We have been following a bit of a theme lately on valuation methods and selecting innovation projects. This was started with my post on some research that we have been doing on valuing innovation projects. Using surveys and quantitative analysis the study showed that traditional valuation methods such as net present value inhibited innovation. However, treating an innovation project as a real option was positively correlated with successful innovation.

Following this post, a few readers pointed us to Clay Christensen’s essay on the NPV trap, which is very supportive of the survey results. Tim subsequently wrote a blog piece on this too.

If traditional valuation methods are so flawed when selecting innovation projects, then it’s probably worth saying a bit more about real options. One reader of the blog, who runs a VC investment company specializing in high-tech ventures, said she had been using real options to make investment decisions for several years and found the outcomes to be much better than traditional methods for valuing projects (thanks Deb!).

What got me thinking about real options in the first place was a finance colleague who gave me an excellent article from the McKinsey Quarterly. It’s quite old now but I still think it’s the best introduction to real options and innovation strategy so I will use some of the ideas in this post.

The first thing to think about is how to value a financial option. I won’t go into detail on this one but some very smart people won the Nobel Prize for this and then nearly brought down the global economy when they used their equations to manage a hedge fund called Long Term Capital Management (now that really is academic impact!).

In the equation for valuing a stock option, increasing uncertainty and time to expiry increase the value of the option. The cost of exercising the option decreases its potential value, as does the revenues that we might lose by holding the option, rather than the underlying stock.

The value of a real option works in exactly the same way, as explained by this diagram from the McKinsey Quarterly overview.

The most important issue for managing innovation projects is that uncertainty and time increase the value of the project. In traditional valuation methods, these variables decrease the value of the project. Innovation always involves uncertainty and longer time frames and is therefore highly compatible with real options.

But taking a real options approach to innovation can tell us more than just valuation. It also tells us how to maximise the value of the project by constructing it as a real option where we can maximise its value by looking at the variables that make the option more attractive. For example, can we take an initial stake in the project as an option that will allow us to hold the option for a long time? Can we decrease the costs of exercising the option by finding potential partners to help us take the innovation to a bigger market?

This gets a bit abstract so I will use a recent example of an Australian oil and gas producer that is buying a ‘real option’ in producing biofuel from algae.

In this case uncertainty is high. The technology is unproven and nobody really knows what the oil or carbon price will be in five years time. Given that Beach Energy owns the tenements in the Cooper Basin where the algae farms are proposed, the potential time to expiry is very long and the exercise price is also relatively low. It’s a very good case for constructing the business case as a real option.

Beach does this by staging the investment with an alliance partner. If the initial scoping study or the pilot plant fails then all they have lost is the minimal investment in the early stage of the project. They can exercise the option by scaling up production if the industry conditions make the project economically feasible.

Again, the most important point is that there is value in embracing uncertainty and learning by trying things out.

Source: Leslie and Michaels (1997) The Real Value of Real Options. McKinsey Quarterly (3), pp. 97-108.

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Last week I talked about how I use Malcolm Gladwell’s TED talk in my innovation courses. Another one that I use to illustrate how the innovation process is changing is the talk by Charles Leadbeater:

The innovation lessons are a little easier to pull out of this one, since it is directly about innovation. Leadbeater talks primarily about the role that collaboration plays now in innovation. Many of the key ideas are explained in his book We-Think, which is definitely worth reading. Here are some of the key ideas that jump out at me in his talk:

