reputational systems in business social network sites
TRANSCRIPT
Teaching excellence for over a hundred years
Reputational Systemsin Business Social Network Sites:
An Empirical Analysis
Riccardo De Vita – University of Greenwich ([email protected])
Ivana Pais – University of Brescia ([email protected])
Teaching excellence for over a hundred years
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
Introduction
Ongoing research on social mechanisms at the base of online interaction on social network sites (SNSs)
o use by professionalso analysis of different types of online relationships
Specific focus on online reputationo theoretical relevanceo accessibility of data (explicit reputation)o managerial implications (online social capital)
Teaching excellence for over a hundred years
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
Hypothesis
1. Recommendations are more likely to occur between people linked by connections through multiple social network siteso 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 connectivityo Number of recommendations received/giveno Expertiseo Number of years spent on the online group
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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
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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 actorso Linkedin Group Vs Facebook Group
4311 1357505
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Method
Structural Variables:o Facebook connection between Milan In members
registered to the two groups – binary, undirectedo Linkedin connection between Milan In members
registered to the two groups – binary, undirectedo 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)
Facebook Group
Linkedin Group
Multiplexity – Linkedin/Facebook
Linkedin Recommendations
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The technological embeddedness of recommendations
Recommendations are sparse in the network under observation
The existence of a ‘technological multiplexity’ is not associated with an increased number of recommendations
Confirming preliminary results it seems to emerge a specialized and selective use of SNSs, reflecting underlying different relationships
Total Intraor. Interor.
# ties*** 92 46 47
% of Linkedin 1.35% 0.68% 0.69%
Also on Fac. 1 0 1
% of total rec. 1.09% - 2.13%
*** Ties counted on dichotomized network. One actor was recommended at two different points in time by the same person, however with a different work relationship
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Online behavior and recommendations
Recommending(outdegree)
Being recommended(indegree)
Connectivity - Facebook ++ ++Connectivity - Linkedin ++ +Expertise +++ +Years in the groupRecommendations 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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Comparing recommendations
Rec. – Interorg. Rec. – IntraorgReciprocity 27.03% 39.39%E-I index - 0.351 - 0.394Centralization 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?)
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Discussion
Preliminary understanding of online recommendationso 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
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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
Reputational Systemsin Business Social Network Sites:
An Empirical Analysis
Riccardo De Vita – University of Greenwich ([email protected])
Ivana Pais – University of Brescia ([email protected])