GUEST POST from Tim Kastelle

The first clocks using mechanical movements kept time by regulating a flow of water. For seven hundred years or so, everyone knew that these clocks were invented in Europe in the 13th and 14th centuries. No one is quite sure where exactly, as several cities at roughly the same time erected central clock towers. However, in the middle of the 20th century, Joseph Needham came across a diary written by Su Song that documented a clock that he built in China at the end of the 11th century. The diary had been lost for hundreds of years, and even in China it was well known that mechanical clocks were a European invention.

Needham continued to dig, and eventually found records of four different clocks that had been built in China about the same time. Each only worked for a short period of time. All early clocks required a huge amount of maintenance, and it appears as though the makers of these clocks all either died or moved on after they were built. No one else had the knowledge required to keep them running.

So can we say that mechanical clocks are a Chinese innovation?

In one sense, who cares? But in another sense, this illustrates an important point.

Innovation isn’t just about having ideas. It’s not even simply about executing ideas. To innovate, you have to do both of these things. But you also have to get the idea to spread.

Su wrote a book describing the workings of his clock, but the plans were insufficient to build or maintain a working clock. His son believed that he left out crucial details so that his ideas wouldn’t be stolen.

Su had a very successful career and was widely acclaimed in his time, so from a personal standpoint, this may have been a good strategy. On the other hand, he didn’t get full credit for his ideas for 900 years, mainly because he tried to keep them from spreading.

Finding the right balance between rewarding inventors for successfully executing ideas and getting these ideas to diffuse to improve overall economic outcomes is a difficult problem – one that still bedevils discussion of intellectual property today.

To me though, if your ideas don’t spread, then you aren’t innovating.

Editor’s Note: If you enjoyed this article you might also enjoy – Innovation is All About Value

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GUEST POST from Tim Kastelle

In an excellent article on the impact of twitter on the Arab Spring revolutions, Blake Hounshell makes an important point about twitter itself:

“But five years since its founding, Twitter has hit a critical mass of activists and casual observers on the ground, journalists in the office and in the field, and analysts behind their desks. Twitter today is always buzzing with news, ideas, rumors, speculation, and juicy gossip. (It was Twitter itself that understood this shift from vanity tool to news platform earlier than anyone else, when in November 2009 it changed its prompt from “What are you doing?” to “What’s happening?” One of the fastest ways to tell whether someone’s not worth following is if they’re still answering that first question.)”

This is true in politics, and it is true in other fields as well (on a related point, you can also tell which critiques of twitter aren’t worth reading – they are the ones that criticize the stream of tweets answering the “what are you doing question”). It is certainly the case that there is a thriving discussion of innovation on twitter now too.

If you think about it, that change in question actually represents a business model innovation on the part of twitter. Once the question being answered changes, so does the value being created.

And once the value created changes, you have a new business model.

If changing one small question can change the business model for twitter, what might change the business model for your organization?

It’s a question worth thinking about…

Editor’s Note: If you enjoyed this article you might also enjoy – Innovation is All About Value

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GUEST POST from Tim Kastelle

Since I put the Innovation Matrix together last year, we’ve been experimenting with it to see if it makes sense. I’ve used it in a couple of classes, and John and I have discussed it with a number of people that are actually responsible for innovation within their organizations. We’ve learned that the basic principle seems to resonate pretty strongly with people. We’ve also learned that the original configuration could use a bit of work (conversations with Mark Dodgson and Kate Morrison also helped in this regard).

Here is the new version of the Innovation Matrix:

This is a bit of a distillation of observations over time. I thought of it because I think that a lot of people that are trying to improve innovation within an organization think that they can go from the bottom left (No Innovation Capability) to the top right (World Class Innovator) in one jump, simply by introducing some sort of innovation program. I think that this is impossible – that you actually have to make the trip in a number of steps, and that there are many different paths that you can take.

