Practice-Based Innovation: Insights, Applications and Policy Implications || Communicating Connections: Social Networks and Innovation Diffusion

Chapter 4

Communicating Connections: Social Networks

and Innovation Diffusion

Pekka Aula and Olli Parviainen

Abstract The role of social networks in promoting the diffusion of innovations

is widely recognised, but networks are used more as a vague metaphor than

an analytic concept. In this chapter, we study the possibilities that social network

analysis (SNA) offers to promote the diffusion of innovations. In addition, we

investigate the roles of opinion leaders and opinion brokers in the networks of

innovation diffusion. We base our findings on a case study of a food industry

organisation. We conclude with some remarks on how the study of innovation

diffusion might benefit from adapting the methods of social network analysis.

4.1 Introduction

According to Harmaakorpi (2006), a valuable source of innovations are factors like

the ability to interact, learn collectively, and build relationships of trust between the

innovating partners. Consequently, an organisation’s innovation system ought to be

understood as a social infrastructure that consists of various networks with different

kinds of social relationships and social ties (Granovetter 2005; Burt 2004;

Harmaakorpi and Melkas 2005) as well as technological, information, and business

models (Carlsson et al. 2002; Gelsing 2010). The emergence and diffusion of

innovations, by definition, stem from situations where individuals or organisations

engage in communication processes (Rogers 2003). The communication system

P. Aula (*)

Department of Social Research, Media and Communication Studies, University of Helsinki,

Helsinki, Finland

e-mail: [email protected]

O. Parviainen (*)

Department of Social Research, Media and Communication Studies, University of Helsinki;

Entrepreneur, Verkostoanatomia, Helsinki, Finland

e-mail: [email protected]

H. Melkas and V. Harmaakorpi (eds.),

Practice-Based Innovation: Insights, Applications and Policy Implications,DOI 10.1007/978-3-642-21723-4_4, # Springer-Verlag Berlin Heidelberg 2012

49

mailto:[email protected]

mailto:[email protected]

needed to enable this interaction has been traditionally understood as the physical

system composed of technical devices and communicative linkages that form the

means of distributing messages and the corresponding organisational actions. The

emphasis has been on the quantity – not the quality – of the channels used.

We argue that the communication perspective is essential to understanding

the complexity and multidimensionality of the processes involved in diffusion of

innovations. We will support this argument with evidence from a case study in

which we identified central actors and defined channels of communication and

innovation diffusion in a company’s social network. We are interested in the

possibilities social network analysis provides at the inter- and intra-organisationallevels in promoting the diffusion of innovations. Instead of focusing on insti-

tutionalised communications channels in an organisation’s innovation system,

we propose that a more holistic view of the communication system involved in

innovation diffusion is needed. Placing emphasis on social relations might yield

a more diverse and complete picture of the communication system that enables

diffusion of innovations within an innovation system. Research on opinion leaders,

for example, has revealed that the biggest influence on individuals’ decision to

adopt an innovation is the opinion of a trusted few (Valente and Davis 1999; Rogers

2003). This power to influence others, opinion leadership, is based on the degree of

expertise and the opinion leader’s position within the particular social network. We

suggest that this influence over others means that content of communication and

position within the communication network matter as much as (or even more than)

channel of communication. Social network analysis as a method has provided

a workable instrument for studying these channels and key position holders,

and it also provides an instrument for studying the channels with specific content.

More importantly, social network analysis enables the detection of innovationpotential by detecting so-called structural holes (Burt 1992).

Social network analysis has been used to facilitate the adoption of innovations in

a variety of settings. However, it has primarily been used to detect individuals in

central network positions who are able to efficiently disseminate existing innovations

(Valente 1995; Valente and Davis 1999; Rogers 2003). Though the term ‘network’ is

widely used in innovation research (Parjanen et al. 2010), the systematic use of social

network analysis throughout the innovation diffusion process has been rare at both

the micro- and the macro-level. Research combining the inter-organisational macro-

and intra-organisational micro-level has been conducted, but the sources used in

gathering network information have been archives of institutional relationships, such

as patent applications (for example Ahuja 2000; Cantner and Graf 2006), which has

been proved problematic (Carlsson et al. 2002).

