ArticlePDF Available

SELECTIVE REVEALING IN OPEN INNOVATION PROCESSES: THE CASE OF EMBEDDED LINUX.

Authors:

Abstract and Figures

This paper provides a quantitative study of free revealing of firm-developed innovations within embedded Linux. Firms voluntarily contribute developments back to public projects, receiving informal development support. Revealing selectively still allows them to protect their intellectual property. Revealing is strongly heterogeneous among firms, which can partly be explained by firm characteristics.
Content may be subject to copyright.
Selective Revealing in Open Innovation Processes:
The Case of Embedded Linux
published in Research Policy 35 (2006) 953969
Joachim Henkel
Schöller Chair in Technology and Innovation Management
Technische Universität München
Arcisstr. 21, D 80333 Munich, Germany
phone: +49-89-28925741, fax: +49-89-28925742, email: henkel@wi.tum.de
CEPR, London
Abstract: This paper provides a quantitative study (N = 268) of patterns of free revealing of
firm-developed innovations within embedded Linux, a type of open source software (OSS). I
find that firms, without being obliged to do so, contribute many of their own developments
back to public embedded Linux code, eliciting and indeed receiving informal development
support from other firms. That is, they perform a part of their product development open to
the publican unthinkable idea for traditionally-minded managers. Such openness obviously
entails the challenge of protecting one’s intellectual property. I find that firms address this
issue by revealing selectively. They reveal, on average, about half of the code they have
developed, while protecting the other half by various means. Revealing is strongly
heterogeneous among firms. Multivariate analysis can partly explain this heterogeneity by
firm characteristics and the firm’s purpose behind revealing. An analysis of reasons for
revealing and of the type of revealed code shows that different types of firms have different
rationales for openness. Implications for management are that the conflict between downsides
and benefits of openness appears manageable. Provided selective revealing is practiced
deliberately, the opportunities of open development dominate.
Key words: open source software, embedded Linux, free revealing, appropriation, IP protection
JEL classification: L86, M11, O31
1
1 Introduction
With some simplification, one can describe the traditional view of innovation as taking place
entirely within one firm. In contrast to such closed innovation, open innovation processes are
characterized as spanning firm boundaries (Chesbrough 2003). This may mean that
technology is treated as a tradable good to be bought and sold on the market (Arora et al.
2001). However, openness in innovation processes can reach far beyond such market-
mediated exchange. Under suitable circumstances, firms may make their technology available
to the public in order to elicit development collaboration, but without any contractual
guarantees of obtaining it.
Open innovation in this sense is the subject of the present paper. I explore the
commercial development of open source software (OSS) for embedded systems such as
machine controls or VCRs (e.g., VDC 2004). One of the benefits that firms can derive from
using OSS is informal development collaboration (Feller and Fitzgerald 2002). Realizing this
advantage requires that a firm reveals its code to the publican obvious prerequisite for open
innovation. Some OSS licenses work in the same direction by restricting means to keep code
proprietary. However, these factors pushing for openness conflict with a firm’s need to protect
its intellectual property. So, how can open innovation be reconciled with intellectual property
protection?
Using a quantitative empirical study of embedded Linux (N = 268), I explore how firms
manage this conflict. First, I clarify that firms, despite the fact that Linux is OSS, indeed have
a choice between openness and protection. I then analyze what share of their developments
firms make public, what type of code they reveal, and what motivates them to do so. Using
multivariate analysis I explore which firm characteristics determine revealing behavior. In
particular, I investigate if and under what conditions openness leads to informal development
collaboration, i.e., open innovation.
For several reasons, embedded Linux is ideally suited for studying the above questions.
First, it is nearly exclusively developed by commercial firms, while hobbyists play only a
minor role. Second, the fact that it comes under an OSS license makes firms consider
revealing their own developments, which they would probably not even think of doing in the
case of proprietary software. Still, they have considerable latitude in either sharing or
protecting their code. As a result, openness is a conscious decision. Third, as one of the most
2
widely used operating systems in this field (Webb 2002, VDC 2004), Linux is of highest
relevance for manufacturers of embedded devices. Such devices, in turn, account for the vast
majority of all processorsaround 6 billion in 2002 (Ganssle and Barr 2003). Hence,
studying the innovation process of embedded Linux is not just instructive for understanding
open innovation, but has implications for the large (and growing) embedded systems industry.
Central results are the following. Firms are aware of and routinely use various means of
protecting their code. However, despite the possibility of protection they reveal on average
about half of the code they develop for embedded Linux. The degree of openness turns out to
be strongly heterogeneous among firms in my sample. Exploring this heterogeneity using
multivariate analysis, I find that the share of its code a firm reveals is far from random.
Instead, the analysis indicates rational cost/benefit considerations. In particular, the more
important obtaining external development support is as a motive for free revealing, the more
code the respective firm reveals. Furthermore, small firms ceteris paribus reveal significantly
more, likely because, due to resource scarcity, they expect to benefit more from external
development support.
Thus, open and collaborative innovation processes indeed take place. The private-
collective model of innovation (von Hippel and von Krogh 2003) is found to work also in a
commercial environment. However, firms practice “selective revealing” so as to minimize
competitive lossesand are able to do so while abiding by the applicable OSS license. The
patterns of free revealing I find are consistent with profit-maximizing behavior. It thus seems
conceivable that OSS, even OSS under the GPL, becomes a standard part of industrial firms’
innovative activity. The key is to understand what to reveal and what to protecti.e., to
repartition innovative activities into an open and a protected part in a manner consistent with
private profits.
The remainder of the paper is organized as follows. In Section 2, background
information is given on firms’ benefits and downsides of developing OSS and on embedded
Linux. Section 3 presents research design and data. In Section 4, analysis and results are
presented. Section 5 concludes with a summary and a discussion.
2 Literature Review
The present paper links to four strands of literature: information trading, revealing of user
innovations, collective invention, and commercial OSS development. In order of increasingly
3
close relation to this paper’s subject, I will briefly review the relevant literature in the
following, and will point out in what respect the present paper differs.
Information trading
Open information exchange between firms often occurs within a dyad of individuals.
This phenomenon of “information trading” has been analyzed, among others, by von Hippel
(1987) and Schrader (1991) and more recently by Dahl and Pedersen (2004). This literature
finds that, despite the lack of formal contractual agreements, the information provider expects
her counterpart to reciprocate when, in the future, she in turn requests information. A parallel
to revealing OSS code is that in both cases the individual developer holds a gatekeeper
position. The important difference, however, is that in a trading situation information is given
to one particular recipient only. The provider thus knows if the conditions of acquaintance and
mutual trust are fulfilled, which Bouty (2000) identified as preconditions for an interpersonal
exchange of strategic resources in her study of R&D scientists. Furthermore, within a dyad a
lack of reciprocation can clearly be attributed to one individual, and retaliation (in particular,
ceasing to exchange information) can thus be targeted precisely.
Free revealing of user innovations
Studies of free revealing have to this point focused on free revealing by firms or
individuals that expect to benefit from use rather than sale of their innovations. Free revealing
by users has been found in fields as diverse as chemistry analyzers (von Hippel 1988), iron
production (Allen 1983, see below), library information systems (Morrison et al. 2000), and
sporting goods (Franke and Shah 2003). Those that freely reveal might be motivated to do so
by expectations of benefit from development support by a user community (Franke and Shah
2003) or a manufacturer (Harhoff et al. 2003). Also building reputation among peers might be
a powerful motivator, as Raymond (1999) argues for the case of (user-developed) OSS. In
addition, users often lack the means to exploit their innovation by selling it, since the required
change of functional role is typically difficult to accomplish (von Hippel 1988).
The development of OSS is indeed often performed by users (e.g., Franke and von
Hippel 2003, von Hippel 2001). However, this need not be the case. As will be laid out below,
firms may contribute to public OSS development for a variety of reasons. In particular, firms
developing embedded Linux are typically not users of the software, but manufacturers (or
suppliers to those) of goods containing embedded software.
4
Collective invention
Open innovation as I use the term in this paper is similar to the phenomenon of
“collective invention”, a particular instance of user innovation. The term has been coined by
Allen (1983) in his study of iron production in 19th century England. During 1850-1875, two
important attributes of iron furnaces were subject to steady improvement. Allen found that in
many cases innovators publicly revealed data on their furnace design and performance in
meetings of professional societies and in published material. He also documented a pattern in
which innovators built on each other’s advances. Nuvolari (2001a) describes another
historical case of collective invention, namely, the development of the Cornish pumping
engine. Further works on this topic are due to Cowan and Jonard (2003), Lamoreaux and
Sokoloff (2000), McGaw (1987), and Russo (1985).
Various authors have drawn parallels between collective invention and OSS
development (Henkel 2004a, Meyer 2003, Nuvolari 2001b, Osterloh and Rota 2004). Yet,
there is an important difference. As Allen (1983, p. 2) points out: Collective invention differs
from R&D since the firms did not allocate resources to inventionthe new technical
knowledge was a by-product of normal business operation. This situationone of
innovation by userscorresponds to user-development of OSS (e.g., in the case of the
Apache web server), but differs from that of embedded Linux. In the latter case, software
development quite clearly is a part of R&D. Another difference is that, in the cases of
collective invention mentioned above, all firms were following the same goal, namely,
increasing efficiency of their furnaces and pumping engines, respectively. In contrast, it is
precisely heterogeneity of needs which supports collaboration in the field of embedded Linux
(Henkel 2004b). Thus, the phenomenon explored in this paper is related to, but clearly distinct
from collective invention.
Costs and benefits of commercial OSS development
The existence of and nature of benefits commercial firms could derive from contributing
to public OSS development have been explored by various authors (Behlendorf 1999, Hecker
1999, Raymond 1999, Feller and Fitzgerald 2002, Lerner and Tirole 2002, Wichmann 2002,
Bonaccorsi and Rossi 2004, Dahlander and Magnusson 2005). Potential benefits that have
been identified can roughly be grouped into four categories: setting a standard and enabling
compatibility; increasing demand for complementary goods and services; benefiting from
5
external development support, in particular from the OSS community
1
; and signaling
technical excellence or good OSS citizenship.
