MTS India is hiring a brand new innovation team, 6-10 people. Looking for unique individuals who are passionate about spearheading innovation projects across the whole company. Junior and senior roles
MTS India is a mobile voice and data service provider.
redsparks is a campaign to find the creative enablers, lateral thinkers and outliers for an Innovation Team. It's the chance of a lifetime! Your decisions can impact the future roadmap of MTS India and change the face of the telecom market in India.
Applications are open now through March 20th on the redsparks Facebook page where you'll also find more information. "Like" the page to stay up-to-date, spread the word to all your friends and get going! To apply, simply click on the "GO" link at the bottom of the Facebook page.
http://www.facebook.com/MTSredsparks
Showing posts with label innovation. Show all posts
Showing posts with label innovation. Show all posts
Friday
India announces a national innovation fund for early stage capital
News alert
The National Innovation Council has recently addressed 2 major issues in the Indian innovation ecosystem
As always the devil will be in the details. Will the fund have the resources to disburse such a huge sum in hundreds of tiny investments, perhaps evaluating thousands before they find the right ones? Will an infrastructure network be enough to increase collaboration and knowledge sharing between dispersed and competing institutes? We'll see. For now, good things are happening.
The National Innovation Council has recently addressed 2 major issues in the Indian innovation ecosystem
- Within the next 2-3 months a national innovation fund will be set up to provide early stage seed capital for ventures in areas such as health and education. The fund will reportedly have Rs. 1'000 crore (or around $220 mio) to invest.
- The Union Budget for 2011-2012 will cover the creation of a new knowledge network - an optical fiber network that will connect 1500 institutes across India
As always the devil will be in the details. Will the fund have the resources to disburse such a huge sum in hundreds of tiny investments, perhaps evaluating thousands before they find the right ones? Will an infrastructure network be enough to increase collaboration and knowledge sharing between dispersed and competing institutes? We'll see. For now, good things are happening.
Wednesday
Science and the city
Johnson, Steven. Ghost Map: The Story of London's Most Terrifying Epidemic -- and How It Changed Science, Cities and the Modern World, Riverhead Books, 2006.
The map in the title of this book shows houses and wells in mid-19th-century London. In 1854 Soho experienced the most violent outbreak of cholera in the city's history. The map, compiled by the physician John Snow, shows the number of dead for each house and which well is closest in terms of walking distance. Its story is usually told as one of cartographic innovation - the map eventually helped to convince the political scientific establishment of the day that keeping drinking water clean (ie. improving and rerouting sewage systems) could prevent cholera epidemics.
Steven Johnson traces Snow's efforts and finds that the map was merely the final product of a much more fascinating story.
The accepted theories of cholera transmission at the time were a) a miasma theory that related disease to "bad air" and stench produced by unsanitary living conditions and b) that the lower classes were more susceptible to disease through unspecified "moral failings."
Snow, a prolific scientist and distinguished physician - and more importantly a rigorous empiricist, could find no support for these theories. His observations suggested that patients contracted cholera by ingesting something harmful, not by inhaling it or through a weak moral constitution. He had a hunch that cholera was spread through water contaminated by sewage and set out to prove it by linking cholera cases and different water sources.
Snow's initial focus of inquiry was an area of London that had a mixed water supply, ie. different houses received their water from different companies. At one point, he spent days going from house to interviewing residents about their water consumption. When the 1854 epidemic broke out in his own neighborhood, he not only tended to the ill, but also kept records of cholera deaths by house and linked them to a specific pump on nearby Broad Street. Again, he spent hours interviewing people about their drinking habits and also managed to link several deaths further afield to the same pump.
Snow presented his data to the Board of Governor's of St. James Parish as they tried to deal with epidemic. The board members were skeptical - after all, the Broad Street pump was known for its particularly pure water. But they had few other options, and the risks of shutting down the pump were low compared to the potential of saving tens or hundreds of lives. So, a week after the first outbreak of cholera, the handle on the Broad Street pump was removed. While this was probably the first scientifically sound reaction in the battle against cholera, the neighborhood public and the national Board of Health were not convinced.
Henry Whitehead, a clergyman, spent most of his days walking around the Broad Street neighborhood, talking with his parishioners. When the cholera epidemic hit, he saw the consequences first-hand. He soon discredited both the "miasma" and the "moral weakness" arguments. He realized that there were fewer higher-class deaths than lower-class deaths per house, simply because the lower-class apartments were more densely populated. In fact, per capita death rates were unrelated to class. One of the most severely affected houses was locally known to be one of the cleanest, unlikely to suffer more from miasma than many filthier houses that had fewer cholera cases. The local work house which should have been hit worst, reported remarkably few deaths.
When Whitehead heard of Snow's water-borne explanation, he set out to debunk it like the others. He had seen several patients recover after drinking large quantities of water from the Broad Street pump (thereby almost stumbling on the cure for cholera), so he thought that discrediting Snow's theory would be easy. However, the more he spoke to survivors and former residents who had fled the epidemic, the more supporting evidence he found. Where Snow had tallied deaths and linked them to the pump, Whitehead added survivors and linked them to alternate water sources (or beer consumption instead of water).
Eventually, it was Whitehead who discovered the source of the epidemic: by chance he stumbled on the record of the death of a baby girl, reportedly from diarrhea, who fell sick a few days before the outbreak. The girl's address was immediately next to the Broad Street pump, and the family used a (officially nonexistent) cesspool located just a few feet from the pump for their household waste. By now, Snow's and Whitehead's case was convincing enough to warrant an inspection of the well. The final confirmation came when the cess pool was found to be leaking into the well.
These results were reported by the vestry of St. James parish, and both Snow and Whitehead wrote extensively about the cholera outbreak in the following years. Still, the scientific community clung to miasma and class/morals-based theories. The official report of the national Board of Health blamed miasma for the 1854 epidemic and all but ridiculed Snow's water-borne theory.
And finally, we come to the map. Snow started working on it several months after the outbreak. Several maps had been created to analyze the cholera epidemic, but these weren't linked to rigorous scientific theorizing and data. By reducing the map to streets, houses, pumps and cholera deaths, Snow made a striking visual case for his theory. A revised version was included in the St. James vestry's report, and this contains the most innovative technique used on the map: a dividing line shows groups houses together in terms of which well is closest by walking distance (a so-called Voronoi diagram). The geography of Soho streets and alleys meant that walking time and distance as the crow flies were not always correlated. This version posed an even more striking argument linking the Broad Street pump to the 1854 cholera epidemic.
While the map was important, Johnson shows that it - and the theories it supported - would never have seen the light of day without the incredible depth of local, even amateur, knowledge that Snow and Whitehead brought to it. And while the map legitimized the first correct science-based response to a cholera epidemic, its authority was based in the scientific inquiry behind it.
Johnson also argues that the epidemic was as much a product of the city as the ultimate measures to prevent it. Cholera could never have spread as virulently without the incredible population density found in London in the 1850s; it would never have found a way to spread if London hadn't been overwhelmed by the sheer quantity of human waste produced by this population. On the other hand, two working class men (John Snow and Henry Whitehead) would hardly have gained the education and reputation to influence public health policy anywhere outside the socially mobile city; high population density also meant that Snow and Whitehead were able to gather enough data to convincingly prove their theory.
The massive reconstruction of the sewage system that followed many years later (and only after another decimating epidemic) essentially rid London of cholera. This example, not only allowed other cities to improve their sanitation cities, it also proved the enduring viability of the city. Widespread ideas that cities of a million of more people were destined to self-destruct and drown in their own waste were refuted once and for all.
Links:
Online resources for the book (incl. links to the map and the UCLA department of epidemiology's John Snow site).
Steven Johnson's TED talk on the cholera epidemic.
The map (without the Voronoi diagram showing walking time).
Review of Johnson's first book, "Interface Culture."
Get The Ghost Map: The Story of London's Most Terrifying Epidemic--and How It Changed Science, Cities, and the Modern World
.
Get Interface Culture
.
The map in the title of this book shows houses and wells in mid-19th-century London. In 1854 Soho experienced the most violent outbreak of cholera in the city's history. The map, compiled by the physician John Snow, shows the number of dead for each house and which well is closest in terms of walking distance. Its story is usually told as one of cartographic innovation - the map eventually helped to convince the political scientific establishment of the day that keeping drinking water clean (ie. improving and rerouting sewage systems) could prevent cholera epidemics.
Steven Johnson traces Snow's efforts and finds that the map was merely the final product of a much more fascinating story.
The accepted theories of cholera transmission at the time were a) a miasma theory that related disease to "bad air" and stench produced by unsanitary living conditions and b) that the lower classes were more susceptible to disease through unspecified "moral failings."
Snow, a prolific scientist and distinguished physician - and more importantly a rigorous empiricist, could find no support for these theories. His observations suggested that patients contracted cholera by ingesting something harmful, not by inhaling it or through a weak moral constitution. He had a hunch that cholera was spread through water contaminated by sewage and set out to prove it by linking cholera cases and different water sources.
Snow's initial focus of inquiry was an area of London that had a mixed water supply, ie. different houses received their water from different companies. At one point, he spent days going from house to interviewing residents about their water consumption. When the 1854 epidemic broke out in his own neighborhood, he not only tended to the ill, but also kept records of cholera deaths by house and linked them to a specific pump on nearby Broad Street. Again, he spent hours interviewing people about their drinking habits and also managed to link several deaths further afield to the same pump.
Snow presented his data to the Board of Governor's of St. James Parish as they tried to deal with epidemic. The board members were skeptical - after all, the Broad Street pump was known for its particularly pure water. But they had few other options, and the risks of shutting down the pump were low compared to the potential of saving tens or hundreds of lives. So, a week after the first outbreak of cholera, the handle on the Broad Street pump was removed. While this was probably the first scientifically sound reaction in the battle against cholera, the neighborhood public and the national Board of Health were not convinced.
Henry Whitehead, a clergyman, spent most of his days walking around the Broad Street neighborhood, talking with his parishioners. When the cholera epidemic hit, he saw the consequences first-hand. He soon discredited both the "miasma" and the "moral weakness" arguments. He realized that there were fewer higher-class deaths than lower-class deaths per house, simply because the lower-class apartments were more densely populated. In fact, per capita death rates were unrelated to class. One of the most severely affected houses was locally known to be one of the cleanest, unlikely to suffer more from miasma than many filthier houses that had fewer cholera cases. The local work house which should have been hit worst, reported remarkably few deaths.
When Whitehead heard of Snow's water-borne explanation, he set out to debunk it like the others. He had seen several patients recover after drinking large quantities of water from the Broad Street pump (thereby almost stumbling on the cure for cholera), so he thought that discrediting Snow's theory would be easy. However, the more he spoke to survivors and former residents who had fled the epidemic, the more supporting evidence he found. Where Snow had tallied deaths and linked them to the pump, Whitehead added survivors and linked them to alternate water sources (or beer consumption instead of water).
Eventually, it was Whitehead who discovered the source of the epidemic: by chance he stumbled on the record of the death of a baby girl, reportedly from diarrhea, who fell sick a few days before the outbreak. The girl's address was immediately next to the Broad Street pump, and the family used a (officially nonexistent) cesspool located just a few feet from the pump for their household waste. By now, Snow's and Whitehead's case was convincing enough to warrant an inspection of the well. The final confirmation came when the cess pool was found to be leaking into the well.
These results were reported by the vestry of St. James parish, and both Snow and Whitehead wrote extensively about the cholera outbreak in the following years. Still, the scientific community clung to miasma and class/morals-based theories. The official report of the national Board of Health blamed miasma for the 1854 epidemic and all but ridiculed Snow's water-borne theory.