• His story of the invention of the mountain bike is an excellent example of customer-led innovation. Over 60% of the bicycles sold in the US now are mountain bikes, but they were not developed by bike companies. They were developed by lead users, who combined the rugged frame of the slow, clunky one-speed bikes, the gears from racing bikes, and brakes from small motorcycles to create bikes they could take off-road. This happens in many industries now – others that we talked about in class include software, adventure sports and medicine. The last one always makes students a bit nervous – who wants to have their doctor experimenting? And yet, many times they have to invent new ways of doing things on the fly when they are faced with an urgent situation. So many medical devices actually originate in use.
• Leadbeater’s discussion of the patent system builds on that last point. He says that the purpose of many innovations is discovered in use, which does not fit well with an IP system that requires inventors to know exactly how their invention will be used in order to file a patent. Inventors often don’t know what their ideas are for! I think that this is a critical point – this is one of the reasons why “release, get feedback, improve, iterate” is a very effective innovation method. It allows you to discover what your great idea is actually good for as you interact with the people using it.
• There is a good discussion of why the organisational imperatives within large firms lead to incremental innovation rather than breakthroughs. It is much easier to get sign off on an idea that builds incrementally on an existing product or service, aimed at current customers, with a reasonably predictable return. Innovation is inherently uncertain, which often makes it difficult to get innovative ideas off the ground within large organisations.
• “The truth is that most creativity is cumulative, and collaborative.” Innovations combine things that already exist. There is no thunderbolt from the sky, but rather a great deal of hard work, discussion, and collaboration that must take place if we are to be consistently innovative.
• The last half of the talk is a great explanation of the conflicts between open and closed innovation models. Leadbeater discusses why established firms try hard to maintain their primarily closed innovation models. He also correctly points out that the most successful business models of the future will probably not be either fully open or fully closed, but rather some type of hybrid combination of the two.

Overall, it is a terrfic talk and well worth your time. Both in this and in We-Think Leadbeater makes a compelling case for the benefits of collaborative innovation, why organisations might use open innovation models, and some of the tensions involved in doing so.

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I was doing some work with a company this week, and ran across one of my pet peeves. The organisation itself was very exciting. We interviewed over 20 people about various projects that were going. To a person they were smart and engaging, with great ideas, vision and energy. It was invigorating. The number of exciting projects that they have on the go is enormous. So what’s the problem? The problem is that many of these projects will never get off the ground. Why? Because they might cannibalise existing business.

This is the worst possible excuse for killing innovation. In many ways, if you worry about cannibalisation, you are falling into exactly the same trap that makes Net Present Value project evaluation so flawed. Earlier this week, Ralph Ohr and Wim Rampen both pointed me to an article that illustrates this flaw perfectly. The article is called Innovation Killers, by Clayton Christensen, Stephen Kaufman and Willy Shih from the January 2008 edition of Harvard Business Review. They include this diagram:

This shows the problem perfectly. NPV analysis assumes that our current level of performance will remain stable and that is the probable future that is used to evaluate new innovative projects. However, the ‘do nothing’ option does not result in staying at the same level of profit and performance. If we do nothing, our performance will decline. We will be worse off because someone out there is figuring out how to destroy our market. If we try new things, and they work, we might stay ahead of them. If we don’t, they’ll knock us over. There’s no way around it.

This same flawed assumption is being made when people worry about introducing new ideas that will cannibalise their current product or service lines. Of course they will. But again, the do nothing option does not result in ‘everything stays the same’. The do nothing option results in someone else doing the cannibalising.

Halfway through the day, our host said this:

When we compare ourselves to our current competitors, we look pretty good. We’re innovative and ahead of the game. But that’s the wrong comparison. I’m not worried about the people already in our market. I’m worried about the people I’ve never heard of.

Exactly.

The simple fact of the matter is this: we must introduce innovations that take away our current market share. There’s no such thing as a cash cow – if you have a product that is dominating the market, but you’re not innovating around it, it’s not a cash cow, it’s already dead.

Cannibalise your current products. Kill your best performers, or someone else will kill them for you.

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In a recent post Tim mentioned a comment by a representative of an Australian university tech transfer office at an investor’s conference the other week. As Tim says, he was declaring the death of open innovation and a return to ’sensible’ IP strategy, where we patent everything and then try to sell it or license it out.

