Modeling a : From Producer to User and Open Collaborative Innovation

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Citation Baldwin, Carliss and Eric A. von Hippel. "Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation." Organization Science 22, 6 (December 2011): 1369-1683 © 2011 INFORMS

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Carliss Baldwin, Eric von Hippel,

To cite this article: Carliss Baldwin, Eric von Hippel, (2011) Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation. Organization Science 22(6):1399-1417. https://doi.org/10.1287/orsc.1100.0618

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Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation

Carliss Baldwin Harvard Business School, Boston, Massachusetts 02163, [email protected] Eric von Hippel Sloan School of Management, Massachusetts Institute of , Cambridge, Massachusetts 02141, [email protected]

n this paper, we assess the economic viability of innovation by producers relative to two increasingly important alternative Imodels: by single-user individuals or firms and open collaborative innovation. We analyze the costs and architectures and communication costs associated with each model. We conclude that both innovation by individual users and open collaborative innovation increasingly compete with and may displace producer innovation in many parts of the economy. We explain why this represents a paradigm shift with respect to innovation research, policy making, and practice. We discuss important implications and offer suggestions for further research. Key words: user innovation; modularity; open innovation; collaborative innovation : Published online in Articles in Advance March 11, 2011.

1. Introduction and Overview and open collaborative innovation. Each of these three Ever since Schumpeter (1934) promulgated his theory forms represents a different way to organize human effort of innovation, entrepreneurship, and economic develop- and investments aimed at generating valuable new inno- ment, economists, policy makers, and business managers vations. In the body of this paper, we will analyze these have assumed that the dominant mode of innovation is three models in terms of their technological properties— a “producers’ model”; that is, it has been assumed that specifically, their design costs and architecture, and their the most important for innovations would orig- communication requirements. In these two technological inate from producers and be supplied to consumers via dimensions, each model has a different profile that gives goods and services that were for sale. This view seemed it economic advantages under some conditions and dis- reasonable on the face of it—producer innovators gener- advantages in others. Each has a valuable role to play in ally profit from many users, each purchasing and using a the economy. single producer-developed design. Individual-user inno- Our modeling of design costs, design architectures, vators, in contrast, depend only on their own in-house and communication costs allows us to place bounds on use of their design to recoup their innovation-related the contexts in which each model will be economically investments. Presumably, therefore, a producer serving viable. Our analysis will lead us to conclude that inno- many customers can afford to invest more in an inno- vation by individual users and user firms as well as open vation design than can any single user. From this it has collaborative innovation are modes of innovating that been generally assumed that producer-developed designs increasingly compete with and may displace producer should dominate user-developed designs in most parts of innovation in many parts of the economy. This shift is the economy. being driven by new —specifically, the tran- This long-held view of innovation has, in turn, led sition to increasingly digitized and modularized design to public policies based on a theory of producer incen- and production practices, coupled with the availability tives. Producers, it is argued, are motivated to innovate of very-low-cost, Internet-based communication. by the expectation of profits. These profits will disappear We will show that under the right technological and if anyone can simply copy producers’ innovations, and architectural conditions, users seeking direct value can therefore producers must be granted subsidies or intellec- disintermediate producers seeking profit. Our analysis tual property rights that give them exclusive control over therefore challenges the assumption that the profits are their innovations for some period of time (Machlup and the only economically important motive causing inno- Penrose 1951, Teece 1986, Gallini and Scotchmer 2002). vators to create new designs. Profits to innovation arise However, the producers’ model is only one mode of because users are willing to pay for a new or superior innovation. Two increasingly important additional mod- product or process. Thus profit itself is derived from els are innovations by single-user firms or individuals users’ willingness to pay. We are not suggesting that the

1399 Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation 1400 Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS profit motive is not a major stimulus to investment in reported that nearly all the most important innovations innovation, but value to users is both a necessary con- in oil refining were developed by user firms. von Hippel dition for profits to exist and an alternate motive for (1976, p. 1977) found that users were the developers investing in innovative designs. of approximately 80% of the most important scientific We will argue that, taken in combination, the patterns instrument innovations as well as the developers of most and findings we describe create a significant change in of the major innovations in semiconductor processing. the “problem field” in innovation research, policy mak- Pavitt (1984) found that a considerable fraction of inven- ing, and practice, and thus represent a paradigm shift in tion by British firms was for in-house use. Shah (2000) these fields (Kuhn 1962). and Hienerth (2006) found that the most commercially In §2 we review relevant literature. In §3 we present important product innovations in several sporting fields and explain conditions under which each of the three tended to be developed by individual users. economic models of innovation we describe is viable. Empirical studies also show that many users—from In §4 we discuss some broader patterns related to our 6% to nearly 40%—engage in developing or modify- models and also offer suggestions for further research. ing products. This has been documented in the case of several specific types of industrial products and con- sumer products (Urban and von Hippel 1988; Herstatt 2. Literature Review and von Hippel 1992; Morrison et al. 2000; Lüthje et al. In this section we briefly review the literature on user 2002; Lüthje 2003, 2004; Franke and von Hippel 2003; innovation, openness of intellectual property, and collab- Franke and Shah 2003). It has also been documented in orative innovation and modular designs. large-scale, multi-industry surveys of firms developing process innovations for in-house use in both Canada and 2.1. Innovation by Users the Netherlands (Arundel and Sonntag 1999, Gault and Users, as we define the term, are firms or individual von Hippel 2009, de Jong and von Hippel 2009). Finally, consumers that expect to benefit from using a design, via a survey of a representative sample of consumers in a product, or a service. In contrast, producers expect the United Kingdom, it has been found that 6.2% of the to benefit from selling a design, a product, or a ser- UK population—about 3 million people—have recently vice. Innovation user and innovation producer are thus developed or modified consumer products to better serve two general “functional” relationships between innova- their personal needs (von Hippel et al. 2010). tor and innovation. Users are unique in that they alone When taken together, the findings of these empirical benefit directly from innovations. Producers must sell studies make it very clear that users have long been innovation-related products or services to users; hence doing and continue to do a lot of commercially signifi- the value of innovation to producers is derived from cant product and process development and modification users’ willingness to pay. Inventors creating knowledge in many fields. to sell rather than use are producers, as are those that innovate to manufacture and sell goods embodying or 2.2. Innovation Openness complementing the innovation. An innovation is “open” in our terminology when all Qualitative observations have long indicated that im- information related to the innovation is a public good— portant process improvements are developed by employ- nonrivalrous and nonexcludable. This usage is closely ees working for firms that use them. Smith (1937, p. 17) related to the meaning of open in the terms “open pointed out that “a great part of the machines made source software” (Raymond 1999) and “open science” use of in those manufactures in which labor is most (Dasgupta and David 1994). It differs fundamentally subdivided, were originally the inventions of common from the recent use of the term to refer to organizational workmen, who, being each of them employed in some permeability—an organization’s “openness” to the acqui- very simple operation, naturally turned their thoughts sition of new ideas, patents, products, etc., from outside towards finding out easier and readier methods of per- its boundaries, often via licensing protected intellectual forming it.” Rosenberg (1976) studied the history of the property (Chesbrough 2003). U.S. machine tool industry and found that important and Economic theorists have long thought that inno- basic machine types like lathes and milling machines vation openness is undesirable—that uncompensated were first developed and built by user firms having a “spillovers” of proprietary innovation-related knowledge strong need for them. Textile firms, gun developed by private investment will reduce innova- manufacturers, and sewing machine manufacturers were tors’ expected profits from innovation investments and important early user developers of machine tools. so reduce their willingness to invest. Accordingly, many Quantitative studies of user innovation document nations have long offered intellectual property rights that many of the most important and novel products grants that afford inventors some level of temporary and processes commercialized in a range of fields are monopoly control over their inventions. The assump- developed by users for in-house use. Thus, Enos (1962) tion has been that losses incurred because of intellectual Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS 1401 property rights grants will be more than offset by gains Lakhani and Wolf 2005, von Hippel and von Krogh to society from related increases in innovation invest- 2003). With regard to cost, protecting design informa- ment, and increased disclosure of information that would tion is generally expensive, requiring security walls and otherwise be kept hidden as trade secrets (Machlup and restricted access or the enforcement of intellectual prop- Penrose 1950, Penrose 1951, Foray 2004, Heald 2005). erty rights (Blaxill and Eckardt 2009). For this rea- Given this argument, empirical research should show son, preventing others from viewing and using a new innovators striving to keep information on their inno- design may be significantly more costly than leaving the vations from being freely diffused. However, open for inspection or use by any interested party instead shows that both individuals and firms often vol- (Baldwin 2008). untarily “freely reveal” what they have developed. When Not surprisingly, the incentive to freely reveal we say that an innovator freely reveals information about decreases if the agents compete with one another—for an innovation, we mean that exclusive intellectual prop- example, if they are firms making the same end product erty rights to that information are voluntarily given up by or individuals competing in a sport (Franke and Shah the innovator, and all interested parties are given access 2003, Baldwin et al. 2006). Selective openness strate- to it—the information becomes a public good (Harhoff gies illustrate this point nicely. Thus, Henkel (2003) has et al. 2003). (Intellectual property rights may still be documented selective free revealing among producers in used to protect the developers of these public goods from the case of embedded Linux software. The producers liability and to prevent expropriation of their innovations partition their code into open modules on which they by third parties; see O’Mahony 2003.) collaborate and closed modules on which they compete The practices visible in open source software develop- (Henkel and Baldwin 2010). ment were important in bringing the phenomenon of free revealing to general awareness. In these projects it was 2.3. Collaboration and Modularity Collaboration is a well-known attribute of online, mul- clear policy that project contributors would routinely and ticontributor projects such as open source software systematically freely reveal code they had developed at projects and Wikipedia (Raymond 1999, Benkler 2002). private expense (Raymond 1999). However, free reveal- Franke and Shah (2003) studied user innovators in four ing of innovations has a history that began long before sporting communities and found that all had received the advent of open source software. Allen (1983) and