And finally, we come to the map. Snow started working on it several months after the outbreak. Several maps had been created to analyze the cholera epidemic, but these weren't linked to rigorous scientific theorizing and data. By reducing the map to streets, houses, pumps and cholera deaths, Snow made a striking visual case for his theory. A revised version was included in the St. James vestry's report, and this contains the most innovative technique used on the map: a dividing line shows groups houses together in terms of which well is closest by walking distance (a so-called Voronoi diagram). The geography of Soho streets and alleys meant that walking time and distance as the crow flies were not always correlated. This version posed an even more striking argument linking the Broad Street pump to the 1854 cholera epidemic.
While the map was important, Johnson shows that it - and the theories it supported - would never have seen the light of day without the incredible depth of local, even amateur, knowledge that Snow and Whitehead brought to it. And while the map legitimized the first correct science-based response to a cholera epidemic, its authority was based in the scientific inquiry behind it.
Johnson also argues that the epidemic was as much a product of the city as the ultimate measures to prevent it. Cholera could never have spread as virulently without the incredible population density found in London in the 1850s; it would never have found a way to spread if London hadn't been overwhelmed by the sheer quantity of human waste produced by this population. On the other hand, two working class men (John Snow and Henry Whitehead) would hardly have gained the education and reputation to influence public health policy anywhere outside the socially mobile city; high population density also meant that Snow and Whitehead were able to gather enough data to convincingly prove their theory.
The massive reconstruction of the sewage system that followed many years later (and only after another decimating epidemic) essentially rid London of cholera. This example, not only allowed other cities to improve their sanitation cities, it also proved the enduring viability of the city. Widespread ideas that cities of a million of more people were destined to self-destruct and drown in their own waste were refuted once and for all.
Links:
Online resources for the book (incl. links to the map and the UCLA department of epidemiology's John Snow site).
Steven Johnson's TED talk on the cholera epidemic.
The map (without the Voronoi diagram showing walking time).
Review of Johnson's first book, "Interface Culture."
Get The Ghost Map: The Story of London's Most Terrifying Epidemic--and How It Changed Science, Cities, and the Modern World
.Get Interface Culture
.
Thursday
Hypios
A while back I was excited to discover Innocentive, an online platform to match scientists around the world with people who needed a problem solved. One of the greatest things about it was the high proportion of solutions that were submitted (and accepted) from Brazil, Russia and other emerging economies. They have since developed to become more of an open innovation platform. Whereas their challenges used to be exclusively in the hard sciences, there are now a few business challenges, searches for suppliers etc. There are also "Ideation" challenges that require less technical know-how. They have an entire section on developing country problems now, too.
Hypios is taking the same idea in a slightly different direction. They have a similar platform, but they extend it to include advertising, marketing, social sciences, humanities etc. They also include a social networking angle and the opportunity to solve problems as a team, not just individually. Right now they're in beta and still setting up the site. They're using their own problems to demonstrate how the platform might work to source algorithms or marketing materials. It will be interesting to see how they do - and how they compete with Innocentive. A comparison of both companies' IP approaches might be interesting too.
Hypios is taking the same idea in a slightly different direction. They have a similar platform, but they extend it to include advertising, marketing, social sciences, humanities etc. They also include a social networking angle and the opportunity to solve problems as a team, not just individually. Right now they're in beta and still setting up the site. They're using their own problems to demonstrate how the platform might work to source algorithms or marketing materials. It will be interesting to see how they do - and how they compete with Innocentive. A comparison of both companies' IP approaches might be interesting too.
Friday
Dissertation abstract
Now that the dissertation draft is complete, I feel it's time to put my abstract online for those people who want a bit more than my "elevator spiel" about what I actually wrote. Find it here thanks to Google Docs.
Monday
Innovation has a long way to go in Bangalore
As the headlines proclaim Bangalore to be the next Silicon Valley, more critical voices are often drowned out. This one will hopefully fare better. NASSCOM and BCG recently published a report on the innovation ecosystem for the Indian IT system. The following graphic from the report basically sums up their conclusions: India has a long, long way to go.
In addition, the authors found that the willingness and initiative of Indian IT firms to invest in firm-level innovation could use a massive increase.
The Indian innovation system has a lot of potential for improvement. Given that the the Bangalore IT cluster developed in response to foreign (not domestic) demand, one could ask whether other elements could also be "imported" into the local innovation system. For example, it appears that much of the more fundamental research in Bangalore's corporate R&D labs is being conducted by US-trained researchers. On the one hand, the knowledge and relationships that these researchers bring with them may help accelerate the development of Bangalore's innovation system. On the other hand, relying on foreign research training is definitely not sustainable.
In addition, the authors found that the willingness and initiative of Indian IT firms to invest in firm-level innovation could use a massive increase.
The Indian innovation system has a lot of potential for improvement. Given that the the Bangalore IT cluster developed in response to foreign (not domestic) demand, one could ask whether other elements could also be "imported" into the local innovation system. For example, it appears that much of the more fundamental research in Bangalore's corporate R&D labs is being conducted by US-trained researchers. On the one hand, the knowledge and relationships that these researchers bring with them may help accelerate the development of Bangalore's innovation system. On the other hand, relying on foreign research training is definitely not sustainable.
Sunday
R&D Outsourcing and the Economics of Innovation
The Institute for the Future (ITFT) has set up a project called Delta Scan, speculating on the future of science and technology for the years 2005-2055. It includes a plausible argument on increased R&D outsourcing and offshoring. And a good, concise collection of references.
A shift in R&D processes from “ivory tower” models to global networks of contractors and alliances could have a significant impact on the economics of innovation. [...]
Over the next 20 years, the geography of R&D may shift again – from regional clusters in the developed world to global networks with large outsourced operations in the developing world. India and China, in particular, will provide large pools of highly skilled workers at 25% to 50% of the cost of their counterparts in the West and Japan. The Indian government estimates that outsourced R&D in India currently generates about $1 billion annually; this is projected to rise to $11 billion by 2008, mostly in software. China's manufacturing capacity gives it a natural advantage in computer hardware R&D. Both nations also have the long-term potential for large-scale work in pharmaceuticals and biotechnology.
The trend towards sourcing R&D off-shore may change the economic significance of sourcing services off-shore generally. Up to now, the practice has tended to free up capital and labour in developing countries and provided resources for the creation of new, higher value-added enterprises. However, some of the R&D jobs that may be outsourced are among the most highly prized.
Serious obstacles still remain, in particular, quality control and the protection of intellectual property. Furthermore, for the near future R&D outsourcing will be limited to 'modular innovation', namely incremental improvements in existing lines of research. Radical, breakthrough innovation will continue to be the domain of regional clusters in developed countries.
A shift in R&D processes from “ivory tower” models to global networks of contractors and alliances could have a significant impact on the economics of innovation. [...]
Over the next 20 years, the geography of R&D may shift again – from regional clusters in the developed world to global networks with large outsourced operations in the developing world. India and China, in particular, will provide large pools of highly skilled workers at 25% to 50% of the cost of their counterparts in the West and Japan. The Indian government estimates that outsourced R&D in India currently generates about $1 billion annually; this is projected to rise to $11 billion by 2008, mostly in software. China's manufacturing capacity gives it a natural advantage in computer hardware R&D. Both nations also have the long-term potential for large-scale work in pharmaceuticals and biotechnology.
The trend towards sourcing R&D off-shore may change the economic significance of sourcing services off-shore generally. Up to now, the practice has tended to free up capital and labour in developing countries and provided resources for the creation of new, higher value-added enterprises. However, some of the R&D jobs that may be outsourced are among the most highly prized.
Serious obstacles still remain, in particular, quality control and the protection of intellectual property. Furthermore, for the near future R&D outsourcing will be limited to 'modular innovation', namely incremental improvements in existing lines of research. Radical, breakthrough innovation will continue to be the domain of regional clusters in developed countries.
Monday
National Innovation System timelines: India, China, USA
Found on Yoshiki Mikami's virtual class website.
This is Mikami's overview of the evolution of the Indian national innovation system from the 18th century to the present. The data on more recent decades isn't quite comprehensive, but it's useful to get a timeline.
Mikami's site also has timelines for China and the USA.
This is Mikami's overview of the evolution of the Indian national innovation system from the 18th century to the present. The data on more recent decades isn't quite comprehensive, but it's useful to get a timeline.
Mikami's site also has timelines for China and the USA.
Sunday
Great new resource
Thursday
Horizontal innovation networks: open source software and surfing
Eric von Hippel, Horizontal innovation networks - by and for users, 2002, MIT Sloan Working Paper
Von Hippel's horizontal innovation networks are in many ways modern day versions of collective invention. He examines various networks of users engaged in the production, distribution and consumption (use) of innovations.
Even as the intellectual property rights discussion is heating up, the evidence suggests that patents (or copyright) and licensing aren't optimal ways of appropriating returns, except in the chemicals and pharmaceuticals industries -- and, therefore aren't the best way of encouraging innovation. One frequently mentioned alternative to patent regimes is the open-source movement, an example of von Hippel's horizontal networks. So, when might such horizontal innovation networks work?
User networks can function entirely independently of manufactureres when
(1) at least some users have sufficient incentive to innovate;
(2) at least some users have an incentive to voluntarily reveal their innovations, and
(3) diffusion of innovations by users is low cost and can compete with commercial production and distribution.
When only the first tow conditions hold, a patten of user innovation and trial and improvement will occur within user networks, followed by commercial manufacture and distribution of innovations that prove to be of general interest.
Non-users might also contribute to these networks (e.g. suppliers, producers of complementary products). However, this isn't necessary for them to work.
While user innovation in open source software is well known, it is not a unique case. Von Hippel's second example is high performance windsurfing. Here, users experiment with new equipment designs and techniques which are traded in the windsurfing community, mainly at competitive events where the core of the community regularly meets.
One question that often comes up in large-scale innovative collaboration is whether participants in an innovation network need to feel a sense of community. Von Hippel argues that windsurfers are members of a community (which forms the basis of trust and sharing), whereas open source programmers aren't. However, even in open source projects there may be communal norms, such as "generalized reciprocity" at work.
He cites a different concern: level of competitiveness. The effect of competition on willingness to free reveal has recently been documented by Franke and Shah (2002) in their study of four communities of sports enthusiasts ... They found that a statement regarding free revealing of innovations ... was significantly less agreed with by innovating members of the more rivalrous communities than by innovators within the less rivalrous communities ... They also found that assistance provided by one community member to another during the innovation development process was significantly less within the more competitive communities.
An interesting question raised by von Hippel at the end of the paper is whether there might be "life cycle" patterns, e.g. that user innovation is stronger in the early stages of a product's life cycle and weaker as it reaches maturity.
Summary
First, von Hippel explains why users innovate in the first place:
- In some product categories users may reasonably expect a higher reward from innovating than can manufacturers. For example, if a user firm develops a new process machine for in-house use that enables it to produce a major new product line, and keeps its innovation secret while benefiting from it, it may make more profit from that machine than would a manufacturer-innovator that must reveal the machine in order to sell it.
- Second, user innovation costs can be significantly lower than manufacturer innovation costs when the problem-solving work of innovation developers requires access to "sticky" -- costly to transfer -- information regarding user needs and the context of use. cf. Ogawa (1997)
Often lead users will be the first to innovate. Given that lead users experience needs in advance of the bulk of a target market, the nature, risks, and eventual size of that target market are often not clear to manufacturers. This lack of clarity can reduce manufacturers' incentives to innovate, and increase the likelihood that lead users will be the first to develop their own innovative solutions for needs that later prove to represent mainstream market demand.