If you’ve been reading Tim’s thoughts on patents or my post on the evidence for the Gollum effect of IP being a barrier to innovation, you can probably guess our opinion on the ‘death of open innovation’ declaration.

We are concerned that there is a growing confusion about what open innovation is (and isn’t) so I thought I’d write about an excellent review of the topic by Linus Dahlander (Stanford) and David Gann (Imperial College, London) that’s about to be published in one of the leading academic journals on innovation.

As Linus and David point out, open innovation is far from being a new phenomenon.

In the late 19th century, Edison’s laboratory – the Invention Factory at Menlo Park, displayed characteristics that in many regards had an open approach to innovation. The commercial development of electric lighting, for instance, was the product of a team of engineers that recombined ideas from previous inventions, collaborating with scientists, engineers, financiers and people in marketing outside the laboratory.

However, what is changing in innovation is the steady increase in the use of external ideas and external paths to market. Far from being a fad, this changing face of innovation is part of a shift towards alliance capitalism where companies pursue specialization but also collaborate to access capabilities that make their specializations more valuable.

I think the highlight of the review paper is a reminder that open innovation is a continuum (not just open versus closed as the only options), and that open innovation takes on several forms. The 2×2 matrix categorizing open innovation into inbound versus outbound and pecuniary versus non-pecuniary is particularly useful.

Inbound and outbound innovation is straightforward but the pecuniary versus nob-pecuniary dimension means that a lot of innovation isn’t based on ownership and formalized transactions. There are times when firms can give away technology and ideas as part of their innovation strategy. As Tim has said previously, free can be a valid business model in the right circumstances.

But here is where the conference speaker got confused. Revealing is not the only form of open innovation. When we think about open innovation as a range of activities that firms undertake to create and capture value by externalizing the innovation process, we start to see how common open innovation really is. Linus and David’s review shows the diversity of open innovation in the following table.

So….open innovation isn’t new, it’s not just about giving away ideas and it’s not going away.

Source: Dahlander, L., Gann, D.M., How open is innovation? Res.Policy (2010), doi:10.1016/j.respol.2020.01.013

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I have a confession to make. Although I have been teaching business strategy and innovation management for over ten years there is always a doubt in my mind over the value of what I bring into the classroom. If you looked at my collection of PowerPoint slides and readings, you would see a bag of tools for analysis but I have never really believed that these tools, such as industry analysis, scenario planning and value chain analysis, were really very important.

My late grandfather was a self-taught businessman who created a large construction company. He was always amused by the idea that people could study business and often asked me when I was going to get a real job. Everything that the managers of the company knew was based on ‘rules of thumb’, learned from experience rather than studying ‘tools’ in a business school. What he was actually saying is that there is no shortcut to learning ‘rules of thumb’.

These rules of thumb are otherwise known as heuristics. In a complicated world, heuristics help us to process information and simplify decision making. We use heuristics every day in all sorts of ways. For example, I never go into an empty restaurant, or a full platform means that I need to run because the next train is coming soon. Malcolm Gladwell’s book, Blink, is an excellent read if you are skeptical about the importance of intuition and heuristics in rapid decision making.

Experienced innovation managers also use heuristics. Tim and I were talking to managers the other day who had worked out that they needed to quarantine innovation projects from other business activities. Now, there are a whole bunch of research results and theories which tell us why this is a good idea, but the company had arrived at this conclusion without any of these.

Experience can allow us to develop heuristics but this can be slow and sometimes very expensive (my grandfather’s business didn’t survive to learn from the effect of borrowing to fund big projects at the top of the business cycle). Is there an alternative and can we teach heuristics?

I think the answer is yes and I was provoked into thinking about this after Tim wrote his post about analysis and interpretation. Here he was questioning the emphasis that we place on tools for analysis at the expense of more expansive exploration of possibilities for innovation.

I agree that focusing on analysis will probably make us less innovative but what if we tried to emphasize the heuristic value of analytical tools? If we actually blended the tools with people’s experience and existing heuristics then possibly we can get past the tool as a thinking constraint to a point where the tool is a catalyst for thinking about new possibilities.