4.2 Diffusion of Innovations in Social Networks

According to Rogers (2003), an innovation is an idea, a practice or an object that

the unit of interest experiences for the first time. Innovations are conceived

when people exchange information on different needs and solutions to them.

The diversity of skills, talents, knowledge, and connections inside and outside of

50 P. Aula and O. Parviainen

the organisation increases the innovativeness of the organisation. Thus, the instru-

mental element in innovation is connections to other people and organisational

entities. In the following sections, we approach our research question from three

overlapping perspectives: diffusion of innovations happens through social networksthat are based on communicative interactions. Figure 4.1 explains the relationships

between these central concepts, most of which are overlapping but not synonymous.

We positioned these concepts in two dimensions: the technological-human dimen-

sion and the planned-emergent dimension. Social networks are more human

than technological and emerge from interaction between people. Communication

networks overlap with social networks but also possess a technological aspect. For

example, the internet-based social networking service Facebook has a social and

a technological aspect. A communication system is a planned technological struc-

ture, and an innovation system is a planned structure that usually consists of both

technological and human elements. In this study, we use social network analysis

to study the area of intersection formed by the central concepts.

4.2.1 Explaining the Diffusion of Innovations

The study of diffusion of innovations has its origins in the agricultural research

of the 1940s and 1950s. Since then, the theory has been used in sociology,

Fig. 4.1 The relationships between the central concepts

4 Communicating Connections: Social Networks and Innovation Diffusion 51

communications, marketing, management, and health care (Wejnert 2002; Rogers

2003). The study of diffusion of innovations can range from the characteristics of

innovations to the results of adopting innovations and to the environment in which

the innovation occurs (Wejnert 2002). Much of the research has focused on the

individual perspective, but this neglects, somewhat ironically, the social context

that is the framework for the diffusion (Rogers 2003).

Usually, diffusion has been conceptualised as a process in which an innovation

is communicated through various communication channels between individuals in

a social system. This means that to be ‘diffused’, the innovation does not have to be

adopted (Strang and Soule 1998). In other words, one can be aware of an innovation

but decide not to adopt it for use. As a matter of fact, much of the research on

innovation diffusion has taken place after the innovation in question has diffused

(for a historical review for research diffusion on innovations, see Rogers 2003).

Research on innovations during diffusion and on innovations that did not diffuseis rare (Rogers 2003).

Explanations for the diffusion of innovations are roughly derived from either

internal or external standpoints with regard to the social system (Strang and Soule

1998). The external view focuses on the innovation itself: the relative advantage of

the innovation, its complexity, trialability, observability, and compatibility (Rogers

2003). That is, the characteristics of the innovation are the critical factor as it is

adopted in a system of rational individuals. From an internal perspective, on the

other hand, diffusion of an innovation can stem from social pressure, learning, or

contagion (Young 2009), from structural equivalency (Burt 1999), or from prestige,

spatial proximity, or culture (Strang and Soule 1998).

A social system can be described as any stabilising framework in which com-

munication and human behaviour occur (Parsons 1991). Traditionally, the social

system within an organisation has been divided in to two parts or subsystems:

the official structure, consisting of hierarchical structures and established com-

munications channels, and the unofficial structure, consisting of social relations

between the members of the social system. It is important to keep in mind that

social systems are not the same as social networks and vice versa. Social systems

are a combination of the official structures mentioned above and informal social

networks. Though this divide is ubiquitous, the two structures overlap and are very

difficult to distinguish from each other (Flap et al. 1998). Official structures are

usually visible and well documented, making them the obvious subject of research.

Unofficial social structures are more dynamic, and thus laborious to capture without

the proper tools.

4.2.2 Social Network View on Diffusion of Innovations

The term network is used in various ways as a metaphor for decentralised, complex

and multiple-actor systems. ‘Network’ can be used as a part of a theory (e.g.

