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Meso organisational development Promoting Innovation Systems Technological change Technological disruption Technology and innovation management Thinking out loud

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.

Categories
About the future Addressing persistent market failure Advancing manufacturing Complexity and Evolutionary Thinking Industrial Policy Meso organisational development Technological change Technological disruption Technology and innovation management Thinking out loud

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.

Categories
Research and development Technological change Technological disruption Technology and innovation management Thinking out loud

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

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Advancing manufacturing Technological change Technological disruption Technology and innovation management Thinking out loud

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)

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

Categories
Complexity and Evolutionary Thinking Promoting Innovation Systems Technological change Technological disruption Technology and innovation management Thinking out loud

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:

  1. 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,
  2. That is followed by an era of ferment from which a dominant design emerges; and
  3. 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)

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

 

 

Categories
About the future Innovation Technological change Technological disruption Technology and innovation management Thinking out loud

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.

Categories
About the future Advancing manufacturing Meso organisational development Organizational Design and Development Technological change Technological disruption Technology and innovation management Thinking out loud

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:

  1. 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.
  2. 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.
  3. 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.

Categories
Leadership Technological disruption Technology and innovation management Thinking out loud Unlocking and Leveraging Knowledge

How to cultivate a more knowledge-intensive team or organisation

It is interesting to reflect how over my career the organisations I work with have gone from trying to gather additional information from beyond the organisation to trying to make sense of all the information around them. Actually, some people I know are actively disengaging from reading newsletters, books, blog sites or other channels because they are feeling overwhelmed.

My career started in the ICT sector in the early days of connecting companies to the internet. In those days, people wanted to connect to the internet because they wanted to access some additional data, information and communications from far away. They sometimes wanted this internet connection, even if the real benefits of connectivity were fluffy and took years to realise. I shifted to the development sector in the 2000s. By then, many organisations had already started to benefit from this new connection to the information highway. The shift towards web sites, online databases, capturing learning and networks had already begun. In many of the projects I worked on, there were attempts to establish web-based knowledge portals, communities of practice, and online knowledge repositories around a vast assortment of topics. No longer was technical knowledge only available to geeks that could navigate obscure corners of bulletin boards.

Now it seems like organisations and individuals* are drowning in information. (I wonder if one could even argue that information is being reduced to data?). Folders are cluttered with documents – of which some are valuable and others not. Some documents are duplicated several times over on local hard drives, cloud drives and in inboxes. Decision-makers have more information at their disposal than they need, and they often have no idea how to figure out what is relevant or more valuable. Everything seems important, and too much content is collected and never used.

I often talk at events about how information and knowledge are critical for innovation. A culture must be fostered where what is known can be leveraged, thus supporting both continuous knowledge development and innovation. At these events, I am often told by participants that their organisations don’t have all the knowledge they need to innovate. What they need is not at their fingertips. Some express that it is crazy to propose that they start with becoming more sensitive about what they know and how they organise themselves around the knowledge they create and depend on daily. Some even claim they are not working in knowledge-intensive workplaces because people are not willing to write up what they are thinking or doing! I don’t think it is so much about documenting everything anymore. I still make for the exit when I am told that I must write up “best practices” or “lessons learnt”!

However, making better sense of what is known, and what is not understood, are critical — both for individuals and collectives. The search for complementary or necessary knowledge takes place both internally and externally. Internally, knowledge management is about continuously reflecting on what is already known by the organisation or team. It is about figuring out what to document, or what to keep in mind, or what to consider next time. Or it is about figuring out how to use what is already discovered to improve, products, services, processes and structures. Knowledge management is increasingly about being more sensitive to weak signals, responsive to new patterns and alert to odd findings. Over time, knowledge management is becoming a more distributed function as organisations become more knowledge-intensive. More and more people are somehow collecting, processing, adapting and synthesising knowledge.

The external focus of knowledge management is about tracking emerging knowledge or discovering new patterns or supplementary knowledge beyond the organisation or team. It is mainly about exploring what others already know and had the time and energy to document, or to track important developments in other domains and bringing the relevant ideas to the attention of the organisation.

To fill in the internal knowledge gaps from the outside, your team must become better at knowing what you know. This is of course only useful if you can turn what you know into value for others. If you are enabling knowledge development, you must be sensitive to what the organisation needs in the short and long term, so that information can be sourced timesously and over a time period. Teams must also understand how what they know is valuable and usable within the organisation and by its clients.

