Created by Materia for OpenMind Recommended by Materia
Estimated reading time Time 26 to read

Ever since Joseph A. Schumpeter (1934) promulgated his theory of economic development, economists, policymakers and business managers have assumed that the dominant mode of innovation is a “producers’ model.” That is, it has been assumed that most important innovations would originate from producers and be supplied to consumers via goods that were for sale. This view seemed reasonable on the face of it—producers generally serve many users and so can profit from multiple copies of a single innovative design. Individual users, in contrast, depend upon benefits from in-house use of an innovation to recoup their investments. Presumably, therefore, a producer that serves many customers can afford to invest more in innovation than any single user. From this it follows logically that producer-developed designs should dominate user-developed designs in most parts of the economy.

However, the producers’ model is only one mode of innovation. A second, increasingly important model is user innovation. Under this second model, economically important innovations are developed by individual users (consumers) and also by user firms. Sometimes, user-developed innovations result from a number of users working together collaboratively.

User innovation is an institution that competes with and, I will argue, can displace producer innovation in many parts of the economy. A growing body of empirical work clearly shows that users are the first to develop many and perhaps most new industrial and consumer products. In addition, the importance of product and service development by users is increasing over time. This shift is being driven by two related technical trends: 1. the steadily improving design capabilities (innovation toolkits) that advances in computer hardware and software make possible for users; 2. the steadily improving ability of individual users to combine and coordinate their innovation-related efforts via new communication media such as the Internet.

The ongoing shift of innovation to users has some very attractive qualities. It is becoming progressively easier for many users to get precisely what they want by designing it for themselves. Innovation by users also provides a very necessary complement to, and feedstock for, producer innovation. And innovation by users appears to increase social welfare. At the same time, the ongoing shift of product-development activities from producers to users is painful and difficult for many producers. User innovation is “attacking” a major structure of the social division of labor. Many firms and industries must make fundamental changes to long-held business models in order to adapt. Further, governmental policy and legislation sometimes preferentially supports innovation by producers. Considerations of social welfare suggest that this must change. The workings of the intellectual property system are of special concern. But despite the difficulties, a user-centered system of innovation appears well worth striving for.

Today a number of innovation process researchers are working to develop our understanding of user innovation processes. In this paper, I offer a review of some collective learning on this important topic to date.

Importance of innovation by users

Users, as I use the term, are firms or individual consumers that expect to benefit from using a product or a service. In contrast, producers expect to benefit from selling a product or a service. A firm or an individual can have different relationships to different products or innovations. For example, Boeing is a producer of airplanes, but it is also a user of machine tools. If one were examining innovations developed by Boeing for the airplanes it sells, Boeing would be a producer-innovator in those cases. But if one were considering innovations in metal-forming machinery developed by Boeing for in-house use in building airplanes, those would be categorized as user-developed innovations and Boeing would be a user-innovator in those cases.

Innovation user and innovation producer are the two general “functional” relationships between innovator and innovation. Users are unique in that they alone benefit directly from innovations. All others (here lumped under the term “producers”) must sell innovation-related products or services to users, indirectly or directly, in order to profit from innovations. Thus, in order to profit, inventors must sell or license knowledge related to innovations, and producers must sell products or services incorporating innovations. Similarly, suppliers of innovation-related materials or services—unless they have direct use for the innovations—must sell the materials or services in order to profit from the innovations.

The user and producer categorization of relationships between innovator and innovation can be extended to specific function, attributes, or features of products and services. When this is done, it may turn out that different parties are associated with different attributes of a particular product or service. For example, householders are the users of the switching attribute of a household electric light switch—they use it to turn lights on and off. However, switches also have other attributes, such as “easy wiring” qualities, that may be used only by the electricians who install them. Therefore, if an electrician were to develop an improvement to the installation attributes of a switch, it would be considered a user-developed innovation.

Both qualitative observations and quantitative research in a number of fields clearly document the important role users play as first developers of products and services later sold by manufacturing firms. Adam Smith (1776) was an early observer of the phenomenon, pointing out the importance of “the invention of a great number of machines which facilitate and abridge labor, and enable one man to do the work of many”. Smith went on to note that “a great part of the machines made use of in those manufactures in which labor is most subdivided, were originally the invention of common workmen, who, being each of them employed in some very simple operation, naturally turned their thoughts towards finding out easier and readier methods of performing it”. Rosenberg (1976) explored the matter in terms of innovation by user firms rather than individual workers. He studied the history of the US machine tool industry, finding that important and basic machine types like lathes and milling machines were first developed and built by user firms having a strong need for them. Textile manufacturing firms, gun producers and sewing machine producers were important early user-developers of machine tools.

Quantitative studies of user innovation document that many of the most important and novel products and processes in a range of fields have been developed by user firms and by individual users. Thus, Enos (1962) reported that nearly all the most important innovations in oil refining were developed by user firms. Freeman (1968) found that the most widely licensed chemical production processes were developed by user firms. Von Hippel (1988) found that users were the developers of about 80% of the most important scientific instrument innovations, and also the developers of most of the major innovations in semiconductor processing. Pavitt (1984) found that a considerable fraction of invention by British firms was for in-house use. Shah (2000) found that the most commercially important equipment innovations in four sporting fields tended to be developed by individual users.

