IAMOT 2000, The Ninth International Conference on Management of Technology February 20-25, 2000, Miami, Florida, USA. Track 4: Industrial Innovation see http://www.iamot.com/

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Elements of Innovation Management in Computer Software and
Services
Michaël Le Duc, Department of Business Studies and Informatics, Mälardalen University,
Box 325, 631 05 Eskilstuna, Sweden. E-mail mlc@mdh.se
Abstract:The paper reviews and analyses theoretically elements of innovation management in
the computer software market. Concepts related to the knowledge-based economy are discussed,
mainly network effects, e.g. that de facto standards are highly demanded to enable users to
conveniently share information. Everett M. Rogers’ seminal work on the diffusion of innovations is
related to the software market, especially characteristics of innovations that lead to adoption or
rejection. A model analyses the possible combinations of adoption criteria with three types of
network effects. Furthermore, what Teece calls “complementary assets” to an innovation play a
central role in determining diffusion of a firm’s software technology.
Keywords:packaged software, software services, innovation management, network effects
Introduction
The following paper is a review and theoretical discussion on some innovation management
aspects of the software market. The main question explored is what makes an innovation in
software adopted by some potential user or other decision-making unit and how software
producers manage these mechanisms. Software services play a central role in the diffusion of
software packages, which makes them relevant for this discussion.
A rich literature covers innovationprocesses in many fields from agriculture to
pharmaceuticals. However, the software and software services market needs much further study
(Teece and Coleman, 1998), including from an innovation management perspective. Elements of
Everett M. Rogers’ influential work on innovation diffusion (Rogers, 1985) is here related to other
concepts explaining salient features of the software market. Rogers’ work contributes with
innovation theory that is strongly supported by his comprehensive review of empirical research in a
wide range of disciplines. Not all elements necessarily apply to software markets however.
Adoption criteria and network effects determine software
innovation diffusion
Adoption is a key concept in the innovation literature. Rogers (1995) also terms it the
innovation-decision process, which “is the mental process through which an individual (or other
decision-making unit) passes from first knowledge of an innovation to forming an attitude toward
the innovation, to a decision to adopt or reject, to implementation of the new idea, and to
confirmation of this decision.”
Rogers (1995) has identified five pivotal characteristics that determine the adoption of an
innovation.(1) Relative advantage is the degree to which an innovation is perceived by a potential

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adopter as being better than the idea it supersedes (Rogers, 1995, p. 212). (2) Compatibility “is
the degree to which an innovation is perceived as consistent with the existing values, past
experiences, and needs of potential adopters” (Rogers, 1995, p. 224). (3) Complexity concerns
the degree to which an innovation is perceived as relatively difficult to understand and use (Rogers,
1995, p. 242). (4) Trialability has to do with how much the potential adopter can experiment with
the innovation. (5) Observability is associated with the degree to which the results of an innovation
are visible or communicated to others.
Roger’s five key characteristics above should be complemented with some new aspects of
innovation diffusion theory and economic theory that are highly visible in the software market.
According to Carayannis (1998), the knowledge-based economy operates with new types of
dynamics where physical limits associated with most traditional capital are not at play. For instance,
when a good software innovation has been produced, often at great effort, it can be easily spread,
e.g. without having to build costly factories, if characteristics associated with adoption are
competitive.