The table has two increasing dimensions. Across the horizontal axis there is increasing commitment to innovation. This can include things like talking about how innovation is important, including it as a core value, putting in systems to support and improve innovation, and explicitly earmarking time, money and other resources to innovation. This is measuring innovation inputs.

Going up the vertical axis shows an increase in innovation competence – mainly the ability to generate and successfully execute new ideas. This measures innovation outputs.

Here is a brief description of each box:

1. No Innovation Capability: these firms don’t innovate. This isn’t necessarily bad – there’s no value judgment being made. They can be successful if they have strong positions in stable industries, or they can be average performers or struggling in other circumstances. I think we can probably all think of examples for this category.
2. Thinking About Innovation: firms in this category are starting to talk about the importance of innovation. They might add it to their list of core values, or have a CEO that is starting to talk it up. Regardless of this increase in awareness and commitment, they are still not very good at it. This is often the first step that organizations take in trying to improve innovation.
3. All Talk, No Action: is a self-explanatory category. They are talking the talk, with official innovation programs, commitment of time and resources, etc. But they’re still lousy at actually executing ideas. They may have an excessive focus on ideation, a bad selection process, or just not be very good at executing.
4. Accidental Innovators: These would be firms that innovate under some other name – so they might be really good at process innovations through a continuous improvement or lean program. They are able to execute ideas reasonably well, but they don’t have any structure in place to support it, nor do they think that they’re innovative. They innovate through stealth.
5. Average at Everything: these firms have some structure in place to support innovation, and they are getting better at doing it. Several firms that I work with have gotten to this level after moving first to Talking About Innovation.
6. Potential Stars: there are two paths to get to this point. Along on, these organisations are good at innovating, and they are putting more resources into getting better at it. They have top-level commitment to innovation, good processes in place, and dedicated resources for innovation. They are reasonably good at executing new ideas and have the potential to become extremely good. The other path is to be very good at executing new ideas, but with less structure. These organisations aren’t sinking huge amounts of resources into the process, but they are consciously trying to innovate. Because they lack full commitment to innovation, it might not become systematized, but they also have the potential to be extremely good.
7. Unicorns: the problem with making a matrix is that you have to put something into every box, even if its mythical.
8. World Class Innovators: Another self-explanatory category. In these firms innovation is deeply embedded in the culture – everything is oriented around innovation. Think Google, Apple, 3M, Procter & Gamble etc.

How to use this:

Here are some things that I think we can do with this:

• Use it to make a better picture of how firms improve at innovation: Many of the people in my classes are in firms towards the bottom left, and many of the examples that we use to illustrate points are from firms in the top right (Google, P&G, 3M, etc.). This might be too big a conceptual jump. Not every firm can get to the top right, and neither should every firm aim to. It is more productive to think of this as an incremental process of steps, rather than one big jump.
• Track the evolution of firms: we can learn about how to best manage innovation by tracking how firms progress through this matrix. For example, one firm I work with started with No Innovation Capability, then started talking about it and moved to Thinking About Innovation, and now that they are getting better at it they are Average at Everything.
• Realize that there are multiple targets to shoot at: Like I said, not every organization can be Google. Thinking about innovation with this matrix, you can see that all of the categories in the top row are excellent at innovation. However, the farther you go to the right, the more resources you have to commit to build and maintain this level of excellence. There are many situations where you can try to be an excellent innovator with a more bottom-up, less resource-intensive system in place.
• Think About the Best Path to Follow: Almost everyone starts by increasing commitment. The danger with this is that you can end up in the All Talk, No Action category. I wonder if we should be figuring out ways to improve capability rather than commitment. Or is this even possible? It’s an interesting question, and you can certainly make a strong argument in favor of increasing capability before you increase how much you talk about innovating.

The main point with The Innovation Matrix is that improving your innovation performance is a journey of many steps, not simply one big leap. The matrix is designed to help us think about this more accurately, and to be more successful at improving our innovation performance.

If you have any thoughts on this, we’d love to hear them.