Empirical studies also show that many users—from 10% to nearly 40%—engage in developing or modifying products. This has been documented in the case of specific types of industrial products and consumer products, and in large, multi-industry studies of process innovation in Canada and the Netherlands as well (table 1). When taken together, the findings make it very clear that users are doing a lot of product development and product modification in many fields.

Studies of innovating users (both individuals and firms) show them to have the characteristics of “lead users” (Urban and von Hippel, 1988, Herstatt and von Hippel, 1992, Olson and Bakke, 2001, Lilien et al., 2002). That is, they are ahead of the majority of users in their populations with respect to an important market trend, and they expect to gain relatively high benefits from a solution of the needs they have encountered there. The correlations found between innovation by users and lead user status are highly significant, and the effects are considerable (Franke and Shah, 2003, Lüthje et al., 2002 and Morrison et al., 2000).

Table 1. Studies of user innovation frequency
Data Sources: a. Urban and von Hippel, 1988; b. Herstatt and von Hippel,1992; c. Morrison et al., 2000; d. Lüthje, 2003; e. Franke and von Hippel, 2003; f. Lüthje, 2004; g. Franke and Shah, 2003; h. Lüthje et al., 2002; i. Arundel and Sonntag, 1999; j. Gault and von Hippel, 2009; k. de Jong and von Hippel, 2009.


Since lead users are at the leading edge of the market with respect to important market trends, one can guess that many of the novel products they develop for their own use will appeal to other users too and so might provide the basis for products producers would wish to commercialize. This turns out to be the case. A number of studies have shown that many of the innovations reported by lead users are judged to be commercially attractive and/or have actually been commercialized by producers.

Research provides a firm grounding for these empirical findings. The two defining characteristics of lead users and the likelihood that they will develop new or modified products have been found to be highly correlated (Morrison et al., 2004). In addition, it has been found that the higher the intensity of lead user characteristics displayed by an innovator, the greater the commercial attractiveness of the innovation that that lead user develops (Franke and von Hippel, 2003a). In figure 1, the increased concentration of innovations toward the right indicates that the likelihood of innovating is higher for users having higher lead user index values. The rise in average innovation attractiveness as one moves from left to right indicates that innovations developed by lead users tend to be more commercially attractive. (Innovation attractiveness is the sum of the novelty of the innovation and the expected future generality of market demand.)

Figure 1. User-innovators with stronger “lead user” characteristics develop innovations having higher appeal in the general market
place (Data Source: Franke and von Hippel, 2003


Why Many Users Want Custom Products

Why do so many users develop or modify products for their own use? Users may innovate if and as they want something that is not available on the market and are able and willing to pay for its development. It is likely that many users do not find what they want on the market. Meta-analysis of market-segmentation studies suggests that users’ needs for products are highly heterogeneous in many fields (Franke and Reisinger, 2003).

Mass producers tend to follow a strategy of developing products that are designed to meet the needs of a large market segment well enough to induce purchase from and capture significant profits from a large number of customers. When users’ needs are heterogeneous, this strategy of “a few sizes fit all” will leave many users somewhat dissatisfied with the commercial products on offer and probably will leave some users seriously dissatisfied. In a study of a sample of users of the security features of Apache web server software, Franke and von Hippel (2003b) found that users had a very high heterogeneity of need, and that many had a high willingness to pay to get precisely what they wanted. 19% of the users sampled actually innovated to tailor Apache more closely to their needs. Those who did were found to be significantly more satisfied.

Users’ Innovate-or-Buy Decisions

Even if many users want “exactly-right products” and are willing and able to pay for their development, we must understand why users often do this for themselves rather than hire a custom producer to develop a special just-right product for them. After all, custom producers specialize in developing products for one or a few users. Since these firms are specialists, it is possible that they could design and build custom products for individual users or user firms faster, better, or cheaper than users could do this for themselves. Despite this possibility, several factors can drive users to innovate rather than buy. Both in the case of user firms and that of individual user-innovators, agency costs play a major role. In the case of individual user-innovators, enjoyment of the innovation process can also be important.

With respect to agency costs, consider that when a user develops its own custom product that user can be trusted to act in its own best interests. When a user hires a producer to develop a custom product, the situation is more complex. The user is then a principal that has hired the custom producer to act as its agent. If the interests of the principal and the agent are not the same, there will be agency costs. In general terms, agency costs are 1. costs incurred to monitor the agent to ensure that it (or he or she) follows the interests of the principal, 2. the cost incurred by the agent to commit itself not to act against the principal’s interest (the “bonding cost”), and 3. costs associated with an outcome that does not fully serve the interests of the principal (Jensen and Meckling, 1976). In the specific instance of product and service development, a major divergence of interests between user and custom producer does exist: the user wants to get precisely what it needs, to the extent that it can afford to do so. In contrast, the custom producer wants to lower its development costs by incorporating solution elements it already has or that it predicts others will want in the future—even if by doing so it does not serve its present client’s needs as well as it could.

