

FOSTER, R.N. 1986b. Working The S-Curve: Assessing Technological Threats. Research Management, 294 17-20.

Citation for this text:

(TIPS, 2018:23-24)

TIPS. 2018. Framing the concepts that underpin discontinuous technological change, technological capability and absorptive capacity. Eds, Levin, Saul and Cunningham, Shawn. 1/4, Pretoria: Trade and Industry Policy Strategy (TIPS) and behalf of the Department of Trade and Industry, South Africa. www.tips.org.za DOWNLOAD

Disruption, radical and incremental innovation

I am continuing with my sprint to write down the ideas and concepts that I am explaining regularly at different events and meetings (The earlier posts are all available on www.cunningham.org.za). The ideas about the fourth industrial revolution being disruptive are now discussed and repeated even by people who have very little understanding of technology or innovation, nevermind management (see post “what is the difference between 4IR and Industrie 4.0?”). There are subtle yet important differences between disruptive, radical and incremental innovation. These differences matter for policymakers, entrepreneurs and economic development practitioners.

In business management literature a distinction is often made between incremental, radical and disruptive innovation[1]. Incremental innovation introduces relatively minor changes to an existing product, process or technology, while radical innovation is based on a different set of engineering, scientific and business principles and often opens up new markets and applications. While incremental improvements may be small, the cumulative effects of an ongoing series of incremental improvements could be huge.

Incremental innovation exploits the potential of an established design and often reinforces the dominance of established firms. It mainly originates from within the sub-sector or system, and the informed or connected firms are often aware of the changing trends[2]. While it hardly requires new science, incremental innovation draws on incredible skills, deductive reasoning and experience, and over time can have significant cumulative economic consequences. Most businesspeople hardly recognise incremental improvement as innovation, although when prompted, many are able to identify several incremental improvements to their products, processes and organisational arrangements. Incremental innovations are chosen by the market if they offer savings, or add more value to what already exists. The market chooses an improved idea if it exceeds their existing expectations.

Radical innovations occur when new technologies are introduced into an existing market or technological domain. In the evolutionary technological change process, a radical innovation can start one of the change cycles (start a fluid phase), or it can be a blip in the performance of the technology during the amplification or selection phases.

Christensen (2000) argues that both incremental and radical innovations based on a specific technological paradigm often benefit incumbent firms, and describes them as sustainable innovations (for incumbent firms). Incumbents and markets can recognise the benefits of the radical innovation and quickly adapt to it, or integrate it into their operations.

Disruptive innovation is different in that it often favours the new entrants (called the attackers by Christensen), who often combine different product, process and marketing innovation with a different business model. This part of the business model is really important. Disruptive innovations are hardly about the product/service or the process, it is really about a different business model. These business model innovation often originate in niche markets where an innovator works very closely with niche market players to refine an idea in an iterative process before it is taken up by other markets. Incumbents have a really hard time to defend against this because they can copy some of the products, service or process features, but they often cannot copy the culture of the attacker.

Christensen et al. (2015) explains that disruptors often challenge incumbent firms with new business models, and attack incumbents by targeting marginal or even low-end markets[3]. Firms with resources and adaptive management systems are often able to exit these markets or to shift into new (often higher-value) market segments. While incumbents may be able to adapt their products and processes, it is often a matter of time before newer business models of the attackers outperform their traditional arrangements.

There are examples of famous and powerful firms going under or losing market dominance because they were disrupted by a new technological paradigm introduced by actors from another sector. Recent research comparing the US Fortune 500 companies in 1955 and 2017 shows that only 60 firms were in both lists (Perry, 2017). It is already hard enough for firms to stay abreast of technological changes and innovations within their sector and in related industries, therefore many established firms are often blindsided by technologies developed in other sectors that may in future disrupt them[4].

Some remarks about these ideas:

For most companies, radical and incremental innovations occur on a frequent basis. It may require rethinking a product, making changes to a process, finding new material suppliers or changing prices. While a competitor launching a new product, or announcing a change in pricing may disrupt your plans or cause a lot of stress, this is not what is meant with disruption. Disruption means that you cannot proceed in the same way. The markets you have served in the past now have new criterial which they use to select between alternatives and you have a weaker offer.

Disruptive innovations are disruptive because they require a rethink or demand a change of the core business model. Clients don’t want a price cut or an added feature. Some countries (like Singapore) promote disruptive technologies into their economies because it leads to increased innovation and much higher awareness by incumbents of global technological changes. Other countries try to defend against disruptive technologies, but in a way, they may only be postponing the inevitable. What is clear to me is that companies cannot afford to only look for technological solutions within their industry or sector, but that they have to scan much broader. For an incumbent company to respond to a big disruption may require more business model innovation. For instance, our South African manufacturers have lost many competitive battles with manufacturers from Asia. Yet, very few manufacturers innovated in the business models by opening their own factories in Asia to learn from those markets.