We can teach heuristics, but it means that the teaching style must be different from the old models based on simple content delivery (sadly, still very prevalent is most business schools). Rather than the educator being the conveyor for information, they become a facilitator of an interaction between the ‘tools’ and the experiential knowledge of the learner.

I run an executive education course in strategy and I think that one way to get the interaction going between tools and heuristics is to use a ‘live case’ teaching method where some of the managers bring strategy problems to the course that are worked through during the week with other participants in the course. The result is that preconceptions are challenged and tools are applied and adapted for purpose, with the result being a new set of heuristics and a new way of seeing business problems and opportunities.

We have a vast mountain of tools and frameworks from authors, academics and consultants. How many of these are genuinely valuable as heuristics that change the way we see the world?

Flickr photo from Mara under creative commons license

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In all of my longer innovation courses, I use this video by Malcolm Gladwell in the first lecture. It’s his TED talk from a few years ago, and if you haven’t seen it yet, it is well worth your time:

Gladwell recounts the story of Howard Moskowitz, who did the consulting work that led to the development of, among other things, extra chunky spaghetti sauce from Prego. The story involves how Moskowitz conducted a number of taste tests for different products. Usually, the data did not make any sense when he analysed it trying to find the perfect taste combination for Pepsi, or spaghetti sauce, or coffee. His insight came when he discovered that instead of one optimum, there were multiple optima for each of these products. This is how Prego went from making just one spaghetti sauce to making regular, robusto and extra chunky.

One of the things that I emphasise in these early lectures is that innovation involves making new connections (Schumpeter had this idea first though, not me!). To encourage people to get some practice in making novel connections, I use a number of videos like this – which are not directly about innovation, but which have something important to say about the process – and I ask them to figure out what the innovation lessons are.

Here are some of the ideas that commonly come up with this one:

• The first interesting bit is how Moskowitz saw the multiple optima idea ‘like a bolt of lightning’. The popular image is that this is the way that inspiration works. But note that Moskowitz was equipped to receive this bolt of lightning by many years of hard work and intensive study. It’s like the story from Gordon Gould, one of the people that invented the laser (quoted from The Myths of Innovation by Scott Berkun):
  

  In the middle of one Saturday night… the whole thing suddenly popped into my head and I saw how to build the laser… But that flash of insight required the 20 years of work I had done in physics and optics to put all of the bricks of that invention in there.

  It’s another version of “chance favours the prepared mind.”

• The second point is that the innovation wasn’t really the new products – it was the process that led to their introduction. It was the idea that there was no perfect spaghetti sauce, just perfect spaghetti sauces. As Hugh MacLeod says:
  

  Products are idea amplifiers. The molecules and/or bytes are secondary.

• The third point, and this one is huge, is that focus group testing never revealed the desire for extra chunky spaghetti sauce. Moskowitz discovered this opportunity by testing, watching and recording what people ate and what they liked. Their actions said that they wanted extra chunky, even though this desire was never articulated. That’s what design-driven innovation is designed to get at – unarticulated needs. And when it works well, the results are like those that Prego achieved with extra chunky – very profitable!
• Finally, it shows the process of innovation. Moskowitz had his flash of insight, and he was pretty sure that he was right. But then he had to go out and execute his idea. This took a huge amount of work. And only once he had proven that process worked through working with Prego did the idea diffuse more broadly. Those are the three steps in innovation: develop a great idea, figure out how to execute it, and get it to spread.

So there’s a lot to be learned about innovation from spaghetti sauce. What ideas strike you when you watch Gladwell’s talk?

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John’s post How Accountants Kill Innovation ended up causing a bit of a stir this week. The press release that went with it was pulled from the UQ Business School website complained about it, but the post here got a huge number of reads, and the press release got picked up by BioTechnologyNews.net, where it was popular enough to end up as that site’s story of the week on Industry-News.net. Part of the traffic on the blog here was driven by the 21 tweets that it got (including one from CPA Australia – so accountants nationally are maybe not as thin-skinned as our local faculty).