However, even when users innovate, they need not necessarily reveal their innovations to a larger public that includes collaborators, competitors, and free riders, essentially making them a public good. Why do they?
Empirical studies show innovating users often choose to freely reveal details of their innovations to other users and to manufacturers as well. ...Free revealing can be the dominant way innovations are diffused in some fields and under some conditions. This happens when the benefits from free revealing exceed the benefits that are practically obtainable from licensing or secrecy:
- obtaining patents and licensing intellectual property may be impossible, too costly, or not an effective form of protection,
- similarly, maintaining a trade secret may be too costly or impractical once a product is on the market,
- faced with the choice between voluntary free revealing now and involuntary free revealing later, innovators may have more incentive to free reveal voluntarily, (which is what happened in Allen's study of collective invention in 19th century iron furnaces),
- in addition to Allen's findings, Harhoff et al find that an innovator may have an interest in rapid diffusion since an innovation that is freely revealed and adopted by others can become an informal standard that may preempt the development an/or commercialization of other versions of the innovation,
- as in the case of collective invention, innovators may be able to benefit through reputation increases among peers and potential employers (and firms may benefit from a reputation of being employers of contributors to open source and similar projects),
- there may be intrinsic benefits in terms of enjoyment and learning that arise from participation in horizontal innovation networks,
- finally communal norms, e.g. "generalized reciprocity," may also play a role.
Even if users free reveal, it is not clear that they will be able to diffuse the innovation. What does this depend on?
Often innovation streams that have a large cumulative impact are likely to be made up of relatively small individual innovations. We have also seen ... that benefits to innovators from free-revealing, while higher than benefits they could expect from licensing or secrecy, may well be low. On this basis we speculate that most innovations diffused via a user innovation network are likely to be of relatively low benefit to both diffusers and adopters, and so must be diffused at a low cost if they are to be diffused at all.
Von Hippel's horizontal innovation networks are in many ways modern day versions of collective invention. He examines various networks of users engaged in the production, distribution and consumption (use) of innovations.
Even as the intellectual property rights discussion is heating up, the evidence suggests that patents (or copyright) and licensing aren't optimal ways of appropriating returns, except in the chemicals and pharmaceuticals industries -- and, therefore aren't the best way of encouraging innovation. One frequently mentioned alternative to patent regimes is the open-source movement, an example of von Hippel's horizontal networks. So, when might such horizontal innovation networks work?
User networks can function entirely independently of manufactureres when
(1) at least some users have sufficient incentive to innovate;
(2) at least some users have an incentive to voluntarily reveal their innovations, and
(3) diffusion of innovations by users is low cost and can compete with commercial production and distribution.
When only the first tow conditions hold, a patten of user innovation and trial and improvement will occur within user networks, followed by commercial manufacture and distribution of innovations that prove to be of general interest.
Non-users might also contribute to these networks (e.g. suppliers, producers of complementary products). However, this isn't necessary for them to work.
While user innovation in open source software is well known, it is not a unique case. Von Hippel's second example is high performance windsurfing. Here, users experiment with new equipment designs and techniques which are traded in the windsurfing community, mainly at competitive events where the core of the community regularly meets.
One question that often comes up in large-scale innovative collaboration is whether participants in an innovation network need to feel a sense of community. Von Hippel argues that windsurfers are members of a community (which forms the basis of trust and sharing), whereas open source programmers aren't. However, even in open source projects there may be communal norms, such as "generalized reciprocity" at work.
He cites a different concern: level of competitiveness. The effect of competition on willingness to free reveal has recently been documented by Franke and Shah (2002) in their study of four communities of sports enthusiasts ... They found that a statement regarding free revealing of innovations ... was significantly less agreed with by innovating members of the more rivalrous communities than by innovators within the less rivalrous communities ... They also found that assistance provided by one community member to another during the innovation development process was significantly less within the more competitive communities.
An interesting question raised by von Hippel at the end of the paper is whether there might be "life cycle" patterns, e.g. that user innovation is stronger in the early stages of a product's life cycle and weaker as it reaches maturity.
Summary
First, von Hippel explains why users innovate in the first place:
- In some product categories users may reasonably expect a higher reward from innovating than can manufacturers. For example, if a user firm develops a new process machine for in-house use that enables it to produce a major new product line, and keeps its innovation secret while benefiting from it, it may make more profit from that machine than would a manufacturer-innovator that must reveal the machine in order to sell it.
- Second, user innovation costs can be significantly lower than manufacturer innovation costs when the problem-solving work of innovation developers requires access to "sticky" -- costly to transfer -- information regarding user needs and the context of use. cf. Ogawa (1997)
Often lead users will be the first to innovate. Given that lead users experience needs in advance of the bulk of a target market, the nature, risks, and eventual size of that target market are often not clear to manufacturers. This lack of clarity can reduce manufacturers' incentives to innovate, and increase the likelihood that lead users will be the first to develop their own innovative solutions for needs that later prove to represent mainstream market demand.
However, even when users innovate, they need not necessarily reveal their innovations to a larger public that includes collaborators, competitors, and free riders, essentially making them a public good. Why do they?
Empirical studies show innovating users often choose to freely reveal details of their innovations to other users and to manufacturers as well. ...Free revealing can be the dominant way innovations are diffused in some fields and under some conditions. This happens when the benefits from free revealing exceed the benefits that are practically obtainable from licensing or secrecy:
- obtaining patents and licensing intellectual property may be impossible, too costly, or not an effective form of protection,
- similarly, maintaining a trade secret may be too costly or impractical once a product is on the market,
- faced with the choice between voluntary free revealing now and involuntary free revealing later, innovators may have more incentive to free reveal voluntarily, (which is what happened in Allen's study of collective invention in 19th century iron furnaces),
- in addition to Allen's findings, Harhoff et al find that an innovator may have an interest in rapid diffusion since an innovation that is freely revealed and adopted by others can become an informal standard that may preempt the development an/or commercialization of other versions of the innovation,
- as in the case of collective invention, innovators may be able to benefit through reputation increases among peers and potential employers (and firms may benefit from a reputation of being employers of contributors to open source and similar projects),
- there may be intrinsic benefits in terms of enjoyment and learning that arise from participation in horizontal innovation networks,
- finally communal norms, e.g. "generalized reciprocity," may also play a role.
Even if users free reveal, it is not clear that they will be able to diffuse the innovation. What does this depend on?
Often innovation streams that have a large cumulative impact are likely to be made up of relatively small individual innovations. We have also seen ... that benefits to innovators from free-revealing, while higher than benefits they could expect from licensing or secrecy, may well be low. On this basis we speculate that most innovations diffused via a user innovation network are likely to be of relatively low benefit to both diffusers and adopters, and so must be diffused at a low cost if they are to be diffused at all.
Wednesday
Collective invention
Collective invention, R. C. Allen, 1983, Journal of Economic Behavior and Organization
All too often R&D and innovation are used interchangeably. Obviously, innovation doesn't always happen in a lab, under a dedicated budget or even with the explicit intention of increasing profits.
Allen describes an alternative way of organizing innovation: collective invention.
Allen focuses on the iron industry and the development of blast furnaces in the 19th century. He finds that competing firms freely exchanged information on improvements to the design of their furnaces through a) informal networks and b) engineering societies and their publications. This meant that current and potential competitors could easily acquire knowledge of best practices. The first firm to experiment with a new design would carry the risk that the change would increase production cost. Subsequent adopters of the design could then benefit from the first firm's experience and data. This let firms "leapfrog" each other and led to a high rate of innovation.
Why would firms share valuable information so freely with their competitors and even potential entrants to the industry? Allen identifies several reasons:
- Design improvements were incremental and didn't legally qualify as "novel," i.e., they couldn't be patented.Under circumstances of competition and non-approbriablility, an individual inventor or a firm allocating resources to invention could expect an economic return far less than the social value of any invention.
- Once a new furnace was built, it was very costly if not impossible to keep the design changes secret since industry consultants and many (poorly paid and easily bribed) workers were involved in setting it up and running it.
- Since returns on design improvements couldn't be appropriated, there was nothing to be gained from keeping the innovations secret. Yet, there could be some gains in making it available.
- There was no R&D budget -- any design improvements were tested and implemented when a new furnace was built, severely limiting the pace of innovation that any single firm could achieve. Firms expected that their sharing of information would be reciprocated so that they would benefit from industry-wide experience and best practices when they built the next furnace. By spreading costs and risks among firms, collective invention meant that competitive industries could have high rates of invention even if the enventions were not patentable.
- Managers of the firm often had professional ambitions that could be advanced by releasing information about the operation of their firms. Under those circumstances the profits of the firm might be sacrificed and information released.
- Firms seem often to have engaged in competitions in advancing size or output. ... Since you cannot win the contest unless you reveal valuable information, these competitions fostered information release.
- Another reason that firms might have released technical information is that that behaviour might have been profitable. Hirschleifer (1971) has argued that inventors can be compensated for their efforts if they successfully speculate in assets that appreciate in value due to the invention. ... The characteristics of the situation that made collective invention profitable were the specificity of the resulting technical progress to lacal conditions and the fact that the Cleveland industry was only a small part of the world industry so that the price of iron could be regarded as exogenous. Under these circumstances, the owners of the natrual resource would actively foster information propagation since they could not lose by it and might well gain.
All too often R&D and innovation are used interchangeably. Obviously, innovation doesn't always happen in a lab, under a dedicated budget or even with the explicit intention of increasing profits.
Allen describes an alternative way of organizing innovation: collective invention.
Allen focuses on the iron industry and the development of blast furnaces in the 19th century. He finds that competing firms freely exchanged information on improvements to the design of their furnaces through a) informal networks and b) engineering societies and their publications. This meant that current and potential competitors could easily acquire knowledge of best practices. The first firm to experiment with a new design would carry the risk that the change would increase production cost. Subsequent adopters of the design could then benefit from the first firm's experience and data. This let firms "leapfrog" each other and led to a high rate of innovation.
Why would firms share valuable information so freely with their competitors and even potential entrants to the industry? Allen identifies several reasons:
- Design improvements were incremental and didn't legally qualify as "novel," i.e., they couldn't be patented.Under circumstances of competition and non-approbriablility, an individual inventor or a firm allocating resources to invention could expect an economic return far less than the social value of any invention.
- Once a new furnace was built, it was very costly if not impossible to keep the design changes secret since industry consultants and many (poorly paid and easily bribed) workers were involved in setting it up and running it.
- Since returns on design improvements couldn't be appropriated, there was nothing to be gained from keeping the innovations secret. Yet, there could be some gains in making it available.
- There was no R&D budget -- any design improvements were tested and implemented when a new furnace was built, severely limiting the pace of innovation that any single firm could achieve. Firms expected that their sharing of information would be reciprocated so that they would benefit from industry-wide experience and best practices when they built the next furnace. By spreading costs and risks among firms, collective invention meant that competitive industries could have high rates of invention even if the enventions were not patentable.
- Managers of the firm often had professional ambitions that could be advanced by releasing information about the operation of their firms. Under those circumstances the profits of the firm might be sacrificed and information released.
- Firms seem often to have engaged in competitions in advancing size or output. ... Since you cannot win the contest unless you reveal valuable information, these competitions fostered information release.