Against these possible benefits a number of potential downsides must be weighed. First,
software that is freely available to anyone can no longer be soldcustomers will at best be
willing to pay for convenient packaging. Second, software revealed as OSS can be used also
by competitors, which may imply a loss of competitive advantage. Third, the firm that
originated the software might lose control over its further development, even when it acts as
the official maintainer of the respective public OSS project.
As a result of balancing the above benefits and downsides, wide diffusion of the
software and its source code may turn out to be desirable to the originator. In other situations,
it may be preferable to keep the code proprietaryprovided this choice exists. If the
respective piece of software is based on existing OSS under the GPL, it must be put under that
same license. In order to circumvent this requirement, the firm would have to abstain from
using GPL’ed software in the first place.
How firms can realize the benefits while minimizing the downsides of public OSS
development has been addressed by a number of authors. A recommendation for firms turning
proprietary software into OSS is to choose “non-copyleft” OSS licenses (which would allow
to reconvert future versions into proprietary software) or to use an OSS and a proprietary
license in parallel (Behlendorf 1999, Hecker 1999, Raymond 1999).
The use of “standard” means of protection—legal mechanisms, secrecy, lead time, and
complementary assetsin the case of OSS has been analyzed by Dahlander and Magnusson
(2005) using case studies. They find that four out of five firms in their sample make sure to
keep the copyright to the entire software program, thus maintaining a higher level of control
over the software. Three of the firms close at least parts of the software, thus using secrecy
and legal protection mechanisms. The remaining two firms keep their software open, relying
instead on a committed developer community as a complementary asset. Note, however, that
only the last of these solutions is viable in the case of derived work based on GPL’ed
software. In a similar, but broader context West (2003) analyzes the success of semi-open
platform strategies by Apple, IBM, and Sun.
1
See Hertel et al. (2003) and Lakhani and Wolf (2005) for empirical studies of individuals’ motives to contribute
to public OSS projects.
6
A protection and appropriation means independent of the type of licensing is provided
by complementary assets (Teece 1986). In the present context, this may be a brand name, as
for distributors such as Red Hat and SuSE (e.g., Feller and Fitzgerald 2002); a developer
community committed to the respective firm and its software (Dahlander and Magnusson
2005); proprietary software specific to the OSS in question; or hardware to which the
respective OSS is tailored.
These results on commercial OSS development leave an important gap. First, several of
the protection mechanisms discussed above are not available when a firm develops software
based on existing OSS under the GPL. Given the wide diffusion of Linux and the dominance
of the GPL among open source licenses (Lerner and Tirole 2005), this case obviously merits
particular attention. Second, there is no study that analyzes the decision to reveal code or not
on the level of individual code contributionsacknowledging the fact that firms might reveal
selectively only some of their developments. Third and finally, there is no quantitative
empirical study that relates a firm’s revealing behavior to its characteristics, thus contributing
to a deeper understanding of where open innovation works and where it does not. This paper
addresses these issues.
3 Research Questions and Hypotheses
The central question of this paper is under what conditions open innovation processes are
feasible—“open” in the sense that firms make their developments freely available to the
public and receive informal development support from outside. The particular case I explore
is that of embedded Linux. In this context, the following research questions will be addressed:
1. What latitude do firms have with respect to revealing or protecting the code they
develop for embedded Linux? What means of protection do they use, and how often?
2. What share of the code for embedded Linux that these firms develop is made public?
3. What type of code is typically revealed? What code is typically protected?
4. What are the reasons for firms to make their OSS code voluntarily public?
5. How is the degree of openness determined by firm characteristics?
Since the context is the development of OSS under the General Public License (GPL,
see Section 4.1) question 1 needs to be addressed first. Obviously, if a firm’s revealing
behavior was entirely determined by the GPL then the only decision of interest would be why
7
it uses OSS in the first place. Having established that firms do have a choice with regard to
openness, the obvious next questions 2 and 3 relate to their actual revealing behavior.
Questions 4 and 5 then deal with how this behavior can be understood and explained.
Question 5 will be addressed using regression analysis. To guide the choice of
explanatory variables, hypotheses are derived from interviews (see 4.2) and the literature.
The share of code developed for embedded Linux that a firm reveals to the public is,
ceteris paribus, larger …
H1: … the smaller the firm.
H2: … if there is a firm policy in place that encourages revealing.
H3: … if there is no firm policy restrictive to revealing in place.
H4: … if there are proprietary complementary assets available.
H5: … the longer the firm has been developing embedded Linux.
H6: … the more important, as a reason to reveal, considerations are regarding…
a) development support, b) marketing, c) reputation, d) the GPL.
Hypothesis H1 is derived from resource considerations. Active participation in the open
source development process can provide firms with external development support as well as a
low-cost marketing channel (Aldrich and Auster 1986, Gruber and Henkel 2005). Due to
resource constraints, both should be relatively more important for small than for large firms.
Hypotheses H2 and H3 are self-explanatory as far as the direction of the effect is concerned.
However, it is not obvious that a significant effect can indeed be found. H4 derives from
considerations by Teece (1986). Assuming that, for device and component manufacturers,
their hardware constitutes a complementary asset to the embedded Linux code they develop I
hypothesize that hardware manufacturers reveal, ceteris paribus, more than software firms.
This effect should be more pronounced for component manufacturers since their code,
typically drivers, shows a higher specificity to the respective hardware than in the case of
device manufacturers.
H5 is posited because firms new to embedded Linux development typically will not yet
have high-quality code to reveal. In addition, full adoption of the open source development
process (and hence, revealing of code) requires a (presumably slow) change from the
traditional proprietary attitude to an “open” one. This change should be slower, due to
organizational inertia, the older a firm is at the time it starts developing embedded Linux
(variable: AgeAtStart). On the other hand, a firm entering the embedded Linux arena with
longer experience in a fully proprietary business model is more likely to have complementary
8
assets, and so might reveal more. Given these counteracting effects, I refrain from positing a
hypothesis regarding the effect of AgeAtStart, but do include it as an explanatory variable.
Finally, H6a through H6d relate to self-reported reasons for revealing. For example, the more
important, ceteris paribus, development support (H6a) is for a firm the more it should reveal.
4 Research Design and Data
4.1 Object of Study: Embedded Linux
In recent years, more and more technical products have become “smart” in the sense of
containing microprocessors and software. The number of processors built into such
“embedded systems” dwarfs that of processors used in computers: of the 6.2 billion
processors manufactured in 2002, more than 98% went into embedded systems (Ganssle and
Barr 2003). Accordingly, software development constitutes an ever increasing part of overall
new product development (NPD), even more so since embedded systems are highly
heterogeneous and thus require more software adaptations than standard computers. In this
situation, efficiency and effectiveness of software development become increasingly
important (Rauscher and Smith 1995).
In this arena of embedded software, embedded Linux plays an increasingly important
role. The term denotes versions of Linux used in embedded devices such as mobile phones,
VCRs, and machine controls. It has experienced rapid development over the last years (Webb
2002) and has become one of the three most widely used operating systems for embedded
devices (VDC 2004). Due to high heterogeneity of embedded devices, there is no standard
version of embedded Linux. Correspondingly, “developing embedded Linux” refers to the
development of modules or extensions that make Linux suitable for embedded systems.
Examples are the RTAI real-time module, the toolkit busybox, the shrinked C library uclibc,
and architecture-specific code for processors used in embedded devices.
Such code is to be regarded as “derived work” in the sense of the GPL, which governs
the use of Linux. This implies that, by the time a device containing embedded Linux comes
onto the market, the source code of the version of Linux it contains must be made available to
all buyers. Obviously, this is a matter of concern for firms using GPL software. Still,
considerable latitude exists with respect to sharing or protecting one’s developments, as will
be discussed in Section 5.1.
9
Further characteristics add to making embedded Linux a suitable object of study for my
purpose. Nearly all developments come from firmsdevice manufacturers, component
manufacturers, and dedicated software firmswhile hobbyists play only a minor role. Hence,
garnering support from hobby developers can be ruled out as a major motivator for firms to
engage in this field of OSS. Furthermore, these firms benefit from contributing to embedded
Linux not by increasing demand for complements nor by pursuing strategic interests as, e.g.,
IBM does by supporting Linux as a server operating system. Instead, embedded Linux
constitutes, for device manufacturers, a part of their products. For that purpose, diffusion of
the respective source code would not be required. Hence, if firms still voluntarily reveal their
code then they must expect other benefits. As it will turn out, external development support
from other firms is indeed the main motivator.
Embedded Linux thus offers an ideal opportunity to explore selective openness in
innovation processes. This opening-up concerns the operating system, a product component
which is of vital importance but typically not an important differentiator (even though this
may be the case). The case of embedded Linux thus demonstrates a repartitioning of
innovative activity into an open and a protected part. The decision that firms face is where to
draw the line.
4.2 Data
Between November 2003 and March 2004 a web-based questionnaire on the development of
embedded Linux was online. It had been developed based on a series of 30 interviews with
industry-participants in the field of embedded Linux and yielded 268 valid responses.
The survey targeted developers, since they actually perform the act of revealing code
and should thus be best informed about it. It was advertised on web portals and mailing lists
dedicated to embedded Linux development. The basic population addressed by the survey
thus consists of all embedded Linux developers who read the web portals or mailing lists
advertising the survey.
2
Given that the questionnaire would have been hard to find and
difficult to answer for people beyond this target group, responses from outside the basic
2
Most participants (51.5%) learned about the survey on the portal LinuxDevices.com. Roughly 10% each came
from the “PowerPC embedded” mailing list, the “RTAI” mailing list, and Handhelds.org. The “Busybox
mailing list accounts for 4.1%. 9% were informed by other sources (e.g., colleagues), and 6% did not disclose
how they learned about the survey.
10
population seem unlikely. A precise response rate can not be given since data on the size of
the basic population was not fully available. However, a nonresponse analysis comparing
early to late respondents (Armstrong and Overton 1977) yields no indication of a non-
response bias.
Asked to indicate what type of organization they work for, 22.4% of respondents
described their employer as a “Software company specializing on embedded Linux”, 42.5%
as a “Device manufacturer”, and 8.6% as a “Manufacturer of components like chips and
boards”. The remaining 26.5% of respondents ticked I am working as a hobbyist” (15.3%) or
“University or other non-profit research organization” (11.2%).