Network effects (e.g. Katz and Shapiro, 1985; Arthur, 1996) should be added to Roger’s
five characteristics above since they strongly reinforce or downplay these criteria in the software
market. Three major network effects are identifiable. Supply side increasing returns to scale, also
called the conversion effect (Majumdar and Venkataraman, 1998), emerge since the up-front
R&D costs of software are high relative to their unit production costs. In the case of Microsoft,
“the first disk of Windows to go out the door cost Microsoft $50 million; the second and
subsequent disks cost $3. Unit costs fall as sales increase” (Arthur, 1996). Arthur’s estimate of the
low distribution cost probably concerns the Original Equipment Manufacturer (OEM) distribution
channel, where a computer manufacturer pre-installs software before shipment. Note that
marketing and sales costs are high in the packaged software market, e.g. in comparison to software
services. Demand side increasing returns to scale, or the consumption effect (Majumdar and
Venkataraman, 1998), adds to the former. The more people use a software package the more
valuable it becomes. For example, users can easily exchange files with others using the same
software and do not have to learn a new software if they switch employer. The third and final
network effect discussed here is industry wide returns to scale, or the imitative effect (Majumdar
and Venkataraman, 1998), which is visible when firms model their behavior after firms perceived to
be similar. The software industry is organized in a network of specialized firms that produce
modules with agree-upon standard interfaces. These modules are combined into the many types of
information systems that are used at the workplace and at home. For example, a client-server
information system can be composed of a server with software from SAP, Oracle and IBM,
personal computers running Microsoft Windows, office productivity tools from Lotus and some
firm-specific software developed by a local software consultancy firm. Each packaged software
firm can specialize where it has the best strategic position and let others supply complementary
components. Consultants specialize in services such as selecting, combining, adapting and
customizing software packages as well as custom software development and maintenance. They
contribute to the network effects for packaged software suppliers as well as on the demand side by
recommending and learning the most common software packages in the market for each
application type. On the other hand, consultants are qualified to recommend competing software to
what a customer uses if it is identified as significantly superior.

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Innovation in this model is basically fueled by supply side push – notably due to occasionally
fierce competition, e.g. thrcough the complex interplay of rivaling product launches or the promise
of future innovation – demand side pull as software use increases in amount and sophistication as
well as industry wide organized innovation, e.g. in the current convergence of computing,
telecommunications and other realms like the media and retail trade.
The combination of Roger’s adoption criteria with the three types of network effects above,
which reinforce or inhibit innovation, leads to a model that is presented in Table 1.
Network
effects
Adoption
criteria
Innovation enhancing and
inhibiting forces associated
with supply side network
effects
Innovation enhancing and
inhibiting forces associated
with demand side network
effects
Innovation enhancing and
inhibiting forces associated
with industry-wide network
effects
Relative
advantage
Supply side increasing returns
to scale; high cost of R&D can
be spread among a large
number of users. Production
costs do not increase
significantly with number of
users
Demand side increasing returns
to scale; the emergence of de
facto standards enable users to
reduce uncertainty and costs
significantly
Industry wide increasing returns to
scale; synergistic collaboration
between firms with
complementary products, e.g.
operating systems suppliers,
application suppliers, add-on
developers and consultants
Oligopoly or monopoly can
hamper innovation at the firm
level. Comfortable but
dangerous for the winners
Users can get locked into
software that is not necessarily
the best
Competition occurs within
established standards. Hard time
for alternative standards or non-
standard products
Compatibility Developers can continually
deliver incremental innovations
(upgrades) as well as
You do not have to re-learn a
new software or a new version of
an existing software. New
By building on
compatible components,

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complementary products (e.g.
suites) to installed base of
users
software adhering to a standard
like Windows leverage its look
and fell
complex systems can
quickly be diffused in
comparison to a single
vendor approach
Innovation process must
consider established
technology, the installed base.
Backward compatibility may
be negative for innovation
Missed opportunities from
better but incompatible software.
Usability is still a major problem
area, the WWW is not exempted
Lock-in of the whole industry in
platforms of compatible
components.
Complexity Increasingly complex products
can be developed as existing
customers become increasingly
proficient with software and
technology matures
The same software package can
be adapted to several user
categories, e.g. the average user,
power user and consultants
The whole structure of the
industry addresses the
opportunities and challenges of
complex software. Standardised
modularity is a key concept.
The market may get saturated
with too many new features in
existing software thus leading
to decreased sales
Complexity increases as more
and more software modules can
be interconnected, e.g. through
the Internet. Usability problems.
Vulnerability due to network
organisation if key components
fail. A component can become
orphan due to obsolescence or loss
of market
Trialability See compatibility and
complexity
See compatibility and complexity See compatibility and complexity
Observability Network effects are strongly at
play
Network effects are strongly at
play
Network effects are strongly at
play
Table 1. Possible combinations of Roger’s adoption criteria and network effects in the
packaged software market.