assistance in their development efforts by at least one Nuvolari (2004) described and discussed 19th-century other user from their communities. The average number examples. Contemporary free revealing by users has assisting each user innovator was three to five. Finally, been documented by von Hippel and Finkelstein (1979) a study of process equipment innovations by high-tech for medical equipment, by Lim (2009) for semiconduc- small and medium enterprises in the Netherlands con- tor process equipment, by Morrison et al. (2000) for ducted by de Jong and von Hippel (2009) found that library information systems, and by Franke and Shah 24% of 364 user firms drawn from a wide range of (2003) for sporting equipment. Gault and von Hippel industries had received assistance in their innovation (2009) and de Jong and von Hippel (2009) have shown development work from other process equipment users. in multi-industry studies in Canada and the Netherlands Modular design architectures are an important aid that user firms developing process equipment often to collaborative work. A modular system is one in transfer their innovations to process equipment suppli- which the elements, which may be decisions, tasks, or ers without charge. In the case of consumer products, components, are partitioned into subsets called mod- several studies have shown that it is quite rare for con- ules. Within each module, elements of the system are sumers to attempt to protect or restrict access to inno- densely interdependent: changing any one will require vations they have developed (Shah 2000, Raasch et al. changes in many others. Across modules, however, ele- 2008, von Hippel et al. 2010). ments are independent or nearly so; a change in one Evidence has now accumulated that innovators who module by definition does not require changes in oth- elect to freely reveal their innovations can gain sig- ers (Baldwin and Clark 2000). Modular systems can nificant private benefits—and also avoid some private be easily broken apart: Herbert Simon called such sys- costs. With respect to private benefits, innovators that tems “near-decomposable” (Simon 1962). Furthermore, freely reveal their new designs often find that others given appropriate knowledge, a nonmodular system can then improve or suggest improvements to the innova- be made modular (or near decomposable) by creat- tion, to mutual benefit (Raymond 1999). Freely revealing ing a set of coordinating design rules that establish users also may benefit from enhancement of reputation, interfaces and regulate the interactions of the mod- from positive network effects as a result of increased ules (Mead and Conway 1980, Baldwin and Clark diffusion of their innovation, and from other factors 2000, Brusoni and Prencipe 2006). Most design-relevant such as obtaining a source of supply for their inno- knowledge and information does not need to cross mod- vation that is cheaper than in-house production (Allen ule boundaries. This is the property of “information hid- 1983, Lerner and Tirole 2002, Harhoff et al. 2003, ing” (Parnas 1972). Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation 1402 Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS

Modularity is important for collaboration in design emerge in history and recede as technologies and pref- because separate modules can be worked on indepen- erences change. dently and in parallel, without intense ongoing commu- Our approach is modeled after Williamson’s (1985, nication across modules. Designers working on different 1991) analysis of transaction costs and especially after modules in a large system do not have to be colocated Fama and Jensen’s (1983a, b) account of how agency but can still create a system in which the parts can be costs affect organizational forms. However, in contrast integrated and will function together as a whole. In small to this prior work, we will not attempt to determine projects or within modules, designers can utilize “action- which model is most efficient in terms of minimizing able transparency” rather than modularity to achieve transaction or agency costs, but we instead will establish coordination. When the targeted design is small, each bounds on the viability of each model. When more than designer’s activities will be “transparent” to his or her one form is viable, we do not expect to see one form collaborators. Each contributor can then take separate drive out the other (as is the common assumption) but action to improve the design, building on the transpar- rather expect to see creative combinations of the forms ently visible contributions of the others. In open collab- to take advantage of what each one does best. orative projects, modularity and actionable transparency Finally, in contrast to virtually all prior work except generally go hand in hand, with both factors contributing for Chandler (1977) and Woodward (1965), we take an to the divisibility of tasks (Colfer and Baldwin 2010). explicitly technological approach to the question of via- Building on the arguments of Ghosh (1998), Raymond bility. We fundamentally assume that in a free economy, (1999), and von Hippel and von Krogh (2003), Baldwin the organizational forms that survive are ones with ben- and Clark (2006b) showed formally that if communi- efits exceeding their costs (Fama and Jensen 1983a, b). cation costs are low relative to design costs, then any Costs in turn are determined by technology and change degree of modularity suffices to cause rational inno- over time. Thus, Chandler (1977) argued that the mod- vators that do not compete with respect to the design ern corporation became a viable form of organization being developed to prefer open collaborative innovation (and the dominant form in some sectors) as a conse- over independent innovation. This result hinges on the quence of the (partly endogenous) decline in production fact that the innovative design itself is a nonrival good: costs for high-flow-through technologies, together with each participant in a collaborative effort gets the value

(exogenous) declines in transportation and energy costs. of the whole design but incurs only a fraction of the Adopting Chandler’s (1977) logic, we should expect a design cost. particular organizational form to be prevalent when its technologically determined costs are low and to grow 3. Where Is Each Model Viable? relative to other forms when its costs are declining rela- Previous work has demonstrated the existence of the tive to the costs of other forms. three basic ways of organizing innovation activity and Today, design costs and communication costs are de- has elucidated their characteristics. However, to our clining rapidly, and modular design architectures are knowledge, there has been no systematic thinking about becoming more common for many products and pro- the conditions under which each model is likely to cesses. In the rest of this section, we argue that appear and whether each is expanding or contracting rel- these largely exogenous technological trends are making ative to the other two. To make progress on these ques- single-user innovation and open collaborative innovation tions, it is necessary to develop a theoretical framework viable across a wider range of innovation activities than that locates all three models in a more general space of was the case before the arrival of technologies such as attributes. That is our aim in this section. personal computers and the Internet. We have seen, and Our methodology is that of comparative institutional expect to continue to see, single-user innovation and analysis. In this diverse literature, laws, social customs, open collaborative innovation growing in importance rel- modes of governance, organizational forms, and indus- ative to producer innovation in most sectors of the econ- try structures are compared in terms of their incentives, omy. We do not believe that producer innovation will economic consequences, and ability to survive and grow disappear, but we do expect it to become less pervasive in a given historical setting or technological context. In and ubiquitous than was the case during most of the the particular branch we are most concerned with, orga- 20th century, and to be combined with user and open nizational forms and industry structures are taken to be collaborative innovation in many settings. endogenous and historically contingent (Chandler 1977; Woodward 1965; Williamson 1985, 1991; Nelson and 3.1. Definitions Winter 1982; Aoki 1988, 2001; Langlois 1986b, 2002; A single-user innovator is a single firm or individual that Baldwin and Clark 2000; Jacobides 2005). Different creates an innovation to use it. Examples are a single forms may be selected to suit different environments firm creating a process machine to use it, a surgeon cre- and then adaptively modified. Thus, organizational forms ating a new medical device to use it, and an individual Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS 1403 consumer creating a new piece of sporting equipment to Demsetz 1988; Hart 1995). For the firm or organiza- use it. tion to continue in existence, each party must perceive A producer innovator is a single noncollaborating himself or herself to be better off within the contracting firm. Producers anticipate profiting from their design by relationship than outside of it. selling it to users or others: by definition, they obtain In finance, a firm assembles resources by issuing no direct use value from a new design. We assume that claims (contracts) in the form of debt and equity. It uses through secrecy or intellectual property rights a producer the proceeds to purchase assets and to bridge the gap innovator has exclusive access and control over the inno- between cash outflows and inflows. A firm is solvent as vation, and so is a monopolist with respect to its design. long as it can pay off or refinance all its debt claims and Examples of producer innovators are (1) a firm or indi- have something left over. If this condition is not met, the vidual that patents an invention and licenses it to others, firm ceases to be a going concern and must be liquidated (2) a firm that develops a new process machine to sell to or reorganized. its customers, and (3) a firm that develops an enhanced In institutional game theory, an institution is defined service to offer its clients. as the equilibrium of a game with self-confirming beliefs An open collaborative innovation project involves (Aoki 2001, Greif 2006). Within the institutional frame- contributors who share the work of generating a design work, participants join or contribute resources in the and also reveal the outputs from their individual and expectation that other parties will enact their respec- collective design efforts openly for anyone to use. The tive roles. If all behave as the others expect, everyone’s defining properties of this model are twofold: (1) the initial beliefs are confirmed: the pattern of action then participants are not rivals with respect to the inno- becomes a self-perpetuating institution. When the par- vative design (otherwise, they would not collaborate), ticipants in the institution are rational actors, one of and (2) they do not individually or collectively plan their self-confirming beliefs must be that “I am better to sell products or services incorporating the innova- off participating in this institutional arrangement than tion or intellectual property rights related to it. Many, withdrawing from it.” In this view, a stable nexus of con- tracts, a solvent firm, and an active open collaborative but not all, open source software projects have these innovation project are all special cases of institutional characteristics. equilibria.

