Cluster Network Model for Inter-SME Diffusion of Innovation
Stefan Kambiz Behfar
1
, Tina Afshar
2
and Parmiss Afshar
3
1
Department of International Business, HEC Montreal, H3T 2B1, Canada
2
Department of Strategy Planning, Parsian Bank, Tehran, Iran
3
Transportation and Traffic Organization, Tehran, Iran
Keywords: SME, Diffusion of Innovation, Adoption, Adaptation, Cluster Network Model.
Abstract: Since Rogers (1995) first gave a typology of innovation diffusion, there have been many studies on the role
of networks in the topic of innovation diffusion and adoption. Bradley (1995) defined technology diffusion
as the spread of a new technology from one SME to another; whereas DiMaggio and Powell (1991)
emphasized that under conditions of uncertainty, inter-organizational diffusion of innovation occurs through
imitation (adaptation). Other authors have investigated rate of innovation where importance was given to the
number of firm linkages and geographical proximity (Florida 1995, Van Oort 2004). Although the role of ties
has been studied with regard to innovation diffusion and knowledge sharing, to the best of our knowledge,
there has been no published research concerning efficient innovation diffusion and adoption within SME
cluster networks, where efficient innovation diffusion with cluster is defined when most SMEs within each
cluster could adopt innovation. Here, we present a cluster network model for inter-SME diffusion of
innovation where SMEs represent nodes, and innovation adoption and adaptation between any two SMEs
represent ties. In such a model, we differentiate between SMEs as either sources or beneficiaries of innovation,
and discuss creation of ties among those SMEs and among cluster of SMEs. This study presents a conceptual
piece, where we provide three propositions a) The network model contains both source and beneficiary of
innovation, where the beneficiary adopts an innovation from source, or adapts to the innovation of another
beneficiary, b) The more efficient diffusion of innovation from one SME cluster to another is when two
clusters interconnect strongly rather than loosely, c) The rate of innovation adoption among SMEs depends
on their network dependency.
1 INTRODUCTION
Innovation is shown to be interactive, cooperative and
cumulative (Ahuja, 2000; Burt, 2004), where its
emergence requires many sources of knowledge
connected through a network. As products become
modular and knowledge within a complex system is
distributed among individuals within the system,
collaboration becomes essential for new product
development, since individuals do not possess all the
required knowledge to accomplish innovation
(Baldwin and Clark, 1997). Innovation usually results
from interactions among different bodies or sources
of knowledge (scientific, educational, public-private
institutions), where these sources of knowledge
aggregate into clusters with industrial, academic or
public players interacting within clusters (intra-
cluster) and between clusters (Inter-cluster).
Diffusion theories of innovation initially
introduced by Rogers (1958) explained adoption of
technological change by farmers. Rogers (1999)
defined “innovation diffusion as the process by which
an innovation is communicated through certain
channels over time among members of a social
system”. The literature includes various papers on
diffusion of innovation in manufacturing and service
industries, public policy, healthcare and education
(Nutley and Davis, 2000). Generally, the study of
innovation covers generation (new product, process,
and market), communication, adoption,
implementation and resulting behavior. Rogers
(1995) gave the first typology of innovation diffusion
covering innovation, innovativeness, opinion
leadership, diffusion networks, and rate of adoption
in different social systems, communication channels,
and consequences of innovation.
Huber (1991) suggested that organisational units
transfer knowledge and learn from other units. But
not all units have external access and internal capacity
to learn knowledge and apply it. Internal capacity can
be achieved by increasing R&D ability. Gurisatti et al
Behfar, S., Afshar, T. and Afshar, P.
Cluster Network Model for Inter-SME Diffusion of Innovation.
In Proceedings of the 1st International Conference on Complex Information Systems (COMPLEXIS 2016), pages 177-186
ISBN: 978-989-758-181-6
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
177
(1997) emphasized accumulated knowledge and
expertise as an important factor determining whether
firms are likely to adopt new technology. On the other
hand, external access to new knowledge can be
improved by networking. In this regard, Hansen
(1999) modeled an organization as a complex
network with inter-unit links, where knowledge
transfer can be investigated by analyzing the inter-
organizational network.
