User Innovation Theory
Summary by Janet Hope in the context of applications in Open Source Biotechnology
"The seminal work in the user innovation literature was Eric von Hippel’s 1988 book, Sources of Innovation. However, the literature also draws on earlier empirical studies in the broader field of innovation studies. There are five key elements to this theory: user innovation, free revealing, collective invention, distributed production, and community support." (http://www.gene-watch.org/genewatch/articles/18-1Hope.html)
"The user innovation literature draws a distinction between users and manufacturers of an innovation, the categories being defined according to the type of benefit an innovator expects to attain from his or her work. Users primarily benefit from using the innovation, while manufacturers primarily benefit by selling it. The conventional assumption is that manufacturers, rather than users, are likely to be the main innovators in any given field, for the simple reason that “making one and selling many” items is assumed to be the most profitable way to exploit a piece of technology. By contrast, the user innovation literature points to empirical evidence that users, rather than manufacturers, are in fact the primary innovators in many contexts, including open source software." (http://www.gene-watch.org/genewatch/articles/18-1Hope.html)
Also from Janet Hope at http://www.gene-watch.org/genewatch/articles/18-1Hope.html
"In such contexts, the information required to come up with new technological developments is “sticky”—it is costly to transfer from one person to another. It makes more sense for a user who already has most of that information to invent a new piece of technology than it does for a manufacturer, who would have to invest in researching what users need and how that particular innovation would function in a given industrial setting.
(Biotechnology research and development (R&D) is an area where it often makes sense for users to do most of the innovating. This is because biological information is particularly sticky; it is complex and highly location-specific. This is partly a function of the nature of biological materials and partly a function of the early stage of development of many biotechnology inventions)" (http://www.gene-watch.org/genewatch/articles/18-1Hope.html)
"The second assumption is that any “free revealing” or uncompensated spillover of proprietary knowledge developed through private investment will reduce the innovator’s profits from that investment. It is therefore assumed that innovators will avoid such spillovers as much as possible. The user innovation literature challenges this assumption. Although the proprietary approach is often the most profitable way to exploit an innovation, user innovation theory shows that this is not always the case.
There are disadvantages to the proprietary approach. Secrecy is one example. Proprietary knowledge must be kept secret for it to remain that way, but this only makes sense for inventions that cannot easily be reverse-engineered. Even though there is no limit to the term of trade secret protections in principle, in practice, most development secrets can be compromised, sometimes very quickly. The costs of preserving secrecy can be significant and must be balanced against the benefits of exclusive access. Further, when it comes to licensing an innovation, a dilemma arises. IP owners want to sell as many licenses as possible to generate revenue, but if the information is disclosed to too many users, the benefit of trade secrecy protection may be lost.
There are also several ways in which a self-interested innovator can benefit from a free revealing approach. First, free revealing of IP may establish a non-proprietary technology that encourages the purchase of proprietary technology with which it works. To use a software analogy, a company may make an operating system free to users because it intends to sell other software that is exclusively compatible with this operating system. These secondary pieces of proprietary technology can offset any monetary losses from the non-proprietary technology.
Free revealing may also facilitate improvements to the core technology. If the original innovator then gains access to those improvements, this represents a cost saving in R&D for that company.
In addition, by revealing its IP, a company may generate a favorable reputation that is useful in selling associated offerings, by enhancing brand value or improving the company’s ability to attract and keep high quality employees.
It is often assumed in discussions of open source that the benefits of adopting a free revealing strategy for exploiting innovations must be unique to software or other information goods, but such benefits can apply to physical goods as well. Biotechnology can benefit from free revealing, and some companies are already beginning to implement such practices to this end.
As mentioned earlier, one can use non-proprietary products as enticements for other proprietary goods or services. Companies are often willing to contribute to the creation of open databases in order to attract customers to a host Web site that offers additional commercial content. Others give away the rights to use valuable cell lines and experimental animals, and sell consulting services on how to maintain them. Microarrays, a way for conducting many experiments at once on a DNA or protein “chip,” are another potential field, as proprietary chips are too expensive for some institutions and applications. Developing open standards for manufacturing microarrays could produce a proprietary market for tools that would conform to those standards.
