1. Bas Reus: Multiple online group membership
May 25th 2013 – Sunbelt 2013 Hamburg

2. Introduction
• “Multiple group membership and behavior on an online collaboration platform”
– A story about posting behavior of bridging members
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3. Multiple group membership consequences
• Individual – focus of this research
– people being member of more than one group
– people being able to take information from one group to other groups
– people must share their available time (attention) between more groups
– people form bridges between groups
– people can exhibit different posting behavior in different groups
• Group – focus for a next research
– group have members that are member in other groups as well (bridges)
– groups overlapping with other groups
– groups have members that exhibit different posting behavior
– …
SNA research Bas Reus: Multiple online group membership

4. What makes a bridging member?
• Focus is on membership in both groups and what it means for the intercohesive position of ego
• Different from classic brokerage
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visual adopted fromVedres & Stark (2008)
Structural hole broker Bridging member

5. Online behavior, looking at form dimension
• Form dimension refers to observable features of communication (message in online group)
• Online forum message markers to determine behavior:
– Text length
– Asking questions
– Replying to many others
– Others replying to you
– Viewing the thread
– Start with “Hello” or “Hi”
– Ending with regards
– Including attachments
– Using hyperlinks
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6. ResearchQuestion
• Background
– Advance in online collaboration tools
– Organizations make more use of these tools to work towards collective goals
– People are active in (many) online groups
– Employees are able to form groups with ‘strangers’ from different physical locations
• RQ
– What is the influence of the number of bridging members on the overlap of two online groups
on their posting behavior in both groups?
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7. SNA research Bas Reus: Multiple online group membership 7

8. What makes a true bridging member?
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9. Setting
• Dutch health insurance company
• Online community for all 2400 employees
– Employees work in different places across the country
– Employees can be complete strangers to each other
• Data used from start in June 2012 to April 2013
• Analyzed posting behavior of messages in online groups
• Some statistics from private groups:
– 108 private groups
– 3421 messages
– 402 active actors
– 115 bridging actors (active in at least 2 groups)
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10. Method of analysis
• Classify posting behavior based on form dimension (Moser, Ganley & Groenewegen, 2013)
– Identify markers of form dimension (e.g. length of message, number of questions)
– Exploratory factor analysis to validate markers
– Cluster analysis based on factor scores to classify each message
• Binary logistic regression to test hypotheses
– Test hypotheses 1 and 3 with control variables
• IV: Number of bridging members (1, 3+)
• DV: Ego plays same or different role (YES, NO)
– Test hypotheses 2a and 2b with control variables
• IV: Ego and alter are interacting in both groups (YES, NO)
• DV: Ego and alter act similar (YES, NO)
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11. Factor analysis on message form markers
• Total variance explained: 79%
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Rotated Component Matrixa
Component
Connector Active Requestor Friendly
Ego replied to unique others in group .958
Unique others replied to ego .930
Number of messages of ego in group .955
Number of views of ego in group .913
Usage of question marks in message .851
Length of message .766
Starts message friendly .823
Ends message friendly .721
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.

12. Cluster analysis on message factor scores
• For logistic regression analysis, removed cluster 3 messages, no clear behavior identified
• Used all messages of all online groups to determine factors
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Final Cluster Centers
Cluster
1 2 3 4 5
Connectors 2.95372 -.16074 -.23195 -.32329 -.09498
Actives -.14847 2.54876 -.31570 -.12269 -.08652
Requestors .03139 -.19741 -.27260 2.67566 .02812
Friendlies -.15830 -.12652 -.40649 -.20984 1.68565
Number of Cases in each
Cluster
Cluster
1 505.000
2 682.000
3 4714.000
4 511.000
5 1299.000
Valid 7711.000
Missing .000

13. Intermediary results roundup
• Messages are classified into four factors (eight markers loaded on four factors)
• Messages are clustered into five clusters, dropped messages of one cluster, remaining four:
– Connectors (505 messages)
– Actives (682 messages)
– Requestors (511 messages)
– Friendlies (1299 messages)
• We can now run the binary logistic regressions to test the hypotheses
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14. Descriptive stats all cases: Cross tabulation
• BridgingMembers: Number of actors active both in group A and group B, sharing membership
• SameCluster: Ego is showing similar behavior in group A and group B
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BridgingMembers * SameCluster Crosstabulation
SameCluster Total
NO YES
BridgingMembers
1
Count 73 41 114
% within BridgingMembers 64.0% 36.0% 100.0%
2
Count 63 54 117
% within BridgingMembers 53.8% 46.2% 100.0%
3+
Count 92 160 252
% within BridgingMembers 36.5% 63.5% 100.0%
Total
Count 228 255 483
% within BridgingMembers 47.2% 52.8% 100.0%