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GUEST POST from Tim Kastelle

Here is a must-watch video from Eli Pariser discussing some of the themes from his new book The Filter Bubble (reviewed well here by Cory Doctorow). It’s only nine minutes, and it is well worth your time:

Pariser’s main point is that the primary filters on the internet these days are algorithmic, and that these filters have a strong tendency to only expose you to viewpoints that reinforce whatever you currently think.

This is very important for how we use the internet, but it also has huge implications for innovation as well. I think that many of us work inside of an innovation filter bubble, and that this makes it much harder for us to innovate.

What is an innovation filter bubble? It is all of the habits and routines that prevent us from being exposed to novel ideas and new points of view. Some of these include:

• The internet filters that Pariser discusses: much of our information comes from the web these days, and as he shows in the talk, this can lead to only running across viewpoints that reinforce our own.
• Who we spend time with: do you always eat lunch with the same people? Or alone? Spending time with people that you know well is great (and we often don’t do enough of this), but at the same time, we usually spend time with these people because they think a lot like us.
• Silos within our organizations: is where you work organized by specialty? Most organizations are. This has benefits in that it makes it easier to find the information that is most relevant to our jobs more easily. Still, this is another form of filtering that reinforces current views.

The end result of the filtering that occurs through these routines is that the information that we are exposed to can become too restricted. As Pariser argues, these filters make it easy to find information that is relevant to the task at hand – and that is what makes them useful. But does access to information that is highly relevant to the task at hand help innovation? Probably not.

Innovation is based on connecting ideas in novel and interesting ways. To do this, we need to run across information that is more than just relevant. We also need information that is important, uncomfortable, challenging, and that reflects other points of view.

We have to make a conscious effort to break out of our innovation filter bubble.

How can we do this? Here are some ideas:

• Actively seek out new and different viewpoints: Ethan Zuckerman has some great ideas about how to do this on the internet. But also do it in your day to day activities. Once a week have lunch or a coffee with someone with a completely different background, area of expertise, or view of life. Go out and find those challenging ideas somewhere.
• Use filters based on expertise instead of algorithms: as I’ve discussed before, there are at least five forms of filtering. The algorithmic filters are more efficient, but they fall prey to the problems outlined by Pariser. Make better use of expertise-based filters. You can do this by accessing people with expertise in different areas, and also by building broad networks and activating them to help you generate new ideas. Algorithms are great, but you still need some people-based filtering as well.
• Encourage enhanced serendipity: this is an idea from Ross Dawson, and it’s also discussed in The Power of Pull. It involves building your networks (both online and personal) to maximize your exposure to new ideas and novel viewpoints. One of my personal rules in this area is that on twitter I always follow people that follow me if they come from outside of Australia, North America or Europe. And I follow nearly all of the people that run into from Europe too. This is one way to run across new viewpoints.

In order to innovate we have to generate new connections between ideas. We can’t do this if all of our routines only expose us to viewpoints that are very similar to our own.

To innovate more effectively, we have to break out of the innovation filter bubble.

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GUEST POST from Tim Kastelle

Here is an outstanding talk from Nilofer Merchant (and an interesting post about the background to it) – it is well worth your time:

Here are some of the key points that jump out at me in this talk:

1. New ideas should change us

One of her first points is that even though people frequently talk about being in favour of innovation, in practice many of these same people actually block it. Why? Because new ideas lead to change, and we often don’t like to change. Do we secretly resist innovation? This is a question that merits serious thought.

I was fortunate enough to talk with Nilofer face-to-face last week. We discussed a variety of things, including management books. She strongly made the point that if we’re going to write things that genuinely have an impact on people, then we should be explicit about how we want them to behave differently after reading these offerings. New ideas should change us, and we should be conscious of this whenever we create, share, or encounter new ideas. And we should be open to this in all of these situations as well.

