Stop and smell the rosesPosted: September 13, 2014
You’ve got to stop and smell the roses
You’ve got to count your many blessings everyday
You’re gonna find your way to heaven is a rough and rocky road
If you don’t stop and smell the roses along the way
From a song written by Carl Severinsen and Mac Davis
It affects nearly all of us, whether we are drumming our fingers in front of the microwave oven telling it to hurry up, wanting ever faster internet connections, or finding our attention spans are getting shorter, we have a need for speed.
One purpose of this blog series is to search out research findings and relate them to innovation ecosystems and particularly to the Rainforest framework. A framework that balances the science of innovation with the science of business is, we suggest, useful for economic development across a great diversity of mindsets, motivations, and worldviews.
July’s and August’s blogs about agile innovation ecosystems suggest that there is a need for rapid diffusion, spread, or flow, of information (knowledge, learning, innovations) if such networks are to be responsive. It is to this feature we shall turn our attention in this blog – with two caveats.
First, the results presented here are from several different contexts and there is no certainty that they will be directly relevant to innovation ecosystems. However, they should at least catalyze our thinking.
Second, all these results are based on modeling information flow along links between nodes connected in networks. There are ongoing investigations among researchers as to just how the structure, or topology, of a network of nodes and links influences information flow. Past research has also investigated the type of network structure, such as clustering, which enables rapid diffusion and social learning – and what features can block social learning. An alternate, and less researched model, is that of flow of fluids through pipes which we will consider in a future blog.
In their 2014 paper Rapid innovation diffusion in social networks http://www.econ2.jhu.edu/people/young/KreindlerYoung.pdf Gabriel E. Kreindlera at MIT and H. Peyton Young at the University of Oxford, derive results that are independent of a network’s structure and size. We will get to their results in a moment.
In other recent work, a team of researchers at Facebook and the University of Michigan have also been looking into information diffusion among over 200 million Facebook users and published their findings in Role of Social Networks in Information Diffusion http://www.scribd.com/doc/78445521/Role-of-Social-Networks-in-Information-Diffusion.
Let’s look at some of the conclusions from these two investigations about factors influencing information flow; those that appear to be common sense, others possibly less so.
Both groups note that innovations often spread through social networks as we respond to what our ‘friends’ are doing. However, in looking at how diffusion of information occurs there is a difficulty: did my behavior influence yours or do you and I behave similarly because we have common characteristics or interests (similar peer behavior)?
It would seem to make sense that if I only interact infrequently with others, that is my links are weak and there is not much similarity between myself and these weakly linked individuals, not much information volume is likely to flow through these weak links. On the other hand information flow should be strong between me and those with whom I interact frequently – my strong links or strongly clustered ones. Strong and weak links, or ties, and their role in stabilizing networks were discussed in our June 2013 blog. The Facebook studies have surfaced results demonstrating the function of strong and weak links in diffusion of information.
To quote the Facebook report: “Weak ties are collectively more influential than strong ties. Although the probability of influence is significantly higher for those that interact frequently, most contagion occurs along weak ties, which are more abundant.” Used in this context, contagion means the spread of information or ideas from person to person.
These results extend the classic studies of Mark Granovetter described in our June 2013 blog.
The MIT/Oxford studies discovered that that diffusion is fast whenever the payoff gain from the innovation is sufficiently high; greater payoffs produce greater speed of diffusion. For example, a technology may be adopted more quickly if the benefit payoffs are substantial. This seems intuitive. Less obvious is another finding that the speed at which an innovation increases when there are a “greater number of errors, experimentation, or unobserved payoff shocks in the system” (also called noise or variability). This may explain the remarkable results which are sometimes achieved in some circumstances by people working under unexpected crisis conditions.
Noise may be interpreted as the weeds in a Rainforest system, born out of uncontrolled environments and necessary for growing innovative companies. Shocks can be good for testing system resiliency, as long as the system as a whole does not tip into a chaos state. We will introduce Ashby’s Law of Requisite Variety next month in discussing resilience. Finally, we know it is the connections between the individual innovation ecosystem components which are critical; non- existent or non-functioning links can destroy communication and knowledge flow without adequate redundancy.
How seriously should we take all these findings? How do they relate to the Rainforest model and Type 2 complex adaptive innovation ecosystems? These questions will be discussed in next month’s blog but I wonder if a paradox is emerging. Might designing lean and agile ecosystems in fact discourage adequate experimentation and learning from mistakes, thus defeating their very purpose? Could rapid diffusion in innovation ecosystem networks be increased if we, as the song says, stop and smell the roses along the way?
Next time: An innovation flow paradox? Shocks and innovation ecosystem resiliency.