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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
How to Establish an Online Innovation Community?
The Role of Users and their Innovative Content
Julia Hautz
University of Innsbruck
julia.hautz@uibk.ac.at
Katja Hutter
University of Innsbruck
katja.hutter@uibk.ac.at
Kurt Matzler
University of Innsbruck
kurt.matzler@uibk.ac.at
Markus Rieger
Hyve AG
markus.rieger@hyve.de
Abstract
We studied the evolution of an online innovation
community and users’ interaction behavior through
social network analysis to explore how to build an innovation community and get a better understanding
about the role of participating users and their innovative content. Throughout our analysis we explicitly
recognize that the primary desired outcome of an online innovation community is the generation of innovation and breakthrough ideas and therefore simultaneously consider the user itself as well as the user generated ideas. Through relying on structural data combined with consideration of the context of our community we were able to identify eight different user types,
who have different importance in supporting the health
and success of the community.
1.
Introduction
Today the creation of ideas is no longer the main
task of isolated inventors or researchers. Instead, innovation processes are opening up and more and more
innovations are created outside the boundaries of firms
through networks of various actors from different organizational backgrounds, e.g., customers, suppliers, or
other partners selected for their unique capabilities [9].
In this networking world, companies detect the power
of the Internet as a platform for collaborative innovation and reaching creative people and customers
beyond the organization. Thereby unique opportunities
emerge to capitalize on users’ innovative potential and
knowledge, resulting in what has been termed as virtual customer integration or co-creation through online
communities [10, 42].
For successful virtual co-creation the participation
of engaged consumers is crucial. Only when talented
users are willing to share their creative ideas and submit their innovative content can significant contribu-
Johann Füller
University of Innsbruck
johann.fueller@uibk.ac.at
tions to organizations’ innovation activities be expected.
It is critical for the success of an online innovation
community to identify those valuable key members who
are actively contributing to support the needs and health
of the community. These members have to be encouraged and even rewarded [37]. More insights into identifying key members and their roles in an online innovation community are needed, as companies’ investments
in building virtual co-creation and community platforms
are substantial. Knowing which different user types are
interested in visiting, joining, and actively participating
in virtual innovation communities, organizations learn
how to establish and successfully manage online innovation communities in order to enhance their innovation
process through valuable consumer contributions.
The goal of our study is to explore how to build an
innovation community and to get a better understanding
about the role participating users and their innovative
contributions play for the creation of a lively online innovation community. We apply Social Network Analysis (SNA) to (1) study structure, patterns, evolution, and
dynamics of interactions among individuals in order to
(2) identify key members, who play critical roles in the
success of innovation communities.
Unlike previous studies on the identification of critical roles in online communities, we explicitly consider
that the primary desired outcome of an online innovation
community is the generation of innovation and breakthrough ideas [55]. Therefore, not only the user as a participating member of the online community and his behavior, but also the generated innovative content is at the
center of our analysis. By investigating user behavior
and user generated content simultaneously, our paper
recognizes the particular purpose and context of an online innovation community. The SNA approach provides
new insights into members’ roles and reveals their importance. Our findings identify opportunities in community design, architecture, and coordination to create successful places for collaborative idea and innovation generation in the future.
978-0-7695-3869-3/10 $26.00 © 2010 IEEE
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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
The paper is structured as follows: we briefly introduce the relevant literature on online innovation communities and co-creation as means for enhancing the
firm’s innovation activities, followed by a discussion
of studies on key members in communities. Further,
we briefly review social network concepts and social
capital theory. Then we describe our network setting,
followed by an analysis of the structure and evolution
of interaction over time at an overall network level.
Based on the findings from the macro level analysis,
we identify different user types and further analyze
local networks of these key users. Finally, we discuss
theoretical as well as practical implications.
role in the design of new buildings, new design concepts, or technology inventions in various industries for
decades [8, 17]. Nowadays, websites feature online idea
contests in different kinds of industries. These virtual
platforms not only allow users to disclose their ideas to
firms, but to interact with other like-minded peers, build
social networks, and establish a sense of community.
New technologies enable interested users to create their
own profiles, upload their creative content, communicate, discuss and share their insights and experiences
with others, interact and collaborate and thereby learn
from the aggregate knowledge and feedback of peers.
2.2.
2.
Literature Review
Terms like crowdsourcing [24, 31], co-creation
[56], user innovation [50], virtual customer integration
[12], and open innovation [9] characterize a recent
movement in innovation management. No longer can
innovation be considered as a discrete event resulting
from isolate inventors or internal R&D departments.
Instead, innovation is considered as an interactive
process involving collaborative relationships, interactions, and exchange of knowledge [2, 26, 38].
2.1.
Online Innovation Communities
Companies detect the power of the Internet and
novel information and communication technologies
(ICT) as a platform for new forms of interaction and a
source of collaborative innovation. The role of communities in creating, shaping, and disseminating innovation activities is taking on new forms that are transforming the nature of innovation management [42, 50].
In this context, the phenomenon of “online innovation
communities” – users as well as manufacturers producing ideas and inspirations for new product development
– have become the subject of considerable interest in
both research and management [50, 52]. Von Hippel
(2005, p. 96) defines innovation communities as
“meaning nodes consisting of individuals or firms interconnected by information transfer links which may
involve face-to-face, electronic, or other communication.” Innovation communities can be purely functional
or may also fulfill the role of social communities defined by networks of interpersonal ties providing support, a sense of belonging, and social identity [50].
