Unlocking knowledge in organisations

A favorite topic that I love to talk, think and write about is the knowledge that is lurking around in organisations, often untapped.

Last week, the University of Stellenbosch Business School, where I am a member of faculty in the Executive Development programme, published an article I wrote in its thought leader newsletter. It is titled “Unlocking knowledge in organisations to enable innovation”. What started off as a 1200 word article was reduced to 700 words by Linton Davies, the wordsmith that always helps me to better express my ideas when I write formal publications. I think this article as it stands now must be the most I have ever said in only 700 words!

I am really proud of this article in its current short form. It started off many years ago as a much a more complicated module in my innovation systems training session. Now it is a practical workshop format that I use often in organisations supporting innovation, but increasingly in businesses, government programmes and even NGOs.

It is informed by evolutionary and complexity thinking, and is thus in line with my current research and the principles that I now pursue and value. Of course, a lot of extremely important theory is left out in this form, but by helping managers become more aware of how the inhibit or promote knowledge generation in their organisations is for me already a great start.

 

Significance over scale when selecting sectors

When promoting territorial economic development from an innovation systems perspective it is important to find ways of increasing the use of knowledge and innovation in the region. However, in mainstream economic development there is a tendency to target the private sector based on scale. This means that practitioners look at quantitative measures such as jobs, numbers of enterprises, numbers of beneficiaries, etc. when deciding where to do analysis and focus support. This is common practice in value chain promotion, sub sector selection, etc. Many development programmes do this as well prioritizing scale measures such as jobs, women, rural individuals, etc.

From my experience of assisting development organisations to strengthen the economic resilience of regional economies (which means more innovation, more experiments, more diversity, increased use of knowledge, more collaboration between different technological domains), I have found that the scale argument is distracting and too focused on the beneficiaries (whatever is counted) and not focused enough on those indirect public or private agents that are significant and that enable a whole variety of economic activities to take place. With significant I mean that there could even be only one stakeholder or entry point (so the direct scale measure is low) but by addressing an issue it enables a whole variety of economic activities to take place.

Of course, scale is very important when a local politicians need votes. It is also important when you have limited budget and must try to achieve wide spread benefit. For this reason scale is very important for social programmes.

However, when local institutions are trying to strengthen the local innovation system, in other words improve the diversity technological capability of a region, then scale becomes a second priority. The first priority then becomes identifying economic activity that enables diversity or that reduces the costs for enterprises to innovate, use knowledge more productively should be targeted. The reason why this does not happen naturally is that these activities are often much harder to detect. To make it worse, “significance” could also be a matter of opinion (which means you have to actually speak to enterprises and their supporting institutions) while crunching data and making graphs often feel safer and appear to be more rigorous.

My argument is that in regions, the long term evolution and growth of the economy is based on supporting diversification and the creation of options. These options are combined and recombined by entrepreneurs to create new economic value in the region, and in so doing they create more options for others. By focusing exclusively on scale, economic actors and their networks increasingly behave in a homogeneous way. Innovation becomes harder, economic diversity is not really increased. I would go as far as saying that success becomes a trap, because once a recipe is proven it is also harder to change. As the different actors becomes more interdependent and synchronized the system becomes path dependent. Some systems thinkers refer to this phenomena as tightly coupled, meaning a failure in one area quickly spills over into other areas. This explains why whole regions goes into decline when key industries are in decline, the economic system in the region became too tightly coupled.

But I must contradict myself just briefly. When interventions are more generic in nature, meaning they address market failures that affect many different industries and economic activities, then scale is of course important.

The experienced development practitioners manage to develop portfolios where there are some activities that are about scale (for instance, targeting a large number of informal traders) and then some activities that are about significance (for instance ensuring that local conformity testing labs are accessible to local manufacturers).

The real challenge is to figure out what the emergent significant economic activities are that improves the technological capability in the region. New emergent ideas are undermined by market failures and often struggle to gain traction. Many new activities requires a certain minimum economic scale before it can be sustained, but this is a different kind of scale than when practitioners use scale of impact as a selection criteria. Many small but significant economic activities cannot grow if they do not receive public support in the form of promotion, awareness raising or perhaps some carefully designed funding support.