- Another reason that firms might have released technical information is that that behaviour might have been profitable. Hirschleifer (1971) has argued that inventors can be compensated for their efforts if they successfully speculate in assets that appreciate in value due to the invention. ... The characteristics of the situation that made collective invention profitable were the specificity of the resulting technical progress to lacal conditions and the fact that the Cleveland industry was only a small part of the world industry so that the price of iron could be regarded as exogenous. Under these circumstances, the owners of the natrual resource would actively foster information propagation since they could not lose by it and might well gain.
Monday
Competition or cooperation?
Much has been made of the importance of alliances in the biotech industry. Large pharmaceutical firms are particularly good at testing and marketing drugs, but are facing pipeline shortages, while smaller start-ups are better at research and innovation. Alliances can help each benefit from the other. Research alliances between firms and with universities are also important drivers of innovation - as demonstrated by W. Powell and others.
Is this a phenomenon specific to biotech or a general one? Are the gales of creative destruction no longer a threat to large incumbent firms? Joshua Gans, David Hsu and Scott Stern set out to formulate some general rules in a paper presented by K@W.
The researchers found that the likelihood of start-ups cooperating with established companies depends upon three factors:
1) the strength of the startups’ intellectual property rights;
2) whether they have relationships with intermediaries such as venture capitalists; and
3) whether their industry requires big investments in things such as manufacturing and distribution.
To draw their conclusions, they surveyed 118 technology start-ups.
“In economic environments like the biotechnology industry – where patents are relatively effective in protecting [intellectual property rights], firms face high relative investment costs, and brokers are available to facilitate trade – start-up innovators tend to earn their returns from innovation through the market for ideas, acting as an upstream supplier of ‘technology’ rather than as a horizontal innovation-oriented competitor,” the authors write. “In contrast, when investment costs for the entrant are relatively low and the technological innovation is not protected by patents, as in the disk-drive industry, the disclosure threat tends to foreclose the ideas market. Start-up innovators in this environment are more likely to commercialize their innovations through product market competition.”
Patents protect start-ups from having their inventions stolen by incumbents. That, in turn, gives them greater leverage in negotiations. “Under cooperation, negotiating over the sale of an idea inevitably involves a disclosure risk, eroding the bargaining position of the start-up and reducing the incumbent’s willingness to pay,” the researchers explain. “Increasing the strength of [intellectual property rights] reduces the expropriation threat for either strategy, and thus it increases the absolute expected returns to start-up innovators.” Negotiations often lead to cooperative relationships such as joint ventures and even acquisitions.
It’s not only the small biotechs that have embraced the cooperative model of innovation, Hsu pointed out in an interview. Merck & Co., the giant drug maker based in Whitehouse Station, N.J., has made partnering a cornerstone of its strategy for bringing new drugs to the market. Two of its leading products – Fosamax, an osteoporosis drug, and Cozaar/Hyzaar, a hypertension medication – came to the company via license agreements.
Of course, negotiating, like marriage, requires a partner, and finding the right one can make the difference between happiness and divorce. But as a rule, start-ups aren’t well-suited to finding good partners. They tend to be small and thus stretched thin. What they need are matchmakers, that is, intermediaries such as venture capitalists, lawyers and accountants.
Intermediaries often specialize in particular industries, working mostly with, say, biotech or information-technology companies. As a result, they have a deep knowledge of the industry’s players; they know whether those players are looking for partners and whether they can be trusted in negotiations. Likewise, they can vouch for the value of a startup’s innovation and the ability of its founders. Hsu and his co-authors find that start-ups that work with intermediaries are more likely to choose cooperation over competition.
Finally, “As the sunk costs of product-market entry increase, the gains from trade between start-up innovators and incumbents also increase, so start-ups will be more likely to forgo competition,” they point out.
What does all this mean if you are an entrepreneur with a company or a manager within a big, established firm? Ideally, it will help you pick the right path, cooperation or competition. But as Hsu points out, no formula fits all companies within an industry. Two of the best-known and biggest biotech companies – Amgen and Genentech, both based in California – partnered early on with established companies. But they invested the earnings from those partnerships in becoming fully integrated pharmaceutical companies.
Is this a phenomenon specific to biotech or a general one? Are the gales of creative destruction no longer a threat to large incumbent firms? Joshua Gans, David Hsu and Scott Stern set out to formulate some general rules in a paper presented by K@W.
The researchers found that the likelihood of start-ups cooperating with established companies depends upon three factors:
1) the strength of the startups’ intellectual property rights;
2) whether they have relationships with intermediaries such as venture capitalists; and
3) whether their industry requires big investments in things such as manufacturing and distribution.
To draw their conclusions, they surveyed 118 technology start-ups.
“In economic environments like the biotechnology industry – where patents are relatively effective in protecting [intellectual property rights], firms face high relative investment costs, and brokers are available to facilitate trade – start-up innovators tend to earn their returns from innovation through the market for ideas, acting as an upstream supplier of ‘technology’ rather than as a horizontal innovation-oriented competitor,” the authors write. “In contrast, when investment costs for the entrant are relatively low and the technological innovation is not protected by patents, as in the disk-drive industry, the disclosure threat tends to foreclose the ideas market. Start-up innovators in this environment are more likely to commercialize their innovations through product market competition.”
Patents protect start-ups from having their inventions stolen by incumbents. That, in turn, gives them greater leverage in negotiations. “Under cooperation, negotiating over the sale of an idea inevitably involves a disclosure risk, eroding the bargaining position of the start-up and reducing the incumbent’s willingness to pay,” the researchers explain. “Increasing the strength of [intellectual property rights] reduces the expropriation threat for either strategy, and thus it increases the absolute expected returns to start-up innovators.” Negotiations often lead to cooperative relationships such as joint ventures and even acquisitions.
It’s not only the small biotechs that have embraced the cooperative model of innovation, Hsu pointed out in an interview. Merck & Co., the giant drug maker based in Whitehouse Station, N.J., has made partnering a cornerstone of its strategy for bringing new drugs to the market. Two of its leading products – Fosamax, an osteoporosis drug, and Cozaar/Hyzaar, a hypertension medication – came to the company via license agreements.
Of course, negotiating, like marriage, requires a partner, and finding the right one can make the difference between happiness and divorce. But as a rule, start-ups aren’t well-suited to finding good partners. They tend to be small and thus stretched thin. What they need are matchmakers, that is, intermediaries such as venture capitalists, lawyers and accountants.
Intermediaries often specialize in particular industries, working mostly with, say, biotech or information-technology companies. As a result, they have a deep knowledge of the industry’s players; they know whether those players are looking for partners and whether they can be trusted in negotiations. Likewise, they can vouch for the value of a startup’s innovation and the ability of its founders. Hsu and his co-authors find that start-ups that work with intermediaries are more likely to choose cooperation over competition.
Finally, “As the sunk costs of product-market entry increase, the gains from trade between start-up innovators and incumbents also increase, so start-ups will be more likely to forgo competition,” they point out.
What does all this mean if you are an entrepreneur with a company or a manager within a big, established firm? Ideally, it will help you pick the right path, cooperation or competition. But as Hsu points out, no formula fits all companies within an industry. Two of the best-known and biggest biotech companies – Amgen and Genentech, both based in California – partnered early on with established companies. But they invested the earnings from those partnerships in becoming fully integrated pharmaceutical companies.
Tuesday
Creative destruction revisited
Among innovation economists Joseph Schumpeter is revered for his concept of 'creative destruction.' Still, he might not have been getting enough credit.
A while back, The Economist published a piece on innovation by monopolists. Traditionally, monopoly (or market dominance) is considered to discourage firms from innovating or doing anything else that might be in consumers' interests. Paradoxically, there is evidence that some of the firms with the largest market shares have above-average rates of innovation, as measured by R&D investments.
Why does it happen? A new paper by Federico Etro, of the University of Milan, aims to resolve Mr Arrow's paradox. He sets out a model in which a market leader has a greater incentive than any other firm to keep innovating and thus stay on top. Blessed with scale and market knowledge, it is better placed than potential rivals to commit itself to financing innovations. Oddly — paradoxically, if you like — in fighting to maintain its monopoly it acts more competitively than firms in markets in which there is no obviously dominant player.
Of course, this doesn't mean that all monopolies are wrongfully accused of abusing their market power. For one, Etro's findings only hold under certain conditions:
The most important requirement for this result is a lack of barriers to entry: these might include, for example, big capital outlays to fund the building of new laboratories, or regulatory or licensing restrictions that make it hard for new firms to threaten an incumbent. If there are no such barriers, a monopolist will have an excellent reason to innovate before any potential competitor comes up with the next new thing. It stands to lose its current, bloated profits if it does not; it stands to gain plenty from continued market dominance if it does.
If the world works in the way Mr Etro supposes, the fact that a dominant firm remains on top might actually be strong evidence of vigorous competition. However, observers (including antitrust authorities) may well find it difficult to work out whether a durable monopoly is the product of brilliant innovation or the deliberate strangulation of competitors. More confusing still, any half-awake monopolist will engage in some of the former in order to help bring about plenty of the latter. The very ease of entry, and the aggressiveness of the competitive environment, are what spur monopolists to innovate so fiercely.
But what if there are barriers to entry? These tend to make the dominant firm less aggressive in investing in new technologies—in essence, because its monopoly with the existing technology is less likely to be challenged. Over time, however, other companies can innovate and gradually overcome the barriers—“leapfrogging”, as Mr Etro calls it. Meanwhile, the monopolist lives on marked time, burning off the fat of its past innovations.
A while back, The Economist published a piece on innovation by monopolists. Traditionally, monopoly (or market dominance) is considered to discourage firms from innovating or doing anything else that might be in consumers' interests. Paradoxically, there is evidence that some of the firms with the largest market shares have above-average rates of innovation, as measured by R&D investments.
Why does it happen? A new paper by Federico Etro, of the University of Milan, aims to resolve Mr Arrow's paradox. He sets out a model in which a market leader has a greater incentive than any other firm to keep innovating and thus stay on top. Blessed with scale and market knowledge, it is better placed than potential rivals to commit itself to financing innovations. Oddly — paradoxically, if you like — in fighting to maintain its monopoly it acts more competitively than firms in markets in which there is no obviously dominant player.
Of course, this doesn't mean that all monopolies are wrongfully accused of abusing their market power. For one, Etro's findings only hold under certain conditions:
The most important requirement for this result is a lack of barriers to entry: these might include, for example, big capital outlays to fund the building of new laboratories, or regulatory or licensing restrictions that make it hard for new firms to threaten an incumbent. If there are no such barriers, a monopolist will have an excellent reason to innovate before any potential competitor comes up with the next new thing. It stands to lose its current, bloated profits if it does not; it stands to gain plenty from continued market dominance if it does.
If the world works in the way Mr Etro supposes, the fact that a dominant firm remains on top might actually be strong evidence of vigorous competition. However, observers (including antitrust authorities) may well find it difficult to work out whether a durable monopoly is the product of brilliant innovation or the deliberate strangulation of competitors. More confusing still, any half-awake monopolist will engage in some of the former in order to help bring about plenty of the latter. The very ease of entry, and the aggressiveness of the competitive environment, are what spur monopolists to innovate so fiercely.
But what if there are barriers to entry? These tend to make the dominant firm less aggressive in investing in new technologies—in essence, because its monopoly with the existing technology is less likely to be challenged. Over time, however, other companies can innovate and gradually overcome the barriers—“leapfrogging”, as Mr Etro calls it. Meanwhile, the monopolist lives on marked time, burning off the fat of its past innovations.
Monday
Global R & D
Harnessing innovation: R&D in a global growth economy, Economist Intelligence Unit, 2004
The Economist Intelligence Unit has published a new white paper: Harnessing Innovation. It is based on an online survey and in-depth interviews with companies such as Agilent, IBM, AMD Siemens, BMW and Nokia.
