On October 23rd, 2014, we updated our
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We need to broaden the work in this arena by looking at:
What happens after access is not a problem anymore?
What happens to the social content / substance of Internet use?
Assumption: The Internet does not singlehandedly create social gaps, or can reduce them
The Internet is a catalyst, is the “yeast” in the social mix
It favors specific behaviors (especially those with socially consequential effects) if these behaviors are already present
Two pronged approach
Individual – what social behaviors are enhanced by the Internet?
Social/group level – is social capital increased / diminished by Internet connections?
The individual approach
A number of studies have noticed “magnification effects”
Those socially active are more likely to
Adopt the Internet
Or to use it for social goals
Empirical evidence for magnification effects
Metamorphosis (Ball-Rokeach and Matei)
GSS 2000 (Robinson and Neustadtl)
Pew Internet Polls (Rainie, Jones and Howard)
Syntopia (Katz, Rice and Aspden)
Cyberville study in Toronto (Wellman and Hampton)
Belonging Mass Communication On-line connections Metamorphosis research strategy Chinese Greater Monterey Park Mexican East LA Caucasian South Pasadena Korean Greater Koreatown Caucasian Westside African-American Greater Crenshaw Central American Pico Union Bilingual Telephone Interviews 1812 Households
Some empirical evidence – Metamorphosis
People who are more solidly anchored to their neighborhoods are more likely to make a friend on-line
7% increase in likelihood of making a friend on-line for each increase in a “belonging index” score
Married people are more likely to make a friend on-line than singles when they know someone in the neighborhood
Singles are less likely to make a friend on-line when they do not know someone in the neighborhood
Community level effects in Metamorphosis study
In white neighborhoods the Internet indirectly contributes to social integration
In Asian and Latino neighborhoods the Internet does not contribute -- directly or indirectly -- to social integration
Group effects at national level
Study of the 48 contiguous union states
states with the highest amount of social capital are more likely to produce virtual groups – Yahoo! clubs – and to produce the most active groups
GSS 2000 Internet module
Contains questions about:
Reasons for using the Internet social reasons included
Time spent with people off-line (distinguishes between family and friends)
Users are more likely to spend more time with neighbors and friends
Those who use the Internet for social reasons are also more likely to spend more time with friends – although not with family members
Further research questions
If high “socializers” in Real Life are high “socializers” in Virtual Space, how sustainable is this in the long run?
Will there be a tipping-off point, which will lead to a “reversal of fortunes”?
Will, in the long run, on-line ties replace off-line ties?
Will this affect especially the virtual class, those living the digital life, isolated in their “nerdistans” (Kotkin)?
This question, although asked many times and allegedly answered, is still to be addressed. It requires longitudinal, national or large scale representative studies.
Further research questions
How should we confront the failure of hardware dissemination to alleviate the problems of the poorest, less vital communities?
Maybe we should address the issue of presence/absence of social capital first, before assuming that technology will create it
Internet connections revitalize pre-existing community resources, cannot invent them from scratch
Social capital theory
In trying to explain when and how the Internet might contribute to / detract from social interaction I rely on Social Capital Theory (Coleman, Putnam)
SCT main goal to explain social action
Social capital = social networks and informal / semi-formal organizations
SCT identifies the resources and motivations that explain social involvement and collective action
Generalized reciprocity: main resource for generating social capital
Social capital is more likely to be produced where social attitudes, expectations and obligations are directed by the principle of “generalized reciprocity”:
“ I’ll do this for you without expecting anything specific back from you, in the confident expectation that someone else will do something for me down the road” (Putnam, 2000, Bowling Alone, p. 21).
Where generalized reciprocity is strong, there is are important “unintended consequence:” groups are easier to form and transactions (social, economic, political) to be negotiated more social capital
Extending social capital theory to studying the social impact of communication technology
Generalized reciprocity is “trained” and developed in geographically situated communities: families, neighborhoods, schools, circles of friends and associates, political units
shared cultures and values facilitate trust
a sense of obligation is stronger to those closer to us (dark side)
A sense of “generalized reciprocity” once acquired, becomes portable and extensible to other realms. We can take it with us wherever we go.
SCT implications for studying the Internet
Generalized reciprocity and the habits of the heart associated with it will inflect our use of communication technology
When colonizing the Internet we take our capacity to generate social capital with us
We will generate social ties in a proportion commensurate with our general ability to produce social capital
The broad research question
Does off-line propensity for sociability influence on-line social interactions?