Bradley (1995) mentioned that "technology
diffusion can be defined as the spread of a new
technology from one SME to another". Many studies
have investigated SMEs’ innovation activities and
some studies have examined networks of innovation
where firms collaborate on projects (Batterink et al
2010, Ngugi et al 2010). However, not many studies
have discussed the role of firms within the network
(Gardet et al 2012). Narula (2004) showed that SMEs
often lack resources and capabilities to innovate
exclusively, and this makes a network essential for
SMEs to access innovation diffusion. Other authors
have investigated the role networks in increasing
knowledge sharing. Ma and Agarwal (2007)
discussed the role of perceived identity in augmenting
knowledge sharing. Kraut (2007) investigated the role
of similarities in direct reciprocity and design of
online communities. An alternative approach argued
that under conditions of doubt and uncertainty, inter-
organizational diffusion of innovation occurs through
imitation or adaptation (see DiMaggio and Powell
1991) where organizations learn from similar
organizations or from industry leaders. We propose a
network model for inter-SMEs diffusion of
innovation, as shown in Figure 1. For any given
technology innovation, some SMEs play a source role
and others act as beneficiaries of innovation, where
the beneficiary can either adopt innovation from the
source, or adapt to the exiting innovation of another
beneficiary.
Proposition 1: A network model contains both source
and beneficiary of innovation, where the beneficiary
adopts the innovation from source, or adapts to the
innovation of another beneficiary.
Innovation cluster is defined as an ensemble of
various firms and institutions that interact formally
and informally via agreement and transactions or
informal occasional meetings and collectively
contribute to innovation within a given industry.
Literature has rendered different perspectives on
clusters: learning, knowledge sharing (geographic
and cognitive distance), governance and transaction
cost economics (Williamson 1975),exploration
(discovery, development of idea) and exploitation
Figure 1: Illustration of SMEs as source and beneficiary of
innovation; where beneficiary adopts from source and
beneficiary imitates (adapts) from another beneficiary.
(implementation of idea) (Holland 1975). Innovation
exists in all areas of products, processes and market
structure that all affect network dynamics. Many
small enterprises cooperate with other small or big
enterprises in order to explore and exploit new
technology, while one has to make distinction
between sharing know-how and physical assets, and
knowledge or information spill-over. Coleman (1990)
and Uzzi (1999) argued that strong ties within a dense
network are efficient for exchanging complex
knowledge, and redundant ties lead to more trustful
and cooperative behavior. Burt (1992) argued on the
contrary that strong ties are inefficient for acquiring
external knowledge because they lack diversity in the
resources needed for innovation, and at the same time
increase communication costs as a result of tie
redundancy. Although the role of ties has been
studied with regard to innovation diffusion and
knowledge sharing, to the best of our knowledge,
there is no published research concerning efficient
innovation diffusion and adoption within SME cluster
networks. Efficient innovation diffusion implies that
most SMEs within each cluster could adopt
innovation. In this study we present a cluster network
model for inter-SME diffusion of innovation where
SMEs represent nodes, and innovation adoption and
adaptation between any two SMEs represent ties, and
propose that:
Proposition 2: The more efficient diffusion of
innovation from one SME cluster to another cluster is
when two clusters interconnect strongly rather than
loosely.
Granovetter (1973) proposed a network theory for
linking micro and macro levels of sociological theory
through an analysis of various types of weak ties.
Strong ties are relationships with individuals whom we
know very well, but weak ties provide bridges which
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178
allow innovations to cross boundaries between social
groups (clusters), which themselves are strongly tied.,
A definition of weak tie was used by Hansen (1999) to
investigate the transfer and sharing of knowledge in an
organizational system. Kogut (1992) and Tsai (2000)
suggested that social networks facilitate creation of
new knowledge within organizations. In another study,
Tsai (2001) focused on the question “How can an
organizational unit gain useful knowledge from other
units to enhance its innovation and performance?”, and
emphasized the role of strong ties in intra-corporate
and strategic alliances.
Authors have also discussed role of ties on rate of
innovation, where Ahuja (2000) and Shane et al
(1994) discussed firm’s network relationship
impacting the rate of innovation, where network
allows for knowledge sharing and information flow.
