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代寫Human Systems Management 29 217–229


DOI 10.3233/HSM-
IOS Press
217
Open source disruptive-innovation strategy
Evangelos G. Katsamakasa,∗ and Nicholas C. Georgantzasb
a
Information Systems Area, Fordham University Business Schools, NY, USA
bManagement Systems Area, Fordham University Business Schools, NY, USA
Abstract. Is open source software disrupting the software industry? If yes, how does this process work and what are its likely
impacts? The purpose of this article is to explore the market effects of open source software as a disruptive innovation. The
research framework it proposes accounts for the dynamic complexity of the open-source disruptive innovation strategy (DIS)
of disrupter ?rms or communities, and the disruptive-innovation response strategies (DIRS) of incumbent software ?rms. The
article articulates essential components of DIS and DIRS, namely new software performance dimensions and organizational
innovations that open source enables. The interaction of DIS and DIRS processes through time can lead either to the failure or
to the co-existence of incumbent and disrupter ?rms or communities. The article extends disruptive innovation theory and helps
managers make sense of the complex competitive dynamics introduced by open source.
Keywords: Open source software, disruptive innovation, information technology, innovation, organizational innovation, compet-
itive dynamics, strategy
Evangelos G. Katsamakas is Assis-
tant Professor of Information Systems
at Fordham University Business Schools
in New York. He holds a Ph.D. from
the Stern School of Business, New
York University and a M.Sc. from the
London School of Economics. Prof.
Katsamakas’ research analyzes the busi-
ness and economic impact of IT and
the Internet, focusing on open source
software, technology platforms and dis-
ruptive innovation.His research interests
include economics and game theory
modeling and simulation of complex
systems. Prof. Katsamakas’ research has appeared in Management
Science, Journal of MIS, Information Resources Management Jour-
nal, System Dynamics Review and other major academic journals
and conferences. Prof. Katsamakas was a Guest Editor of the System
Dynamics Review special issue on IS Research with SD published in
the Fall of 2008.
∗Corresponding author: Evangelos G. Katsamakas, Information
Systems Area, Fordham University Business Schools, 113West 60th
Street, Suite 625-A, New York, NY 10023, USA. Tel.: +1 212 636
6192; Fax: +1 212 765 5573; E-mail: katsamakas@fordham.edu.
Nicholas C. Georgantzas is Professor,
Management Systems Area, and Direc-
tor, System Dynamics Consultancy,
Fordham University Business Schools,
New York, NY, USA. Both an Associate
and a Guest Editor, System Dynamics
Review, he is also consultant to senior
management, specializing in simulation
modeling for learning in strategy, pro-
duction and business process (re)design.
Author of Scenario-Driven Planning
(Greenwood 1995), Dr. Georgantzas has
published expansively in refereed schol-
arly journals, conference proceedings
and edited books. Mostly trans-disciplinary, his research interests,
publications and consulting entail systems thinking, knowledge tech-
nology and strategy design, focusing on the necessary theory and
modeling for learning in and about the dynamically complex systems
in which we all live.
1. Introduction
Open source software growth is an exciting emerging
technological and organizational phenomenon studied
in an extensive, trans-disciplinary literature. von Krogh
and von Hippel [56] group open source research into
three areas:Motivation of contributors, governance and
the innovation process, and competitive dynamics. This
0167-2533/10/$27.50 © 2010 – IOS Press and the authors. All rights reserved218 E.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy
article contributes a research ‘bridge’ between the sec-
ond and third research areas that von Krogh and von
Hippel identify. It proposes a research framework link-
ing the innovation aspects of open source with the
competitive dynamics [19].
The article builds on the disruptive innovation lit-
erature [11, 17] in order to understand the potential
business success of open source software and its impact
on the software industry. The Linux operating system
has grown, for example, into a mainstream operating
system in data centers. Borland, one of the traditional
leaders in application development tools (IDE), has
retreated from the IDE market because of the com-
moditization of development tools by the open source
Eclipse1. Similarly, the open source web server Apache
dominates the web server market, with more than a 54
percent market share2.
When and why is open source software a potentially
disruptive innovation? How must established software
?rms respond to the emergence of the open source phe-
nomenon, and how can open source players increase
their likelihood of success? What can open source
exploration teach us about disruptive innovation toward
building a superior disruptive innovation theory?
