User-contested and user-complemented markets for innovations:
Impacts on market outcomes and social welfare
Eric von Hippel* and Christina Raasch**
February, 2012
ABSTRACT
There are two ways to diffuse innovations: for “free” via peer-to-peer channels, and at a
price via market channels. Economic scholarship and policymaking have traditionally focused
only upon marketplace diffusion. In this paper, we also consider peer-to-peer diffusion, and so
are able to consider the effects of rivalry and complementarity between these two important
channels.
When innovations that are close substitutes are being diffused via both channels,
producers face a rival that has rarely been considered in competitive analyses: the option for users
to self-supply independent of the market. We show that this additional option for adopters –a
“user-contested market’ - exerts price discipline on producers, and also increases social welfare.
More specifically, social welfare increases as does the proportion of users capable of self-supply,
and decreases as the costs of self-supply increases.
It is also the case that innovations diffused peer-to-peer can be essential complements to
products diffused via the market. For example, surgeons performing arthroscopic surgery require
both specialized equipment (diffused via the marketplace today) and specialized techniques
(largely diffused peer-to-peer today) to perform arthroscopic surgery. If, as in this example,
producers find it profitable to commercialize only some essential system elements, a “user
complemented market” – peer-to-peer diffusion of essential complements - will be essential both
to customer access to desired functionality, and to producer profits. Further, when producers’
products are required for system functionality, we show they can extract increased profits from
the system as a whole.
In a discussion, we further explain and explore the finding that benefits from peer-to-peer
diffusion of user innovations is largely an externality from the point of view of innovating users –
users are, after all, giving their innovations away. This market failure may require attention from
policymakers. We also note that the concept of user contested and user complemented markets,
here explored within the specific context of innovation, extends beyond that context.
* MIT Sloan School of Management, evhippel@mit.edu
** Hamburg University of Technology and MIT Visiting Scholar, craasch@mit.edu
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User-contested and user-complemented markets for innovations:
Impacts on market outcomes and social welfare
1. User and producer innovation paradigms
Product and service innovations can be diffused peer-to-peer, and/or via the marketplace.
In this paper, we explore rivalry and complementarity between these two important diffusion
channels. We define peer-to-peer diffusion as the transfer of innovation-related information to
potential adopters at no cost to recipients. We define marketplace diffusion as the transfer of an
innovation to potential adopters via a transaction where something of value is given in exchange.
The user innovation paradigm is represented by the broad arrow shown in the top half of
figure 1. Here, at the left side of the arrow, we see users innovating prior to producers. This is a
common pattern because user innovators justify their investments based only upon expected inhouse benefits, and users have first-hand certainty regarding those. The producer, in contrast,
must be concerned about the potential for a larger market, upon which his profits depend.
Understanding of the detailed nature and likely extent of the market for an innovation tends to
emerge only after a period of use and experimentation by early users (Baldwin et al. 2006).
Users generally openly reveal, or at least do not actively protect, their innovations. Others
then may, if they choose, adopt those innovations and perhaps also modify and improve them in
turn. Diffusion of innovation-related information to non-innovators via peer-to-peer transfer also
occurs, as is shown at the right end of the user innovation paradigm arrow. This peer-to-peer
diffusion is typically “free,” because users generally are not rivals, and their private benefit
generally does not decline if others adopt their innovation without payments. No producers need
to be involved in peer-to-peer diffusion. From left to right, it is a user innovation paradigm (von
Hippel 2005, Baldwin and von Hippel 2011).
USER%
INNOVATION%
AND%DIFFUSION%
Innova- on!
by!users!!!
!
!
!
!!
!
!Collabora- ve!Evalua- on/
!
!!!! !Replica- on/Improvement!!! !
!
!
!
!
!
!
!
!
Market! !
Research!!!!
!Peer?to?Peer!!
!Diffui on !!
!
!
!!!!! !
!!Product !!!!!!!!!!!!!Produc- son!
!!Development!
Market!!
Diffui on!
PRODUCER%
INNOVATION%
AND%
DIFFUSION%
Figure 1: The user and producer innovation and diffusion paradigms
The bottom broad arrow in the figure is a schematic representation of the “linear”
producer innovation paradigm (Godin 2006). In this paradigm, producers start by studying user
needs, and then perform R&D as needed to develop and produce novel products and services.
Next they diffuse what they have created via sales in the marketplace. As producers would lose
profits and sales if other producers adopt their innovations without payment, innovating
producers generally try to prevent this via such means as secrecy and intellectual property rights.
The producer-centered innovation paradigm has been the standard model for innovation
researchers and policymakers ever since Schumpeter placed producers at the center of his theory
of economic development, saying, “It is … the producer who as a rule initiates economic change,
and consumers are educated by him if necessary” (Schumpeter 1934, p. 65).
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Finally, the red arrow connecting the two paradigms represents those innovation designs
pioneered and initially diffused by users that are later picked up and commercialized by
producers. As noted earlier, producers will generally wait to see the extent of peer-to-peer
diffusion in order to determine whether and when commercialization of a user-developed
innovation becomes a viable option for them. Commercialization may involve improvement of
the user design to increase producibility and reliability, followed by production and marketplace
diffusion.
1.1 Organization of the paper
In section 2, we review related literature. In section 3, we describe and model the
economics of rivalry between diffusion via the user and producer paradigms, and explore the
impact on market outcomes. In section 4 we describe and model the provision of essential
complements comprising user innovation systems via peer-to-peer diffusion or via marketplace
diffusion. In section 5, we conclude with a discussion of major implications for research, policy
and practice.
2. Literature review
2.1 Evidence for the user innovation paradigm
Many empirical studies clearly document the scale and scope of the first step in the user
innovation paradigm. With respect to development of consumer products by individual
consumers for their own use, three national surveys of representative samples of consumers over
age 18 found that 6.1% of the UK population, 5.2% of the US population, and 3.7% of the
Japanese population had within the last 3 years created or modified consumer products for their
own use. In aggregate, the amount consumers in each of the three countries spend upon
developing consumer innovation per year is similar in magnitude to aggregate firm expenditures
on consumer product R&D in those countries: Consumers spend 144% of firm expenditures in the
UK, 36% in the US, and 13% in Japan (von Hippel et al. 2012; Ogawa and Pongtanalert 2011).
In studies of narrow fields and communities, e.g. sports, software, toys, outdoor products, and
medical products, the proportion of user innovators has been found to be still higher, typically 2030% (Franke and Shah 2003; Lüthje et al. 2005; Prügl and Schreier 2006; Janzik et al. 2011.)
National cross-industry surveys also confirm the significance of innovation by user firms:
A survey in the Netherlands of a sample of 498 high-tech SMEs finds that 54% modified or
created process equipment and/or process software for their own use over the last three years.
Per-project expenditure averaged €184.4K (de Jong and von Hippel 2009). In a UK survey among
1,004 firms from 15 industrial sectors 15.3% of responding firms were user innovators,
expending an average of £45K for equipment and materials plus 107 person days per project
(Flowers et al. 2010). Surveys of large samples of Canadian manufacturing plants asked about inhouse innovations affecting a specific list of advanced processing technologies. These found a
proportion of around 20% answering in the affirmative (Arundel and Sonntag 1999; Schaan and
Uhrbach 2009). In their survey of 1,219 Canadian firms, all of them user innovators, Gault and
von Hippel (2009) find firm spending to average $634.7K when modifying existing technologies
and $995.8K when developing new technologies for in-house use.
Individual user innovators are motivated by personal need for a solution, and also by selfactualization, learning and community status gained during the innovation process. Such “process
benefits” can only be attained by engaging in the innovation process first-hand (Lakhani and
Wolf 2005; Franke et al. 2010; von Krogh et al. 2012, Hienerth et al. 2011).
In step 2 of the user innovation paradigm, users collaboratively develop and improve a
user innovation. The major advantage of collaborative innovation is that all participants can
benefit from using the entire design, while only incurring part of the costs to develop it (Benkler
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2006; Baldwin and Clark 2006; Baldwin and von Hippel 2011). They gain access to skills that are
not available in-house, but required to complete the design (Giuri et al. 2010).
Findings from two aforementioned national consumer surveys show that most product
innovations developed by individuals are not developed in collaboration with others (US: 11.4%,
Japan: 8.4%). As regards collaborative innovation by user firms, de Jong and von Hippel (2009)
show that 24% of innovators received assistance from other users. Studies of (online and offline)
communities of users focused upon activities of strong mutual interest show much higher levels
of collaboration. Thus, all innovators in a sample from four extreme sports communities received
assistance by at least one other user, and typically by several (Franke and Shah 2003). Similarly,
an analysis of the 100 most active, mature open source software projects on Sourceforge shows
that the average number of code contributors per project is 6.6, with additional users participating
by suggesting features, offering advice, or reporting bugs (Krishnamurthy 2002). We speculate
that the reason for this difference is that when an innovation topic is of continuing interest to
users, they tend to find each other, form communities, and collaborate.
