Contagious Innovation: How Bacteria and Innovation are Connected

By: Scott Orsey

Recently I got lost in the “Wikis” while researching innovation on the web. For me, Wikipedia can be a bottomless reservoir of knowledge and time – tempting me to dig through a never ending chain of links that are at times deeper and at other times broader than the topic at hand.

Shockingly, I found myself questioning some very basic tenets of my understanding of innovation. My journey began innocently enough. I was seeking background information for an upcoming panel I was facilitating on the topic “diffusion of innovation.” I wound up with my mind in knots over a simple question that I already thought I could answer: what qualifies as an innovation? Let me catch you up.

Much of our understanding of diffusion of innovation was popularized by a professor of communications studies named Everett Rogers. He literally wrote the book “Diffusion of Innovation” in 1962 (if that doesn’t make you think twice about how “advanced” we are with our modern social networks, then you should check out the theory’s forefather from the 19th century). Rogers outlines how “diffusion” describes the way in which an innovation is spread among the participants of a social system. Factors influencing diffusion include the innovation itself, time, the social system and the communication channels that are used.

A famous chart (below) that describes the rate of adoption versus time that looks like a statistical bell curve. Diffusion starts slowly with the innovation first spreading from the innovators to a small group of early adopters. Then it accelerates as the early majority and the late majority adopt. The rate then slows as the innovation saturates the network and a smaller group of laggards finally acquiesce to its use.

The diffusion of innovations according to Rogers. With successive groups of consumers adopting the new technology (shown in blue), its market share (yellow) will eventually reach the saturation level. In mathematics, the yellow curve is known as the logistic function. The curve is broken into sections of adopters. (Wikipedia)
The diffusion of innovations according to Rogers. With successive groups of consumers adopting the new technology (shown in blue), its market share (yellow) will eventually reach the saturation level. In mathematics, the yellow curve is known as the logistic function. The curve is broken into sections of adopters. (Wikipedia)

That is what you would learn if you read the first three paragraphs of the Wikipedia entry. I should have left my investigation at that, but what about all the hyperlinks that the authors so artfully cast throughout the article? Certainly there is more color to this topic.

When I clicked on the blue word “innovation,” I was confronted with this statement: Innovation is defined simply as a “new idea, device, or method.” I was struck by the brevity of this definition. There is nothing here that implies that an innovation must promise a net benefit. I checked with Merriam-Webster, and the dictionary agreed. Being called an innovation merely means that something is new. It can either add or detract from the system – the word is agnostic in terms of the value-add.

While I am the first to appreciate a broad definition, I cannot bring to memory an example where I have seen someone use the word “innovation” to mean anything other than something that promises a net positive. And, although it is not hard to recollect innovations that were ultimately detrimental, these examples invariably had a positive result in the eyes of the innovator or those that adopted them. Why else would they have been adopted?

Armed by this new understanding, I now liken innovations to bacteria. There are good bacteria, and there are bad bacteria. Some help us digest our food and are essential to human life while others cause us to become miserably sick and in some cases die. In truth, this oversimplifies bacteria; the value of a bacterium lies somewhere between these two extremes. Take E. coli. While some strains cause severe illness, there are hundreds of types that cause us no problems. Even in our first hour of life, E. coli is present, often crowding out more dangerous organisms. That’s a good thing!

Regardless of good, bad, or somewhere in the middle, all bacteria diffuse through our world based on a set of factors that include the bacteria themselves, time, the ecosystem and the communication channels they use to spread. Sound familiar?

Like the biological laws that control how bacteria propagate, the theories of diffusion apply as equally to all innovations.

This bestows a burden on those among us who have identified critical challenges that can only be met through innovation: When we make the call to innovate, how might we ensure that the innovations that promise to rise to our challenges actually propel us forward? We cannot make such guarantees, but here are some thoughts for how we might best proceed.

First, good policy is essential. We should create systemic incentives and disincentives that nurture positive outcomes and tamp down counterproductive work. These policies should not pick winners and losers but rather demand transparency, feedback and even some healthy competition. Let the ecosystem weed out the failures.

Second, we must insist upon rigorous evaluation that includes a wide range of metrics to ensure we encourage the best possible innovations. Evaluation must incorporate metrics that demonstrate a strong correlation, if not direct causation, to the outcomes we desire and metrics that test how efficiently those results are delivered.

Third, when we do find an innovation, we must imbue it with every advantage necessary to assist its diffusion including enough definition such that others can assess and replicate the work, while at the same time, providing leeway for others to advance the innovation further. In other words, the innovation must be communicated clearly and concisely.

Fourth, we can nurture the network in which the innovation will be diffused – creating safe and efficient communication channels.

Finally, we must not forget the human element. We should focus some attention on identifying strong innovators and building innovators’ capacity knowing that good innovators will notice and respond to the environment. They will take in information including the policy landscape, the data from their measurement and feedback from others working on similar activities to pivot and strengthen their innovations.

These sorts of efforts shift the focus from identifying promising innovations to supporting strong innovators. That may not seem like a big shift, but my recent revelation makes me question our ability to accomplish the former. Promising innovations may be ultimately either good or bad, yet they diffuse according to the same set of rules. I would much rather nurture an ecosystem where strong innovators succeed. As we so often say, it is all about the system.

Scott Orsey is the Director of Operations & Strategy for Connecticut Children’s Office for Community Child Health. Learn more »

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