Predicting Social Capital in Nonprofits’ Stakeholder Engagement on Social Media
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. 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. 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. 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
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
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
11. • # of retweets per tweet
• List count
• One month increase in list count
RETURN
Word-of-mouth
Reputation
MEASURES OF RETURNS
11
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. • Social (media) capital can be acquired through relationship investment
• The best practice is connecting with diverse ties through rich messages
MAJOR FINDINGS
13
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. # 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. # 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. # 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. # 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. # 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. • Acquired social capital helps diffuse organizational messages and build
online reputation.
MAJOR FINDINGS
20
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. 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. 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
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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
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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.
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