3
Hence, only a minority of respondents conform to the cliché of the OSS developer as a
hobbyist. Weighted by the number of hours per week spent on embedded Linux, this result
becomes even more pronounced, with hobbyists contributing only about 7% of total hours in
my sample. In addition, hobbyists are likely over-represented due to lower time pressure and
higher emotional involvement (which is likely also true for university programmers). The
notion that hobbyists play a minor role in embedded Linux development is thus supported.
Participants have considerable experience as programmers, on average 14.2 years.
Average experience in developing OSS and embedded software, resp., is 4.9 and 7.1 years.
Those respondents working for commercial firms were asked some information on their
employer. It turns out that software firms in the sample are comparatively young and small,
with a median founding year of 1997 and 64% of respondents working for smaller firms
(max. 50 employees). Device manufacturers have a median founding year of 1988, and 54%
of respondents work for smaller firms. Component manufacturers, finally, are on average the
oldest and largest firms, with median founding year of 1986 and only 26% of participants
working for smaller firms. As to experience in developing embedded Linux, the distribution is
strongly left-skewed, with very few firms (12.5%) having started between 1994 (the earliest
date) and 1999. The mean starting year is 0.75 years earlier for software than for hardware
firms (p < 0.01 in one-sided t-test).
3
In order to avoid biased responses, participants were granted anonymity. In particular, they were not asked for
the name of their employer. This means, of course, that some firms could appear more than once in the sample
without being identified. Still, multiple occurrences seem to be rare. Most commercial developers (166 out of
197) had provided an email address. Of these, 83 could clearly be identified as company email addressesand
only three of them appear twice.
11
5 Results and Discussion
Following the research questions posited above, this section is divided into five parts. The
first three subsections provide a descriptive analysis, addressing means of protecting OSS
code (5.1), the share of code (5.2) and the type of code (5.3) that is actually revealed. The
question why firms make their developments public is addressed in 5.4 using self-reported
reasons. Finally, the central part of this study (5.5) provides a multivariate analysis of
revealing behavior, allowing deeper insights into how firms balance openness and protection.
5.1 Ways to Protect Code Based on OSS
The GPL stipulates that the source code of derived work based on GPL’ed software must be
made available to all receivers of the software. In the case of embedded Linux this means that
when a device comes to market, any buyer is entitled to obtain the source code. This
regulation, strict as it appears, nonetheless leaves room for protection.
First, contrary to a common misconception, derived work does not need to be made
public. The GPL merely requires the seller of derived workbe it on a storage medium or
embedded in a deviceto make the source code available to its customers. If these are few, or
even only one as in the case of commissioned OSS development, and if the customers
themselves are not interested in revealing the code (and not obliged to by the GPL), then the
software can effectively be kept secret. As Table 1 shows, 46.6% of respondents working for
software firms stated their firm reveals code “sometimes” or more often only to its customers.
Second, even if a device is sold to the mass market, the developing firm can nonetheless
delay and restrict diffusion by providing the source code on demand only, and without active
support. Since typically more than a year passes between development of the code and market
launch of the device, and since lead time is generally considered a rather effective means of
protection (e.g., Sattler 2003), considerable advantage can be attained this way. Also this
means is frequently used: 45.0% of all respondents working for commercial firms
4
stated that
“sometimes” or more often their firm reveals code only when the device containing it comes
onto the market, and only when buyers request it.
4
Unless noted otherwise, all further percentages refer only to respondents working for commercial firms
(N=197).
12
Third, the period of time between code development and market launch can be used to
optimize the timing of active revealing. This means of protectioni.e., to actively reveal
code, but only after a certain delaywas said to be used at least “sometimes” by 35.7% of
respondents. Fourth and finally, it is accepted (if disputed) practice to make drivers available
only as loadable binary modules, not as source code (Marti 2002). This turns out to be the
most commonly used means, with 53.1% of respondents stating that their firm uses it at least
sometimes; 16.1% even indicated always. Further means of protection, which were not
surveyed, must be applied before code is written as derived work of GPL’ed code. Often, re-
designing the code architecture allows to shift to the (proprietary) application layer those
functionalities that require protection (Henkel 2003).
Hence, despite the GPL various means to obtain a certain protection of one’s code exist,
and are routinely used.
5
Still, firms do reveal considerable amounts of code nonetheless, as
will be reported in the following.
5.2 Revealing of CodeExtent and Change over Time
When quantifying the amount of code for embedded Linux that a firm reveals, a distinction is
appropriate at the outset. For code that is so specific to a particular device that it is of no value
for other firms, revealing (and also protection) is not an issue.
6
Hence, the corresponding
survey question focuses on those embedded Linux developments by your firm that are
potentially useful for others. That is, they are not too specific to your firm.” “Revealing” was
defined as follows: “the code is actively made public, be it for downloading on a website, as a
posting on a mailing list, or as a submission to the maintainer.”
Table 2 provides descriptive statistics of the answers to the above question. Hobbyists
and developers in universities exhibit “typical” open source behavior, revealing nearly all of
5
Some parts of Linux are licensed not under the GPL but under the LGPL. When a firm links its own
developments to such code, these can be kept proprietary more easily than in the case of GPL code. In my
empirical study, I can not distinguish the two cases since information on the licensing schemes is not available.
Still, due to the general dominance of the GPLLerner and Tirole (2005), in their empirical analysis of projects
on SourceForge, count 18,133 projects under the GPL compared to 2,501 under the LGPLit seems justified to
focus the argument on code under the GPL.
6
Morrison et al. (2000) report similar considerations in their study on user innovation on library computer
systems.
13
the code in question (mean = 92%, median = 100%). In contrast, the mean across the three
commercial categories is 49.3% (median = 50%). So, firms on average do make a
considerable share of their developments public49% is very much for an organization used
to proprietary processes. However, they do not blindly follow the popular myth that derived
work of GPL’ed code has to be made public.
A closer look at Table 2 shows that revealing behavior varies strongly between firms.
For all three categories, the standard deviation of the share of revealed code is above 35%, the
minimum at 1%, and the maximum at 100%. It will be the subject of Section 5.5 to explain
some of this heterogeneity by firm characteristics.
One might conjecture that revealing in the field of embedded Linux is a left-over from
the hype that surrounded OSS in 1999 and 2000, and that it decreases further over time.
However, the survey yields exactly the opposite result. Only 10.3% of “commercial”
respondents (total responses: 145) stated that their company reveals less now (i.e., at the time
they did the survey, late 2003 / early 2004) than in 2000. 40.7% did not see a change, while
nearly half of all respondents (49%) said their company reveals “somewhat more” or “much
more” than in 2000. Hence, code sharing by commercial firms in the field of embedded Linux
is anything but a dying-out left-over from a hype; quite the contrary, it seems to be on the way
to becoming mainstream behavior.
5.3 Type of Code that is Revealed
Having established how code is protected and how much of it is revealed, the next question is
what type of code firms typically reveal. The questionnaire offered five statements relating to
the type of code, and participants were asked to indicate their agreement on a 5-point ordinal
scale. Figure 1 shows the share of agreeing and disagreeing responses, separately for software
and hardware firms.
The highest agreement, for both types of firms, received the statement that the revealed
code is “generic”—with 63% agreement from hardware firms and even 85% from software
firms. This is a very plausible finding, since revealing generic code should not harm the
competitive position of the company (cf. Schrader 1991, Fauchart 2003).
Yet, this is not the whole story. Also the statements “is important for our competitive
position” and “helps to differentiate our product from others” received a positive net
agreement (i.e., share agreement minus share disagreement). While it is small in the case of
14
hardware manufacturers, it is considerable in the case of software firms (27.1% and 30%,
resp.). A likely interpretation of this finding is that revealing for these firms is not at odds
with, or may even support, differentiation and competitive advantage. This may be the case
when revealing signals the firm’s competences or when it creates demand for related services
(for which the respective firm is best qualified) or related proprietary software.
The logic of complementary assets as a protection mechanism applies when the code is
specific to some hardware or application software. Net agreement to the statement “is specific
to our hardware” is relatively large (34.5%) from hardware firms; similarly, respondents
working for software firms show a relatively high net agreement (28.6%) to the statement “is
specific to our application software”.
Two responses to the open question what types of developments the respondent’s
company would typically make public add a further perspective to the above statistical
findings. The trade-off between costs and revenues linked to proprietary software is behind
the first quote: [We reveal code] where the cost of support is more then the profit made on
sales. We then give it out without support.” The second quote addresses the protection of
competitive advantage: Anything that will not give our competitors the possibility to copy
our products one to one in a short time. Against this (and other) downside, firms weigh a
number of potential benefits, which are dealt with in the following.
5.4 Reasons to Reveal
Based on the interviews I conducted as well as a literature analysis twelve potential reasons
why firms would reveal their code were identified. Participants were offered these reasons
and were asked to indicate their agreement to the statements “My company reveals code
because [reason x] on a 5-point ordinal scale. Results are shown in Figure 2, for hardware
manufacturers only. This differentiation yields a clearer picture because hardware and
software firms differ considerably with respect to some of these reasons. The focus is laid on
hardware manufacturers since they are more numerous (137 vs. 60) in the sample. I will
address the corresponding results for software firms at the end of this section.
The highest agreement received the statement “My company reveals code because the
GPL requires it.” This was to be expecteddespite ways to circumvent it, the GPL obviously
15
does have a strong effect on revealing behavior.
7
The four reasons following on ranks 2 to 5
received only slightly less net agreement. Three of thembugfixes by others, further
development by others, and reduced maintenance effortare directly related to informal
outside development support, that is, to the technical benefits of the open source process. The
remaining one, on rank 2 (“to appear as a good OSS player”), is indirectly related to that same
aspect, since receiving informal outside support requires to be regarded as a good open source
player (Osterloh et al. 2001, Franck and Jungwirth 2003). Hence, firms do perceive technical
benefits from the open source development process in embedded Linux, and are willing to
share their code with others in order to realize these benefits.
For the reasons on ranks 6 and higher, the level of agreement drops significantly
compared to rank 5. On rank 6, still receiving twice as much agreement as disagreement, a
reason related to marketing appears: “revealing good code improves our company’s technical
reputation.” Further details can be obtained from Figure 2.