Finally, consider the evidence for the third step in the user innovation paradigm, diffusion
to others via peer-to-peer transfer. Both the consumer and industry surveys cited in the previous
sections find that almost all innovations developed by consumers are not protected from imitators
(UK: 98%; US: 92%; Japan: 100%). Many user firms do not formally protect their innovations
either (Netherlands: 87%; Canada: 40-54%; UK: 59%). However, not formally protecting an
innovation is different from actively attempting to diffuse it to adopters. The private-collective
model of innovation explains the conditions under which innovators rationally choose to expend
private resources to share their design information as a public good (von Hippel and von Krogh
2003): Sharing may increase their private benefits (e.g. due to network effects or enhanced
learning and fun) or decrease their private costs (e.g. due to reciprocal sharing by other
community members) (Allen 1983; Lerner and Tirole 2002; Lakhani and Wolf 2005). Users may
also incur losses from diffusion when there is rivalry with others, and it has been found that
diffusion does indeed decrease as rivalry increases (Harhoff et al. 2003; Franke and Shah 2003;
Baldwin and Clark 2006).
Findings for individual user innovators from the UK show that 33% of innovators say
that information about their innovations has diffused, and 17% are aware that their designs have
actually been adopted by some other users and/or producers. Again, case studies of user
communities often show considerably larger figures, revealing communities to be “islands of
extensive sharing” of innovation (Franke and Shah 2003; Raasch et al. 2008). In the area of the
SIMs computer game, for instance, 75% of innovative user-created files are shared; the 53 most
popular ones were downloaded 58,914 times, on average, with some files being downloaded more
than a million times (Prügl and Schreier 2006).
User firms share their innovations with process suppliers or other users, often free of
charge. In the UK, 25% of innovating firms share, 50% without recompense (Flowers et al.
2010). Among Canadian manufacturing plants, 27% of those developing new technologies (24%
of those modifying existing technologies) are aware that their innovations have been adopted by
other user firms (Gault and von Hippel 2009).
2.2 The producer innovation paradigm
The model used to describe producer innovation today is the “linear model of
innovation”. This model postulates that innovation starts with basic research, followed by applied
research, then development of the design for the innovation itself, and finally progressing to
production and marketplace diffusion (Bush 1945; Godin 2006). Variants of this model are used
today both for government statistics on innovation, and to guide the commercial practices of
innovating producer firms.
Empirical research on innovation has never found support for the idea that basic research
is invariably or even often is the initiating event for producer development of an innovation (e.g.,
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Sherwin and Isenson 1967). Nonetheless, the sequence of basic research followed by applied
research and development has been built into national data collection efforts. Today the Frascati
Manual (OECD 2003), used to guide national data collection efforts for OECD countries,
recommends collecting data on these three categories of innovative effort. Economists have also
adopted this taxonomy, employing these three categories in their analyses of the contribution of
science to economic progress (Godin 2006).
In the 1970’s a simpler market pull model variant of the linear model became popular
among scholars, in which the market rather than R&D was shown as the source of new ideas, and
research findings were shown as feeding into all phases of innovation rather than initiating the
innovation process (Kline and Rosenberg 1986). Today’s management textbooks follow
essentially a market pull version of the linear model, with research and development viewed as a
single category. Managers are instructed that “the innovation process” involves following the
basic sequence of steps in our Figure 1: conduct market research to identify customer needs and
marketplace demand; develop a responsive product or service via R&D and market testing;
produce and sell the innovation (Urban and Hauser 1993; Ulrich and Eppinger 2012).
2.3 Conditions under which either paradigm has a comparative advantage
Users and producers differ in terms of their benefits from innovating (in-house use vs.
sale in the marketplace), as well as in terms of their design costs, communication costs,
production costs, and transaction costs. Because of these inherent differences, users and
producers have different but overlapping sets of innovation opportunities that are viable for them.
Exogenous changes in technologies are radically reducing both design costs and communication
costs, and thereby shifting steadily more innovation opportunities into the sets that are viable for
single and/or collaborating user innovators (Baldwin and von Hippel 2011).
As a result of sticky information effects, functionally novel innovations tend to be
developed by users, while producers tend to develop improvements to product and services of
known function serving larger markets (Riggs and von Hippel 1994; Ogawa 1998; von Hippel
1998). Markets serving novel needs tend to be both small and uncertain at the start, making such
opportunities less attractive to producers (von Hippel 2005). In addition, innovation opportunities
that are encumbered by high costs of transacting with producers favor in-house solutions
(Williamson 1985; Pisano 1990; Hart 1995).
Diffusion via the two channels also operates under different conditions. In terms of
production costs, producers have a comparative advantage when high fixed costs render low-scale
in-house production by users uneconomical (Chandler 1977; Hounshell 1985). On the other hand,
peer-to-peer diffusion has an advantage when users possess superior knowledge about potential
adopters and therefore require low diffusion effort (e.g. because they are members of the same
community); when users enjoy higher credibility among their peers than producers do (Mayzlin
2006); and when differential regulation of the two channels allows user diffusion to proceed
“under the radar”.
2.4 Interactions between the user and producer innovation paradigms
As describe earlier, there is considerable evidence that many innovations developed by
users are adopted by producers as the basis for commercial products. In such cases, what was
diffused only via peer-to-peer diffusion is also offered via a second diffusion channel, the market.
In many instances, this occurs when user innovators elect to commercialize their innovations
themselves (Hienerth 2006; Häfliger et al. 2010). For an individual user innovator, this entails a
transition to user entrepreneur; Shah et al. (2011) find that “46.6% of innovative startups [those
founded to commercialize an innovation] founded in the United States that survive to age 5 years
are founded by users.”
A user firm commercializing its user innovation vertically diversifies towards process
technologies; case evidence from the construction, tea packaging, and mining industries shows
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user firms selling their process innovations to other firms, including competitors (Block et al.
2010). In other instances, user innovations enter the market channel when incumbent producers
adopt and commercialize them. The national surveys of consumers cited above are unclear on the
percentage of user innovations transferred that are transferred to producers rather than peer-topeer. Approximately 25% of innovations developed by user firms have been found to be
transferred to producers for commercial production (de Jong and von Hippel 2009; Gault and von
Hippel 2009).
Baldwin et al. (2006) model how user innovations become commercial products by
traversing both of the above steps in sequence: The first producers who elect to commercialize
are typically user entrepreneurs (cf. Shah and Tripsas 2007; Shah et al. 2011). They produce their
own designs small-scale, using high-variable/low-capital cost production methods. Once a stable
design emerges for which there is a broader market demand, high-capital/low-variable cost
manufacturing becomes economically viable. Larger incumbent producers then adopt and supply
the user design, or a variation that better fits mass-market needs.
2.5 Users as rivals and complementors to producers
Much of the economic literature on innovation competition focuses on the supply side
and the decisions taken by producer firms (Pollock 2008 provides an overview). Accordingly,
studies of the viability of innovation and market entry is focused on the supply of competing and
complementary products – both of which are assumed to originate from other producer firms
(Kamien and Schwartz 1975; Dasgupta and Stiglitz 1980; Dosi 1988).
A notable exception is the recent literature on innovation and competition among “open
source” and “closed source” software suppliers (Casadesus-Masanell and Ghemawat 2006; Sen
2007). According to this literature, “open source projects” supply their code for free; they do not
behave strategically in competition with commercial substitutes. Users can choose to contribute
to the supply of open source software code, to free-ride on the effort of others, or to buy the
producer software. Their choices are determined by heterogeneity in users’ willingness to pay,
development capabilities, adoption costs, and impatience for a solution meeting their needs (Kuan
2001; Lin 2008; Baldwin and Clark 2006; Casadesus-Masanell and Hervas-Drane 2010). Some
key findings from this literature are: Open source projects can establish themselves as
competitors to closed software producers; producers lose profit due to this competition;
consumers benefit from the existence of an open source alternative unless it forces proprietary
firms to exit the market. (Note that “open” is not equivalent to “user-created”; part of this
literature models open source software as being provided collaboratively by producers of closed
complementary offerings.)
It is understood that individual product and services operate within larger systems (Simon
1962). For example, semiconductor manufacture involves a series of steps, each carried out by a
specific type of process machine; and each of these specialized machine types only has value
when coupled together with all the others to form a complete production system (von Hippel
1988, p. 23). Similarly, consumer products and services are typically part of larger, multicomponent systems. For example, smartphones as a user system involve multiple components
such as the handset, the operating system, application software, a cloud storage service, and
digital content such as music.
Modules that constitute a system may be supplied separately by different producers
(Sanchez and Mahoney 1996; Economides 1999). Such inter-firm modularity allows producers to
specialize in accordance with their resources and capabilities and thereby become more profitable
(Barney 1991; Baldwin 2010). Customers can assemble their own multi-vendor system
configuration by selecting and combining components in the way that best meets their specific
requirements (Langlois and Robertson 1992; Schilling 2000). In this view, systems are created
and supplied by producers. The role of users is to select and assemble components - to the extent
that they are able to do so. (If most users have low capabilities in this regard, the literature
6
suggests that producers should take the decisions for them and offer a bundled solution; cf. Holt
and Sherman 1986; Schilling 2000.)
In a separate stream of research, users are shown as sometimes creating or assembling
entire systems (von Hippel 1988, Harhoff and Mayrhofer 2010; Hienerth et al. 2011). Baldwin
and Henkel (2011) analyze how value may be captured by suppliers of system modules. They
explain how all producers of essential modules can appropriate a share of the total system
surplus. For this reason, a producer opening up parts of the system to external contributors would
want to put essential modules under a regime that prevents value capture by others, e.g. an open
source software license.
3 User and producer paradigms as rivals
The user and producer paradigms each have an innovation component and a diffusion
component. We begin this section by briefly describing the economics of both of these aspects of
the user and producer paradigms. Then we go on to explore interactions between the two
paradigms.