A design is a set of instructions that specify how to We define an innovation opportunity as the opportu- produce a novel product or service (Simon 1981; Romer nity to create a new design. With respect to a particular 1990; Suh 1990; Baldwin and Clark 2000, 2006a). These innovation opportunity, each of the three models of inno- instructions can be thought of as a recipe for accom- vation may be viable or not, depending on the benefits plishing the functional requirements of the design (Suh and costs flowing to the actors. 1990, Winter 2010, Dosi and Nelson 2010). In the case In terms of benefits, we define the value of an inno- of products or services that themselves consist of infor- vation 4V 5 as the benefit that a party expects to gain mation such as software, a design for an innovation can from converting an innovation opportunity into a new be virtually identical to the usable product itself. In the design—the recipe—and then turning the design into a case of a physical product such as a wrench or a car, useful product, process, or service. Different individuals the design recipe must be converted into a physical form and organizations may benefit in different ways. By def- before it can be used. inition, users benefit from direct use of the product, pro- A given mode of innovation is viable with respect to cess, or service specified by the new design. Producers a particular innovation opportunity if the innovator or benefit from profitable sales, which may take the form each participant in a group of innovators finds it worth- of sales of intellectual property (a patent or license) or while to incur the requisite costs to gain the anticipated sales of products or services that embody the design. value of the innovation. By focusing on anticipated ben- Ultimately, however, a producer’s benefit (hence, value) efits and costs, we assume that potential innovators are derives from the users’ willingness to pay for the inno- rational actors who can forecast the likely effects of their vative design. design effort and choose whether to expend the effort Each innovation opportunity has four generic costs: (Simon 1981, Langlois 1986a, Jensen and Meckling design cost, communication cost, production cost, and 1994, Scott 2001). Our definition of viability is related transaction cost. Consistent with our assumption that to the contracting view of economic organizations, the innovators are rational actors, we assume that these costs concept of solvency in finance, and the concept of equi- (as well as benefits) are known ex ante to potential inno- librium in institutional game theory. vators, although there may be uncertainty in their assess- In the contracting literature, firms and other organi- ments. As with value, the costs may differ both across zations are viewed as a “nexus of contracts,” that is, a individuals and across the three models of innovation. set of voluntary agreements (Alchian and Demsetz 1972; Design cost (d) is the cost of creating the design for Jensen and Meckling 1976; Fama and Jensen 1983a, b; an innovation—the instructions that when implemented Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation 1404 Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS will bring the innovation into reality. Following Simon costs.) In open collaborative innovation projects, how- (1981), these costs include (1) the cost of identifying ever, there are several or many contributors, and the the functional requirements (that is, what the design is inequality must hold for each one individually. Notice supposed to do); (2) the cost of dividing the overall prob- we have defined the criterion as a strict inequality: we lem into subproblems, which can be solved separately; assume that the actors must anticipate a strictly posi- (3) the cost of solving the subproblems; and (4) the cost tive gain to undertake the effort and cost of innovation. of recombining the subproblems’ solutions into a func- We do not rule out the possibility that the activities of tioning whole. Many of these tasks require calculations, design, communication, production, or exchange might and thus design cost is strongly affected by the cost of be pleasurable for some agents: if this is the case, the computation. relevant cost would be negative for those agents. How- Communication cost (c) is the cost of transferring ever, the cases of interest here are those for which the design-related information among participants in differ- sum of costs is positive—that is to say, the innovation is ent organizations during the design process. Under this not a free good. definition, single-user innovators, because they are in As indicated in the introduction, design costs and the same organization, incur no communication cost. communication costs have declined and are continuing (Of course, there can be intraorganization costs of com- to decline very rapidly because of the advent of personal munication. However, for our purposes it is sufficient computers and the Internet. We believe these largely if the costs of communication are less within an orga- exogenous technological trends are the main causes of nization than across organizational boundaries.) Pro- the increasing importance of single-user and open col- ducer innovators and innovators collaborating in an open laborative innovation models in the economy at large. project must communicate across organizations and thus To make this argument as clear as possible, we will first incur communication costs. focus our analysis on these costs alone, holding produc- Production cost (u) is the cost of carrying out the tion costs and transaction costs constant across all three design instructions to produce the specified good or ser- economic models. Once we have established bounds on vice. The input is the design instructions (the recipe) viability for the three models with respect to design and plus the materials, energy, and human effort specified communication costs, we will reintroduce the other two in those instructions; the output is a good (the design dimensions of cost and show how they affect the results. converted into usable form). To simplify our notation in the next few sections, we Transaction cost (t) is the cost of establishing prop- define v as the value of an innovation opportunity net erty rights and engaging in compensated exchanges of of production and transaction costs. Because it subtracts property. For an innovation, transaction cost includes out production and transaction costs, v can be thought the cost of creating exclusive rights to the design, by of as the (expected) value of the design alone, before it keeping it secret or by obtaining a patent or copy- is put up for sale or converted into a useful thing. The right. It also includes the cost of controlling opportunis- bounds of viability can then be restated as follows. tic behavior (Williamson 1985), writing contracts (Hart For a given innovation op- 1995), and accounting for transfers and compensation Bounds on Viability 2. portunity, if production and transaction costs are con- (Baldwin 2008). stant across models, a particular model of innovation is 3.2. Bounds on Viability viable if and only if for each necessary contributor to Every innovation opportunity, that is, every potential the model v > d c 0 new design, can be characterized in terms of its value i i + i (2) and the four dimensions of cost described above. The With this simplifying assumption, we can now rep- criterion of viability can thus be specified mathemati- resent innovation opportunities with different costs as cally as follows. points in a graph with design cost and communication Bounds on Viability 1. For a given innovation op- cost on the x and y axes, respectively. We next ask the portunity, a particular model of innovation is viable if question, for what combinations of design and commu- and only if for each necessary contributor to the model nication cost will each model be viable? 3.3. Single-User Innovation Vi > di + ci + ui + ti0 (1) Consider first a single-user innovator—an individual or a (The subscripts indicate that the benefits and costs may firm—contemplating investment in a design whose value vary by contributor and across models.) to her is vs. This measure of benefits includes all aspects For single-user innovators and producer innovators, of the innovation that the user values. It may reflect, for there is only one contributor to be considered. (Producer example, improved performance, lower cost, lower envi- innovators may employ many people, but the producer’s ronmental impact, greater flexibility, and/or enhanced contracts with employees are subsumed in the producer’s capabilities. The effort of innovation is worthwhile (for Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS 1405

Figure 1 Bound on Single-User Innovation small-scale firms and individual users are not. In Fig- d

A B 3.4. Producer Innovation Producers can often economically justify undertaking larger designs than can single users because they expect to spread their design costs over many purchasers. Even Communication costs ( c) though they are single organizations, however, they are affected by communication costs, because to sell their products they must make potential buyers aware of what they have to sell. Noninnovating users will purchase the innovation vs vi Design cost (d) from a producer as long as their value is greater than the producer’s price: vi > p, where vi denotes the value of the innovation to the ith user, and p denotes the producer this innovator and this design) if this value is greater innovator’s price. (Both value and price are measured than the user’s design cost: ds < vs. In Figure 1, we draw net of production and transaction costs.) a vertical line at d = vs. Points to the left of the vertical As we mentioned earlier, we assume that if the line will satisfy the constraint and hence be viable; those producer undertakes a design effort, it will obtain prop- to the right will not. Thus the constraint d = vs bounds erty rights that give it some predictable degree of effec- the region in which single-user innovation is viable for tive monopoly on the design. We also assume that the this opportunity. producer knows the value vi that each potential user Communication costs do not enter the analysis be- places on the innovation. In other words, it knows its cause the user is a single agent that both designs and customers’ willingness to pay for the innovative prod- benefits from the use of an innovation. As was men- uct or service and can subtract the relevant production tioned earlier, a single-user innovator does not need to and transaction costs from their willingness to pay. The engage in interorganization communication as part of producer innovator can convert this customer knowledge either the design process or the process of reaping value into a demand function, Q4p5, which relates each price from the design. For this reason, as shown in Figure 1, it might charge to the number of units of the product or the institution of single-user innovation is viable inde- service it will sell at that price (Baldwin et al. 2006). pendent of the cost of communication. Single users From the demand function, the producer innovator can will innovate even if communication technology is very solve for the price, p∗1 and quantity, Q∗, that maximize primitive and the costs of communication are very high. its expected contribution (expected revenues net of pro- Some innovations developed by users are intended to duction and transaction costs). It can subtract its design be consumed in-house with little or no external eco- (dp) and communication (cp) costs from this net revenue nomic impact—for example, innovations intended to to calculate expected profit, ç: improve the comfort or convenience of individual users ∗ ∗ or the safety of user firm employees. Others, especially ç = p Q − dp − cp0 (3) in the case of process innovations developed by user firms, reduce the process user’s costs (production, trans- If the producer anticipates positive profit, then as a actions, communication, design) without changing con- rational actor, it will enter the market to supply the inno- sumers’ willingness to pay for the product. (When a vation. In this case, the producer innovator model is process innovation changes the consumer’s willingness viable with respect to the innovation opportunity. Con- to pay by changing product or context characteristics, it versely, if its anticipated profit is negative, the producer is at least in part a product innovation by a producer— will not enter, and the producer model of innovation is which we consider in the next section.) not viable. A small unit cost savings, when applied to a large The zero profit constraint on the producer defines a volume of production, will have a large total value. negative 45ž line in the space of design and communica- Hence, as a general rule, firms running large-scale oper- tion costs: p∗Q∗ = d + c. Figure 2 shows this bound in ations will be viable as user innovators in regions where relation to a hypothetical single-user innovator’s bound Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation 1406 Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS

Figure 2 Bound on Producer Innovation a rough measure of what a given producer spends com- municating with customers. In effect, a producer innova- Single-user innovators tor must split its (net) revenue between design cost and only communication cost and still have something left over. Thus, if communication costs fall because of technolog- ical progress, a producer innovator may become viable even if design cost stays the same. Single-user X not viable innovators and Producer’s producer 3.5. Open Collaborative Innovation innovators bound: Consider finally the model of open collaborative innova- coexist p*Q*=d+c Producer tion. Recall that open collaborative innovation projects innovators Communication costs ( c) only involve users and others who share the work of generat- ing a design and also reveal the outputs from their indi- X viable vidual and collective design efforts openly for anyone to opportunity use. In such projects, some participants benefit from the design itself—directly in the case of users, indirectly in Design cost (d) the case of suppliers of complements that are increased in value by that design. Each of these incurs the cost of doing some fraction of the work but obtains the value for the same opportunity. As we show in the figure, the of the entire design, including additions and improve- design costs are higher than the value of the innovation ments generated by others. Other participants obtain pri- to a single user; hence the single-user innovation model vate benefits such as learning, reputation, fun, etc., that is not viable for this design. We then show two possible are not related to the project’s innovation outputs. outcomes for the producer. In the first case, communica- For ease of exposition, we will derive the bounds of tion costs are low so that the sum of design and commu- the model for user innovators first and then consider the nication costs falls below the producer’s bound. In the impact of other participants on those bounds. second case, the sum falls above the bound. Producer For the contributing user innovators, the key advan- innovation is a viable model for the first combination of tage of open collaborative innovation is that each con- costs but not for the second. tributor can undertake some of the work but rely on From this analysis, we learn three things. First, through the demand curve, a producer’s profit is deter- others to do the rest (von Hippel and von Krogh 2003, mined by its customers’ willingness to pay. Producers’ Baldwin and Clark 2006b). The ability to divide up incentives are derived from and depend on users’ valu- design tasks via modular design architectures eliminates ations. As a general rule in a free market, without use the design cost bound, d < vs, that made large-scale value there can be no profit. (Products whose purchase is innovations infeasible for single-user innovators. mandated by the state or some other authority constitute Creating a modular architecture that supports distri- exceptions to this rule.) buted innovation across geographical and organizational Second, like large-scale single-user innovators, pro- boundaries will add to the upfront cost of a new design ducer innovators are affected by the size of the market if detailed and comprehensive modularity is designed in for their goods. In large markets, the producer will have from the start. This was famously the case, for example, many customers; thus its net revenue (after production for IBM’s System/360 and Pirelli’s modular integrated and transaction costs) will generally be far in excess of robotized system for tire production (Baldwin and Clark the value of the product to any one customer. Because 2000, Brusoni and Prencipe 2006). However, modular the producer can “aggregate demand,” it can invest in architectures need not add significantly to costs if, as both product and process innovations that its own cus- is typical modern practice, modularity is largely emer- tomers (acting as single-user innovators) would not find gent. In such cases, the original architect designs a small attractive. functional core and a set of interfaces. New modules Finally, the need to communicate differentiates pro- can then be attached to the system even if they were ducer innovators from single-user innovators. As was not originally envisaged by the architects, as long as mentioned earlier, to learn about which designs and they comply with the interface specifications. Examples goods will be profitable to sell, producers must com- of this type of architecture include the IBM personal municate with and learn from their potential customers computer and the Linux operating system (Ferguson via marketing research. To sell goods, a firm’s potential and Morris 1993, Raymond 1999). Emergent modular- customers have to be told about the innovative and mer- ity is compatible with the constraints of open collabora- itorious features of the firm’s products and services. The tive projects, where the design cost of each contributor percentage of cost devoted to “marketing expenses” is (including project architects) needs to be relatively low. Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS 1407