Social software applications like web based toolkits
[46], virtual concept testing [12], virtual worlds [22]
have been introduced as mean to benefit from for collaborative innovation and virtual co-creation. Online
idea and design contests are enjoying a renaissance
among companies. They have already played a major
The Community and its Key Members
Websites like designboom.com, crowdspring.com,
deviantARt.com, or newgrounds.com feature one design
contest after the other for all kinds of subjects. Although
idea and design contests are quite popular, the overwhelming majority is challenged by too few interested
users and therefore too few activities that make the platforms a vibrant source of great connections and innovations. A recent study showed that they fail in evoked
consumer interest, number of creative contributions as
well as quality of ideas [27].
Only when users are qualified and motivated to contribute and spread their promising ideas may they be
able to add value to the organization’s innovation activities. Organizations need to find out which participants or
which contributions are needed to create a vibrant platform for innovation communities. In the literature a
number of approaches are discussed how to establish an
online community [15, 25, 41]. For example, Kim [25]
presents nine aspects for building a community and out
of it five different community roles emerge. Kim recommends an approach of visitors, novices, regulars,
leaders, and elders who each have specific tasks, roles,
and rights, which enables certain self-control of the
community.
Much of the current work on identifying key members within an online community has focused on frequency of participation and volume of contributions. In
such a way Kozinets (1999) defines four user types in
virtual communities of consumption according to their
consumption activity and their relation to the community. Furthermore, Füller et al. (2007) categorize three
different member types in an online basketball community based on their posting frequency.
In contrast to the ethnographic approach applied by
Kozinets (1999) and Füller et al. (2007), Nolker and
Zhou (2005) view an online community as a social network connected by member-member relationships and
try to get new insights into members’ roles by using
SNA and behavioral-based measures. They identify
leaders, motivators, and chatters as three key member
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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
roles in an online knowledge sharing community [37].
Similarly, in this study, we consider innovation
communities as a particular form of network [40] offering special interaction possibilities [43]. SNA serves
as a theoretically and practically appropriate method to
answer our research questions. Considering that an
online innovation community aims to generate ideas
and innovative solutions outside the boundaries of the
firm, the key member roles that support these specific
needs and the success of the online community can be
assumed to be different from other community types
serving different purposes [23, 37].
2.3.
Social Networks & Social Capital Theory
Social networks are graph theoretical representations of the relationship between social entities (such
as people or organization), which are represented by a
set of nodes and the relationships between these nodes
(such as communication, information flow, or trade),
are represented as a set of ties [23]. Hence, social ties
are viewed as channels for the flow of information,
knowledge, or other resources. In this context the
processes and relationships that take place between
people facilitating coordination and cooperation for
mutual benefit are referred to as social capital [6, 36].
Social capital theory considers the social network as
well as the knowledge or other resources that may be
transferred through the network [34, 35]. Researchers
have recognized the significant overlaps between networks and communities such as the emphasis on reputation, norms of reciprocity, and mutually beneficial
interactions. In an online community, social capital is
mostly embedded in the communication or messaging
activities as these provide participants with opportunities to find social support, to establish new social contacts for collaboration, exchange knowledge and information, and create social wealth [6, 36, 39]. The use
of social interaction technologies in online communities inherently creates network data [43]. Various
forms of computer-mediated social interaction – like
SMS, e-mail, blogs, or chats– create digital records of
interaction between the content creator and other creators as they view, reply, annotate, or link to one another’s content. Recent studies support the notion that
online communication serves as much social function
as face to face conversation and can therefore be used
to study the underlying network of social relationships
by online communication [5]. Thus, they applied SNA
to investigate the social capital of online communities
[18, 20, 37, 39]. In the community context, it is the
mutual support, contact to others, exchange of knowledge and information which creates social capital for
its members [6, 36, 39].
SNA is a quantitative method for studying complex
social phenomena such as community structure and
groups of interacting individuals [19]. So far SNA has
been applied to study virtual communities designed with
a clear emphasis on the social aspect of interactions like
social friendship networks, Internet dating communities,
or online communication networks. No study exists
which is exploring innovation communities from a SNA
perspective. Therefore, in our analysis we viewed the
Swarovski EnlightenedTM innovation community as a
social network connected by member-member relationships. This approach allows taking advantage of SNA,
which reveals patterns that are not discernable with other
methods and which provides new insights into the member’s roles in an online innovation community, thereby
revealing the importance of members that would otherwise be misinterpreted [18]. In our study, we explore the
relevance of members as well as the importance of their
contributions – ideas and comments in our case – to establish a lively innovation community.
3.
Network Data and Method
Our study is based on the data from an online innovation community in which the users are participants in
an online idea and design contest established by the
world leading crystal producer.
3.1.