Castells 1996), when it is used to model actors’ relationships to each other.


‘Network’ can also be used as an analytic concept that is formed from methods

of gathering, handling, and modelling action (Johanson et al. 1995). In addition,

‘network’ is used in identity construction and cognitive conceptualisation of

organisations (Ibarra et al. 2005).

Network theories do not form a cohesive and uniform set. In contemporary

network studies, there are two different but converging traditions: the first tradition,

the social sciences approach, applies network theories with an emphasis on quanti-

tative natural sciences, and second tradition, the social anthropology approach, uses

it more as a metaphor (Knox et al. 2006). In addition, in the studies the unit of

interest varies greatly; for example, from cognitive semantic networks to trade

networks between nations. This same diversity can also be found in communication

network theories. Monge and Contractor (2003) offer a multi-theoretical, multilevel

model (MTML) that tries to bring together various theories and interest levels. They

argue that, instead of one level of interest, one should take in to account multiple

levels and theories at the same time. The theory of diffusion of innovations provides

one such MTML approach. Networks form conduits for attitudes and behaviours to

spread among the network members. This contagion can occur through being in

a similar position within a network or by direct contact (Burt 1999). In this study,

we will use the MTML as a guideline for involving networks at the organisational,

group, and individual level.

Social network analysis (SNA) is a set of various methods for describing and

studying human interaction, and it is used to study repeating patterns in connections

linking social actors (Wasserman and Faust 1994). This means that the unit of

analysis is not the individual actor, but the entity consisting of these actors and the

connections connecting them (Wasserman and Faust 1994). Usually the aim of

social network studies is to locate a set of actors forming a distinct subgroup and

actors filling a specific role or position (Freeman 2005).

SNA carries a few central concepts. Subjects interacting within a network are

referred to with various terms (nodes, points, actors), as are the relationships that

connect them (links, ties, arcs, connections). In this study, we use the terms actorand connection for clarity’s sake. Actors in networks can be people, groups of

people, or larger organisations. Connections between actors can describe, for

example, family ties, the flow of support or resources, emotional valuing, interac-

tive behaviour or membership in a group (Wasserman and Faust 1994). Depending

on the relationship, connections can be directed or undirected. A connection

describing e.g. ‘discusses with’ is undirectional, whereas in ‘asks advice from

someone’, the direction of the connection is important. In a directed connection,

the sender and the receiver can be distinguished. The strength of the connection can

vary as well. It can be either dichotomous (connection is present or absent) or

valued (Wasserman and Faust 1994). The value of the connection represents its

strength, intensity, or frequency (Monge and Contractor 2003).

SNA has been used, in form or another, as a research method in the social and

behavioural sciences since the 1930s. The first famous study is from 1932, when

Jacob Moreno stated that personal relations more than personal characteristics were

responsible for some of people’s actions (Moreno 1934). The popularity of network


analysis grew slowly until the 1990s, when the number of studies using SNA started

to grow rapidly. The reasons for this growing popularity lie in three phenomena: the

increased use of the network metaphor (e.g., Castells 1996), the rise of social

networking services (such as Facebook, Twitter, etc.), and increased computing

power and more developed computer software (Carrington et al. 2005).

Usually, quantitative social science relies on statistical tools designed to study

the background information (age, gender, education, etc.) – or attributes – of

subjects. These statistical methods assume that all attributes are independent from

the attributes of other subjects. In social network analysis, the individual study

subjects are considered interdependent. Specialised social network analysis tools

have been developed by combining social sciences, empirical studies, mathematics,

and traditional statistical science (Wasserman and Faust 1994).

Though the attributes of actors are also used in various statistical tools in social

network analysis, they are usually connected to specialised network statistics.