There are a wide range of market failures such as high coordination costs with other actors, high search cost, adverse selection, information asymmetry and public good failures that undermines emergence in local economies. It is exactly for this reason that public sector support at a territorial level (meaning sub national) must be sensitive to these market failures and how they undermine the emergence of new ideas that could be significant to others. The challenge is that often local stakeholders such as local governments have limited influence over public institutions in the region that are funded from other spheres of public administration.

Let me wrap up. My argument is that scale is often the wrong place to start when trying to improve the innovation system in a region. Yes, there are instances where scale is important. But my argument is that some things that could be significant, like the emergence of variety and new ideas often get lost when interventions are selected based on outreach. Furthermore, the focus on large scale impact draws the attention to symptoms of problems and not the the institutional or technological institutions that are supposed to address market failures and support the emergence of novelty.

I will stop writing now, Marcus always complains that my posts are too long!

Let me know if I should expand on the kinds of market failures that prevent local economies from becoming technologically more capable.

 

 

REPOST: The difference between academic and industrial science

In the last 5 years I have posted my blog articles on the topics around my work. I re-use many of these articles in my ongoing consulting and training work. Below is an article that I originally posted on 20 August 2011. This is one of the popular posts on my blogsite that was posted before I had the current following.

For my frequent readers, please forgive my trip down the archives!

 

One of my favourite authors on the topic of science is the late John Ziman. Ziman played an important role in popularising science and its role in the technological evolution of societies. We have some of his books on our Mesopartner bookstore (You can also click on the images on the right of the screen) .

In his last book, Real Science, he made an important distinction between science in academia, and science in industry. This is relevant to me because I am assisting universities to conduct more relevant scientific research that will benefit industry. At the same time I am assisting industries to intensify their scientific research.

According to Ziman, academic science works towards the Mertonian norms introduced by Robert K Merton in 1942, also known as CUDOS. Merton advanced our understanding of the ethos of the scientific process. I like Ziman’s (2000) discussion of the Mertonian principles. CUDOS is as an acronym that denotes good academic research and stands for:

  • Communalism – fruits of academic science should be public knowledge (belongs to the whole scientific community), and the communication and dissemination of results are as almost as important as the research itself,
  • Universalism – researchers and scientists relate to each other regardless of the rank and experience of the researcher. The norm of universalism requires that scientific findings are evaluated objectively regardless of the status, race, gender, nationalism or any other irrelevant criteria,
  • Disinterestedness – academic scientists have to be humble and disinterested. Work is done in a neutral, impersonal and is often recorded in the passive voice. It disassociates with the personal or social problems, and focus on advancing knowledge or solving a very specific problem in an almost clinical way.
  • Originality – every scientist is expected to contribute something new to the archive, while building on the knowledge of predecessors. Unfortunately this also sometimes constrains how creative academic research can become. “new” could mean new data, questions, methods and insights.
  • Scepticism – This norm triggers important brakes on scientists, as it involves critical scrutiny, debate, peer review and contradiction before being accepted. It is important as it deepens understanding and knowledge from different research perspectives, and should not seen as being completely negative, rather it should be seen as being necessary.

 

Industrial science works towards what Ziman (2000:78-79) calls PLACE:

  • Proprietary – the knowledge is not made public (or at least as little as necessary is made public),
  • Local – it is focused on local technical problems rather than on increasing general understanding,
  • Authoritarian – Industrial researchers act within a hierarchy and must work to please senior management, in other words, it is not serendipitous,
  • Commissioned – it is undertaken to achieve practical goals rather than to just improve knowledge, and
  • Expert – industrial researchers are employed as expert problem solvers, rather than for their personal creativity and writing or teaching skills.

 

Ziman argues that when universities undertake contract research for industry, they somehow cross the boundaries between these two approaches to research. For instance, industry is more interested in solving a specific technological challenge and would prefer that senior researchers work on a problem. In the last 50 years it has increasingly become necessary for universities to raise 3rd stream income, so it a universally accepted practice that universities undertake research for and in cooperation with industry.  However, a university must prioritise the development of interns and junior researchers (and achieve other social goals). Furthermore, industry may not be interested in registering a patent (immediately), otherwise their secrets gets shared with the whole world. Academic researchers on the other hand, are expected to deliver publications when they cannot deliver patents or licenses, thus there is another conflict of their objectives. Perhaps a last comment is that universities are under pressure to solve social problems that are deemed “relevant” by prevailing political pressures, while industry prefer to solve problems that are immediate, relevant and that may even be in contrast with the desires of the prevailing political and social debates. Practically this means that at the moment industry may need to automate to remain competitive, thus incurring job losses, while government and the society may be demanding job creation for people with little or no technical education.