Design Contest
Swarovski EnlightenedTM invited designers and creative consumers from all over the world to engage in the
jewelry-design community (see Figure 1). The community creation was based on a design-contest, which was
open for designers (e.g., design students) but also for
people who are generally interested in jewelry, gemstones, and related topics. The contest was conducted
within two categories: With the jewelry configuration
toolkit, participants were able to create their own jewelry selecting from 24 components in various colors and
through the selection and free placement of 108 different
EnlightenedTM gemstones. In the second category, freely
created jewelry designs could be uploaded. More than
1,700 participants joined the community to showcase
their talent and submit their designs. In total, they
created over 3,100 pieces of jewelry in different segments ranging from classic to sporty and from abstract
to realistic. A lively community evolved across cultural
boundaries. The contest spread from Austria to the US
and further to Brazil and created a buzz in countries such
as China, India, Russia, Turkey and Iran. The website
created more than 770,000 page impressions.
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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
TM
Figure 1: Enlightened Design Competition
Participants uploaded their own avatars and pictures and evaluated the configured and freely created
designs either with short evaluations (1-5 points) or
with “vote&win” evaluations within different categories (from “The design of jewelry looks good” to “In
particular I like the shape”). Further, comments on
designs and suggestions for improvements have been
provided. An emotional but also detailed voting took
place. Community members placed 147,000 gemstones, made more than 33,000 evaluations and contributed 2,900 qualitative comments. A brief exploratory
content analysis of these comments revealed that the
vast majority were not competitive but rather of a supportive nature. Hence, as shown by the examples below, members supported and inspired each other, explored relationships, and provided feedback and ideas
for further improvements for each other:
“… its very beautiful! I like the diagonal symmetry. Blue and
gold is very Egyptian and very in,” suggests designer212
“… I have got inspiration from this design.nice one,” says
designer427
“… like this one best out of your designs. maybe you could
use cooler colors,” argues designer100
“… very very interesting...the idea that your design allows
multiple combinations for each user taste (maybe you can
add a wider range of passion colors),” suggests designer290
“… when can I see a photo from the dot-designer? - I love
your lovely dot´s” answers designer385
3.2.
Network Setting
In the analysis each registered user represents a
node (vertex) in the network. The users in the network
are connected through their interactions. A relationship
(edge) between two users was established when one
user commented on the design of another participant.
Therefore relationships between users are directional,
whereas the direction of an edge indicates who com-
mented and who received the comment. In defining the
network, we adopted a weak notion of social relationship [19]. In this context a directed tie is established
between two users, if the former user writes a comment
for the latter one. This implies that a relationship is already established if only one single comment has been
sent from one user to another, even if no answer to this
initial contact occurs. By adopting this network definition, we do not follow the approach of Kossinets and
Watts [28], who only focus on reciprocated ties and
transform them into undirected ones. However, as our
goal is to investigate the expected differences in user
behavior in our community, mainly captured by directed
network measures, it is essential for us to maintain the
directionality of ties.
A further reason for choosing such a low threshold
of one contact already representing a relationship is the
very low average strength of a directed tie of 1.15 (defined as the ratio of all comments sent and number of
directed ties in the network). Further, more than 95% of
all directed edges in the network have a strength of three
or less (measured as the number of comments sent along
one particular tie). Setting higher limits in spite of these
low strength measures would have resulted in a high
number of isolated users, which would have substantially reduced the connectivity in the network and our ability to investigate the role of interaction in the community
[39]. These facts justified our adoption of the notion of
weak social relationships between users.
Our network dataset covers the period from the beginning of the design contest on the 24th October 2008
until the 15th February 2009. Although the contest itself
ended on 31st December 2008, we did not stop our analysis, but continued to collect data on interaction and
communication, since the participants still contributed in
discussions about the designs and new members attended. During the fifteen weeks of our analysis, all users who participated in the interaction in the network by
either sending or receiving one comment were included.
Users who simply registered, but did not participate in
the interaction were not included in the analysis.
3.3.
Network Evolution Analysis
To the best of our knowledge, online innovation
communities have not yet been the subject of SNA, in
which user activity as well as provided content have
been considered simultaneously. Due to this lack of existing analyses we studied the interaction network of our
innovation community at the macro level as a first explorative step to grasp basic insights into its overall
structure (see Table 1) Basic properties of social networks include size, defined as the count of the number
of nodes [21, 53]. During the observation period a total
of 1,127 users were recorded, who wrote a total of 3,275
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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
comments. In addition, overall network density – the
ratio of the number of ties actually observed to the
number of ties theoretically possible – provides a
measure on the extent to which a user’s contacts are
connected among themselves [45, 53]. As density is a
decreasing function of network size, in our large network only 0.25% of the theoretically possible ties are
realized. However, this absolute number is of little
value. It would be more informative to compare the
density of different online innovation communities set
up slightly differently, which is intended to be done in
future studies.
Figure 3: Interactions until 29.12.2008
Table 1: Overall Network Metrics
Unique Edges
Edges with Duplicates
Total Edges
Self Loops
Vertices
Graph Density
1,841.0
1,434.0
3,275.0
44
1,127.0
0.00254609
The next step in our analysis involved the observation of the evolution of the innovation community over
time. The reliance on the exact time point at which
each tie is formed allows to examine the evolution of
the users’ behavior. Through this approach to social
networks as evolving systems, interesting patterns of
interactions between users or content attracting the
attention of other users can be identified [3, 44, 57].