2. The key question that you encounter about innovation is: Am I Allowed Here?

Are organizations serious about wanting ideas from people? In many cases, again, there is a gap between the rhetoric and the practice. This relates very strongly with the first point. Seeing the world differently drives innovation. But in many places, seeing the world differently is not welcome – and this contributes to the feeling that you’re not allowed to innovate. Giving yourself permission is often the biggest step you take in becoming innovative. If you’re a manager, supporting people who see the world differently is critical.

3. We often filter out ideas that we haven’t encountered before

Normal is good, and fitting in is good. At least, that’s what our brain tells us. One consequence of thinking this way is that if we run across an idea that is novel (at least to us), we block it out. This is a problem. A lot of our management practice is designed to reduce variation – to make things more efficient, to eliminate problems, to turn as much of our work as possible into a routine, or an algorithm. But innovation requires increasing variation.

4. Ideas grow when they are shared

The last big idea in the talk is that ideas get better when they’re built upon. In order for this to happen, we have to let them go. This again is counter-intuitive. Our great ideas are ours, right? Nope.

These are just three of the ways that we block innovation, even if we think we’re in favor of it.

How I’d Like You to Change Your Behavior After Reading This

Here are some things you can do to address these issues:

• Give yourself permission to innovate, even if you think you’re not allowed to.
• If you’re a manager, find ways to support people that increase the diversity of ideas in your organisation. Don’t just focus on improving efficiency.
• Make a conscious effort to run across new ideas. Subscribe to blogs that you don’t agree with, or that make you mad, and engage with these ideas.
• Let your next idea go. Share it, and see how it grows.

Ideas have power, and great ideas should change how we act. And remember, that’s how we act, collectively.

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GUEST POST from Tim Kastelle

I’m still working my way through Being Wrong by Kathryn Schulz. It’s a very interesting book, and nicely written. I’ll tell you more about it when I’m done. In the meantime, I’d like to share a fantastic quote from Schulz, which is in her review of Join the Club: How Peer Pressure Can Transform the World by Tina Rosenberg.

Schulz uses the review to critique Big Idea Books, and her argument applies to a majority of business books too. Here is one of the key issues that she raises (I added the emphasis):

“Solutions are not one size fits all—they are, in fact, maddeningly bespoke. That’s because neither problems nor people are fungible. Rosenberg is a brilliant reporter, but here she exhibits the characteristic blind spot of the blind-spot-obsessed Big Idea books. Like totalizing religious or political stories, these books promise to hand over the master key that will unlock our lives. Or, more precisely, they tell us that we have had the key all along, but that we have been holding it upside down.

To which I say: key-shmey. There is no rule, process, peer group, leader, or best seller that can absolve us of the responsibility of thinking our way through life on our own two feet. What irks me most about this infinite parade of gigundo solutions isn’t their glibness or even the borderline theology (of some) and borderline Babbitry (of others) involved in promising audiences easy, happy, profitable ideas. Nope. What irks me is that when you rigidly apply grand theories to everybody, sooner or later everybody feels like nobody, whether you’re in Communist Belgrade or the local DMV. There is a reason we call such systems soul-crushing: They ignore or annihilate individual difference and inner life.”

This is the problem we have in dealing with complex systems. There are no one-size-fits-all solutions. If anyone tells you that there is, beware.

Furthermore, there in complex systems, there are nearly always unintended consequences to action. These two things together make it very difficult to plan out actions in advance.

This is why I like Schulz’ advice to think our way through life on our own two feet – it’s the only way to go. A big part of this is experimenting. One of the themes of Being Wrong is that wrongness is a natural state. We can learn from error, in fact, we must learn from error as this is the only way to improve.

I ran across a great website today called Admitting Failure. They are trying to use the site to help international development efforts learn from things that don’t work in other contexts. Here is their reasoning:

“The development community is failing… to learn from failure. Instead of recognizing these experiences as learning opportunities, we hide them away out of fear and embarrassment.