An analogous analysis was carried out for software firms. The main difference is that
reasons related to marketing (“revealing good code improves our company’s technical
reputation” and “visibility on the mailing list is good marketing”) rank much higher (ranks 3
and 6, in contrast to ranks 6 and 10 for hardware manufacturers). This can be explained by the
fact that these software firms act as suppliers to hardware manufacturers, performing
commissioned development and/or selling tools or specific distributions of embedded Linux.
In order to check consistency of responses and to construct meaningful indices to be
used in the subsequent analysis (see 5.5), an exploratory factor analysis is carried out. With
four components, it explains 64.6% of total variance and yields good quality measures (KMO:
0.70, p < 0.001). The resulting components can be interpreted as “development support”
(ranks 3, 4, 5, 7, 9 in Figure 2), “reputation” (ranks 2, 6), “marketing” (ranks 10, 11, 12), and
“GPL” (rank 1).
8
7
Given the survey’s definition of “revealing” (see 5.2), the GPL does not require revealing at all, strictly
speaking. Still, the requirement of the GPL to make the code eventually available may constitute a reason to do
so proactively. In any case, this reason being on rank 1 does not affect the findings on the importance of the
other reasons.
8
The factor analysis uses principal component analysis and Varimax rotation. Cronbach’s for the components
development support, reputation, and marketing obtains, respectively, as 0.80, 0.64, and 0.60. Compatibility as a
reason to reveal (rank 8) can not clearly be attributed to any one factor since all factor loadings are below 0.5.
16
5.5 Multivariate Analysis of Revealing Behavior
The descriptive analysis of the amount of revealed code showed high heterogeneity between
firms (Table 2). In the following, I present a multivariate analysis that aims at explaining this
heterogeneity by firm characteristics. Due to missing values mainly in the dependent variable,
the number of observations is reduced from 197 (number of commercial respondents) to 119.
9
Table 3 shows descriptive statistics of the explanatory variables, Table 4 the correlation
matrix.
10
As to the type of firm, device manufacturers are taken as the reference group since
they exhibit the lowest average share of revealed code (see Table 2).
Since the share of revealed code is restricted to the interval [0%,100%], a Tobit model is
the natural choice.
11
In order to check robustness of results with respect to the model
specification, I additionally use an Ordered Probit model. In this model, the dependent
variable lies in the set {1, …, 5} where “1” indicates that the share lies in the first quintile
(0% 20%), “2” codes “21% 40%” etc. This model allows for a non-linear dependence of
the share of revealed code on the explanatory variables inside the interval [0%,100%].
Table 5 shows the regression outcome. Specifications (5) through (8) contain, in
contrast to (1) (4), indices “Reason_XY” (based on the factor analysis performed in Section
5.4) which indicate how important the respective group of reasons is for revealing.
Specifications (1), (2), (5), and (6) employ a Tobit model, while (3), (4), (7), and (8) are based
on an Ordered Probit estimation. Finally, specifications with even numbers are obtained by
successive elimination of insignificant variables. In all cases, the hypothesis that the
Factor analysis for software and hardware firms separately yields slightly different components, which mirrors
the specificities of the two groups. Only the pooled analysis is reported since its output will be used in 5.5.
9
Missing values in the variables related to reasons for revealing (see Figure 2) would reduce this number further
to 105. In order to avoid this loss, missing values were estimated using the impute command in Stata 9 for those
observations in which no more than four out of twelve values were missing.
10
One might propose to use also the type of code that a firm typically reveals (see 5.3) as an explanatory
variable. However, as a second dimension of revealing behavior (in addition to the extent of revealing) it would
rather have to be treated as a dependent variable in another regression, which is beyond the scope of this paper.
11
In contrast to an OLS regression, a Tobit model accounts for the censoring of the dependent variable. In the
present case this means that the share of revealed code can not be less than 0%, nor larger than 100%.
17
eliminated variables are also jointly equal to zero can not be rejected.
12
Comparing the
columns of Table 5 one finds that signs and significances are, with a few exceptions regarding
significance levels, consistent across the various specifications.
13
Firm size: The regression results confirm Hypothesis H1. The coefficient for the dummy
variable “SizeLarge” (indicating that the firm has more than 200 employees) is significantly
negative in all specifications. This finding is consistent with the high level of agreement
(90.0% of N=185) that the statement “Open source software allows small enterprises to afford
innovation” received. The dummy variable “SizeMedium” coding medium sized firms (11
200 employees) is insignificant; obviously, this group is not different enough from the
reference group (1 10 employees).
Firm policies: Two dummy variables capture the effects firm policies might have on
revealing: “PolicyEncouraging” equals 1 if the respondent ticked the statement My company
encourages me to contribute source code that is not critical for competition and
“PolicyRestrictive” equals 1 if the person agreed to “My company is very restrictive in
revealing code”. While “PolicyEncourages” does not exhibit the expected positive effect (thus
not confirming H2), “PolRestrictive” carries a significantly negative coefficient in
specifications (1) through (4). Its significance disappears when, in specifications (5) through
(8), reasons to reveal are introduced as explanatory variables. This is a plausible finding since,
as the correlation matrix (Table 4) confirms, policies towards revealing should result from the
importance attached to various reasons to reveal. H3 is thus partly supported.
Complementary assets: Since the descriptive analysis (Table 2) revealed that device
manufacturers make, on average, less code public than the other two commercial groups, they
were chosen as the reference group. This already suggests what the regression clearly shows,
namely, that H4 can not be fully confirmed. Despite the hypothesized protection by a
complementary asset (the device), device manufacturers reveal, in all specifications,
12
A Likelihood Ratio test for the Tobit regressions yields F(2,111) = 0.12 (p = 0.889) for specifications (1)/(2)
and F(5,107) = 0.45 (p = 0.812) for specifications (5)/(6). A Wald test for the Ordered Probit regressions yields
χ2(2) = 0.12 (p = 0.944) for specifications (3)/(4) and χ2(5) = 3.27 (p = 0.658) for specifications (7)/(8).
13
The pseudo R2 value seems rather low for all specifications. However, a pseudo R2 can not be interpreted as an
R2 in an OLS regression. To give an idea of the size of the explained variance, a standard OLS regression of
specification 5 (leading to roughly the same coefficients and significances as the Tobit model) yields an R2 of
0.27. This still leaves much variance unexplainedas expectedbut is a much more reasonable value than 0.02.
18
significantly less than software firms. This may be due to the fact that hardware
manufacturers are more entrenched in a proprietary culture than software firms (especially
those that specialize on OSS), and/or that their hardware does in fact not constitute a
sufficiently protective complementary asset.
14
In contrast, comparing software firms to
component manufacturers does lend some support to H4. Across all specifications, the
estimated coefficient of TypeCompMan is roughly 1.5 times as large as that of TypeSWFirm.
However, the hypothesis of equality between the two coefficients cannot be rejected (for all
specifications). H4 is confirmed, however, insofar as component manufacturers reveal
significantly more than device manufacturers (the reference group). In addition to the higher
specificity of their complementary assets (as conjectured), also demand from component
buyers for driver source code may serve to explain this finding.
Familiarity with embedded Linux: The variable CompYearsEL, capturing a firm’s
experience in developing embedded Linux, carries a significant (5% level) and positive
coefficient in all specifications. H5 is thus confirmed.
Company age when it started embedded Linux: The variable AgeAtStart is positive and
significant (on the 5% resp. 10% level) in all specifications. Hence, of the two counteracting
effects discussed above the argument related to accumulation of complementary assets seems
to dominate.
Reasons to reveal: Of the four variables relating to reasons for revealing, those related to
marketing and to the GPL do not show a significant effect. H6c and H6d can thus not be
confirmed. A plausible interpretation is that firms for which marketing is more important as a
reason to reveal may contribute code in a more visible way, but not in higher quantity.
Similarly, firms that consider the GPL’s stipulations as important reasons for revealing may in
fact be quite reluctant to make their code public, and only do so when indeed they feel forced
to. In contrast, the variables Reason_Development and Reason_Reputation carry positive
coefficients in all specifications (with one exception), significant on the 5% resp. 10% level.
14
In fact, a device manufacturer might combine commodity hardware with specifically developed software,
based on OSS, to create a highly differentiated good. In one of my interviews, the interviewee described the case
of a device manufacturer who had developed a board for bundling 48 UMTS channels. All elements of the board
were commodities. In contrast, the driver software contained important intellectual property, in particular
proprietary protocols. Had it been publicly revealed as OSS, competitors could easily have copied the device. In
such a case, the hardware, as a complementary asset, obviously affords little protection to the software.
19
The effect sizes of both are large, and nearly identical. For illustration, an increase of 1 in the
value of Reason_Development (e.g., a change from “agree somewhat” to “agree strongly” in
the answers to the reasons that make up this index, see 5.4) is predicted to result in an increase
of the share of revealed code of 8.8% (specification 6). H6a and H6b are thus confirmed.
To summarize, the regression analysis provides a lucent picture of firms’ revealing
behavior. In particular, confirmed Hypotheses H1 and H6a underline the importance of
external development support as a motivator to reveal one’s own code.
6 Conclusion
There is a long-standing debate on how profits from innovations can best be appropriated.
15
This discussion usually makes an implicit a priori: It presupposes that exclusivity is desirable
for the innovator. It thus focuses on the protection of innovations, while what actually matters
is appropriation of profits from innovation. The two often go along with each otherbut
there are important exceptions. As the rise of OSS has impressively shown, freely revealing
one’s developments may be a sensible thing to do, in particular when community-based
development is viable (von Hippel 2001). This is favored when, as for embedded Linux,
needs are strongly heterogeneous (cf. Bessen 2001, Franke and von Hippel 2003, Henkel
2004b) and the underlying technology is highly modular (Baldwin and Clark 2003,
MacCormack et al. 2004).
On the other hand, developing OSS is often considered to imply free revealing. As I
point out in this paper, also this view is not correct: Commercial OSS development, even if
based on GPL’ed software, perfectly well accommodates a combination of free revealing and
various means of protecting one’s code. Firms thus have the chance to practice selective
revealing.
A case where such combination of revealing and protection is common has been
analyzed in this paper, namely, embedded Linux. Firms in the sample routinely use various
means of protection that are consistent with the GPL. Among them are restricted or delayed
revealing and the use of “binary-only” drivers. As a result firms reveal, on average, about half
of the code they have developed for embedded Linux, while protecting the other half.