What is much harder is to get better at sensing where there are areas where more knowledge is needed, where things are not yet clearly understood or mastered. Many people I know spend a lot of time rediscovering what they already knew, or what their teams already sorted, stored or processed. This (re)discovery wastes a lot of valuable time and mental bandwidth. It often just adds more noise in the form of documents that are easily collected but rarely used or synthesised effectively.

It is easy to test how knowledge-intensive a organisation is. Ask your team where they start when they need to gain access to information they know should be captured somewhere. Do they begin internally, or do they open their browsers and start externally?

In knowledge-intensive organisations, the knowledge search starts internally. That is due to knowledge synthesising taking place and adding value to everything the organisation does. The internal search could begin with looking at data already collected, or with information captured in reports, files, photos or physical documents. Or it can start in more informal spaces, like in Slack, MS Teams or even Whatsapp or asking around in the corridors.

In knowledge-starved environments, the search for new or supplementary knowledge almost always starts on the outside. It begins in a browser or at an online resource. In these organisations, the value of improving how information is collected, organised, synthesised, evaluated is low. The pressure to use what is known, or sensed in innovative ways is also low. Improving how information is organised is simply simply not worth it. Lazy or ill-disciplined team members can undermine knowledge-intensification, because organisations have to keep legacy systems running in parallel to newer systems. For instance, some organisations communicate internally via e-mail, slack and other messaging systems. Documents are both stored in shared systems and are emailed about. This is clumsy and it reduces to ability of teams to build a coherent picture of what is going on, what is important, what needs to be maintained, expanded or deleted.

While permanent connectivity makes it much easier to search externally quickly, the habit of failing to collect, synthesise and create a more customised combination of knowledge also comes with other risks. The person doing the searching is at the mercy of tags developed by others, search rankings influenced by advertising spend, and increasingly a lot of fake news, reports, statistics. We all know that what is captured in the form of explicit knowledge is almost always far behind the curve of context-specific tacit knowledge that is hard to capture. This strategy of external search could work if your clients are less informed than you are. Besides, using search engines to find knowledge is also an art. There is also a lot of luck involved.

However, if clients want synthesised knowledge that fits their context, then organisations have to become better at enabling their own knowledge culture. Is your organisation the go-to place for certain kinds of knowledge? In a knowledge culture, it is not just about the technicalities of the search internally and externally. It is also about evaluating, refining and maintaining what is retained (and how) and how what is kept is organised or retrieved. Making it clear why something is kept or how a module can be used in combination with others is also valuable. If along the way documents are found that are no longer relevant, they are marked as unimportant and moved aside or deleted/archived.

Organisations (and individuals) also need to find a balance between documenting what is known for sure and exploring what is tacit, but not yet ready to be captured in an explicit form. This is where applications like Slack and Microsoft Teams, Trello and others are really valuable.

A knowledge culture values collecting, combining and synthesising information. It thrives on sharing hunches, talking about fears and opportunities. It is not just about proven content and technicalities. It is about:

  • constantly reflecting on what information is valuable,
  • collectively or individually thinking about which ideas, concepts or knowledge modules are used often to support decisions,
  • It is about talking about which concepts are drawn on frequently, and;
  • Exploring where explicitly captured or better-organised knowledge could be valuable for the organisation to draw on in future.

From this base, it is then easy to combine internal knowledge with different external knowledge. For me, starting the search outside is like searching for second-hand knowledge, while the raw material of ideas and insights of the internal organisation are overlooked or undervalued.

When an organisation becomes conscious of the value of their collective wisdom, far more care is taken to identify frequently used materials, modules, processes, tools, patterns, labels, thoughts and proven concepts. It is not just about the habit of collecting, sorting, storing and retrieving. It is also about reflecting on what works, what can be used better, and what kind of conversation or effort is missing. Then it becomes straightforward to combine internal knowledge with external ideas to innovate. If organisations reflect more about what knowledge is valuable and how this knowledge can best be kept alive for future refinement or use, then they will already be on their way to becoming more knowledge-intensive cultures.

*For those that are interested to know more about personal knowledge mastery, I recommend you take a look at the PKM course offered by Harold Jarche.