3.1 Economics of innovation creation by single or collaborating users
The user innovation paradigm shown in figure 1 begins at the leftmost side of the arrow
with a single user or collaborating user group developing an innovation. The economic
justification that might bring them to innovate is in brief outline as follows. A single user, h, (who
may be an individual or a firm) will find it economically rational to innovate if and as she expects
to obtain a positive net return from doing so. Her return consists of two components: the increase
in in-house use value created by the innovation (vh), compared to the next best alternative, plus
benefits experienced from the process of innovating itself, such as fun and learning (bh). (As we
saw in our literature review, innovation process benefits can be substantial.). Her cost, dh, consists
of the cost of developing the innovation (including buying materials, work hours, etc.) and of
putting it to work in-house. Given these considerations, we see that a user innovation is viable for
the single user h, if
(1)
vh + bh > dh.
Note that inequality (1) gives a necessary, but not a sufficient condition for users to innovate.
Users considering whether to innovate will also think about “outside options” that do not involve
innovation, such as waiting to see whether any other user, or a producer, comes up with a solution
to their need that they can adopt or buy.
If and as h’s design – or, at an earlier stage, just her idea for a new design – is of interest
to additional users, they may decide to participate in a collaborative process of further
developing, extending and improving the original design or design idea (cf. figure 1).
The conditions required for user j to participate in a collaborative innovation process
involve the same cost and benefit components described earlier. In addition, j incurs costs of
adoption, aj, i.e. costs of understanding and replicating the existing design, as created by others
before j, and incorporating it into her own use system. If and as she extends and improves the
design, she also incurs design cost dj for the portion of the entire collaborative innovation process
carried out by user j. vj refers to j’s use value from the collaborative innovation, including j’s own
improvements of it. There may be process benefits bj in adoption as well as in further
development. (E.g. replicating the design may involve fun or learning, or gain the user access to a
community.)
The condition for user j to engage in collaborative development therefore is
(2)
vj + bj > aj +dj.
Users considering whether to participate in collaborative innovation will, of course, also consider
other options, such as adopting the existing design “as is” or waiting till the design is more
advanced.
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3.2 Economics of peer-to-peer diffusion
We next discuss the economics of peer-to-peer (user-to-user) diffusion and adoption in
more detail. These two activities occur throughout the user paradigm. When users co-create or
improve, they must diffuse what they have done and adopt what others have done as an element
in their work. Diffusion and adoption also occur at the final stages of the paradigm, where many
simply adopt and use a relatively stable design “as is.”
There are two parties involved in diffusion within the user innovation paradigm: the userinnovator(s) and the potential adopter. The user-innovator may (or may not) make design
information available. Potential adopters must then incur additional costs to actually obtain and
understand that information, reproduce the innovation, and learn to use the innovation effectively.
As noted in equation (1) user innovators are primarily motivated by benefits expected
from in-house use, and from benefits associated with executing the innovation process. Neither of
these sources of benefit requires diffusion, although diffusion may enhance or decrease them in
some cases. The net consequence is that innovating users entirely or largely may regard benefits
obtained by potential adopters as an externality. Consequently, there is no necessary connection
between user innovators’ chosen level of investment in diffusion and the socially optimal level.
(An example of an investment in diffusion would be providing potential adopters with detailed
drawings (CAD files) of the inner workings of a new medical device, instead of just a general
description.) As we will see later, an improvement in an innovation description by an innovator
can sometimes greatly reduce adoption costs for many adopters, this can be an important market
failure from the perspective of social welfare.
Innovators will invest in diffusing their innovation-related information if and as their
incremental benefits exceed their incremental costs (Benker 2006, Baldwin and von Hippel
2011). On the other hand, if their expected benefits are negative, they may invest in retarding
diffusion, via such means as maintaining secrecy or intellectual property protection.
3.3 Economics of innovation adoption
Potential adopters i are those non-innovating individuals or firms expecting a positive use
value for a user design, once adopted, vi >0. (In many cases, there will be several similar usercreated designs. For simplicity, we focus on one ‘best’ design, following Baldwin et al. (2006)
who explain how, within a given area, adopters will converge on ‘the best’ user-developed
design.)
Suppose there are N potential adopters of the best user design, including users involved in
the innovation process who want to upgrade. These users will find it economically viable to adopt
a design via the user-to-user channel if their net adoption costs are less than their improvement in
use value, vi. Let ci denote user i’s adoption costs net of process benefits. We obtain the adoption
condition
(3)
vi > ci , with ci = ai - bi .
Only some users with potential use benefit will find this condition satisfied. Some might, for
example, expect relatively high in-house use benefits. Others might expect high adoption process
benefits and/or low adoption costs.
We next segment all N potential adopters into two categories that are novel relative to
prior literature, and that will be important for microeconomic analyses of producer and mixed
market diffusion that we will consider in later sections of the paper.
All users i for whom adoption via self-supply is viable (ci < vi) are “DIY users” – they
can, but need not, adopt an innovation from the peer-to-peer channel by “Doing It Yourself.” All
others are “non-DIY users” (ci ≥ vi) for whom self-supply via the peer-to-peer channel is not
viable. (Taken together, these two groups sum to all potential adopters, N.)
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Peer-to-peer diffusion
v
vi
viNDIY
viDIY
!
(1 ! s)N
Self-suppliers
u
sN
N
n
In the figure we show a market of N users,
ordering them by their use value, vi, in
descending order, starting with the highest,
(rightmost black line). For simplicity, we
suppose that the increment in use value
between successive users is constant, such
that we obtain a linear downward sloping
demand curve of vi.
Next, we split the market to show the two
sub-groups, (1-s)N DIY users and sN nonDIY users. Note how the two segment
curves sum horizontally to the market
demand curve. We assume that the highest
use value is the same in both groups, .
Figure 2: Diffusion from user to user
All DIY users choose to adopt the design from other users (peer-to-peer). They will do
this because their adoption costs are less than their use value. In contrast, non-DIY users will not
adopt the innovation from the peer-to-peer channel because their adoption costs would be higher
than their use value. The potential use value lost by those non-DIY users, a measure of maldiffusion, is represented by the large shaded triangle marked Xu. (We use superscript u to denote
the user-to-user diffusion case.)
3.4 Economics of innovation by producers
The incentives of producers to innovate are fundamental to the traditional producer
innovation paradigm, and have been explored elsewhere by many (Demsetz 1967; Dosi 1988;
Liebeskind 1996). In brief overview, producers innovate when they expect their returns from
sales to meet their profit hurdles after their innovation and production and diffusion costs. For
them, as was mentioned earlier, the decisions to innovate and to diffuse what they have developed
are tightly coupled. Unlike users, producers have no in-house use for their innovations, so their
innovation incentive is entirely based upon expected returns from diffusion of those innovations
via sales in the market.
Producers are known to maximize their profit, Π, by setting either price p or quantity n.
(Throughout this analysis, we assume that the producer has market power, i.e. that the market is
imperfectly competitive. We also focus on the simple case of just one producer.)
(4)
Π = n (p - c) - f .
c is the producers’ variable cost of production and distribution, and f denotes their fixed cost.
Fixed cost includes the cost of innovating as well as building mass production facilities,
marketing, etc. Producers can often economically justify incurring larger fixed costs of
developing and diffusing innovations than can any single user, because they expect to spread
these costs over many purchasers. As a result of fixed investment, average cost decreases with
output quantity, i.e. there are increasing returns to scale.
According to a fundamental assumption in the economics of the producer paradigm,
users’ maximum willingness to pay (often called their ‘reservation price,’ denoted by r,) for the
producer product equals the use value they expect to derive from it:
(5)
ri = vi.
(Note that buying from a producer rather than DIY making removes innovation process value
benefits for the user: cf. inequalities (1) and (3).)
9
Following this identity, each user will buy the product from a producer if and only if her
use value exceeds the producer price, pp. Let np denote the producer’s profit-maximizing output
quantity. The condition for viable producer entry then is that there must be at least np users i
whose reservation price exceeds the producer’s average cost of supplying np units,
(6)
vi > c +
f
np .
Producer-only market
ac
v
The producer’s supply curve is given by his
average cost (downward sloping, due to
increasing returns to scale). If the producer’s
average cost is as shown by curve ac, the
producer will make exactly zero profit
selling quantity np at price pp = ac (the point
where ac intersects with line vi).
If his average cost were lower, say as shown
in the average cost curve ac’, he could make
profit Πp by selling the product at price
pp > ac.
vi
ac'
pp
!
p
!
n
p
p
N
n
Buyers
Figure 3: The case of diffusion by a monopolistic producer
This is shown in figure 3. If the producer’s average cost is lower than shown by curve ac,
(6) holds and the producer can enter the market at a profit.
What is the outcome for a market where users can only buy from producers and have no
self-supply option (as is traditionally assumed)? All np users whose use value exceeds the
producer’s profit-maximizing product price pp choose to buy the product from the producer; all
N-np others go without. A producer with market power maximizes profit by scarcity and higher
prices, inefficiently limiting diffusion. Use value lost equals area Xp.
