Category: Technological change

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

 

 

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:

  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.

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.

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

Pondering disruptions and industrial revolutions

I am asked almost daily about my opinion about “the fourth industrial revolution”, technological disruptions and the impact on jobs.

Depending on who asks, I might fire off a statement like “I don’t believe there is a fourth industrial revolution underway”. Or perhaps I might be a little bit more popular and say “I don’t think there is one, but probably many smaller revolutions going on”. I must be honest, I have also told several leaders in business and government, “definitely, and you had better pull up your socks and scan the horizon so that you don’t get caught with your pants down”.

I do feel a certain responsibility towards those that ask me these questions. I am all too aware that my response might encourage somebody to think more seriously about their organisation’s ability to sense change and to respond. Or my response might paralyse, or maybe even give somebody a reason to remain complacent. The truth is, we simply do not know the exact answer or extent of the technological changes around us.

When the change is as complex as it is now, and so dispersed across many actors in the economy and the world, we simply do not know. We can measure patents, imports, exports, value add, jobs, but we simply do not know how many entrepreneurs, government leaders or citizens are reading up on new ideas, trying new combinations, dreaming in the middle of the night of new business models and arrangements. These changes, when they aggregate into a pattern or a groundswell, often only make sense looking back. When we look back we see those moments where shifts took place, where tipping points were reached, where narrow or broad revolutions took place. But in the present moment, it is just foam, sweat and conflicting messages in the news that seems to make us numb.

Maybe it deserves a blog post on its own, but what we have to bear in mind is that in the original meaning of an industrial revolution, the “industrial” should be understood as technological change. The revolution describes what happens to many forms of social institutions. That means small and large, formal and informal social institutions are too clumsy, too rigid, fitting an older order but not ready for the new order. So it is not the autonomous vehicle that will disrupt us (well, maybe us geeks might be very distracted by them); the disruption will come from the massive investments that would be required in transport infrastructure, in the way we move around, in the way governments regulate, collect taxes, and so on. Maybe it challenges how companies are organised, maybe it completely challenges global supply chains or creates new markets that are much better than older markets. The physical technology, when it outpaces the evolution of the social technologies, disrupts the latter.

I must say this in stronger terms. When the evolution of the physical technologies is too far ahead it destabilises the society, because the required social technology modules are not available. It destabilises because the “have’s” can draw from other societies social institutions, while the rest are left out behind a huge and growing barrier.

For me, that means that we should figure out ways to enable experimentation and innovation in social technologies because this is the hard part. Investing in a specific physical technology and the required knowledge to use is still the easier bit. Figuring out how to crowd in a broad cross-section of the society, how to get more people to try new ways of managing, new forms of enterprise, new arrangements of market and non-market actors; that is where we need resilience and creativity.

In South Africa, I feel that we are all too focused on the physical technologies, the gadgets. Yet, our societies ability to raise new enterprises, to experiment with new management models, new ways of doing business enabled by new technologies, is just too low. Despite having richly diverse demography, having people with great experience and qualifications unemployed or employed and frustrated, we are simply creating or encouraging too few people to venture out and start something new.

Four functions of innovation and technology management

This article is meant for my business clients and colleagues managing technology transfer and innovation extension services.

In the past I have written much about the professionals and organisations who are responsible for helping entrepreneurs to improve and strengthen their innovation portfolios on my personal blog site.

To recapitulate: I believe that many industries are struggling to modernise because their supporting institutions use completely different frameworks to manage innovation (or perhaps the supporting institutions make their choices as randomly as enterprises do).

One of the first concepts that a tech transfer institute or industry support organisation should transfer to enterprises is “how to manage innovation and technology”. Just because there is an engineer or an MBA/PhD in a company does not guarantee effective or creative management of innovation and technology.

Today I shall focus on the four broad functions that must be managed strategically in every enterprise and supporting institution. Even if someone in the organisation has the job title of Innovation Manager or Technology Manager, these functions should still be visible throughout the organisation. In other words, this is not somebody’s job, but it helps if somebody coordinates these activities. Also, see these four functions as the minimum. More mature innovating organisations will have far more depth than these four high level headings.

The four functions agreed by most scholars and innovation experts can be summarised roughly as:

  1. Searching and scanning for new ideas and technologies, both within and beyond the organisation. This includes looking at technologies that could affect the clients of the organisation, and technologies that could disrupt markets and industries.
  2. Comparing, selecting and imagining how different technologies could impact the organisation, its markets and its own innovation agenda.
  3. Next comes integrating or deploying the technology or innovation into the organisation. This includes adjusting processes and systems, scaling up implementation, and project managing the whole change process.
  4. The last step is often overlooked, but new technology and innovation often make new ideas, innovations and improvements possible. I call this last step exploiting the benefits of a new technology or idea. This could involve leveraging some of the additional benefits or features of a technology, perhaps by creating a new business unit focused on an adjacent market or particular offering.

