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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One of the major themes running through the blog is that innovation is a complex process made from different activities. Tim and I have pushed this pretty hard because innovation is often confused with invention (the generation of an idea resulting in a new physical process or thing). There is a lot to be gained from looking at innovation as a value chain because it allows us to see where the weak links are in the innovation process. In nearly all cases, ideas are never the problem. The other advantage of the value chain is that it tells us that we should be measuring different things to get a complete view of performance. Like the story of the blind men and the elephant, measuring only one part of the process will invariably lead us to the wrong conclusion.

Now, a value chain approach to managing and measuring innovation at the firm is relatively straightforward. Surveying people in the business and collecting data about new products takes some time, but it is easy to do. But what happens at the level of industries and nations? How do we measure performance here?

Of course what tends to happen is that the data that are already available and easy to collate get first preference. Things like formally reported R&D expenditure and patents are cheap to access but the obvious downside is that they tell us nothing about how well the innovation process is working. This means that governments who rely on these data to devise policy to support national competitiveness are flying blind.

But it’s actually worse than that. Not only do these measures tell us nothing about the innovation process, they are also an inaccurate measure of innovation!

Now, I have a confession to make. I have used patent counts to measure innovation in my research. In my defense I would like to add that this was in the biotechnology industry where most innovations are patented. However, while writing a review article on innovation networks, I stumbled across a survey that makes me very skeptical about any measure of innovation that relies on patents.

In a survey of 604 large industrial firms in Europe, Arundel and Kabla (1998) found that the average percentage of product innovations that were patented varied from 8% in textiles to 79% in pharmaceuticals, with the average for the entire sample being 36%. With process innovations, the range was 8% in textiles up to 47% in precision instruments. The study also found that larger firms tended to patent more of their innovations and that some firms used secrecy agreements rather than patents.

So, if you are using patents to measure innovation, what does your innovation elephant look like. Well, it’s going to look like the pharmaceutical industry is highly innovative, with precision instruments and machinery a distant third and second. Also, smaller firms won’t look very innovative. On the other hand, the underachievers will be textiles, food and transport. Given this information, it would be tempting to support a pharmaceutical industry and accept that the underachievers will struggle and die.

This is a problem because more detailed surveys of firms in the EU have shown that transport and food are among the fastest growing and most innovative industries. In rich economies like Norway, innovative industries such as fishing, forestry and mining not only produce their own innovations, but they are intense consumers of innovations from other industries. Patents tell us very little about this story.

Little knowledge is a dangerous thing and in measuring and managing innovation, it is deadly.

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I was in a business presentation session with a large engineering firm last week, which got me thinking about what I like to see in new product strategies. In this instance, the firm was considering a move into the renewable energy industry, based upon how big this industry might be in the years ahead.

Now this sort of thinking concerns me because it it is often the first step to failure. While Tim and I talk about the value of experimentation and trying things out, there is a real tendency for managers to talk themselves into big bets when new businesses and potentially big markets are involved. In the spirit of Tim’s excellent post on the value of checklists, I’d like to put forward a simple checklist that tests for three main reasons for failure when committing to a new business.
1) The company doesn’t have competitive advantage in the industry.
2) The industry is chronically unprofitable for structural reasons.
3) Interdependencies in the value network within the industry means that buyers and suppliers face a significant cost in using your product.

While these tests are based on established thinking in strategy, my conclusions are not based on years of exhaustive statistical research. Instead, they are based on my investing in (and mostly losing money on!) small technology stocks.

Many years ago I would look at a stock and if the potential market was huge then I would probably go ahead and buy some shares. It’s probably the same optimism bias that makes us buy lottery tickets. When there is a big pot of money at stake there is a real tendency for rational thinking to go out the window.

So let’s look at examples of failure in each of these tests and an example of an innovation that meets the three criteria.

No Competitive Advantage- Jackgreen
Jackgreen was an Australian specialist green energy retailer that went into administration in late 2009. I’m happy to say that this isn’t one of my failed investments, but it did catch a very high profile Australian fund manager. While consumer demand for renewable energy has been growing steadily in Australia, there is absolutely nothing to stop other energy companies doing exactly what Jackgreen has been doing. The two main drivers of competitive advantage are providing a product or service that is valuable to customers AND having something in the production chain (such as IP, skilled staff, exclusive alliances, location, brand) that makes it hard for competitors to imitate. Jackgreen never had any barriers to imitation.

Bad Industry- Australian Biodiesel
A few year ago when oil prices were spiraling upwards towards $200/barrel it seemed that the obvious bet in energy was biofuels. Australian Biodiesel had IP rights over a process that allowed a wider variety of oils, including animal fats to be turned into diesel. It passed the first test of competitive advantage but it soon became apparent that this was always going to be a very low margin industry and almost impossible for small players. What made the industry so tough can be easily described with Porter’s five forces model of industry profitability. Buyers didn’t have to buy biodiesel (they could do just as well with ordinary diesel) and suppliers could find other industries that could use canola oil and animal fats. Margins in the industry were always going to be a problem and volume was the only answer. In the end, the rising cost of canola and fat finally put the company into administration.

Value Network Interdependencies- Magnesium International
Magnesium International was an Australian company that bought exclusive rights to the Dow process for producing magnesium. In addition they also had exclusive patents for rolling magnesium into thin sheets. Once again, in a era where we are trying to make cars and electronic goods lighter, there should have been a massive global market and a rapidly rising magnesium prices as it became more widely used by manufacturers. Nice idea, but even with the thin sheet product interest in magnesium was at best marginal, mainly because manufacturers faced massive switching costs in changing production equipment to use the new material. In the absence of a profitable business model the company went looking internationally for government support. After committing to Egypt, the company soon went into receivership.

Meeting the Checklist- Nanosonics Limited
Nanosonics is an Australian company that produces devices that disinfect hospital equipment. Nanosonics meets the competitive advantage test because it can disinfect faster than existing methods with the use of less toxic chemicals and has a fairly strong IP position (although defending patents can be tough and expensive). The industry looks good because hospitals must have fast and effective disinfection processes and are therefore relatively insensitive to price. Switching to the new disinfection process has minimal costs for the buyers as well.

These three simple tests should provide a reality check for scoping new businesses. I only wish that somebody had told me about them ten years ago. I wonder if my accountant will let me write off my losses as an educational expense?

Case studies used in this post are not intended as investment advice. John is famous in his MBA strategy classes for putting the kiss of death on successful firms by using them as case studies.

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