The still evolving disruptive innovation literature
provides a lense for answering these questions. In
addition to Christensen’s work that brought disrup-
tive innovation to the research focus, Adner’s [1] and
Danneels’ [17] work on disruptive innovation also
in?uences this paper. Adner’s [1] is a ?rst attempt to
formalize disruptive innovation research by identify-
ing conditions under which the process leads either to
incumbent failure or to the coexistence of the incum-
bent and new entrant ?rms. Following Adner, we seek
accurately de?ned conditions that enable the disrup-
tion process and the various possible outcomes in the
context of open source software. Danneels [17] gives a
constructive review of the related literature. He argues
that disruptive innovation research must strive to de?ne
disruption clearly, to increase the predictive value of
disruption theory and to explain incumbent success.
A major contribution of this paper is that it provides
initial insights and develops a research framework to
pursue Danneels’ themes in the software industry that
is being transformed by open source.
1 eWeek, “Open source popularity forces Borland to
retrench”, Feb. 8 2006, ,
1923549,00.asp.
2 Netcraft, “Web Survey”, June 2010, .
Christensen [11] emphasizes ?rms’ performance
trajectories as the most important determinants of dis-
ruption. Adner [1] shows that the combination of
performance and price trajectories is important. This
article moves that it is the co-evolution of software
performance and organizational innovation trajectories
that are the strongest determinants of the disruption pro-
cess. The article outlines this co-evolution’s entities in
the context of open source software.
The dynamic framework that the article presents
accounts for the dynamic complexity ([51], pp. 21–23)
seen both in the open-source DIS of disrupter ?rms or
communities and in the disruptive-innovation response
strategies (DIRS) of incumbent ?rms. The frame-
work articulates crucial DIS and DIRS entities, namely
the new software performance dimensions and the
organizational innovations that open source generates.
The software market conditions that both current and
potential over- and under-served users stipulate, as
well as the emergence of new markets, provide a
fertile ground for DIS and DIRS processes which,
through time, can lead either to the failure or to the
competitive coexistence of incumbent and disrupter
?rms.
Only a few IS papers study disruptive innovation.
Lyytinen and Rose [40, 41] focus on the organiza-
tional impact of disruptive innovation adoption in the
context of Internet computing. Sherif et al. [49] study
software reuse as a disruptive innovation. Lucas and
Goh [39] discuss how organizational change and cul-
tural considerations led Kodak to respond poorly to
the disruptive impact of digital photography. Within
the open source software context and the emerg-
ing IS dynamics research agenda [25], this article
adds to the literature its focus (a) on disruptive inno-
vation strategies (DIS and DIRS) and (b) on the
market dynamics and outcomes of a complex disruption
process.
Section 2 below reviews the disruptive innovation
and related literature, identifying major issues relevant
to open source software research. Section 3 proposes
the research framework, focusing on the dynamics of
disruptive innovation that explain the evolution of open
source software. Section 4 discusses extensions and
section 5 concludes with a short discussion.
2. Disruptive innovation
The innovation management literature has delin-
eated multiple ways to dichotomize innovation, suchE.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy 219
as radical vs. incremental, competency destroying
vs. competency enhancing, and component vs. archi-
tectural. Tushman and Anderson [53] argue that
established ?rms ?nd it hard to adapt to competency-
destroying innovations, but they do well in the case
of competency-enhancing innovations. Henderson and
Clark [29] argue that established ?rms’ organiza-
tional structures facilitate component innovation, but
are not well suited to adopting architectural inno-
vations. Christensen’s [11] view of the DIS theory
offers a new dichotomy of innovation: Sustaining
vs. disruptive. The de?ning feature of a DIS is that
it emphasizes new performance dimensions that dif-
fer from the primary performance dimensions in a
mainstream market. Conversely, sustaining innovation
emphasizes the improvement of mainstream-market
performance metrics, such as incremental or radical
improvement of existing product functionality and per-
formance metrics.
Typically, disruptive innovations start as cheaper
and lower performance technologies [11]. However, as
the performance of the disruptive innovation improves
through sustaining innovation and begins to meet the
needs of themainstreammarket, itmay drive the incum-
bent serving the mainstream market out of business.
Data fromthe hard disk industry suggest that disruptive
innovation has been a major failure source of previ-
ously dominant hard disk makers [11]. Christensen
et al. [14] describe the failure of Western Union, the
telegraph incumbent, to respond to the disruptive inno-
vation of telephone at the end of the 19th century.
