ON TECHNOLOGY INNOVATION
A Community Succession Model for Software Enterprise
Qianhui Liang and Weihui Dai
School of Information Systems, Singapore Management University, Singapore
School of Management, Fudan University, China
Keywords: Software enterprise, Technology innovation, Ecological community, Succession model.
Abstract: In this paper, we have taken an economic approach of technological innovations to studying the issue of
evolution in software enterprise. Based on Lotka–Volterra equations and equilibrium formula, we have built
a model for the dynamics of software technological innovations. The model is applied in order to derive the
typical succession patterns of communities and a method for optimal co-existence and interactions among
the communities. We validate our model by presenting a case study on the development process of the
software enterprises.
1 INTRODUCTION
To achieve efficient and sustained innovations of
software enterprise, a number of key factors such as
effective motives, a complete element market and a
tactical technology transfer system, have to evolve
to its optimal status ultimately. These factors are
implemented by components such as research and
development, software production, financial and
investment, technology service brokerage,
technology administration, and etc. These
components co-exist in the software sector and work
closely with each other in a collaborative and
interactive way. The components and their
interactions result in the evolution of software
enterprise, which are marked by the continuous
innovations created by them.
It is reported in a number of research that
technology innovation community can be seen as a
social “technology” community that is based on
industry associations, characterized by geographical
closeness, and composed of interrelated and
interaction-al innovative organizations. The theory
of ecosystem communities and economics of
technology innovation provide several implications
that can be used to study technology autonomous
innovation community and their organizational
patterns and operational mechanisms.
In this paper, we have taken an economic
approach of technological innovations to studying
the issue of evolution of the software enterprise. The
components of the software enterprise innovation
are modelled as communities in an ecosystem that
evolve over time. Based on Lotka–Volterra
equations and equilibrium formula, we have built a
model for the dynamics of technology innovation on
software enterprise. The model is applied to derive
the typical succession pattern of communities and a
method for optimal co-existence and interactions
among the communities.
2 RELATED WORK
The innovation system of the software enterprise has
evolved from a linear, static pattern to a systematic
and dynamic pattern with a lot of complex
interactions. The earlier innovation pattern can be
seen as driven by the technology push and market
pull, which is described in the theory of Schumpeter.
(Schumpeter., 1990). With an increase of the
complexity and scale of software products, this kind
of innovation pattern can no longer meet the
requirements for software innovation. This has
resulted in the emergence of a new innovation
pattern that is more adaptive and complex. This new
pattern consists of various related software
organizations and environments in various
geographic regions at certain periods of time.
(Weihui Dai, Mingqi Chen, Nan Ye, to be
published).
The operational mechanism in natural ecosystem
411
Liang Q. and Dai W. (2009).
ON TECHNOLOGY INNOVATION - A Community Succession Model for Software Enterprise.
In Proceedings of the 11th International Conference on Enterprise Information Systems - Information Systems Analysis and Specification, pages
411-415
DOI: 10.5220/0002013704110415
Copyright
c
SciTePress
gives us inspirations in exploring the complex
interactions and evolution rules on innovation
system. The study of interrelationships among
species and between species and their physical-
chemical environments in ecology has defined
community as a group of species occurring in a
particular area, and ecosystem as assembles of
species, communities and the physical and chemical
components forming a more or less stable system.
In the early of 1960’, some researchers already
noticed the similar ecological characteristics in
social and economic organization. This lead to
successful findings in the evolution mechanism of
economic communities by Nelson and Winter in
1982. (
Nelson, R.R., Winter, S.G., 1982). Freeman and
Hannan gave a systematic summary of the theory,
methodology and research experience on
organizational ecology. (
Hannan, M.T., Freeman, J.H.,
1989
). Those theory and methodology were also
applied to technology innovation. An innovation
ecological community can be described as some
entities and their inter-relative institutions which
jointly and individually contribute to the
development and diffusion of new technologies. In
1993, Gerry Martin and John Ziman organized a
mult-discipline team to explore the evolution
features and rules of technology innovation from
philosophy, ecology and behavior science. (
Lucheng
Huang, 2004
). Therefore, ecosystem on technology
innovation has been a new hot point. (Lucheng
Huang, 2003
).