Others have studied role of networks within topic of
knowledge sharing and innovation adoption where
importance was given to the number of firm linkages
and geographical proximity (Florida 1995, Van Oort
2004) impacting rate of adoption. This also applies
to our proposed network model based on innovation
adoption and adaptation within SMEs network.
Proposition 3: Rate of innovation adoption and
adaptation among SMEs depends on their network
dependency.
There are some conceptual and contextual
assumptions regarding our proposed theory:
Innovation usually results from interactions
among different sources of knowledge (here,
SMEs are sources of knowledge).
Intra-cluster ties are assumed to be strong which
allow for faster exchange of knowledge and
inter-cluster ties are to be weak and long which
allow for better access to external knowledge.
At any given time, an SME is either the source or
beneficiary of innovation, not both
simultaneously. However, at another time, it
could reverse its status as source or beneficiary
of innovation.
SME-beneficiary will adopt innovation from
another SME-Source provided that the source
agrees to transfer the innovation and at the same
time, the beneficiary has the internal capacity to
adopt the innovation.
If SME-beneficiary cannot adopt innovation
from another SME-Source, it attempts to adapt
(imitate) to innovation of another SME-
beneficiary.
Our proposed theory constructs are yet to define
(Innovation adoption and adaptation). Innovation
adoption refers to adoption of SME beneficiary from
source of innovation. Innovation adaptation refers to
imitation of SME beneficiary to existing innovation
of another beneficiary. Our phenomenon of interest is
“Efficient diffusion of Innovation”.
In the first section of paper, Source and
Beneficiary of Innovation, we discuss definitions and
differences of adoption and adaptation, the types of
innovation are, and the reasons for adoption and
adaptation. In the second section of the paper, we
discuss business and social network models and how
our proposed network model is different in terms of
network structure. In the next section, we discuss the
roles of ties in diffusion of innovation and the
ambiguities in the literature with respect to efficient
role of ties on innovation diffusion. In the next
section, we propose our cluster network model by
showing a model diagram and investigate the role of
cluster coupling in efficient diffusion of innovation.
Finally, we propose that the rate of adoption is
influenced by resources acquisition, number of
linkages, and tie number and heterogeneity.
2 SOURCE AND BENEFICIARY
OF INNOVATION
New technology exists in all the areas of products,
processes and market structure that all affect network
dynamics. Many small enterprises cooperate with
other small or big enterprises in order to explore and
exploit new technology. In the network of SMEs,
there are a few percentages of SMEs as innovators
which are source of innovations, while others adopt
innovations. How can we differentiate between those
SMEs: 1) those which innovate themselves and are
source of innovation; 2) those which adopt innovation
but are also source of innovation; 3) those which
adopt innovation and are beneficiary for innovation;
and 4) those which imitate innovation? Therefore, we
pose the questions:
a) What are the differences of adoption and
adaptation? and what are the types of innovation?
b) What are the reasons for adoption and adaptation?
2.1 Adoption versus Adaptation
Adoption is defined as an organization’s emulation of
another organization innovation. Organization’s
definition of core purpose and domain and expertise
specify whether to adopt or not. Alternatively,
adaptation is defined in two ways:
1. as organization’s reformatting to the needs of its
Cluster Network Model for Inter-SME Diffusion of Innovation
179
environment after adoption. In the topic of
technology innovation, an existing technology is
either sufficiently adapted to new circumstances, or a
new or improved technology will be adopted. By this
definition, both processes of adaptation and adoption
are inter-linked, where adaptation of existing
technology happens before adoption of new
technology, or adaptation happens during the process
of adoption of new technology, or adaptation happens
after adoption of new technology.
Table 1: SME adoption and adaptation of innovation.
1. Source SME
1. Product 2. Source MNE
Adoption 2. Market -----
3. Process 1. Source SME
SME
2. Source MNE
Adaptation
1. Beneficiary SME
1. Product 2. Beneficiary MNEs
2. Market -----
3. Process 1. Beneficiary SME
2. Beneficiary MNEs
2. as organization’s imitation of existing technology.
