Macro Innovation - I-O II

So in the last post I suggested that when we analysis innovation effects at the macro level we need to think differently.

At the recent AAAS meeting in Vancouver I surprised to learn the 'plastic cups' we were drinking from were in fact corn starch. I don't know which company they were from but here is a picture of what is possible:

http://www.flickr.com/photos/becs027/2318310270/
They look like plastic, they feel like plastic and they perform like plastic - except they are compostable.

So what difference would this make to an I-O table. Now I haven't looked into the exact processes of the technology so this is just suggestive at the moment.

(click on image to enlarge).

I have suggested here that the changes in the I-O table would be minimal. Some added agriculture into the plastics industry with some necessary increase in chemical / petroleum products into the ag industry to increase production. The petroleum industry's input into the plastics industry would decrease.

Okay, so to a different example; the digital camera revolution I discussed last time.
So just a note. As it happens - the Canadian I-O table which I am using as an example doesn't have the category 'C33 Medical, precision and optical instruments' populated with data - it is amalgamated with a different category. But that is okay for this example, I have simply highlighted the appropriate cells.

First we need to include not just the B2B but the entire sheet because the impact has been with final consumers.

Chemicals has dropped and with it the entire column of purchases, as well as supplies through to retailers and consumers. Optical devices have increased along with the entire column as more devices include optics.

Finally to some speculation. There has been much talk lately about 3D printing. Now here again this approach can help us think more clearly about the possible effects of such technology and why it become highly problematic to talk sensibly. The big benefit of 3D printing is that it cuts down on waste - additive manufacturing as it is called. That changes the coefficients.

Second it makes manufacturing possibly more local.

I would suggest that at the very least 3D printing at some point has the potential to change the very structure of manufacturing both in what it supplies and how it is supplied and in what it buys.

So in this way I would suggest we can use the structural data we already have but then develop new approaches to analysis that don't take existing structures for granted but begin to layer it with new meaning that represent changes in the operating models of segments of economic activitiy.

Conceptualising innovation at the macro landscape level

Recently I have been puzzling over the idea of whether we could look back across time and measuring the impact of a particular singular or group of innovations.

The problem is very rapidly you run up against numerous problems some of which are intertwined.

So lets start with a simple example; the transformation of camera from film to digital. Papers that I have seen on this topic divide on how they deal with it. One type of study is the case method that seamlessly describe both the camera manufacturers and the film manufacturers and suppliers such as Kodak and Fuji.

The second method often uses some case data but also draws upon industry data.

And here is the problem; film manufacturing is in the chemicals industry and photographic equipment has essentially its own category and never the twain shall meet. Standard industry data cannot address the interactions between these categories. If you you turn to I-O data that won't help much either. Input-output data helps us to see interactions where those interactions are a part of actual the transformation of products such as minerals into steel and steel into cars.

Film and Cameras have no such interactions.

So this is point 1 - there are common sense business models that link complementary products for which there is no obvious data.

We then turn to another problem. We have come to understand innovation has been one of five categories:

• product innovations
• process innovations
• new suppliers (inputs)
• new markets (outputs)
• new organisational structures

But if we look at innovation in the aggregate there is a conundrum. The example above is obviously product innovations isn't it? But what of the impacts:

• Product innovation - a camera transformed from mechanical to electronic.
• Process innovation - the entire manufacturing process for cameras has changed.
• Suppliers - camera companies needed to link up with electronics companies
• Markets - cameras are now in everything so the camera companies are selling to electronics companies. Retail market for film processing has collapsed. Film production has collapsed. The market for high quality lens making has probably also declined.
• All of this must have require organisational changes.

So point 2: from a post innovation aggregate point of view there are only innovations - dividing between categories is extremely difficult to determine. 

This begins to suggest that the innovation literature has an unexposed assumption nested within it. I won't call it a paradigm as that means something different. Such assumptions are extremely hard to reveal and map. I showed up two of these in my book Innovation Systems Frontiers. Innovation literature focuses on exports ignoring imports and does not ptoblematise political borders. But that took 7 years of literature mapping to highlight. I don't propose to do the same here.

So I will simply assert that scholars are more interested in corporate strategies for innovations and industry approaches to innovation. This developmental perspective includes all the geography literature. Now I realise that there is a growing research impacts literature but that is a different matter  which I want to leave aside in this blog.

So if you go here http://timkastelle.org/blog/2012/06/the-complete-innovation-matrix/ and read Tim's really interesting ideas on corporate innovation strategies you will see what I mean. I do encourage you to take the time and this blog is in part a response to think about the problem from the other end of the process.

We have spent less time thinking through the linkages in economies. We have wasted a lot of time in my opinion devising taxonomies (Pavitt) and technology flows (literature on embodied R&D), but the more this work rests on existing data structures rather than new surveys the more limited the results will be because of point 1 above.

A more idiosyncratic approach as an intermediate step may be a better starting point. If we adopt the model in Arthur's 'The nature of technology" we can construct examples such as that below. Lets start with one artefact - a car:

Endo-system technologies:

• engines
• glass
• electronics
• rubber
• plastics
• fabrics
• seats
• sensory systems (autopiloted cars)
• etc etc

Exo-system technologies

• fuels - processing quality
• fuels - sourcing geological science / deep sea drilling / oil sands etc
• roads
• traffic management
• reconfiguration of urban systems
• public transport systems 
• etc

So point 3: thinking backwards forces us to see that lying across our conventional data are multiple networks of intersecting business models and technological systems. 

What is my answer? Instead of looking for answers either within the existing data structures or ways to reform data collections, lets face the problem and working with the existing data build link activities that are currently not linked. Think of international airline network maps and you will have a vision of where I am going with this idea.