Network statistics describe either the actors’ network properties or the properties

of the whole network. Individual actors’ network statistics describe one’s position

within the network in relation to others. The most important of these measures

include degree, betweenness, and closeness1 (Everett and Borgatti 2005). Statistics

describing the whole network can describe values such as density, reciprocity, or

centralisation.2 A central node within a network is an actor whose position allows

her to access, spread, or control information, attitudes, and communication. While

a central position benefits the actor (see, for example, Burt 1992), it also embeds

the actor more deeply in the network (Wasserman and Faust 1994). This allows

the actor to monitor and control the network, while at the same time limiting her

options if she wishes to remain in the central position (Valente and Pumuang 2007).

4.2.3 Communication as Social Networks

Monge and Contractor (2003) have performed a comprehensive review of

network theories in communication. Most network theories have a structuralisticapproach. According to the perspective of constructive structuralism, people’s

values, beliefs, and attitudes are the result of their position within the organisation

(Monge and Contractor 2003). Burt’s (1992) theories of structural equivalence are

a great example of constructive structuralism in action (see also Knoke 1994).

1 Degree centrality measures the number of connections the actor has. Betweenness measures the

number of shortest paths (lines of connection) that go through the actor. Closeness measures the

actor’s distance to other members of the network.2 Density is figure between 0 and 1, with 0 meaning none of the possible connections are present,

and 1 meaning every possible connection is present. Reciprocity indicates what percentage of the

directed relationships in a network is mutual. Centralisation indicates how much structural power

within the network is centralised in a single actor.


The relativist structuralist approach argues that communication structures are

formed dynamically through action and therefore connect people regardless their

official roles and positions (see, for example, Rogers’s (2003) extensive research on

diffusion of innovations in networks). However, many network theories do not fit

clearly in either the constructive or relativist structuralistic approach. Burt’s theory

(1992) of structural holes and social capital is a good example of this. According to

Johansson (2000), a person’s fitting into a role is the result of his position within

official networks and direct unofficial communications.

Human communication by definition requires more than one participant. When

the number of participants exceeds two, the communication event can be

approached as a network. According to Monge and Contractor (2003), communi-

cation networks are recurring patterns that are formed by the flow of messages in

time and place, where a message is something that can move between the actors in

a network or be created by them. These messages form a relationship between the

communicating actors. Relationships can be described as information exchange,

knowledge of something, or communicating with someone. Through these

relationships, people are embedded in several different but often overlapping

networks.

However, in a social network bound together by communication, the number of

connections between the actors is not evenly distributed (Barabasi 2003); that is,

some actors have more and more diverse connections than others. According to the

structuralistic approach, this inequality of connections means that two groups of

actors have more influence in social networks than others: opinion leaders and

opinion brokers (Burt 1999; Valente and Davis 1999; Rogers 2003).

Opinion leaders are people who affect other peoples’ opinions, attitudes, beliefsand behaviour (Rogers 2003; Valente and Pumuang 2007). Opinion leaders are

central figures in social networks and usually adopt innovations early (Rogers 2003;

Valente and Pumuang 2007). The term opinion leadership originates from the two-

step flow of communication (Lazarsfeld et al. 1948), which states that mass media

do not influence people directly but through certain key people. Other people ask

these opinion leaders for advice, and their opinion is well regarded. Opinion

leadership is more often directed at some specific area of interest instead of

taking the form of all-encompassing leadership in all walks of life (Merton 1957;

Goldsmith and Hofacker 1991; Rogers 2003; Weimann et al. 2007). Studies (e.g.,

Childers 1986; Rogers 2003) have revealed that opinion leadership is not a strict

division into followers and leaders, but more of a continuum of a person’s ability to

influence others that is part of everyday interaction (Katz and Lazarsfeld 1955).

With this distinction, it is possible to use opinion leadership as metaphor for

influence in a network with specific content.

Opinion brokers bring new information to groups and act as go-betweens

between groups of similar composition (Burt 1999). A broker can influence or

regulate the flow of information within a network (Burt 1999; Monge and Contrac-

tor 2003). A broker fills a structural hole in a network. Structural holes refer to an

absence of strong ties within a network. Structural holes are not always a negative

phenomenon, as they allow more efficient functioning of the group, insulating it


from continuous and time-consuming communication with every other member of

the network (Burt 1999). Burt’s (1992) theory of structural holes describes how an

actor connecting separate groups gains a competitive advantage by making the

groups dependent on the brokers’ connections. The theory of structural holes is

related to Granovetter’s (1973) famous theorem on the ‘strength of weak ties’.

