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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
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.
52 P. Aula and O. Parviainen
‘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
4 Communicating Connections: Social Networks and Innovation Diffusion 53
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.
54 P. Aula and O. Parviainen
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
4 Communicating Connections: Social Networks and Innovation Diffusion 55
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
56 P. Aula and O. Parviainen
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,
4 Communicating Connections: Social Networks and Innovation Diffusion 57
‘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.)
58 P. Aula and O. Parviainen
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.
4 Communicating Connections: Social Networks and Innovation Diffusion 59
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.)
60 P. Aula and O. Parviainen
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.
References
Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study.
Administrative Science Quarterly, 45(3), 425–455.Barabasi, A. L. (2003). Linked. New York: Plume.
Black, J. (1982). Opinion leaders, is anyone following? Public Opinion Quarterly, 46, 169–176.Burns, T., & Stalker, G. (1961). The Management of Innovation. London: Tavistock.
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
4 Communicating Connections: Social Networks and Innovation Diffusion 61
Burt, R. (1992). Structural Holes. Cambridge: Harvard University Press.
Burt, R. (1999). The social capital of opinion leaders. The Annals of the American Academy ofPolitical and Social Science, 566, 37–54.
Burt, R. (2004). Structural holes and good ideas. The American Journal of Sociology, 110(2),349–399.
Cantner, U., & Graf, H. (2006). The network of innovators in Jena: An application of social
network analysis. Research Policy, 35(4), 463–480.Carlsson, B., Jacobsson, S., Holmen, M., & Rickne, A. (2002). Innovation systems: Analytical and
methodological issues. Research Policy, 31(2), 233–245.Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and methods in social network
analysis. New York: Cambridge University Press.
Castells, M. (1996). The rise of the network society. Cambridge: Blackwell.
Chan, K., & Shekhar, M. (1990). Characteristics of the opinion leader: A new dimension. Journalof Advertising, 19(3), 53–60.
Childers, T. (1986). Assessment of the psychometric properties of an opinion leadership scale.
Journal of Marketing Research, 23, 184–188.Everett, M., & Borgatti, S. (2005). Extending centrality. In P. J. Carrington, J. Scott, &
S. Wasserman (Eds.), Models and methods in social network analysis (pp. 57–76). New
York: Cambridge University Press.
Flap, H., Bulder, B., & V€olker, B. (1998). Intra-organizational networks and performance:
A review. Computational and Mathematical Organization Theory, 4(2), 109–147.Freeman, L. (2005). Graphic techniques for exploring social network data. In P. J. Carrington,
J. Scott, & S. Wasserman (Eds.), Models and methods in social network analysis(pp. 248–269). New York: Cambridge University Press.
Gelsing, L. (2010). Innovation and the development of industrial networks. In B. A. Lundvall
(Ed.), National systems of innovation: Toward a theory of innovation and interactive learning(pp. 119–132). London: Anthem Press.
Goldsmith, R., & Hofacker, C. (1991). Measuring consumer innovativeness. Journal of theAcademy of Marketing Science, 19(3), 209–221.
Granovetter, M. (1973). The strength of weak ties. The American Journal of Sociology, 78,1360–1380.
Granovetter, M. (2005). The impact of social structure on economic outcomes. Journal ofEconomic Perspectives, 19(1), 33–50.
Harmaakorpi, V. (2006). The regional development platform method as a tool for regional
innovation policy. European Planning Studies, 14(8), 1085–1104.Harmaakorpi, V., & Melkas, H. (2005). Knowledge management in regional innovation networks:
The case of Lahti, Finland. European Planning Studies, 13(5), 641–659.Ibarra, H., Kliduff, M., & Tsai, W. (2005). Zooming in and out: Connecting individuals and
collectivities at the frontiers of organizational research. Organization Science, 16(4), 359–371.Johanson, J. E. (2000). Intraorganizational influence: Theoretical clarification and empirical
assessment of intraorganizational social influence. Management Communication Quarterly,13(3), 393–425.
Johanson, J. E., Mattila, M., & Uusikyl€a, P. (1995). Johdatus verkostoanalyysiin [An Introductioninto Network Analysis]. Helsinki: Kuluttajatutkimus. (In Finnish.)
Katz, E., & Lazarsfeld, P. F. (1955). Personal influence. Glencoe, Illinois: Free Press.Knoke, D. (1994). Political Networks: The Structural Perspective. New York: Cambridge Univer-
sity Press.
Knox, H., Savage, M., & Harveyn, P. (2006). Social networks and the study of relations: Networks
as method, metaphor and form. Economy and Society, 35(1), 113–140.Lazarsfeld, P., Berelson, B., & Gaudet, H. (1948). People’s choice (2nd ed. [1st ed. 1944]).
New York: Columbia University Press.
Merton, R. (1957). Social theory and social structure. Glencoe, Illinois: Free Press.Monge, P., & Contractor, N. (2003). Theories of communication networks. New York: Oxford
University Press.
62 P. Aula and O. Parviainen
Moreno, J. (1934). Who shall survive? Washington, DC: Nervous and Mental Disease Publishing
Company.
Parjanen, S., Harmaakorpi, V., & Frantsi, T. (2010). Collective creativity and brokerage functions
in heavily cross-disciplined innovation processes. Interdisciplinary Journal of Information,Knowledge, and Management, 5, 2–21.
Parsons, T. (1991). The social system. London: Routledge.Rogers, E. (2003). Diffusion of innovations (5th ed. [1st ed. 1962]). New York: Free Press.
Strang, D., & Soule, S. (1998). Diffusion in organizations and social movements: From hybrid
corn to poison pills. Annual Review of Sociology, 24, 265–290.Tsai, W. (2001). Knowledge Transfer in Intraorganizational Networks: Effects of Network
Position and Absorptive Capacity on Business Unit Innovation and Performance. Academyof Management Journal, 44(5), 996–1004.
Valente, T. (1995). Network Models of the Diffusion of Innovations. Cresskill: Hampton Press.
Valente, T., & Davis, R. (1999). Accelerating diffusion of innovations using opinion leaders. TheAnnals of the American Academy AAPSS, 566, 55–67.
Valente, T., & Pumuang, P. (2007). Identifying opinion leaders to promote behavior change.
Health Education & Behavior, 34(6), 881–896.Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications.
New York: Cambridge University Press.
Weimann, G., Deon, H., van Vuuren, D., & Joubert, J. P. R. (2007). Looking for opinion leaders:
Traditional vs. modern measures in traditional societies. International Journal of PublicOpinion Research, 19(2), 173–190.
Wejnert, B. (2002). Integrating models of diffusion of innovations: A conceptual framework.
Annual Review of Sociology, 28, 297–326.Young, P. (2009). Innovation diffusion in heterogeneous populations: Contagion, social influence,
and social learning. The American Economic Review, 99(5), 1899–1924.
4 Communicating Connections: Social Networks and Innovation Diffusion 63