Get involved – share failures, build knowledge and encourage others to do the same – so we all benefit, today.”

We have to take failure seriously precisely because there are no one-size-fits-all solutions to problems. Contexts are always slightly different, so not all lessons will transfer from one arena to another. Nevertheless, if we embrace the messiness of the world, we’ll see that we don’t need grand theories. We just need to try things, and learn from what works and what doesn’t.

I’m pretty sure that this approach will work for, well, nearly everyone.

Editor’s Note: You might also want to check out – Don’t Fail Fast, Learn Fast – by Braden Kelley

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GUEST POST from Tim Kastelle

What happens when the people that are supposed to be creative and innovative in your organization are neither?

I ran across an interesting quote from one of the people interviewed in the new book Herding Cats: Being Advice to Aspiring Academic and Research Leaders by Geoff Garrett and Graeme Davies:

“The biggest thing that I have found through the years is that many people in research are actually bureaucrats. I would have expected them all to be interested in the future, wanting to change the world, brimming over with enthusiasm to get on with the job and deliver useful results. This took me a long time to realize and I think I would have been much more effective if I had understood that there are a lot of people who really do not want to see much in the way of change, and that includes a lot of people in R&D.”

How do you deal with this?

It’s actually a really tough question. This is why effective change management is a critical part of innovation management. It’s also why in research studies, innovation success correlates with so many other good outcomes – higher profits, better firm survival rates, more engaged employees and so on.

The reason for this is that all of these things are driven by good management.

Garrett and Davies conclude their excellent book with a quote from another interview:

I reckon there are five key dimensions to leadership in a research and development or academic environment…

Garrett and Davies undersell their book when they say it is only about managing academic-style research. Those are all the things you need to do to manage effectively anywhere.

Good innovation managers are simply good managers.

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GUEST POST from Tim Kastelle

“The future’s already here, it’s just not evenly distributed, and it doesn’t look like we expect it to”

When scientists first started talking about Artificial Intelligence in the 1950s and 1960s, a lot of the discussion centered around how to best create AI that would think like people do. This view of AI has dominated our imagination ever since.

Think of HAL in 2001: A Space Odyssey, Deep Blue and other chess-playing computers, Skynet and the rise of the robots in the Terminator movies, and all the current discussion about the singularity. All of these are pictures of Artificial Intelligence doing what human intelligence does – just doing it faster, or better.

Over 50 years later, except for the chess-playing computers, we’re still waiting for this form of AI to take off.

Because we’re not seeing this type of AI, AI has been a failure, right?

Well, not really. There’s actually tons of different types of AI in practical use right now. An article in Wired UK outlines many of the current uses of AI, and it’s an impressive list: warehouse stocking robots, the Google search engine, algorithmic financial trading, credit card fraud detection, and self-driving cars, just to name a few.

Even though we don’t have Skynet yet, we’re still interacting with AI throughout our day, often without even realizing it.

The Long S-Curve of Innovation Diffusion

The story of AI illustrates a common innovation myth – that ideas spread quickly.

Around the time that computer scientists first started thinking seriously about AI, Everett Rogers was showing that most innovations follow an S-Curve as they diffuse through the economy. The Innovation Diffusion curve looks something like this:

Ideas are first picked up by people that Rogers referred to as Innovators, then Early Adopters. This is happening over the time period that I’ve labeled “X” in the diagram. Eventually, the new idea either dies off, or it takes off. Once the tipping point occurs, the idea then spreads rapidly throughout the market, until a saturation point is reached.

When people are innovating, or thinking about innovations, one huge mistake that they commonly make is to underestimate how long the idea will stay in the X range.

Here’s an example: email. The first email was sent around 1971, just a few years after the internet started. For a long, long time, email was only used by researchers, the military, and academics. It wasn’t until the late 1980s that universities started to make email available to students (that’s when I got my first email address).

