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Predicting Social Capital in Nonprofits’ Stakeholder Engagement on Social Media

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A new framework for understanding organizational social capital online

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Predicting Social Capital in Nonprofits’ Stakeholder Engagement on Social Media

  1. 1. Wayne Weiai Xu PhD Candidate Department of Communication, SUNY-Buffalo Advisor: Dr. Gregory D. Saxton, Associate Professor Department of Communication, SUNY-Buffalo Predicting Social Capital in Nonprofits’ Stakeholder Engagement on Social Media 1
  2. 2. THE SOCIAL (MEDIA) CAPITAL MODEL INVESTMENT SOCIAL CAPITAL RETURN Message-based investment Connection-based investment Network locations Embedded resources Word-of-mouth Reputation Context: Community foundations’ public communication on Twitter 2
  3. 3. Why another social capital model/term?1. WHY THE STUDY? Is social media anything new?2. Is organizational social capital different from its interpersonal counterpart?3. Social capital was treated as an outcome or antecedent, rather than a theory (which it really is). Social media could be a great equalizer in the distribution of social capital. Mass-interpersonal approach 3
  4. 4. A lack of empirical testing of the social capital as a process, rather than an antecedent or an outcome, in particular, in computer-mediated contexts. 1. THE GAP A lack of conceptualization and measures of the social capital process unique to the social media context, especially considering that organizations build/maintain online contacts through interpersonal approaches. 2. 4
  5. 5. INVESTMENT SOCIAL CAPITAL Message-based investment Connection- based investment Network locations Embedded resources P1 P2 P3 P4 STUDY ONE STUDY MODEL RETURN Word-of-mouth Reputation P5 P6 P7 P8 STUDY TWO 5
  6. 6. Twitter mentions/replies, distinguishable by the symbol “@” MEASURES OF RELATIONSHIP INVESTMENT 6
  7. 7. • The cue richness of messages • The number of targeted stakeholders • The frequency of targeting • The variety of targeted stakeholders • The number of targeted stakeholders • The frequency of targeting • The variety of targeted stakeholders INVESTMENT Message-based investment Connection-based investment MEASURES OF RELATIONSHIP INVESTMENT 7
  8. 8. NETWORK LOCATIONS EMBEDDED RESOURCE MEASURES OF SOCIAL (MEDIA) CAPITAL 8
  9. 9. • In-degree centrality • Betweenness centrality • The size of acquired stakeholder network • The influence of acquired stakeholders • The strength of ties with acquired stakeholders • The variety of acquired stakeholders SOCIAL CAPITAL Network locations Embedded resources MEASURES OF SOCIAL (MEDIA) CAPITAL 9
  10. 10. WORD-OF-MOUTH REPUTATION MEASURES OF RETURNS 10
  11. 11. • # of retweets per tweet • List count • One month increase in list count RETURN Word-of-mouth Reputation MEASURES OF RETURNS 11
  12. 12. U.S.-based community foundations Based on a complete list of 1,308 community foundations by the Council on Foundations (www.cof.org/community-foundation-locator). 258 were present on Twitter at the time of study INVESTMENT: Three-month data, 07/30/2014 to 10/30/2014 SOCIAL CAPITAL: Three-month data, 10/31/2014 to 01/31/2015 RETURN: One-month data, 02/01/2015 to 02/28/2015 DATA SOURCE 12
  13. 13. • Social (media) capital can be acquired through relationship investment • The best practice is connecting with diverse ties through rich messages MAJOR FINDINGS 13
  14. 14. # of targeted local stakeholders # of targeted non-local stakeholders Frequency of stakeholder- targeting Variety of targeted stakeholders # of tweets Message complexity INVESTMENT SOCIAL CAPITAL the size of acquired stakeholder network β = .24* β = .17* β = .17* F (8, 193) = 40.99, .61** RESULTS – HOW INVESTMENT PREDICTS SOCIAL CAPITAL 14