Communication costs are a major concern for open other forums of open collaborative innovation (Raymond collaborative innovation projects. To divide their work 1999, Franke and Shah 2003, Baldwin et al. 2006). effectively and then to put it back together to form a Note that open collaborative projects do not as a gen- complete design, contributors must communicate with eral rule discourage or prevent free riding. This feature one another rapidly and repeatedly. This means that low of their makeup can be understood in light of the fact communication costs, as recently enabled by the Inter- that the cost of screening or other protective measures to net, are critical to the viability of the open collaborative exclude free riders would raise communication costs and innovation model. thus shrink the pool of potential contributors and, hence, User innovators will choose to participate in an open the overall scale of the project. The network properties collaborative innovation project if the increased commu- of the open collaborative model (the fact that the value nication cost each incurs by joining the project is more to everyone increases as the total number of contribu- than offset by the value of designs obtained from others. tors increases) mean that this reduction in the contributor To formalize this idea, assume that a large-scale inno- pool would reduce the value of the project to the con- vation opportunity is perceived by a group of N com- tributors that remain as well as to free riders (Raymond municating designers. As rational actors, each member 1999, Baldwin and Clark 2006b, Baldwin 2008). of the group (indexed by i) will estimate the value of The second bound determines the maximum scale of the large design and parse it into two subsets: (1) the the design. If there are N members of the group and part, valued at vsi, that the focal individual can complete each contributes his or her own part, the total design himself at a reasonable cost (by definition, vsi > dsi5; investment will be the sum of their individual design and (2) the part, valued at voi, that would be “nice to costs. The upper bound on design cost is then have” but that he cannot complete at a reasonable cost N N given his skills and other sticky information on hand (by X X dsi < vsi =N v¯s1 (5) definition, voi · doi). i=1 i=1 We assume that member i has the option to com- municate his portion of the design to other members where v¯s is the average value each places on his or and receive their feedback and complementary designs her own portion of the design. Note that this bound is N times greater than the bound on the design cost at a cost ci. It makes sense for i to share his designs if he expects to receive more value from others than of the average single-user innovator. Thus, given low- his communication cost. His expected benefit from enough costs of communication, open collaborative user communicating can be parsed into (1) the probability  innovators operating within a task-divisible and modular j architecture can pursue much larger innovation opportu- that member j will respond in kind, (2) the fraction j of the remaining design that member j can provide, and nities than single-user innovators acting alone. Open collaborative projects, as we said earlier, may (3) the total value voi that i can obtain from others’ contri- butions. As a rational actor, member i will communicate attract participants who do not plan to use the design his design to the other members of the group if created by the project but are instead motivated by incen- tives such as learning, reputation, and the fun of partic- N −1 ipation. For such contributors, the sum of their design X j · j · voi > ci0 (4) cost and communication cost must be less than whatever j6=i benefit they do obtain from the project. Thus, instead of This is the first bound on the open collaborative inno- inequality (4), the ancillary contributors (denoted by the subscript a) criterion is, “Does my expected benefit— vation model. It establishes the importance of communi- such as reputational benefits—exceed the sum of my cation cost and technology for the viability of the open design and communication costs?”: collaborative model of innovation. The lower the cost of communicating with the group, the lower the thresh- v > d + c 0 (40) old other members’ contributions must meet to justify a a a an attempt to collaborate. Higher communication costs Other things equal, this bound is more likely to be sat- affect inequality (4) in two ways: they increase the direct isfied if the ancillary contributors’ communication costs cost of contributing (ci), and they reduce the probability are low. Thus, communication costs constrain nonuser that others will reciprocate (j ). It follows that if com- participants as well as users. munication costs are high, an open collaborative project The presence of ancillary contributors further relaxes cannot get off the ground. However, if communication the upper bound on the scale of the design. If there costs are low for everyone, it is rational for each mem- are M ancillary contributors in addition to N users, the ber of the group to contribute designs to the general pool upper bound on total design value is and expect that others will contribute complementary N M designs or improve on his own design. This is in fact the X X 0 dsi + da < N v¯s + M4v¯a −c ¯a51 (5 ) pattern observed in successful open source projects and i=1 a=1 Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation 1408 Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS where v¯a −c ¯a is value of participating (net of communi- Figure 3 Bounds of Viability for All Three Innovation Models cation cost) to the average ancillary contributor. Thus the Single-user scale of an open collaborative project is expanded—and innovators may be greatly expanded—by attracting ancillary con- only tributors who value learning, fun, reputation, etc., more than using the design itself. All in all, the two bounds indicate both the limitations Single-user and the possibilities associated with the open collabo- innovators rative innovation model. The first bound (Equations (4) and producer and (40)) shows that this mode of innovation is severely innovators coexist restricted by communication costs. If the value of the Producer “other” part of the system is low or the expectation that innovators others will actually contribute is low relative to the cost Communication costs (c) only of communication, single-user innovators will “stick to their knitting” and not attempt to collaborate, and ancil- All three Open collaborative Open collaborative lary contributors will find some other outlet for their types are innovators and producer viable innovators compete innovators only talents. But if communication costs are low enough to clear these hurdles, then the second bound (Equations (5) Design cost (d) and (50)) shows that, using a modular design architec- ture as a means of coordinating their work, a collab- to cover the “upstream” costs of design and communi- orative group can develop an innovative design that is cation. The range of viability for the model with higher many times larger in scale than any single member of costs is then reduced. In terms of the bounds derived the group could manage alone. above, the single-user innovator’s bound would move Figure 3 places the three models of innovation— to the left, the producer’s bound would move toward single-user, producer, and open collaborative—in the the origin, and the open collaborative project’s bounds same figure. The shadings and text in the figure indicate would move both down and left. areas in which one, two, or all three models are viable. We now consider whether there are systematic differ-

Basically, single-user innovation is viable when design ences in production or transaction costs across the three costs are low for any level of communication cost. Open models. collaborative innovation is viable when communication costs are low for high levels of design cost, as long as 3.6.1. Production Costs. At the start of this section, the design can be divided into modules that one or a we explained that a design is the information required few contributors can work on independently. Producer to produce a novel product or service—the “recipe.” innovation is viable when the sum of design and com- For products that themselves consist of information such munication costs falls below the producer’s expected net as software, production costs are simply the cost of revenue as indicated by the negative 45ž line. copying and instantiating the design. For digitized prod- ucts and services, these costs are now very low. In the 3.6. Bringing Back Production and case of a physical products, however, the design recipe Transaction Costs must be converted into a physical form before it can be At the beginning of this section, to focus on the con- used. In such cases, the input is the design instructions trasting effects of design and communication costs on (the recipe) plus the materials, energy, and human effort the three models of innovation, we made the simplifying specified in those instructions; the output is a good (the assumption that production costs and transaction costs design converted into usable form). were similar across all three and thus had no effect on One of the major advantages producers have histori- any model’s viability relative to the other two. We did cally had over single-user innovators and open collab- this by defining the value of the design (vi) as the total orative innovation projects is economies of scale with value of the innovation to the innovator (Vi) minus the respect to mass production technologies. Mass produc- costs of production and transactions: tion, which became widespread in the early 20th century, v ≡ V − u − t 0 (6) is a set of techniques whereby certain physical products i i i i can be turned out in very high volumes at very low unit (Subscripts indicate that values and costs may differ cost (Chandler 1977, Hounshell 1985). The economies across individuals and models.) of scale in mass production generally depend on using From this definition it is clear that if production costs a single design (or a small number of designs) over and or transaction costs are systematically higher for a par- over again. In classic mass production, changing designs ticular model of innovation, then for the same willing- interrupts the flow of products and causes setup and ness to pay (Vi) there will be less value in the design (vi) switching costs, which reduce the overall efficiency of Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS 1409 the process. There is no room for variety, as indicated Producer innovators must incur transaction costs. By by Henry Ford’s famous quote, “[A] customer can have definition, they obtain revenue and resources from com- a car painted any color 0 0 0 so long as it is black” (Ford pensated exchanges with users, employees, suppliers, 1922, Chapter 4, p. 34). and investors. A considerable amount of analysis in the Can single-user innovators or open collaborative inno- fields of , management, and strategy consid- vation projects convert their various designs into physi- ers how to minimize transaction costs by rearranging the cal products that will be economically competitive with boundaries of firms or the structure of products and pro- the products of mass producers? Increasingly, the answer cesses. (For reviews of this literature, see Williamson is yes. Consider that, today, modularization is affect- 2000, Lafontaine and Slade 2007.) The bottom line is ing the interface between design and production as well that for producer innovators, transaction costs are an as the interfaces between design tasks. This means that inevitable “cost of doing business.” mass producers can design their production technolo- Single-user innovators, including process innovators, gies to be independent of many of the specifics of the incur transaction costs when they seek to assert exclu- designs they produce. Such processes are said to pro- sive rights over their innovative designs. Patents on inter- vide “mass customization” (Pine 1993, Tseng and Piller nal processes and equipment, the enforcement of secrecy 2003). When mass customization is possible—that is, and “need-to-know” policies within a firm, and non- when particular designs are no longer for technical rea- compete agreements with key employees are all visible sons tied to production technologies—producers can in evidence of transaction costs that single-user innova- principle make their low-cost, high-throughput factories tors incur to protect valuable intellectual capital. In such available for the production of designs created by single cases, as rational actors, single-user innovators would users and collaborative open projects. have to find a net gain after subtracting both design and Some producers might resist this idea, wanting to cap- transaction costs from the expected value of an innova- ture profits from proprietary product designs as well as tive design to themselves. proprietary production capabilities. But if there is com- However, single-user innovators have a choice as to petition among producers, some will be willing to pro- which innovations are worth protecting and which are duce outside designs as well as their own and forgo the not. As discussed in the literature review, empirical rents they formerly obtained from proprietary designs. research suggests that single-user innovators generally Indeed, this possibility is manifest in many industries do not treat all or even most of their innovations as valu- where “toll” production is common. For example, “sil- able property that must be sequestered within their walls. icon fabs” produce custom designs to order via very They often find it more practical and profitable to freely sophisticated and expensive production processes, as do reveal their designs to achieve network effects, reputa- producers of specialty chemicals and contract manufac- tional advantages, and other benefits and/or to avoid the turers of consumer electronic goods (Sturgeon 2002). cost of protecting their innovations. For example, many Nevertheless, for a long time to come, there will user firms that develop process equipment innovations continue to be instances where economies in mass for in-house use freely reveal them to get an outside production significantly depend on careful and subtle source of equipment production and improvements from codesign of products and product-specific production others (Harhoff et al. 2003, de Jong and von Hippel systems. In such instances, we expect producer innova- 2009). This free revealing decreases as the importance of tors to continue to have an advantage in designing and process innovation as a source of competitive advantage producing goods and services for mass markets. increases (Franke and Shah 2003, Raasch et al. 2008). 3.6.2. Transaction Costs. If producer innovators By definition, when single-user innovators freely reveal have a production cost advantage for some (but not all) an innovation, they do not incur transaction costs, and production technologies, free-revealing single-user and the region of viability for the innovation opportunity is open collaborative innovators have an advantage with thereby expanded. respect to transaction costs. As indicated, the transac- Open collaborative innovation projects do not sell tion costs of innovation include the cost of establishing products, nor do they pay members for their contribu- exclusive rights over the innovative design, for example, tions. In this respect, they do not incur transaction costs. through secrecy or by obtaining a patent. Also included However, when an open collaborative project becomes are the costs of protecting the design from theft, for large and successful, its members generally find that example, by restricting access, and enforcing noncom- they must incur costs to protect the now-valuable design pete agreements (Teece 2000, Marx et al. 2009). Finally, from malfeasance and expropriation. For example, vir- transaction costs include the costs of legally transfer- tually all large open source projects have a system of ring rights to the good or service embodying the innova- hierarchical access that prevents anyone from changing tion, receiving compensation, and protecting both sides the master copy of the source code without authoriza- against opportunism (Williamson 1985, Baldwin 2008). tion by a trusted member of the project. The General Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation 1410 Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS

Public License (GPL) was explicitly designed to pro- to battle their competitors.” However, like all human tect the rights of users to view, modify, and distribute endeavors, the organizations and institutions that create code derived from the licensed code (Stallman 2002, innovations are historically contingent. They are solu- O’Mahony 2003). The costs of restricting access and tions to the problems of a specific time and place using enforcing the GPL are like classic transaction costs in the technologies of that time and place. It is the case that they assert and enforce property rights to prevent that throughout most of the 20th century, single-user and vandalism and theft. open collaborative innovation were extant, but central- Notwithstanding these necessary expenditures, open ized R&D, product development, and process engineer- collaborative innovation projects do avoid the “mun- ing groups within firms were the most economical way dane transaction costs” of defining, counting, and paying to design mass-produced products and related produc- for goods in formal legal transactions (Baldwin 2008). tion processes (Chandler 1977, Hounshell 1985). Their contributors do not have to figure out what to Four technological factors contributed to the preem- sell, how much to charge, or how to collect payment— inence of mass-produced products, and thus the dom- costly activities that producers must perform in the nor- inance of producer and large-scale process innovators mal course of business. In this respect, free-revealing in technologically advanced economies in the early and single-user innovators and open collaborative innovation middle parts of the 20th century. First, computational projects have a transaction cost advantage over producer resources were scarce, and therefore the cost of creating innovators. individual designs was quite high. Second, as discussed Regulation can be viewed as a form of transaction above, there was generally a close tie between design cost imposed by the government on all three innovation of items to be produced and the complex requirements models. Drugs, commercial aircraft, and automobiles are of process technologies. Third, modular design methods among the product types that must meet heavy safety- were not well understood and seldom implemented. And related regulatory burdens before being allowed to enter fourth, cheap, rapid communication enabling distributed the marketplace. Regulation in the form of standard set- design among widely separated participants in a design ting affects many other industries, such as telecommuni- process was not technically possible. Taken together, cations. Within our theoretical framework, regulation and these factors made it cheaper to design standardized and standard setting tend to decrease the value of innovation uniform products centrally and in conjunction with their opportunities, thus shrinking the bounds of viability. In manufacturing processes. Given these conditions, it is Figure 3, all three bounds will move down and to the reasonable to speculate that Schumpeter and later Teece, right, and the areas of viability will become smaller. Romer, and Baumol were simply observing the most vis- ible innovation processes of their times when they stated that producers (business firms) were the leading devel- 4. Discussion opers of innovation in market economies. As we said at the start of this paper, there is a widespread Today, as was mentioned earlier, conditions facing and longstanding perception among academics, policy would-be innovators are changing rapidly and radically. makers, and practitioners that innovation by producers is Just as the rise of producer innovation was enabled by in- the primary mode of innovation in market economies. In terdependencies between centralized product design and this view, innovations are undertaken by firms that can the technologies of mass production, today the rapid aggregate demand or not at all. In the 1930s, Schumpeter growth of single-user and open collaborative innovation (1934, p. 65) placed producers at the center of his theory is being assisted by technologies that both enhance the of economic development, saying, “It is 0 0 0 the producer capabilities of individual designers and support distri- who as a rule initiates economic change, and consumers buted, collaborative design projects. These technologies are educated by him if necessary.” Sixty years later, include powerful personal computers; standard design Teece (1996, p. 193) echoed Schumpeter: “In market languages, representations, and tools; the digitization of economies, the business firm is clearly the leading player design information; modular design architectures; and in the development and commercialization of new prod- low-cost any-to-any and any-to-all communication via ucts and processes” (see also Teece 2002, p. 36). At the Internet. Of course, we should remember that the about the same time, Romer (1990, p. S74) made the institutions of single-user and open collaborative inno- predominance of producer innovation the basis for his vation have long existed (Rosenberg 1976, von Hippel model of endogenous growth: “The vast majority of 1976, Shah 2005). However, they are growing more designs result from the research and development activ- prominent today because of the largely exogenous tech- ities of private, profit-maximizing firms.” And Baumol nological developments just mentioned. (2002, p. 35) placed producer innovation at the center of Technological trends suggest that both design costs his theory of oligopolistic competition: “In major sec- and communication costs will be further reduced over tors of U.S. industry, innovation has increasingly grown time. To visualize this effect, imagine Figure 3 being in relative importance as a instrument used by firms populated with numerous points each representing an Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS 1411 innovation opportunity. As design and communication 4.1. Interactions Between the Three Models costs fall, each point would move down and to the left. From Figure 3 it is evident that for some combinations As a result of this general movement, some points would of design and communication costs, two or even all three cross the thresholds of viability for single-user and open models of innovation will be viable. How will the pres- collaborative innovation. Conversion of some designs ence of one influence the other(s)? In other words, how from small-scale to mass production would cause some will the models interact? Prior research allows us to points to move in the opposite direction, against the gen- elaborate on this basic matter in several interesting ways, eral trend. But for the most part, technological progress as we discuss next. along both dimensions of cost will have the effect of When single-user innovation and producer innovation moving whole classes of innovation opportunities from are both viable, the single-user innovators must eval- the regions where only innovation by firms able to aggre- uate an innovation opportunity, not only in relation to gate demand is viable to regions where single-user inno- their design cost but also in relation to the producer’s product and price. If the producer offers a good-enough vation or collaborative innovation are also viable. In product at a low-enough price, purchasing the innovation these cases, what was previously a dominant model— may dominate developing it in-house, and some poten- the only feasible way to cover the costs of innovation— tial single-user innovators may switch to becoming cus- becomes subject to competition from other, newly viable tomers of the producer. (This happens regularly when models. This means that producer innovators increas- companies switch from custom software developed by ingly must contend with single-user innovators and open an in-house IT department to off-the-shelf, purchased collaborative innovation projects as alternative sources software.) However, to attract users who can innovate of innovative products, processes, and services. on their own, the producer’s price must be less than The declining cost of computation most directly the user’s design cost, which by definition is less than affects the cost of designs that can be developed and the user’s value: p < ds < vs. Given differentiated users, tested on computers—and today, one is hard-pressed to rational producers are likely to target as customers users think of a type of design that does not fall into this with high design costs and leave those with low design domain. Of course, computer-based design is central to costs to work out their own solutions. the design of software. Increasingly, it is also central Indeed, because of their distinct roles, producer inno- to the development of hardware-embodied designs rang- vators and single-user innovators may develop a sym- ing from consumer products to buildings. Design pro- biotic relationship. Empirical studies have shown that cesses for both software and hardware generally require most single-user innovation is done by a subset of all designers to represent different states of the design using users called “lead users” that are ahead of the bulk of text, pictures, and models; store and analyze data; com- the market with respect to an important trend and also pute rates, bounds, and tolerances; and simulate behav- have a high incentive to innovate to solve the needs ior under various conditions. Technologies that automate they encounter at the leading edge (von Hippel 1986). the tasks of writing, drawing, modeling, data storage and Some of these lead users have no interest in commer- analysis, computation, and simulation all have the effect cializing their innovations. However, their innovations of reducing design cost. may nonetheless serve as an attractive feedstock of field- The declining cost of communication most directly tested product prototypes for producers. By monitoring affects the design of products and services where design and incorporating lead-user innovations into their own is informed by community trial-and-error learning. The offerings, producer innovators may enhance their prod- uct and service offerings while at the same time reducing design of physical games and the equipment used for their design costs and increasing their likelihood of suc- them are an example, because the final shape of suc- cess in the marketplace (Lilien et al. 2002, von Hippel cessful games only emerges via collaborative play and 2005). In other instances, individual lead users may related redesign. Wikis like Wikipedia are an example of found companies for the purpose of commercializing open collaborative innovation in the world of text: they their designs. Such firms, which generally serve market coexist and compete with producer-designed compendia. niches not large enough to attract established firms, are Social networking sites like Facebook also have features often the first to introduce new products into the econ- of both single-user innovation (each person designs her omy (Baldwin et al. 2006, Shah and Tripsas 2007). page) and open collaborative innovation (she and her Open collaborative innovation projects are more likely friends contribute content to each others’ pages). to pose a threat to producer innovators than are single- Although not all designs are equally affected, we user innovators. In the first place, by attracting effort and believe declining computation and communication costs dividing up tasks, collaborative projects can in princi- are having enough of an impact across the economy ple match the scale of a producer’s design. Second, as to change the relative importance of the three different a matter of policy and to reduce transaction costs, open models of innovation discussed previously. collaborative projects make their designs available to all Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation 1412 Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS comers at little or no charge. The existence of a free individual gamers or groups of gamers acting collabora- design, even if it must be adapted by end users, puts tively develop “mods” (Jeppesen 2004). price pressure on a producer innovator. Indeed, users Large indivisible design projects, which have tradi- have incentives to collaborate for this purpose precisely tionally been in the producer-only zone of Figure 3, may in those cases where the producer innovator can deter become hybrids as a result of the rearchitecting of tradi- other producers from entering its market. If the price of tional, producer-centered design approaches. For exam- a design will collapse on entry of a second contender, ple, drug development costs are commonly argued to no profit-seeking producer will find the second-in oppor- be so high that only a producer innovator, buttressed tunity attractive. In contrast, users directly gain from by strong intellectual property protection for drugs, can any price drop and hence will benefit by supporting a succeed. Increasingly, however, we are learning how to collaborative project aimed at breaking the producer’s subdivide drug trials—a large cost traditionally borne monopoly (Baldwin and Clark 2006b). Supporting this by drug producers—into elements suitable for voluntary, logic, some open source projects have been founded with unpaid participation by users acting within a collabo- the aim of preventing or breaking producers’ monopo- rative open innovation framework. This possibility has lies. The most famous is the GNU Project, begun by recently been illustrated in a trial of the effects of lithium Richard Stallman in 1984 for the express purpose of pro- on amyotrophic lateral sclerosis (ALS; Lou Gehrig’s dis- viding a free alternative to commercially owned software ease) carried out by ALS patients themselves with the (Stallman 2002). support of a toolkit and website developed by the firm However, the openness and modularity of open col- PatientsLikeMe. laborative projects make pure head-to-head competition Innovation platforms and the innovations appended to with producers an unlikely end result. Once an open col- them are often a hybrid of single-user innovation, pro- laborative project has been started, producer innovators ducer innovation, and/or collaborative open innovation. can adapt their own strategies in response. The monop- Innovation platforms are components that provide a sta- olist challenged by the project may withdraw to parts of ble framework or binding surface that serves to sup- the market that are locked in, are not price sensitive, or port and organize the innovation contributions of many demand high levels of service (Casadesus-Masanell and complementors (Gawer and Cusumano 2002, Gawer