We refer to innovation adoption and adaptation as
new technology adoption from a source or imitating
an existing technology from another beneficiary. As
illustrated in Table 1, SME-beneficiary adopts from
SME or MNE source, but adapts (imitates) to
innovation of other SME or MNE beneficiary.
2.2 Reasons for Adoption and
Adaptation
As illustrated in Table 2, SME-beneficiary adopts
innovation from SME-Source based on the conditions
if the source agrees to transfer innovation; and at the
same time beneficiary has the internal capacity to
adopt innovation. If any of these two conditions is not
fulfilled, then the beneficiary adapts to innovation of
other SME beneficiary, as demonstrated in Table 2.
Table 2: Reasons of adoption and adaptation, where S.A.
implies whether Source Agree to let beneficiary adopt
innovation or not; B. I.C implies whether Beneficiary have
Internal Capacity or not.
S. A./B. I.C. 1 0
1 Beneficiary adopt
innovation from
this cluster Source
Beneficiary adapts to
innovation of one of this
cluster beneficiaries
0 Beneficiary adopt
innovation from
another cluster source
Beneficiary adapts to
innovation of one of
another cluster beneficiaries
3 CLUSTER NETWORK
MODELS
Networks are defined as relationships between actors
where these actors include individuals, groups or
organizations (Aldrich and Zimmer 1986, Burt 1982,
1992, Ireland et al 2001).
3.1 Firm Business and Knowledge
Sharing Network Models
Network relationships among organizations
constitute different forms such as joint ventures, sub-
contracting, strategic alliances, and more that in fact
exchange or share, co-develop new products (A. J.
Groen, 2005).
Researchers have investigated importance of
network on knowledge sharing and impact of
collaboration on network overall performance.
Authors have discussed firm’s network relationship
impacting the rate of innovation (Ahuja 2000, Shane
et al 1994), where network allows for knowledge
sharing and information flow. Knowledge sharing
network elements are categorized in Table 3.
Table 3: Details of firm knowledge network model.
Node
F
irm
Tie
K
nowledge sharing activity
Tie strength
F
requency of activity
Tie diversity
Type of activity (joint team, projec
t
collaboration)
Tie content
K
nowledge (know-how, information, asset)
3.2 Proposed SME Cluster Network
Model
Innovation cluster is defined as ensemble of various
firms and institutions that interact formally and
informally via agreement and transactions or informal
occasional meetings and they collectively contribute
to innovation within given industry. Nonetheless we
discuss how SMEs network made of source (S) and
beneficiary SMEs are connected and propose a new
clustered network model detailed in Table 4 in terms
of node type, tie strength, diversity, and content.
Table 4: Details of proposed SME Cluster Network model.
Node SME source, SME beneficiary
Tie Innovation adoption, adaptation
Tie strength Rate of adoption, adaptation
Tie diversity Weak-vs-Strong and Intra-vs-Inter cluste
r
Tie content Innovation (new product, process,
market)
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180
As observed in Figure 2, if clusters overlap via SMEs,
then those clusters strongly inter-connect, while
separated clusters loosely inter-connect. We define
three types of SME clusters’ connections.
Figure 2: Illustration of intra (inter) SME-cluster links,
where SME adopts from Source (S), and SMEs adapt to the
innovation of other SMEs.
Two beneficiaries could inter-connect, two sources
could inter-connect, or one beneficiary could inter-
connect with one source.
1. When two clusters overlap via one or more SMEs,
then these two clusters interconnect strongly.
2. When two clusters do not overlap, but interconnect
via source-source connection, then these two clusters
interconnect strongly.
3. When two clusters do not overlap, but inter-
connect via dependency among two beneficiaries
from two clusters, then these two clusters
interconnect loosely.