From the results:
Market- and customer-driven innovation has moved from buzz-word to reality, although keeping customers focused on innovation projects is a significant challenge once initial enthusiasm has ebbed.
The survey clearly indicates that R&D is high up the hierarchy of corporate priorities — asked to identify their overall strategic priorities, respondents put product development third, just behind cost-cutting and strengthening customer relationships. But the emphasis rests heavily on the D(evelopment)part of the equation. Market pressures to keep up with competitors’ innovations and to satisfy more demanding customers are the two top drivers of R&D activity, according to survey respondents. In this environment, anything companies can do to reduce the odds of failure as they embark on new research projects is critical.
A more market-oriented approach to R&D is driving R&D leaders to work more closely with customers as they develop new products and services. But there are challenges in this approach. While collaboration is key for creating demand-driven innovations, survey participants also noted that maintaining customer involvement ranked as one of the leading roadblocks to successful R&D projects. This evolving innovation landscape promises a more effective R&D process, one that sharpens the decision-making process as firms choose where to allocate their R&D spend and that increases the chances of launching commercially viable new products. It may also encourage an even more pronounced division of labour in the world of research, with governments, universities and start-up companies focusing on “blue-sky” research projects and companies working on more incremental development activities.
R&D has gone global with many firms distributing their R&D centers around the world. This is driven by quality and time-to-market factors rather than just cost-cutting. In some cases research is still centralized and locations further afield focus on development and process innovation.
Competition for talent, new technologies and easier market access have accelerated the process of R&D globalisation, with countries such as India and China hosting significant volumes of R&D activity for multinationals. Cost is a driver of globalisation too, but its significance can be overplayed as far as R&D goes. Once infrastructure and coordination costs for managing distributed R&D facilities are included, the total savings are not as huge as popular headlines suggest. Speed of development is a more important benefit of the global research economy.
The survey finds organizational problems to be the single greatest challenge for R&D efforts. It is therefore especially interesting to note the different models implemented by firms that distribute R&D activities globally.
If you have research and development activities in time zones around the world,how do you coordinate that activity?
The report provides very little information on how global R&D management is implemented, although the challenges are certainly highlighted.
Although there are clear economic reasons for locating certain R&D work in lower-cost countries, relocating R&D resources solely because of labour costs is a losing proposition. “There is no such thing as low-cost intellectual property”, declares IBM’s Dr Horn. Aside from the travel, coordination and communications expense, the labour rate itself is climbing as recipient countries become more sophisticated economies. The current rule of thumb among India’s IT professionals is to expect a 15% pay rise every year. Such narrowing becomes even more pronounced once top management cost is included.
More important to the globalisation trend is the ability to innovate around the clock. But it is no small task to manage this process. According to Fred Weber, chief technology officer for AMD, a leading semiconductor manufacturer, companies must avoid the pitfalls of compartmentalisation. “Whenever possible, we try to ensure that a remote site does not build up its own little fiefdom of products that it is making. Rather we aim to develop an integrated global engineering force. So we might split projects across multiple locations.”
AMD ’s latest Opteron processor resulted from teams working simultaneously in Texas, California, Singapore and Dresden in Germany, for example. But the challenges posed by this structure can be formidable, acknowledges Mr Weber. “When you see somebody everyday and you have lunch with them, you understand them a lot better than when you talk on the phone to them once a week and see them once a year.” Cultural differences further exacerbate this lack of face-to-face contact.
The Economist Intelligence Unit has published a new white paper: Harnessing Innovation. It is based on an online survey and in-depth interviews with companies such as Agilent, IBM, AMD Siemens, BMW and Nokia.
From the results:
Market- and customer-driven innovation has moved from buzz-word to reality, although keeping customers focused on innovation projects is a significant challenge once initial enthusiasm has ebbed.
The survey clearly indicates that R&D is high up the hierarchy of corporate priorities — asked to identify their overall strategic priorities, respondents put product development third, just behind cost-cutting and strengthening customer relationships. But the emphasis rests heavily on the D(evelopment)part of the equation. Market pressures to keep up with competitors’ innovations and to satisfy more demanding customers are the two top drivers of R&D activity, according to survey respondents. In this environment, anything companies can do to reduce the odds of failure as they embark on new research projects is critical.
A more market-oriented approach to R&D is driving R&D leaders to work more closely with customers as they develop new products and services. But there are challenges in this approach. While collaboration is key for creating demand-driven innovations, survey participants also noted that maintaining customer involvement ranked as one of the leading roadblocks to successful R&D projects. This evolving innovation landscape promises a more effective R&D process, one that sharpens the decision-making process as firms choose where to allocate their R&D spend and that increases the chances of launching commercially viable new products. It may also encourage an even more pronounced division of labour in the world of research, with governments, universities and start-up companies focusing on “blue-sky” research projects and companies working on more incremental development activities.
R&D has gone global with many firms distributing their R&D centers around the world. This is driven by quality and time-to-market factors rather than just cost-cutting. In some cases research is still centralized and locations further afield focus on development and process innovation.
Competition for talent, new technologies and easier market access have accelerated the process of R&D globalisation, with countries such as India and China hosting significant volumes of R&D activity for multinationals. Cost is a driver of globalisation too, but its significance can be overplayed as far as R&D goes. Once infrastructure and coordination costs for managing distributed R&D facilities are included, the total savings are not as huge as popular headlines suggest. Speed of development is a more important benefit of the global research economy.
The survey finds organizational problems to be the single greatest challenge for R&D efforts. It is therefore especially interesting to note the different models implemented by firms that distribute R&D activities globally.
If you have research and development activities in time zones around the world,how do you coordinate that activity?
30% Research and development is coordinated on a regional basis (eg,EMEA,the Americas)
30% Research and development is managed globally,with round-the-clock teams who work consecutively on the same projects
25% Research and development is carried out separately in each country
16% Other
The report provides very little information on how global R&D management is implemented, although the challenges are certainly highlighted.
Although there are clear economic reasons for locating certain R&D work in lower-cost countries, relocating R&D resources solely because of labour costs is a losing proposition. “There is no such thing as low-cost intellectual property”, declares IBM’s Dr Horn. Aside from the travel, coordination and communications expense, the labour rate itself is climbing as recipient countries become more sophisticated economies. The current rule of thumb among India’s IT professionals is to expect a 15% pay rise every year. Such narrowing becomes even more pronounced once top management cost is included.
More important to the globalisation trend is the ability to innovate around the clock. But it is no small task to manage this process. According to Fred Weber, chief technology officer for AMD, a leading semiconductor manufacturer, companies must avoid the pitfalls of compartmentalisation. “Whenever possible, we try to ensure that a remote site does not build up its own little fiefdom of products that it is making. Rather we aim to develop an integrated global engineering force. So we might split projects across multiple locations.”
AMD ’s latest Opteron processor resulted from teams working simultaneously in Texas, California, Singapore and Dresden in Germany, for example. But the challenges posed by this structure can be formidable, acknowledges Mr Weber. “When you see somebody everyday and you have lunch with them, you understand them a lot better than when you talk on the phone to them once a week and see them once a year.” Cultural differences further exacerbate this lack of face-to-face contact.
Tuesday
Diseconomies of scale
Another interesting piece by James Surowiecki, 'the pipeline problem' explains the new industry structure in life sciences. Large pharmaceutical firms may have branding and sales power, but encounter diseconomies of scale when it comes to R&D or innovation in general. As a consequence, their 'pipelines' have been drying up, and they have become to rely on smaller, more flexible and more entrepreneurial biotech firms to deliver efficient research.
The traditional pharmaceutical research model harks back to processes developed by German and Swiss chemical firms in the late nineteenth century, when chemists synthesized and screened thousands of compounds in search of a few potential new drug candidates. Although the methodology is more sophisticated now, success is still in many ways thought to be a matter of brute force: throw hundreds of scientists at a problem and hope for the best. It’s crapshoot economics; a few great successes can pay for myriad failures. So bigger has always been seen as better.
Today, though, the advantages of size are trumped by what are called “diseconomies” of scale: inertia, bureaucracy, risk aversion, clock-watching, office politics. Joseph Kim saw a lot of this firsthand, as a scientist at Merck for nine years, and now he likes to compare Merck to the Titanic. “Companies like Merck have fantastic scientists working for them, but they also have these middle and upper layers of managers who are just taking up space,” he said last week. “I like to call them ‘anti-bodies,’ because they just sit there being anti-everything. No one ever gets fired for saying no to a new idea.” Now, as the founder and the C.E.O. of a little biotech called VGX Pharmaceuticals, Kim has a novel type of aids drug in clinical trials and a promising drug for cancer in development.
It turns out that research and development doesn’t scale—that bigger may be worse. That’s why the engines of pharmaceutical innovation have for some time now been smaller biotech firms like VGX, which can concentrate on a few promising avenues of research and can offer enterprising scientists the freedom—and the potentially enormous rewards—of working as entrepreneurs. Just as, in the seventies, the locus of innovation in the tech business shifted from Goliaths like Digital and I.B.M. toward the smaller, more narrowly focussed start-ups of Silicon Valley, so it is shifting now from big to little pharma.
The traditional pharmaceutical research model harks back to processes developed by German and Swiss chemical firms in the late nineteenth century, when chemists synthesized and screened thousands of compounds in search of a few potential new drug candidates. Although the methodology is more sophisticated now, success is still in many ways thought to be a matter of brute force: throw hundreds of scientists at a problem and hope for the best. It’s crapshoot economics; a few great successes can pay for myriad failures. So bigger has always been seen as better.
Today, though, the advantages of size are trumped by what are called “diseconomies” of scale: inertia, bureaucracy, risk aversion, clock-watching, office politics. Joseph Kim saw a lot of this firsthand, as a scientist at Merck for nine years, and now he likes to compare Merck to the Titanic. “Companies like Merck have fantastic scientists working for them, but they also have these middle and upper layers of managers who are just taking up space,” he said last week. “I like to call them ‘anti-bodies,’ because they just sit there being anti-everything. No one ever gets fired for saying no to a new idea.” Now, as the founder and the C.E.O. of a little biotech called VGX Pharmaceuticals, Kim has a novel type of aids drug in clinical trials and a promising drug for cancer in development.
It turns out that research and development doesn’t scale—that bigger may be worse. That’s why the engines of pharmaceutical innovation have for some time now been smaller biotech firms like VGX, which can concentrate on a few promising avenues of research and can offer enterprising scientists the freedom—and the potentially enormous rewards—of working as entrepreneurs. Just as, in the seventies, the locus of innovation in the tech business shifted from Goliaths like Digital and I.B.M. toward the smaller, more narrowly focussed start-ups of Silicon Valley, so it is shifting now from big to little pharma.
Thursday
What is innovation?
What is innovation? Approaches to distinguishing new products and processes from existing products and processes, Bruce S. Tether, 2003
In this paper, Bruce Tether from CRIC discusses various definitions of innovation and some tools for identifying innovations in research.
Tether argues that innovation is sometimes defined as an achievement, sometimes as the results or consequences of that achievement, and sometimes as a business approach - and that there is considerable confusion between the three.
Innovation as achievement
It can be difficult to know how easy it will be to develop a new technology, and the extent to which people will want to use it when it is available. For economists, these represent two types of uncertainties in the development of new technologies - technological uncertainties (will it work?) and market uncertainties (will it sell, how quickly, and will competitors quickly introduce their own versions of the product if it proves successful?). The presence of these uncertainties is often an indicator of innovation and will be important in various definitions of innovation.