The entries in Table 1’s cells are aimed at discussing key phenomena without claiming
theoretical or empirical completeness. Innovation in the software is so complex that a model should
only be used to clarify some aspects. The model and some other innovation issues of the software
market are discussed in the following.
The compatibility of a software package with the technical infrastructure of a user as well as
him or her as a person is an area of great effort in the industry. Usability management, or the
management of human-computer interaction, is a central area associated with compatibility. This
includes visual clarity, consistency, compatibility, informative feedback, error prevention as well as
user guidance and support (Ravden and Johnson, 1989).
Complexity needs special attention in software, since modern information systems are
extremely complex. The art of software engineering is to create the illusion of simplicity in the user
community (Booch, 1994, p.6). The foremost instrument used to this end is software modularity,
i.e. Object-Oriented Design (OOD) and Object-Oriented Programming (OOP). One key element
in OOD/OOP is the encapsulation of complex implementation details in separate modules
combined with publicizing essential properties and functions in interfaces between modules.
Rogers’ adoption criteria as well as the three types of network effects discussed in this paper
can explain the rapid diffusion of the Internet. The most striking example of software adoption in
the nineties is the diffusion of the Internet during the last half a dozen years or so. The basic
technology has been used since the 1980s, mainly by scientists and students. It was text-based and
mainly used on the UNIX platform with arcane commands that you had to remember. As a user,
you also had to know the exact address of each Internet site and other time consuming tasks.

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When a graphical user interface was introduced in combination with a hypertext structure to
navigate anywhere in the world, complexity was managed by software such as NSCA Mosaic and
Netscape, not the user. Thus, the tremendous potential of this new medium could be unleashed to
the ordinary software user, a case of relative advantage.
The Internet has contributed to the diffusion of a number of software technologies that
previously had been used on a much lesser scale. For example, hypertext was limited to specific
application types such as multimedia, e.g. the Macintosh HyperCard system in the 1980s and
digital encyclopedias. Online help systems also have traditionally used hypertext technology.
However, with the advent of the World Wide Web a dramatic diffusion of hypertext and
hypermedia has occurred. Even operating systems are now modified to take advantage of this
innovation. Other technologies that the WWW has accelerated the diffusion process of include e-
mail, home computing, digital network technologies, decision support systems (especially in
financial sites), groupware and electronic commerce.
Innovation can be hampered on the supply side by hindering entrants with superior
technology to challenge a firm dominating a segment of the market more strongly than in many other
industries due to the mechanism of increasing returns to scale. On the demand side users can be
locked-in (Katz and Shapiro, 1998) by their investment in a particular software, thus creating
switching costs (Katz and Shapiro, 1998) when changing to a competing product or even buying
an upgrade. Established firms have even to compete with older versions of their own software
when making a release. In the case of packaged software, rapid change equates a purchase to a
sunk cost. This is not necessarily the case of software developed in-house, as the Y2K problem
indicates. The effect of lock-in is so powerful in the network economy that Arthur (1996) asserts
that a ”new product often has to be two or three times better in some dimensionprice, speed,
convenience − to dislodge a locked-in rival”. The required relative advantages mentioned by
Arthur may not be universal. For example, Rice et al. (1998) define a “game changer”, or radical
innovation, “to have the potential, (1) for a 5-10-times improvement in performance compared to
existing products; (2) to create the basis for a 30-50-percent reduction in cost; or (3) to have new-
to-the-world performance features.” One reason for these negative aspects in an innovation
perspective is that there is a problem of incompatibility and complexity in the software market.
Thus, once an industry standard has been established incremental process innovation dominates.
The power of lock-in is illustrated by the case of IBM that tried in the early 1990s to
forcefully challenge Microsoft Windows with the operating system OS/2 but failed. Though partially
compatible with Windows OS/2 did not become adopted on a wide scale, e.g. by the lack of
applications specifically written for OS/2. IBM could not leverage enough commitment from the
developer community to start the imitative effect.
Software producers have however learned that lock-in is not eternal. Once-dominant
products whose market shares have plummeted include: WordStar, WordPerfect, Lotus 1-2-3,
dBase, Paradox (Katz and Shapiro, 1998), all winners of the DOS era, and Netscape.