The graphs displayed in Figures 2-4 illustrate the
establishment of relationships in the overall network
over time. From this visualization it can be observed
that communication and interactions start within a
smaller user group, which seems to be very highly
connected. However, over time more and more peripheral users engage in interaction by commenting only
once on a particular design without participating in
further relationships [11]. From the illustrations, this
kind of evolution of the network can be assumed to
continue nearly until the official deadline of the design
contest.
Figure 2: Interactions until 14.11.2008
Figure 4: Interactions until 15.02.2009
However, two days before the contest closed, the dynamic analysis revealed a single user who was at the
center of high interaction activity, thereby also attracting
the attention of users who had not participated in the
network before.
3.4.
Different Roles of User and Content
The first exploratory evolution analysis at the overall
network level shows that some network users are involved in a higher number of relationships than others.
Further, it reveals that some nodes are the origin of ties,
while others seem to be mainly the goal of comments.
These observations raise the following question:
Is it the user and his interaction behavior, or rather
the content generated by a particular user, hence the
attractiveness of designs that is responsible for arousing
interaction?
To answer this question it is investigated if heterogeneity in the way nodes generate or attract relationships
can be found. If differences among users exist it will be
difficult to use average properties to describe the behavior of a “typical, prototype” user [39]. While in randomly generated networks any two nodes are connected
with equal probability, in most real word networks it is
very likely to find the majority of nodes poorly connected with a minority of nodes many times better con-
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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
nected than the average, constituting key users [1, 14].
Key members are defined as those who contribute to
the success and health of the community. By identifying those key members particular traits and characteristics which promote health and success of the community can be encouraged and even rewarded [18, 37].
In order to detect the degree of variation in user behavior, structural properties on the micro level - the
individual user level - have to be analyzed. A first
rough statistical analysis of the two user level indices
in-degree centrality (the number of ingoing relationships of a node) and out-degree-centrality (the number
of outgoing relationships of a node) reveals that on
average a member posted around three comments Further an “average user” submitted around two designs
(see Table 2).
Table 2: Statistical indicators in- and out-degree,
number of designs
Mean
Median
Std Deviation
Variance
Kurtosis
Skewness
Minimum
Maximum
in-degree
2.91
0.00
32.81
1076.47
1030.30
31.46
0.00
1079.00
out-degree
nr of designs
2.91
2.34
2.00
0.00
5.90
7.64
34.85
182.61
344.64
10.66
15.32
162.00
0.00
0.00
149.00
162.00
However, the zero median of in-degree-centrality
shows that a very large proportion of users in the
community didn’t receive any comments at all. The
large standard deviation of in-degree compared to outdegree further indicates that some designs elicit a lot of
comments from many different users. When analyzing
the number of submitted designs per user, again the
median of zero highlights that a large portion of community members didn’t contribute through submitted a
design, but only through posting comments.
Previous empirical research on network structure
has shown that many real-world networks share fattailed out- and in-degree distributions as structural
property [4]. In these networks some nodes are acting
as hubs, with out- and in-degrees far above the mean.
In the case where the degree distribution of a network
can be formalized by a power-law function, the network is said to be ‘‘scale-free’’, indicating a high level
of heterogeneity in the way nodes generate or attract
connections [4]. For directed networks, the power law
distribution takes the following form:
p(kO) ∼ (kO)−τ (out-degree)
p(kI ) ∼ (kI )−τ (in-degree)
Figure 5 illustrates the empirical out- and in-degree
distributions of our network. Both distributions are
well approximated by the linear behavior on the double
logarithmic scale, as indicated by the straight lines.
The distributions can be fitted by power-law functions
with an exponent τ of 1.5 for the out-degree distribution,
and 1.5 as well for the in-degree distribution. These
power-law exponents below two are a distinctive signature of a network structure that is dominated by a few
highly connected nodes, while the vast majority of nodes
remain poorly connected [7, 14].
out-degree
in-degree
Figure 5: Out- and In-degree distribution (log-values)
However, this distribution analysis only indicates
different importance of users, but it is not sufficient to
identify important key members. Rather, the different
roles within a community can be defined by relations
between different users, their behavior, or a combination
of these two aspects [18, 37]. Relationship-based roles
follow the traditional network node structures of hub,
broker, and bridge [13]. Behavioral roles are defined on
the basis of an individual behavioral pattern as, for example, debater, motivator, or spammer [49]. Overall, it
has to be kept in mind that the identification of roles is
more a point of reference than a factual statement.
However, it has to be considered that different communities have different needs and the roles that support
these needs are different as well [37]. In the case of our
innovation community with the goal to generate new
breakthrough innovations, this analysis should not only
focus on the interaction behavior of participating users
but also on the user generated content. Thus, based on
the review of relevant literature, the data from our online
community setting and the considerations discussed
above, we identified different types of roles, based on a
combination of user behavior, user relationships, and the
attractiveness of user generated content (see Table 3).
In this context, the social network constructs, indegree and out-degree centrality are used for identification of key member roles. Prior research shows that
these measures are useful predictors of the importance of
an individual in a network, as both capture how active
an individual user is and how central he is positioned in
the network [23, 33, 37, 39, 58].
However, unlike the previous studies, we do not use
in-degree centrality to define the popularity of a user
itself [39]. As the comments a user receives are directed
to the particular designs he has submitted, we instead
applied in-degree to measure the potential of a user’s
ideas to generate a high level of attention. Thus, the
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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
number of comments a user receives can be seen as an
indication for the future ability of his idea to capture
customer attention and arouse their curiosity.