Innovation as achievement (in the face of risk and uncertainties) can be two-fold:
1. Achieving a significant leap forward in the technological frontier
2. Re-conceptualising existing problems and thereby restructuring technological systems
Innovation as the consequence of achievement
'Great innovations' are primarily thought great because of the consequences of technologies, and not necessarily because of the novelty of the achievement itself, which in any case has usually transformed substantially from the original achievement through the accretion of little details.
What is important here is that innovation has unintended consequences that benefit everyone. Economists call these unintended consequences spillovers or positive externalities. (There can, of course, also be negative externalities.)
Innovation as dynamic capabilities
The conceptualisation of innovation - as a process - is becoming more widespread. Here, innovation is less associated with particular acts or achievements (and their consequences), and is more associated with an attitude of mind, and a whole ensemble of behaviours and practices associated with that attitude.
A truly innovative firm is not one that introduces a new product 'once in a blue moon', but is instead one that is continuously engaged in practices intended to enhance the probability that it will 'discover' new or better products or processes of making them. ... Central to this concept of innovation is being alive to change. Being flexible - being able to adapt what is done in different circumstances, such as to particular customers needs - is usually insufficient to constitute being truly innovative.
Firms that are innovative tend to have 'dynamic capabilities'. 'A dynamic capability is a learned and stable pattern of collective activity through which the organisation systematically generates and modifies its operating routines in pursuit of improved effectiveness.' (M. Zollo and S. Winter, 2002). This requires a combination of strategic and organisational skills.
Innovation in this sense does not necessarily coincide with innovation in the form of introducing new products and services.
Product innovation
One conceptualization of product innovation (Saviotti and Metcalfe, 1984) distinguishes between technical characteristics and service characteristics of a product, and maps the two onto each other. (E.g. number of cylinders in a car's engine vs/ acceleration.) Innovation can then happen in 5 conceptually different ways. (In practice they are often interdependent.)
1. A change in the absolute values of one or more of the technical characteristics
2. A change in the mixture or balance of the technical characteristics
3. A change in the pattern of mapping between the technical characteristics an the service characteristics
4. A change in the mixture or balance of the service characteristics
5. A change in the absolute values of one or more of the service characteristics.The problem with this approach is that it is practically impossible to measure service characteristics, such as brand value objectively.
Process innovation
The pattern of innovation in processes is likely to differ from that of products, and particularly from standardised 'mass produced' products. With standardised products, a new product is typically introduced (following processes of experimentation and prototype development) after which it will remain unchanged for some time. A few minor upgrades will be introduced over time, after which a bigger, generational change will be undertaken. ... The cycle is then repeated with the third and fourth generations of the product. Over time, the scale of improvement between the generations is likely to decline. ... Innovation is fairly easily measured, at least in principle, by the scale of the jumps, or steps between the products available.
By contrast, innovation in processes - and indeed in services as well as customised products - can follow a different path. Sometimes there is rapid learning immediately after the introduction of the process - this is the learning curve, initially the process is slow and inefficient, but gradually and on a continuous basis, minor improvements are introduced. The same pattern is repeated if a substantially new process is later introduced. Alternatively, after the initial innovation, there may be a slow process of continuous improvement, but after a while the scope for improvement diminishes.
The point here is that improvements, and innovations, may be much harder to identify in processes ... than with the archetypal mass-produced standardised products because improvements are less likely to occur in definite steps. In particular, it can be difficult to distinguish between variations and innovations.
Radical innovation and the hierarchical decomposability of technologies
The literature on innovation is replete with references to radical and incremental innovations, yet there is considerable confusion about what distinguishes an incremental from a radical innovation.
3 definitions:
Freeman (1982): Radical innovations are those that transcend the technical limitations (of the existing technologies)
Saviotti, Stubs, Coombs, and Gibbons (1982): Incremental innovation can be defined as a series of quantitative changes in known parameters or in the introduction into a given product of technical characteristics already used in some similar product. A radical innovation would be, instead, the appearance of a new technical characteristic.
Tushman and Anderson (1986) focus on the impact on the industry: Competence-destroying discontinuities are so fundamentally different from previous dominant technologies that the skills and knowledge base required to operate the core technology shift. ... Competence-enhancing discontinuities are order of magnitude improvements in price/performance that build on existing know-how within a product class.
Unfortunately, each case leaves significant room for disagreement in trying to categorize specific innovations.
Constant (1987) clarifies some of the issues by conceptualising technological systems. Ontologically, systems are composed of sub-systems which are composed of an immense variety of components. ... This hierarchical decomposability suggests the absolute relativity of all change: Whether a given change is perceived as radical or incremental depends solely on the hierarchical level chosen. A new valve, a new turbine material or fabrication technique may represent a revolutionary solution to a specific sub-problem at that level; yet the same change, viewed from the level or the total aircraft system may appear only as a typical incremental innovation.
Practical approaches to identifying innovation
Tether distinguishes between 'object based' approaches (the researcher identifies innovations) and 'subject based' approaches (the researcher asks firms about their innovative behaviour).
Several approaches are presented with a special emphasis on OECD's Oslo Manual, which is considered the international standard. Although it helps in specifying innovations, in Tether's opinion it doesn't distinguish enough between adoption of innovations and innovative activity. According to the Oslo Manual, a firm is innovative if it adopts a new technology - even if this adoption occurs without any learning, adaptation or risk-taking on the part of the firm.
Recommendations
Tether recommends a 3-dimensional approach to the evaluation of innovations.
For products: Conceptual novelty, technological uncertainty, and market uncertainty.
For processes: Conceptual novelty, technological uncertainty, learning & adaptation.
Comments
This could be a useful approach in justifying my choice of industry for case studies. A problem remains, however, in that the case studies will most likely be in the service industry. How do Tether's recommendations apply to services? The analytical dimensions of product innovations work for services. However, many will develop more along the line of processes with many incremental improvements, rather than large 'jumps'.
In the end, it might be more useful to work with the concept of innovation as a process/attitude/set of practices.
A footnote: Tether emphasizes that he is talking about innovation, not invention. The difference being that an innovation has been commercialised where an invention has not.
In this paper, Bruce Tether from CRIC discusses various definitions of innovation and some tools for identifying innovations in research.
Tether argues that innovation is sometimes defined as an achievement, sometimes as the results or consequences of that achievement, and sometimes as a business approach - and that there is considerable confusion between the three.
Innovation as achievement
It can be difficult to know how easy it will be to develop a new technology, and the extent to which people will want to use it when it is available. For economists, these represent two types of uncertainties in the development of new technologies - technological uncertainties (will it work?) and market uncertainties (will it sell, how quickly, and will competitors quickly introduce their own versions of the product if it proves successful?). The presence of these uncertainties is often an indicator of innovation and will be important in various definitions of innovation.
Innovation as achievement (in the face of risk and uncertainties) can be two-fold:
1. Achieving a significant leap forward in the technological frontier
2. Re-conceptualising existing problems and thereby restructuring technological systems
Innovation as the consequence of achievement
'Great innovations' are primarily thought great because of the consequences of technologies, and not necessarily because of the novelty of the achievement itself, which in any case has usually transformed substantially from the original achievement through the accretion of little details.
What is important here is that innovation has unintended consequences that benefit everyone. Economists call these unintended consequences spillovers or positive externalities. (There can, of course, also be negative externalities.)
Innovation as dynamic capabilities
The conceptualisation of innovation - as a process - is becoming more widespread. Here, innovation is less associated with particular acts or achievements (and their consequences), and is more associated with an attitude of mind, and a whole ensemble of behaviours and practices associated with that attitude.
A truly innovative firm is not one that introduces a new product 'once in a blue moon', but is instead one that is continuously engaged in practices intended to enhance the probability that it will 'discover' new or better products or processes of making them. ... Central to this concept of innovation is being alive to change. Being flexible - being able to adapt what is done in different circumstances, such as to particular customers needs - is usually insufficient to constitute being truly innovative.
Firms that are innovative tend to have 'dynamic capabilities'. 'A dynamic capability is a learned and stable pattern of collective activity through which the organisation systematically generates and modifies its operating routines in pursuit of improved effectiveness.' (M. Zollo and S. Winter, 2002). This requires a combination of strategic and organisational skills.
Innovation in this sense does not necessarily coincide with innovation in the form of introducing new products and services.
Product innovation
One conceptualization of product innovation (Saviotti and Metcalfe, 1984) distinguishes between technical characteristics and service characteristics of a product, and maps the two onto each other. (E.g. number of cylinders in a car's engine vs/ acceleration.) Innovation can then happen in 5 conceptually different ways. (In practice they are often interdependent.)
1. A change in the absolute values of one or more of the technical characteristics
2. A change in the mixture or balance of the technical characteristics
3. A change in the pattern of mapping between the technical characteristics an the service characteristics
4. A change in the mixture or balance of the service characteristics
5. A change in the absolute values of one or more of the service characteristics.The problem with this approach is that it is practically impossible to measure service characteristics, such as brand value objectively.
Process innovation
The pattern of innovation in processes is likely to differ from that of products, and particularly from standardised 'mass produced' products. With standardised products, a new product is typically introduced (following processes of experimentation and prototype development) after which it will remain unchanged for some time. A few minor upgrades will be introduced over time, after which a bigger, generational change will be undertaken. ... The cycle is then repeated with the third and fourth generations of the product. Over time, the scale of improvement between the generations is likely to decline. ... Innovation is fairly easily measured, at least in principle, by the scale of the jumps, or steps between the products available.
By contrast, innovation in processes - and indeed in services as well as customised products - can follow a different path. Sometimes there is rapid learning immediately after the introduction of the process - this is the learning curve, initially the process is slow and inefficient, but gradually and on a continuous basis, minor improvements are introduced. The same pattern is repeated if a substantially new process is later introduced. Alternatively, after the initial innovation, there may be a slow process of continuous improvement, but after a while the scope for improvement diminishes.
The point here is that improvements, and innovations, may be much harder to identify in processes ... than with the archetypal mass-produced standardised products because improvements are less likely to occur in definite steps. In particular, it can be difficult to distinguish between variations and innovations.
Radical innovation and the hierarchical decomposability of technologies
The literature on innovation is replete with references to radical and incremental innovations, yet there is considerable confusion about what distinguishes an incremental from a radical innovation.
3 definitions:
Freeman (1982): Radical innovations are those that transcend the technical limitations (of the existing technologies)
Saviotti, Stubs, Coombs, and Gibbons (1982): Incremental innovation can be defined as a series of quantitative changes in known parameters or in the introduction into a given product of technical characteristics already used in some similar product. A radical innovation would be, instead, the appearance of a new technical characteristic.
Tushman and Anderson (1986) focus on the impact on the industry: Competence-destroying discontinuities are so fundamentally different from previous dominant technologies that the skills and knowledge base required to operate the core technology shift. ... Competence-enhancing discontinuities are order of magnitude improvements in price/performance that build on existing know-how within a product class.
Unfortunately, each case leaves significant room for disagreement in trying to categorize specific innovations.
Constant (1987) clarifies some of the issues by conceptualising technological systems. Ontologically, systems are composed of sub-systems which are composed of an immense variety of components. ... This hierarchical decomposability suggests the absolute relativity of all change: Whether a given change is perceived as radical or incremental depends solely on the hierarchical level chosen. A new valve, a new turbine material or fabrication technique may represent a revolutionary solution to a specific sub-problem at that level; yet the same change, viewed from the level or the total aircraft system may appear only as a typical incremental innovation.