Examples from other industries show that incumbents
are quite prone to failure when they fail to respond
to disruptive innovation [16]. This pattern of failure
is interesting because the incumbent ?rm is attacked
and defeated by an initially inferior performance
technology.
Several explanations are plausible for these incum-
bent failure patterns. Drawing on resource dependence
theory, Christensen suggests that incumbents fail
because they listen closely to their current users,
who demand sustaining innovations, and their current
shareholders, who demand pro?t growth. Incumbents
tend to move up-market, where pro?t margins are
high, by investing in features and performance desir-
able by the high-end market. The processes (e.g.,
budgeting) and values (e.g., ?nancial performance
growth) that incumbent ?rms have in place tend
to be biased toward sustaining innovations [16].
Christensen et al. [14] also suggest that the cur-
rent management paradigm of making decisions based
on solid analysis of historical data breaks down
in the face of disruptive innovation, which ren-
ders the future different than the past. Likewise,
Henderson [30] focuses on organizational competen-
cies in explaining incumbent response to disruptive
innovation.
Over-served, low-end costumers and non-
consumption market segments constitute attractive
targets for disrupter ?rms. A DIS that targets low-end
market segments is called low-end disruption, while
a DIS targeting non-consumption segments is called
new-market disruption. While Christensen’s [11] early
work focuses on describing disruptive innovation and
its impact, Christensen and Raynor [16] focus on how
?rms can design and bene?t from DIS. The typical
recommendation for incumbent ?rms is to develop
an independent business unit dedicated to disruptive
innovation opportunities. Christensen et al. [10] argue
that “CIOs at established companies can help identify
and foster disruptive innovation to ensure future
growth in existing and emerging markets”.
A few research articles provide useful and critical
discussions of Christensen’s seminal work. Danneels
[17] and Tellis [52] argue that the essential features of
a disruptive technology must be de?ned. Speci?cally,
Danneels ([17], p. 249) argues that “a disruptive
technology is a technology that changes the bases
of competition by changing the performance metrics
along which ?rms compete”. He also questions to
what extent Christensen’s early work can be used to
make ex-ante predictions, instead of merely to analyze
cases ex-post. Indeed, Christensen’s early work tends
to use the term ‘disruptive innovation’ to describe
both a type of innovation that emphasizes different
performance attributes, as well as the market impact
of that innovation. However, as a type of innovation,
disruptive innovation may not always have a dis-
ruptive outcome, i.e., incumbent failure. Moreover,
an innovation that dramatically disrupts the current
market is not necessarily a disruptive innovation,
i.e., the type of innovation that Christensen describes
([47], p. 347).
Indeed, while Christensen [11] focuses on disrup-
tive technological innovations that lead to incumbent
failure, Markides [43] argues that business-model
3
innovation usually leads to just a bounded disrupter
market-share, e.g., 10–20 percent for Internet banking
and brokerage. Likewise, Chandy and Tellis [7], King
3 This is especially relevant for ?rms such as JBoss,MySQL, Com-
piere and others that use open source as a business model.220 E.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy
Fig. 1. A dynamic DIS-DIRS research framework4.
and Tucci [34] and Chesbrough [9] seek a better expla-
nation of why some incumbents succeed. Danneels
([17], p. 255) points out that customer-orientation
“does not imply an exclusive focus on current
customers”.
While the disruptive innovation theory focuses on
low-end and new-market disruption, high-end dis-
ruption is also possible [27]. Moreover, creating an
independent organization to focus on the disruptive
technology might not always be optimal, particularly
in light of potential synergies [8, 17, 43]. Christensen
[12] admits that his early contributions on disruptive
innovation do not constitute a complete theory, but a
theory under iterative development.He argues that “this
lap continues along the identi?cation and application
track... the next lap should move toward developing
more detailed metrics that indicate circumstances and
quantifying the implications of those circumstances”
([14], p. viii).
We answer these research calls by adopting the
DIS and DIRS notions, in order to describe strate-
gies that rely on disruptive open source innovations,
but we do not assume that DIS will by de?nition
‘kill’ incumbent ?rms. We propose that the outcome
of the process is determined by the dynamic inter-
action of DIS and DIRS through time, and provide
a starting point for clarifying the crucial debate: Is
co-existence the typical outcome of the open source dis-
ruption process or the elimination of the incumbent and
why?