Out-degree centrality, in contrast, is used to measure the level of how actively a user participates in giving feedback, suggesting enhancements and motivating
others to further improve the submitted designs. Hence,
a high out-degree of a user indicates that he is actively
involved in information transfer and knowledge sharing between users.
Finally, the number of designs uploaded by a single
user is used to capture his idea generating ability.
Modern design literature supports the assumption that a
person’s creativity is based on idea quality as well as
on idea quantity [33]. In this context it has to be kept in
mind that the number of submitted designs does not
reveal if the user submitted all of his ideas or only his
“final, best idea.”
Based on these three characteristics, eight different
types of users were identified, which are associated
with different responsibilities in the network and to
which varying importance for the success of the online
innovation community can be attributed. A user was
categorized as a key user if he was ranked high on at
least one of the three criteria. A high level was defined
as being part of the highest 1.5% percent based on the
respective measure. Figure 8 visualizes those identified
key community members in the network structure:
Table 3: Types of User roles
ROLES
Motivator, attention-grabbing
Motivator
Attention attractor
passive User
Attention attractor, motivating
idea generating
Motivator, idea generating
Attention attractor, idea
generating
Idea generator
communication
(out-degree)
idea
design
generation atractiveness
(designcount) (in-degree)
color
high
high
low
low
low
low
low
low
high
low
high
low
violett
blue
green
others
high
high
high
orange
high
high
low
black
low
high
high
red
low
high
low
brown
Figure 6: Key members in the Innovation Community
When superficially observed, it could be easily as-
sumed that users who submit the highest number of designs, the idea generators, provide the highest commitment to goal of the online innovation community as a
high number of ideas increases the probability to find a
new breakthrough innovation. However, users who are
able to attract a high level of attention in the community
through their designs, attention grabbing users, can be
regarded as very valuable and useful. They allow a first
verification about how much attention a new idea will
arouse among potential customers. If a user’s design
attracts a high degree of curiosity among community
members, it can be assumed that it has the potential to
arouse the attention and catch the eye of future customers. Further, through the high number of ingoing comments, this attention grabbing user receives a high number of valuable feedback, which yield the potential for
further enhancement of his idea.
Finally, users, who do not participate in the actual
contest through the upload of a self created design, could
be easily regarded as playing no active or valuable role
in the online innovation community. However, the
community of practice literature points out that knowledge transfer, sharing of information, giving feedback,
sharing experiences, and discussions are the key prerequisite to pool, refine, and disseminate ideas and new
product developments. Thus, only those users who actively comment on the designs posted by others, the socalled motivators, enable the online community to be
more than just an open innovation tool, which allows
users to disclose their ideas to a firm. Rather, through
their active feedback, their approval, disapproval and
their suggestions, users with a high out-degree centrality
provide the opportunity to collaborate in the creative
process and to enhance and perfect individual ideas
through their collaborative interactions.
To point out the differences between the identified
user types, which might initially be evaluated as equally
important for the network, we investigated the egocentric networks of typical motivators, passive users, idea
generators, and attention attractors. The visualization of
local networks that center around a particular node allow
identifying patterns of interaction behavior associated
with particular roles [54].
Figures 7 and 8 show the local network patterns of a
typical attention attractor with numerous inward connections. Although both users have only submitted a
relatively low number of designs, they were able to attract a lot of attention among the other community
members, indicating that the designs submitted by those
two users have the potential to arouse discussion and
curiosity, both supporting fast dissemination and adoption of innovation. In Figure 8 it can be also seen that
the ego-centric network of this user is not very dense,
which means that the numerous commentators did not
engage in discussions among themselves, which can be
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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
seen from the low number of existing triangles. Further, it has to be noticed that even though these users
receive a high amount of feedback and attention, they
do not participate in this kind of network interaction,
which can be seen from the few, if any, outgoing relationships.
Figure 7: User 2254
5 designs
Figure 8: User 2913
2 designs
Figures 9 and 10 show the egocentric network that
is associated with a motivator and feedback giver. It is
characterized by its very high number of outgoing ties,
indicating that those users are actively engaged in
commenting on many different designs and providing a
high amount of feedback inside the network. Compared to Figure 8, these local networks are dense with
many interconnected ties and triangles.
tions nor does it automatically lead to high attention and
curiosity from other community members.
Although User 2091 and User 2345 submitted a very
high number of ideas they were only able to attract a
very low number of comments, shown by the low number of ingoing relationships. Therefore, it can be assumed that their ideas, despite their vast number, do not
have the potential to make people curious, to initiate
discussion and spread word about them. These are very
important implications for new product development,
which crucially depends on fast dissemination and adoption. But these users are not only unable to benefit the
community through attractive ideas, they also missed
participating actively in giving feedback and evaluating
the design of others and thereby disregard this important
possibility to add value to the community. These observations show that the sheer amount of designs is no indicator for identifying crucial key players in an online
innovation community.
Figure 11: User 2091
162 designs
Figure 9: User 2109
3 designs
Figure 10: User 1921
34 designs
However, very important differences can be detected when further investigating these two motivators.
