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:
- sensory systems (autopiloted cars)
- etc etc
- fuels - processing quality
- fuels - sourcing geological science / deep sea drilling / oil sands etc
- traffic management
- reconfiguration of urban systems
- public transport systems
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.