15
See, e.g., Arundel (2001), Cohen et al. (2000), Cohen et al. (2002), Gallini and Scotchmer (2002), Harabi
(1995), Horstmann et al. (1985), Levin et al. (1987), and Sattler (2003).
20
Between firms, revealing behavior is strongly heterogeneous, which can partly be
explained by firm characteristics. Among other things I find that the amount of revealed code
ceteris paribus is larger for smaller firms, which likely benefit more from external
development support. It is also higher for component manufacturers, likely because these
firms enjoy a good protection by proprietary complementary assets (the components they
sell). Finally, experience with OSS matters: the longer a firm has been using embedded Linux
and the higher hence its familiarity with the OSS development process, the more it reveals.
Among the reasons to reveal, informal outside development support in the form of
bugfixes, code improvement, and code maintenance figures prominently, and turns out to be a
significant driver of revealing. Hence, firms indeed seem to derive technical benefits from the
open source development process, increasing efficiency and effectiveness of embedded
software development. Thus, even in this mainly commercial environment, the private-
collective model of innovation described by von Hippel and von Krogh (2003) is found to
work.
A point that merits discussion and further research is the role played by the individual
developer. One might conjecture that revealing by firms is in fact driven by OSS enthusiasm
of the programmerpossibly even against his employer’s interests. However, neither the
survey nor my interviews support this view. Employed programmers are somewhat
enthusiastic about OSS, but significantly less so than hobbyists and university programmers.
While a detailed discussion is beyond the scope of this paper, there are indications that
programmers do often act as “champions of revealing”, but not as uncontrollable OSS
enthusiasts disregarding their firm’s interest.
Open innovation processes as observed in the field of embedded Linux carry a
considerable potential for efficiency gains (Foray 2004, Henkel and von Hippel 2005), in a
similar way as open science supports the accumulation of knowledge (David 2005). They
allow to reduce duplication of effort and to avoid transaction costs of commercial licensing.
Embedded Linux being OSS and being licensed under the GPL certainly helped to spawn the
observed open innovation process. However, the fact that firms are far more open than they
are obliged to by the license shows that voluntary revealing takes place. Hence, open
innovation as observed here should just as well be feasible for software other than OSS, and
for goods other than software. The main obstacle seems to be that most firms are entrenched
in a proprietary attitude and unfamiliar with openness. As one respondent phrased it,
[revealing code] is new territory for this company. The spirit is willing, but the legal staff is
21
weak. This paper suggests that this attitude needs rethinking. Firms can benefit from open
innovation by striking the right balance between sharing and protection.
22
References
Aldrich, H., E. Auster, 1986. Even dwarfs started small: Liabilities of age and size and their
strategic implications. In: L. Cummings, B. Staw (eds.) Research in Organizational
Behaviour. San Francisco, CA: JAI Press. 165-189.
Allen, R. C., 1983. Collective Invention. Journal of Economic Behaviour and Organization 4,
1-24.
Armstrong, J. S., T. S. Overton, 1977. Estimating nonresponse bias in mail surveys. Journal
of Marketing Research 14, 396-402.
Arora, A., A. Fosfuri, A. Gambardella, 2001. Markets for technology: The economics of
innovation and corporate strategy. Cambridge, MA: MIT Press.
Arundel, A., 2001. The relative effectiveness of patents and secrecy for appropriation.
Research Policy 30(4), 611624.
Baldwin, C. Y., K. B. Clark, 2003. The architecture of cooperation: how code architecture
mitigates free riding in the open source development model. Working Paper, Harvard
Business School.
Behlendorf, B., 1999. Open source as a business strategy. C. Dibona, S. Ockman, M. Stone,
eds. Open-sources: Voices from the open source revolution. Sebastopol, CA: O’Reilly,
149-170.
Bessen, J., 2001. Open source software: free provision of complex public goods. Working
Paper 5/01. http://www.researchoninnovation.org/opensrc.pdf.
Bonaccorsi, A., C. Rossi, 2004. Comparing motivations of individual programmers and firms
to take part in the open source movement. From community to business. Working paper
Sant’Anna School of Advanced Studies Pisa.
Bouty, I., 2000. Interpersonal and interaction influences on informal resource exchanges
between R&D researchers across organizational boundaries. Academy of Management
Journal 43(1), 50-65.
Chesbrough, H., 2003. Open Innovation: The New Imperative for Creating and Profiting from
Technology. Boston, MA: Harvard Business School Press.
23
Cohen, W. M., A. Goto, A. Nagata, R. R. Nelson, J. P. Walsh, 2002. R&D spillovers, patents
and the incentives to innovate in Japan and the U.S. Research Policy 31(8-9), 1349
1367.
Cohen, W. M., R. R. Nelson, J. P. Walsh, 2000. Protecting their intellectual assets:
appropriability conditions and why U.S. manufacturing firms patent (or not). NBER
Working Paper No. w7552.
Cowan, R., N. Jonard, 2003. The dynamics of collective invention. Journal of Economic
Behavior and Organization 52(4), 513-532.
Dahl, M. S., C. O. R. Pedersen, 2004. Knowledge flows through informal contacts in
industrial clusters: myth or reality? Research Policy 33(10), 1673-1686.
Dahlander, L., M. G. Magnusson, 2005. Relationships between open source software
companies and communities: Observations from Nordic firms. Research Policy 34(4),
481-493.
David, P. A. 2005. The economic logic of “open science” and the balance between private
property rights and the public domain in scientific data and information: A primer.
SIEPR Discussion Paper No. 02-30.
Fauchart, E., 2003. On knowledge sharing patterns among rival firms: The case of knowledge
on safety. Working Paper. http://userinnovation.mit.edu/papers/safety3.pdf.
Feller, J., B. Fitzgerald, 2002. Understanding Open Source software development. Boston,
MA: Addison Wesley.
Foray, D., 2004. Economics of Knowledge. Cambridge, MA: MIT Press.
Franck, E., C. Jungwirth, 2003. Reconciling investors and donatorsThe governance
structure of open source. Journal of Management and Governance 7, 401-421.
Franke, N., S. Shah, 2003. How communities support innovative activities: An exploration of
assistance and sharing among end-users. Research Policy 32(1), 157-178.
Franke, N., E. von Hippel, 2003. Satisfying heterogeneous user needs via innovation toolkits:
The case of Apache security software. Research Policy 32(7), 1199-1215.
Gallini, N. T., S. Scotchmer, 2002. Intellectual Property: When is it the best incentive
system?, in: A. Jaffe, J. Lerner, S. Stern (eds.), Innovation policy and the economy, vol.
2 , Cambridge, MA: MIT Press.
24
Ganssle, J., M. Barr, 2003. Embedded Systems Dictionary. Lawrence, KS: CMP Books.
Gruber, M., J. Henkel, 2005. New ventures based on open innovationan empirical analysis
of start-up firms in embedded Linux. International Journal of Technology Management,
accepted for publication.
Harabi, N., 1995. Appropriability of technical innovations an empirical analysis. Research
Policy 24(6), 981992.
Harhoff, D., J. Henkel, E. von Hippel, 2003. Profiting from voluntary information spillovers:
How users benefit by freely revealing their innovations. Research Policy 32, 1753
1769.
Hecker, F., 1999. Setting up shop: The business of open-source software. IEEE Software
16(1), 45-51.
Henkel, J., 2003. Open-Source-Aktivitäten von UnternehmenInnovation in kollektiven
Prozessen. Unpublished Habilitation thesis, University of Munich.
Henkel, J., 2004a. Open source software from commercial firms tools, complements, and
collective invention. Zeitschrift für Betriebswirtschaft, Supplement 4, 1-23.
Henkel, J., 2004b. The jukebox mode of innovation a model of commercial open source
development. CEPR Discussion Paper 4507.
Henkel, J., E. von Hippel, 2005. Welfare implications of user innovation. Journal of
Technology Transfer 30(1/2), 73-87.
Hertel, G., S. Niedner, S. Herrmann, 2003. Motivation of software developers in open source
projects: An Internet-based survey of contributors to the Linux kernel. Research Policy
32(7), 1159-1177.
Horstmann, I., G. M. MacDonald, A. Slivinski, 1985. Patents as information transfer mecha-
nisms: To patent or (maybe) not to patent. Journal of Political Economy 93(5), 837-858.
Lakhani, K. R., R. G. Wolf, 2005. Why hackers do what they do: Understanding motivation
and effort in free/open source software projects, in: J. Feller, B. Fitzgerald, S. Hissam,
and K. R. Lakhani (eds.), Perspectives on Free and Open Source Software. Cambridge,
MA: MIT Press.
Lamoreaux, N. R., K. L. Sokoloff, 2000. The geography of invention in the American glass
industry. Journal of Economic History 60(3), 700729.
25
Lerner, J., J. Tirole, 2002. Some simple economics of open source. Journal of Industrial
Economics 50(2), 197234.
Lerner, J., J. Tirole, 2005. The scope of open source licensing. Journal of Law, Economics
and Organziation 21, 20-56.
Levin, R. C., A. Klevorick, R. R. Nelson, S. G. Winter, 1987. Appropriating the returns from
industrial research and development. Brookings Papers on Economic Activity (3), 783
820.
MacCormack, A., J. Rusnak, C. Baldwin, 2004. Exploring the structure of complex software
designs: An empirical study of open source and proprietary code. Harvard Business
School Working Paper 05-016.
Marti, D., 2002. Use binary-only kernel modules, hate life. LinuxJournal.com, 06/19/2002.
http://www.linuxjournal.com/article.php?sid=6152.
McGaw, A., 1987. Most wonderful machine: Mechanization and social change in Berkshire
paper making, 1801-1885. Princeton, NJ: Princeton University Press.
Meyer, P. B., 2003. Episodes of collective invention. Working Paper 368, Bureau of Labor
Statistics, U.S. Department of Labor, http://www.bls.gov/ore/pdf/ec030050.pdf.
Morrison, P. D., J. H. Roberts, E. von Hippel, 2000. Determinants of user innovation and
innovation sharing in a local market. Management Science 46(12), 1513-1527.
Nuvolari, A., 2001a. Collective Invention during the British industrial revolution: The case of
the Cornish pumping engine. DRUID Working Papers 01-05, Copenhagen Business
School.