Ghemawat 2006). Alternatively, producers may become and Henderson 2007, Baldwin and Woodard 2009, contributors to open collaborative projects for which Gawer 2009). Platforms can range from interface stan- they supply complements. Thus, in 1999, IBM became dards such as an application programming interface or a an important supporter of and contributor of code to screw thread specification, to open source software plat- Linux. IBM sells products and services that complement forms like Apache or Linux, to social networking sites Linux, ranging from computers to proprietary software like Facebook. In the case of Apache, the platform is to consulting services. Similar examples are legion, such an open, collaboratively built one, and appended innova- as Google’s support for an open source software stack tions are developed by innovators representing all three for mobile devices (Android). of our polar models. In contrast, Facebook and YouTube are producer-built and producer-owned platforms, and 4.2. Hybrid Innovation Models appended creative content is generated primarily by indi- A hybrid innovation model combines elements of vidual users. Innovation platforms can be a great source the three polar models analyzed in previous sections. of profit for their owners when entry costs are low and Hybrids of the three basic models thrive in the real network effects are strong. Indeed, under such conditions world. This is because the architecture of a design to platform owners often face a “winner-take-all” situation achieve a given function can often take a number of and so vie fiercely to attract free content to leverage forms suited to development by combinations of our their internal resources, attain category leadership, and three basic models. For example, producers or users can thereby improve their financial performance (Gawer and choose to modularize a product architecture into a mix of Cusumano 2002). large components that can today best be created by pro- Closed collaborative innovation, often termed “crowd- ducers, plus many smaller components suited for devel- sourcing,” is a hybrid of open collaborative innovation opment by single-user innovators or open collaborative and producer innovation. In this hybrid model, a pro- innovation projects. Thus, Intel develops expensive and ducer innovator poses a problem, solicits proposed solu- complex central processing unit chips for computers. tions from numerous third parties (the “crowd”) and then Complementary software and hardware designs are then selects the best solution or combination. Members of the developed by for-profit producers, by single-user innova- crowd do not see nor do they have rights to use the tors, and by open collaborative projects. Another exam- proposed solutions: the outputs are closed and owned ple is the development of software “engines” for com- by the sponsor (Howe 2006, Pisano and Verganti 2008, puter games by producer firms upon which platforms Jeppesen and Lakhani 2010). Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS 1413

To understand closed collaborative innovation, con- producer innovation is viable to regions where single- sider that there are two reasons to open up a collabo- user innovation or open collaborative innovation is also rative project. In the first place, open access to the out- viable. In these cases, what was previously a domi- puts of a collaborative project acts as an inducement nant model—the only feasible way to cover the costs of to single-user innovators to join and contribute instead innovation—becomes subject to competition from other, of innovating on their own (Baldwin and Clark 2006b). newly viable models. In the second place, when effective problem solving In the course of explaining the viability of these addi- requires contributors to know and understand the solu- tional innovation models, we have also uncovered a chal- tion being developed, open access is the low-cost default lenge to two fundamental assumptions in prior work on solution. innovation. Recall that, since the time of Schumpeter, Sponsors of collaborative projects can close and own the preeminence of producer innovation as well as the innovative output of a collaborative project if they the need for intellectual property rights to enable pro- can escape these two constraints. To escape the first, ducer innovators to protect their rents have gone largely sponsors can create incentives that will attract nonuser unquestioned by scholars and policy makers alike. These contributors to their project. For example, they can offer two assumptions have deeply permeated academic schol- payment or process-related rewards such as learning or arship, policy making, and practice in many fields. Both fun (Raymond 1999, Lerner and Tirole 2002, Lakhani assumptions are now challenged by the viability of the and Wolf 2005, Benkler 2006). To escape the second single-user and open collaborative innovation models we constraint, project sponsors can employ an extreme form have described in this paper. Users are now seen to be of modularity in which no participant knows (or needs an important source of innovation, and value to users to know) what the others are doing, and only the spon- is seen as an alternate motive to profits for investing in sor sees everything. Examples of closed collaborative innovative designs. The net result is, we think, to greatly projects are design contests such as the Netflix Prize or change the problem field in innovation research, policy the Cisco I-Prize, in which many contestants compete making, and practice. for a monetary prize and reputational gains (Jeppesen With respect to research, consider that one of the sig- and Lakhani 2010, Boudreau et al. 2011). nal accomplishments in economics in the 1990s was Like open collaborative projects, hybrids generally to incorporate rent-seeking investment into macroeco- rely on an underlying modular architecture to provide nomic growth models, thereby endogenizing technolog- the basis for splitting up design tasks among vari- ical change at the level of the economy (Romer 1990, ous participants. The modular architecture also makes Aghion and Howitt 1998). In its most common inter- it possible to create a structure of intellectual prop- pretation, “endogenous growth theory” supports politi- erty rights so that producer innovation, single-user inno- cal and legal moves to strengthen intellectual property vation, and open collaborative innovation can all take rights. The basic argument is that without the ability place within the same technical framework (Henkel and to exclude others from using new designs, incentives to Baldwin 2010). invest in innovation would disappear, and in the absence of such investments, technological progress and eco- 4.3. Suggestions for Further Research nomic growth would not occur. In this paper, we have argued that a paradigm shift Recall that our analysis suggests that this view is too is occurring in our understanding of innovation. Kuhn stark. We have shown that users working alone, or in (1962, p. 103) writes, the context of open collaborative projects and in con- [Paradigms] are the source of the methods, problem-field, junction with ancillary contributors, have the potential to and standards of solution accepted by any mature scien- supply innovations for their own direct benefit without tific community at any given time. As a result, the recep- the inducement of excludability or monopoly. Such users tion of a new paradigm often necessitates a redefinition cannot themselves be strong rivals, but the nonrivalrous of the corresponding science. Some old problems may be nature of consumers is a longstanding implicit assump- relegated to another science or declared entirely “unsci- tion in most of economics. (If a firm can sell goods with entific.” Others that were previously nonexistent or trivial the same design to many users, by definition, those users may, with a new paradigm, become the very archetypes do not need to exclude other users from access to the of significant scientific achievement. design. Such users are excellent prospects for forming Taken in combination, we think the patterns and find- a collaborative project to supply the design without the ings we have described represent a significant change in intermediation of a monopolistic producer innovator.) the “problem field” of innovation research, policy mak- Thus, one avenue for future theoretical research is to ing, and practice. In brief recapitulation, we have found develop micro- and macroeconomic models that allow that technological progress is moving whole classes of for the possibility of nonrivalrous user innovation as well innovation opportunities from the region where only as producer innovation. Another is to explore further the Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation 1414 Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS technological and institutional factors needed for col- producers. Early evidence shows there are ways for pro- laborative nonrivalrous innovation to become an impor- ducers to thrive by adopting open innovation designs tant contributor to technological progress and economic and shifting to profit seeking based on other forms of development. private advantage, such as lead time, high-quality ser- With respect to policy making, an area that clearly vice, and superior distribution channels (Raymond 1999, must be reviewed—and that would strongly benefit from von Hippel 2005). Of course, producers will predictably research along the lines described above—is intellec- resist open innovation until they have made successful tual property rights. From the time of the Enlighten- transitions to these new business models. Energetic pur- ment, many have held the view that providing inventors suit of practitioner-oriented research can aid and speed with incentives in the form of property rights to their up these needed transitions. In addition, when and where “writings and discoveries” would induce them to invest each organizational form is most efficacious is an impor- in the creation of useful new ideas, i.e., innovations. tant question for both theory and practice in innovation Of course, it was also known that grants of intellectual management. property rights would create undesirable monopolies. The paradigm shift we describe from producer innova- Producers create deadweight losses when they exploit tion to user and open collaborative innovation offers both intellectual property rights to reap monopoly profits inescapable challenges and opportunities to researchers, and spend money to protect or extend their monopoly to policy makers, to firms—indeed, to all of us who positions (Machlup and Penrose 1950, Penrose 1951, have a stake in innovation. We think that both personal MacLeod 2007). Value in use as an alternative incentive freedoms and social welfare will increase as a result of for single-user innovators and participants in open col- this shift. We suggest that further explorations will be laborations indicates that there can be ways to support valuable. robust innovation without the “devil’s bargain” inherent in the granting of intellectual property rights. Acknowledgments Today, essentially all national governments support The authors thank Jeroen de Jong, Joachim Henkel, Karim costly intellectual property rights infrastructures to sup- Lakhani, Anita McGahan, Eric Raymond, Steven Roper, port inventors who wish to restrict access to their inno- Suzanne Scotchmer, Scott Stern, and Michael Tushman, as vations. At the same time, governments have done very well as three anonymous reviewers and the associate editor, little to create an infrastructure to support inventors Lori Rosenkopf, for comments and suggestions that led to sig- nificant improvements of this paper. and innovators who may wish to practice open inno- vation. The result is that “open” innovators are forced References to operate within an framework of intellectual prop- Aghion, P., P. Howitt. 1998. Endogenous Growth Theory. MIT Press, erty rights designed for closed innovators (Strandburg Cambridge, MA. 2008). This framework imposes significant costs on open Alchian, A. A., H. Demsetz. 1972. Production, information costs, and innovation: because innovation-related information can economic organization. Amer. Econom. Rev. 62(5) 777–795. be “owned,” all developers and users of such informa- Allen, R. C. 1983. Collective invention. J. Econom. Behav. Organ. tion must conduct extensive and expensive searches for 4(1) 1–24. potential owners—and still run the risk of litigation from Aoki, M. 1988. Information, Incentives, and Bargaining in undiscovered owners—before they can freely utilize or the Japanese Economy. Cambridge University Press, Cam- reveal what they know. In effect, public policy making bridge, UK. today has created a nonlevel playing field between open Aoki, M. 2001. Toward a Comparative Institutional Analysis. MIT and closed innovation (Dreyfuss 2010). Press, Cambridge, MA. Research is urgently needed into the basic matter of Arundel, A., V. Sonntag. 1999. Patterns of advanced manufactur- the social welfare effects of closed versus open inno- ing technology (AMT) use in Canadian manufacturing: 1998 vation to establish the basis for an appropriate balance. AMT survey results. Report 88F0017MIE, 12, Statistics Canada, If, as we expect, increased support for open innova- Ottawa, ON, Canada. tion is found to be desirable from the perspective of Baldwin, C. Y. 2008. Where do transactions come from? Modular- social welfare, then expansion of “fair use” rights and ity, transactions and the boundaries of firms. Indust. Corporate Change 17(1) 155–195. other forms of safe harbors for those seeking to freely use and reveal innovation-related information will be Baldwin, C. Y., K. B. Clark. 2000. Design Rules: The Power of Mod- ularity, Vol. 1. MIT Press, Cambridge, MA. among the types new policies that should be considered (Strandburg 2008). Baldwin, C. Y., K. B. Clark. 2006a. Between “knowledge” and the “economy”: Notes on the scientific study of designs. B. Kahin, With respect to corporate practice of innovation, D. Foray, eds. Advancing Knowledge and the Knowledge Econ- research should be pursued on new business models that omy. MIT Press, Cambridge, MA, 299–328. create private profits for producers without proprietary Baldwin, C. Y., K. B. Clark. 2006b. The architecture of participation: control of innovative product and service designs. Crea- Does code architecture mitigate free riding in the open source tion of new product and service designs is a cost for development model? Management Sci. 52(7) 1116–1127. Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS 1415

