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Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
Lurking as trait or situational disposition: Lurking and contributing in enterprise social media
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Lurking as trait or situational disposition: Lurking and contributing in enterprise social media

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This CSCW 2012 short-paper tests hypotheses from three theories to account for behaviors of 200,000+ people in 8600+ online enterprise communities in IBM. We find little support for theories based on …

This CSCW 2012 short-paper tests hypotheses from three theories to account for behaviors of 200,000+ people in 8600+ online enterprise communities in IBM. We find little support for theories based on binary traits (either or lurker OR a contributor) or for social learning (legitimate peripheral participation). We propose a theory of (a) general disposition to engage (through either or both of lurking and contributing) and (b) personal decision regarding the method of engagement, depending on factors such as job-role, topic-interest, or social commitment to other participants.

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  • 1. Lurking as Personal Trait or Situational Disposition:Lurking and Contributing in Enterprise Social Media Michael Muller IBM Research Cambridge, MA, USA michael_muller@us.ibm.com 1
  • 2. Outline• Study hypotheses based on three theories of lurking and contributing in social media:• … using a rich data source of over 200,000 users, in over 8700 online communities …• … allowing new analyses that examine, for each person, their lurking and contributing activities in multiple online communities• Implications for – Theory – Facilitation of communities 2
  • 3. Multiple Online Spaces• Source of data – 8711 online communities in IBM Connections Communities, an enterprise online communities service – Basic statistics • Users 224,232 • Contributors 22,949 • Communities 8,711 • Communities/Member 1 - 184, median=2 • Members/Community 1 - 14,997, median=9• Code each person – Count # Communities lurked (member) # Communities Lurked – Count # Communities contributed # Communities Contributed 3
  • 4. Theories that describe contributing and lurking• Binary trait theory: Lurkers – Lurking vs. contributing # Lurked – Curien et al. 2006 – “loafer”, “freeloader” – Panciera et al. 2010 – “born vs. made? born” Contributors Either a lurker or a contributor, but not both # Contributed• Continuity-of-engagement theory: t en m – Lurking and contributing combined ge # Lurked ga En – Nonnecke et al. 2006 Engagement drives both lurking and contributing # Contributed• Social learning theory: – Lurking as preparation for contributing Contributing g – Lave and Wenger, 1990 – “social learning” u tin rib – Preece and Shneiderman, 2009 – “reader to leader” nt Co Lurk immediately after joining; then contribute later Lurking Time after joining 4
  • 5. Very Little Support for Binary Trait Theory 84% of people who Contributed (in one community) 201283 “pure” lurkers Lurked (in another community) 19386 people who both contribute and lurk 3563 “pure” contributors 5
  • 6. Moderate Support for Continuity-of-Engagement Theory Correlation of Contributing & Lurking Pearson r = .375, p<.01 201283 “pure” lurkers Spearman rho = .352, p<.01 19386 people who both contribute and lurk 3563 “pure” contributors 6
  • 7. Decile Analysis: Lurking and Contributing Over Time• Decile analysis Contributing – Define activity span 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 1 10 = joindate to lastactivitydate – Divide into equal tenths Join Last activity (deciles) date date • Discard last date, because Time after joining it is guaranteed to have activity – Analyze contributions per decile• Social Learning theory predicts – Lurk immediately after joining – Then begin to Contribute later Contributing g u tin• Can test predictions for nt rib Co – Individual users Lurking – Entire communities Time after joining 7
  • 8. Little Support for Social Learning Theory – Individual Users 45 40 40 A Real Estate Manager, Sweden 35 D A Sales Manager, Czech Republic Real Estate Manager, Sweden Sum of Contributions across Communities Sum of Contributions across Communities 35 30 30 25 25 20 20 15 15 10 10 5 5 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Decile Decile 250 30 B Collaboration Toolsmith, Canada E A Development Lead, United Kingdom Real Estate Manager, Sweden Sum of Contributions 25 across Communities Sum of Contributions 200 across Communities 20 150 15 100 10 50 5 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Decile Decile 300 40 C Business Analyst (HW products), USA Business Analyst (HW products), USA 35 F A Software Specialist, Ireland Real Estate Manager, Sweden Sum of Contributions Sum of Contributions 250 across Communities across Communities 30 200 25 150 20 15 100 10 50 5 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Decile Decile 8
  • 9. Lurking as Disposition• Theory: Engagement + disposition• Yes, but what contributes to the user’s disposition? – Future ethnographic research into questions of • Relationship to topic • Relationship to people in the community • Work role and responsibilities • Other factors to be discovered• Implications for Design: Influencing the user’s disposition – Burke and colleagues: Users receiving responses to their posts tend to continue in the online community Catch them early, before they decrease their activity • Awareness tools for community facilitators • Rapid recommendation service to recruit other members to reply – Engagement is important • Identify not only early contributors, but also early lurkers • Encourage both styles of engagement 9
  • 10. Conclusion and Contribution• Tested three theories related to lurking and contributing Binary trait theory Continuity-of-engagement theory Social learning theory• Proposed theory based on engagement (trait) + disposition – Need for future research to understand factors in users’ dispositions• Developed a decile analysis for fine-grained understanding of – Individuals’ patterns of contribution across communities – Communities’ patterns of contribution across individuals Decile analysis can be extended for lurkers when view data become available• Potential design implications – Importance of early intervention to maintain user interest – Opportunities to strengthen engagement of both contributors and lurkers – Opportunities for new tools and practices to make use of these new understandings 10
  • 11. Thank youmichael_muller@us.ibm.com 11

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