Another way is to pre-empt the establishment of a proprietary standard. The most obvious example of this strategy is the participation of companies in the SNP consortium, which pays academic scientists to place genome sequence data in the public domain. For these companies, giving away data is not a charitable act—it avoids having to negotiate IP access among themselves and with other companies down the line. Interestingly, the human genome sequencing project considered adopting open licenses, but the idea was abandoned because it was decided that any restrictions on the data, even in the form of a license designed to ensure it stayed non-proprietary, would create a dangerous precedent. In that case, open source development was successfully taken to its extreme.
(An even more ambitious proposal for large-scale collaboration has been made by Steve Maurer, an economist at UC Berkeley. Maurer suggests harnessing and combining volunteer efforts across the life sciences community to research potential malaria drugs that would then be made publicly available. Maurer argues that an open source approach would reduce the total costs of drug development. As highly trained volunteer labor would perform the research, sponsors could avoid overpaying R&D costs, which are difficult to estimate in early stages. In addition, because the IP would be available to everyone, any company could manufacture the drug, and the resulting competition would keep down the market price for the completed product.
Biotechnology companies can also enhance their reputations by using open source techniques. One example of this strategy is the contract research company Millennium Research. Millennium makes much of the technology it develops available to the general public, boosting its reputation for innovation and expertise, as well as for user-friendliness and social-mindedness. This kind of attitude seems to resonate with users.)
Along with the benefits of adopting a free revealing, non-proprietary strategy for exploiting an innovation, there are costs to contend with as well. Opportunity costs are the gains that an innovator could have made by adopting an exclusive proprietary approach. Actual costs include the expense of diffusing an innovation. Clearly, choosing the best strategy for exploiting any particular innovation involves weighing the costs and benefits of a proprietary versus a non-proprietary approach. It is a trade-off, and in many cases, in biotechnology as elsewhere, the balance may tip in favor of the traditional proprietary approach. This is generally the case in the pharmaceutical industry.
Normally, the opportunity cost of adopting a free revealing approach is not prohibitively high, as patents have a limited term and can be easy for other industry members to circumvent. However, the pharmaceutical industry has structured itself so as to make patent ownership particularly profitable. It has successfully pushed for patents that are broad enough to effectively cover not just a particular molecule that has value as a drug, but all the variations of that molecule that might be effective. This means that pharmaceutical patents are almost impossible to invent around. Combined with legal tactics for extending patent terms, the opportunity costs of giving up an exclusive proprietary approach to drugs in favor of an open source approach are likely to be too high for big pharma to be interested. As stated earlier, cases like these illustrate some of the negative effects of proprietary IP regimes." (http://www.gene-watch.org/genewatch/articles/18-1Hope.html)
"Collective invention comes about when enough innovators adopt free revealing approaches to producing a particular innovation. The result is a cycle of free revealing developments which may or may not be reinforced by a copyleft-style licensing regime.
There are several conditions that favor the development of a collective invention regime in any given industry; two are particularly relevant to biotechnology. First, collective invention is more likely to take root where R&D is expensive and its outcomes are uncertain. This is because each firm’s expected payoff from working privately may not be enough to cover the costs, or it may be too expensive to do at all. In general, biotechnology is significantly more capital intensive than other fields, and often the only way to get a desired end product is to share the burden.
Collective invention is also more likely to take place where standardization within an industry makes collective learning easier. While genetic engineering is inherently more complex and diversified than software engineering, there are certainly circumstances to which open source development can be applied." (http://www.gene-watch.org/genewatch/articles/18-1Hope.html)
Distributed Production and Community Support
"The final two elements of an open source development approach, according to the user innovation literature, are distributed production and community support. Both are possible in a biotechnology context.
Community support for collective invention is the crux of open source development. As stated earlier, there is no question that one could implement the legal framework for open source development in the life sciences. The real question is whether anyone would be willing to use it. Many of the community-based incentives and support structures that help to drive open source software development have an equivalent in the life sciences. While the two fields are markedly different, the advantages displayed in the former can definitely entice a community of adopters in the latter.
The potential for distributed production is less clear-cut. Distributed production is a non-issue for information-based products, as it can be transported and reproduced by users with no real cost. For physical goods, production and distribution involve economies of scale that are best exploited by manufacturers. This certainly seems to apply in some areas of biotech, such as the large-scale manufacture of pharmaceuticals, a field heavily entrenched in the principle of proprietary IP. On the other hand, it is clearly not a major factor in relation to self-replicating biological materials, such as seeds. In fact, the inherently dispersed and localized nature of the agricultural enterprise suggests that a distributed approach to technology development may make much more sense than a centralized approach." (http://www.gene-watch.org/genewatch/articles/18-1Hope.html)