Hello everyone, 

This is a very interesting discussion. I enjoyed all posts. Terry offers me an opportunity to share a few ideas. The whole project about invention and innovation should be developed first at methodogological (metatheoretical in the Anglo discourse) level. There are a number of methodological questions to be formulated and resolved before the project can continue productively. For example, it is very important to make the right decision about what to compare and contrast or simply use as examples. This decision depends on the ontological realities that are considered and methodological opportunities that the researchers embrace. However, this is also a cyclical structure where methodology informs about ontological choices and ontology presupposes particular methodological approaches. The picture becomes even more complex when we have to take into account the teleological context. That can make a real difference to the direction of the project. 

I personally have some hesitation about working with Bell and Facebook, but I am not sure I know the teleological background of the project. Bell is designed to produce inventions that are marketable. But still, inventions. Facebook is designed to generate web traffic that brings advertising revenue. The social consequences of Facebook are still to be seen. Facebook generates social innovation without producing invention. It just provides infrastructure for something. For us it is social networking, for them it is advertizing revenue. They are not interested in the social networking by itself, only in the traffic that it generates. They also use very simple techniques to generate more traffic. They send automatic messages on behalf of someone that shares with you university affiliation, interest group, or another common feature. In some way, to put Facebook next to Bell is an offence to the engineers and inventors. However, if a company is interested to study how they can make money by making other people work for them for free, they can study Facebook. It is a great example. By the way, there were several large networking companies that are in decline. Despite of this, there are many such startups right now. Besides the multiple failures, there are many people that believe they can replicate the success of Facebook by finding a different niche. The key is to understand how Facebook made it, while many others failed. I don't think this is a design question. It cannot be resolved in the realm of design. This is much more than design. The design of all networking companies is very simple from a designerly point of view. What is very complex is the understanding how to make social potential to work for you for free and bring revenue. At this time, the key word is advertising. And advertising revenue depends on numbers of viewers. How to bring viewers to the site. How to attract them. What content. And how to make viewers provide this content on they own, for free, and feel happy about that. Not everyone can make this simple think work. For some reason, Facebook made it. How -- this is a good question.

Just a few thoughts,

Lubomir 

-----Original Message-----
From: PhD-Design - This list is for discussion of PhD studies and related research in Design [mailto:[log in to unmask]] On Behalf Of Terence Love
Sent: Saturday, March 03, 2012 8:45 AM
To: [log in to unmask]
Subject: Re: Invention and Innovation

Dear Rosan and  Ken and  Gunnar and all,

There is a different and potentially more fruitful way of viewing this situation - taking an overview of how the situation is analysed, i.e. a meta-analysis.

What  follows is a kind of overview of   'meta-analysis of  theory-making'
about innovation. (I've now learned to draw attention to when something is a meta-analysis of theory. Hopefully to avoid it being seen as being about application of  particular theories - a PhD student/colleague has  just had a paper bounced because of that misunderstanding!). It's usually a useful thing to do in any design situation where there is some contraditions...

One thing often overlooked is that all disciplines use the same structure for their theory models. In fact, all disciplines  use the same theories; with different issues such as  innovation, price, aesthetics, radiation, social capital etc  being fitted into the same theory structures . One can easily map 'all theory models across all disciplines' as simple combinations of  theory building bricks. These are found much the same in all disciplines. There is a limited number of these theory structures and it isn't that  large.

In other words, theories in most disciplines are much the same, except for some variation between disciplines in the average level of complication of theory structures that they use.

Different disciplines use  slightly different selections from these  'theory blocks, but there is substantial overlap in basic theory models across all disciplines.

I suggest the above similarity of theory across all disciplines  is simply the result of the biologically limitations of human thinking. We make similar theories across disciplines because we are all humans and humans think this way. Yes, I know, it's tempting to feel the similarity  of theory across disciplines is from some deep mystical thread  connecting all aspects of the world. It  just doesn't make as much sense as that its limitation of humans - after all it's humans that make up the theories, not the world.

So across all disciplines, we all use the same bunch of theory 'patterns'
that can be roughly divided into three  different levels of complexity

	*  Some are really simple (e.g. the pattern found in 'equals', balance, justice, equations, weight of evidence...)