Granovetter divides people’s connections into strong ties and weak ties. Strong ties

are the connections that define us: family, friendship and spousal relationships,

whereas weak ties are the less-emotional ties: people that one knows but has not

invested lot of emotions in (acquaintances, etc.). According to Granovetter, weak

ties determine a person’s success, not strong ones.

Opinion leaders and opinion brokers are an important and highly influential part of

a network’s properties (Barabasi 2003). Opinion leaders and opinion brokers share

regulation of communication within the social network. Opinion brokers bring

new information and innovation to the network, but opinion leaders determine if

the innovation is adopted by and diffused within the social network (Valente and

Pumuang 2007). Using opinion leaders to diffuse new products and innovations has

been proved effective (Rogers 2003; Chan and Shekhar 1990), as people tend seek

advice from friends and other influential people in uncertain situations (Black 1982).

Opinion leadership and brokership are ways of describing influence within

a network, while mapping the structural holes makes it possible to detect untapped

innovation potential within it. Human interaction is not easily simulated, so to best

evaluate the possibilities of SNA in diffusion of innovations research, some practi-

cal measures are needed.

4.3 Case Study

In order to reflect our ideas on innovation diffusion and to analyse the roles of the

central actors in social networks, we conducted a case study of a company. The

practical aim of the study was to find the central actors and to define the channels of

communication and innovation diffusion within the company’s social network. We

followed the theoretical framework outlined above, where the central actors within

a social network are categorised into opinion leaders and opinion brokers.

Analysing communication patterns requires determining of the level and unit of

analysis, defining the set of actors, and choosing the right metrics to describe the

networks’ and actors’ properties. The level of analysis can be the organisational

level, but at the same time the unit of interest can be an individual. In this study, the

level of analysis varies, but the unit of interest is always an individual. Predefining

the actors in the network facilitated conducting of the analysis. We focused on the

staff and management of the case company. Selection of network metrics depends

on the level of analysis: analysing the position of individuals within the network

requires a different set of metrics than analysing the entire network.

In our study, we approached the diffusion of innovations and social and commu-

nication networks from the three different perspectives or levels of analysis


suggested by the MTML (Monge and Contractor 2003): organisational, group, and

individual. The first of these, the organisational level, focuses on the general

properties of the network. The second, the group level, concentrates on the kinds

of groups that are formed within a network and how they communicate with one

another. The third level, the individual, focuses on the network’s central actors. We

incorporated these three levels of analysis into the insights described earlier in light

of the theories of innovation diffusion, social networks, and communication

networks in order to investigate the possibilities of social network analysis in

diffusion of innovations.

4.3.1 Research Design

The study case is a food-industry plant and research unit (henceforth ‘the Com-

pany’) located in Lahti, Finland. It has 43 employees (including temporary workers

and a few researchers from the Company’s parent company). Data gathering was

conducted through a survey questionnaire administered at the Company’s facilities;

a researcher was onsite to assist in this process. The purpose of the study was

explained in a pre-survey letter circulated throughout the Company two weeks prior

to the survey. The letter proved to be very successful, as almost all respondents

were anticipating the study and little explanation was required during the study. The

response rate was 82%. The data were analysed by using the network analysis

program Ucinet v.6 and visualised using Netdraw.

Three types of networks were of interest in the study. The first network type was

formed on the basis of the listing of organisational connections outside the Com-

pany. The Company is a part of an industry cluster situated in the Lahti region, and

in the questionnaire the names of members of the industry cluster were given as

options for outside connections; a free text field was also offered for respondents to

list other connections. In addition, the respondents were asked to report the impor-

tance of the connection. Most of the connections reported were from the pre-defined

list of names from the cluster. The second network was formed from the answers to

the question, “From whom you have received information about new methods or

techniques that have been useful in your work?” In other words, the answers

provided the basis for the Company’s innovation network. The third network was

formed based on the answers to the question, “How often do you contact the

following people in work-related matters?” This network forms the setting for the

two previous questions, as it describes the daily and weekly interactions happening

in the Company.