Vendors furthermore endeavor to continue to lock-in users by releasing upgrades regularly
(whether they are innovations for the customers or not); promising new features in future releases to
preempt competitors’ releases, etc. The phenomenon of competitive upgrades offering substantial

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discounts to customers who switch from a competitor’s software are used partially to counteract
demand side network effects.
Another example of obstacles to diffusion due to network effects and critical adoption
criteria is the initiative of the United States Department of Defense (DoD) that stemmed from the
problem of the multitude of software programming languages used in its procured systems,
especially in embedded software such as in fighter planes, from the 1950s and onward. In the
eighties, the DoD imposed a standard programming language, Ada. However, few non-defense
applications have been developed in the Ada programming language since it is not object-oriented,
which is now mainstream in the civilian software sector, and many functional features are specific to
defense applications (Mowery and Langlois, 1996). Ada was not compatible with the mainstream
programming community and thus not adopted outside defense applications.
One concept that ought to be considered in this context is what Teece (1986) calls
complementary assets. Complementary assets of companies encompass assets that support the
innovation process. Such assets for a software firm include critical human resources, marketing and
sales power, accumulated capital, service network effectiveness, accumulated goodwill and the
existence of complementary technologies in the involved industry. Another key complementary
asset in software is installed base, which can be leveraged if a new software or software version is
sufficiently compatible. All these complementary assets can block competitors’ entry, despite their
innovations’ potential technological superiority. The perception of a firm’s complementary assets
furthermore influence adoption strongly in the software sector, in part due to switching costs of
users associated with changing an existing software with an incompatible one, e.g. through a
learning effort. The media image of a company and its leaders is another complementary asset that
can turn against the company, e.g., in the case of negative articles in the press that can lead to key
people leaving their jobs, which leads to further negative signals, etc. in a vicious cycle.
A central concept here is software platform. When two alternative platforms or standards
compete, the one that gets ahead early in the establishment of the market tends to get even further
ahead in a positive feedback loop (Arthur, 1994) since the value of a platform increases rapidly
with the number of users. The winning standard takes it all or almost all as in the case of the
Windows and Intel (“WINTEL”) platform (over 90% of the personal computer market). In the
computing market, different platforms compete in networks of firms (Bresnahan & Greenstein,
1999).
One well-known example, is how the personal computer platform Intel/MS-DOS in the mid-
eighties overtook the early market dominated by the CP/M platform as well as lesser platforms like
Apple and Atari. One of the factors that made MS-DOS a winner was that Microsoft actively
stimulated application software firms thus gaining advantage through imitation network effects (see
next section), i.e. by creating a platform of hardware, compatible operating systems and thousands
of applications for an unprecedented number of computing segments in comparison to mainframes
and minicomputers.
There is a significant difference in the market mechanisms, especially pertaining to network
effects, in early and rapidly expanding markets, when product innovations from different firms
compete, and mature markets, where process innovations of the winning designs and firms

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overtake the market (Abernathy and Utterback, 1978). In mature markets dominant designs have
emerged which leads to a shakeout of firms to a few surviving suppliers. Utterback (1994)
substantiates this in a number of industries. The software industry is not an exception
In the computer industry, Borrus and Zysman (1997) even write about the "Wintelist" era
that has emerged, which “is a struggle over setting and evolving de facto product standards in the
market, with market power lodged anywhere in the value-chain, including product architectures,
components, and software. Those constituent system elements--from components and subsystems
through operating and applications software--become separate and critical competitive markets.”
Network effects and other factors lead to a number of expert Wintelists that in their platform
network control “open-but-owned” systems built to “restricted” standards (Borrus and Zysman,
1997). Completely open and successfully adopted standards can be imitated by competitors that
do not have to amortize R&D costs as much as if they entered early in the R&D process, thus
mitigating first-mover advantages. Candidate “Wintelist” companies include Microsoft (operating
systems and office automation software for personal computers), IBM (mainframe computing
technology and even services in very large information systems), Oracle (non-PC relational
databases), and SUN Microsystems (workstations and UNIX servers). The judicious point here is
for a company to control the standard of important components of information systems. Customers
also require de facto standards in the different components of their information systems.
Consultants have to comply with this.