Nuvolari, A., 2001b. Open source software development: Some historical perspectives.
Working Paper, http://opensource.mit.edu/papers/nuvolari.pdf.
Osterloh, M., S. G. Rota, 2004. Open source software development Just another case of
collective invention? Working paper, http://papers.ssrn.com/sol3/Delivery.cfm/
SSRN_ID561744_code383702.pdf?abstractid=561744&mirid=1.
Osterloh, M., S. Rota, M. von Wartburg, 2001. Open source New rules in software
development. Working Paper Institute for Research in Business Administration,
University of Zurich. http://www.ifbf.unizh.ch/orga/downloads/OpenSourceAoM.pdf.
26
Rauscher, T. G., P. G. Smith, 1995. Time-driven development of software in manufactured
goods. Journal of Product Innovation Management 12(3), 186-199.
Raymond, E. S., 1999. The cathedral and the bazaar: Musings on Linux and open source by
an accidental revolutionary. Sebastopol, CA: O’Reilly.
Russo, M., 1985. Technical change and the industrial district: The role of interfirm relations
in the growth and transformation of ceramic tile production in Italy. Research Policy
14(6), 329343.
Sattler, H., 2003. Appropriability of product innovations: An empirical analysis for Germany.
International Journal of Technology Management 26(5-6), 502516.
Schrader, S., 1991. Informal technology transfer between firms: Cooperation through
information trading. Research Policy 20(2), 153170.
Teece, D. J., 1986. Profiting from technological innovation: Implications for integration,
collaboration, licensing and public policy. Research Policy 15(6), 285305.
VDC., 2004. White paper, Venture Development Corporation. Referenced in: Linux now top
choice of embedded developers. LinuxDevices.com.
http://www.linuxdevices.com/news/ NS2744182736.html.
von Hippel, E., 1987. Cooperation between rivals: informal know-how trading. Research
Policy 16, 291302.
von Hippel, E., 1988. The Sources of Innovation. New York: Oxford University Press.
von Hippel, E., 2001. Innovation by user communities: Learning from open source software.
MIT Sloan Management Review 42 (Summer), 8286.
von Hippel, E., G. von Krogh, 2003. Open source software and the “Private-Collective”
innovation model: Issues for organization science. Organization Science 14(2), 209
223.
Webb, W., 2002. Pick and Place: Linux grabs the embedded market. edn.com.
http://www.reed-electronics.com/ednmag/contents/images/253780.pdf.
West, J., 2003. How open is open enough? Melding proprietary and open source platform
strategies. Research Policy 32(7), 1259-1285.
27
Wichmann, T., 2002. FLOSS final report part 2: Free/Libre open source software: Survey
and study firms’ open source activities: Motivations and policy implications. Berlecon
Research. http://www.berlecon.de/studien/downloads/200207FLOSS Activities.pdf.
28
Appendix
Table 1: Frequency of use of various means to protect code
Means of protection
al-
ways
often
never
mis-
sing
Revealing only to customers*
7.0%
14.0%
41.9%
17
Revealing only on request of
device buyers
5.8%
22.5%
38.4%
59
Revealing only after delay
2.8%
11.4%
44.7%
56
Loadable binary modules
16.1%
19.5%
36.2%
48
* This means of protection applies to software vendors only. Observations with missing data
or with a reply “don’t know” are not included in the calculation of percentages.
Table 2: Share of code that is revealed
Type of organization
Mean
Median
St.
dev.
Min.
N
mis-
sing
Software company
57.5%
60%
35.9%
1%
39
21
Device manufacturer
42.3%
25%
36.3%
1%
69
45
Component manufacturer
58.8%
60%
40.2%
1%
17
6
Commercial, all
49.3%
50%
37.3%
1%
125
72
University / non-profit research org.
90.0%
100%
21.0%
30%
20
10
Hobbyist
93.3%
100%
19.8%
25%
31
10
Non-commercial, all
92.0%
100%
20.1%
25%
51
20
ALL
61.7%
75%
38.4%
1%
176
92
29
Table 3: Descriptive statistics of explanatory variables used in Table 5 (N = 119)
Variable
Dummy variable, equal to 1 if …
Frequency of “0”
Frequency of “1”
SizeMedium
firm has 11 to 200 employees
75
(63%)
44
(37%)
SizeLarge
firm has more than 200 employees
83
(69.7%)
36
(30.3%)
PolicyEncouraging
firm has encouraging policy towards revealing OSS code
81
(68.1%)
38
(31.9%)
PolicyRestrictive
firm has restrictive policy towards revealing OSS code
108
(90.8%)
11
(9.2%)
TypeSWFirm
firm is a software firm
81
(68.1%)
38
(31.9%)
TypeCompMan
firm is a component manufacturer
103
(86.6%)
16
(13.4%)
AgeAtStart
the difference between the company’s age and its
experience with Embedded Linux is above the median
56
(47.1%)
63
(52.9%)
Variable
Explanation
Minimum
Maximum
Median
Mean
St. Dev.
CompYearsEL
Number of years firm has experience developing
embedded Linux
1
10
4
3.647
1.911
Reason_
Development
Importance (-2 … 2) of reasons to reveal code related
… to development
-1.93
2
0.8
0.728
0.821
Reason_Marketing
… to marketing
-2
2
0
0.059
0.848
Reason_Reputation
… to reputation
-2
2
1
0.776
0.926
Reason_GPL
… to GPL (i.e., the license requirements)
-2
2
1
1.149
0.938
30
Table 4: Correlation matrix of explanatory variables (N = 119). Only correlations with p < 0.1 are shown.
Size
Medium
Size
Large
Policy
Encouraging
Policy
Restrictive
Type
SWFirm
Type
CompMan
Comp
YearsEL
AgeAtStart
Reason
Development
Reason
Marketing
Reason
Reputation
Reason
GPL
SizeMedium
1.000
SizeLarge
not
1.000
meaningful
PolicyEncouraging
-0.176*
1.000
(0.055)
PolicyRestrictive
0.295***
-0.156*
1.000
(0.001)
(0.090)
TypeSWFirm
-0.176*
0.188**
1.000
(0.055)
(0.041)
TypeCompMan
0.223**
-0.270***
1.000
(0.015)
(0.003)
CompYearsEL
0.165*
1.000
(0.073)
AgeAtStart
0.584***
-0.257***
0.185**
-0.365***
0.322***
1.000
(0.000)
(0.005)
(0.044)
(0.000)
(0.001)
Reason_Development
-0.157*
0.240***
-0.317***
1.000
(0.088)
(0.009)
(0.001)
Reason_Marketing
-0.238***
0.173*
-0.251***
0.223**
-0.213**
0.358***
1.000
(0.009)
(0.060)
(0.006)
(0.015)
(0.020)
(0.000)
Reason_Reputation
-0.299***
-0.206**
-0.211**
0.335***
0.556***
1.000
(0.001)
(0.025)
(0.022)
(0.000)
(0.000)
Reason_GPL
1.000
* significant at 10%; ** significant at 5%; *** significant at 1%; significance level p in parentheses
31
Table 5: Multivariate analysis of share of code that firms reveal
Tobit
Ordered Probit
Tobit
Ordered Probit
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
SizeMedium
-3.256
-0.031
-2.681
-0.008
(9.461)
(0.253)
(9.025)
(0.253)
SizeLarge
-29.780**
-27.139***
-0.965**
-0.936***
-22.692*
-22.257**
-0.810**
-0.860**
(12.487)
(10.028)
(0.390)
(0.337)
(12.016)
(9.630)
(0.402)
(0.340)
PolicyEncouraging
-2.664
-0.077
-6.376
-0.149
(8.113)
(0.243)
(7.937)
(0.247)
PolicyRestrictive
-23.833*
-23.433*
-0.923**
-0.903**
-13.732
-0.645
(12.871)
(12.787)
(0.381)
(0.377)
(12.738)
(0.423)
TypeSWFirm
19.050**
19.259**
0.518**
0.514**
18.989**
18.777**
0.552**
0.501**
(8.735)
(8.621)
(0.248)
(0.245)
(8.482)
(8.262)
(0.245)
(0.243)
TypeCompMan
29.113**
28.880**
0.759**
0.754**
25.396**
25.555**
0.669*
0.652*
(11.669)
(11.649)
(0.346)
(0.348)
(11.347)
(11.318)
(0.350)
(0.338)
CompYearsEL
4.233**
4.052**
0.143**
0.139**
3.883**
3.391*
0.135**
0.121**
(2.001)
(1.965)
(0.061)
(0.061)
(1.940)
(1.903)
(0.061)
(0.059)
AgeAtStart
19.543*
19.006*
0.549*
0.553**
18.908*
19.171**
0.572*
0.588**
(10.258)
(9.664)
(0.293)
(0.279)
(9.840)
(9.300)
(0.305)
(0.286)
Reason_Development
8.605*
8.787*
0.257
0.264*
(4.797)
(4.473)
(0.166)
(0.150)
Reason_Reputation
10.042**
9.985**
0.305**
0.278*
(4.659)
(4.209)
(0.149)
(0.147)
Reason_Marketing
-0.998
-0.087
(5.186)
(0.163)
Reason_GPL
1.848
0.063
(3.757)
(0.124)
Constant
31.030***
29.029***
14.864
14.093
(9.827)
(8.891)
(12.003)
(9.344)
Observations
119
119
119
119
119
119
119
119
Pseudo R-squared
0.02
0.02
0.082
0.082
0.03
0.03
0.108
0.100
Likelihood ratio (Tobit) /
Wald test (Probit)
2(8)=25.4
p=0.0013
2(6)=25.2
p=0.0003
2(8)=25.6
p=0.0012
2(6)=25.4
p=0.0003
2(12)=37.2
p=0.0002
2(7)=34.9
p<0.0001
2(12)=43.3
p<0.0001
2(7)=28.1
p=0.0002
σ (Tobit) / cuts (Probit)
38.40
38.41
0.30, 0.58, 0.92, 1.33
0.33, 0.61, 0.95, 1.37
36.38
36.71
0.81, 1.10, 1.47, 1.91
0.76, 1.05, 1.41, 1.84
* significant at 10%; ** significant at 5%; *** significant at 1%; standard errors in parentheses (for Ordered Probit, robust standard errors)
32
Figure 1: Type of code that is revealed
Note: Shares of agreement and disagreement shown for software and hardware firms (see last
column). The number of valid responses varies between 48 and 50 for software firms and
between 109 and 113 for hardware firms. Responses “neither agree nor disagree” are not
shown.