4 ROLE OF TIES IN DIFFUSION
OF INNOVATION
4.1 Innovation via Access to External
Knowledge
Organizational systems have provided three solutions
for access to external knowledge in complex
networks (Goduscheit 2009): a. integrated system, b.
modular system, c. networks.
a. Simon (1962) viewed firms as hierarchical systems
made of subsystems that are loosely coupled
vertically and horizontally and interact based on input
and output. Loose coupling implies that interactions
among subsystems are much weaker than interactions
within subsystems.
b. When systems grow big, the number of interactions
among subsystems becomes numerous and an
integrated structure could be no longer used for
coordination and management. An alternative
solution for organization of production and
innovation would be a modular system (Baldwin and
Clark 1997 and Langlois 2002) which implies a
nearly decomposable system. However, in reality
firms do not appear as purely integrated or purely
modular in terms of organization of production and
innovation, but feature a variety of interactions and
benefit from both modularity and integration
(Brusoni and Prencipe, 2001).
c. The third solution would be a network, which relies
on heterogeneous resources across firms. Integration
among firms reduces costs of accessing dispersed
knowledge leading to innovation (Ahuja 2000, Kogut
2000, Powell 1990). Innovation usually results from
interactions among different bodies or sources of
knowledge, where these sources of knowledge
aggregate into clusters with players interacting inside
(intra-cluster) and outside (Inter-cluster). In the
context of organizational systems, innovation cluster
is defined as an ensemble of various firms and
institutions that interact formally and informally via
agreement and transactions or informal occasional
meetings and they collectively contribute to
innovation within a given industry.
4.2 Ambiguity in the Role Efficiency of
Tie in Diffusion of Innovation
Granovetter (1973), Burt (1992), Hansen (1999)
emphasized the role of weak ties (distant ties) in
acquiring external knowledge needed for innovation.
Granovetter (1973) proposed a network theory for
linking micro and macro levels of sociological theory
through an analysis of various types of weak ties that
bridge groups. Burt (1992) argued that strong ties are
inefficient for acquiring external knowledge as they
lack the diversity in resources needed for innovation,
and at the same time increase communication costs as
a result of redundancy of ties. Therefore, weak ties
(non-redundant, less-frequent) are more appropriate
for communicationto allow access to a variety of
knowledge. In the context of organizational systems,
Hansen (1999) also noted that weak ties between
units are more advantageous than intra-unit ties,
because infrequent and distant relationships are less
likely to provide redundant knowledge and more
likely to preclude duplicity of documents.
On the other hand, Coleman (1990) and Uzzi
(1999) argued to the contrary that strong ties within
dense network are required for exchange of complex
knowledge, and redundant ties lead to more trustful
Cluster Network Model for Inter-SME Diffusion of Innovation
181
and cooperative behavior. Kogut (1995) argued that a
dense innovative cluster provides quick transfer of
information, knowledge sharing, more interactions,
better integration, and better coordination; it also
favors the organization in terms of lower transaction
cost and risk and shared trust and identity.
As one sees, for the purpose of innovation, there
are ambiguities in the benefits of networks: one
concerns the distinction between strong and weak ties
(Granovetter 1973, Nelson 1989), the second is
between sparse network structures (Burt 1992) versus
dense network structure (Walker et al 1997).
5 EFFICIENT DIFFUSION OF
INNOVATION AMONG SME
CLUSTERS
5.1 Proposed Model Diagram
We investigate efficient diffusion of innovation
among SME clusters by proposing the hypothesis that
the more efficient diffusion of innovation from one
SME cluster to another cluster is when two clusters
interconnect strongly rather than loosely, where
efficient innovation diffusion implies that most SMEs
within each cluster could adopt innovation.
In the model map, given in Figure 3, we use a
moderator variable (cluster coupling) in order to
explain relation of the constructs “inter-SME tie
(innovation adoption and adaptation)” with the
outcome “efficient innovation diffusion”. When
cluster coupling is strong, this leads to more SMEs
adopting innovation, i.e. efficient cluster diffusion.
When cluster coupling is loose, this leads to just
immediate neighbors adopting innovation, i.e. less
efficient cluster diffusion of innovation.
Inter-cluster tie moderated by its strength allows
access to external knowledge via weak ties and faster
exchange of knowledge via strong ties; and that lead
to efficient role of tie in innovation diffusion.
5.2 Role of Cluster Coupling in
Efficient Diffusion of Innovation
In this section, we attempt to elaborate role of cluster
coupling shown in Figure 4 in efficient diffusion of
innovation. If clusters overlap via SMEs strong inter-
connection, this leads to efficient diffusion of
innovation among SMEs, most SMEs of either cluster
can connect to each other via joint SME connections.
