Technological change Technological disruption

What does it mean to be disrupted?

Updated on 15 September 2020

It felt to me as if the refrain that technology is disrupting our lives had reached a crescendo in 2019 and early 2020. For most people, it feels as if the implications of technological advancements are creeping slowly but surely into their everyday lives. The “industrial revolution” is not happening in distant factories or remote industries or in faraway countries. A continuous stream of technological changes is confronting us and demanding that we change how we travel, commute, engage with government, learn, shop and work.

During the Covid-19 pandemic which caused the global lockdowns imposed by governments and the decision by some to self-isolate, the refrain changed a little. Now, the downsides of technological advancement are mainly concentrated on topics of unequal access and the challenges of the dissemination of misinformation (and fake news). Talk of a “new normal” and a post-COVID world includes forecasts that more people will in future work from home, that education will increasingly occur in a more hybrid digital and physical mode and that governments and corporations will have to become more digitally savvy.

Messenger pigeon released from British tank 1918 IWM Q 9247

A message-carrying pigeon being released from a port-hole in the side of a British tank on 9 August 1918. This is photograph Q 9247 from the collections of the Imperial War Museums. The photograph was taken by David McLellan, and was retrieved from the Wikimedia Commons Imperial War Museum Collection.

I even heard a futurist on a radio talkshow forecast the imminent closing of business districts and corporate office buildings in favour of everyone working from home and the demise of shopping malls and inner-city promenades.

I have three objections to these sweeping statements about the speed of technological change and disruption:

  1. They ignore the architecture of technologies. Technologies, industrial revolutions and disruptions are all dumped into broad and ambiguous categories with vague boundaries. While this makes the message easier to spread and receive, it does not help those who must make short and longer-term investment decisions.
  2. They ignore the embeddedness of technologies. Lumping many technologies together makes it harder for all of us to understand what and who will be disrupted, and what the implications of the disruptions are for policies, communities, enterprises and social institutions. Increasingly, we also have to worry about control of data, usage rights and even the concentration of wealth, and the fact that many technology companies are behaving as if they were beyond accountability.
  3. They ignore the irregularity of technological change. In the photo reproduced here of a British tank taken on 9 August 1918, it can be seen the military technology has advanced further than the communications technology. With the wisdom of hindsight, many historians have also questioned how well social arrangements regarding tank warfare in the British Military had evolved in the early 20th century. There are accounts of how military strategies and the organisation of tank commanders were still mainly dictated by what worked for the cavalry that dominated the battlefields over the previous three centuries.

Previously I described the different kinds of disruption in some detail. In the next series of posts I will explore:

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.


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.

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).


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.


[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.


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. DOWNLOAD