40% 20% 0% 20% 40% 60% 80%
The embedded Linux
code that my company
typically reveals ...
... is generic; many other
companies can use it.
... is important for our
competitive position.
... helps to differentiate our
product from others.
... is specific to our
hardware.
... is specific to our
application software
HW
SW
HW
SW
HW
SW
HW
SW
HW
Share disagreement Share agreement
disagree strongly disagree somewhat agree somewhat agree strongly
SW
40% 20% 0% 20% 40% 60% 80%
The embedded Linux
code that my company
typically reveals ...
... is generic; many other
companies can use it.
... is important for our
competitive position.
... helps to differentiate our
product from others.
... is specific to our
hardware.
... is specific to our
application software
HW
SW
HW
SW
HW
SW
HW
SW
HW
Share disagreement Share agreement
disagree strongly disagree somewhat agree somewhat agree strongly
SW
33
Figure 2: Share of respondents working for hardware manufacturers (N = 137) that agree /
disagree to various reasons to reveal
Note: The number of valid responses varies between 105 and 113. The remainder is either
missing or the answer was “don’t know”. The share of respondents answering “neither agree
nor disagree” is not shown.
0% 40% 80%40%
1. the GPL
requires it
2. we want to appear as a good player in
the open source community
3. other developers make bugfixes
and reveal them
4. others develop the code further and
reveal their developments in turn
5. it reduces our maintenance effort when code
becomes part of the standard distribution
6. revealing good code improves
our company's technical reputation
7. others add functionality
that we did not anticipate
8. this way, our products stay compatible
to other products
9. we often do not have sufficient resources
to make developments on our own
10. visibility on the mailing list
is good marketing
11. our own development should fast become
the standard (i.e. be widely adopted)
12. we identify potential employees
by looking at suggestions on our code
My company reveals code because ... Share disagreement Share agreement
0% 40% 80%40%
1. the GPL
requires it
2. we want to appear as a good player in
the open source community
3. other developers make bugfixes
and reveal them
4. others develop the code further and
reveal their developments in turn
5. it reduces our maintenance effort when code
becomes part of the standard distribution
6. revealing good code improves
our company's technical reputation
7. others add functionality
that we did not anticipate
8. this way, our products stay compatible
to other products
9. we often do not have sufficient resources
to make developments on our own
10. visibility on the mailing list
is good marketing
11. our own development should fast become
the standard (i.e. be widely adopted)
12. we identify potential employees
by looking at suggestions on our code
My company reveals code because ... Share disagreement Share agreement
0% 40% 80%40%
1. the GPL
requires it
2. we want to appear as a good player in
the open source community
3. other developers make bugfixes
and reveal them
4. others develop the code further and
reveal their developments in turn
5. it reduces our maintenance effort when code
becomes part of the standard distribution
6. revealing good code improves
our company's technical reputation
7. others add functionality
that we did not anticipate
8. this way, our products stay compatible
to other products
9. we often do not have sufficient resources
to make developments on our own
10. visibility on the mailing list
is good marketing
11. our own development should fast become
the standard (i.e. be widely adopted)
12. we identify potential employees
by looking at suggestions on our code
My company reveals code because ... Share disagreement Share agreement
34
Acknowledgements
I am grateful to Carliss Baldwin, Paul David, Marc Gruber, Dietmar Harhoff, Georg von
Graevenitz, Eric von Hippel, two anonymous reviewers and to participants at the Workshop
on User Innovation and Open Source Software in Munich, the OWLS workshop in Oxford,
and a lunch seminar at Harvard Business School for helpful comments and discussions. I
thank Mark Tins for valuable help in data collection. All remaining errors are mine.
... For a successful dominant logic, a platform owner must meet an optimised balance between appropriability and adoption (Gawer and Cusumano, 2014b, Ondrus et al., 2015, West, 2003. A platform owner must have a clear dominant design; however, at the same time, it must protect the core part of the technology secret to maximise economic benefits and not lose its influence and leadership in the platform-based ecosystem (West, 2003, Hein et al., 2019, Loux et al., 2020, Henkel, 2006. When a platform firm use out-bound OI, it can examine the potential of disruptive technology via external partners or consolidate its platform dominance by embracing them. ...
... First, out-bound OI not only helps firms to bring ideas to markets by sharing knowledge with external partners but also supports both value co-creation processes and market exploitation (Enkel et al., 2009, Henkel, 2006, Henkel et al., 2013. Moreover, it can ease the process through which a platform owner attracts external complementors. ...
... To address this paradox of openness, an optimised equilibrium point that simultaneously addresses an appropriation issue and a revealing dilemma is critical (Kim and Ahn, 2019). As noted by Laursen and Salter (2006), Laursen and Salter (2014), extreme openness is detrimental not only to innovation performance but also for knowledge appropriation (Henkel, 2006). Admittedly, various appropriation schemes, such as intellectual property (IP), cross-licensing, and informal approaches (e.g. ...
Article
Full-text available
Technology advancements are underpinning firms in shaping their products and services into digital platforms to foster value co-creation in their platform-based ecosystems. While existing research has mainly focused on business-to-customer (B2C) platforms, relatively little research has been conducted on business-to-business (B2B) platforms. To address this research gap, this study employs a case study approach to collect and examine three out-bound open innovation (OI) application cases in the context of B2B platforms, namely, TSMC, IBM and CNT Tech. The case analysis results show that the coverage of B2B markets can be expanded and diversified by OI. To improve the quality of platform offerings (not only platform services but also complementary innovations), the case firms implemented OI applications comprising two phases that manage knowledge outflows (with boundary resources) and inflows (with input/output controls) across their organisational boundaries. Knowledge sharing provided a B2B platform owner new market creation opportunities, and complementors combined and pivoted some of the platform owners’ core technologies, consequently diversifying the platforms’ applications and making platform ecosystem more dynamic and vibrant.
... al., 2014). Despite the growth of mass market production, user-innovation continues to occur in many fieldsincluding scientific equipment (von Hippel, 1988), sporting goods (Franke and Shah, 2003;Luthje et al., 2005;Tietz et al., 2005;Raasch et al., 2008), consumer goods (Luthje, 2004), and software (Urban and von Hippel, 1988;Franke and von Hippel, 2003;Henkel, 2006). Moreover, there are cases demonstrating how Do-It-Yourself (DIY) products open new market niches (Shah, 2006;Fox, 2013) and brings public into industry (Lee, 2014;Ng et. ...
... The software is then made available through Peer-to-Peer Free Diffusion via a website and one or more servers. In some cases, producers get involved either by contributing to the open-source software making it more suitable for their internal use (Innovation Support arrow) or by identifying a related service such as training for which they can appropriate value and make a profit (Lerner and Tirole, 2002;Henkel, 2006;Stewart et al., 2006;West and Gallagher, 2006;Dahlander and Magnusson, 2008). The for-profit pathway initiates with the downward arrow Innovation Design and proceeds through Production and Market Diffusion. ...
Article
The paper introduces a conceptual approach explaining how end users, user communities and /or for-profit firms provide benefits to society through new product or service development. We show that innovation may occur in different economic environments including non-market ones as well as that social innovation will not occur on its own in a purely producer for-profit environment. To explain such cases, we suggest integrating product and user innovation paradigms into the Producer-User Social Innovation (PUSI) Model that demonstrates how infrastructure and enabling technology is provided either by producer or user to introduce new market product or service. To provide face validity and illustrate the versatility of the proposed approach we consider five very different cases. These illustrative examples allowed to provide evidence that user-driven innovation is socially oriented in its nature as it implicitly addresses community or societal needs. In addition to providing insights into the nature of social innovation, the model can be utilized to help understand why social innovation may fail and how to increase the likelihood of success by engaging with appropriate for-profit producers, communities, and users. Implications to policy and practice are provided, including the opportunity for government to encourage social innovation directly and indirectly.
... Thus, intermediaries have entered the online space by structuring knowledge to identify providers who can provide solutions beyond the immediate exigencies of the problem and helps provider selection among many potential matches (Ye et al., 2012;Dong and Pourmohamadi, 2014). In open innovation for a, they can also selectively reveal information as trusted parties (Henkel, 2006;Alexy et al., 2013;Henkel et al., 2014) and therefore help in supporting collaboration innovation in a non-partisan way. ...
... Intermediaries have also emerged in other roles associated with the design, implementation and 'after-contest' provision of services to both winners and losers. Among new roles, the study revealed intermediaries as being trusted, third party 'revealers', expanding previous knowledge as noted earlier on how firms 'selectively reveal' information in open innovation (Henkel, 2006;Alexy et al., 2013;Henkel et al., 2014), although in a wider intermediary context such neutrality may be altered by regulatory or institutional changes (De Beer and Clemmer, 2009). ...
Article
Full-text available
The aim of this paper is to explore how innovation search is conceptualised, given that firms increasingly use innovation intermediaries. The paper examines the search processes which involves the role of innovation intermediaries in different stages of the innovation search process. The study discovered that innovation search activity is a much more extended and complex process, not being as targeted or as specific than previously conceptualised, and involves a set of search stages, which are associated with a loosely coupled iterative search process. Innovation intermediaries were also discovered to be undertaking new, more extended roles in the search process, through, for example, combining new search procedures with online digital platforms.
... To boost a company's innovation capabilities, the in-and outflow of knowledge as part of open innovation initiatives became not only accepted but encouraged by industry leaders (Chesbrough 2017). Open and sharing initiatives soon extended to other objects such as source code (Henkel 2006), which enabled collaboration in digital ecosystems and led to new forms of value creation (Schlagwein et al. 2017). ...
Conference Paper
Full-text available
Increasing competitive pressure and digitalization demand organizations to rethink their approaches to collaboration and innovation. By permitting organizational boundaries to become permeable, firms have started to allow the in-and outflow of knowledge to enable new forms of value creation. This also applies to data as a form of knowledge. Yet, firms still struggle to systematically evaluate the benefits of engaging in open data initiatives. Hence, a comprehensive and empirically grounded model is needed. Therefore, we apply an exploratory research approach by triangulating insights from a literature review, expert interviews, and multiple case studies to derive three clusters of benefit concepts: internal improvements, innovation driver, and visibility & participation. Our work contributes to a better understanding of open data and lays the foundation for quantitative empirical studies. For practitioners, we outline a novel path to extract value from data and, hence, call for additional investments into open data initiatives.