Baldwin, C. Y., C. J. Woodard. 2009. The architecture of platforms: Ford, H. 1922. My Life and Work. Garden City Publishing Company, A unified view. A. Gawer, ed. Platforms, Markets and Innova- Garden City, NY. tion. Edward Elgar, London, 19–44. Franke, N., S. Shah. 2003. How communities support innovative activ- Baldwin, C. Y., C. Hienerth, E. von Hippel. 2006. How user innova- ities: An exploration of assistance and sharing among end-users. tions become commercial products: A theoretical investigation Res. Policy 32(1) 157–178. and case study. Res. Policy 35(9) 1291–1313. Franke, N., E. von Hippel. 2003. Satisfying heterogeneous user needs Baumol, W. J. 2002. The Free-Market Innovation Machine: Analyzing via innovation toolkits: The case of Apache security software. the Growth Miracle of Capitalism. Princeton University Press, Res. Policy 32(7) 1199–1215. Princeton, NJ. Gallini, N., S. Scotchmer. 2002. Intellectual property: What is the best Benkler, Y. 2002. Coase’s penguin, or, Linux and The Nature of the incentive system? A. Jaffe, J. Lerner, S. Stern, eds. Innovation Firm. Yale Law J. 112(3) 369–447. Policy and the Economy, Vol. 2. MIT Press, Cambridge, MA, 51–78. Benkler, Y. 2006. The Wealth of Networks: How Social Produc- tion Transforms Markets and Freedom. Yale University Press, Gault, F., E. von Hippel. 2009. The prevalence of user innovation New Haven, CT. and free innovation transfers: Implications for statistical indica- tors and innovation policy. Working Paper 4722-09, MIT Sloan Blaxill, M., R. Eckhardt. 2009. The Invisible Edge: Taking Your Strat- School of Management, Cambridge, MA. egy to the Next Level Using Intellectual Property. Portfolio, New York. Gawer, A. 2009. Platform dynamics and strategies: From products to services. A. Gawer, ed. Platforms, Markets and Innovation. Boudreau, K. J., N. Lacetera, K. R. Lakhani. 2011. Incentives and Edward Elgar, London, 45–76. problem uncertainty in innovation contests: An empirical analy- sis. Management Sci. 57(5) 843–863. Gawer, A., M. A. Cusumano. 2002. Platform Leadership: How Intel, Microsoft and Cisco Drive Industry Innovation. Harvard Busi- Brusoni, S., A. Prencipe. 2006. Making design rules: A multidomain ness School Press, Boston. perspective. Organ. Sci. 17(2) 179–189. Gawer, A., R. Henderson. 2007. Platform owner entry and innova- Casadesus-Masanell, R., P. Ghemawat. 2006. Dynamic mixed tion in complementary markets: Evidence from Intel. J. Econom. duopoly: A model motivated by Linux vs. Windows. Manage- Management Strategy 16(1) 1–34. ment Sci. 52(7) 1072–1084. Ghosh, R. A. 1998. Cooking pot markets: An economic model for Chandler, A. D., Jr. 1977. The Visible Hand: The Managerial Rev- the free trade of goods and services on the Internet. First Mon- olution in American Business. Harvard University Press, Cam- day 3(3), http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/ bridge, MA. fm/article/view/580/501. Chesbrough, H. 2003. Open Innovation: The New Imperative for Cre- Greif, A. 2006. Institutions and the Path to the Modern Econ- ating and Profiting from Technology. Harvard Business School omy: Lessons from Medieval Trade. Cambridge University Press, Press, Cambridge, MA. New York. Colfer, L. J., C. Y. Baldwin. 2010. The mirroring hypothesis: The- Harhoff, D., J. Henkel, E. von Hippel. 2003. Profiting from voluntary ory, evidence and exceptions. Working Paper 10-058, Harvard information spillovers: How users benefit by freely revealing Business School, Boston. their innovations. Res. Policy 32(10) 1753–1769. Dasgupta, P., P. A. David. 1994. Toward a new . Hart, O. 1995. Firms, Contracts and Financial Structure. Oxford Uni- Policy Res. 23(5) 487–521. versity Press, Oxford, UK. de Jong, J. P. J., E. von Hippel. 2009. Transfers of user process Heald, P. J. 2005. A transaction cost theory of patent law. Ohio State innovations to process equipment producers: A study of Dutch Law J. 66(3) 473–509. high-tech firms. Res. Policy 38(7) 1181–1191. Henkel, J. 2003. Software development in embedded Linux— Demsetz, H. 1988. The theory of the firm revisited. J. Law, Econom. Informal collaboration of competing firms. W. Uhr, W. Esswein, Organ. 4(1) 141–161. E. Schoop, eds. Proc. 6. Internationalen Tagung Wirtschaftsin- formatik, Vol. 2. Physica, Heidelberg, Germany, 81–99. Dosi, G., R. R. Nelson. 2010. Technical change and industrial dynamics as evolutionary processes. B. H. Hall, N. Rosenberg, Henkel, J., C. Y. Baldwin. 2010. Modularity for value appropriation: eds. Handbook of the Economics of Innovation, Vol 1. North- How to draw the boundaries of intellectual property. Working Holland, Amsterdam, 51–127. Paper 11-054, Harvard Business School, Boston. Dreyfuss, R. C. 2010. Does IP need IP? Accommodating intellectual Herstatt, C., E. von Hippel. 1992. From experience: Developing production outside the intellectual property paradigm. Cardozo new product concepts via the method: A case study Law Rev. 31(5) 1437–1473. in a “low-tech” field. J. Product Innovation Management 9(3) 213–222. Enos, J. L. 1962. Petroleum Progress and Profits: A History of Process Innovation. MIT Press, Cambridge, MA. Hienerth, C. 2006. The commercialization of user innovations: The development of the rodeo kayak industry. R&D Management Fama, E. F., M. C. Jensen. 1983a. Agency problems and residual 36(3) 273–294. claims. J. Law Econom. 26(2) 327–349. Hounshell, D. A. 1985. From the American System to Mass Produc- Fama, E. F., M. C. Jensen. 1983b. Separation of ownership and con- tion, 1800–1932. Johns Hopkins University Press, Baltimore. trol. J. Law Econom. 26(2) 301–325. Howe, J. 2006. The rise of . Wired (14.06), http://www Ferguson, C. H., C. R. Morris. 1993. Computer Wars: How the West .wired.com/wired/archive/14.06/crowds_pr.html. Can Win in a Post-IBM World. Times Books, New York. Jacobides, M. G. 2005. Industry change through vertical dis- Foray, D. 2004. The Economics of Knowledge. MIT Press, Cam- integration: How and why markets emerged in mortgage bank- bridge, MA. ing. Acad. Management J. 48(3) 465–498. Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation 1416 Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS

Jensen, M. C., W. H. Meckling. 1976. Theory of the firm: Managerial Mansfield, E. 1968. Industrial Research and Technological Innova- behavior, agency costs, and ownership structure. J. Financial tion: An Econometric Analysis. Norton, New York. Econom. 3(4) 305–360. Mansfield, E. 1985. How rapidly does new industrial technology leak Jensen, M. C., W. H. Meckling. 1994. The nature of man. J. Appl. out? J. Indust. Econom. 34(2) 217–223. Corporate Finance 7(2) 4–19. Marx, M., D. Strumsky, L. Fleming. 2009. Mobility, skills, and Jeppesen, L. B. 2004. Profiting from innovative user communities: the Michigan non-compete experiment. Management Sci. 55(6) How firms organize the production of user modifications in the 875–889. computer games industry. Working Paper WP-04, Copenhagen Business School, Copenhagen. Mead, C., L. Conway. 1980. Introduction to VLSI Systems. Addison- Wesley Publishing Company, Reading, MA. Jeppesen, L. B., K. R. Lakhani. 2010. Marginality and problem- solving effectiveness in broadcast search. Organ. Sci. 21(5) Morrison, P. D., J. H. Roberts, E. von Hippel. 2000. Determinants 1016–1033. of user innovation and innovation sharing in a local market. Kuhn, T. S. 1962. The Structure of Scientific Revolutions. University Management Sci. 46(12) 1513–1527. of Chicago Press, Chicago. Nelson, R. R., S. G. Winter. 1982. An Evolutionary Theory of Eco- Lafontaine, F., M. Slade. 2007. Vertical integration and firm bound- nomic Change. Harvard University Press, Cambridge, MA. aries: The evidence. J. Econom. Literature 45(3) 629–685. Nuvolari, A. 2004. Collective invention during the British industrial Lakhani, K. R., R. G. Wolf. 2005. Why hackers do what they do: revolution: The case of the Cornish pumping engine. Cambridge Understanding motivation and effort in free/open source soft- J. Econom. 28(3) 347–363. ware projects. J. Feller, B. Fitzgerald, S. Hissam, K. R. Lakhani, O’Mahony, S. 2003. Guarding the commons: How open source con- eds. Perspectives on Free and Open Source Software. MIT Press, tributors protect their work. Res. Policy 32(7) 1179–1198. Cambridge, MA, 3–22. Lamoreaux, N. R., K. L. Sokoloff. 2009. The rise and decline of Parnas, D. L. 1972. On the criteria to be used in decomposing systems the independent inventor: A Schumpeterian story? S. H. Clarke, into modules. Comm. ACM 15(12) 1053–1058. N. R. Lamoreaux, S. Usselman, eds. The Challenge of Remain- Pavitt, K. 1984. Sectoral patterns of technical change: Towards a tax- ing Innovative: Lessons from Twentieth-Century American Busi- onomy and a theory. Res. Policy 13(6) 343–373. ness. Stanford University Press, Stanford, CA, 43–78. Penrose, E. 1951. The Economics of the International Patent System. Langlois, R. N. 1986a. Rationality, institutions and explanation. R. N. Johns Hopkins University Press, Baltimore. Langlois, ed. Economics as a Process: Essays in the New Institu- tional Economics. Cambridge University Press, Cambridge, UK, Pine, B. J., II. 1993. Mass Customization: The New Frontier in Busi- 225–255. ness Competition. Harvard Business School Press, Boston. Langlois, R. N. 1986b. The new institutional economics: An introduc- Pisano, G. P., R. Verganti. 2008. Which kind of collaboration is right tory essay. R. N. Langlois, ed. Economics as a Process: Essays for you? Harvard Bus. Rev. 86(12) 78–86. in the New Institutional Economics. Cambridge University Press, Raasch, C., C. Herstatt, P. Lock. 2008. The dynamics of user innova- Cambridge, UK, 1–26. tion: Drivers and impediments of innovation activities. Internat. Langlois, R. N. 2002. Modularity in technology and organization. J. Innovation Management 12(3) 377–398. J. Econom. Behav. Organ. 49(1) 19–37. Raymond, E. S. 1999. The Cathedral and the Bazaar: Musings Lerner, J., J. Tirole. 2002. Some simple economics of open source. on Linux and Open Source by an Accidental Revolutionary. J. Indust. Econom. 50(2) 197–234. O’Reilly, Sebastopol, CA. Lilien, G. L., P. D. Morrison, K. Searls, M. Sonnack, E. von Hippel. Romer, P. M. 1990. Endogenous . J. Political 2002. Performance assessment of the lead user idea-generation Econom. 98(5) S71–S102. process for . Management Sci. 48(8) 1042–1059. Rosenberg, N. 1976. Perspectives on Technology. Cambridge Univer- Lim, K. 2009. The many faces of absorptive capacity: Spillovers of sity Press, New York. copper interconnect technology for semiconductor chips. Indust. Schumpeter, J. A. 1934. The Theory of Economic Development. Har- Corporate Change 18(6) 1249–1284. vard University Press, Cambridge, MA. Lüthje, C. 2003. Customers as co-inventors: An empirical analysis Scott, W. R. 2001. Institutions and Organizations. Sage, Thousand of the antecedents of customer-driven innovations in the field Oaks, CA. of medical equipment. Proc. 32nd EMAC Conf., Glasgow, Scot- land, European Marketing Academy, Brussels. Shah, S. 2000. Sources and patterns of innovation in a con- sumer products field: Innovations in sporting equipment. Work- Lüthje, C. 2004. Characteristics of innovating users in a consumer ing Paper 4105, MIT Sloan School of Management, Cam- goods field: An empirical study of sport-related product con- bridge, MA. sumers. Technovation 24(9) 683–695. Lüthje, C., C. Herstatt, E. von Hippel. 2005. User-innovators and Shah, S. K. 2005. Open beyond software. C. DiBona, D. Cooper, “local” information: The case of mountain biking. Res. Policy M. Stone, eds. Open Sources 2.0: The Continuing Evolution. 34(6) 951–965. O’Reilly Media, Sebastopol, CA, 339–360. Machlup, F., E. Penrose. 1950. The patent controversy in the nine- Shah, S. K., M. Tripsas. 2007. The accidental entrepreneur: The emer- teenth century. J. Econom. Hist. 10(1) 1–29. gent and collective process of user entrepreneurship. Strategic Entrepreneurship J. 1(1–2) 123–140. MacLeod, C. 2007. Heroes of Invention: Technology, Liberalism and British Identity, 1750–1914. Cambridge University Press, Simon, H. A. 1962. The architecture of complexity. Proc. Amer. Phi- Cambridge, UK. los. Soc. 106(6) 467–482. Baldwin and von Hippel: From Producer Innovation to User and Open Collaborative Innovation Organization Science 22(6), pp. 1399–1417, © 2011 INFORMS 1417

Simon, H. A. 1981. The Sciences of the Artificial, 2nd ed. MIT Press, von Hippel, E., S. N. Finkelstein. 1979. Analysis of innovation in Cambridge, MA. automated clinical chemistry analyzers. Sci. Public Policy 6(1) Smith, A. 1937. An Inquiry into the Nature and Causes of the Wealth 24–37. of Nations. E. Cannan, ed. Random House, New York. [Orig. von Hippel, E., G. von Krogh. 2003. Open source software and the pub. 1776, Methuen & Co., London.] “private-collective” innovation model: Issues for organization Stallman, R. M. 2002. Free Software Free Society: Selected Essays of science. Organ. Sci. 14(2) 208–223. Richard Stallman. J. Gay, ed. GNU Press, Boston. von Hippel, E. A., J. de Jong, S. Flowers. 2010. Comparing business Strandburg, K. J. 2008. Users as innovators: Implications for patent and household sector innovation in consumer products: Find- doctrine. University Colorado Law Rev. 79(2) 467–542. ings from a representative survey in the UK. Working paper, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1683503. Sturgeon, T. J. 2002. Modular production networks: A new Ameri- can model of industrial organization. Indust. Corporate Change Williamson, O. E. 1985. The Economic Institutions of Capitalism. 11(3) 451–496. Free Press, New York. Suh, N. P. 1990. The Principles of Design. Oxford University Press, Williamson, O. E. 1991. Comparative economic organization: The Oxford, UK. analysis of discrete structural alternatives. Admin. Sci. Quart. 36(2) 269–296. Teece, D. J. 1986. Profiting from technological innovation: Implica- tions for integration, collaboration, licensing and public policy. Williamson, O. E. 2000. The new institutional economics: Taking Res. Policy 15(6) 285–305. stock, looking ahead. J. Econom. Literature 38(3) 595–613. Teece, D. J. 1996. Firm organization, industrial structure, and techno- Winter, S. G. 2010. The replication perspective on productive knowl- logical innovation. J. Econom. Behav. Organ. 31(2) 193–224. edge. H. Itami, K. Kusunoki, T. Numagami, A. Takeishi, eds. Dynamics of Knowledge, Corporation Systems and Innovation, Teece, D. J. 2000. Managing Intellectual Capital. Oxford University Part 1. Springer, Berlin, 95–121. Press, Oxford, UK. Woodward, J. 1965. Industrial Organization: Theory and Practice. Tseng, M. M., F. T. Piller, eds. 2003. The Customer Centric Enter- Oxford University Press, Oxford, UK. prise: Advances in Mass Customization and Personalization. Springer, New York. Urban, G. L., E. von Hippel. 1988. Lead user analyses for the Carliss Baldwin is the William L. White Professor of development of new industrial products. Management Sci. 34(5) Business Administration at the Harvard Business School. She 569–582. received a doctorate in business administration from Harvard Business School. She studies the modularity of products and von Hippel, E. 1976. The dominant role of users in the scientific

instrument innovation process. Res. Policy 5(3) 212–239. processes and the impact of design structure and openness on competitive outcomes, industry structure, and evolution. With von Hippel, E. 1977. The dominant role of the user in semiconduc- Kim Clark, she is the coauthor of Design Rules: The Power of tor and electronic subassembly process innovation. IEEE Trans. Modularity. Engrg. Management EM-24(2) 60–71. Eric von Hippel is the T. Wilson Professor of Innovation von Hippel, E. 1986. Lead users: A source of novel product concepts. at the MIT Sloan School of Management and also a professor Management Sci. 32(7) 791–805. of engineering systems at the Massachusetts Institute of Tech- von Hippel, E. 1988. Sources of Innovation. Oxford University Press, nology. His academic research examines the sources of and New York. economics of innovation. His most recent book, Democratiz- von Hippel, E. 2005. Democratizing Innovation. MIT Press, ing Innovation (MIT Press, 2005), is available free on the Web Cambridge, MA. (http://mit.edu/evhippel/www/books.htm).

CORRECTION In this article, “Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation” by Carliss Baldwin and Eric Von Hippel (first published in Organization Science, 2011, vol. 22, no. 6, DOI:10.1287/ orsc.1100.0618), the authors have now made this article Open Access under CC BY-ND License below: This work is licensed under a Creative Commons AttributionNoDerivatives 4.0 International License. You are free to download this work and share with others commercially or noncommercially, but cannot change in any way, and you must attribute this work as “Organization Science. https://doi.org/10.1287/orsc.1100.0618, used under a Creative Commons Attribution License: https://creativecommons.org/licenses/by-nd/4.0/.