A user wants to preserve its need specification because that specification is chosen to make that user’s overall solution quality as high as possible at the desired price. For example, an individual user may specify a mountain-climbing boot that will precisely fit his unique climbing technique and allow him to climb Everest more easily. Any deviations in boot design will require compensating modifications in the climber’s carefully practiced and deeply ingrained climbing technique—a much more costly solution from the user’s point of view. A custom boot producer, in contrast, will have a strong incentive to incorporate the materials and processes it has in stock and expects to use in future even if this produces a boot that is not precisely right for the present customer. For example, the producer will not want to learn a new way to bond boot components together even if that would produce the best custom result for one client. The net result is that when one or a few users want something special they will often get the best result by innovating for themselves.

A model of the innovate-or-buy decision (von Hippel, 2005) shows in a quantitative way that user firms with unique needs (in other words, a market of one) will always be better off developing new products for themselves. It also shows that development by producers can be the most economical option when n or more user firms want the same thing. However, when the number of user firms wanting the same thing is between 1 and n, producers may not find it profitable to develop a new product for just a few users. In that case, more than one user may invest in developing the same thing independently, owing to market failure. This results in a waste of resources from the point of view of social welfare. The problem can be addressed by new institutional forms, such as the user innovation communities that will be mentioned later.

It is important to note that an additional incentive can drive individual user-innovators to innovate rather than buy: they may value the process of innovating because of the enjoyment or learning that it brings them. It might seem strange that user-innovators can enjoy product development enough to want to do it themselves—after all, producers pay their product developers to do such work! On the other hand, it is also clear that enjoyment of problem solving is a motivator for many individual problem solvers in at least some fields. Consider for example the millions of crossword-puzzle aficionados. Clearly, for these individuals enjoyment of the problem-solving process rather than the solution is the goal. One can easily test this by attempting to offer a puzzle solver a completed puzzle—the very output he or she is working so hard to create. One will very likely be rejected with the rebuke that one should not spoil the fun. Pleasure as a motivator can apply to the development of commercially useful innovations as well. Studies of the motivations of volunteer contributors of code to widely used software products have shown that these individuals too are often strongly motivated to innovate by the joy and learning they find in this work (Hertel et al., 2003; Lakhani and Wolf, 2005).

Users’ Low-Cost Innovation Niches

An exploration of the basic processes of product and service development shows that users and producers tend to develop different types of innovations. This is due in part to information asymmetries: users and producers tend to know different things. Product developers need two types of information in order to succeed at their work: need and context-of-use information (generated by users) and generic solution information (often initially generated by producers specializing in a particular type of solution). Bringing these two types of information together is not easy. Both need information and solution information are often very “sticky”—that is, costly to move from the site where the information was generated to other sites (von Hippel, 1994). It should be noted that the observation that information is often sticky contravenes a central tendency in economic theorizing. Much of the research on the special character of markets for information, and the difficulty of appropriating benefit from invention and innovation, has been based on the idea that information can be transferred at very low cost. (Thus, Arrow observes that “the cost of transmitting a given body of information is frequently very low. . . In the absence of special legal protection, the owner cannot, however, simply sell information on the open market. Any one purchaser can destroy the monopoly, since he can reproduce the information at little or no cost” (1962: 614-615).

When information is sticky, innovators tend to rely largely on information they already have in stock. One consequence of the resulting typical asymmetry between users and producers is that users tend to develop innovations that are functionally novel, requiring a great deal of user-need information and use-context information for their development. In contrast, producers tend to develop innovations that are improvements on well-known needs and that require a rich understanding of solution information for their development. Similarly, users tend to have better information regarding ways to improve use-related activities such as maintenance than do producers: they “learn by using” (Rosenberg, 1982).

This sticky information effect is quantitatively visible in studies of innovation. Riggs and von Hippel (1994) studied the types of innovations made by users and producers that improved the functioning of two major types of scientific instruments. They found that users are significantly more likely than producers to develop innovations that enabled the instruments to do qualitatively new types of things for the first time. In contrast, producers tended to develop innovations that enabled users to do the same things they had been doing, but to do them more conveniently or reliably (table 2). For example, users were the first to modify the instruments to enable them to image and analyze magnetic domains at sub-microscopic dimensions. In contrast, producers were the first to computerize instrument adjustments to improve ease of operation. Sensitivity, resolution, and accuracy improvements fall somewhere in the middle, as the data show. These types of improvements can be driven by users seeking to do specific new things, or by producers applying their technical expertise to improve the products along known general dimensions of merit, such as accuracy.

Table 2. Source of innovations by nature of improvement effected (Source: Riggs and von Hippel, 1994.)