When an SME adopts or adapt to an external
innovation, all other SMEs within cluster adapt to
innovation as well.
If separated clusters strongly interconnect via
SME Source-Source connection, this leads to
efficient diffusion of innovation among SMEs, as
SMEs of either cluster can adopt innovation from its
cluster source (S). Whereas, if two clusters
interconnect loosely via SME-SME link, one SME
(beneficiary) adapts to the innovation of other SME
(beneficiary) and this leads to:
Source (S) of each cluster cannot adopt this
innovation from the beneficiary SME, since the
S – SME link is one-sided from source to
beneficiary. Therefore, this weak inter-cluster
connection renders less-efficient innovation
diffusion.
Immediate neighbors of SME (not ALL SMEs)
can adapt to the innovation that has been adapted
from the other cluster. Therefore, this weak inter-
cluster connection renders less-efficient
innovation diffusion.
Figure 3: Illustration of the theory model diagram.
Efficient Implemen-
tation
(
Walker 1997
More efficient cluster
diffusion
of innovation
Firm network tie:
agreement, transaction
Tie strength (weak-strong)
Cluster coupling (loose-tight)
Cluster network tie: Inno
adoption, adaptation
Inter-SME tie
Efficient innovation
diffusion (Burt 1992)
Access to external knowledge
via weak ties
Faster exchange of knowledge
via strong ties
More SMEs adopt innovation
via tight inter-cluster coupling
Immediate neighbors adapt to
innovation via loose coupling
Less efficient cluster
diffusion of innovation
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182
Figure 4: Illustration of intra (inter) SME-cluster links, where clusters overlap (strongly connect), S-S interconnects, or SME-
SME interconnects (Weakly connect).
6 RATE OF ADOPTION
As Asheim and Isaksen (2002) mentioned, network
afford SMEs access to resources otherwise lacking,
and can be means of overcoming liability for
entrepreneurial firms. The rate of dependency
depends on several factors such as number of firms
within network, geographic spread, and previous
experience of cooperation, hub firm resources, and
hub firm size.
Many studies have investigated SMEs innovation
activities, and some have studied networks of
innovation such as Batterink et al (2010), where firms
collaborate on projects. Very few have studied the
role of hub firms within SME networks and
capabilities that lend to ways and mechanisms to
improve coordination between hub firms and other
SMEs (Ngugi et al 2010, Gardet et al 2012).
Table 5: Details of SME network analysis approaches.
1. Theory of power
Prfeffer and
salancik
(1998)
SME
network
Analysis
2. Linkages and geo-
graphical proximity
Florida (1995)
Van Oort
(2004)
3. Number of ties and
ties heterogeneity
Borgatti and
Foster (2003)
As shown by Gardet and Mothe (2012), through
networks, firms want to gain control over resource
flows, and hub firms try to maintain their dependency
on other firms in order to achieve innovation
objectives. There are different approaches to
dependency analysis with other SMEs. We provide
different SME network analyses in Table 5 explaining
our third proposition “Rate of innovation adoption
and adaptation among SMEs depends on their
network dependency”.
1. Pfeffer and Salancik (1978) and Proven et al.
(1980) proposed a theory of power within an
innovation network to analyse the dependency
relations that an SME hub firm has with other
members (similar to our proposed model). The source
of dependency is the acquisition of resources if a hub
firm does not have all the required resources for an
innovation project.
2. Florida (1995) has shown that number of linkages
of the firm affect geographical proximity within an
innovation network. Dollinger (1999) also stated that
the way a network is built affects knowledge creation
and sharing.
3. As shown by Sullivan and Marvel (2011), an
entrepreneur knowledge set is inadequate, and this
inadequacy is usually predominant during early
stages of business (Collinson and Gregson 2003).
Network ties are one way to overcome this
shortcoming, where ties are the individuals or firms
with whom entrepreneurs are in business contact.
These ties could be between two SMEs where an
entrepreneur adopts knowledge from another firm.