By the early 1990s, the World Wide Web was built on top of the internet, and then email started to spread a bit more quickly. By 1993 or so, it was becoming relatively common among early adopters outside of academia. But even then, the question that you asked if you wanted to send someone an email was “do you have email?”. And in just a few years, suddenly everyone had email. By 1996 or so, the question was a simple “what’s your email address?” That was the tipping point. In another five years, it was “which email address should I use for you?, because everyone had a personal email address, one for work, and often a few more. Email had reached saturation.

If you thought that email started with the WWW in the early 1990s, X was only four or five years. But if you think of the whole story, X actually lasted about 25 years.

This is pretty common for new ideas. Xerography was patented in 1936, but the first Xerox machine didn’t hit the market until 1949. The technology didn’t take off for another seven years or so. X was about 20 years for this idea.

Even in the fast-moving internet age, X is often a lot longer than we expect it to be. Jeff Bezos had the idea for Amazon in early 1993. It took about two years to get the site up and running. In 2000, people were still calling it amazon.bomb, among other things. It didn’t take off until about 2002. X was about nine years for Amazon – and that’s one of the shortest time periods that I’m aware of.

The Reasons for the Long X

The unusually long period of X for new ideas is due to several things. Most of them have to do with uncertainty – we don’t actually know what the new idea is for yet. This happens in a few ways:

1. We have to figure out how to make the new idea work: the best use of a new idea is often not obvious. In fact, because we tend to think in analogies, we often get this wrong at the start. In the AI example, the technology didn’t start to really take off until people stopped asking “how can we make computers that think like people?” and they started asking “we have computers that do some things that people can’t do well – how can we make use of this?” Going through this process takes time, and it requires a lot of experimentation. Many of these experiments will fail – but one of the critical things that we have to figure out is under which circumstances the new ideas work, and under which ones they don’t work so well.
2. We have to fight against the hype cycle: the long X is a direct contributor to the hype cycle. The Early Adopters get excited about the new idea, and it gets oversold. Then the people that are threatened by the new idea fight back. When it doesn’t spread as quickly as expected, the excitement wanes and cynicism sets in. Eventually, though, through experimentation we figure out what the best use of the new idea will be, and at that point it is finally poised to take off.

Greg Satell explains this process very well in his post Why Trends Are For Suckers. This is what the hype cycle looks like – you can see the long X at work in it!

3. Most importantly, we have to figure out how to create value for people with the new idea. This is the part that the Early Adopters tend to ignore – they usually like new things simply because they’re new. For everyone, the new idea needs to solve a problem. Avinash Kaushik explains the issue perfectly in 11 Digital Marketing Crimes Against Humanity:

“When I look at winners and I separate them from the losers there is one thing that stands out. Winners have a sophisticated understanding of the holistic success of their digital existence. It comes from undertaking two simple steps:

I tend to talk about the need to create value more broadly, not simply economic value – but in either case, without clear value creation, the new idea will never take off. Again, it takes some time to figure out where how to create this value, and often the value being created isn’t the value that was originally expected.

The Myth of Quick Adoption

Our tendency to dramatically underestimate the true value of X in innovation diffusion causes all kinds of problems. If we’re early adopters, we expect new ideas to spread quickly. And yet, they don’t. If we’re threatened by new ideas, the long X can give us a false sense of security. As it becomes clear that early predictions are exaggerated, we become complacent. But eventually, once all the experimentation has been done, and people have figured out what the new ideas are really good for, and how to create value with them, the threat begins to bite.

I’m not sure of any way to move through the innovation diffusion curve more quickly. It is by its very nature slow, experimental, unpredictable, exciting, revolutionary and wasteful. It is part of what makes innovation both exhilarating but also frustrating.

Being aware of the myth of quick adoption is the first step towards figuring out how to deal with it.

Editor’s Note: If you enjoyed this post, you’ll probably enjoy these two:

1. Premature Innovation – by Braden Kelley
2. Can You Have Slow Innovation? – by Jeffrey Phillips

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GUEST POST from Tim Kastelle

One question that comes up all the time is: “how can I innovate when my manager won’t let me?”