3.5 Producer entry into a user-contested market
Next, we turn to the red arrow in figure 1 – which shows the commercialization of a user
design by producers. What difference does it make for producers, compared to the traditional
producer paradigm summarized in section 3.3, that the user innovation paradigm is diffusing the
user design for free in parallel to their marketplace entry? First, there is a source of benefit to
producers: producers can take users’ diffused designs for free. Second there is a cost for
producers: the peer-to-peer diffusion channel competes with the producer by offering DIY users a
non-market option of self-supply. As an example of these benefits and costs, consider Linux and
Red Hat. Linux is open source software, freely offered to all via the peer-to-peer diffusion
channel. DIY users can viably adopt Linux software via that channel, and many do. Red Hat is a
commercial firm – a ‘producer’ - that also adopts the design of Linux software from the peer-topeer channel, and in this way saves design costs. Red Hat then adds ‘user-friendly’ installation
and use features which lower adoption costs for users – and so render adoption for the first time
feasible for at least some non-DIY users. At the same time, however, DIY users have the option
to continue to self-supply, which offers competition for Red Hat in the DIY segment of the
market.
We term markets with a segment of DIY users capable of self-supply “user contested
markets.” Many results of traditional producer paradigm analyses are inaccurate when applied to
10
such markets. As we will see next, they systematically over-estimate users’ willingness to pay,
and consequently also provide inaccurate results for other market outcomes, such as equilibrium
price, quantity, and profit.
Recall the fundamental identity of the producer paradigm given in (5), whereby the
reservation price ri of non-DIY users is given by their use value of the product,
ri
= vi .
(7)
The reservation price of DIY users, by contrast, is lower than that. By definition, DIY users can
create copies of and adopt the design at a cost below their use value. Thus, their maximum
willingness to pay is
NDIY
ri = ci .
(8)
Therefore, to adequately describe user contested markets, analyses traditionally used
must be changed to interpret users’ willingness to pay for the producer product as either their use
value or their individual cost of self-supply, whichever is smaller,
DIY
ri = min (vi , ci ) .
(9)
Note that this simple representation includes the possibility of quality differences,
specifically vertical dimension-of-merit improvements, between the user design diffused via the
peer-to-peer channel and the producer product. We incorporate such vertical differentiation, in
basic form, as cost reductions by the producer relating to a product of the same quality, i.e.
providing the same use value.1
The overall reduction in willingness to pay in the user-contested market will affect
producer entry. Let nc be the producer’s profit-maximizing output quantity in the user-contested
market. The producer can make a positive profit in a user-contested market, if there are at least nc
users i whose reservation price (9) exceeds the producer’s average cost of supplying nc units,
min(vi ,ci ) > c +
f'
nc .
(10)
Whether marketplace diffusion via producers is viable thus depends on the fraction of DIY users
as well as their costs of self-supply (Appendix: Proof 1). The more prevalent and the more
attractive the option of self-supply, the fewer users, possibly fewer than nc, will have a
sufficiently high willingness to pay to attract a producer to the market (cf. (10)).
To understand the market outcome in a user-contested market, we again use our diagram,
now integrating elements from the user-to-user and producer configurations described earlier
(Figure 4; cf. algebraic solutions in the appendix). As is realistic, let us assume that the producer
cannot price discriminate based on users’ ability to self-supply; i.e. he has to choose one profitmaximizing price, taking into account both segments.
1
Viewed in this way, investment in dimension of merit improvements helps the producer to optimize its
mix of fixed and variable costs of producing and diffusing a product of a given quality, i.e. to get the
average cost structure that maximizes its profit. Modeling its profit-maximizing investment is an obvious
next step, as are considerations of other forms of differentiation. Baldwin et al. (2006) present a related
model of vertical and horizontal differentiation.
11
User-contested market
v
min (vi ,ci )
ac '
pc
viNDIY
ciDIY
n
DIY
!
n
Self-suppliers
Buyers
NDIY
n
c
c
N
n
User-created free design spill-overs lower the
producer’s average cost (f’<f, as previously
explained), say to ac’. Demand is represented
by the kinked total demand curve shown in
red. Why is it kinked? It is the horizontal sum
of the demand curves of the DIY and nonDIY segments respectively (as given by the
two downward-sloping lines). We reorder
DIY-users by their cost of self-supply, in
descending order starting from to obtain the
bottom curve. As explained, willingness to
pay in the DIY segment is lower than use
value in the DIY segment (cf. (8)). At very
high prices, therefore, the producer will only
sell to non-DIY users. Only if he sets his price
below , will DIY users begin to prefer
buying to making. Note from the diagram that
we make the conservative assumption that
adoption costs are non-negative for all users,
process benefits notwithstanding.
Figure 4: Diffusion in a user-contested market
In market equilibrium, nc users, some being DIY and some non-DIY, will buy the
producer product. All remaining DIY users obtain the product by self-supply (as shown in the
diagram). The producer makes profit Πc>0. sN - nNDIY non-DIY users do not get the product.
Their use value lost is given by the triangle marked Xc in the diagram.
3.6 Comparing outcomes across three cases
To this point we have separately considered innovation entry and diffusion where only
users are active (sections 3.1 to 3.3) and where only producers are active (section 3.4). We have
also discussed what we call ‘user-contested markets’ where both peer-to-peer diffusion and
marketplace diffusion are available options (section 3.5). We now compare these three cases,
comparing aggregate social welfare created in each case, and exploring who is better off and who
is worse off in each.
In brief summary, we find that the user-contested market provides the highest social
welfare of the three cases. We also find that both DIY and non-DIY users are best off in the usercontested market, while the producer may prefer either the producer-only market or the usercontested market, depending on the comparative strengths of the user supply and the user
competition effects on profit. We show all these points by integrating all three cases in Figure 5,
as well as by algebraic proofs provided in the Appendix.
12
Producer-only market
Peer-to-peer diffusion
User-contested market
ac
v
vi
v
vi
v
ac'
viNDIY
viDIY
!
Self-suppliers
ac'
pp
!
(1 ! s)N
min (vi ,ci )
pc
p
!
u
sN
N
np
n
Buyers
viNDIY
DIY
i
c
p
N
n
n
DIY
!
n
NDIY
n
c
c
N
n
Self-suppliers
Buyers
Figure 5: Comparison of all three cases, as previously shown
We begin with the important observation that the market price in the user-contested
market, pc, is always lower than price in the producer-only market, pp. The magnitude of the
price-depressing effect depends on the fraction of DIY users and on the magnitude of the
decrease in their willingness to pay due to the DIY option (Appendix: Proof 2). A market with
more DIY users is more competitive and therefore more efficient. High costs of self-supply
weaken users’ outside option of DIY.
Due to this price depression, non-DIY users are clearly better off – they would not have
got any product in the P2P-only configuration, and they would have had to pay a higher price in
the producer-only market. In other words, non-DIY users benefit from the presence of their more
DIY-savvy peers. DIY users also benefit compared to both other market configurations: they can
choose whichever sourcing option is cheapest for them.
Diffusion is highest in the user-contested market. We see in the diagram that more nonDIY users than in the producer-only market get to buy the product; and all DIY users end up with
the product, too, whether by making or buying. Use value lost, our measure of mal-diffusion
captured by the grey triangles, is clearly lowest in the user-contested case. It is easy to see from
the diagram that it depends on the fraction of DIY users whether the producer-only or peer-topeer only configuration ranks second (Appendix: Proof 3).
What about the comparative positions of the producer in the user-contested and produceronly markets? At first look, it would seem that the producer profit shrinks from Πp to Πc; the
producer loses. However, recall that the producer’s own production and distribution technology
gave him average cost ac – at which he did not make any profit whatsoever in this market. It was
the users’ free supply of information that brought down his cost to ac’. Thus, at least for the case
shown in the diagram, it would be more accurate to say that, due to user innovation and
competition, the producer’s profit increases from zero to Πc.
Thus, there are two countervailing effects: free user-generated innovation-related
spillovers lowering the cost base (f’) and increasing profit, and user competition via DIY selfsupply decreasing profit. Whether producer profit rises our falls due to user innovation and
competition, depends on the relative strength of these effects (Appendix: Proof 4; cf. (10) vs. (6)).
Thus, it may be that market entry is more likely to be profitable in a user-contested market.
Aggregating all these effects accruing to the different groups, we find that social welfare
in the user-contested market is always higher than in both the producer-only market and the P2Ponly mode. I.e. even in cases in which the producer loses profit, this is overcompensated by a
higher surplus of DIY and non-DIY uses. It is noteworthy that the user-contested market is
socially preferable even when we assume that users have less efficient means of production than
13
the producer, such that their net costs of replicating and adopting one unit are always higher than
the producer’s marginal cost. (Appendix: Proof 5)
We also note that social welfare in the user-contested market rises along with the share of
DIY users – as long as the producer is not forced to exit the market - and it decreases if the costs
of self-supply are high (Appendix: Proof 6). This finding has important policy implications that
we will discuss later.
In sum, our analytical model shows that if a user-contested market is viable, it is socially
preferred. An important factor not included in our model reinforces this conclusion. Consider
that, as was shown in figure 1, users often innovate before producers. User innovation is only
undertaken if it is self-rewarding and confers an innovator’s surplus. In other words, welfare is
being created ahead of producer entry, which further improves the welfare balance.
4. Peer-to-peer and market channels as complements
In section 3, we explored user and producers’ incentives to create and diffuse a novel
product (or service) – call it ‘product K’. Now we introduce an important added matter: product
K is often or generally only one component in a larger user system that is made up of tightlycoupled complementary components – all being required or useful to provide a valued output to
users. When this is so, users must have access to all essential system components if product K is
to provide any value to them in that application. In this section, we consider the situation that
pertains if producers only elect to supply some of these essential components via the market –
while others are only available via the peer-to-peer diffusion channel.
4.1 Examples of systems with components diffused via markets OR peer to peer
In the case of many user systems, only some of the essential components are diffused by
producers via the marketplace, while other essential components are only available peer-to-peer.