Two characteristics of these network ties are the
number as well as heterogeneity among ties (Borgatti
Cluster Network Model for Inter-SME Diffusion of Innovation
183
and Foster 2003). These ties may provide
entrepreneurs with a knowledge advantage as well as
access to other resources. Other authors such as Greve
and Salaff (2003) found that the number of network
ties has an inverse correlation with the early-late
stages of the venture, whereas early stage ventures
have usually more ties.
7 CONCLUSION
There have been many studies on the role of networks
within the topic of knowledge sharing and innovation
adoption. On one hand, Walker, Kogut and Shan
(1997) stressed the efficient role of close ties within
clusters on network outcome; on the other hand Burt
(1992) emphasized the efficient role of structural
holes between clusters on network outcome.
Although the role of tie for the purposes of innovation
diffusion and knowledge sharing has been
emphasized, to the best of our knowledge, there has
been no research in the literature in regard to efficient
innovation diffusion and adoption within an SME
cluster network, where efficient innovation diffusion
implies that most SMEs within each cluster could
adopt innovation. We presented a cluster network
model for inter-SME diffusion of innovation where
SMEs represent nodes, and innovation adoption and
adaptation between any two SMEs represent ties. We
provided and explained three propositions:
1) A network model contains both source and
beneficiary of innovation, where the beneficiary
adopts the innovation from source, or adapts to the
innovation of another beneficiary. Sources could be
SMEs or MNEs or other sources of innovation, while
beneficiaries are always SMEs. SMEs aggregate to
clusters where those SME clusters strongly or loosely
interconnect. We define three types of SME clusters’
connections.
When two clusters overlap via one or more
SMEs, then these two clusters interconnect
strongly.
When two clusters do not overlap, but
interconnect via source-source connection, then
these two clusters interconnect strongly.
When two clusters do not overlap, but
interconnect via link between two SMEs from
two clusters, then these two clusters interconnect
loosely.
There are several contextual assumptions in regard to
our proposed theory as follows:
At any given time, an SME is either source or
beneficiary of innovation, not both
simultaneously. However, at another time, it
could reverse its status as source or beneficiary
of innovation.
SME-beneficiary will adopt innovation from
another SME-Source provided that the source
agrees to transfer innovation to the adopter and
at the same time, the beneficiary has the internal
capacity to adopt innovation.
If SME-beneficiary cannot adopt the innovation
from another SME-Source, it will attempt to
adapt (imitate) the innovation of another SME-
beneficiary.
2) The more efficient diffusion of innovation from
one SME cluster to another cluster is when two
clusters interconnect strongly rather than loosely. If
clusters overlap via SMEs (strong inter-connection),
this leads to efficient diffusion of innovation among
SMEs. When one SME adopts innovation or adapts to
innovation of another SME, all other SMEs within the
cluster adopt or adapt the innovation as well, because
when two clusters overlap, most SMEs within these
clusters connect to each other. Therefore, this
connection renders more efficient innovation
diffusion. When two clusters interconnect via source-
source connection (strong inter-cluster connection),
SMEs of either cluster can adopt innovation from its
source (S) connected to the other source. Therefore,
this connection renders more-efficient innovation
diffusion too.
If two clusters interconnect loosely via SME-SME
link, one SME (beneficiary) adapts to the innovation
of other SME (beneficiary), and this leads to:
Source (S) of each cluster cannot adopt this
innovation from the beneficiary SME, since the
S - SME link is one-sided from source to
beneficiary. Therefore, this weak inter-cluster
connection renders less-efficient innovation
diffusion.
Only immediate neighbors of SME (not ALL
SMEs) can adapt to the innovation that has been
adapted from the other cluster. Therefore, this
weak inter-cluster connection renders less-
efficient innovation diffusion.
3) Rate of innovation adoption (adaptation) among
SMEs depends on their network dependency. There
are different analyses to network dependency among
SMEs: Theory of Power (Pfeffer and salancik 1998),
Linkages and geographical proximity (Florida 1995
Van Oort 2004), Number of ties and ties
heterogeneity (Borgatti and Foster 2003). Pfeffer and
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184
Salancik (1978) and Proven et al. (1980) proposed a
theory of power within an innovation network to
analyse the dependency relations that an SME hub
firm has with other members. The source of
dependency is the acquisition of resources if a hub
firm does not have all the required resources for an
innovation.
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