The sticky information effect is independent of Stigler’s (1951) argument that the division of labor is limited by the extent of the market. When profit expectations are controlled, the impact of sticky information on the locus of innovation is still strongly evident (Ogawa, 1998).

If we extend the information-asymmetry argument one step further, we see that information stickiness implies that information on hand will also differ among individual users and producers. The information assets of some particular user (or some particular producer) will be closest to what is required to develop a particular innovation, and so the cost of developing that innovation will be relatively low for that user or producer. The net result is that user innovation activities will be distributed across many users according to their information endowments. With respect to innovation, one user is by no means a perfect substitute for another.

Why Users Often Freely Reveal Their Innovations

The social efficiency of a system in which individual innovations are developed by individual users is increased if users somehow pass on what they have developed to others. Producer-innovators partially achieve this when they sell a product or a service on the open market (partially because they disseminate the product incorporating the innovation, but often not all the information that others would need to fully understand and replicate it). If user-innovators do not somehow also pass on what they have done, multiple users with very similar needs will have to independently develop very similar innovations—a poor use of resources from the viewpoint of social welfare. Empirical research shows that users often do achieve widespread diffusion by an unexpected means: they often “freely reveal” what they have developed. When we say that an innovator freely reveals information about a product or service it has developed, we mean that all intellectual property rights to that information are voluntarily given up by the innovator, and all interested parties are given access to it—the information becomes a public good (Harhoff et al., 2003).

The empirical finding that users often freely reveal their innovations has been a major surprise to innovation researchers. On the face of it, if a user-innovator’s proprietary information has value to others, one would think that the user would strive to prevent free diffusion rather than help others to a free ride on what it has developed at private cost. Nonetheless, it is now very clear that individual users and user firms—and sometimes producers—often freely reveal detailed information about their innovations.

The practices visible in “open source” software development were important in bringing this phenomenon to general awareness. In these projects it was clear policy that project contributors would routinely and systematically freely reveal code they had developed at private expense (Raymond, 1999). However, free revealing of product innovations has a history that began long before the advent of open source software. Allen, in his 1983 study of the eighteenth-century iron industry, was probably the first to consider the phenomenon systematically. Later, Nuvolari (2004) discussed free revealing in the early history of mine pumping engines. Contemporary free revealing by users has been documented by von Hippel and Finkelstein (1979) for medical equipment, by Lim (2000) for semiconductor process equipment, by Morrison, Roberts, and von Hippel (2000) for library information systems, and by Franke and Shah (2003) for sporting equipment. Henkel (2003) has documented free revealing among producers in the case of embedded Linux software.

Innovators often freely reveal because it is often the best or the only practical option available to them. Hiding an innovation as a trade secret is unlikely to be successful for long: too many generally know similar things, and some holders of the “secret” information stand to lose little or nothing by freely revealing what they know. Studies find that innovators in many fields view patents as having only limited value (Harhoff et al., 2003). Copyright protection and copyright licensing are applicable only to “writings,” such as books, graphic images, and computer software.

Active efforts by innovators to freely reveal—as opposed to sullen acceptance—are explicable because free revealing can provide innovators with significant private benefits as well as losses or risks of loss. Users who freely reveal what they have done often find that others then improve or suggest improvements to the innovation, to their mutual benefit (Raymond, 1999). Freely- revealing users also may benefit from enhancement of reputation, from positive network effects due to increased diffusion of their innovation, and from other factors. Being the first to freely reveal a particular innovation can also enhance the benefits received, and so there can actually be a rush to reveal, much as scientists rush to publish in order to gain the benefits associated with being the first to have made a particular advance.

Innovation Communities

Innovation by users tends to be widely distributed rather than concentrated among just a very few very innovative users (table 3). As a result, it is important for user-innovators to find ways to combine and leverage their efforts. Users achieve this by engaging in many forms of cooperation. Direct, informal user-to-user cooperation (assisting others to innovate, answering questions, and so on) is common. Organized cooperation is also common, with users joining together in networks and communities that provide useful structures and tools for their interactions and for the distribution of innovations. Innovation communities can increase the speed and effectiveness with which users and also producers can develop, test and diffuse their innovations. They also can greatly increase the ease with which innovators can build larger systems from interlinkable modules created by community participants.

Table 3. User innovation is widely distributed: Few users developed more than one major commercialized innovation
Table Source: von Hippel, 2005, table 7-1.
Data Sources: * von Hippel, 1988, Appendix: GC, TEM, NMR Innovations
** Riggs and von Hippel, Esca and AES
*** von Hippel, 1988, Appendix: Semiconductor and pultrusion process equipment innovations.
**** Shah, 2000, Appendix A: skateboarding, snowboarding and windsurfing innovations developed by users.


Free and open source software projects are a relatively well-developed and very successful form of an Internet-based innovation community. However, innovation communities are by no means restricted to software or even to information products, and they can play a major role in the development of physical products. Franke and Shah (2003) have documented the value that user- innovation communities can provide to user-innovators developing physical products in the field of sporting equipment. The analogy to open source innovation communities is clear.