Practical approaches to identifying innovation
Tether distinguishes between 'object based' approaches (the researcher identifies innovations) and 'subject based' approaches (the researcher asks firms about their innovative behaviour).
Several approaches are presented with a special emphasis on OECD's Oslo Manual, which is considered the international standard. Although it helps in specifying innovations, in Tether's opinion it doesn't distinguish enough between adoption of innovations and innovative activity. According to the Oslo Manual, a firm is innovative if it adopts a new technology - even if this adoption occurs without any learning, adaptation or risk-taking on the part of the firm.
Recommendations
Tether recommends a 3-dimensional approach to the evaluation of innovations.
For products: Conceptual novelty, technological uncertainty, and market uncertainty.
For processes: Conceptual novelty, technological uncertainty, learning & adaptation.
Comments
This could be a useful approach in justifying my choice of industry for case studies. A problem remains, however, in that the case studies will most likely be in the service industry. How do Tether's recommendations apply to services? The analytical dimensions of product innovations work for services. However, many will develop more along the line of processes with many incremental improvements, rather than large 'jumps'.
In the end, it might be more useful to work with the concept of innovation as a process/attitude/set of practices.
A footnote: Tether emphasizes that he is talking about innovation, not invention. The difference being that an innovation has been commercialised where an invention has not.
Saturday
Bringing innovations to market in networked industries
The New Rules for Bringing Innovations to Market, Bhaskar Chakravorti, 2004
Bhaskar Chakravorti, author of 'The Slow Pace of Fast Change,' discusses the pitfalls of innovation in networked industries in this HBR article. (See also this earlier post.)
Chakravorti bases his argument on game theory and network economics.
When a market is in equilibrium (i.e. Nash equilibrium) every player in a market believes that he or she is making the best possible choices and that every other player is doing the same. Equilibrium in a market lends stability to the players' expectations, validates their choices, and reinforces their behaviors. When an innovation enters the market, it upsets the players' expectations and choices and introduces uncertainty in decision making.
So, once a market reaches equilibrium, it resists new ideas and new products and significantly favors incumbents who maintain the status quo.
A market's hostility to innovations becomes stronger when plazers are interconnected. In a networked market, each participant will switch to a new product only when it believes others will do so, too. ... When America's first transcontinental railroads were built in the 1860's, for example, factories and businesses that were close to waterways did not immediately relocate near railways. They did so only when they felt their customers and suppliers were making the switch, too.
Communications technology provides virtual connections between market participants and can affect adoption of new products. Using these technologies, market participants sent signals about their behavior and allow others to form expectations.
For instance, E. Remington and Sons introduced the first typewriter in 1874, a time when penmanship was still a highly respected skill. Most writers (with the exception of Mark Twain) initially shunned the typewriter. The growth of railroads, telephones, and telegraph lines led to the dispersal of companies and the depersonalization of communications. the typewritten document became the standard for written communications in business. Use of the typewriter spread. Thus, the railroads, the telephone, and the telegraph implicitly increased the speed with which consumers accepted the typewriter.
That influence is a two-edged sword.
Networked markets allow for the rapid diffusion of news, ideas, and, in theory, innovations. But they also erect formidable barriers to the adoption of innovations - primarily because of the interdependencies between players.
This reminds me of Barabasi's work on power laws in networks.
Once enough plazers in a networked market decide to switch to a new product, other players' motivation to do so becomes stronger. Beyond that threshold, the network becomes innovation's ally rather than its foe.
This sounds much like a game theoretical explanation of Shumpeter's 'creative destruction.'
Chakravorti also emphasizes the importance of hubs for innovators. Aligning interests with the most connected industry players gives an innovator access to a large network with very little effort.
While he goes on to recommend a strategy for innovators who are trying to move from one equilibrium to another in order to promote their new products, there is little mention of markets that are not yet in equilibrium. I could imagine that the bioinformatics industry is still too young to have reached a Nash equilibrium and that network ties are not yet stable enough to deter innovation. Or, to put it another way, the biomedical industry experienced two 'waves:' Has the industry reached equilibrium in the second wave yet?
And what will happen to an industry so dependent on, even defined by, innovation once it does reach equilibrium?
Bhaskar Chakravorti, author of 'The Slow Pace of Fast Change,' discusses the pitfalls of innovation in networked industries in this HBR article. (See also this earlier post.)
Chakravorti bases his argument on game theory and network economics.
When a market is in equilibrium (i.e. Nash equilibrium) every player in a market believes that he or she is making the best possible choices and that every other player is doing the same. Equilibrium in a market lends stability to the players' expectations, validates their choices, and reinforces their behaviors. When an innovation enters the market, it upsets the players' expectations and choices and introduces uncertainty in decision making.
So, once a market reaches equilibrium, it resists new ideas and new products and significantly favors incumbents who maintain the status quo.
A market's hostility to innovations becomes stronger when plazers are interconnected. In a networked market, each participant will switch to a new product only when it believes others will do so, too. ... When America's first transcontinental railroads were built in the 1860's, for example, factories and businesses that were close to waterways did not immediately relocate near railways. They did so only when they felt their customers and suppliers were making the switch, too.
Communications technology provides virtual connections between market participants and can affect adoption of new products. Using these technologies, market participants sent signals about their behavior and allow others to form expectations.
For instance, E. Remington and Sons introduced the first typewriter in 1874, a time when penmanship was still a highly respected skill. Most writers (with the exception of Mark Twain) initially shunned the typewriter. The growth of railroads, telephones, and telegraph lines led to the dispersal of companies and the depersonalization of communications. the typewritten document became the standard for written communications in business. Use of the typewriter spread. Thus, the railroads, the telephone, and the telegraph implicitly increased the speed with which consumers accepted the typewriter.
That influence is a two-edged sword.
Networked markets allow for the rapid diffusion of news, ideas, and, in theory, innovations. But they also erect formidable barriers to the adoption of innovations - primarily because of the interdependencies between players.
This reminds me of Barabasi's work on power laws in networks.
Once enough plazers in a networked market decide to switch to a new product, other players' motivation to do so becomes stronger. Beyond that threshold, the network becomes innovation's ally rather than its foe.
This sounds much like a game theoretical explanation of Shumpeter's 'creative destruction.'
Chakravorti also emphasizes the importance of hubs for innovators. Aligning interests with the most connected industry players gives an innovator access to a large network with very little effort.
While he goes on to recommend a strategy for innovators who are trying to move from one equilibrium to another in order to promote their new products, there is little mention of markets that are not yet in equilibrium. I could imagine that the bioinformatics industry is still too young to have reached a Nash equilibrium and that network ties are not yet stable enough to deter innovation. Or, to put it another way, the biomedical industry experienced two 'waves:' Has the industry reached equilibrium in the second wave yet?
And what will happen to an industry so dependent on, even defined by, innovation once it does reach equilibrium?
Thursday
The Bioeconomy and what it means for regional economies
Prospects for a Bioeconomy: The Biomedical Industry and Economic Development, Cinda Herndon-King and Richard S. Seline, 2000
Without many too many facts to fall back on, I have proposed that hi-tech industry industries are moving away from a pure cluster model towards a 'network of competing and cooperating clusters.' This report backs me up as far as the biomedical industry is concerned. There's more on networks of innovation and regions collaborating to compete at the website of New Economy Strategies.
Cinda Herndon-King and Richard Seline analyzed 28 regions in the United States, with a special emphasis on the 4 most important clusters: Boston, San Diego, the Bay Area and Seattle. At the time the report was written, biotech was poised to pick up investments and momentum from the slacking internet bubble economy.
Herndon-King and Seline provide a comprehensive overview of the biomedical industry. They point out the enormous market potential of the health care industry in the U.S., mainly due to a population with a higher life expectancy that is aging overall. However, much of the potential also arises from the fact that genomic pharmaceuticals allow much more personalized healthcare and a much vaster scope of treatments - beginning with highly targeted preventive care.
They cite Mark Dibner and list 7 factors which distinguish the biomedical industry from other high tech sectors:
1. Financing: The start-up costs of business are high, and generally not financed by the entrepreneur
2. Reliance on research base: Most (55%) of biotechnology companies engage in activities which are in the research and development phase only.
3. Time to market: Typically, between five to twelve years is required. Return on investment for early investors is not based on product sales but from increasing valuation of the company, realized upon exit.
4. Regulatory environment: The cost of the the drug development and approval process is estimated at an average of $300 to $500 million per drug. The time required for approvals can be highly variable, and can often depend on factors outside the control of the submitting company.
5. Dependence on patent issues: Attracting investment requires a strong global intellectual property position.
6. Alliances and outsourcing: Due to the high costs of doing business, biotechnology firms extensively leverage outside skills, technology and capital through alliances. Reliance on academic innovation has emerged as the primary factor affecting biotechnology industry cluster devlopment.
7. Influence of public perception and environment.
Two major trends that form a recurring theme throughout the report are:
1. the interrelationship of tools and enabling technology with basic scientific discovery. The distinction between providing equipment or software and conducting basic research is blurred since so much discovery depends on the development of specialized or custom-made new tools.
2. the requirement for interdisciplinary approaches to biomedical research, bioinformatics being a case in point for both trends.
The authors go on to describe 2 phases of the industry:
The first wave business model centered on the 'full integrated pharmaceutical company' that licensed, financed, managed, and fought the federal regulatory labyrinth around (typically) a university patent or paper. This fully-integrated model housed the research, the testing, the manufaturing, and the distribution and sales for all aspects of bringing a drug or product to the market.
The second wave of the biotech industry is best defined by the reliance upon outsourcing and business networks rather than the integration model. Simply, the biotech and life science industry has found alliances, networks among researchers-vendors-suppliers, and a more concentrated and accelerated focus of both the science and the economics to be not just valuable but competitive propositions.
This has implications for regional economies that focus on biotech/biomed:
The Second Wave therefore is permeating regional strategies: proximity is no longer a value proposition in all elements of the lifecycle. Proximity to new ideas, to faculty, to research facilities promises greater innovation (defined as a social process among inputs of the science and outputs of entrepreneurial formation), but as firms mature the proximity demand within a region is challenged. Seattle for instance found in the late 1980s that no strategic marketing firms existed in their region and thus turned to Los Angeles and New York for assistance. Over a three year period, enough demand was created in Seattle that approximately 30 firms were established to serve the growing strategic marketing and sales requirements – many were outpost from Los Angeles and New York, others were home-grown. Currently San Diego has exceeded its manufacturing capacity – land is in short supply and costly; an initiative is underway to partner with border cities in Mexico and communities outside of California for non-essential manufacturing services.
There is a shift from self-contained regional clusters to specialized networked regions (see graph on page 44 of the report).
This is a reflection of changes in the industry itself as it moved from full vertical integration within one firm to a greater reliance on networks and alliances.
Proximity matters but not as it once did - like a fully-integrated company, regions believed that they must manage or control all aspects of the product cycle. With the determination that not every region has all the critical ingredients, more and more expectations arise for networking with other institutions, knowledge, talent and entrepreneurs beyond the local community. Proximity matters because innovation is a social process but not all aspects of the product testing and development must rely on the capacity to 'rub shoulders' with the testing, trials, and manufacturing aspects of the industry.
But the question remains: Which aspects require shoulder rubbing, and which don't?
Without many too many facts to fall back on, I have proposed that hi-tech industry industries are moving away from a pure cluster model towards a 'network of competing and cooperating clusters.' This report backs me up as far as the biomedical industry is concerned. There's more on networks of innovation and regions collaborating to compete at the website of New Economy Strategies.