3. Open source DIS
The research framework focuses on disruption pro-
cess dynamics (Fig. 1). It posits that salient features of
open source – new software performance dimensions
and organizational innovations – and software market
conditions – over served and under served users and
new markets – enable the software disruption process,
which starts with disrupter ?rms entering the software
market. The dynamic interaction of disruptive innova-
tion strategy (DIS) and the incumbent ?rm’s disruptive
innovation response strategy (DIRS) determines the
process outcome. Through time, the outcome is either
the coexistence of the two ?rms or the failure of one of
them. Yet it is the disruption process itself that is of out-
most interest because of the genuinely dynamic nature
of the disruptive innovation phenomenon. The strategy
dynamics that disrupter (DIS) and incumbent (DIRS)
software ?rms follow shape the disruption process in
a dynamically complex way. The research framework
shown here is a ?rst step toward understanding this
dynamic complexity.
The entire current and potential market consists
of over-served (OsU), ?tly-served (FsU) and under-
served (UsU) current and potential users (Fig. 2).
4 Note that incumbent market share =1– disrupter market share.
For expositional clarity and in order to avoid unnecessary combinato-
rial complexity, we describe a two-?rm industry (one incumbent and
one disrupter), but the insights should easily extend to any number of
?rms.E.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy 221
Fig. 2. Market evolution through time.
At time t = 0, UsU preferences for software perfor-
mance exceed the actual performance (P0)ofthe
software in the market. So the UsU segment would
seek performance improvements. Conversely, P0 beats
OsU performance preferences, hence the OsU seg-
ment of the current and potential market would seek
software with reduced performance and price. But
because markets do evolve through time, our DIS-
DIRS framework opts for a dynamic characterization
of markets, where both product performance and
user preference for performance also change through
time.
Both the OsU and UsU segments are ideal targets for
low-end and high-end DIS. When actual performance
increases faster than user preferences, then over-served
users increase and a DIS targeting the OsU segment
is preferable (Fig. 2b). When user performance prefer-
ences increase faster than actual product performance,
then under-served users increase and a DIS targeting
the UsU segment (high-end disruption) is preferable
(Fig. 2c).
The outcome of the process depends crucially on the
relative effectiveness ofDIS andDISR.WhenDIS dom-
inates DISR (DIS > DIRS) the incumbent is driven out
of the market. When DIRS dominates DIR (DIRS >
DIS) the disrupter is driven out of the market. In all
other cases the outcome is the coexistence of the two
?rms.
Through time, DIS and DIRS may focus on dis-
ruptive innovation, sustaining innovation or both.
Essential DIS and DIRS building blocks are ?rms’
product performance and organizational innovation
trajectories.
Clearly, open source is a disruptive innovation.
But this does not lead deterministically to incum-
bent failure (Fig. 3). Through time, an incumbent
?rm may imitate the open source software along
certain performance dimensions or may adopt cer-
tain open source organizational innovations. These
possibilities increase signi?cantly the dynamic com-
plexity of the DIS-DIRS co-evolution. In the database
market, for example, Oracle felt a threat
5 from
the numerous low-end open-source database offer-
ings, e.g., MySQL, in early 2006. As a response,
Oracle acquired open source database products Sleep-
ycat and InnoDB6, offered a free low-end database,
made its development tool free and supported popu-
lar open source languages, such as PHP, to encourage
open source developers in order to build applications
for Oracle products7. MySQL was acquired by Sun
Microsystems, and ultimately Oracle acquired Sun in
2010.
5 Business Week, “Taking on the Database Giants”, Feb 6,
2006,
tc20060206 918648.htm.
6 eWeek, “Oracle can’t sti?e open source databases”,March 1 2006,
.
7 eWeek, “Oracle hands developers a free database”, October 31
2006, E.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy
Fig. 3. DIS-DIRS co-evolution trajectories computed with system dynamics simulations.
3.1. New software performance dimensions
Open source emphasizes a number of new software
performance dimensions or metrics and thereby shifts
the basis of competition in the software industry and
enables the disruption process. This suggests that open
source is a disruptive innovation. This section discusses
some of the most salient performance dimensions, and
compares open source and proprietary software (Fig. 4).
Identifying salient performancemetrics and their impli-
cations for DIS and DIRS can help explain the business
impact of open source, guide future research as open
source evolves and quantify the differences between
open source and proprietary software. The salient per-
formance dimensions proposed here include: Price,
source code access, functionality, trialability, modular-
ity and emphasis on open standards.