The collective or community effort to provide a public good—which is what freely revealed innovations are—has traditionally been explored in the literature on “collective action”. However, behaviors seen in extant innovation communities fail to correspond to that literature at major points. In essence, innovation communities appear to be more robust with respect to recruiting and rewarding members than the literature would predict. The reason for this appears to be that innovation contributors obtain some private rewards that are not shared equally by free riders (those who take without contributing). For example, a product that a user-innovator develops and freely reveals might be perfectly suited to that user-innovator’s requirements but less well suited to the requirements of free riders. Innovation communities thus illustrate a “private-collective” model of innovation incentive (von Hippel and von Krogh, 2003).

Adapting Policy to User Innovation

Is innovation by users a “good thing”? Welfare economists answer such a question by studying how a phenomenon or a change affects social welfare. Henkel and von Hippel (2005) explored the social welfare implications of user innovation. They found that, relative to a world in which only producers innovate, social welfare is very probably increased by the presence of innovations freely revealed by users. This finding implies that policy making should support user innovation, or at least should ensure that legislation and regulations do not favor producers at the expense of user-innovators.

The transitions required of policy making to achieve neutrality with respect to user innovation vs. producer innovation are significant. Consider the impact on open and distributed innovation of past and current policy decisions. Research done in the past 30 years has convinced many academics that intellectual property law is sometimes or often not having its intended effect. Intellectual property law was intended to increase the amount of innovation investment. Instead, it now appears that there are economies of scope in both patenting and copyright that allow firms to use these forms of intellectual property law in ways that are directly opposed to the intent of policy makers and to the public welfare (Foray, 2004). Major firms can invest to develop large portfolios of patents. They can then use these to create “patent thickets”—dense networks of patent claims that give them plausible grounds for threatening to sue across a wide range of intellectual property. They may do this to prevent others from introducing a superior innovation and/or to demand licenses from weaker competitors on favorable terms (Shapiro, 2001; Bessen, 2003). Movie, publishing, and software firms can use large collections of copyrighted work for a similar purpose (Benkler, 2002). In view of the distributed nature of innovation by users, with each tending to create a relatively small amount of intellectual property, users are likely to be disadvantaged by such strategies.

It is also important to note that users (and producers) tend to build prototypes of their innovations economically by modifying products already available on the market to serve a new purpose. Laws such as the (US) Digital Millennium Copyright Act, intended to prevent consumers from illegally copying protected works, also can have the unintended side effect of preventing users from modifying products that they purchase (Varian, 2002). Both fairness and social welfare considerations suggest that innovation-related policies should be made neutral with respect to the sources of innovation.

It may be that current impediments to user innovation will be solved by legislation or by policy making. However, beneficiaries of existing law and policy will predictably resist change. Fortunately, a way to get around some of these problems is in the hands of innovators themselves. Suppose many innovators in a particular field decide to freely reveal what they have developed, as they often have reason to do. In that case, users can collectively create an information commons (a collection of information freely available to all) containing substitutes for some or a great deal of information now held as private intellectual property. Then user-innovators can work around the strictures of intellectual property law by simply using these freely revealed substitutes (Lessig, 2001).

This pattern is occurring in the field of software—and very visibly so. For many problems, user-innovators in that field now have a choice between proprietary, closed software provided by Microsoft and other firms and open-source software that they can legally download from the Internet and legally modify as they wish, to serve their own specific needs. It is also happening, although less visibly, in the case of process equipment developed by users for in-house use. Data from both Canada and the Netherlands show that about 25% of such user-developed innovations get voluntarily transferred to producers. A significant fraction—about half—is transferred both unprotected by intellectual property and without charge (Gault and von Hippel, 2009, de Jong and von Hippel, 2009).

Policy making that levels the playing field between users and producers will force more rapid change onto producers but will by no means destroy them. Experience in fields where open and distributed innovation processes are far advanced show how producers can and do adapt. Some, for example, learn to supply proprietary platform products that offer user-innovators a framework upon which to develop and use their improvements (Jeppesen, 2004).

Diffusion of user-developed innovations

Products, services, and processes developed by users become more valuable to society if they are somehow diffused to others that can also benefit from them. If user innovations are not diffused, multiple users with very similar needs will have to invest to (re)develop very similar innovations which, as was noted earlier, would be a poor use of resources from the social-welfare point of view. In the case of information products, users have the possibility of largely or completely doing without the services of producers. Open-source software projects are object lessons that teach us that users can create, produce, diffuse and provide user field support, update, and use complex products by and for themselves in the context of user innovation communities. In physical product fields, the situation is different. Users can develop products. However, the economies of scale associated with manufacturing and distributing physical products give producers an advantage over “do-it-yourself” users in those activities.