Although User 2109 contributed through his feedback,
he was not able to attract a high level of attention with
his three designs. User 1921, in contrast, not only actively engaged in sharing his knowledge and but also
received a high number of comments for his designs.
This way this user benefits the online innovative community in two very important ways. He represents a
very valuable user type, the motivating, idea generating attention attractor, which is critically necessary for
the communities’ success.
Figures 11 and 12 impressively highlight that a
high number of uploaded designs does not necessarily
indicate either a high user participation in network interaction like feedback giving or improvement sugges-
Figure 12: User 2345
44 designs
To investigate if users who post a high number of
designs inevitably get more attention for their ideas, we
calculated the Pearson correlation coefficient between
the number of designs a user submitted and the number
of comments he attracted with all his ideas [39]. The
pairwise correlation of 0.115 (see Table 4) shows that
people submit a high number of new ideas do not necessarily attract the most attention. Further, the relationships between nodes’ out- and in-degree as well as between out-degree and number of designs were investigated in the Correlation analysis. Again, the low correlation coefficients suggest that users who actively comment on ideas, give feedback, encourage improvements
and give suggestions for further development of posted
ideas do not have to be those who attract interest and
arouse curiosity through their design.
Table 4: Pearson Correlation Coefficients
In-Degree
In-Degree
Out-Degree
Nr. of designs
Out-Degree nr of designs
1
0.12989811
1
0.11542744 0.21884588
1
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Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
4.
Discussion
In this article, we studied the patterns in the evolution of an online innovation community and users’
interaction behavior. In our analysis we recognized that
an online innovation community has a very specific
purpose and therefore requires a special kind of user
participation and interaction. Thus, we simultaneously
considered the user itself as well as the user generated
ideas. We found users to be heterogeneous in the way
they participate in the community. Through relying on
structural data combined with consideration of the context of our community we identified eight different
user types who have different levels of importance in
supporting the health and success of the innovation
community. By viewing our online community as a
social network connected by member-member relationships, we took advantage of SNA, which revealed important key members that would otherwise have been
misidentified.
Our findings show that the sheer amount of contributed designs should not be regarded as the only indicator to identify the key players in an online innovation
community and therefore it is not the pure idea generating behavior which should be encouraged in the
community. To be able to benefit from the interaction
between users and be more than just a platform of presenting an idea to a firm, it is rather essential for the
community to attract people who are interested in evaluating ideas and sharing their knowledge. Through
their active feedback, their approval, disapproval, their
suggestions, and votes this users activate the knowledge transfer, sharing of information and experiences
within the community and therefore also contribute in
pooling, refining, and disseminating ideas and new
product developments. Further, users should be encouraged who are able to attract a high level of attention in through their designs as a lot of community
members start to vote and discuss their ideas thereby
providing valuable feedback and suggestions for further enhancement. It is critical to be able to identify,
encourage and reward these members as their ideas
yield high probabilities for successful new products.
In the future, more and more companies may consider using the enormous creative potential of online
innovation communities to enrich their product development process. This affects the work of classical new
product development as Swarovski managers, for example, had to interact with the community but also had
to come up with a process to effectively integrate the
community generated ideas and to use the design to its
full extent. Based on our results, in the future, companies can devise and align their actions regarding an
appropriate management and leadership approach of
online innovation communities, community rules, and
norms. Based on our identified community member
types, strategies can be developed for either supporting
different users in their behavior, or for transforming
them, for example, form passive users to motivators. It
gives also practical implications for appropriate rewards
and encouragement for users who are actively contributing to supporting different needs and the health of the
community. In this context, different user types have to
be considered differently based on their very specific
contributions to the community. But also implications
for technical design and inclusion of special, critical
functionalities of the web platforms and software applications are given.
Our study shows that only computer-mediated interaction allows turning innovation by a community without intra-community interaction where users only disclose their ideas to firms, into innovation within the
community. Communication and interaction between
users enable information and knowledge sharing resulting in collaborative and more creative innovation.
One limitation of our study and, therefore, potential
for future research is the investigation of the content of
given comments. A netnographic approach, the qualitative assessment of explicitly verbalized and implicitly
existing attitudes, perceptions, imagery, and feelings of
community members [29, 30] could enable us to gain
further insight into the behavior of innovation community members. Users could not only be evaluated based on
the number of comments but also based on the length
and value of their suggestions. This way, the type and
quality of the knowledge shared and transferred could be
studied. Also, comments not containing sense-making
messages could be excluded from the analysis.
A further interesting way for future research could be
the investigation of the motives and personalities hidden
behind individual community member types and how
these personal characteristics affect the quantity and
quality of contributed content. Although several studies
have engaged in identifying different member types in
online communities, so far very little research investigates the actual motivation of users behind those types.
To capture participants’ motives and personal characteristics, it is planned to conduct an online survey among
the members of the Swarovski EnlightenedTM Community to be able to further investigate personal motivations
and characteristics of the particular key user types.
5.
References
[1] Albert, R., and Barabasi, A.: ‘Statistical mechanics of
complex networks’, Reviews of Modern Physics, 2002, 74, (1),
pp. 47-97.
[2] Almeida, P., Phene, A., and Grant, R.: ‘Innovation and
Knowledge Management: Scanning, Sourcing, and Integra-
9
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
tion’, in Easterby-Smith, M., and Lyles, M.A. (Eds.): ‘Handbook of Organizational Learning and Knowledge Management’ (Blackwell, 2003), pp. 357-371.