The spider graphs on Fig. 4 show an ordinal com-
parison of open source and proprietary software across
salient software dimensions based on: a) academic and
practitioner research and b) a scenario of co-evolving
open source and proprietary software, i.e., a possible
scenario of performance evolution.
3.1.1. Price (low to high)
The ?rst important dimension of competition is very
low or zero software licensing price, i.e., the open
source de?nition.Open source increases the importance
of price competition, typically associated with low-end
disruption [16]. As the business press points out: “it’s
really hard to compete with free – especially when the
free stuff is really good”.
8 Proprietary software ven-
dors often reduce their prices as a competitive response
to open source offerings [19]. But beyond the licensing
fee, normatively speaking, organizations ought tomake
software adoption decisions based on the total cost of
ownership (TCO). Yet TCO is a multidimensional con-
8 eWeek, “Open source popularity forces Borland to retrench”,
Feb. 8 2006.E.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy 223
Fig. 4. A comparison of open source and proprietary software along salient software performance dimensions based on: a) current academic and
practitioner literature and b) a scenario of co-evolving open source and proprietary software.
struct, controversial
9 and subjective because it varies
from organization to organization, while different soft-
ware providers emphasize different TCO components,
depending on the advantages they offer.
3.1.2. Source code access (open to closed)
Open source code enables users to access the source
code of software products. Access to the source code
enables organizations to customize and to optimize soft-
ware to their exact needs, to correct de?ciencies, to
assess the quality of the software and to provide feed-
back to the open source software developers.Moreover,
source code transparency may increase developers’
incentives to write high quality source code. Access
may not be equally important for all software products
or all organizations. But it is particularly important for
high IT capability organizations [33].
3.1.3. Functionality (low to high)
Typically, the functionality of open source software
is minimalist (basic) compared to proprietary software,
9 For example, there is a controversy about the TCO compari-
son of Windows Server and Linux. Microsoft is arguing that its
products have actually lower TCO, when you consider manage-
ment, staf?ng and other costs. Other studies contradict that argument,
.
and users often complain that open source applica-
tions are not as easy to use as proprietary applications.
A plausible explanation is the user-driven nature of
open source innovation [22, 37, 55]. As open source
software functionality increases through time, however,
there is no guarantee that it won’t overshoot, thereby
risking disruption by another type of future software
innovation.
3.1.4. Trialability (low to high)
The fourth important dimension that open source
emphasizes is trialability. IT user ?rms are free to try
and to experiment with open source software as much
and as long as theywish.Trialability is positively related
to innovation adoption in the innovation diffusion liter-
ature [46]. Proprietary software vendors can respond by
increasing the trialability of their own offerings, but this
is not without cost and limitations. It is typical to offer,
for instance, a trial version for 30 days, but technology
has to be built into the software product to enforce that.
3.1.5. Modularity
Modularity of the source code is anothermajormetric
of open source software. Modularity enables the effec-
tive distributed development of open source software.
MacCormack et al. [42] develop a method of measur-224 E.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy
ing the degree ofmodularity of software products. They
compare one proprietary and one open source product
and they ?nd that the open source is more modular
than the proprietary software. This is an interesting
example of how different modes of organization are
associatedwith differences in software design. Baldwin
and Clark [5] argue that a modular source code base
increases voluntary project participation and software
quality.
3.1.6. Open standards (open to closed standard)
The ?fth important dimension is emphasis on open
standards. Emphasis on open standards is important for
users in all software markets because open standards
intensify market competition, mitigate user lock-in
and dependence on a single vendor [48], and enable
IT organizations to mix-and-match their preferred IT
infrastructure components.
The conventional belief that new technologies start
with low performance but eventually surpass old tech-
nologies is not always valid [50]. It remains unclear
whether open source software will eventually sur-
pass the functionality or performance of proprietary
software, and whether open source performance will
overshoot creating opportunities for future disruption
by another type of software innovation, leading to new
waves of disruption.
Yet, future theoretical and empirical research has
to validate these salient performance dimensions or
metrics, and to estimate the relative importance of
each within the disruption process. One major research
theme might be the co-evolution of performance of
proprietary and open source software, i.e., how the
performance evolution of one is affected by and in
turn affects the performance of the other. Among other
effects, one might expect that the emergence of open
source could lead to an increase of the quality and
performance of proprietary software attributed to inten-
si?ed competitive pressures.