 

Universities must understand this tension, and must operate within and between different modes of conducting research. Current legislation perhaps assumes one standard approach to university research, that always results in something that can be published and or patented (licensed), and it further assumes that the value (and cost) or research is known at the time of start of the research or after completion. Practical experience indicates that this is not always the case. Sometimes the value of research only becomes apparent when it faces market forces.

 

Sources:

ZIMAN, J.M. 2000.  Real Science: what it is, and what it means. Cambridge: Cambridge University Press.

 ZIMAN, J.M. 2003.  Technological Innovation as an Evolutionary Process. Cambridge Cambridge University Press.

Absorbed into the networks behind the systems we see

Its been a while since I have last posted here. The reason for my absence is two-fold.

Firstly, I am busy with a course offered by Coursera and the University of Michigan about Social Network Analysis (SNA). My business partners and one of our associates in Mesopartner are participating in this course. The course is 9 weeks long and I must admit that it is taking much more of my time than I originally anticipated.

The second reason I am hardly online is that the industrial policy in South Africa is starting to have positive effects on local industry. As I work mainly with the manufacturing sector on topics like innovation systems, industrialization, identifying and addressing market failures, and the competitiveness of regions, it means that there is suddenly an upsurge in demand. The demand is lead by state owned companies that are suddenly obliged to procure manufactured content locally, and by local industries that realize that years of underinvestment and fighting to survive against cheap and sometimes lower quality goods have left many sub-sector uncompetitive.

But these two reasons are also having an effect on each other. I have been applying many of the principles and tools of Social Network Analysis in my diagnostic work for the last 2 years, and for the last year I have been using SNA as my main diagnosis instrument. This recent course have simply forced me to read up more and more on many of the theories and the concepts behind the instruments I have been using. I am still trying to figure out how to do this kind of diagnosis fast, and how to teach these instruments and theories to the practitioners that we (Mesopartner) are working with around the world. At this moment the diagnosis that I am doing in valve, pump, tooling, automotive and industrial equipment is still slow and it takes all my attention.

What is the benefit of taking a SNA approach to sub-sector development?

  1. Well, firstly, a network diagnostic very quickly reveals whether there is a cluster or even a value chain. We often assume that these constructs are real, but in the last few years we have learned that just because all the actors that should be in a chain are there doesn’t mean that a value chain exists. Same goes for a cluster, just because all the elements are there doesn’t mean there is a dense network of cooperation, knowledge exchange and systemic competitiveness.
  2. Secondly, a network view assists with understanding the deeper relationships, trust patterns and information flows in a small part of a real system. These relationships makes it possible to predict how information flows, who the thought leaders are and how influential institutions, leaders, officials and business people are. This is directly relevant for my work with innovation systems.
  3. Lastly, Social Network Analysis also highlights how complex even a single link in a value chain can be. When you look at the spider web of relations, ownership structures, communication channels and knowledge spillovers, then you see how traditional development interventions have completely missed the leverage points.

All I can do at this moment is to commit to blog more frequently once this course is done. I will share some of the results of the industrial diagnosis that I am currently busy with in a few weeks time. Below I will give a sneak preview of the network map of the valve manufacturing cluster in South Africa. You will immediately see that some manufacturers (in red) and some foundries (in blue) are more connected than others. The yellow dots are valve manufacturers that are not yet part of the formal valve cluster structure. Hardly any additional analysis is needed to show that the more connected firms are the ones we should work with.

Cluster drawing 4

However, the additional analysis that we can run on this cluster further narrows the choices of whom to work with to get both the highest impact (in terms of both ability to grow their business, increase employment and meet customers needs) and in terms of getting the highest demonstration and spill over effects. The latter is important, because when you want to upgrade an industry you should prioritize firms that are able to create positive spillovers and that others are willing to follow. To do this kind of analysis we need a combination of qualitative and quantitative information, and we use specialized software applications. But more about this in a future post!