[17] Fullerton, R., Linster, B.G., MCKee, M., and Slate, S.:
‘An experimental investigation of research tournaments’, Economic Inquiry, 1999, 37, (4), pp. 624-636.
[3] Banks, D.L., and Carley, K.M.: ‘Models for network
evolotion’, Journal of Mathematical Sociology, 1996, 21, (12), pp. 173-196.
[18] Gleave, E., Welser, T., Lento, T., and Smith, M.: ‘A
Conceptual and Operation Definition of 'Social Role' in Online
Community’, Proc. 42nd Hawaii International Conference on
System Science, 2009, IEEE Press.
[4] Barabasi, A., and Albert, R.: ‘Emergence of Scaling in
Random Networks’, Science, 1999, 286, (5439), pp. 509-512.
[19] Granovetter, M.: ‘The strength of weak ties’, American
Journal of Sociology, 1973, 78, pp. 653-667.
[5] Boase, J.: ‘Personal Network and the Personal Communication System ’, Information, Communication and Society, 2008, 11, (4), pp. 490-508.
[20] Han, S., and Kim, B.J.: ‘Network analysis of an online
community’, Physica A, 2008, 387, (23), pp. 5946-5951.
[6] Burt, Ronald S.: ‘Structural Holes and Good Ideas’,
American Journal of Sociology, 2004, 110, (2), pp. 349-399.
[21] Hannemann, R.A.: ‘Introduction to Social Network Methods’, University of California, Riverside, CA, 2001.
[7] Caldarelli, G.: ‘Scale-Free Networks: Complex Webs
in Nature and Technology (Oxford Finance)’, Oxford University Press, USA, 2007.
[22] Hemp, P.: ‘Avatar-Based Marketing’, Harvard Business
Review, 2006, (June), pp. 48-57.
[8] Che, Y.-K., and Gale, I.: ‘Optimal Design of Research
Contests’, The American Economic Review, 2003, 93, (3),
pp. 646-671.
[23] Hinds, D., and Lee, M.R.: ‘Social Network Structure as a
Critical Success Condition for Virtual Communities’. Proc.
Proceedings of the 41st Annual Hawaii International Conference on System Sciences2008 pp. 323–337 .
[9] Chesbrough, H.: ‘The Era of Open Innovation’, MIT
Sloan Management Review, 2003, Spring 2003, pp. 35-41.
[24] Howe, J.: ‘Crowdsourcing: Why the Power of the Crowd
Is Driving the Future of Business ’, Crown Business, 2008.
[10] Cohen, W., and Levinthal, D.: ‘Absorptive capacity: a
new perspective on learning and innovation’, Administrative
Science Quarterly, 1990, 35, pp. 128-152.
[25] Kim, A.J.: ‘Community Building on the Web’, Peachpit
Press, Berkley, 2000.
[11] Crowston, K., Wei, K., Li, Q., and Howison, J.: ‘Core
and Periphery in Free/Libre and Open Source Software Team
Communications’. Proc. 39th Annual Hawaii International
Conference on System Sciences - Volume 062006 pp. 118.
[12] Dahan, E., and Hauser, J.: ‘The Virtual Customer’,
Journal of Product Innovation Management, 2002, 19, (5),
pp. 332-353.
[13] Denning, P.J.: ‘Network laws’, Communications of the
SCM, 2004, pp. 15-20.
[14] Dorogovtsev, S., and Mendes, J.: ‘Evolution of networks. From biological nets to the Internet and WWW’, Oxford University Press, New York, 2003.
[15] Figallo, C., and Rhine, N.: ‘Building the Knowledge
Management Network. Best Practices, Tools and Techniques
for Putting Online Conversation to Work’, Wiley, New York,
NY, 2002.
[16] Füller, J.: ‘Why Consumers Engage in Virtual New
Product Developments Initiated by Producers’, in Pechmann,
C., and Price, L. (Eds.): ‘Advances in Consumer Research’
(2006).
[26] Kogut, B., and Zander, U.: ‘Knowledge of the Firm,
Combinative Capabilities and the Replication of Technology’,
Organization Science, 1992, 3, (3), pp. 383-398.
[27] Kohler, T., Matzler, K., and Füller, J.: ‘Avatar-based
innovation: Using virtual worlds for real-world innovation’,
Technovation, 2009, (forthcoming).
[28] Kossinets, G., and Watts Duncan, J.: ‘Empirical Analysis
of an Evolving Social Network’, Science (Washington, D.C.),
2006, 311, (5757), pp. 88-90.
[29] Kozinets, R.: ‘On netnography: Initial reflections on
consumer research investigations of cyberculture’, Advances
in Consumer Research, 1998, 25, (1), pp. 366-371.
[30] Kozinets, R.: ‘The Field Behind the Screen: Using Netnography for Marketing Research in Online Communications’,
Journal of Marketing Research, 2002, 39, (1), pp. 61-72.
[31] Kozinets, R.V., Hemetsberger, A., and Schau, H.J.: ‘The
Wisdom of Consumer Crowds: Collective Innovation in the
Age of Networked Marketing’, Journal of Macromarketing,
(forthcoming).