15. Testing hypotheses 1 and 3: Logistic Regression
• Number of bridging members (1, 2, 3+) is most important independent variable in prediction
• Model can predict 71% correctly instead of 47% (step 0)
• Model fits well according to 1. Hosmer and Lemeshow (.452) and 2. Nagelkerke (.298)
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B S.E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Step 1a
Bridging members .000
Bridging members(1) .815 .316 .010 2.259 1.217 4.194
Bridging members(2) 1.366 .292 .000 3.920 2.212 6.946
Groups actor is active in -.063 .009 .000 .939 .923 .955
Total messages in group 1 .005 .001 .000 1.005 1.002 1.008
Total messages of actor in group 1 .020 .006 .001 1.020 1.008 1.033
Constant -.326 .242 .179 .722

16. Descriptive stats H2a and H2b: Cross tabulation
• IsConnectedBoth: ego and alter interact with each other in both groups
• SameRoles: ego and alter act similar in groupA and similar in group B
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IsConnectedBoth * SameRoles Crosstabulation
SameRoles Total
NO YES
Ego interacts with alter
in both groups
NO
Count 63 7 70
% within IsConnectedBoth 90.0% 10.0% 100.0%
YES
Count 41 19 60
% within IsConnectedBoth 68.3% 31.7% 100.0%
Total
Count 104 26 130
% within IsConnectedBoth 80.0% 20.0% 100.0%

17. Testing hypotheses 2a and 2b: Logistic Regression
• Ego is connected to alter (YES, NO) is most important independent variable in prediction
• Model can predict 87% correctly instead of 80% (step 0)
• Model fits well according to 1. Hosmer and Lemeshow (.711) and 2. Nagelkerke (.540)
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B S.E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Step 1a
Ego is connected to alter 2.549 1.017 .012 12.788 1.743 93.817
Total messages in group1 -.020 .007 .002 .980 .968 .993
Groups actor is active in .075 .029 .011 1.078 1.017 1.142
Total messages of actor .042 .011 .000 1.042 1.020 1.065
Constant -15.642 3.889 .000 .000

18. Results
• Hypotheses 1 and 3 supported
– Ego can exhibit different posting
behavior when ego is the only
bridging member
– Ego is constrained to exhibit the
same posting behavior due to
Simmelian tied bridging members
• Hypotheses 2a and 2b partially supported
– Connectedness of ego and alter in
both online groups leads to more
similar posting behavior
– However 80% of total behavior is
different between ego and alter in
all cases with two bridging members
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19. Implications and relevance
• For theory
– Multiple group membership: we only know impact on learning and productivity for individual
and group, nothing on behavior of people, and not in an online setting
– Can we can use network theories to explain behavior of bridging members in online groups?
• Structural holes – not constrained in behavior
• Structural equivalence – diverging (competitive) vs. converging (cooperative) role behavior
• Simmelian ties – constrained in behavior
• For practice (online groups and communities)
– Understanding behavior as it happens (show position of actor in relation to other groups)
– Making interventions (invite members to group or better not)
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20. Discussion
• Qualitative text analysis can enhance results (knowledge transfer between online groups)
– Who act as gatekeepers and representatives?
• Usage of network measures as independent variables such as:
– Betweenness of the bridging actors
– Closure of overlapping clique
– Tie strength within and outside of overlap
• Study longitudinal change of ego behavior due to changing number of bridging members in time
• Study overlap of more than two groups
• Make step from multiple group membership to overlapping groups, and ultimately to group social
capital and group effectiveness (original submitted abstract)
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21. Thank you!
• Q&A
• Please leave your business-card for a copy of this presentation
• Authors:
– Bas Reus b.reus@vu.nl
bas.reus@favelafabric.com
– Christine Moser c.moser@vu.nl
– Peter Groenewegen p.groenewegen@vu.nl
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