	* Some are a little  more complicated (e.g. multiplication, the idea of 'factors' acting on a situation, simple reversibility, linear modelling, adding components, simple additive combinations between models, models that use variables that have multiple dimensions, .g. multi-dimensional vectors,
)
	
	* Some  theory patterns  are much more complex (e.g. theory patterns in  in which different factors transform each other and themselves in ways that vary across space and time or even across abstract aspects of reality)

Theories used to model design and innovation, for example, typically use theory patterns from the lower (simpler) end of  the middle group.
Descriptions of innovation activities/processes and types of organisation (vertical integrated/distributed/star etc), and theories of causality fall mainly into these simpler middlish kinds of linear categories of theory. I suggest the reason they often don't work very well is primarily because they are too simple for the situations being theorised about.

From observation, however, the problem here  is more about people and education than about how well particular theories fit situations.  Which theory patterns get used  in particular disciplines  depends more on the education and skills of the people in the discipline than on whether the theories fit the world. Using complicated theories is difficult! Mostly, it requires special education. Using *really* complicated theories is hard and usually requires strong ability in mathematics (degree level and beyond).
Typically, those who  have   found out how to make theories that predict and
explain reality have done it the hard way. The difference between that and the easy way is that the hard way works (thanks TP). 

I suggest the reason the simpler theory  patterns get used in design research is mainly because of human limits in which people are  not comfortable with using more complex theory structures. Thus the question of innovation and design and whether and which Bell Labs, Facebook  and  IBM illustrate whether innovation is best described  as linear or due to oppressive genius, is a furphy in light of  the types of theory being used.
If the theory isn't complex enough to represent the situation, then no amount of careful choosing will solve the problem ('will a half litre glass or pint glass best hold this 20,000 litres of orange juice...').

The overall problem  is perhaps easiest understood in terms of  Ashby's Law of Requisite Variety in which control system (theory model) variety has to be at least as great as system variety before  starting to discuss which control system (theory model)  is the best choice.

This leaves the field of design in an unusual position. The theories that are used are not complex enough to do justice to the situations being discussed , and most of us doing the discussing haven't been educated to use theory patterns with the complexity necessary (of course there is always the option to try to force the situation into the theory by simplification...aargh.).

Fortunately, the world has a discipline that specialises in the abstract understanding of  theory patterns - mathematics. This is not exactly the complete box and dice as mathematics gets a bit fuzzy round the edges about the relation between the theory patterns and the real world bits that are being represented  and that also needs some good skills in  epistemology and ontology. Maths, however, gives access to  the treasure chest of theory patterns and how to use them. People can simply  open  the chest and revel in the treasures to improve their creative understanding of situations.

So where does this go in terms of your problem and questions  about theories about comparative evaluation of the drivers and cause of innovation (and Bell Labs and Facebook ) ? (...perhaps  IBM would have been a better choice...?).

First, it suggests there may be some benefits in looking at the situation in terms of the complexity of the theory patterns that are being used to analyse the situation and whether they are complex enough for what you are trying to do - to me it looks like they are not.

Second, it suggests a need to look with an epistemological eye to de-messify the epistemological problems about the concept of innovation and  the surrogates it is measured by.

All of this points to the same conclusions as  Don Norman proposed: design researchers need way  more maths education.

Best wishes,
Terry
==
Dr Terence Love
Love Services Pty Ltd
PO Box 226, Quinns Rocks
Western Australia 6030
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+61 (0)4 3497 5848
==



-----Original Message-----
From: PhD-Design - This list is for discussion of PhD studies and related research in Design [mailto:[log in to unmask]] On Behalf Of Rosan Chow
Sent: Thursday, 1 March 2012 4:52 PM
To: Dr Terence Love
Subject: Re: more than the place where Claude Shannon and William Shockley worked

Hi Gunnar, 

As you know I have been reading, let me try to see what I retain from the
readings: (this is an exercise for me)

This story of the Bell Labs might be said to be based on the so-called linear model of innovation (basic research, applied research, development & innovation). This model, however, has been challenged, someone please helps here. 

As everybody knows, invention is not the same as innovation. Innovation, understood today as commercialization of new/novel technology was made by Rupert Maclaurin (and not J.Schumpeter, Surprise!) (Bodin 2008 http://www.csiic.ca/PDF/IntellectualNo2.pdf  ).  

On the one hand, I think the author has a point - to challenge how innovation is defined and understood. (For a fascinating read for a history of innovation as a category, again Bodin 2008 http://www.csiic.ca/PDF/IntellectualNo1.pdf ).