... In managing the resulting tensions, it is essential to find the right governance modes and organizational designs (Rouyre & Fernandez, 2019). Consequently, governing OI projects entails a dynamic process combining structural (formal) and relational (informal) interactions (Faems, Janssens, Madhok, & Looy, 2008;Henkel, 2016). Transferring knowledge across the actors' boundaries thus requires strategies involving not only contracting, but also governance mechanisms based on relationships (e.g., Dyer & Nobeoka, 2000;Saebi & Foss, 2015;Zhu, Xiao, Dong, & Gu, 2019). ...
Article
More and more self-interested businesses are organizing themselves in interdependent, non-hierarchically controlled networks to jointly create superior value by engaging in open innovation projects. In aligning the heterogeneous actors toward a focal value proposition, it is crucial for the orchestrators of such arrangements to manage their inter-organizational relationships by navigating the interplay of contractual and relational governance mechanisms. Despite the relevance of how contracts and relational governance co-evolve, knowledge on the interaction of contracts and relational governance in specific contexts is still missing. Through a multiple-case study of ten multinational ecosystem orchestrators, we explore how large multinational orchestrators govern the interplay inter-organizational relationship mechanisms in open innovation projects across ecosystems. Based on our findings, we propose a five-dimensional, sequential model of governing the interplay of inter-organizational relationship mechanisms in open innovation projects across ecosystems and discuss our contributions in the context of the current literature.
... Over the years, literature has produced various understandings and conceptualizations of what is meant by openness [4]. [7] and open source software [8]. In contrast, open processes include open innovation [2,9] and crowdsourcing [10]. ...
Chapter
Inspired by governmental institutions publishing data for more than a decade, also private sector organizations have started engaging in open data initiatives in recent years. While monetary expenses to engage in open data are tangible, its benefits remain vague, thus fueling the open data paradox. We conduct a set of expert interviews to untangle this paradox to elicit potential benefits that may originate from engaging in open data in the private sector. Our preliminary results show three distinct groups of benefits: internal improvements, innovation driver, and external visibility. With this paper, we lay the foundation for a comprehensive model on exploring open data benefits. For practitioners, we showcase a novel path to extract value from data and to monetize it.
... Contributions to open source software projects no longer come only from individual developers who volunteer their work, but more from for-profit companies 3 . In certain product markets such as embedded systems, open source software has been widely adopted by commercial firms (Henkel, 2006). As a matter of fact, studies show that 40% of all open source software codes are contributed by corporate employed programmers (Lakhani & Wolf, 2005). ...
Chapter
Full-text available
The open source software ecosystem has become a vibrant innovation ecosystem and the main stream innovation model in IT industries over the past two decades. In recent years, China also started to embrace this innovation model. Specifically, many firms in China have sponsored their own open source software platforms, in order to create their platform ecosystems. What is the knowledge strategy associated with open source software? What is unique about open source innovations in China? What lessons can we learn from the experiences of Chinese firms, in their open source software endeavors? This chapter sets out to examine open source software ecosystems in China and answer these questions. As a result, this chapter not only enriches the technology innovation management knowledge base by studying open source innovations in China, but also sheds new lights on the managing of open source software systems in general.
... 184, C. Garcés-Ayerbe i in. 185 oraz S. Lee i in 186 . Charakterystycznym rodzajem firm z sektora MMŚP są tzw. ...
... Research has mainly focused on such networks in the business-to-consumer (B2C) markets like computing and communications industries where these forms are common (West & Bogers, 2016). Advanced research looks at understanding the underlying motivation of firms engaging in OI and how individuals influence the OI process (Henkel, 2006;Afuah & Tucci, 2012;Poetz & Schreier, 2012). Quantitative research results show evidence that even companies outside high-tech industries are adopting OI methods Lichtenthaler, 2008). ...
Thesis
Full-text available
This paper-based dissertation aims to contribute to the open innovation (OI) and technology management (TM) research fields by investigating their mechanisms, and potentials at the operational level. The dissertation connects the well-known concept of technology management with OI formats and applies these on specific manufacturing technologies within a clearly defined setting. Technological breakthroughs force firms to continuously adapt and reinvent themselves. The pace of technological innovation and their impact on firms is constantly increasing due to more connected infrastructure and accessible resources (i.e. data, knowledge). Especially in the manufacturing sector it is one key element to leverage new technologies to stay competitive. These technological shifts call for new management practices. TM supports firms with various tools to manage these shifts at different levels in the firm. It is a multifunctional and multidisciplinary field as it deals with all aspects of integrating technological issues into business decision-making and is directly relevant to a number of core business processes. Thus, it makes sense to utilize this theory and their practices as a foundation of this dissertation. However, considering the increasing complexity and number of technologies it is not sufficient anymore for firms to only rely on previous internal R&D and managerial practices. OI can expanse these practices by involving distributed innovation processes and accessing further external knowledge sources. This expansion can lead to an increasing innovation performance and thereby accelerate the time-to-market of technologies. Research in this dissertation was based on the expectations that OI formats will support the R&D activities of manufacturing technologies on the operational level by providing access to resources, knowledge, and leading-edge technology. The dissertation represents uniqueness regarding the rich practical data sets (observations, internal documents, project reviews) drawn from a very large German high-tech firm. The researcher was embedded in an R&D unit within the operational TM department for manufacturing technologies. The analyses include 1.) an exploratory in-depth analysis of a crowdsourcing initiative to elaborate the impact on specific manufacturing technologies, 2.) a deductive approach for developing a technology evaluation score model to create a common understanding of the value of selected manufacturing technologies at the operational level, and 3.) an abductive reasoning approach in form of a longitudinal case study to derive important indicator for the in-process activities of science-based partnership university-industry collaboration format. Thereby, the dissertation contributed to research and practice 1.) linkages of TM and OI practices to assimilate technologies at the operational level, 2.) insights about the impact of CS on manufacturing technologies and a related guideline to execute CS initiatives in this specific environment 3.) introduction of manufacturing readiness levels and further criteria into the TM and OI research field to support decision-makers in the firm in gaining a common understanding of the maturity of manufacturing technologies and, 4.) context-specific important indicators for science based university-industry collaboration projects and a holistic framework to connect TM with the university-industry collaboration approach The findings of this dissertation illustrate that OI formats can support the acceleration of time-to-market of manufacturing technologies and further improve the technical requirements of the product by leveraging external capabilities. The conclusions and implications made are intended to foster further research and improve managerial practices to evolve TM into an open collaborative context with interconnectivities between all internal and external involved technologies, individuals and organizational levels.
... Communities in which virtual is a type of community for problem solving are external sources to firms even if they contain employees of the focal firm (e.g. Henkel, 2006;Dahlander and Wallin, 2006). It is conformed to a decision making process that occurs from the employment structure which is guided in the workplace (O'Mahony, 2007:144). ...
Conference Paper
Nowadays, more firms tend to open up their innovation activities and obtain innovative solutions through collective wisdom by novel notion of crowdsourcing among crowds instead of individuals. This paper aims to contribute to the literature by highlighting some prominent topics in crowdsourcing as a new smart strategic approach. This is interesting to know how crowdsourcing strategies could be applied for the firm's strategic problem solving, not just focusing on how crowd systems and platforms operate. This paper responds how various types of crowdsourcing strategies as smart open innovative solutions contribute firms to solve their organizational open call problems. The research design was based on providing, collecting, classifying and reviewing different literature streams from different academic papers and scientific reports relevant to the concept of crowdsourcing by searching relevant keywords. The results show Crowd Communities as external collaboration sources contribute firms to solve their strategic innovative problems through open call platforms.
Article
Currently, two models of innovation are prevalent in organization science. The “private investment” model assumes returns to the innovator result from private goods and efficient regimes of intellectual property protection. The “collective action” model assumes that under conditions of market failure, innovators collaborate in order to produce a public good. The phenomenon of open source software development shows that users program to solve their own as well as shared technical problems, and freely reveal their innovations without appropriating private returns from selling the software. In this paper, we propose that open source software development is an exemplar of a compound “private-collective” model of innovation that contains elements of both the private investment and the collective action models and can offer society the “best of both worlds” under many conditions. We describe a new set of research questions this model raises for scholars in organization science. We offer some details regarding the types of data available for open source projects in order to ease access for researchers who are unfamiliar with these, and also offer some advice on conducting empirical studies on open source software development processes.
Article
Geographic clustering in inventive activity has often been attributed to clustering in production. For the glass industry, we find that despite a general association between location of invention and production, there were significant deviations. Centers of production were not always centers of invention, and some of the most inventive areas, such as southern New England, had very limited production. We hypothesize that the growth of a market for technology facilitated a geographic division of labor between invention and commercial exploitation and stimulated inventive activity in places where there were institutions capable of mediating among inventors, suppliers of capital, and firms seeking new technologies.
Article
The technological advancements in Linux, an open-source operating system (OS) which is used as a commercial products for embedded applications, are discussed. The reasons for the growth of this OS include a powerful and growing set of software features and some fundamental changes in the embedded landscape. RTLinux is one of the more popular and successful real-time Linux implementations and is available in both proprietary and open-source versions from FSMLabs. The Embedded Linux Consortium is also working on a platform standard that will formalize the kernel-interface specifications. Now even the single-board-computer manufacturers also find Linux a valuable sales tool for their products.
Article
This paper analyses the perceived effectiveness of patents and other means of appropriation for protecting the competitive advantages of new products. Data were obtained from the "Mannheim Innovation Panel", which includes more than 1800 German‐based firms with at least some new product development activities. In line with past research in the USA and several European countries, we found for Germany that, on average, patents are a rather ineffective appropriation tool. However, we identified one cluster of firms accounting for about 20% of all firms, where patents are perceived as the most effective mechanisms of appropriation. We further analysed, by means of logistic regression, how this cluster can be characterised and by what factors the perceived effectiveness of patents as a method of appropriation are moderated.