Linking – Beyond Linear Development Trajectories: What if there were 5 clusters of quite different developing countries?

For my first post of 2013 I share a post from “Aid on the edge of Chaos” that I found challenged my thinking. The title and all the content relates directly to the site.

We humans are supposedly very good at recognizing patterns, with some evolutionary theorists even crediting our survival and evolution with this trait. However, we also tend to struggle to see beyond patterns that we have classified, almost like a needle on a old record. Some examples are the way we divide the world into developed and underdeveloped, industrial and emerging. Although we all know that these classifications are in conflict with our own experience of the world (think of the sophistication of the Indian Pharmaceutical sector) we still are trapped in our labels that we use.

Below is a link to a post on Aid on the Edge of Chaos, featuring the work of Andy Sumner and Sergio Tezanos Vázquez where they explore new approaches to classify developing countries.

Beyond Linear Development Trajectories: What if there were 5 clusters of quite different developing countries?.

The image below is from the original post on the Aid on the edge of chaos blog site. Take a look at their post and then think again how you label the countries that you work in.

  • What happens if you classify the countries differently?
  • What are the implications of just changing the classification?
  • How does their classification scheme challenge your own way of classifying regions?

Clusters proposed by Andy Sumner and Sergio Tezanos Vázquez

Is there a hierarchy of the different levels of innovation?

In my daily work I often switch between working on firm level issues about innovation to working on the more systemic level of innovation systems. My focus is mainly on the institutions that are trying to get whole regions or sub-sectors to uprgrade technologically. In other words, they want modernization of a particular sub-sector or region for a specific reason.

In the last few years I have noticed some patterns that explain why these technology intermediaries are not hitting their targets:

1) they focus mainly on the micro level of the firm, and don’t move to the innovation system level. Moving from one firm to many is not necessarily systemic or holistic.

2) an underlying assumption in many Technology Transfer or economic development programmes with an emphasis on technology is that the problem is that firms cannot innovate (for whatever reason), therefore agencies must innovate on their behalf. It therefore takes a very narrow perspective that innovation is about products or processes, and that technology is about hardware + training. It completely miss the point that innovations emerge from within a specific framework, and that giving a firm a new product on a platter is not technology transfer nor sustainable.

3) a third pattern is the assumption that improving innovation in industry is an engineering problem (see my post on what is meant with technology). It completely ignores that fact that an innovation system is a dynamic system that is mainly about how different economic agents interact, engage, share information, learn together, and remember (learn) what works and what doesn’t work. Freeman (1987:1) defined an innovation system as “the network of institutions in the public and private sectors whose activities and interactions initiate, import and diffuse new technologies.The emphasis is mainly on the dynamics, process and transformation of knowledge and learning into desired outputs within an adaptive and complex economic system.

4) Innovation is somehow disconnected from creativity and creative thinking. Creativity in innovation is all about getting different people to think together. Maybe they agree, most often they don’t. But somehow they need to recognize constraints, threats, opportunities and then work from there. It requires some tension and often a lot of argumentation. It isn’t serendipitous journey. It requires strong leadership and a lot of guts. And it takes time.

Let me stop here.

Earlier in a post I have written about the different levels of innovation that are commonly identified as:

  1. Product or service innovation
  2. Process innovation
  3. Business model or organizational innovation
  4. Social or societal innovation

The funny thing is that everyone is focusing on helping firms to develop new products or maybe even a better process. Yet, the biggest obstacles to product and process innovation is not a lack of effort, or funding or ideas. It is complacent or outdated management, or perhaps business models that worked in another time but that has not kept pace with change. How often do we hear that someone we know or even a whole group quit a firm to start their own enterprise because management wouldn’t listen to their ideas?

Lets get practical. For example, large parts of our South African manufacturing sector is focused on the manufacturing of components designed somewhere else in the value chain. This is most likely explained by several factors including the concentration of corporate ownership in a few industrial holdings (a left over from sanctions and import substitution) and the presence of highly organized supply chains in many sectors like Automotives or electronics. Partial success in getting larger firms to compete internationally, combined with local framework conditions that inhibit the growth of small firms (for instance inflexible labour laws, collective bargaining, Black Economic Empowerment and a preference to procure through tenders) re-inforce this pyramid structure, with many component manufacturers at the base and product integrators (OEMs) at the top of the pyramid. The product owners dominates both their supply chain, the product architecture and the performance criteria. Most component manufacturers are squeezed both on their margin but also on the processes that they may use.

