1. Reputational Systems
in Business Social Network Sites:
An Empirical Analysis
Riccardo De Vita – University of Greenwich (r.devita@gre.ac.uk)
Ivana Pais – University of Brescia (pais@jus.unibs.it)
Teaching excellence for over a hundred years

2. Agenda
 Theoretical background: lack of studies about online
personal recommendations
 Preliminary hypothesis
 Methodology: empirical setting, data and variables
 Results
 Discussion, implications and limitations
Teaching excellence for over a hundred years

3. Introduction
 Ongoing research on social mechanisms at the base of
online interaction on social network sites (SNSs)
o use by professionals
o analysis of different types of online relationships
 Specific focus on online reputation
o theoretical relevance
o accessibility of data (explicit reputation)
o managerial implications (online social capital)
Teaching excellence for over a hundred years

4. Theoretical background
 Research on online reputation mechanisms but mainly for
seller-buyer relationships (Ebay and Amazon) (Ockenfels,
Roth 2006; Houser 2006; Resnick, Zeckhauser Swanson
2006; Resnick, Kuwabara, Zeckhauser 2000; Bolton,
Katok, Ockenfels 2002; Dellarocas 2001)
 Research gap!
Teaching excellence for over a hundred years

5. Hypothesis
1. Recommendations are more likely to occur between
people linked by connections through multiple social
network sites
o Recommending implies emotional closeness – multiple
online ties as “strong ties” (Haythornthwaite, 2002)
o Facebook is associated with friendship
2. Recommendations are positively associated with:
o Online connectivity
o Number of recommendations received/given
o Expertise
o Number of years spent on the online group
Teaching excellence for over a hundred years

6. Hypothesis
3. Recommendation relationships with people from the same
organization are (a) similar to, (b) different from
recommendation relationships with people from a different
organization
Teaching excellence for over a hundred years

7. Milan In
 A non-profit association set up in 2005 to allow members
of LinkedIn living in Milan to physically meet up with each
other.
 Comparative study:
o same organization & same actors
o Linkedin Group Vs Facebook Group
4311 505 1357
Teaching excellence for over a hundred years

8. Method
 Structural Variables:
o Facebook connection between Milan In members
registered to the two groups – binary, undirected
o Linkedin connection between Milan In members
registered to the two groups – binary, undirected
o Linkedin recommendation (requires Linkedin
connection) – weighted, directed
 Composition Variables: gender, education, job title,
number of connections,...
 Analysis of network properties at the global and local level
- UCINET 6 (Borgatti, Everett and Freeman, 2002)
Teaching excellence for over a hundred years

9. Facebook Group

10. Linkedin Group

11. Multiplexity – Linkedin/Facebook

12. Linkedin Recommendations

13. The technological embeddedness
of recommendations
Total Intraor. Interor.  Recommendations are sparse
# ties*** 92 46 47 in the network under
% of Linkedin 1.35% 0.68% 0.69% observation
Also on Fac. 1 0 1  The existence of a
% of total rec. 1.09% - 2.13% ‘technological multiplexity’ is not
associated with an increased
*** number of recommendations
Ties counted on dichotomized network.
One actor was recommended at two
different points in time by the same  Confirming preliminary results it
person, however with a different work seems to emerge a specialized
relationship and selective use of SNSs,
reflecting underlying different
relationships
Teaching excellence for over a hundred years

14. Online behavior and
recommendations
Recommending Being recommended
(outdegree) (indegree)
Connectivity - Facebook ++ ++
Connectivity - Linkedin ++ +
Expertise +++ +
Years in the group
Recommendations given NA +++
Recommendations received NA
 Different social mechanisms associated with recommending and being
recommended
 The time spent on the LinkedIn group is never associated with
recommendation
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15. Comparing recommendations
Rec. – Interorg. Rec. – Intraorg
Reciprocity 27.03% 39.39%
E-I index - 0.351 - 0.394
Centralization 1.373% 0.388%
Prevailing industry ICT ICT
 No major differences emerge from a very exploratory analysis
 Issue#1: people working for the same organization declaring different
industries
 Issue#2: biased sample (online recommendation and ICT?)
Teaching excellence for over a hundred years

16. Discussion
 Preliminary understanding of online recommendations
o Different mechanisms supporting recommending
and receiving a recommendation
 Selective nature of online interactions: different platforms
for different needs/uses
o Implications for users and organizations
Teaching excellence for over a hundred years

17. Limitation & the next steps…
 Preliminary results, WIP
 Refining analysis including other SNA measures and
extending the empirical setting
 Focus and comparison across different industries
Teaching excellence for over a hundred years

18. Reputational Systems
in Business Social Network Sites:
An Empirical Analysis
Riccardo De Vita – University of Greenwich (r.devita@gre.ac.uk)
Ivana Pais – University of Brescia (pais@jus.unibs.it)
Teaching excellence for over a hundred years