Cinda Herndon-King and Richard Seline analyzed 28 regions in the United States, with a special emphasis on the 4 most important clusters: Boston, San Diego, the Bay Area and Seattle. At the time the report was written, biotech was poised to pick up investments and momentum from the slacking internet bubble economy.
Herndon-King and Seline provide a comprehensive overview of the biomedical industry. They point out the enormous market potential of the health care industry in the U.S., mainly due to a population with a higher life expectancy that is aging overall. However, much of the potential also arises from the fact that genomic pharmaceuticals allow much more personalized healthcare and a much vaster scope of treatments - beginning with highly targeted preventive care.
They cite Mark Dibner and list 7 factors which distinguish the biomedical industry from other high tech sectors:
1. Financing: The start-up costs of business are high, and generally not financed by the entrepreneur
2. Reliance on research base: Most (55%) of biotechnology companies engage in activities which are in the research and development phase only.
3. Time to market: Typically, between five to twelve years is required. Return on investment for early investors is not based on product sales but from increasing valuation of the company, realized upon exit.
4. Regulatory environment: The cost of the the drug development and approval process is estimated at an average of $300 to $500 million per drug. The time required for approvals can be highly variable, and can often depend on factors outside the control of the submitting company.
5. Dependence on patent issues: Attracting investment requires a strong global intellectual property position.
6. Alliances and outsourcing: Due to the high costs of doing business, biotechnology firms extensively leverage outside skills, technology and capital through alliances. Reliance on academic innovation has emerged as the primary factor affecting biotechnology industry cluster devlopment.
7. Influence of public perception and environment.
Two major trends that form a recurring theme throughout the report are:
1. the interrelationship of tools and enabling technology with basic scientific discovery. The distinction between providing equipment or software and conducting basic research is blurred since so much discovery depends on the development of specialized or custom-made new tools.
2. the requirement for interdisciplinary approaches to biomedical research, bioinformatics being a case in point for both trends.
The authors go on to describe 2 phases of the industry:
The first wave business model centered on the 'full integrated pharmaceutical company' that licensed, financed, managed, and fought the federal regulatory labyrinth around (typically) a university patent or paper. This fully-integrated model housed the research, the testing, the manufaturing, and the distribution and sales for all aspects of bringing a drug or product to the market.
The second wave of the biotech industry is best defined by the reliance upon outsourcing and business networks rather than the integration model. Simply, the biotech and life science industry has found alliances, networks among researchers-vendors-suppliers, and a more concentrated and accelerated focus of both the science and the economics to be not just valuable but competitive propositions.
This has implications for regional economies that focus on biotech/biomed:
The Second Wave therefore is permeating regional strategies: proximity is no longer a value proposition in all elements of the lifecycle. Proximity to new ideas, to faculty, to research facilities promises greater innovation (defined as a social process among inputs of the science and outputs of entrepreneurial formation), but as firms mature the proximity demand within a region is challenged. Seattle for instance found in the late 1980s that no strategic marketing firms existed in their region and thus turned to Los Angeles and New York for assistance. Over a three year period, enough demand was created in Seattle that approximately 30 firms were established to serve the growing strategic marketing and sales requirements – many were outpost from Los Angeles and New York, others were home-grown. Currently San Diego has exceeded its manufacturing capacity – land is in short supply and costly; an initiative is underway to partner with border cities in Mexico and communities outside of California for non-essential manufacturing services.
There is a shift from self-contained regional clusters to specialized networked regions (see graph on page 44 of the report).
This is a reflection of changes in the industry itself as it moved from full vertical integration within one firm to a greater reliance on networks and alliances.
Proximity matters but not as it once did - like a fully-integrated company, regions believed that they must manage or control all aspects of the product cycle. With the determination that not every region has all the critical ingredients, more and more expectations arise for networking with other institutions, knowledge, talent and entrepreneurs beyond the local community. Proximity matters because innovation is a social process but not all aspects of the product testing and development must rely on the capacity to 'rub shoulders' with the testing, trials, and manufacturing aspects of the industry.
But the question remains: Which aspects require shoulder rubbing, and which don't?
Saturday
Cities as drivers of innovation and economic growth
The economy of cities, Jane Jacobs, 1969
Jane Jacobs sees cities as fundamentally different from other economic regions - not as larger and more complicated villages. In her book, she describes an anthropological view of the earliest cities as centers of trade between non-agricultural tribes that develop sophisticated agricultural techniques as they grow. This conflicts with the view traditionally held at the time that hunter/gatherer villages developed agriculture and subsequently grew to become cities.
Jacobs perspective allows a new analysis of cities and their interactions with the rural areas around them. Since she focuses on cities' imports and exports, her's could be seen as simply an input-output model that treats the city (ie. agglomeration economy) as black box. However, the anthropological background she provides and her analysis of divisions of labor within the city go a long way towards opening the black box and understanding it. Her description of entrepreneurship and incremental innovation makes intuitive sense: People doing their job, naturally find ways to improve on certain aspects - mainly through trial and error. If they get positive feedback, they may shift their focus from the original work to elaborating the improvements. One of Jacobs' examples is Mrs. Rosenthal, a New York seamstress. Mrs. Rosenthal was unhappy with the way her dresses fit her customers, so she used her sewing and fitting skills to create bras. She was so successful with this improvement that she went on to give up on sewing dresses and founded Maidenform instead. One of the most important factors in this example is that Mrs. Rosenthal was originally engaged in local work, which she adapted. Maidenform, by contrast became an export business (ie., exporting out of the city of New York). Once the new line of business was firmly established, factories could be set up in rural areas to save costs and improve efficiency. This example is representative of Jacobs's description of the city as a self-reciprocating, open system.
Jacobs emphasizes that this kind of innovation can only happen in cities where there is a high density of people and jobs as well and if a high degree of specialization can be supported by the economy. Since established businesses are constantly being moved to rural areas, the city must create new sources of income, ie. new exports. To achieve this, a vibrant local economy is necessary to serve as the basis for innovation. The inefficiency of cities is an advantage for the trial and error process of incremental innovation: a large variety of different kinds of work is happening in one place, which encourages new combinations; the sustainability of high degrees of specialization lets innovations succeed before they are fully developed, and replication of work means that many instances of trial and error can occur in tandem.
While individual specialization is important, according to Jacobs, cities should not specialize. They need to maintain many different avenues of innovation/trial and error because only a fraction will succeed. Efficiency works against innovation in this case. Some of the most important specializations/innovations that cities provide have been the generic ones that allow new businesses of all kinds to form: venture capitalists, lawyers, printers, leasing of factory equipment, agencies to provide temporary workers, etc.
Even though Jacobs doesn't touch on radical high-tech innovations or consumer-driven innovation (cf. von Hippel's work), she provides a very tangible, practical account of how agglomeration economies work. Her model gives many insights into what it takes to create an agglomeration economy or to revive a stagnating city.
An interview with Jane Jacobs.
Jane Jacobs sees cities as fundamentally different from other economic regions - not as larger and more complicated villages. In her book, she describes an anthropological view of the earliest cities as centers of trade between non-agricultural tribes that develop sophisticated agricultural techniques as they grow. This conflicts with the view traditionally held at the time that hunter/gatherer villages developed agriculture and subsequently grew to become cities.
Jacobs perspective allows a new analysis of cities and their interactions with the rural areas around them. Since she focuses on cities' imports and exports, her's could be seen as simply an input-output model that treats the city (ie. agglomeration economy) as black box. However, the anthropological background she provides and her analysis of divisions of labor within the city go a long way towards opening the black box and understanding it. Her description of entrepreneurship and incremental innovation makes intuitive sense: People doing their job, naturally find ways to improve on certain aspects - mainly through trial and error. If they get positive feedback, they may shift their focus from the original work to elaborating the improvements. One of Jacobs' examples is Mrs. Rosenthal, a New York seamstress. Mrs. Rosenthal was unhappy with the way her dresses fit her customers, so she used her sewing and fitting skills to create bras. She was so successful with this improvement that she went on to give up on sewing dresses and founded Maidenform instead. One of the most important factors in this example is that Mrs. Rosenthal was originally engaged in local work, which she adapted. Maidenform, by contrast became an export business (ie., exporting out of the city of New York). Once the new line of business was firmly established, factories could be set up in rural areas to save costs and improve efficiency. This example is representative of Jacobs's description of the city as a self-reciprocating, open system.
Jacobs emphasizes that this kind of innovation can only happen in cities where there is a high density of people and jobs as well and if a high degree of specialization can be supported by the economy. Since established businesses are constantly being moved to rural areas, the city must create new sources of income, ie. new exports. To achieve this, a vibrant local economy is necessary to serve as the basis for innovation. The inefficiency of cities is an advantage for the trial and error process of incremental innovation: a large variety of different kinds of work is happening in one place, which encourages new combinations; the sustainability of high degrees of specialization lets innovations succeed before they are fully developed, and replication of work means that many instances of trial and error can occur in tandem.
While individual specialization is important, according to Jacobs, cities should not specialize. They need to maintain many different avenues of innovation/trial and error because only a fraction will succeed. Efficiency works against innovation in this case. Some of the most important specializations/innovations that cities provide have been the generic ones that allow new businesses of all kinds to form: venture capitalists, lawyers, printers, leasing of factory equipment, agencies to provide temporary workers, etc.
Even though Jacobs doesn't touch on radical high-tech innovations or consumer-driven innovation (cf. von Hippel's work), she provides a very tangible, practical account of how agglomeration economies work. Her model gives many insights into what it takes to create an agglomeration economy or to revive a stagnating city.
An interview with Jane Jacobs.
How technology aids innovation
Experimentation Matters: Unlocking the Potential of New Technologies for Innovation, Stefan Thomke, 2003.
Some time ago HBS Working Knowledge carried an interview with Stefan Thomke about his book, 'Experimentation Matters: Unlocking the Potential of New Technologies for Innovation.'
Thomke emphasizes that experimentation is vital to innovation and survival in today's business world. Computer modeling and simulation, rapid prototyping, and combinatorial technologies drive down the marginal cost of experimentation and allow companies to create more learning more rapidly.
He also proposes that it may make sense to shift experimentation from producers to customers: some companies have abandoned their efforts to understand exactly what products their customers want and have instead equipped them with tools to design and develop their own new products, ranging from minor modifications to major new innovations. The user-friendly tools, often integrated into a “toolkit” package, deploy new technologies (e.g., computer simulation and rapid prototyping) to make innovation faster, less expensive and, most importantly, better, as customers run “what-if” experiments themselves.
This suggests that since experimentation can be moved up and down the supply chain (relatively) easily, the technology matters more than the location. More on this when I've read the book...
Some time ago HBS Working Knowledge carried an interview with Stefan Thomke about his book, 'Experimentation Matters: Unlocking the Potential of New Technologies for Innovation.'
Thomke emphasizes that experimentation is vital to innovation and survival in today's business world. Computer modeling and simulation, rapid prototyping, and combinatorial technologies drive down the marginal cost of experimentation and allow companies to create more learning more rapidly.
He also proposes that it may make sense to shift experimentation from producers to customers: some companies have abandoned their efforts to understand exactly what products their customers want and have instead equipped them with tools to design and develop their own new products, ranging from minor modifications to major new innovations. The user-friendly tools, often integrated into a “toolkit” package, deploy new technologies (e.g., computer simulation and rapid prototyping) to make innovation faster, less expensive and, most importantly, better, as customers run “what-if” experiments themselves.
This suggests that since experimentation can be moved up and down the supply chain (relatively) easily, the technology matters more than the location. More on this when I've read the book...
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