Are we getting things the wrong way around? Picture: Unknown source

To help manufacturers to design new products and services is not entirely a bad idea, but this doesn’t address the systemic problem. We need business model innovation. We need new OEMs to emerge with new product combinations that draw on existing or easy to develop component competencies. Or we need some business model innovation where some traditional component manufacturers expand their business by manufacturing their own products. Perhaps we need some manufacturers to diversify horizontally, or vertically.

I have played with this idea with students in my classes, and almost all business model innovations will lead to interesting product, service and process innovations. However, we can generate long lists of product/service and process innovations that have not resulted in business model innovations. Partly because these firms cannot sell their new innovative products to their existing customers, they also need to diversify their markets which sometimes requires a completely different business approach.

To stimulate a sub-sector or a region to upgrade cannot be achieved only by helping one firm or a few firms at a time. Somehow we have to challenge management models, we have to help business people identify areas for management innovation. This will result in business model, process and product/service innovations that are self perpetuating; meaning businesses can do it again and again because their competence have increased. Actually, the best impulse into innovation is still modern management that is strategic not only about the internal dynamics of the enterprise, but that is also looking outside of the firm into the market place, at their collaborators, new technologies and their competitors. With firms that are aware of what is going on inside and outside the discussion about innovation is a fantastically creative discussion about what is possible or impossible, with the latter gives rise to very interesting discussions. But a firm that is under-managed or managed with outdated principles is very difficult to assist. Giving the latter group a new product, or taking them to a new market simply won’t do the trick.

Perhaps this is where creative destruction of Schumpeter comes in. Sometimes the only way to upgrade a sector is to allow enterprises with new combinations of management, ideas, products and processes to outcompete older more complacent firms. Hopefully some of the incumbents will at least be able to imitate the signals from the new entrants.

I propose a toast to business model innovation.

Complexity and international development

A while ago I posted an article about the exciting developments in the various fields around complexity science and development (actually there are several earlier articles making reference to this topic). Recently Marcus Jenal wrote a great review of the work of Ben Ramalingam (author of the blog Aid on the Edge of Chaos) and Harry Jones with Toussaint Reba and John Young. The paper can be downloaded here.

 

Perhaps you have noticed that I often make reference in my posts to “complexity”, “evolution” and “complex systems” in the context of development. Some have even asked me why I do this. Well, already there are moves by donors and monitoring bodies to start using a more complexity-sensitive approach to evaluation. This is not entirely fair, as too many development programmes are still designed in a very linear way (log frames, impact chains are mostly used in a linear fashion). This means that to reach your impact you must combine your programme activity with faith and good luck (plus good weather) because most programmes are operating in a sea of complexity. There are just too many factors that can influence your outcomes. And even if you hit all your targets the system may remain exactly the same way. (wink wink: I wonder why no-one is making more of a fuss of the poor track of donor programmes in South Africa that were supposed to deal with systemic failures in education, rural development and even Local Economic Development?)

Another reason I am interested in these topics (other than my usual curiosity) relates to my practical activities around building industrial systems from the bottom up. Although I am still biased towards manufacturing with some emphasis on specialized services, I am trying my best to understand the complexity of not only relations between the actors, but also between the factors that are influencing their behavior. Then throw in some factors like policies several self justified meso-level organizations, mix in some government failure, market failure, network failure and also just the uncertainty from Europe. That makes for a complex system where there are a myriad of vicious and virtuous cycles and then the dynamism of time delays.Mix into this that the political system in South Africa also fights bottom up decision making. Local stakeholders have a limited number of instruments at their disposal and can hardly hold other spheres of the public sector (and other organisations) accountable. Despite this all kinds of firms are innovating, and there are even innovation systems that involves individuals in public agencies that are committed to support local actors (even if their institutions is unwilling or incapable to assist).

I find a lot of comfort and maybe some good questions in the literature on complexity and perhaps also the literature on evolutionary economics. Perhaps I even find some comfort that even the so-called industrialized world is struggling with the increasingly complex and interrelated policy environment.

If you are working on bottom-up industrial policy then please let me know, perhaps we can exchange notes.

%d bloggers like this: