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Archive for April, 2010

What Motivates Knowledge Brokers?

I wrote a post last week about the importance of connecting different groups as a precursor for innovation and the special role that some people have as bridges between disparate knowledge communities where ‘structural holes’ exist within the network between the communities. After writing this post I went looking for more research on the psychological characteristics of these “knowledge brokers” who bridge these groups and are a major factor in both generating new ideas and also getting ideas to spread.

What I found was an excellent review article by Garry Robbins from the network group at the University of Melbourne. The leaders of this research group, Garry Robbins and Pip Pattison are both outstanding research academics and genuinely nice people who are generous with their expertise and experience in network analysis. They run a network analysis course each year in July, which I highly recommend to anyone wanting to learn about network theory and analysis.

While Garry says that we need to do a lot more to understand the connections between networks and the personalities of people within networks, he says that we already have good evidence for the traits and motivations of knowledge brokers.

Last week I wrote about ‘self-monitoring’ or the ability to be socially adaptable as a characteristic of knowledge brokers. There are several studies that support this, but knowledge broking isn’t an easy job. While knowledge brokers are entrepreneurs in the sense that they can see valuable opportunities because of their position on the bridge, occupying the bridge can also be very stressful.

Knowledge brokers aren’t insiders, they are on the edge of two or more communities. It’s a precarious position to be in and knowledge brokers need to work hard to stay involved with multiple groups with different cultures and expertise. If you have ever asked yourself why everyone isn’t a knowledge broker, then this is probably the answer. It’s both intellectually demanding and stressful.

Knowledge broking can have significant personal payoffs but also high rates of stress and failure. It’s a high-risk/high-return personal strategy. If we think about bridging this way then just being multidisciplinary and socially adaptable isn’t enough to be a broker, other psychological traits must be involved too. For those of you who believe that some people are natural brokers, there is definitely evidence to support the idea that fundamental psychological traits can support the role of a broker, as Garry explains:

Burt found that respondents with networks rich in structural holes were inclined to be independent outsiders in search of change and authority; whereas those with few structural holes tended to seek conformity, obedience, security and stability.

In other words, knowledge brokers aren’t just social chameleons, they can also be disruptive change agents and a real pain for those interested in keeping everything just as it is. They are risk-takers and iconoclasts – two very important characteristics for entrepreneurs in general. However, Garry elaborates on these characteristics by referring to some of his own research:

….people who saw themselves as vulnerable to external forces tended to inhabit closed
networks of weak connections; whereas people who sought to keep strong tie
partners apart, and so to bridge structural holes, tended to be individualists, to
believe that they controlled events, and to have higher levels of neuroticism. Finally,
people with strong network closure and “weak” structural holes tended to categorize
themselves and others in terms of group memberships, akin to the social identity
effects discussed above; and they were more extroverted and less individualistic.

So knowledge brokers tend to highly individualistic with strong self-belief. Exactly the kind of person that could be brought into the manager’s office and given counseling about not being a team player. In the psychological sense, neuroticism is the tendency to experience negative emotional states such as anxiety and depression. Not exactly what you would call an ideal work colleague. On the other hand, the team player is unlikely to make a good knowledge broker and will instead create lots of links to close colleagues.

So here’s the paradox. Brokers are essential for innovation but harder to manage and work with. We don’t need everyone in the organization to be a broker but we do need some of these idiosyncratic people to make the networks form across holes and create new opportunities for recombining knowledge.

But if these people are individualistic and need change, then how do we align their goals with the organization. I think this is a really important question for sustaining innovation and it’s come up in discussions with several managers who we are doing research projects with. What do you think?

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The Importance of Business Model Innovation

I frequently have people say something like this to me: “But my organisation can’t be innovative – we’re a service company” (or a government agency, or a university department, and so on). This is why the definition of innovation is so important. A lot of people think that their organisation isn’t innovative because they’re not making iPads, or some other sexy new product. It’s important to remember that there are many types of innovation. Schumpeter listed five: new products and services; new methods of production; opening new markets; finding a new source of supply; changing the structure of an industry.

The last one is important, because you can reframe it as business model innovation – one of the most under-rated forms of innovation. And one that is available to everyone. It’s a topic that we’ve discussed here a lot – this post has a good summary of the idea, along with some resources to investigate. This figure from the Boston Consulting Group report Business Model Innovation: When the Game Gets Tough, Change the Game (here’s the pdf of the report) shows the benefits from this form of innovation:

Quite simply, firms that undertake business model innovation make more money. And if your organisation isn’t designed around maximising profits, you still want to think about business model innovation, because it will likely lead to greater levels of success, however you’re measuring it.

Here is how Hugh MacLeod puts it in Ignore Everybody (see number 11 here):

Don’t try to stand out from the crowd; avoid crowds altogether.

Your plan for getting your work out there has to be as original as the actual work, perhaps even more so. The work has to create a totally new market. There’s no point trying to do the same thing as 250,000 other young hopefuls, waiting for a miracle. All existing business models are wrong. Find a new one.

Or, as Chris Brogan and Julien Smith say in Trust Agents, change the game.

I really do think that this is a huge opportunity. Too few people think seriously about business model innovation. But it is a critical form of innovation, and a great way to set your organisation apart, whatever you’re doing. And one more great thing about it is that you can work on business model innovation using experiments – you don’t have to change everything all at once.

So that’s my assignment for today – give some serious thought to business model innovation.

Note: I’ll be talking about this idea in a series of talks around Australia over the next couple of weeks – if you’re interested in hearing them, the links below take you to pdf versions of the information sheets for each of them:

Australian Industry Group Breakfast Forums

13 April – Brisbane

14 April – Sydney

20 April – Melbourne

21 April – Adelaide

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Was I Better Today than Yesterday?

Stefan Lindegaard wrote two interesting posts this morning. The first talked about how a number of people in his open innovation workshop last week were very frustrated with the innovation process within their company. The second followed up a comment that I made on the first one – where he said that he sometimes advises people that are unable to innovate within their firm to find another job.

Both posts resonated with me. The first because I also run into a lot of frustrated people in my classes and workshops. A lot of people feel that they want to be more innovative but that the environment within their organisation prevents this. The first thing that I tell people in this situation is that they should start by trying to innovate as much as they can within the scope of authority and budget that they control. This is similar to Stefan’s suggestion that people manage up in an effort to influence their managers.

This often works. I’ve talked about this problem before – it actually reflects issues both on the part of the frustrated people and on the part of their managers. In many cases, people are waiting for someone to give them permission to innovate – they’re too scared to start on their own. This is a problem – and the first step to being more innovative inside of non-innovative firms is to try stuff.

Most of the time, this will work. Set things up as experiments, and find out what works. Once you learn this, then tell your manager about it. In the vast majority of cases, they’ll be happy to hear about new ideas that work.

But sometimes, they won’t. I’ve been in jobs where I haven’t been able to implement new ideas. It’s incredibly frustrating. It’s disempowering too. That’s why Stefan’s second post also struck home. I learned a lot about frustration from a women that worked for me in New Zealand. She was incredibly smart, and talented. She was innovative and consistently came up with great ideas. So I was incredibly disappointed when she told me that she was leaving for another job.

I did what I could to keep her. But in the end, her problem wasn’t with her salary, or with me. It was with the rest of the team, which was always blocking her ideas. We had been working on that problem together, but couldn’t find a way around it. That’s why she decided to go. I learned a lot about motivation from that experience.

I think that the main points in Dan Pink’s Drive are fundamentally correct. That people want autonomy, mastery and purpose in their jobs – and that if you give them this, they will do well. Here are two questions that get at these points:

Two questions that can change your life from Daniel Pink on Vimeo.

The second one in particular is critical – “was I better today than yesterday?” Are you building skills? Are you improving? Are you happier? If you don’t like the answers to these questions, than maybe you do need to think about looking for another job.

(the graphic is another one from the awesome Hugh MacLeod)

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Don’t Fear the Social Media Bubble

In a blog post last week, Umair Haque put forward the idea that we’re currently experiencing a social media bubble, and he also explained why he thinks this is a bad thing:

Call it relationship inflation. Nominally, you have a lot more relationships — but in reality, few, if any, are actually valuable. Just as currency inflation debases money, so social inflation debases relationships. The very word “relationship” is being cheapened. It used to mean someone you could count on. Today, it means someone you can swap bits with.

He then says that this relationship inflation leads to inefficient allocation of attention, investment in low-quality content because that is what’s most popular, and a weakening of the internet as a force for good.

That’s pretty alarming.

As much as admire Haque’s work, I think there are a couple of flaws in his argument. I’m 100% with him in his desire for the creation and sustenance of thick, meaningful relationships and for the creation of awesome things that make our lives genuinely better. But I’m not convinced that social media is preventing progress towards these goals.

There are two parts of his argument that I’m not so sure about. The first is the contention that all ties within social media are weak ties. Now, social tools like facebook and twitter certainly do conflate genuine friends with acquaintances. But does it follow that because the tools do, that we do as well? I don’t think so. In a highly recommended post, Stowe Boyd responds:

He suggests that because we are creating and expending time on a growing number of weak ties then we are diminishing our involvement with intimates. I think this is debatable. While the time I spend writing this blog or twittering could in principle be applied to talking to loved ones directly, in reality many of my closest friends read this blog and my twitter stream to remain in contact with me, at no extra cost (here I am adopting Umair’s economics jargon). This in no way weakens my strongest ties, and certainly is the wellspring of thousands of weak ties.

And, as Erica Glasier points out, there are lots of valuable things that we can do with weak ties:

Thin relationships, or “weak connections” make these upper-Maslow interactions possible. You don’t need a high level of investment in someone to trade ideas. Their input is valuable precisely because they come from a different perspective, and aren’t bound by politeness or concern for your ego. I’ve mentioned the findings that novel input from new friends sparks more innovative, creative solutions. The more the merrier.

Weak ties can actually be pretty useful. There are tons of things we can do with our weak ties:

  • We can get a job. The first use of the idea of strong and weak ties came through Mark Granovetter’s paper The Strength of Weak Ties (summarised here). His study showed that people are far more likely to find a job through weak ties than they are through strong ties, because people that we are less closely attached to are more likely to know things that we don’t already know ourselves.
  • Like Erica says, we can use our weak ties to swap ideas, and to get ideas to spread. For examples of this, just look at the ways that Umair, Stowe and Erica use twitter, just for starters.
  • Large numbers of weak ties can be incredibly useful for finding out specific information quickly. Paul Gillin has a good example in the comments to his recent post on search:

    OK, I want to take a run after dark and avoid bad areas of town. In that case, recommendations from my Twitter followers would be my most useful source of advice. In fact, I probably couldn’t even find that kind of information on a search engine.

So, I agree with Boyd that having social tools equate strong and weak ties does not necessarily devalue weak ties. Furthermore, there are plenty of uses for weak ties, many of which actually do create thick value. I think that the key to this issue is ensuring that we keep the distinction between friends and acquaintances clear in our minds.

The second thing I’m not sure I agree with is the idea that bubbles are bad. They’re certainly inefficient, which is bad from a neo-classical economic view. But in many cases bubbles create an incubator for experimentation, which is often the only way we can discover the value in novel technologies. Here’s the way Jason Potts put it (a link to the pdf):

Bubbles are good because they promote variety and experimentation in an economic system. The bubble process facilitates the sort of structural change that economic growth always, in some form, requires. Economic systems, when they are open and therefore competitive, need bubbles to grow. So they require institutional systems and policy frameworks capable of (perhaps vigorous) interaction with bubbles.

A bubble works by concentrating financial liquidity and entrepreneurial attention onto an asset class and its forward prospects. Inside a bubble, the cost of experimentation, and therefore variety generation, is lowered and, by incentive effect, the process of structural change is accelerated. Access to finance is easy inside a bubble. Similarly, the cost of failure is reduced, and the uptake of novelty is high. The economy becomes energised around the bubble, as do the entrepreneurial spirits of agents who happen upon it. Learning is accelerated within a bubble, and radically new business ideas can get a start, as can radically new products. Real bubbles theory, then, is the idea that from a bubble environment there flows the incipient variety upon which the evolutionary economic process of enterprise and wealth creation feeds. Bubbles breed variety, and variety feeds economic evolution, and therefore growth.

Jason is talking about bubbles more like the dot.com boom than policy-driven bubbles like those in housing. The issue is that when we have something new, like the internet, or social media, we have to figure out what the best use of it will be. In the dot.com bubble, it is only obvious in retrospect that pets.com was a dumb idea (those huge shipping charges!) and amazon.com was brilliant. At the time, this was still an open question – and plenty of people thought that amazon was pretty stupid when it launched (Who buys books without seeing them first? How will we know what we’re looking for without book store clerks? etc.). It’s only through experimentation that we discover what the killer ideas are.

It’s the same with social media. It wasn’t obvious when it launched that twitter was going to work as it had – it couldn’t be because twitter is built on the network of connections within the tool. Bubbles are useful precisely because we can’t pick the winners in advance.

The issue that Umair might take with this is that growth isn’t by definition good. I agree with this, so in as far as bubbles drive growth, then they aren’t necessarily good. But I don’t think that they are by definition bad either. Our challenge is to figure out how to drive investment in creating things that have genuine sustainable value. This problem is the same whether or not the social media bubble exists. What we really need is an awesomeness bubble.

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The First Follower is the One That Transforms a Lone Nut into a Leader

Since this is the Innovation Leadership Network, it’s probably time to talk about leadership directly. My thoughts today are spurred by two things: first, this is our 300th post, second, this TED talk from Derek Sivers that was posted yesterday – it’s only 3 minutes, and you must watch it!

It’s a great talk, and it raises several important points, including:

  • Leadership is based on relationships. The main point that he makes is that it is the first follower that makes someone a leader. Leadership is a network property, not a personal one.
  • Secondly, can you believe how many people end up dancing in that mob? It’s astonishing! Small acts can have huge effects. Remember that the next time you’re feeling helpless or overmatched.
  • Nurture your first few followers as equals. We’re trying to build movements, not a group of sycophantic followers. This reminds me of Umair Haque’s Builder’s Manifesto – where he says:

    Builders forge better building blocks to construct economies, polities, and societies. They’re the true prime movers, the fundamental causes of prosperity. They build the institutions that create new kinds of leaders — as well as managers, workers, and customers.

    When Sivers is talking about leadership, he’s really talking about building too – that’s why the people that get involved are so important.

  • This leads directly to the last point, leadership is overrated. The point is not to become the head of something, but rather to empower people to go out and make things better. When we’re talking about leading innovation here, we’re talking about empowering people to be autonomous, to help them experiment, to encourage them to make novel connections, and to help them implement their great new ideas. The most important job that a manager has is not leaderhip – it is removing the obstacles that prevent the people on the team from doing these things.

Which brings me to the topic of the 300th post. John and I have been working on this blog a little over a year now – and we are both consistently astonished and thankful at how much impact we’ve had with it. I never would have imagined that the number of people reading our thoughts would have grown as much and as fast as it has. We are sincerely grateful to everyone that has read a post, made a comment, argued with us about an idea, and contributed to the ideas that are developing here. Thank you!

And even though we have ‘leadership’ in the title of the blog, we’re really trying to build. We’re trying to be innovation catalysts. We don’t want you to follow our ideas – we want you take our ideas and use them to help you execute your own.

That’s the best possible response to what you read here – go out and do something. Let’s start building.

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How to Experiment to Support Innovation

Earlier this week I did a talk on innovation for a local firm that was the opening session of their strategy-making work for the year. During the questions, one person asked for suggestions about a specific initiative that they had been trying to get off the ground for a couple of years, but which just kept stalling. Before I could even answer, someone else jumped in and suggested that maybe the problem was that they simply hadn’t figured out yet how to test out the idea quickly and cheaply.

I’d like to say that he came up with such a great answer as a result of my inspirational talk, but I think that the truth of the matter is more mundane – he’s a smart guy. They talked about some of the ways that they might try an experiment to get the initiative off the ground, and the discussion gave me a clue about how to integrate a couple of ideas that we’ve been talking about here recently.

