Presentation of the paper "Supporting Collaborative Networks in Organizational Settings using an Enterprise 2.0 platform" at NETSCI 09 International Workshop and Conference on Complex Networks and their Applications, Venezia, Italy. July 2009 The paper is at http://www.gnuband.org/papers/supporting_collaborative_networks_in_organizational_settings_using_an_enterprise_20_platform/

1. Supporting Collaborative
Networks in Organizational
Settings using an
Enterprise 2.0 platform
Michela Ferron, Paolo Massa and
Francesca Odella
FBK Foundation and University of Trento

2. The SoNet project
SoNet project, an explorative project at FBK Foundation involving the development
and the implementation of an open source Enterprise 2.0 web desktop, had its spring
1 year ago.
Since its start the project involved both programming and social research and
specifically Social Network Research over the developed web desktop, used as a
virtual laboratory for research purposes.
In particular the group of the developers was motivated by a set of working strategies
such as:
• always in beta (never release a final version, continuous development)
• eat your own dog food
• produser (“producer” + “user”)
• open source (developed code is released under an open source license. Hence
visible and reusable just fitting the open source license terms)

3. Taolin: open source Enterprise 2.0 web desktop
Enterprise 2.0: Social networking in an enterprise contest
Taolin demonstrative screenshot
Taolin platform is designed to support the researchers and collaborators at FBK in
their working environment and involves a series of elements such as a customizable
user profile (on the left), chat, widgets, instruments of users interaction, virtual space
for knowledge sharing....

4. Taolin ( http://taolin.fbk.eu )
Taolin development follows a network strategy: only users enabled have the grants to
log into the web platform.
Enabled users are called champions. The size (i.e. number of nodes) of the network
increases in time together with champions' adoption
Currently there are 122 champions on
~ 400 researchers, technologists and
administrative (started in April, 2008)
Champions:
• favorite adoption among
colleagues (word-of-mouth, direct
suggestion)
• provide direct feedbacks
Taolin evolved over champions
feedbacks, collected through the web
platform as well as natural data (email,
interviews, surveys)
Champions adoption over time

5. The reasons for a collaborative platform: Taolin
Coordination and networking among people working in the same organization is
not a trivial activity.
Collaborative web platforms, sometimes termed Enterprise2.0, have a great
potential of improving effectiveness inside the working environment.
Taolin is finalized to support this working philosophy and to assist the process of
knowledge creation.
Hence the idea of collaborate with sociologists and psychologists and monitor
with social network analysis and other social enquiry techniques the involvement
of the users (motivation and behaviour).

6. Taolin as a virtual lab: Research Goals
The adoption of a communicative technology also modifies the quantity and quality
of interactions among users and among users and non users.
The procedural analysis of the interactions between Taolin champions and
specifically the evolution of the relationships was considered as a relevant source
of information for the developers of the platform.
In this sense Taolin acts as a virtual research laboratory for a social network
research.
Social network derived from real users activity performed on Taolin → Actions
performed within the web platform are logged and then analyzed.
In addition the development of open source automatic analysis tools for this
platform (hence analysis could be replied) is another goal of the project.

7. Networks analyzed
• Chat network
• Nodes are the champions (people who had the grant to log into Taolin at April, 1st 2009)
• Edges are chat messages exchanged between champions
• Edge's weight is the discrete amount of chat messages between champions
• Profile views network
• Nodes are the champions (people who had the grant to log into Taolin at April, 1st 2009)
• Edges linked two champions each time a champion views another champion's profile
• Edge's weight is the discrete amount of these profile views
Both networks are directed and weighted.
For both of the considered networks we analyzed a timespan of two months from April, 1st to May, 31st 2009.
Profile-views network summary:
● 110 nodes, 760 edges, directed
● Number of components: 51
● Diameter: 6
● Density: 0.0634
● Reciprocity: 0.0872
● Average path length: 2.4954

8. Working Hypotheses
1. WH 1: Members of a specific group tend to chat more internally (with other
members of the group) than externally (with users who are NOT members of the
group). Members of a specific group tend to view profile more externally (with
users who are NOT members of the group) than internally (with other members of
the group).
2. WH 2: There is a correlation between "organizational centrality" and "network
centrality" in the platform usage. People who are very network central are the one
in the middle as "organizational centrality".
Working hypotheses have been computed and tested using igraph, an open source
library for complex network analysis (link: http://igraph.sourceforge.net/)

9. Testing hypothesis 1
Hypothesis 1 has been tested either on profile views network than on chat networks.
Both networks are directed graph. We considered only outdegree values for each
nodes, taking into account that those networks are weighted.
The reason for our hypothesis is that social context may activate imitation dynamics
or social control inside one's own group.
We excluded from the analysis groups that are too small, i.e. that have less than five
champions.
To test this hypothesis we computed for every group the “internal” outdegree (number
of edges between two members of that group) and the “external” outdegree (number
of edges directed outside the group) summing these values for each champions in the
group, and then normalizing.
Further testing will be performed on indegree values too.

10. In the chat network users tend to chat more with users of the same group ( internal
communications). But some groups - 30.3% of champions - behave differently: they
relate more with other groups (external communications)

11. Analyzing the profile views network we discovered a similar behavior: users tend to
view profiles of members associated to the same group.

12. Testing hypothesis 2
“Organizational centrality” is a value we computed for each champions on her/his seniority
(how long she/he works for FBK foundation).
We divided all our champions into three organizational classes (OC) representative of the
three previously defined seniority groups.
Hence we assigned to each node (i.e. to each champion) an attribute containing its own
organizational class.
Testing was performed both on chat and profile views networks computing average
betweenness centrality for each organizational class,removing all the possible loops
(champions have the possibility to look at own profile)

13. Testing hypothesis 2
Hypothesis 2 explicits how champions who recently joined FBK make a larger use
of the profile view feature, to know colleagues (people sensemaking) and discover
competitors (→ comparative purpose).
Champions with higher seniority visit less profile, probably because they already
know the environment and need to keep in touch only with a restricted set of
colleagues, linked to their research field. (→ campaigning)
On the other hand the chat is more used by people belonging to OC 1, whereas the
class with the higher level of seniority use it less than other classes.

14. Supporting social networks results
The analyses put in evidence how communications that takes place among
champions (organized in sub-groups) are related both to organizational position and
shared adoption of collaborative instruments.
Activities such as evaluation of information (profile view), adaptive strategies (widget
adoption, internal chat) are also developed according to specific group's preferences
and motivations.
Content and qualitative analysis of the motivations of the champions (via personal
interviews) supported these findings and improved our working hypotheses.

15. Future work
• Reorganize data in time sequences → Longitudinal analysis
• Analyse the impact of recruitment criteria (and also, a regime, no more
champions, but all the members of organization).
• Expand the datasets with
o Widget adoption (imitation effect)
o Cascade behaviors (innovation effect)

16. Thanks!
Questions?
(or - even better - suggestions?)

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