The answer is one people usually don’t want to hear: “Innovate anyway.” But it’s true.

Here’s a clip from the Management Innovation Exchange of Jeffrey Pfeffer talking about how to create your own job – it’s short and well worth watching:

Jeffrey Pfeffer’s recommendations are based on research, but they are awfully similar to those of Seth Godin, which are based on experience:

The number of people you need to ask for permission keeps going down:

When in doubt, see #1.

My recommendations are based on a combination of experience and research.

When people ask me how to innovate when they don’t have permission, my answer is “how much can you get away with?” If you can sign of on projects worth $100 without your manager’s approval, then you can test out any new idea that costs less than $100.

When I started one of my management jobs a while ago, I read a few of books by Tom Peters and a few other people, and I wrote down 48 ideas that I could try with my new team. None of them cost anything more than time and energy to execute. Over the course of two years, we tested all but two of those ideas.

Not every one worked, but a lot of them did. Our team was responsible for recruiting students for a tertiary institute, and as we tried out those ideas together, our team got better and better at matching up people with the courses that interested them.

In our first year of experimenting, we increased enrolments by over 10%, which had about a $2 million impact on the bottom line. All from trying out ideas that cost nothing. That’s all that I had to work with in that job. After that year, I had a bit more slack.

The point here is that I didn’t ask permission. I was given the job to manage, and that’s what I did. I tried out as many new ideas as I could within the authority that I had. And I gave my team as much slack as I could so that they could try out as many ideas as they could. It was a collective effort.

That’s how you innovate when you don’t have permission.

So, how much can you get away with?

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GUEST POST from Tim Kastelle

When I went to visit Neil Kay last year, we talked a bit about novelty. He said that the way that we frame PhD research is all wrong – that it is a mistake when we tell people that they need to make a novel contribution to knowledge. Instead, we agreed that people should be looking to advance knowledge, which is a bit different than making a novel contribution.

For example, you could write an economics PhD connecting the theories of Alfred Marshall with those of Justin Bieber. That would certainly be novel (at least, I hope there aren’t too many people trying that), but would it materially advance knowledge? Probably not.

Here’s another example – what do you think this is? I’ll give you a hint – it’s a symbol.

It was on a door at the conference venue that I was in at the end of last week. Throughout the two days that we were there, we had a stream of people walk up, look at the symbol, pause, continue walking down the hall and then compare the symbol with those on two more doors. The women then shrugged and walked into the last door, while the men returned to this one.

Somehow, that’s the symbol for “Men.”

This is an example of bad innovation.

It’s a novel way to indicate which room the men should use, but it’s not a good way to do so.

There are a few innovation lessons contained in the cryptic symbol:

1. Novel ideas are not automatically innovative. Just because an idea is completely new, it doesn’t mean that it’s good. Like the Marshall plus Bieber PhD, novelty doesn’t tell us anything about the quality of the idea. You need more than novelty – in addition:
2. Innovations need to create value. The weird door sign creates negative value – it confuses people, it may lead to potentially embarrassing mix-ups, and it wastes time. All of these are bad outcomes. To innovate, we need to execute new ideas to create value. To create value, we must remember that:
3. Our innovations have to fit within the existing economic network. As Jeffrey Phillips pointed out in our discussion of flying cars, that is a technology that will only work when there are a large number of related technical and social innovations in place that are required to support flying cars.

The problem with the “Men” symbol is that it doesn’t connect to any normally accepted method for communicating that this is the room into which men should go. This lack of specificity might be find in the signage for a trendy new nightclub, where part of the mystique comes from being difficult to find. I don’t know about you, but I prefer toilets without that mystique…

When you’re thinking up ideas, it is critical to think about how executing these ideas might create value. If they don’t create clear value for people, then it might be smart to spend your limited resources executing a different idea that does.

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