3.2. Organizational innovations
The most important innovation in the open source
context is not the technical or product innovation
but organizational innovation, ranging from software
development, distribution, licensing, and marketing
[45, 56, 58]. Klincewitcz [35] ?nds low levels of tech-
nical innovativeness of open source project outputs
on SourceForge.org, but his study does not consider
organizational innovations such as development meth-
ods. This section focuses on salient open source
organizational innovations that affect the disruption
process.
3.2.1. Software development (hierarchical
to distributed)
The major development innovation is the devel-
opment and testing of software in global distributed
and self-organizing communities over the Internet.
As von Krogh and von Hippel ([56], p. 975) point
out, open source projects offer “eye-opening exam-
ples of novel innovation practices for students and
practitioners in many ?elds”. Open source software
development is innovation by users for users [22,
37, 55], as opposed to innovation by ?rms for users,
combining elements of the private and the collec-
tive innovation model. Several studies provide insights
into the organization and evolution of open source
projects [36, 44] and businessmodels [57].Open source
projects are typically characterized by considerable
heterogeneity of participant roles and contributions,
and may be independent or sponsored by ?rms.
Modularity too facilitates distributed development
[5].
3.2.2. Software distribution
Open source is typically distributed through the Inter-
net, which provides a low cost and ef?cient distribution
channel for information goods, especially when one is
not concerned about piracy.
3.2.3. Software licensing (closed to open)
A software license de?nes the use, modi?cation and
distribution rights assigned to users. The invention of
GPL (General Public License) by the Free Software
Foundation has created a large number of novel open
source licenses10. The major licenses among them are
GPL, LGPL, BSD, MIT and Mozilla Public License.
Compared to closed (proprietary) licenses, GPL pro-
vides users with the right to use, to modify and to
redistribute software. West [59] explores the trend
towards more openness of several major IT vendors.
Open licenses along with ef?cient distribution of soft-
ware over the Internet enable the diffusion of open
source software development and use.
10 The open source initiative website provides a comprehensive list
of approved licenses complying with the open source de?nition, see
. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy 225
Fig. 5. A comparison of open source and proprietary software along
salient organizational innovation dimensions based on: a) current aca-
demic and practitioner research and b) a scenario of co-evolving open
source and proprietary software.
3.2.4. Software marketing (commercial
to word-of-mouth)
Open source marketing emphasizes community and
Internet-enabled word-of-mouth, rather than the tradi-
tional commercial marketing channels and methods.
The in?uence diagram (ID) on Fig. 6 shows salient
relations that characterize the entanglement
11 of orga-
nizational innovation and performance dimensions,
causing the co-evolution, of organizational innovations
and software performance dimensions through time
(Figs 4 and 5). For example, open source code positively
affects distributed development methods. Distributed
development in turn, affects the modularity of the code,
and is also affected by the modularity of the code,
with a time lag and/or delay. Distributed development,
word-of-mouthmarketing, and open licensing affect the
cost and thereby the price of the software product, dis-
tributed development and open licensing negatively so.
Open standards facilitate open licensing, which in turn
positively affects software trialability.
A comprehensive analysis and modeling of the role
of salient organizational innovations in the disruption
process as well as the co-evolution of organizational
innovations and software performance dimensions in
the context of DIS and DIRS are beyond the scope
11 The concept of entanglement is borrowed fromQuantumphysics.
The states of two particles are entangled when the one depends on
the other, and this relationship holds even if the particles are spatially
separated. In the context of the disruption process, the concept empha-
sizes the strong interdependence of software performance dimensions
and organizational innovations (see e.g. Brian Clegg, 2006, The God
Effect: Quantum Entanglement, Science’s Strangest Phenomenon, St
Martin’s Press).
Fig. 6. Salient relations among organizational innovation and per-
formance dimensions that affect the co-evolution of organizational
innovation and performance dimensions.
of this exploratory article. However, the organizational
innovations ?rst introduced by open source have a
strong transformational effect: Open source develop-
ment practices and tools are widely adopted, replacing
the pre-existing developmentmethods across the whole
software industry [18] and beyond software.
3.3. Software market enabling conditions
The enabling conditions of the disruption process
include new markets, over- and under-served users and
new value networks.