When I visit institutions, organisations and companies, I always ask “who is thinking about change taking place beyond your industry or key technology?”. I cannot tell you how often I hear that “the CEO” or “the production manager” are on top of new developments and will be attending a tech fair next year. How can this huge responsibility fall on the shoulders of one or two people, who are at the same time biased towards the current strategy which favours justifying past (sunk) investments? Or if you ask “How did you choose between two technologies?”  you will be surprised how little time was spent considering new business opportunities, or how few companies asked for on-site demonstrations or samples from their preferred technology providers.

I will refrain from being too critical of technology transfer institutions and industry-supporting organisations, except to say that these organisations should be a prime example to industry of how to scan, evaluate, compare and integrate new ideas and technologies. We don’t just want to see the shiny machines and neat facilities, we want to understand how you arrived at your decisions, and how you made the best of your investments after implementing the change. Furthermore, industry wants to know what’s next, or what’s beyond their vision and how it may affect their industry.

To bring it all together, the technological upgrading of industries is plagued by many different market failures. These failures include the tendency NOT to invest due to high research costs, due to fears about making the wrong choices, or because so many decisions and changes must be made at the same time – this while the business continues, markets fluctuate, and technologies change faster and faster. Companies (and institutions) cannot afford just to kick start innovation management immediately before making a change (or when forced by external forces to make a decision). These functions must be managed strategically on a continuous basis, both at the level of top management and within the different functions of the organisation. Both companies and their supporting institutions need to manage innovation and technology, not only from an operational perspective (striving for continuous improvement, etc.) but also from a strategic point of view.

Revised: Industry 4.0, IoT, 3D printing and more. Why some technologies diffuse so quickly and others don’t

I wrote this article yesterday on my thinking-out-loud site and was pleasantly surprised at the interest it sparked. My language guru Linton helped me to fix many grammar errors, so here is the revised version.

I receive questions daily about the Internet of Things, Industry 4.0, 3D printing and many other technologies and whether and how I think these technologies will disrupt manufacturing and education in particular and the world in general. These questions are not only from government officials, but also from businesspeople, friends and fellow geeks.

Let me briefly state that I don’t believe it is possible to spot a paradigm shift in the future or in the present. So I would be hesitant to predict whether or when all these big changes will happen. However, when we look back we can spot shifts. Technological change typically takes places slowly but surely, and then at a certain point there is a massive shift. The point I would like to make is that even the futurists have great problems predicting the direction of that sudden shift. We must also consider that technological paradigm shifts almost invariably do not work out the way they are predicted to do before they occur.

For the last few decades many major technological advancements have been heralded as game changers. The advances are often generalised as sweeping statements about large-scale change. However, in most cases, new advances take a long time penetrating our daily lives, if they ever get that far.

So let me rephrase the original question a little. Perhaps the question is more about figuring out which technologies are diffused quicker than others, and why. This is something that we can calculate to some degree using a short history and the current status quo of assessments of technologies that are being touted as near-term game changers.

Dissemination of technology or knowledge always consists of at least three elements. I will for now ignore the process of diffusion for the sake of brevity. There is a supply side, a demand side and some kind of institutional or social construct that enables and even multiplies the diffusion.

The supply side is often most optimistic about how their ideas are going to change the game. The demand side is often naive about how useful a new technology is in real terms. Many potential users simply wait and see. Then there are the institutional mechanisms that operate at local, national, regional and international levels. There are lots of tensions at this institutional level, because this is where a whole range of social technologies, formal and informal, have to emerge or change. Just think of how US-based software companies are constantly coming up against data privacy groups in Europe. I am sometimes grateful that the institutional level takes time to change. Changing institutions to enable knowledge dissemination often requires multiple knowledge domains, different management levels and social play-offs. Often changing institutional support to improve diffusion must also cater for integrating and synchronising many other simultaneous change processes that are not only technological. They could be about regulations, rights and creating new forms of organisation. Furthermore, physical technology does not always change things the way we expect. After all, innovation is a process of combination and recombination, both at the level of physical technologies and also at the level of social technologies.

There are typically a few constraints that frustrate the diffusion of new technologies broadly speaking. The first is the fixed costs of the technology itself. Fixed costs slow down supply (otherwise we would already have electric vehicle charging points throughout the country), and also slow down demand (I cannot afford a Tesla yet).