We offer two research-based illustrations.
Consider first the example of whitewater kayaking. That extreme sport involves a
“system” of specialized equipment tightly linked to specialized techniques. Both (and many
varieties of both) are required for a user to be able to accomplish the desired sporting outputs of
acrobatic “tricks” such as spins and flips, and riding kayaks down high waterfalls. A detailed
study of the innovation history of that sport shows that only some of these essential components
were ever commercialized by producers (Hienerth et al. 2011).
Whitewater kayaking as a sport was initiated developed and diffused by users in
accordance with pattern of the user innovation paradigm. First, a few users decided that they
wanted to play in very rough whitewater – something that had never been done before. The users
then designed and built the specialized equipment they needed (such as very short, very robust
kayaks) and the devised the novel techniques they needed (such as ways to flip the boat end to
end) to create the sport. They also freely shared these equipment and technique design
innovations via the peer-to-peer diffusion channel. Over a 50-year span, 73% of the most
important equipment innovations, and 91% of the most important technique innovations were
developed by users. During that same span, participation in the sport grew to a present total of
about one million enthusiasts worldwide.
When the sport (and potential market) grew to a commercially interesting size, producers
began to produce and sell kayaking equipment based upon user-developed designs. Due to
economies of scale realized by producers, almost all users today find it cheaper to purchase
whitewater kayaking equipment on the market rather than fabricate it for themselves utilizing free
user designs available user to user. However, still today the tightly-coupled complement of
specialized techniques still diffuses almost entirely user to user (Hienerth et al. 2011). Producers
cannot compete with users in technique coaching because they cannot provide that service more
cheaply than can users via peer-to-peer diffusion. User-to-user coaching is provided
opportunistically as a low-cost adjunct to performances and activities undertaken by the “coach”
14
for their own sake. (For example, “I will try to follow you down this stretch of rough whitewater
– but you need to show me how you do it.” Or, “When we go paddling today, I will teach you
how to do a flip.”)
As a second, more recent example of a user system consisting of three major types of
essential complements, consider the case of DIY 3D printing. 3D printers follow the instructions
of computers to “print” 3D objects out of plastics, metals, or ceramics. They do this by building
up the object layer by layer, each thin layer being “printed” on top of the previous one until a full,
3D object is produced.
In 2005, a design for cheap (under $1 000) 3D printers for hobbyist DIY use was
developed by Adrian Bowyer, a Senior Lecturer in mechanical engineering at the University of
Bath in the United Kingdom. Bowyer made the hardware and software details of his designs
available for free download on the Internet in March, 2007. The idea of a hobbyist 3D printer
turned out to have great appeal to many, and communities of users quickly formed to build
printers for themselves. Following the user innovation paradigm, some improved Bowyer’s
designs, exchanged free advice and help, and freely shared designs for printable objects they had
created for themselves. By 2011 online 3D printer communities had grown to number several
thousand individuals across the world.
Starting about 2010, producers recognized the emergence of a commercially attractive
market and began to supply printers based upon user designs. Most users then began to buy
printers, finding that path to adoption cheaper than building their own. As of 2011, there were on
the order of 10,000 DIY printers in existence. Those built prior to 2010 were homemade, while
the great majority acquired in 2010 and 2011 were purchased from commercial producers. In
contrast, open source printer software and also designs for printable objects, continue to be
developed and diffused by printer users. Users diffuse their innovations for free, encouraged by
significant reputational benefits in the community from sharing innovations and product designs
that are widely admired (de Jong et al. 2012). Producers at this point appear unable to compete
with peer-to-peer diffusion by selling these specialized software products or product designs in
the market, because there are no economies of scale in production or diffusion involved.
4.2 Implications of diffusion of some system components peer to peer only
There are three important implications of user systems for which some components are
diffused from user to user and others via the market: (1) if the user components are essential,
producer diffusion only has value because peer-to-peer diffusion also exists; (2) a producer of an
essential component K can exploit its position to extract profits from non-DIY system users only,
up to the level of the net value those users place upon the entire system; and (3) the producer’s
profit will be higher if his complementors are users, rather than for-profit producers like himself.
Point (1) is clear on the face of it – when producers want to market only some of the
essential system components required to generate an output users desire, the users must obtain the
remaining ones via peer-to-peer diffusion for the producer product to have value.
Note that under these conditions the producer’s market is restricted to users who can selfprovision or buy all other components of the system (i.e. users who have a positive quasi-surplus
from system self-supply). For this reason, the producer may consider supplying additional
components, or consider investing in tools or training to enable more users to self-provide the
required complements and thereby to expand overall system market size.
With respect to point (2), a supplier of essential component K has opportunities to
capture rents, via the sale of K, from the system of user-created essential and non-essential
complements. However, DIY users limit his potential for value capture. The larger the non-DIY
segment, the higher the producer’s potential to capture system-related rents.
Recall that the willingness to pay of DIY users for K cannot exceed their cost of selfsupply for K (cf. (8)). This is still true, even if there is a system around K: the producer cannot get
DIY users to pay more. By contrast, he can get non-DIY users to pay more for K, because, as a
15
sole provider of that component, he holds the key to their entire system value. Therefore, nonDIY users will be willing to pay the producer up to whatever quasi-surplus they derive from the
system: If K is an essential part of a system, their reservation price for K is given by their total
K
system value Vi minus their private net costs Ci of creating all components of the system but K,
r
= Vi - Ci .
(11)
This disaggregation into the two market segments highlights that the producer’s ability to
capture system value via his essential component is overestimated by traditional analyses that do
not consider DIY users as a source of competition for that component and only look to for-profit
substitutes. This error increases along with the fraction of DIY users (Appendix: Proof 7).
An important implication for producers is that if system surplus rises (either because
system use value increases or net system adoption costs fall), a producer can extract still more
rents. This may induce an insightful producer to support user creation of larger system value by,
for example, encouraging users to create more system complements, or creating a cheaper way
for them to diffuse what they have developed peer-to-peer.
As relates to social welfare, our earlier findings still hold. Rivalry in the producer
component increases social welfare. Importantly, the system view emphasizes that the users’
costs of self-supplying all the essential components must be taken into account. Higher costs in
self-provisioning other components can turn a user from DIY to non-DIY with respect to the
producer component – her quasi-surplus from the system falls below her cost of self-supplying
that component, too. Thus, supply conditions for the entire system impact on the competitive
situation of the producer, consumer-surplus, and social welfare.
According to our final point (3), a producer will prefer that those who provide
complements to his component K are users rather than other producers (cf. Baldwin and Henkel
2011). Producer complementors do at least three things that are disadvantageous for our focal
producer: They reduce their own innovation effort due to fear that the focal producer will try to
take part of their rent (hold-up, if the market for complements is competitive; cf. Farrell and Katz
2003); they raise their price, thereby restricting the market for the whole system, and thus for the
focal producer’s component K (this is true if the complementor has market power, cf. Cournot
1838, and a lot of subsequent literature); and they compete with the focal producer for any
additional rent that is created by user complementors. In contrast, users do not behave
strategically in any of these ways.
NDIY
K
5. Discussion
In this paper, we developed the following ideas about innovation and innovation
diffusion: Users often create and at least initially diffuse functionally novel innovations and
systems. In doing so, they may combine some pre-existing components sold by producers with
their own new insights and creations. Next, if and as there is a wider market, producers
commercialize on those innovative parts of the system that they can supply at a profit. In doing
so, they may modify the user design to better fit mass-market needs. As a natural consequence of
this innovation pathway, the producer’s offering is in (potential or actual) competition with
continued user diffusion of innovative system components via the peer-to-peer channel. Under
these ‘user contested market’ conditions, users capable of self-supply have an option, which both
exerts price discipline on the producer and increases social welfare.
As we also discussed, the utility and marketplace value of the innovation system
components producers elect to offer on the marketplace can be dependent on essential
complements being diffused by users peer-to-peer. Under those ‘user complemented market’
conditions, producer markets and profits depend upon the functioning of the peer-to-peer channel.
In addition to benefitting from the peer-to-peer channel in this way, producers may be able to
capture profits from the portions of the overall user system users diffuse peer-to-peer. As in the
case of user-contested markets, user-complemented markets increase social welfare.
16
5.1 Shortfalls in diffusion of user innovation
A crucial matter that ran through our analyses is the cost of self-supply for innovation
adopters. In cases where peer-to-peer diffusion is a rival to marketplace diffusion of an
innovation, the proportion of users able to self-supply and the cost at which they can do so
directly affects producer pricing and social welfare. In cases where peer-to-peer diffusion
provides essential complements to producer products, the ability of users to self-supply
determines the size of producer markets.
Adopters’ incentive and ability to self-supply depends upon the technical requirements
and costs of doing so. These requirements and costs are strongly affected by the form in which
user-innovators reveal their innovations, and the energy they do or do not exert to diffuse that
information. Thus, public posting of a digital file containing the detailed design of a user
innovation in a standard “plug and play” format familiar to adopters can render an innovation
relatively easy and cheap for potential adopters to discover and replicate. In contrast, a user
innovation that is “freely revealed” only in outline – for example, via a general description of a
novel process machine, or via a novel mountain bike only viewable at a distance during a contest
- will be much more costly to replicate and adopt.