3.3.1. New markets
The emergence of new markets in a time period that
coincided with the emergence of open source is an
enabler of the disruption process. For example, the web
server market, dominated by the open source Apache
appeared in mid-90s, exactly when open source was
emerging. Also, the initial foothold of Linux was estab-
lished on the emerging Internet upstart ?rms’ segment,
characterized by high IT capability ?rms seeking to
scale their operations and to grow aggressively relying
on a relatively low-cost infrastructure. Linux clearly
dominates high-performance computing and scienti?c
research computing. The success of embedded Linux
may also coincidewith themajor trend of increasing the
computational power of every kind of hardware device.
There is also strong evidence of non-consumption
segments that are now being served by open source
offerings. For example, open source databases such as
MySQL and enterprise application offerings, such as,
for example, SugarCRM, enable many smaller com-
panies to use software functionality that did not have226 E.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy
access to before because of the high cost and complex-
ity of the offerings bymajor proprietary vendors in these
markets.
3.3.2. Over-served users
Across the whole software value stack, there is
evidence of low-end, over-served users. Over-served
customers are a direct result of performance overshoot
[14], usually the result of many features and function-
ality that users never use. Performance overshoot may
also have negative effects, such as: Increased complex-
ity of using or managing the software and associated
high costs.
3.3.3. Under-served users
Under-served users are high-end users not satis-
?ed by the performance of existing software products,
therefore demanding high performance. This condition
enables high-end disruption. One could classify in this
category ?rms in IT-intensive industries that demand
cutting-edge performance and customization. Because
of its open licensing and access to source code, open
source software enables that company type to achieve
high degrees of customization and performance with
low cost. Typical examples would be Internet com-
panies, such as: Google, Amazon and others, which
developed much of their infrastructure using Linux and
other open source components.
3.3.4. New value network
Christensen [11] points out that new and emerging
value networks create opportunities for disruptive inno-
vations. This aspect of the disruptive innovation theory
is particularly interesting in the IT industry,where users
seek system solutions, therefore the success of each
and every innovation is inseparable from the success
of the whole value network [32], in which innova-
tion is embedded. In the open source context, Linux
is emerging as the hub, around which a whole new
value network is emerging, creating numerous oppor-
tunities for disruption in applications. Interestingly,
open source applications strive to be cross-platformand
do not commit only to the Linux value network. As
a result, open source and proprietary value networks
are strongly interconnected. A value network perspec-
tive that takes into account network structure would
be a most promising research direction in the disrup-
tive innovation literature. It is worth noting that, within
the open source context, network structure analysis has
focused primarily on community structure [28] and not
on market competition.
4. Extensions
4.1. Industrial organization of information
technology
The industrial organization of information tech-
nology [54] can complement and enhance our
understanding of disruptive innovation.Versioning, i.e.,
the practice of offering more than one product ver-
sions, is a way to mitigate the threat of disruption, by
alleviating over-served or non-consumption segments.
This software practice is widely used. Versioning can
be both a preventive strategy and a responsive strat-
egy. Interestingly, preventive strategies in the context
of disruption have been largely ignored in the existing
literature. Software bundling [4] could play a preven-
tive role similar to versioning. Compatibility strategies,
complementarities and network effects and switching
costsmay also affect the likelihood of disruption in soft-
ware markets. Technology platform leadership [19, 20,
23] might be associated with a lower likelihood of dis-
ruption. Synthesis of this industrial organization body
of literature with the disruptive innovation literature is
another promising avenue for future research.
The software value stack (Table 1) shows the rela-
tions between the different types of software used
in organizations, and emphasizes that end-user value
is created by a stack of complementary software
components, rather than individual products. Market
conditions, and ?rmstrategies and disruption outcomes
maybe different at different layers of the software stack,
but all layers are interdependent, because of strong com-
plementarities.
As a result, strategic ?rm relationships or battles
between ?rms matter across layers too (not only within
layers).An incumbentmay bene?t by encouraging open
source disruption and commoditization of an adjacent
Table 1
Software value stack
Software value stack (layers) Open source examples
Productivity OpenOf?ce
Browser Mozilla ?refox, Opera
Content management Bricolage, Plone
ERP/CRM Compiere, SugarCRM, OpenMFG
Web server Apache
Development tools (IDE) Eclipse
Application server JBoss, Geronimo
Database MySQL
Operating system Linux, AndroidE.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy 227
level on the software value stack. For example, hard-
ware, application, and service providers, such as IBM,
for example, bene?t from the spread of open source
within the infrastructure or operating systems markets
[19].
