Cluster Network Model for Inter-SME Diffusion of Innovation

Stefan Kambiz Behfar

, Tina Afshar

and Parmiss Afshar

Department of International Business, HEC Montreal, H3T 2B1, Canada

Department of Strategy Planning, Parsian Bank, Tehran, Iran

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

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

irm

Tie

nowledge sharing activity

Tie strength

requency of activity

Tie diversity

Type of activity (joint team, projec

collaboration)

Tie content

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

Tie content Innovation (new product, process,

market)

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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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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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