In comparison with other innovation
communities, the innovation community of software
enterprise has its unique characteristics. (
Jianping
Wang, 2003
). In the startup stage, software
enterprises usually infest with universities and hi-
tech parks in order to obtain shared talents, capital,
and infrastructures. When entering in the mature
stage, they appear to be inquilinous with some other
industries from a gregarious situation, to avoid
excessive competition. (
Libing Shen, Weihui Dai, 2006;
Nan Ye, 2006
). The “food” sources and “species”
competition play a leading role in the evolution of
software communities. (
Weihui Dai, Mingqi Chen, Nan
Ye, to be published
).
3 LOTKA-VOLTERRA MODEL
The innovation community of the software
enterprise contains some innovation populations,
supporting populations and their environments.
Through the transforming of capital, information and
material, these elements build up a complex and
open system. (
Weihui Dai, Mingqi Chen, Nan Ye, to be
published). To study the competition and its
mechanism in the evolution of software innovation
communities, we used Lotka-Volterra model in this
paper. This model was presented by Lotka and
Volterra in 1925 (
Lotka A. J., 1925), 1926 (Lotka A.J.,
1926
) and 1931 (Volterra. V., 1931) to describe the
quantitative relationship of rival species that in
natural ecosystem.
Lotka and Volterra firstly applied the growth
Logistic equation to the dynamic process of two
competing populations, as shown in (1) and (2)
(
Volterra. V., 1931), where N
1
and N
2
represent the
population size of two species, r
1
and r
2
represent
their In-increase rate, K
1
and K
2
represent their
maximum size of population restricted by resources,
α and β are the competing coefficients of two
species. Competing coefficient is used to represent
the affect and competence imposed by one species to
another. If two species are not in unsolvable conflict,
both α and β are zeros. If they request the exact same
type of resources, both α and β are ones. If species
one consumes a lot more resources than species two,
α is much larger than β.
'
2
CNN
K
rN
rN
dt
dN
=
(1)
1112
11
1
()
dN K N N
rN
dt K
α
=
(2)
The Lotka-Volterra competence equation can be
use to describe the competitions among populations
of software companies that adopt different software
technology standards. Let us assume two
populations of application software development
have different technology innovations or different
software technology standards, for example, .NET
platform from Microsoft and J2EE platform from
Sun, noted by population-.NET and population-
J2EE. The sizes of both populations, i.e.
1
N
and
2
N
, can be modelled as (1) and (2). Their sizes
are affected by the self-crowd existing among the
individuals within the same population as well as
competitions among the individuals across different
populations. There are three outcomes of the
competition: either population survives or both
populations survive. Figure 1 shows the density of
both two populations, with the vertical axis as the
density of population-.NET and the horizontal axis
as the density of population-J2EE
ICEIS 2009 - International Conference on Enterprise Information Systems
412
2
N
2
K
2
/0dN dt >
2
/0dN dt <
0/2
=
dtdN
2
/K
β
1
N
Figure 1: Increase rate of population-.NET.
Similarly, in figure 2, the vertical axis represents the
density of population-J2EE and the horizontal axis
represents the density of population-.NET.
1
/K
α
1
/0dN dt >
1
/0dN dt
=
1
/0dN dt <
2
N
1
K
1
N
Figure 2: Increase rate of population-J2EE.
Please notice the above model does not consider any
situation factors. Therefore,
1<×
α
is used to
constrain the effect of co-existence and co-beneficial
such that the populations will not grow to infinity in
the model. When the co-beneficial of the two
populations reaches equilibrium, the population
sizes are larger. As shown in (3) and (4), as the co-
beneficial increases (
β
α
×
is larger), the sizes also
increase.