On the open-but-owned principle in the software market, commercial software is usually
sold in a compiled form, which means that users can execute it but not analyze how it is designed
and implemented in all its details. The source code in which the software is written is a highly
protected asset, which is not publicized to guard against imitation from competitors. The software
vendor can choose to publicize interfaces, so called "Application Programming Interfaces” (APIs),
to the compiled software to allow developers in the market to hook onto the software, e.g. to
develop complementary software. For example, in Microsoft Windows compiled Dynamic Linked
Libraries (DLLs) are available for many routine tasks such as setting the properties of a printer
driver and playing a multimedia file. A developer does not have to write the printer driver or the
multimedia library anew. A simple call to the library suffices. Furthermore, Microsoft and others
supply powerful development tools that allow the average developer to produce software quickly.
In conclusion, as long as users have a license for the operating system, developers can use many
ready-made functions in the operating system, database software and development tools. This is an
instance of how operating systems suppliers take advantage of imitative network effects.
Organizational implementation issues, which are essential for organizational adoption, have
been studied extensively in the information systems literature since they are often challenging.
Rogers (1995) conceptualizes five steps in the adoption process: (1) knowledge, (2) persuasion,
(3) decision, (4) implementation, and (5) confirmation. The stages of the adoption process of
software and information systems have been studied extensively and Roger’s model is theoretically
compatible with those studies, even if iterations are frequent. See for example Cooper and Zmud
(1990) for an influential article, Lai and Malapert (1997) for a meta-analysis of relevant scientific
articles, Wildemuth (1992) on the adoption of intellectual technologies such as software, Barnett
and Siegel (1988) and, on client/server technology adoption, Chengalur-Smith and Duchessi
(1999). Other dimensions in the software adoption process within an organization often mentioned

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is the role of top management support, the role of innovative champions (Beath, 1991), available
slack resources, etc.
Software adoption within organizations has however some distinctive characteristics.
Fichman and Kemerer (1995) discuss for example the “assimilation gaps” that portray the
difference over time between acquisition of software and deployment. Jurison (1993) has found in
a study on office information systems that software adoption varies over time and by the type of
profession involved (managers, project engineers, professionals and secretaries). E-mail was
shown in the study to be highly adopted over time (3 years) and among all studied professions. In
contrast, project management software had a low and decreasing average adoption level in the
studied population. Adoption was limited to project engineers. Anyone using an office suite such as
Microsoft Office or Lotus SmartSuite knows that only a small part is used by each user. The
package or bundle targets a number of user categories. The problem in software package design
that significantly differentiates it from software consulting services is that the design must be set for
the total market when the package is released. Microsoft and other major packaged software firms
conduct extensive and continual market intelligence on a global scale to match software
functionality with needs for each release thus managing the innovation concepts of assimilation gaps,
compatibility, complexity, etc. OOD/OOP is an important instrument in this R&D process.
Conclusions associated with winning the adoption and network
effects game
In the early market of an information technology, the positive feedback mechanisms
ultimately leading to dominant designs force companies to struggle to control their organization and
market like if they would be on a soaped slope resembling an inverted S-curve with haphazard
and sometimes fatal obstacles. Companies have to frequently measure the trajectories and stops
in order to adjust their path. This uncertainty explains in part why companies concentrate on
one or a few selected segments of the IT-market.
Innovation theory with concepts such as adoption and network effects can explain salient
mechanisms of the software market. Further theoretical and empirical research on network effects
and innovation are needed to increase our understanding of the complex software market.
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Acknowledgements: I would like to thank Dr. Frédéric Delmar, Entrepreneurship and
Small Business Research Institute, Stockholm, Sweden and Professor Dilek Cetindamar
Karaomerlioglu, reviewer of the IAMOT 2000 conference, for precious feedback. The Swedish
Council for Planning and Coordination of Research (FRN) as well as Mälardalen University have
funded this work.
Bio-Sketch of Author: Michaël Le Duc is an Assistant Professor. He received his Ph. D. in
informatics from the Stockholm University in 1996. His research interests include innovation
management in software and information systems services, Decision Support Systems, Software
Engineering and Geographical Information Systems.