All of which is interesting from the standpoint of tracking how ideas spread. Idea diffusion is one of the key things we talk about here, but that’s not what I want to talk about today. I’m interested in the analytical aspect of John’s post. He was talking about research findings that show that in Australian Biotechnology firms, using a Real Options valuation approach leads to better innovation results than using Net Present Value to evaluate potential projects. Firms using NPV actually had a negative return to innovation in their study – so the title of the post really should have been How NPV Kills Innovation. But then the idea wouldn’t have spread nearly as well.

The thing that concerns me about the study though is the use of tools in the first place. I recently read Innovation: The Missing Dimension by Richard Lester and Michael Piore. It’s one of the most insightful books that I’ve read in a while, and I recommend it strongly. Lester and Piore are reporting on the results of a massive number of case studies that they conducted in the cell phone, blue jeans and medical devices industries.

Their key finding is that to innovate succesfully, firms need to be good at both analytical and interpretive processes. Real Options and NPV are typical analytical processes that are used to evaluate potential new products. Lester and Piore argue that in addition to these types of tools, firms also need to be able to interpret the needs of their customers (which is essential the argument of Roberto Verganti in Design-Driven Innovation as well). Here’s how they describe it:

Analytical processes work best when alternative outcomes are well understood and can be clearly defined and distinguished from one another. Interpretive processes are more appropriate when the possible outcomes are unknown – when the task is to create those outcomes and determine what their properties actually are. These two ways of proceeding involve very different kinds of skills, different ways of working together, different forms of managerial control and authority, and, ultimately, different ways of thinking about the economy. The two processes are in fundamental opposition to each other, making it difficult for people to think about both of them at the same time. Yet the ability of businesses to think about these two approaches separately and to manage them simultaneously is the central challenge of product development.

That’s the real problem – analytical tools like NPV kill innovation, but we need them (or at least something like them) to innovate. The real achievement of Lester and Piore is to document how we need both analysis and interpretation in a wide variety of industries, and they include some suggestions for doing this successfully. The main one is to be aware of the necessity of both processes and how to use them effectively.

Interpretive processes are most important at the start of developing our new ideas. Design-driven tools are very effective early in the process, and they do lead to finding new competitive spaces. Disruptive innovations are most likely to arise from interpretive processes.

Because interpretation requires ambiguity and extended conversation, the biggest thing that managers need to do is to prevent shutting down discussion too early. This is the tendency in organisations dominated by analysis – they jump to a preferred option far too early.

The time to use analysis is once you have settled on the ideas that are best. This is the time to optimise things.

Innovation is filled with difficult contradictions to manage – incremental versus radical, short-term versus longer term, open models versus closed, and analytical versus interpretive. In most cases, firms need to find a balance between each of these oppositions. It is hard to do this, but the firms that are successful at finding these balances also tend to be the most innovative. And the most profitable.

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This isn’t going to be one of those rants against ‘beancounters’. We have actually collected and analysed data which tells us that traditional accounting and valuation methods will damage innovation performance. To be fair to my accounting collegues (some of my best friends are accountants) the main conclusion from our study is that we need a different type of accounting to manage the innovation process. In particular it’s the project selection step (or the filtering step in Tim’s aggregate, filter, connect model) where planning and valuation methods can make or break innovation.

In the study we surveyed Australian biotech executives on how they used financial criteria to select innovation projects. With responses from about 100 firms, we were able to see that there were two styles of innovation planning.

The traditionalists used best estimates of market size, cash flows and chances of success to arrive at a value of the project. For those of you with a working knowledge of financial management, their style was closely aligned to net present value analysis.