How can or should user innovations of general interest be transferred to producers for large-scale diffusion? We propose three general methods for accomplishing this. First, producers can actively seek innovations developed by lead users that can form the basis for a profitable commercial product. Second, producers can draw innovating users into joint design interactions by providing them with “toolkits for user innovation.” Third, users can become producers in order to widely diffuse their innovations. We discuss each of these possibilities in turn.

To systematically find user-developed innovations, producers must redesign their product development processes. Currently, almost all producers think that their job is to find a need and fill it rather than to sometimes find and commercialize an innovation that lead users have already developed. Accordingly, producers have set up market-research departments to explore the needs of users in the target market, product-development groups to think up suitable products to address those needs, and so forth. In this type of product development system, the needs and prototype solutions of lead users—if encountered at all—are typically rejected as outliers of no interest. Indeed, when lead users’ innovations do enter a firm’s product line they typically arrive with a lag and by an unconventional and unsystematic route. For example, a producer may “discover” a lead user innovation only when the innovating user firm contacts the producer with a proposal to produce its design in volume to supply its own in-house needs. Or sales or service people employed by a producer may spot a promising prototype during a visit to a customer’s site.

Modification of firms’ innovation processes to systematically search for and further develop innovations created by lead users can provide producers with a better interface to the innovation process as it actually works, and so provide better performance. A natural experiment conducted at 3M illustrates this possibility. Annual sales of lead user product ideas generated by the average lead user project at 3M were conservatively forecast by management to be more than 8 times the sales forecast for new products developed in the traditional manner—$146 million versus $18 million per year. In addition, lead user projects were found to generate ideas for new product lines, whereas traditional market-research methods were only found to produce ideas for incremental improvements to existing product lines. As a consequence, 3M divisions funding lead user project ideas experienced their highest rate of major product line generation in the past 50 years (Lilien et al., 2002).

Toolkits for user innovation custom design involve partitioning product-development and service-development projects into solution-information-intensive subtasks and need-information-intensive subtasks. Need-intensive subtasks are then assigned to users along with a kit of tools that enable them to effectively execute the tasks assigned to them. In the case of physical products, the designs that users create using a toolkit are then transferred to producers for production (von Hippel and Katz, 2002). Toolkits make innovation cheaper for users and also lead to higher customer value. Thus, Franke and Piller (2004) in a study of consumer wrist watches, found the willingness to pay for a self-designed product was 200% of the willingness to pay for the best-selling commercial product of the same technical quality. This increased willingness to pay was due both to the increased value provided by the self-developed product and the value of the toolkit process for consumers engaging in it. (Schreier and Franke, 2004).

Producers that offer toolkits to their customers can attract innovating users into a relationship with their firms and so obtain an advantage with respect to producing what the users develop. The custom semiconductor industry was an early adopter of toolkits. In 2003, more than $15 billion worth of semiconductors were produced that had been designed using this approach (Thomke and von Hippel, 2002).

Innovations developed by users sometimes achieve widespread dissemination when those users become producers—setting up a firm to produce their innovative product(s) for sale. Shah (2000) showed this pattern in sporting goods fields. In the medical field, Lettl and Gemunden (2005) have shown a pattern in which innovating users take on many of the entrepreneurial functions needed to commercialize the new medical products they have developed, but do not themselves abandon their user roles. New work in this field is exploring the conditions under which users will become entrepreneurs rather than transfer their innovations to established firms (Hienerth, 2004; Shah and Tripsas, 2004).

I summarize this overview article by saying again that users’ ability to innovate is improving radically and rapidly as a result of the steadily improving quality of computer software and hardware, improved access to easy-to-use tools and components for innovation, and access to a steadily-richer innovation commons. Today, user firms and even individual hobbyists have access to sophisticated programming tools for software and sophisticated CAD design tools for hardware and electronics. These information-based tools can be run on a personal computer, and they are rapidly coming down in price. As a consequence, innovation by users will continue to grow even if the degree of heterogeneity of need and willingness to invest in obtaining a precisely-right product remains constant.

Equivalents of the innovation resources described above have long been available to a few within corporations. Senior designers at firms have long been supplied with engineers and designers under their direct control, and with the resources needed to quickly construct and test prototype designs. The same is true in other fields, including automotive design and clothing design: just think of the staffs of engineers and model makers supplied so that top auto designers can quickly realize and test their designs.

But if, as we have seen, the information needed to innovate in important ways is widely distributed, the traditional pattern of concentrating innovation-support resources on a few individuals is hugely inefficient. High-cost resources for innovation support cannot efficiently be allocated to “the right people with the right information”: it is very difficult to know who these people may be before they develop an innovation that turns out to have general value. When the cost of high-quality resources for design and prototyping becomes very low (the trend we have described), these resources can be diffused very widely, and the allocation problem diminishes in significance. The net result is a pattern in which development of product and service innovations is increasingly shifting to users—a pattern that will involve significant changes for both users and producers.


Allen, R. C. (1983), “Collective Invention”, Journal of Economic Behavior and Organization 4(1), pp. 1-24.