Sometimes users in-house innovation design efforts produce an outcome that is also easy
for others to adopt “as is.” For example, today many users create and store innovation designs by
using standard digital design tools. Designs stored as digital files can then often be directly
diffused to others via the Internet, who can then easily replicate them at low cost. Software code
has this attribute, as do designs for physical objects intended to be produced on widely available
computer-driven machine tools or 3D printers. In other cases, in order to reduce adoption costs
for others, significant additional investments would be required beyond those required by the
innovator for in-house use purposes. For example, if a user-innovator did not use standard digital
design tools - perhaps because he developed the novel design by ‘cut and try’ methods involving
physical prototypes – then easing adoption costs for others might involve considerable additional
effort and expense to describe the design in precise detail.
When extra effort is required to lower adoption costs, there can be an important market
failure that may need policymaking attention. The issue is that, as we saw earlier, users who
innovate for themselves are primarily motivated by expected benefits that do not depend upon
diffusion. This is the case for benefits from in-house use of their innovation, and for personal
innovation process rewards, such as fun and learning, as well. For this reason, benefits that others
may derive from adopting an innovation are largely or entirely an externality for user innovators.
This potential shortfall in incentives to diffuse in the user innovation paradigm is not present in
the producer paradigm: producer profits depend directly on the extent of diffusion, while
producers profit from sales to others.
Reduction of any shortfall in incentives to diffuse can take either or both of two general
pathways. First, efforts to increase the proportion of user designs created in inherently easily
diffusible forms – standard digital design formats – would be helpful. Cheap and free digital
design tools and support for open design standards are examples of such support. Second,
lowered cost of diffusion of user designs would be helpful. Here, free design depositaries like
Thingiverse.com for designs replicable on 3D printers, and biobricks.org for posting of designs
for the fundamental building blocks of synthetic biology are very valuable. Second, there are
some pathways by which user-innovators can benefit from diffusion of their innovations, and
enhancement of these pathways should increase user innovators’ incentives to invest in diffusion.
For example, reputation effects can depend upon the extent of diffusion (Allen – Lerner and
Tirole -- ), and reputation enhancement mechanisms like leaderboards can play a role here.
For user innovations that producers will find commercially attractive, the shortfall in user
incentives to lower adoption costs for adopters using the peer-to-peer channel will be a less
important matter. Even if user innovations are more costly to adopt than need be the case due to
17
their low incentives to invest in diffusion, producers will find this to be less of an issue than will
individual user adopters. Producers can spread their adoption costs over many potential
purchasers. In addition, user innovators themselves may elect to commercialize their innovations
– to become producers. When they do this, they will experience producers’ incentives to invest in
diffusion.
If research finds systematic underinvestment by user innovators in diffusion then, given
the social welfare implications, corrective action by innovation policymakers could be merited.
As noted above, such action could involve reducing innovating users’ costs and/or increasing
their incentives to invest in diffusion. In addition, there could even be monetary subsidies for
investments in innovation diffusion by user-innovators. Policy actions on these matters will be
entirely new, but in concept they are highly reminiscent of the policies and investments
governments have made and make today in creating standard forms for encoding of patented
inventions, establishing and supporting sites for public posting of inventions, and providing
subsidies for invention.
5.2 Generalizability of the concepts of user contested and user complemented markets
At the start of this paper, we noted that the concept of user contested and user
complemented markets, here explored within the specific context of innovation, extends well
beyond that context. With respect to user contested markets, peer-to-peer diffusion of any
product or service in competition with producers’ marketplace offerings can discipline producer
pricing and enhance social welfare. The items diffused by both channels can arise in either. For
example, an out-of-copyright book that is diffused peer-to-peer via the Internet in competition
with a producer’s commercial offering may well have been developed for and first offered via the
marketplace channel. As was noted earlier, Casadesus-Masanell and Ghemawat 2006 and Sen
2007 among others have explored this matter in the specific context of the competitive
interactions between open and closed software offerings, and this literature provides a good
platform for additional research on the general matter.
With respect to user complemented markets, less is known – indeed, to our knowledge
we are the first to propose and explore this concept. The likely extent of user complemented
markets still must be empirically explored, but seems to us likely that such markets will be found
both to be quite general, and to be expanding over time. In effect anything that can be costjustified for development by a single user innovator or a user collaborative, and that does not
involve significant economies of scale in replication and diffusion is a candidate for open peer-topeer diffusion at a cost that is competitive with closed, commercial production and diffusion.
As was shown in Baldwin and von Hippel (2011), the range of innovation opportunities
where single user and open collaborative innovation are viable is steadily increasing. This is due
to increased understanding of modular design practices, and also due to radical decreases in
design and collaboration costs due to cheaper and more capable computerized design tools, and
radical reductions in communication costs due to the Internet. These same advances contribute,
as we saw, to reductions in diffusion of designs due to digitization, and to reductions in adoption
costs as well. The net result is that producers will increasingly find they have an advantage over
peer-to-peer diffusion channels with respect to net costs to adopters with respect to physical
“things” that are produced at large scale. For this reason, we see autos, surgical equipment, and
kitchen mixers all offered on the market by producers, while driving techniques, surgical
techniques, and cooking techniques all largely diffuse peer-to-peer – resulting in usercomplemented markets in each case.
5.3 Implications for producers
Producers need to consider the user-contestability of their market, which we have argued
to be increasing across our economy. Any producer who focuses on use value in order to
understand his market demand but who is unaware of user competition, will set the price above
18
his profit-maximizing price and overestimate his future profits. As user self-supply flourishes,
these deviations become increasingly large. To make more accurate forecasts and optimize their
supply strategy, producers in user-contested markets need to understand adoption conditions in
the peer-to-peer channel.
With respect to producer complemented markets, producers should first understand
whether they are participating in such a market. If they are, they might wish to invest in
supporting peer-to-peer innovation and diffusion of complementary products and services. As we
saw, this can increase their own potential markets. After all, the more individuals or firms that are
able to self-provide complements and learn from the peer-to-peer diffusion channel innovations
related to how to do arthroscopic surgery, or how to cook using a frying pan, the greater the
potential market for related commercial products. In fact, some firms do this today, as when video
game firms offer support for the posting of video game ‘mods’ developed by fans, and when the
firm Makerbot, a 3D printer producer, supports a website where fans can post their designs for
free, peer-to-peer diffusion of designs for 3D objects printable on 3D printers.
6. Suggestions for further research
The likely extent of two channel competition and complementarity. What kinds of users
will be DIY users, and what is the amount? What kinds of products are producers likely to leave
to peer-to-peer diffusion?
References
Allen, R. C. (1983). "Collective invention." Journal of Economic Behavior and Organization
4(1): 1-24.
Arundel, A. and Sonntag, V. (1999). Patterns of advanced manufacturing technology (AMT) use
in Canadian manufacturing: 1998 AMT Survey results. Ottawa, Statistics Canada. Catalogue No.
88F0017MIE, No. 12.
Baldwin, C. Y. (2010). When open architecture beats closed: The entrepreneurial use of
architectural knowledge. Harvard Business School Working Paper. Boston, MA.
Baldwin, C. Y. and Clark, K. B. (2006). "The architecture of participation: Does code architecture
mitigate free riding in the open source development model?" Management Science 52(7): 11161127.
Baldwin, C. Y. and Henkel, J. (2011). The impact of modularity on intellectual property and
value appropriation. Harvard Business School Working Paper.
Baldwin, C. Y., Hienerth, C. and von Hippel, E. (2006). "How user innovations become
commercial products: A theoretical investigation and case study." Research Policy 35(9): 12911313.
Baldwin, C. Y. and von Hippel, E. (2011). "Modeling a paradigm shift: From producer
innovation to user and open collaborative innovation." Organization Science 22(6): 1399-1417.
Barney, J. (1991). "Firm resources and sustained competitive advantage." Journal of Management
17(1): 99-120.
Benkler, Y. (2006). The wealth of networks - How social production transforms markets and
freedom. New Haven, CT, Yale University Press.
Block, J., Bock, A. and Henkel, J. (2010). Commercializing user innovations by vertical
diversification: The user-manufacturer innovator. SSRN Working Paper. Munich University of
Technology.
Bush, V. (1945). Science the endless frontier. A report to the President. United States
Government Printing Office. Washington DC.
Casadesus-Masanell, R. and Ghemawat, P. (2006). "Dynamic mixed duopoly: A model motivated
by Linux vs. Windows." Management Science 52(7): 1072-1084.
19
Casadesus-Masanell, R. and Hervas-Drane, A. (2010). "Peer-to-peer file sharing and the market
for digital information goods." Journal of Economics & Management Strategy 19(2): 333-373.
Chandler, A. D. (1977). The visible hand: The managerial revolution in American business.
Cambridge, MA, Harvard University Press.
Cournot, A. A. (1838). Researches into the mathematical principles of the theory of wealth. New
York, Kelly.
Dasgupta, P. and Stiglitz, J. (1980). "Industrial structure and the nature of innovative activity."
The Economic Journal 90(358): 266-293.
de Jong, J., de Bruijn, E. and von Hippel, E. (2012). The relationship between using and
innovation value. MIT Working Paper.
de Jong, J. and von Hippel, E. (2009). "Transfers of user process innovations to process
equipment producers: A study of Dutch high-tech firms." Research Policy 38: 1181–1191.
Demsetz, H. (1967). "Toward a theory of property rights." American Economic Review 57(2):
347–359.
Dosi, G. (1988). "Sources, procedures, and microeconomic effects of innovation." Journal of
Economic Literature 26: 1120-1171.
Economides, N. (1999). "Quality choice and vertical integration." International Journal of
Industrial Organization 17: 903–914.