I’ve talked frequently about the importance of experimenting in innovation, and John has been discussing the use of real options in costing innovation initiatives. The key question is: what do these ideas mean in practical terms? Saul Kaplan makes the case compellingly in his post Think Big, Start Small, Scale Fast – here’s a quote, but go read the whole post:

Systems transformation is all about experimentation. It is about combining and recombining capabilities from across silos until something clicks and value is delivered in a new way. It is never just one thing. It starts with a big idea that gets the juices flowing and attracts others with similar passion to the purposeful network. The big idea has to be translated from the white board on to a real world test bed to demonstrate that the idea is feasible. Starting small and demonstrating progress is key to building credibility and expanding a network of interested stakeholders. An ongoing portfolio of small-scale experiments to fail fast on those without merit and to prioritize those with the potential to scale is critical. Those experiments that demonstrate the feasibility of a new model or approach become candidates for expansion.

There’s a great case study of how this can work in Seizing the White Space: Business Model Innovation for Growth and Renewal by Mark Johnson. One of the cases that he describes in detail is that of Hindstan Unilever (HUL), and their introduction of the Shakti Programme. You can read more about this initiative in this article from Fast Company – I will just focus on a couple of the key points.

The goal of the scheme was to reinvent the firm’s distribution model. Previously, they had sold all of their personal care products through stores in India. Unfortunately, the majority of the population of India still lives in villages, most of which don’t have stores – which meant that HUL was unable to get their products to nearly 60% of the population.

The Shakti Programme set up a completely new distribution channel – HUL helped local women set up their own entrepreneurial ventures within the villages. The women, called Shakti Ammas, received training from HUL, and then sold soap, shampoo and other small goods door-to-door. This was a radical business model innovation.

How did they do it? They started with a group of 17 women – this was the experiment. The point of the experiment was to figure out if they could help these women set up their own successful businesses by helping them build their skills, to figure out if there was sufficient demand for these products in the poorer regions of the country, and to sort out how to best build the distribution network. In the second year, they expanded the program to sixty women. During this time, the focused on learning the answers to the first two questions. Then they expanded to 2800 women covering 12,000 villages – this was still an experiment, and it was designed to address the third question.

The experiment was a success. They learned that in this model, the real customers were the Shakti women, and so the supply chain was built around meeting their needs effectively. Once they had proven that there was a market in these villages, that they could successfully train local women to effectively reach it, and that they could build a supply chain that could scale, only then did HUL invest heavily and roll it out through the entire country. There are now 45,000 Shakti Ammas in India, covering over 100,000 villages. The program is profitable (low margin but high volume) and it has become a substantial source of revenue for HUL. Furthermore, they are getting ready to start experiments to try out similar programs in other developing countries.

This case illustrates several key points. The first is that it is a great example of how to experiment. The small launch, with just seventeen women, was relatively inexpensive, and it was designed to answer specific questions. Even if it had failed, HUL would have learned valuable lessons about how to work in the villages. And if it had failed, no one would have ever heard about it, because it was a really small experiment. These are the kinds of experiments we should be trying – small ones, aimed at answering specific questions.

This also illustrates how the real options approach works. Instead of evaluating whether or not they should try out a program employing 45,000 women covering a huge geographical range – which would have looked awfully risky – HUL was able to make scaled investments. The real options approach is designed to figure out how to do exactly this – scale your investments, with clear ideas about whether or not to proceed to each new step. You can see how this worked for HUL – at each step they could have pulled out if the experiment hadn’t worked.

Here are some conclusions:

  • Follow Saul’s mantra – think big, start small, scale fast.
  • Set up your experiments to test specific questions.
  • Make sure that you learn from experiments that don’t work.
  • Use a real options valuation approach to valuing your projects.

Whenever you are trying to get a new innovation off the ground, ask yourself how you can test it out cheaply and quickly. People are much more willing to help you scale up a project that has already been demonstrated to work, than one that is just a great idea.