*
112 1
()/(1)NKK K
ααβ
=+ >
(3)
*
221 2
()/(1)NKK K
βαβ
=+ >
(4)
4 EVOLUTION PATTERN
We have defined two evolution dimensions for the
innovation community of the software enterprise in
terms of the integrity of innovation chain and a
rational ratio of different populations. Through the
observation on the different trends of software
enterprise on two dimensions, we have derived two
representative evolution patterns of population
succession in the software enterprise on shown as
follows:
Type I evolution pattern conforms to the
following process: first, companies take up the
activities of coding and services at the bottom of
innovation ecology chain and accumulate their
knowledge of product development, including the
knowledge of system software; afterwards they
improve the population structure and specialized
into multiple populations on all kinds of software
products, including system software, support
software and application software. Lastly, upon
enough accumulation of knowledge on software
products, brand and market development, they climb
up the chain and forms a new population with an
independent brand and IT property.
Type II evolution pattern can be described as the
following. Software companies first build up the
complete ecology chain in a detailed product field
and perform all of the activities ranging from
research, design to coding in order to improve the
R&D capacity of the population and the individuals;
then they spawns new populations and individuals
that are capable of performing R&D on higher level
of software products via population succession of
the software enterprise, which results in the
succession of the entire community.
There may be variance to the theoretic patterns
above in the real development process of the
software enterprise. First, when the innovation
ecology community of software enterprise came into
being, all of the species may not be at the lower-end
of these two dimensions. It is probable that some
species exist at the middle of these two dimensions;
Second, the ideal state for the innovation ecology
community of the software enterprise is at the right-
top, which means that all of species in the software
enterprise present a better innovation chain and a
reasonable product composition. The analysis on the
evolving pattern of innovation ecology community
in the software enterprise is based on the above
framework basically reflects the development
process of the software enterprise.
5 CASE STUDY
In the development history of software enterprise
originated from the US, which has experienced four
stages. Especially from the 1980s, the software
enterprise went through rapid development. It is
represented by the mergence of numerous new
products and even new generations. This is also
indicated by the fact that more countries taking their
shares in the world software industry and become
the large countries of software, such as Indian,
Ireland and Israel. They have made great progress in
ON TECHNOLOGY INNOVATION - A Community Succession Model for Software Enterprise
413
the software industry and the detailed data are listed
in the following table, where the development index
of software refers to the ratio between the rate of
total output value of software industry in the total
value of GDT and the personal income of GDP. This
table shows the basic situations of different
countries’ software industries in 2002. The given
data tell us that the US takes the most weight in the
market of software industry, which takes the
dominated place in the world software industry.
However, other countries, such as Indian, Ireland,
Israel, Brazil and China, are the new members in the
software enterprise. In the analysis on these data, it
is apparent that the total output value of software
enterprise in China approximates those of Indian and
Ireland. But there are huge differences in the several
inspection data, such as the rate of this total output
value compared with GDP. Besides, though the
average personal output value is similar with that of
Indian, it is in the lowest list. The huge deviations of
software industries in Indian, Ireland, Israel and
Brazil are resulted from their different evolving
patterns in the different environments. The detailed
evolving processes for different countries are given
as follows in table 1.
Table 1: Evolving processes for different countries.
Account software sub-populations, the first
software cluster in Shanghai, China, has experienced
the process as Figure 3. From both figures, we can
see that the changes of sizes of populations very well
inline with our succession model.
Figure 3: Average numbers of competitors in account
software population.
If we consider the two species in this sub-
populations: N
1
for domestic account software
companies and N
2
for foreign account software
companies, the number changes of above two
species shown as Table 2 are accorded with the
Lotka-Volterra model in equation (1) and (2). The
equilibrium point of (3) and (4) are: N
1
*
=19.245,
N
2
*
=6.341.
Table 2: Number change of species in account software
population.
6 CONCLUSIONS
Technology innovation in software enterprise is an
adaptive complex system with its special mechanism
and evolution patterns. With the ecological theory,
we have explored their dynamic mechanism. Based
on the research of rival relationship with Lotka-
Volterra model, this paper derives two evolution
patterns of software innovation community
succession model. We are in the process of refining
the model and colleting more data for a better
validation.
ACKNOWLEDGEMENTS
This research is supported by the National High-tech
R&D Program of China (No.2008AA04Z127) and
Shanghai Leading Academic Discipline Project
(No.B210).
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