Arrow, Kenneth J. (1962), “Economic Welfare and the Allocation of Resources of Invention”, The Rate and Direction of Inventive Activity: Economic and Social Factors, A Report of the National Bureau of Economic Research, 609-625, Princeton, NJ: Princeton University Press.

Arundel, A., and V. Sonntag (1999), “Patterns of Advanced Manufacturing Technology (AMT) Use in Canadian Manufacturing: 1998 AMT Survey Results”, Research Paper no. 12, Ottawa: Science, Innovation and Electronic Information Division, Statistics Canada.

Benkler, Y. (2002), “Intellectual Property and the Organization of Information Production”, International Review of Law and Economics 22(1), pp. 81-107.

Bessen, J. (2003), “Patent Thickets: Strategic Patenting of Complex Technologies”, Research on Innovation, Boston University School of Law, Working Paper.

de Jong, Jeroen P. J., and Eric von Hippel (2009), “Measuring User Innovation in Dutch High Tech SMEs: Frequency, Nature and Transfer to Producers”, MIT Sloan School of Management Research Paper No. 4724-09 (March), Research Policy, forthcoming

Foray, D. (2004), Economics of Knowledge, Cambridge, MA: The MIT Press.

Franke, N., and F. Piller (2004), “Value Creation by Toolkits for User Innovation and Design: The Case of the Watch Market, Journal of Product Innovation Management 21(6), pp. 401-415.

Franke, N. and H. Reisinger (2003), “Remaining Within Cluster Variance: A Meta Analysis of the ‘Dark’ Side of Cluster Analysis”, Vienna Business University, Working Paper.

Franke, N., and S. Shah (2003), “How Communities Support Innovative Activities: An Exploration of Assistance and Sharing Among End-Users”, Research Policy 32(1), pp. 157-178.

Franke, N., and E. von Hippel (2003a), “Finding Commercially- Attractive User Innovations”, MIT Sloan School of Management Working Paper No. 4402-03.

Franke, N., and E. von Hippel (2003b), “Satisfying Heterogeneous User Needs via Innovation Toolkits: The Case of Apache Security Software”, Research Policy 32(7), pp. 1199-1215.

Gault, Fred, and Eric von Hippel (2009), “The Prevalence of User Innovation and Free Innovation Transfers: Implications for Statistical Indicators and Innovation Policy”, MIT Sloan School of Management Working Paper #4722-09 (January).

Harhoff, D., J. Henkel and E. von Hippel (2003), “Profiting from Voluntary Information Spillovers: How Users Benefit by Freely Revealing their Innovations”, Research Policy 32(10), pp. 1753-1769.

Henkel, J. (2003), “Software Development in Embedded Linux – Informal Collaboration of Competing Firms”, in W. Uhr, W. Esswein and E. Schoop (eds.), Proceedings der 6. Internationalen Tagung Wirtschaftsinformatik 2003 2, Heidelberg, pp. 1-99.

Henkel, J. and E. von Hippel (2005), “Welfare Implications of User Innovation”, Journal of Technology Transfer, forthcoming.

Herstatt, C. and E. von Hippel (1992), “From Experience: Developing New Product Concepts via the Lead User Method: A Case Study in a “Low Tech” Field”, Journal of Product Innovation Management 9(3), pp. 213-222.

Hertel, G., S. Niedner and S. Herrmann (2003), “Motivation of Software Developers in Open Source Projects: an Internet-Based Survey of Contributors to the Linux Kernel”, Research Policy 32(7), pp. 1159-1177.

Hienerth, Christoph (2004) “The commercialization of user innovations: Sixteen cases in an extreme sporting industry”, Vienna University of Economics and Business Administration Working Paper.

Jensen, M. C., and W. H. Meckling (1976), “Theory of the Firm: Managerial Behavior, Agency Costs, and Ownership Structure”, Journal of Financial Economics 3(4), pp.305-360.

Jeppesen, L. B. (2004), “Profiting from Innovative User Communities: How Firms Organize the Production of User Modifications in the Computer Games Industry”, Department of Industrial Economics and Strategy, Copenhagen Business School, Copenhagen, Denmark, Working paper WP-04.

Lakhani, K. R. and B. Wolf (2005), “Why Hackers Do What They Do: Understanding Motivation and Effort in Free/Open Source Software Projects”, J. Feller, B. Fitzgerald, S. Hissam, and K. R. Lakhani (eds.), Perspectives on Free and Open Source Software, Cambridge, MA: The MIT Press.

Lessig, L. (2001), The Future of Ideas: The Fate of the Commons in a Connected World, New York: Random House.

Lettl, C., C. Herstatt and H. Gemünden (2005), “The entrepreneurial role of innovative users”, Journal of Business and Industrial Marketing 20(7), pp. 339-346.

Lilien, Gary L., Pamela D. Morrison, Kathleen Searls, Mary Sonnack, Eric von Hippel (2002), “Performance Assessment of the Lead User Idea Generation Process”, Management Science 48(8) (August), pp. 1042-1059.