Categories
From the field Thinking out loud

How my praxis is changing

For many years, my practice was mainly about process consulting, with some research on the side. Because I love reading and theorising, my work always combined operational with conceptual development. I think my most significant value add to my clients was in the informal coaching and decision support I gave them on the side.

Over the years, the commissions I received to mainly do research, conceptual development or decision support work steadily increased. Still, I wanted more, as my programme was still mainly organised around consulting assignments. Then last year it happened. For the first time, research, conceptual development and decision support was my primary source of income. These are longer commissions to figure something out, develop a framework, or synthesise a lot of literature and research.

I get these commissions because my clients are finding value in the topics I am researching, and they are interested in leveraging these insights in their work. So my consulting assignments now become the place of integration, while my self-funded and commissioned research becomes the source of inspiration, ideas and curated content.

At last, I am consulting on the side. Almost all my short term process consulting assignments are now about applying or leveraging my research. My consulting contracts are now about weaving together my research in a way that helps my clients make better decisions and lead healthier and more innovative organisations. What I find rewarding, is that research topics that I struggle to keep apart in my mind, all seem to flow together at my clients. The consulting work is still important, but now my world is increasingly organised around my research interests.

For instance, some of my current research themes are:

  • Strengthening meso organisations, and figuring out how societies create, modify and measure these organisations
  • Establishing technological intelligence in industries, regions and organisations to sense discontinuous technological change Enabling innovation cultures that leverage tacit knowledge Enabling teams to draw on complexity thinking to search and discover for opportunities for systemic change.
  • Developing our systemic insight methodology and tools that enable teams to search and discover for opportunities of systemic change in complex or ambiguous environments.
  • How do societies learn, adapt and develop appropriate physical and social technologies? How does this dissemination happen? Is there really a paradigm shift to a “fourth industrial revolution?” or is this just hype?

I have seven or eight of these themes, with some being more coherent while others are still more disordered.

Now at my clients, these themes weave together in amazing ways:

  • I am helping a ministry of trade and industry to establish a technological change observatory to better anticipate and respond to technological disruption. This assignment combines my exploration in meso organisations, but it also harnesses my work on technological change and measuring change.
  • In another country, I am assisting a newly established think tank in developing a strategy, and in promoting knowledge intensification in the broader economy. This project draws on my work on meso change, but it also draws on my earlier experience in helping teams to conduct and make sense of industrial analysis.
  • In yet another context, I am helping an industry body to make sense of an industry diagnosis that we conducted on their trade members to understand their reality and the dynamics in their industry. This was part of a larger assignment to help a skills development project figure out how it can better support dual vocational education and job creation.

I am enjoying this new balance. It is gratifying to synthesise many loose strands into simple organising frameworks. It draws on my strengths of reading broadly, tinkering with ideas, finding literature from wise scholars on these ideas, and finding ways to make these concepts useful to my clients.

Categories
About the future Promoting Innovation Systems Technological change Technological disruption Technology and innovation management Thinking out loud

Is the Fourth Industrial Revolution a paradigm shift?

I am excited that the Helvetas Eastern European team asked me to write a blog post for their Mosaic newsletter about the Fourth Industrial Revolution. The blog article and many others can be found here.

Regular readers will know that I am not so convinced of one big revolution; rather that there are many smaller disruptions. In this article, I argue that it is hard to imagine what a paradigm shift would look like. I make six arguments of why there are rather several smaller disruptions taking place. The credit for coming up with the image in the article goes to Zenebe Uraguchi from Helvetas. He is also the person that convinced me to write this article, and who guided me when I felt stuck. Thank you, Zenebe! Take a look at some of Zenebe’s posts on the Inclusive Systems blog of Helvetas.

The second half of the article I wrote is about figuring out which social technologies to develop that are needed to make certain technologies usable or beneficial to societies. Many of these social technologies are cultural or organisational, but there are also many public institutions and public goods that are lacking in developing countries.

To me, it feels that we are still just scratching the surface when it comes to helping the meso organisations of developing countries cope with technological change.

However, it is exciting that my research into discontinuous technological change and the necessary social and technological institutions that are required in developing countries is of interest to development organisations and governments.

I am looking forward to your comments, questions, contradictions and ideas!

Best wishes,

Shawn