The other group placed less emphasis on prediction and valuations and were prepared to stage investments in the project. As the project showed promise (or not) funding would be increased or discontinued. In finance terms, these were the ‘real options’ managers. Like a stock option, they were prepared to ride the uncertainty by taking an initial stake in the upside but also recognized that options are valuable because they limit how much will be lost if the project doesn’t perform after the early stages of development.

When we compared these financial management orientations to innovation performance (as measured by patents, which is valid in biotech) the first result was unsurprising. Real-options management was positively and significantly correlated with innovation. However, the second result was a bit unexpected.

The traditionalists using mainstream planning approaches (NPV managers) were negatively correlated with innovation performance. In other words, imposing strong traditional financial criteria for project selection made the firm less innovative than firms that had no particular financial criteria for the selection of projects!

I think this leaves us with three takeaways for managing innovation.

1. Innovation means trying things out and failing. Attempting to provide detailed plans and forecasts regarding what is going to work will mean missed opportunities.

2. Large firms with traditional planning processes and valuation tools need to create different procedures for managing innovation.

3. We need to change the way we value innovation projects and include the upside of uncertainty in our assessment. While we focus on the downside risks with innovation projects, how many of us consider that risk has an upside too. Risk reduces the value of businesses in traditional valuation tools.

This research paper was written with Mat Hayward, Andrew Caldwell and Peter Liesch. It is currently under review for a journal.

Abacus picture from Flickr by Obraka under Creative Commons license

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There were some good (all reactions are good!) reactions to the blog post on the problems of using patent counts to measure innovation so I thought I would follow up with some evidence on patents and intellectual property (IP) strategy.

Conventional thinking is that any IP generated within an organization should be carefully guarded by secrecy agreements, trademarks, patents or a combination of these things. As many universities become more commercially focused, researchers and students are now encouraged to safeguard their IP as well. In fact, I taught a Masters course in technology management last year where several grad students could not discuss their projects in class because they has signed confidentiality agreements.

Many years ago when I was doing biochemistry research my laboratory director took on a project in the aquaculture industry. After early results the director thought that he had made a breakthrough in aquaculture nutrition and consulted the commercialization office of the university. Secrecy agreements were signed by him and the research assistant, who was instructed not to talk to anyone in the laboratory about the project.

One day the research assistant asked me for some technical advice on processing samples and I was more than happy to give it, but what followed next was a very nasty surprise. Within an hour I had the director screaming at me and accusing me of industrial espionage. The closest thing I can compare it to is the scene from the Lord of the Rings where an aging Bilbo sees Frodo wearing his old ring and turns into Gollum with his desperate obsession over “my precious”.

So much for academics and IP protection but what happens to businesses when they get obsessed by IP?

The best study of the relationship between IP protection and innovation is by Keld Laursen (Copenhagen Business School) and Ammon Salter (Imperial College London). They are world class researchers and if you disagree with their results then its worth reading the original paper.

Using a dataset of 2700 UK manufacturing firms, Laursen and Salter looked at the relationship between different IP protection strategies and innovation outcomes. While they looked at formal protection such as patents, trademarks and designs, they also looked at informal protection tactics such as secrecy agreements, complexity of design and lead times over competitors. What they found, for both types of IP protection was that moderate rates of IP protection correlated with increased innovation but intensive IP protection resulted in poor innovation performance. They explain this result as follows….

There are several possible interpretations of this finding. We suggested that firms
might develop a myopia of protectiveness, being overly protective of their new
innovations. They focus their managerial resources and attention towards the
acquisition of legal protection to the detriment of other activities, such as the
mobilization of complementary assets. They may become obsessed with secrecy,
limiting their opportunities to work with others, such as lead users, or to trade
knowledge informally with suppliers, customers and competitors. In this respect,
firms may suffer from a “Gollum effect”, locking themselves away from the rest of
society in the vain pursuit of full protection.

Next time someone tells you that you should be protecting all of your IP, ask yourself why they are telling you this. IP is a means to an end and not an end in itself.

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