Lim, K. (2000), “The Many Faces of Absorptive Capacity: Spillovers of Copper Interconnect Technology for Semiconductor Chips”, MIT Sloan School of Management, Working paper # 4110.

Lüthje, C. (2003), “Customers as Co-Inventors: An Empirical Analysis of the Antecedents of Customer-Driven Innovations in the Field of Medical Equipment”, Proceedings from the 32th EMAC Conference 2003, Glasgow.

Lüthje, C. (2004), “Characteristics of Innovating Users in a Consumer Goods Field: An Empirical Study of Sport-Related Product Consumers”, Technovation, forthcoming.

Lüthje, C., C. Herstatt and E. von Hippel (2002), “The Dominant Role of Local Information in User Innovation: The Case of Mountain Biking”, MIT Sloan School, Working Paper #4377-02.

Morrison, P. D., J. H. Roberts and D. F. Midgley (2004), “The Nature of Lead Users and Measurement of Leading Edge Status”, Research Policy 33(2), pp. 351-362.

Morrison, P. D., J. H. Roberts and E. von Hippel (2000), Determinants of User Innovation and Innovation Sharing in a Local Market”, Management Science 46(12), pp. 1513-1527.

Nuvolari, A. (2004), “Collective Invention during the British Industrial Revolution: The Case of the Cornish Pumping Engine”, Cambridge Journal of Economics 28(3), pp. 347-363.

Ogawa, S. (1998), “Does Sticky Information Affect the Locus of Innovation? Evidence from the Japanese Convenience-Store Industry”, Research Policy 26(7-8), pp. 777-790.

Olson, Erik L., and Geir Bakke (2001), “Implementing the Lead User Method in a High Technology Firm: A Longitudinal Study of Intentions versus Actions”, Journal of Product Innovation Management 18(2), (November), pp. 388-395.

Pavitt, K. (1984), “Sectoral Patterns of Technical Change: Towards a Taxonomy and a Theory”, Research Policy 13(6), pp. 343-373.

Raymond, E. (1999), The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary”, Sebastopol, CA: O’Reilly.

Riggs, William, and Eric von Hippel (1994), “The Impact of Scientific and Commercial Values on the Sources of Scientific Instrument Innovation”, Research Policy 23 (July), pp. 459-469.

Rosenberg, N. (1976), Perspectives on Technology, New York: Cambridge University Press.

Rosenberg, N. (1982), Inside the Black Box: Technology and Economics, New York: Cambridge University Press.

Schreier, M. and N. Franke (2004), “Tom Sawyer’s Great Law in Action: Why Users are Willing to Pay to Design their own Products via Toolkits for User Innovation and Design”, Working Paper, Vienna University of Economics and Business Administration.

Schumpeter, Joseph A., The Theory of Economic Development, New York: Oxford University Press, 1934.

Shah, S. (2000), “Sources and Patterns of Innovation in a Consumer Products Field: Innovations in Sporting Equipment”, MIT Sloan School of Management, Working paper # 4105.

Shah, S., and M. Tripsas (2004), When Do User-Innovators Start Firms? Towards A Theory of User Entrepreneurship, University of Illinois, Working Paper #04-0106.

Shapiro, C. (2001), “Navigating the Patent Thicket: Cross Licenses, Patent Pools, and Standard Setting”, A. Jaffe, J. Lerner, and S. Stern (eds.), Innovation Policy and the Economy 1, pp. 119-150, Cambridge, MA: The MIT Press.

Stigler, George J. (1951), “The Division of Labor is Determined by the Extent of the Market”, Journal of Political Economy 59(3) (June), pp. 185-193.

Thomke, S. H., and E. von Hippel (2002), “Customers as Innovators: A New Way to Create Value”, Harvard Business Review 80(4), pp. 74-81.

Urban, G. L. and E. von Hippel (1988), “Lead User Analyses for the Development of New Industrial Products”, Management Science 34(5), pp. 569-82.

Varian, H. R. (2002), “New Chips Can Keep a Tight Rein on Consumers”, New York Times, July 4.

von Hippel, E., and S. N. Finkelstein (1979), “Analysis of Innovation in Automated Clinical Chemistry Analyzers”, Science & Public Policy 6(1), pp. 24-37.

von Hippel, E. (1994), “Sticky Information and the Locus of Problem Solving: Implications for Innovation”, Management Science 40(4), pp. 429-439.

von Hippel, E., and G. von Krogh (2003, “Open Source Software and the ‘Private-Collective’ Innovation Model: Issues for Organization Science”, Organization Science 14(2), pp. 209–223.

von Hippel, Eric (2005), Democratizing Innovation, Cambridge MA: The MIT Press.

von Hippel, E., and R. Katz (2002), “Shifting Innovation to Users via Toolkits”, Management Science 48(7), pp. 821-833.

Quote this content

Comments on this publication

Name cannot be empty
Write a comment here…* (500 words maximum)
This field cannot be empty, Please enter your comment.
*Your comment will be reviewed before being published
Captcha must be solved