  15. 15. # of targeted local stakeholders # of targeted non-local stakeholders Frequency of stakeholder- targeting Variety of targeted stakeholders # of tweets Message complexity INVESTMENT SOCIAL CAPITAL the influence of ties with acquired stakeholders β = .18* F (8, 193) = 10.88, .28** RESULTS – HOW INVESTMENT PREDICTS SOCIAL CAPITAL 15
  16. 16. # of targeted local stakeholders # of targeted non-local stakeholders Frequency of stakeholder- targeting Variety of targeted stakeholders # of tweets Message complexity INVESTMENT SOCIAL CAPITAL the strength of ties with acquired stakeholders β = .20* β = .29* F (8, 193) = 10.72, .28** RESULTS – HOW INVESTMENT PREDICTS SOCIAL CAPITAL 16
  17. 17. # of targeted local stakeholders # of targeted non-local stakeholders Frequency of stakeholder- targeting Variety of targeted stakeholders # of tweets Message complexity INVESTMENT SOCIAL CAPITAL the variety of acquired stakeholders β = .30* β = .11* F (8, 193) = 28.17, .52** RESULTS – HOW INVESTMENT PREDICTS SOCIAL CAPITAL 17
  18. 18. # of targeted local stakeholders # of targeted non-local stakeholders Frequency of stakeholder- targeting Variety of targeted stakeholders # of tweets Message complexity INVESTMENT SOCIAL CAPITAL Betweenness centrality β = .34* β = .16* F (8, 193) = 15.05, .36** β = -.24* RESULTS – HOW INVESTMENT PREDICTS SOCIAL CAPITAL 18
  19. 19. # of targeted local stakeholders # of targeted non-local stakeholders Frequency of stakeholder- targeting Variety of targeted stakeholders # of tweets Message complexity INVESTMENT SOCIAL CAPITAL Indegree centrality β = .39* β = .18* F (8, 193) = 9.70, .26** β = -.30* RESULTS – HOW INVESTMENT PREDICTS SOCIAL CAPITAL 19
  20. 20. • Acquired social capital helps diffuse organizational messages and build online reputation. MAJOR FINDINGS 20
  21. 21. Betweenness centrality The size of acquired local stakeholder network The size of acquired non- local stakeholder network The influence of acquired stakeholders The strength of ties with acquired stakeholders The variety of acquired stakeholders SOCIAL CAPITAL RETURN Retweet β = .30* F (8, 193) = 16.92, .39** β = .14* RESULTS – HOW SOCIAL CAPITAL PREDICTS RETURNS 21
  22. 22. Betweenness centrality The size of acquired local stakeholder network The size of acquired non- local stakeholder network The influence of acquired stakeholders The strength of ties with acquired stakeholders The variety of acquired stakeholders SOCIAL CAPITAL RETURN list count β = .17* F (8, 193) = 116.55, .82** β = .27* β = -.08* RESULTS – HOW SOCIAL CAPITAL PREDICTS RETURNS 22
  23. 23. Betweenness centrality The size of acquired local stakeholder network The size of acquired non- local stakeholder network The influence of acquired stakeholders The strength of ties with acquired stakeholders The variety of acquired stakeholders SOCIAL CAPITAL RETURN One-month increase in list count β = .19* F (8, 193) = 20.95, .44** β = .22* RESULTS – HOW SOCIAL CAPITAL PREDICTS RETURNS 23
  24. 24. Centrality The size of acquired local stakeholder network The size of acquired non-local stakeholder network The influence of acquired stakeholders The strength of ties with acquired stakeholders The variety of acquired stakeholders SOCIAL CAPITAL# of targeted local stakeholders # of targeted non-local stakeholders Frequency of stakeholder- targeting Variety of targeted stakeholders # of tweets Message complexity INVESTMENT RETURN # of retweet per tweet List count Increase in list count RESULTS – MEDIATED RELATIONSHIPS 24
  25. 25. An empirical testing of social capital as a (causal) process CONTRIBUTIONS 1. The development of measurement scheme for social media capital2. Further empirical or philosophical debates on whether social capital is inherited or acquired 3. Practical implications in strategic Twitter communication4. 25

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