Suppliers like to think that their solutions will fix social mechanisms, but this is often the area where change is the slowest. Social technologies often take the longest time to evolve (for instance in developing standards and regulations for electric vehicles, charging points and recycling of batteries). By evolving, the technology itself often changes with respect to its use, meaning and value  – often beyond what the originators had in mind. Thus while individual users can quickly adopt a new technology or idea, formal institutions, regulations and supporting infrastructure often take longer to adapt to new ideas. This means that the supporting ecosystem that enables new ideas to be quickly diffused perhaps adds additional costs (perhaps massive infrastructure investment or learning is needed), or fails to reduce costs in the diffusion of ideas. This is where the second constraint comes in. It depends on how complex are the required social changes. I mentioned earlier that institutional diffusion must also integrate different complementary technologies. For instance, using a smartphone to make phone calls is easy (single technological paradigm). Using a smartphone to manage or monitor a part of a production line requires many complementary and concurrent capabilities and technologies. It may even require completely rethinking organisational structures, production lines and supplier networks. Simply put, if the new idea is very complicated to use (due to the many concurrent investments and capabilities that are needed), then the costs goes up in terms of education, regulation, infrastructure, coordination, specialisation, management and so on. Just think of what it would take for South Africa to adopt driverless electric vehicles …

Perhaps this also explains why individual companies (think hierarchies) tend to absorb technologies easier than societies or economic sectors. Inside a company management can overcome coordination failures much easier than within a sector or broader society. Meso institutions such as universities and technology transfer organisations are very important for overcoming these coordination costs, but they tend to change slower.

The complexity of technology and its demands on the meso organisation is important in my work. I help these organisations figure out how to navigate the complexity of new technology adaptation and diffusion. It requires an understanding of users, some understanding of technologies, but a lot of understanding of the process of change and organisation. I don’t think I would be able to do my work without my understanding of market failures, especially with regard to failures in the capturing, dissemination, absorption or valuing of knowledge.

There are lots of amazing technological ideas out there that have been tried, tested and measured and found to be effective. Many companies here in South Africa are already using these technologies. So supply and demand exists, and in many cases there are transactions. Yet many of our industries, enterprises, universities and policy makers don’t know how these technologies can save costs, improve efficiency or strengthen resilience. Nor do they know which ideas will stick or have the most impact. So there is a missing institutional capability that reduces the complexity of the technology. What is often missing are institutions that make the dissemination of new ideas easier and cheaper. It is often more the case that the users (and possibly suppliers) don’t know how much the full implementation or use of these ideas would cost, or what skills, complementarities or networks are needed to master new ideas. Many market-supporting social technologies (in the form of institutions and networks) are lacking. Somebody must reduce the search, evaluation and coordination costs. This is where the complexity lies. And neither do we want our institutions to try and implement every new technology – this is where social balance and a longer-term vision are required.

So now I can get back to trends such as the Internet of Things or digitisation of the manufacturing environment. Many manufacturers know about Computer Aided Design (CAD) simulation or even rapid prototyping. But how can we reduce their risk of trying 3D printing, or how can they add more sensors to their production facilities so that they can improve measurement and control? It is not just about the cost of using the technology once or twice. There are issues that are holding entrepreneurs back from simply rushing to an online store and hitting “buy now”. Where would they get the trained staff from? How would they train existing staff? How would they manage a new competency? What would it cost to certify or maintain? Where would they find new customers or suppliers, and what would it cost them to develop the complementary capability and optimally use the new technology? And most importantly, how do we reduce their risks of trying something in different combinations? These are the issues that a network of institutions must consider as they craft their technology extension and demonstration strategies.

For me there is a strong role for technology intermediaries to play in demonstrating, perhaps on a small scale, how new technologies can be integrated into existing workplaces. This means that technology intermediaries must be funded to host (and master) a wide range of complementary technologies, so that entrepreneurs can combine what they have in place with the capabilities of these technology intermediaries. Or that new entrepreneurs not burdened by sunk investments can use their agility to gain access to complementary technologies in order to create new markets. These institutions should not be measured by how many companies fully absorb new technologies (this could lead to perverse incentives), but perhaps by how many companies have tried, engaged with and been exposed to new ideas.

At the same time, policy makers should look at ways to introduce new technologies into developing countries beyond demonstration or technology extension. Some countries such as Germany or Singapore have also been purposefully supporting disruptive incumbent enterprises by supporting the uptake of new technologies. Sometimes you can demonstrate until you are blue in the face, but incumbents won’t change if they don’t have to, and small enterprises sometimes simply cannot build up the momentum to challenge the status quo.

I would like to end this blog by briefly summarising what I’ve been discussing. For me the question of how new technologies may affect our lives is too focused on the hardware  and the geeks who love it. Even though I admire the suppliers and developers of new technologies, and I really admire the sophisticated users who are constantly inducing the emergence of newer and greater technologies, I believe that the real change we need is in getting better at creating responsive institutions that lower the costs for suppliers and buyers to try new things. This is where we can overcome many of the costs that slow down the absorption or dissemination of new technologies.