10
Proceedings of the 43rd Hawaii International Conference on System Sciences - 2010
[32] Lakhani, K.R., and Jeppesen, L.B.: ‘Getting Unusual
Suspects to Solve R&D Puzzles’, Harvard Business Review,
2007, 85, (5), pp. 30-32.
[33] Lettl, C., Herstatt, C., and Gemuenden, H.G.: ‘Learning
from users for radical innovation’, International Journal of
Technology Management, 2006, 33, (1), pp. 25-45.
[34] Lin, N., Cook, K., and Burt, R.S.: ‘Social captial:
theory and research’, Aldine de Gruyter, New York, 2001.
[35] Moody, J., and Paxton, P.: ‘Building Bridges: Linking
Social Capital and Social Networks to Improve Theory and
Research’, American Journal of Scientist, 2009, 52, (11), pp.
1491-1506.
[36] Nahapiet, J., and Ghoshal, S.: ‘Social Capital, Intellectual Capital, and the Organizational Advantage’, Academy of
Management Review, 1998, 23, (2), pp. 242-266.
[37] Nolker, R., and Zhou, L.: ‘Social Computing and
Weighting to Identify Member Roles in Online Communities’, Proceedings of the 2005 IEEE/WIC/ACM International
Conference on Web Intelligence, 2005.
els and Methods in Social Network analysis’ (Cambridge University Press, 2005), pp. 215-247.
[45] Stocker, R., David, G., and David, N.: ‘Consensus and
cohesion in simulated social networks’, Journal of Artificaial
Societies and Social Simulation, 2001, 4, (4)
[46] Thomke, S., and von Hippel, E.: ‘Customers as innovators: a new way to create value’, Harvard Business Review,
2002, 80, (2), pp. 74-81.
[47] Toubia, O.: ‘Idea Generation, Creativity, and Incentives’,
Marketing Science, 2006, 25, (5), pp. 411-425.
[48] Tuomi, I.: ‘Networks of Innovation: Change and Meaning in the Age of the Internet’, Oxford University Press, Oxford, UK, 2003.
[49] Viegas, F.B., and Smith, M.: ‘Newsgroups Crowds and
AuthorLines: Visualizing the Activity of Individuals in Conversational Cyberspace’, HICCS 2004.
[50] von Hippel, E.: ‘Democratizing innovation’, The MIT
Press, Cambridge, 2005.
[38] Nooteboom, B.: ‘Learning by Interaction: Absorptive
Capacity, Cognitive Distance and Governance’, Journal of
Management and Governance, 2000, 4, (1-2), pp. 69-92.
[51] von Hippel, E., and Katz, R.: ‘Shifting Innovation to
Users via Toolkits’, Management Science, 2002, 48, (7), pp.
821-833.
[39] Panzarasa, P., Opsahl, T., and Carley, K.M.: ‘Patterns
and dynamics of users' behavior and interaction: Network
analysis of an online community’, Journal of the American
Society for Information Science & Technology, 2009, 60,
(5), pp. 911-932.
[52] von Krogh, G., and von Hippel, E.: ‘The promise of
research on open source software’, Management science, 2006,
52, (7), pp. 975.
[40] Powell, W.: ‘Neither market nor hierarchy: Network
forms of organization’, in Staw, B., and Cumming, L. (Eds.):
‘Research in Organizational Behavior’ JAI Press, 1990.
[53] Wasserman, S., and Faust, K.: ‘Social Network Analysis:
Methods and Applications’, Cambridge University Press,
Cambridge, 1994.
[41] Preece, J.: ‘Online Communities - Designing Usability,
Supporting Sociability’, John Wiley & Sons, Chichester,
2000.
[54] Welser, H., Gleave, E., Fisher, D., and Smith, M.: ‘Visualizing the Signatures of Social Roles in Online Discussion
Groups’, The Journal of Social Structure, 8, (2), retrieved from
http://www.cmu.edu/joss/content/articles/
volume8/Welser, 2007.
[42] Sawhney, M., and Prandelli, E.: ‘Communities of creation: Managing distributed innovation in turbulent markets’,
California Management Review, 2000, 42, (4), pp. 25-54.
[55] West, J., and Lakhani Karim, R.: ‘Getting Clear About
Communities in Open Innovation’, Industry and Innovation,
2008, 15, (2), pp. 223-223.
[43] Smith, M., Shneiderman, B., Milic-Frayling, N., Rodrigues, E.M., Barash, V., Dunne, C., Capone, T., Perer, A., and
Gleave, E.: ‘Analyzing (Social Media) Networks with NodeXL’, C&T '09: Proceedings of the fourth International
Conference on Communities and Technologies, 2009, Springer
[56] Winsor, J.: ‘SPARK: Be More Innovative Through CoCreation ’, Kaplan Business, 2005.
[44] Snijders, T.A.B.: ‘Models for longitudinal network data
’, in Carrigton, P., Scott, J., and Wasserman, S. (Eds.): ‘Mod-
[58] Zemljic, B., and Hlebec, V.: ‘Reliability of measures of
centrality and prominence’, Social Networks, 2005, 27, (1), pp.
73-88.
[57] Wu, F., and Huberman, B.A.: ‘Persistence and Success
in
the
Attention
Economy’,
SSRN:
http://ssrn.com/abstract=1369484, 2009.
11