The Company’s three social networks are for the most part structured around the

organisational chart. Most of the central actors in the networks are also central in

the organisational hierarchy. In order to get a better sense of the subgroups within

the Company, the actors were divided roughly into three groups according to

communication pattern and position. The first group, ‘management and experts’

(Group I), communicated mainly outwards, while the second and third groups,


‘technical support and experts’ (Group II) and ‘production’ (Group III), were

oriented inward. All of the groups communicate with each other. The tendency to

communicate repeatedly with the same actors was pronounced. The Company is a

production plant, and the task-based organisation of work positions actors in fixed

relationships to each other (see, for example, Burns and Stalker 1961).

4.3.2 Three Levels of Investigation

Organisational perspective. In this study, no concrete innovations were the subject

of innovation diffusion. Instead, we focused on the possible connections for

innovations to enter the Company and diffuse within it. The Company members’

connections to other organisations are numerous. Almost all of the actors whose

connections form the only link from the Company to another organisation are also

actors central to the inner social network. These actors fill structural holes that bring

new innovations into the network. There are seven actors that fill 17 structural holes

(Fig. 4.2). This may cause challenges, as an average of more than two structural

holes are recreated when one of those actors is not present. All of the actors that fill

the structural holes belong to Group I.

Our research focused primarily on the internal view of innovation diffusion, as

the research was conducted solely within one organisation. Furthermore, the con-

tent of the channels was broadly defined as work-related matters. More could have

been accomplished by asking a more specific network question and including more

than one organisation in the analysis.

Group perspective. All of the networks analysed were connected, i.e. every actorcould reach every other actor within the network. The social networks within the

Fig. 4.2 The company’s external connections (Notes: Round nodes are actors and square nodes

are organisations. The green organisations are connections that only one actor has.)


Company form a connected structure that makes the diffusion of innovations

possible. The amount of interaction is about one third of the network formed of

daily work-related interactions. In the innovation diffusion network, one tenth of

the relationships were reported as mutual, suggesting a one-way mode of commu-

nication was more prevalent. The average distance for an innovation to spread

through the network is three steps, and the maximum distance nine steps. This

means that for an innovation to spread to every corner of the social system, it takes

an average of three communicative actions, and a maximum of nine. In this kind of

elongated network structure, communication from one side of the organisation to

the other takes time. The clustering coefficient3 of the innovation network is rather

high. The combination of low density and high clustering indicates that information

on innovations spreads within small, tight-knit groups. According to structural

equivalence (Burt 1999), people in a tight cluster are most likely to have similar

information and require opinion leaders and opinion brokers to spread information

between them.

The degree centrality in the innovation network is about the same as in the daily

interaction network, but the betweenness centrality is more than 50% higher in the

innovation network than in the daily interaction network. This means that the

innovation network relies more on brokers than the daily interaction network.

Groups I and II receive information from outside of the group, while members of

Group III mainly share information on new innovations within the group. Group I

also is well connected inside the group, and its members have the most connections

outside the Company.

Individual perspective. There were clear central actors in the Company’s social

networks. These actors have better possibilities to influence the opinions of other

actors, the flow of communication, and the diffusion of innovations. The same

actors occupied similar network positions in all of the networks. These key actors

were almost all part of the Company’s upper hierarchy or experts and could be

deduced from the organisational chart.