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How You Define a Problem Determines if You Can Solve It

How we define things is incredibly important. I’ve been reminded of this almost constantly this week. Here are some examples:

  • I was talking with a friend of mine over the weekend about using social media to improve the flow of ideas within an organisation. She is a high-ranking manager in a very large organisation, and she was curious to hear about this blog, and about how John and I have used it as a communication tool. She is thinking about implementing some kind of strategy to improve communication across the silos that are deeply entrenched within her organisation. When we started talking about the various tools that are available to help – wikis, blogs, message boards, instant messaging, and so on – I reminded her that if her problem is defined as a technological one, it’s unlikely that she’ll be able to solve it. When silos are approached as an IT problem, you get a lot of tools put in, but no real change in communication patterns. You must define silo problems as communication issues if you want to improve things. The way that you define this problem can determine your success in solving it before you even start.
  • Michael Schrage just wrote a post about how many different types of failure there are when we are trying to innovate. He argues that there are many different types of failure, and that we have to be more clear about what we are talking about.

    The underlying distinctions between failures typically overwhelm their similarities. …

    What kinds of failure make the best or most useful resources for your organization? Which failures matter most for the future? …

    When do you declare an “underperformer” a “failure?” Is it determined by semantics, the marketplace, or your CFO? In my experience, managers talk rather differently about “learning from failure” than they do “boosting underachievers.” Indeed, a Google search for “learning from underperformers” got no results. Needless to say, underperformance is a form of failure. Which brings the end of this post right back to its beginning: What are the kinds of failures that matter most to you and your organization? It’s ironic to the point of perverse that our failure to define failure can undermine our ability to learn from it.

    So how we define failure determines whether or not we can learn from it.

  • I’ve argued previously that one of the reasons that people have problems with the idea of innovation is that it is intentionally broadly defined. Here’s what I said:

    … a penguin and an eagle are both birds, yet they look completely different, they act differently, they live in different environments, there’s no clear connection between the two of them. If we try to use the word ‘bird’ to describe two such obviously different things, then it is a useless word, and we shouldn’t use it at all.

    Innovation can be defined clearly. It does get used to signify many different things – because it describes a broad phenomenon: executing new ideas so that they have economic value. It’s a classification equivalent to ‘bird’. Of course there are different ways to do this. There are many different ways to do this. Which is why we have so many different types of innovation to discuss. Incremental and radical, open and closed, design-driven and customer-focused: penguins and eagles. They are among the many different ways that we can execute ideas with economic importance.

    Innovation is a higher level term, which means many things. It’s important to classify the various sub-types as carefully as possible. Last year John and I were doing some consulting and we ran into a guy that said “our firm has to stop encouraging innovation.” This statement was surprising because in all the people we interviewed in that organisation, his ideas were by far the most radical, and by far the most innovative. So how could he argue against innovation? He could because in that organisation, “innovation” actually meant “incremental innovation.” Because his ideas were far from incremental, they weren’t considered. The way that they defined innovation determined the type of innovative ideas they were willing to entertain.

Taxonomy is the basis of all of the earth sciences – like biology, geography and biogeography. But in economics and business research, no one wants to take the time to classify things. Articles that are merely “descriptive” are discounted as being worthless – they’re not real research – real research is theory building. The problem is that you can’t build theory without having clearly defined building blocks. And to have those, you need a bunch of that boring descriptive work.

It’s a real problem – how can make a call to action to encourage more classification? It’s a boring topic. Admit it – even though this is hugely important, how likely are you to share this post? I think the answer is “not very likely at all”. Because classification isn’t interesting. But accurate classification is absolutely essential to solving problems successfully.

We must have clear, usable definitions of innovation and all of its sub-categories, and of failure and all of its sub-categories. The trap that we fall into is that we think of an idea only in terms of one of the sub-categories (say, innovation to mean incremental innovation) – and if someone else uses the term to refer to a different sub-category (innovation to mean radical innovation), we think that they’re wrong, or misguided.

Clear definitions are the foundation of all science. If we want to manage better, if we want to build our innovation processes on a solid foundation, we must spend some time classifying. Because how you define a problem determines if you can solve it.

(photo of Darwin’s Tree of Life sketch from flickr/speakingoffaith under a Creative Commons License)

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