Which brings me to a final remark. To get more companies hyper-sensitive to technological change, policymakers have to find ways to promote competition. It is only when small improvements make a big difference that incumbents would be willing to search beyond their current sectors for alternatives that offer even a small advantage.

Notes:

This is the 4th post that draws from the research and advisory work I am currently busy with to strengthen South Africa’s technological capability to detect and better respond to discontinuous technological change. The citation information for this post is at the bottom of this post, and a link to the research report that I have copied this from is here.

[1] While this literature is increasingly popular since the publications of Clayton Christensen, it is not new. Schumpeter (1934) and Freeman and Soete (1997), among others, already wrote about this much earlier.

[2] Several trends, such as the increasingly important knowledge-intensive business service sector, or new ways of sharing and protecting knowledge, play an important role in providing firms with access to new or relevant information.

[3] Christensen, Raynor and McDonald (2015) argue that from a disruptive theory perspective Uber is not seen as disruptive, as many taxis have been using apps for a long time, and Uber did not really enter the market by starting in underserved markets. However, due to the violent protests by traditional taxi owners, Uber is often described as being disruptive.

[4] An ironic example of a company that failed to recognise one of its own innovations as disruptive is Kodak. Management was so set on its film-based business and technology model that it chose to ignore its own market research that showed the disruptive potential of digital technology that one of their engineers developed in 1975. Not only did digital technology disrupt Kodak, it created many completely new industries, markets and applications.

Sources:

CHRISTENSEN, C.M. 2000. The innovator’s dilemma: when new technologies cause great firms to fail. 1st Ed. New York, NY: HarperBusiness.

CHRISTENSEN, C.M., RAYNOR, M.E. and MCDONALD, R. 2015. What Is Disruptive Innovation? Harvard Business Review, December 2015.

FREEMAN, C. and SOETE, L. 1997. The Economics of Industrial Innovation. 3rd. London: Pinter.

PERRY, M. 2017. Fortune 500 firms 1955-v-2017.: http://www.aei.org/

Citation for this text:

(TIPS, 2018:21-22)

Technological change cycles

This is the 3rd post that draws from the research and advisory work I am currently busy with to strengthen South Africa’s technological capability to detect and better respond to discontinuous technological change. The citation information for this post is at the bottom of this post, and a link to the research report that I have copied this from is here.

During the 1980s several scholars[1] recognised that technological change follows a cyclical pattern and several models were put forward to explain the phenomena. These models are still in use today and have been found to be active at different levels of technological change. The broad consensus was that a technological change cycle:

Starts with a technological discontinuity or disruption, followed by a period of unstructured and often chaotic innovation when a new idea or concept is made possible (based on preceding developments). This disruption results in a fluid or turbulent development phase during which many ideas are developed, tried and promoted as the next best thing,
That is followed by an era of ferment from which a dominant design emerges; and
This is followed by an era of incremental change during which the dominant design is elaborated.

This can be illustrated with the widely recognised Abernathy and Utterback (1978) model with its three phases of change that are illustrated in Figure 2. The three phases are a fluid phase, a transitional phase, and a specific phase, and is similar to the cyclical pattern described in the bullet list above. Other scholars used slightly different labels, but the characteristics in the different phases are all more or less the same.

Abernathy and Utterback

Figure 2: The Abernathy-Utterback model of technological change

Source: Abernathy and Utterback (1978)

The rate of innovation is highest during the fluid phase, during which a great deal of experimentation with product features and operational characteristics takes place between different competitors[2]. Because of all the changes in the product composition and characteristics, process innovation typically lags. Buyers and users are often confused or overwhelmed during this phase fearing that the benefits are overstated and that the costs of adaptation are uncertain. Only the brave and the innovative engage in finding, adapting and integrating new ideas and concepts.

In the transitional phase, the rate of product innovation slows down and the rate of process innovation increases. At this point, product variety gives way to standard designs that have either proven themselves in the market, or that are shaped by regulations, standards or legal constraints. The pace of innovation of how to produce the product increases. What was done earlier by highly skilled technicians may become automated or developed to a point when low-skilled operators can take over. Or lower-skills jobs are displaced from the production process to other functions like logistics, while the skills intensity on the production line is enhanced. At this point it is easier for bystanders and followers to engage in exploration. The early adopters are already over the horizon, while many early adopters have exited, sold out or moved on.

The final phase, the specific phase, is when the rate of major innovation dwindles for both product and process innovation. In this phase, the focus is on cost, volume, and capacity. Most innovations are very small incremental steps, improvements on what is already known and accepted. Latecomers can now engage with the technology, although it might already be too late.