Farrell, J. and Katz, M. (2003). "Innovation, rent extraction, and integration in systems markets."
Journal of Industrial Economics 48(4): 413-432.
Flowers, S., von Hippel, E., de Jong, J. and Sinozic, T. (2010). Measuring user innovation in the
UK - The importance of product creation by users. NESTA Report. London.
Franke, N., Schreier, M. and Kaiser, U. (2010). "The "I designed it myself" effect in mass
customization." Management Science 56(1): 125-140.
Franke, N. and Shah, S. (2003). "How communities support innovative activities: An exploration
of assistance and sharing among end-users." Research Policy 32(1): 157-178.
Gault, F. and von Hippel, E. (2009). The prevalence of user innovation and free innovation
transfers: Implications for statistical indicators and innovation policy. MIT Sloan School of
Management. Cambridge, MA.
Giuri, P., Ploner, M., Rullani, F. and Torrisi, S. (2010). "Skills, division of labor and performance
in collective inventions: Evidence from open source software." International Journal of Industrial
Organisation 28: 54-68.
Godin, B. (2006). "The linear model of innovation: The historical construction of an analytical
framework." Science, Technology & Human Values 31(639-667).
Häfliger, S., Jäger, G. and von Krogh, G. (2010). "Under the radar: Industry entry by user
entrepreneurs." Research Policy 19(10): 1198-1213.
Harhoff, D., Henkel, J. and von Hippel, E. (2003). "Profiting from voluntary information
spillovers: How users benefit by freely revealing their innovations." Research Policy 32: 17531769.
Harhoff, D. and Mayrhofer, P. (2010). "Managing User Communities and Hybrid Innovation
Processes: Concepts and Design Implications." Organizational Dynamics 39(2): 137-144.
Hart, O. (1995). Firms, contracts and financial structure. Oxford, UK, Oxford University Press.
Hienerth, C. (2006). "The commercialization of user innovations: The development of the rodeo
kayak industry." R&D Management 36(3): 273-294.
Hienerth, C., von Hippel, E. and Berg Jensen, M. (2011). Innovation as consumption: Analysis of
consumers’ innovation efficiency. SSRN Working Paper. Cambridge, MA.
20
Holt, C. and Sherman, R. (1986). Quality uncertainty and bundling. Empirical approaches to
consumer protection economics. P. M. Ippohto and D. T. Scheffman. Washington DC,
U.S.Government Printing Office.
Hounshell, D. A. (1985). From the American system to mass production, 1800-1932. Baltimore,
MD, Johns Hopkins University Press.
Janzik, L., Raasch, C. and Herstatt, C. (2011). "Motivation in innovative online communities:
Why join, why innovate, why share?" International Journal of Innovation Management 15(4):
797-836.
Kamien, M. I. and Schwartz, N. L. (1975). "Market structure and innovation: A survey." Journal
of Economic Literature 13(1): 1-37.
Kline, S. J. and Rosenberg, N. (1986). An overview of innovation. The positive sum strategy:
Harnessing technology for economic growth. R. Landau and N. Rosenberg. Washington DC,
National Academy Press.
Krishnamurthy, S. (2002). "Cave or community? An empircial examination of 100 mature Open
Source projects." First Monday 7(6): 10.
Kuan, J. (2001). Open source software as consumer integration into production. Berkeley,
California, Haas School of Business, University of California-Berkeley working paper: 53.
Lakhani, K. R. and Wolf, B. (2005). Why hackers do what they do: Understanding motivation
and effort in free/open source software projects. Perspectives on Free and Open Source Software.
J. Feller, B. Fitzgerald, S. A. Hissam and K. R. Lakhani. Cambridge, MA, MIT Press: 3-21.
Langlois, R. and Robertson, P. (1992). "Networks and innovation in a modular system: Lessons
from the microcomputer and stereo component industries." Research Policy 21: 297-313.
Lerner, J. and Tirole, J. (2002). "Some simple economics of open source." The Journal of
Industrial Economics 50(2): 197-234.
Liebeskind, J. P. (1996). "Knowledge, strategy, and the theory of the firm." Strategic
Management Journal 17(Winter Special Issue): 93-107.
Lin, L. (2008). "Impact of user skills and network effects on the competition between open source
and proprietary software." Electronic Commerce Research and Applications 7(1): 68-81.
Lüthje, C., Herstatt, C. and von Hippel, E. (2005). "User-innovators and "local" information: The
case of mountain biking." Research Policy 34(6): 951-965.
Mayzlin, D. (2006). "Promotional chat on the Internet." Marketing Science 25(2): 155-163.
Ogawa, S. (1998). "Does sticky information affect the locus of innovation? Evidence from
Japanese convenience-store industry." Research Policy 26(7-8): 777-790.
Ogawa, S. and Pongtanalert, K. (2011). Visualizing the invisible innovation continent: Evidence
from global consumer innovation surveys. SSRN Working Paper.
Pisano, G. (1990). "The R&D boundaries of the firm - An empirical analysis." Administrative
Science Quarterly 35(1): 153-176.
Pollock, R. (2008). The economics of knowledge: A review of teh theoretical literature.
Cambridge University Working Paper. Cambridge, UK.
Prügl, R. and Schreier, M. (2006). "Learning from leading-edge customers at The Sims: Opening
up the innovation process using toolkits." R&D Management 36(3): 237-251.
Raasch, C., Herstatt, C. and Lock, P. (2008). "The dynamics of user innovation: Drivers and
impediments of innovation activities." International Journal of Innovation Management 12(3):
377-398.
Riggs, W. and von Hippel, E. (1994). "Incentives to innovate and the sources of innovation: The
case of scientific instruments." Research Policy 23: 459-469.
21
Sanchez, H. and Mahoney, J. (1996). "Modularity, flexibility, and knowledge management in
product and organizational design." Strategic Management Journal 17: 63-76.
Schaan, S. and Uhrbach, M. (2009). Measuring user innovation in Canadian manufacturing, 2007.
Statistics Canada. Ottawa.
Schilling, M. (2000). "Toward a general modular systems theory and its application to interfirm
product modularity." Academy of Management Review 25(2): 312-334.
Schumpeter, J. A. (1934). The theory of economic development. Cambridge, MA, Harvard
University Press.
Sen, R. (2007). "A strategic analysis of competition between open source and proprietary
software." Journal of Management Information Systems 24(1): 233–257.
Shah, S., Winston Smith, S. and Reedy, E. J. (2011). Who are user entrepreneurs? Findings on
innovation, founder characteristics & firm characteristics. Kauffman Foundation Report. Kansas
City.
Shah, S. K. and Tripsas, M. (2007). "The accidental entrepreneur: The emergent and collective
process of user entrepreneurship." Journal of Strategic Entrepreneurship 1: 123-140.
Sherwin, C. W. and Isenson, R. S. (1967). "Project HINDSIGHT." Science 156(3782): 15711577.
Simon, H. (1962). "The architecture of complexity." Proceedings of the American Philosophical
Society 106: 467-482.
Ulrich, K. T. and Eppinger, S. D. (2012). Product design and development. New York, McGrawHill.
Urban, G. L. and Hauser, J. R. (1993). Design and marketing of new products. Englewood Cliffs,
NJ, Prentice-Hall.
von Hippel, E. (1988). The sources of innovation. New York and Oxford, Oxford University
Press.
von Hippel, E. (1998). "Economics of product development by users: The impact of "sticky" local
information." Management Science 44(5): 629-644.
von Hippel, E. (2005). Democratizing innovation. Cambridge, MA, MIT Press.
von Hippel, E., de Jong, J. and Flowers, S. (2012). "Comparing business and household sector
innovation in consumer products: Findings from a representative survey in the UK." Management
Science, forthcoming.
von Hippel, E. and von Krogh, G. (2003). "Open source software and the "private-collective"
innovation model: Issues for organization science." Organization Science 14(2): 208-223.
von Krogh, G., Häfliger, S., Späth, S. and Wallin, M. (2012). "Carrots and rainbows: Motivation
and social practice in open source software development." MIS Quarterly, forthcoming.
Williamson, O. E. (1985). The economic institutions of capitalism. New York, Free Press.
22
Appendix
Model set-up
We focus our exposition on the simplest situation of supply-side market power: one
monopolist producer. Our findings apply, in essence, to other imperfectly competitive supply
structures, but not to perfect competition among producers.
We normalize the producer’s variable cost to c = 0, and keep in mind that other prices
and costs are then net of this cost. Specifically, each user’s cost of self-supply captures the cost
differential between the user’s and the producer’s cost of producing one unit. We assume this
differential to be positive, i.e. peer-to-peer diffusion to be always more costly at the margin. This
assumption makes sense as producers undertake fixed investment to lower marginal costs of
production and distribution. At the same time, our assumption is a conservative one. In some
areas where there are high process benefits to peer-to-peer adoption and/or where at least some
users have very low costs of adoption, we overestimate the costs of the peer-to-peer channel
relative to the producer channel. In theses cases, our results hold a fortiori.
We assume that the producer has sufficient knowledge of users’ willingness to pay to
construct demand functions for both sub-markets, and that these functions are linear in the
relevant range. The demand function of non-DIY users for product K is
p
n NDIY = sN(1 - )
v .
(A1)
where v is the reservation price of the user with the highest willingness to pay. The demand
function in the DIY user sub-market is
n DIY = (1 - s)N (1 (A2)
p
)
c ,
where c is the maximum willingness to pay (the intersection with the vertical axis) in this submarket, with v > c .
At very high prices, p > c , only non-DIY users will buy producer product K; all DIY
users will self-supply it, along with the rest of the system.2 Only when the price drops below c ,
will there be any buyers capable of self-supply via DIY. From this kink on to the right, total
demand for the producer’s product equals the horizontal sum of the demand quantities in the two
sub-markets, with
(A3) n = N (1 - mp), with
m=
(A4)
s 1 - s sc + (1 - s)v
+
=
v
c
vc
.
Market outcomes
2
If the share of non-DIY users is very large, the producer maximizes profit by only selling to non-DIY
users at a very high price,
s>
p > c . This is optimal if the share of non-DIY users exceeds
v
2(v - c) .
We believe that few markets are so completely segmented into DIY and non-DIY segments that not a
single user would switch from making to buying, once a producer product becomes available. We therefore
do not further pursue this possibility and focus on solutions to the right of the kink.
23
The producer can solve for the price, pc, and quantity, nc, that maximize its expected
profit, Πc. We find the profit-maximizing price pc,
pc =
1
2m .
(A5)
p always exceeds zero, the marginal cost of production and distribution, and thus the first-best,
efficient market price. Output quantities are
c
n c = n NDIY + n DIY =
N
2
with n NDIY = sN(1 -
1
1
) and n DIY = (1 - s)N(1 )
2mv
2mc .
(A6)
This gives the producer a maximum profit of
Pc =
N
-f
4m
.
(A7)
A producer can viably enter a market if the market is “large enough”. The minimum
market size Nmin at which the producer can enter into a user-contested market is (channel
switching condition)
(A8) N min = 4mf .
The smaller the fraction of DIY users, and thus the larger the monopolistic market segment, the
more the producer will exploit his position and raise the price above marginal cost. A market with
more DIY users is more competitive and therefore more efficient. High costs of self-supply
weaken users’ outside option of DIY.
Proof 1
The strength of user competition is captured by the fraction of DIY users and by their
cost of self-supply. Ceteris paribus, the producer’s profit rises with s and c ,
¶Pc N vc(v - c)
=
>0
4 (sc + (1 - s)v)2
(A9) ¶s
,
2
¶Pc N
(1 - s)v
=
>0
4 (sc + (1 - s)v)2
¶c
.
Proof 2
If s=1, we have the standard producer-monopoly case. Our earlier results then simplify to
v
N
Nv
p p = , np = , Pp =
-f
2
2
4
(A10)
.
The difference in price between the producer-only market and the user-contested market is
p p - pc =
v 1
v
c
= (1 )>0
2 2m 2
sc + (1 - s)v
.
(A11)
This difference falls with the fraction of non-DIY users s,
¶( p p - p c ) cv
c-v
=
<0
¶s
2 (sc + (1 - s)v)2
(A12)
,
and increases with the cost of self-provisioning,
2
¶( p p - p c ) 1 (1 - s)v
=
>0
2
2
¶c
(sc
+
(1
s)v)
(A13)
.
24
Proof 3
It follows from (A10) that use value lost in the producer-only case is
Xp =
N
v
8 .
(A14)
And use value lost in the case of peer-to-peer diffusion only is
Xu =
sN
v
2 .
(A15)
Comparing (A14) and (A15), we find that
Xu < X p Û s <
1
4.
(A16)
In other words, unless the fraction of DIY users is high, the producer mode yields wider
diffusion.
Proof 4
Producer profit rises due to user innovation and competition if the decline in fixed cost is
sufficiently large,
f - f'>
(A17)
N
1
(v - )
4
m .
Proof 5
Social welfare (WF) created in the producer-only market equals producer profit plus
consumer surplus of N/2 buyers,
WF p =
Nv
N
3
- f + (v - p p ) = N v - f
4
4
8
.
(A18)
When diffusion is solely peer-to-peer, social welfare equals the surplus of (1-s)N DIY
users, i.e. their use value minus their cost of self-provision. In reality, use value and cost of selfprovision are at best imperfect correlates, maybe even independent. For convenience, we assume
that, for all DIY users, the ordering of users by user value and by adoption cost are identical, i.e.
the user with the highest use value also has the highest adoption cost, same for the second, third,
etc. We make this assumption only for welfare calculations. Any other distribution of surplus
across DIY users that leaves the total surplus of the DIY segment unchanged would be just as
good. Social welfare then equals
WF u =
(1 - s)N
(v - c)
2
.
(A19)
Finally, social welfare in the user-contested market consists of three components:
producer profit, the surplus of those non-DIY users who choose to buy the product, and the
surplus of all DIY users from either making or buying,
WF c =
N
1
1
pc
- f + n NDIY (v - p c ) + (1 - s)N(v - p c ) + n NDIY ( - p c )
4m
2
2
2
,
(A20)
which, after some rearranging, simplifies to
WF c =
N
1
(v )- f
2
4m
.
(A21)
First, we compare WFp and WFc and find that social welfare in the user-contested market
is always higher than in the producer-only market,
25
WF c - WF p =
N
1
3v
N
(v - ) - ( f '- f ) = ( p p - p c ) + ( f - f ') > 0
2
4m 4
4
.
(A22)
Note that this would be true even if the spillover from user innovation to producers were not
valuable, such that the producer’s fixed cost would remain unaffected.
Second, we compare WFu and WFc and find that social welfare in the user-contested
market is always higher than welfare from peer-to-peer diffusion only. To prove this, we assume
that fixed costs are very high (f =1/4m), such that the producer makes exactly zero profit in the
user-contested market. This is a conservative assumption; with fixed cost lower than that, the
welfare advantage of the mixed mode increases. After some rearranging, we find that the usercontested outcome is socially preferable if
1
æ 1 1ö
s(1 - s)(v - c) ç - ÷ > è c vø
4,
(A23)
which is true, since the left-hand side is always positive.
Proof 6
Social welfare in the user-contested market rises in the share of DIY users: The fewer DIY users,
the lower welfare,
1
¶WF c
N ¶m
N
vc(v - c)
==<0
8 ¶s
8 (sc + (1 - s)v)2
(A24) ¶s
.
Social welfare decreases if the costs of self-supply are high,
1
2
¶
¶WF
N m
Nv
(1 - s)v
==<0
8 ¶c
8 (sc + (1 - s)v)2
(A25) ¶c
.
c
Proof 6
The new equilibrium price and profit are
c
psys
'=
(A26)
It holds that
1
N
sc + (1 - s)(1 + q)v
, Pcsys ' =
- f ', with m' =
2m'
4m'
vc(1 + q)
.
c
c
psys
' > psys
if
1
1
sc + (1 - s)(1 + q)v
> , m' =
m' m
vc(1 + q)
(A27)
,
c
c
Psys ' > Psys
which is true for all q>0.
follows, as the quantity sold remains unchanged.
Proof 7
When traditional analyses of value capture via essential system modules do not take into
account the possibility of user self-supply of these modules, they overestimate profit by fraction
æ (1 + q)v ö
E
= (1 - s) ç
- 1÷
c
P
c
è
ø.
sys
(A28)
As the fraction (1-s) of DIY users increases, the magnitude of the error increases, too.
26
SCRAP
1.2 Overview of major findings
First, we explore what we term ‘user-contested markets’ in which a user innovation is
diffused both peer-to-peer and via marketplace diffusion. In user-contested markets, to the extent
that some users can continue to adopt the commercialized innovation via the peer-peer channel as
well as via the market, a source of demand-side competition exists that disciplines producer’s
pricing behavior. All users profit if the market is user-contested – even those who cannot selfsupply – due to the price moderating pressure exerted on producers by those users who can selfsupply. At the same time, the producer can benefit from free user-generated design spillovers
(the user design it adopted). Due to these counteracting factors, producer profits can be higher or
lower in a user-contested market relative to a producer-only market.
Second, we explore ‘user-complemented markets’ in which user-developed products or
services diffused peer-to-peer complement those diffused by producers via the marketplace.
Consider that a “system” of complements is often required to deliver the novel functions
innovating users seek. For example, novel arthroscopic surgical equipment has no value absent a
second essential type of system component - novel surgical techniques and operations that utilize
that novel equipment. Often, as we will see, producers elect to commercialize only some of these
interdependent system elements. In such cases, continuing peer-to-peer diffusion to supply the
remaining essential system complements is essential to overall system value, and to producers’
marketplace success and profits as well.
To the extent that the system components producers elect to commercialize represent a
“bottleneck” that controls user access to value derived from the entire system, the presence of a
larger user system can increase producer profits in an additional way. The producer can then use
his monopoly power over system components he sells to extract additional rents up to the value
the user places upon the entire system – including system complements that users continue to
self-supply. Producers prefer users over producers as the providers of system complements,
because user-innovators do not compete for system rents garnered by other users. So they may
have an incentive to support users in that role.
Finally, we consider the impact of peer-to-peer and marketplace diffusion on social
welfare when these modes are rivals and also when they are complements. We show that social
welfare increases with the proportion of users capable of self-supply, and decreases as the costs of
self-supply increases. We also note that users, unlike producers, do not have an in-built incentive
to diffuse their innovations - they motivated to innovate primarily by benefits from in-house use.
For this reason, the benefits that others may obtain from their innovations are largely an
externality from their perspective. It is therefore likely that diffusion of user innovations will be
suboptimal from the social welfare perspective, and that novel policies to support peer-to-peer
diffusion, exerted by both governments and producers, may be desirable.
27