4.2. Disruption by a community
The disruptive innovation literature discusses disrup-
tion by small or large ?rms. The possibility that an
open source ‘community’ may disrupt a market cer-
tainly is a novel phenomenon. There is no research
linking the organizational dimensions of a community
with its market disruption potential. For example, a
community’s lack of strategic marketing may lessen its
potential for disruption; strategic marketing is essential
also for crossing the chasm of the technology life-
cycle curve. But the fact that a community consists of
developers-users facilitates adoption, at least within the
development community itself.Which of the two effects
dominates is unclear. Indeed, understanding how dis-
ruption by a community works is a major promising
future research direction. Understanding the commu-
nity disruption process has implications for the strategic
response of an incumbent. The incumbent’s optimal
response to a community and a ?rm’s disruptive threat
might differ widely.
4.3. Simultaneous disruption by competing
technologies
The conventional belief that innovations are intro-
duced primarily by small start-ups is not valid [50].
One can provide several examples where a large
?rm has been the potential disrupter. Microsoft is,
for example, disrupting the server operating systems
market and is planning to disrupt the enterprise appli-
cations market. The simultaneous disruption of the
mainstream server operating systems market by two
technologies (Windows NT/Server and Linux) raises
another interesting question: what are the dynam-
ics of simultaneous disruption by two competing
technologies?
4.4. Modeling and analysis
A major contribution of this article is that is pro-
vides the conceptual foundations for future research
efforts in modeling the disruption process in the con-
text of open source. Adner [1] and Adner and Zemsky
[2] provide a ?rst attempt to formalize disruptive inno-
vation using simulation and game-theoretic analyses.
They focus on the demand conditions that can lead
an entrant to drive the incumbent out of the market.
Using simulation, Adner [1] analyzes a model with two
?rms, each offering products characterized by two per-
formance metrics. There are two market segments with
different preferences over the twometrics, and the ?rms
may invest to improve the one or the other performance
metric of their products through time.
Adner’s paper shows that when preference overlap
between market segments increases and is asymmet-
ric, then disruption is likely, i.e., one ?rm invades the
market segment of the other.Adner andZemsky [2] ana-
lyze a related but static model and characterize a Nash
equilibrium when disruption takes place. Georgantzas
and Katsamakas [24] continue the formalization of
disruptive innovation, by developing a system dynam-
ics model that analyzes the dominant feedback loops
responsible for the overshoot-and-collapse dynamics
of the number of ?rms in the hard-disk industry. And
Georgantzas and Katsamakas [26] propose a dynamic
simulation model of disruptive service innovation.
5. Concluding remarks
Open source has introduced an unprecedented tur-
bulence in the software industry and there is a strong
need to clarify its impact. This article proposed a
research framework that highlights the antecedents
and the potential outcome of the dynamic disrup-
tion process enabled by the emergence of the open
source movement. The process entities include new
software performance dimensions that are attractive to
under-served and/or over-served users, and new organi-
zational innovations. The disruption process is shaped
by the co-evolution of DIS and DIRS. Major DIS and
DISR components are the disrupter’s and incumbent’s
software performance and organizational innovation
trajectories. The software performance dimensions are
entangledwith organizational innovations, i.e., they co-
evolve through time.
We showed that open source is a disruptive inno-
vation, but this does not lead deterministically to the
failure of the incumbent ?rm, as the early work by
Christensen implies. In fact, studying disruptive inno-
vation is interesting exactly because we don’t know
ex-ante what will be its market impact. The concep-
tual framework we proposed is a starting point to
understanding the impact of open source in a dynamic
market.228 E.G. Katsamakas and N.C. Georgantzas / Open source disruptive-innovation strategy
Moreover, this exploratory research provides man-
agers with a starting point in their evaluation of the
open source impact on their markets. To that direc-
tion, a manager needs to evaluate the strength of
the antecedents in a particular market. In particular,
managers need to evaluate how strongly performance
dimensions can be affected, how applicable are the new
organizational innovations, and howstrong are themar-
ket conditions for over- and under-served users that
might enable the disruption process. Managers of open
source ?rms or leaders of open source communities
must strengthen their DIS appropriately, focusing on
under- or over-served or new-market segments. Man-
agers of incumbent software ?rms must be pro-active
or respond fast, initiating their DIRS by introducing
open source, organizational and software performance
elements in their strategy designs.
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