reputational systems in business social network sites

Teaching excellence for over a hundred years

Reputational Systemsin 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

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

Teaching excellence for over a hundred years

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?)

Teaching excellence for over a hundred years

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

Teaching excellence for over a hundred years

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 (r.devita@gre.ac.uk)

Ivana Pais – University of Brescia (pais@jus.unibs.it)