During the research, it became clear that a strict division into opinion leaders and

opinion brokers is not as feasible and clear-cut might be assumed. Degree centrality

and betweenness centrality can be used to identify key actors (Fig. 4.3), but no

single metric can be used to identify opinion leaders or opinion brokers for

innovation diffusion purposes. A combination of metrics offers a much more

detailed picture. For example, a high degree combined with low clustering coeffi-

cient indicates that the actor has many alters4 that are not connected to each others’

alters (Valente 1995). This indicates brokership status in a social network. If degree

3 The clustering coefficient is the network’s general tendency to form triangles: for example, if A is

connected to B, who in turn is connected to C. The clustering coefficient in the example situation is

the overall situation ranging from 0 to 1 in networks where A is also connected to C.4 In social network analysis, the term ego is used to describe the actor whose connections are underscrutiny. The ego has connections to alters, other actors that may have connections with each

other.


centrality and the clustering coefficient are both high, the actor has the potential for

opinion leadership. In the Company, the central actors were found in all of the

groups, though Groups I and III had more central actors than Group II.

All of the Company’s networks tend to follow Burns and Stalker’s (1961) notion

that the form follows the function: when working procedures and processes are

strictly defined, there is little room for improvisation and authority figures are more

central. Also, as Barabasi (2003), among others, has noted, connections among

actors are not distributed randomly. Several actors were highly central with regard

to the network, but their centrality type (opinion leadership/brokership) varied.

Networks are dynamic, and their composition changes according to the environ-

ment (Monge and Contractor 2003). Comparison to other networks is, therefore,

difficult, as the study subject changes constantly.

4.4 Summary and Conclusion

In this chapter, we have been interested in the possibilities social network analysis

can offer to the study of innovation diffusion. In addition, and more precisely, we

wanted to ponder the roles of opinion leaders and opinion brokers in innovation

diffusion networks. We also argued that the communication perspective is essential

to understanding the complexity and multidimensionality of the processes involv-

ing diffusion of innovations.

Based on the theory and a case study, we conclude that social network analysis

can be utilised in active diffusion of innovations in at least three ways. Firstly, it can

Fig. 4.3 Innovation network within the company (Notes: The size of the node represents the

degree centrality, and size of the label indicates betweenness centrality. Black nodes belong to

Group I, red to Group II and blue to Group III. The actors’ names are coded.)


be used to monitor to current inner social network structure. Measuring the width,

density, centrality, and reciprocity of the network reveals issues that hinder or

promote diffusion of innovations. Innovations in, for instance, knowledge-based

organisations should have alternative routes (Tsai 2001). Secondly, at the group

level, measuring communication densities between groups can show potential

places for innovation within the organisation. Connecting two units or groups that

were previously less connected provides new interactions and possibilities for

innovation. Thirdly, social network analysis can be utilised to identify people

central to innovation diffusion. These opinion leaders and brokers can be identified

if the innovation type is specified. For example, using a health innovations network

to diffuse IT-related innovations may not yield good results. There is no clear

division into opinion leaders, opinion brokers, and followers. Actors have different

opportunities for influencing the network. The methods of social network analysis

provide ways of guaranteeing a thorough spread if the central actors approve the

innovation. Table 4.1 is used to summarise the benefits of utilising social network

analysis in diffusion of innovations.

The results of this study are limited, as the study did not focus on any particular

innovation and its diffusion process. Furthermore, we included only one organisation,

which limited inter-organisational results. However, expanding the scope of the study

to include an entire innovation and communication system and systemically analyse

the social interactions enabling them could provide some practical results for manag-

ing diffusion of innovations.

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Table 4.1 Utilising social network analysis to promote diffusion of innovations

Diffusion of innovations Social networks Communication

Organisational

level

Number of formal ties

between

organisations

Amount of informal

connections between

organisations

Processes supporting

communication

across organisational

boundaries

Group level Structural holes between

groups

Physical distance between

groups (units, functions,

etc.)

Bridging gaps between

groups

Detecting communities

Individual

level

Inter-organisational

actor interface

plurality

The role of key individuals

(opinion leaders and

opinion brokers)

Enabling individual

network positions to

diffuse innovations


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Practice-Based Innovation: Insights, Applications and Policy Implications || Communicating Connections: Social Networks and Innovation Diffusion