The description of technological change provided above follows the generic three-step process of technology evolution: a process of variety creation, selection, and then amplification or retention.

During the variety creation phase there are many competing designs and no dominant logic. Towards the end of this phase a few dominant designs may emerge, but there is still much competition between ideas. This is not only a technical selection process, there are important social, political and industrial adjustments taking place at the same time.
During the selection phase, standards emerge for positively selected ideas, with a few designs dominating. It is a relatively stable process of incremental improvements in features, performance and results. This may be interrupted occasionally by leaps in performance as some designs are substituted by better technologies, or from breakthroughs often coming from other industries or contexts. In general, designs become simpler as a learning process unfolds about how best to design, manufacture, distribute and use a particular technology around dominant designs. This period is characterised by growing interdependence as modules are developed, substituted and standardised. There is a growing exchange and increased competence within and between different communities of practitioners. Often there is industry consolidation during this phase. It is important to note the dominant designs are only visible in retrospect. They reduce variation, and in turn, uncertainty, but within the process it is hard to predict which designs will survive the next set of radical innovations. Once a design becomes an industry standard it becomes hard to dislodge.
This leads to an amplification phase, in which the best ideas are not necessarily used as intended, but when technological changes spill over into areas not originally intended. This is a relatively stable process that can continue for long periods, until is it suddenly interrupted by a radically different idea, resulting in the process starting all over again.

Anderson and Tushman (1990) state that, from the perspective of the sociology of technology, technological change can be modelled as evolving through long periods of incremental change punctuated by revolutionary breakthroughs[3]. The innovation activities that take place that lead to these phenomena will be discussed in Chapter 3.

Arthur (2009:163) contends that change within technological domains is a slow process. He explains technology domains do not develop like individual technologies like a jet engine: focused, concentrated and rational. It is rather more like the development of legal codes: slow, organic and cumulative. With technology domains, what comes into being is not a new device or method, but a new vocabulary for expression, similar to a new language for creating and combining new functionalities.

A current example is the “Internet of things”, where the connectivity of physical devices are spreading from the office and smartphone devices to interconnect household appliances, industrial applications and an endless list of technologies enabling data exchange, control and new functionalities . It could be argued that this is not a new technology, digital sensors have been around for a long time, our cars, smartphones and equipment have contained them for a long time. However, the language, standards, distributed nature of processing, and developments in big data visualisation have all contributed to this technology appearing to arise from obscurity into the limelight of the popular media. A similar argument could be made for artificial intelligence, drone technology and others.

Notes:

[1] The work of Tushman and Anderson (1986), Abernathy and Utterback (1978) are still frequently cited today.

[2] Kuhn (1962) noted that in the early stages of research in a given field, the most that scholars typically can do is to report the phenomena they observe, without a unifying theory or framework to help them categorise or make sense of what they see. As a result, this stage of knowledge accumulation is characterised by confusion and contradiction. Theories are put forward but reports of deviating phenomena accumulate.

[3] This is often referred to as punctuated equilibrium by political scientists.

Sources

Abernathy, W.J. and Utterback, J.M. 1978. Patterns of Industrial Innovation. Technology Review, Vol. 80No. 7 (June/July 1978) pp. 40-47.

Anderson, P. and Tushman, M.L. 1990. Technological Discontinuities and Dominant Designs: A Cyclical Model of Technological Change. Administrative Science Quarterly, Vol. 35No. 4 (Dec 1990) pp. 604-633.

Arthur, W.B. 2009. The nature of technology : what it is and how it evolves. New York: Free Press.

Kuhn, T.S. 1962. The Structure of Scientific Revolutions. Chicago & London: University of Chicago Press.

Tushman, M.L. and Anderson, P. 1986. Technological Discontinuities and Organizational Environments. Administrative Science Quarterly, Vol. 31No. 3 pp. 439-465.

Citation for this text:

(TIPS, 2018:12-13)

Technological architectures

An important distinction can be made between architectural innovation and component-level innovation. The architecture defines the way different components or subsystems are organised and how they interact with other components. Often architectures themselves form part of even larger webs of architectures.

Innovations at the component level, which is a physically distinct portion of the technology that embodies a separate design concept, mostly reduce costs of production, and often take place at high frequency with a wide range of choices available. While the organisations that innovate at the component level are more dependent on past experience as well as economies of scale, the organisations that determine the architecture are able to depend far more on their value addition, as well as the sunken investments of many other agents into the system.

To change the architecture of a system requires many simultaneous changes to different sub-architecture and component levels, which may be beneficial to some agents in the system, but not to others (thus vested interests often create a path dependency). A change to the architecture could even disrupt industry structure, and it changes the way the markets judge whether a specific architecture is suitable for the function or tasks it fulfils. A combination of path dependency and architectural change can be used to describe why many industries (or architectures) have disappeared.

However, architectures such as the vehicle example in the figure above change slowly over time and can certainly be influenced by improvements at the component level. For instance, better electronic management of the engine may result in less frequent services, but the architecture hardly changes. Interestingly, the architecture of the vehicle also forms part of a wider architecture of road networks and urban designs, again reinforcing another higher level of path dependency. This nested nature of technologies at the level of architectures is what slows down massive technological change. To continue with the example of a car, passenger vehicles depend on the architecture of a road network. It is also dependent on fuel and maintenance systems, parking arrangements, insurance and all kinds of traffic and safety laws.

I find it interesting that two decades ago, electric vehicles were described as being massively disruptive resulting in the demise of the fossil-fuel vehicle. Now, many established car manufacturers have jumped onto the bandwagon and are investing heavily in their own electric vehicle technologies, and in doing so reducing the disruptive effect of alternative fuels. In doing so, they are making massive strides in fuel efficiency, reducing the weight of their cars and substituting harmful and heavy materials with materials that have less impact on the environment. The component and sub-system level innovations offered by electric vehicles are being incorporated into the designs of the older fossil fuel architecture, while the architecture itself is only changing slowly. In South Africa, the network of charging stations and points are slowly expanding, but the use of electric vehicles is still minute compared to the fossil-car usage.

Some examples of architectures and components are computers (architecture) and an internal graphics card (component) or a jet airliner (architecture) and in-air entertainment systems (components).

The reason why I thought it a is a good idea to go back to such a basic distinction as the difference between architectural innovation and component level innovation is that in much of the popular discussion about technological disruption (the fourth industrial revolution-talk) this distinction is not made. What I appreciate about the World Economic Forum is that they are raising awareness of what will happen to social arrangements when one architecture displaces another. But what is not receiving enough attention are the many challenges that we will face in developing countries at the level of sub-systems and components. This is where competitiveness, resilience and innovation are critical because this where the disruptions and discontinuities of industries will occur. This is also the area where developing countries usually follow (as outsourced manufacturers) and where we are the most vulnerable to the design capabilities and dense networks that existing in clusters in the developed world.

I will explore how these changes occur in the next few posts.

Becoming better at tracking how technologies change over time

The subject of how technologies evolve over time have been receiving a lot of attention over the last 40 years. Actually, much of the research work done in the late 80s and 90s are still relevant today. With all the talk of the fourth industrial revolution, the attention has shifted towards innovations coming from elsewhere away from what do we have to do in our own organisation to improve our performance, offer our clients amazing value, and to create the future we want to be part of.

I am working with several think tanks, research organisations and policy advisors to help governments and key meso-organisations to become better at tracking technological change and potential disruptions. This work draws on my experience of supporting industry and innovation systems diagnostic processes as well as my experience in supporting organisation development and change.

To be better able to predict technological disruptions meso organisations and policymakers must become much better at anticipating future demands. That means they have to shift from being demand responsive (in other words waiting for the private sector to clearly articulate what they need) to anticipating what is needed. This requires a deep understanding of how user needs are changing (market knowledge), but also of how key technological capabilities in the industries they serve are changing (technological knowledge).

The challenge here in South Africa is that most of the organisations that are supporting innovation and technological change are focused on fixing the past. Due to our countries past, they are trying to get marginalised people (women, the youth, black entrepreneurs) into the mainstream economy. These disadvantaged groups need a lot of support because they are expected to compete against incumbents who have access to capital, suppliers and markets.

This research agenda has three pillars:

Figure out how well South Africa is doing in terms of technological change. Which sectors are changing faster, where is productivity and manufacturing value add improving, and where are we falling behind? This area of research is also about mobilising sector organisations, like industry associations or a whole range of meso organisations supporting the private sector to become better at tracking technological change.
Make the landscape of technological support organisations more visible. These organisations can assist both the private and the public sector to embrace, experiment with or adapt to technological change. A next step would be to make sure that these organisations are incentivised to disseminate technological knowledge and that they are not only measured on how they assist individual enterprises or technology transfer projects.
The third pillar is to improve the dynamism in how public sector organisations work together and collaborate with the private sector to promote industrialisation, upgrading and innovation. This is an essential ingredient to strengthen the countries technological capability, to reduce coordination costs and to foster healthy and pro-active public goods that encourage entrepreneurs to search and discover new economic opportunities.

The current research agenda is not yet comprehensive but for me the synergies between these three pillars are great. It is about technological change, about making sense, about promoting innovation within and between organisations and also about strengthening meso organisations.