The need for a dual level of analysis strategy
Generalized reciprocity is a form of “positive externality:” neighborhood watch groups benefit even those who do not participate
IS NOT an entirely individual phenomenon
It is BOTH an individual and group process
Not only individuals that present high propensity for generating social capital will be more likely to generate on-line social ties
Social groups with potential for high social capital will manifest the same tendencies
More specific research questions
Do individuals who have a higher propensity for generating social capital (GR), also have a higher propensity for involvement on-line?
Do social environments with higher potential for generating social capital (GR) produce more on-line activity?
Individual-level side of the question
Explored in Los Angeles using a geographically focused sample
Results reported in American Behavioral Scientist (2001) and in the Journal of Communication (in press)
Belonging Index captures level of social capital at individual level – assumes trust and generalized reciprocity
Number of neighbors known to:
Talk about a personal problem
Ask for a ride
Watch over your home
Assist with a repair
It is easy to make friends with your neighbors
You enjoy talking with your neighbors
Your neighbors borrow things from you
You are interested in knowing what your neighbors are like
Cronbach alpha .8
The question, again, is…
Are people with higher level of belonging (social capital) more likely to establish bonds on-line?
Analysis: logistic regression
Depedent variable is binary
“ Yes” / “No” answers to the question: “Have you ever met someone you consider a personal friend?”
How much does a predictor variable increase the odds of choosing one of two categories of the binary variable, controlling for other variables
Logistic regression results
Those who “belong” more are 7% more likely to make a friend on-line for each “belonging index unit increase”
Controlling for gender, ethnicity, income, education, age, immigration history
Belonging is positively associated with making friends on-line
Social capital might be involved in generating sociability on-line
Switching levels of analysis
Do individuals who have a higher propensity for generating social capital (GR) also have a higher propensity for involvement on-line?
Do social environments with higher potential for generating social capital (GR) produce more on-line sociability?
Study started as part of an undergraduate research methods class Paper presented in Maastricht, at the 3rd Conference of Internet researchers, under review at the Journal of Broadcasting and Electronic Media Methodology Using states as units of analysis: Do states with higher capacity for producing social capital generate more on-line sociability?
Operationalizing “on-line sociability”
Number of Yahoo! groups associated with a specific state of the union
Yahoo! groups: Web-based electronic spaces where people interested in a specific location (state or smaller locations) can meet and communicate
Bulletin board, chat, file sharing, photo uploads, community databases
4,597 Groups (M=95 / state)
Group size range: 1 - 2,239 members
Multiple (OLS) regression:
Predict number of groups per 100,000 using capacity for generating social capital
trust level (proxy for generalized reciprocity)
Controlling for population homogeneity and density.
Clubs per 100,000 inhabitants dependent variable
Main predictor variable
State-level of trust (capacity to generate social capital)
% of those who answered “Yes” to the GSS question – “ Most people can be trusted ”
Rough indicator of “ generalized reciprocity ”
Used by Putnam in his “social capital” index.
% Yes “Most people can be trusted”
Population homogeneity (% foreign born)
Variables dropped after exploratory analysis
Gross state product
Highly correlated between them (r=0.6).
Highly correlated with level of trust (r=0.6).
Results The higher the social capital, the more numerous the groups The more homogeneous the population, the more numerous the groups Adjusted R 2 =.18 .01 -2.546 -.420 .032 -.08 Percent population foreign born .02 2.285 .327 .014 0.03 % Yes: Most people can be trusted β Std. Error B p t Standardized Coefficients Unstandardized Coefficients
Is the consequence of high potential for social capital – higher social involvement – connected to on-line sociability?
Can on-line sociability directly be predicted by off-line sociability?
Predict number of Yahoo! groups using number of NGOs (501c3) / 1000 people
Follow up analysis results Dependent variable: Yahoo! groups per 100,000 Adjusted R-square = . 12 The more numerous the non-profit organizations, the more numerous the on-line groups The more homogeneous the population, the more numerous the groups -- ns
On-line sociability probably has a “magnifying glass effect” – helps those who have high level of belonging to extend their relationships on-line
High social capital states generate more on-line groups – sociability on-line reflects sociability off-line
For designing on-line venues:
Sociability builds on sociability
Sticky sites and groups are made of:
sticky individuals who share at least some proximity
Seed the group with high social capital opinion leaders and motivators
For the community activist / policy practitioner
Hardware alone does not revitalize community or democracy
Is the level of social capital sufficient to expect a specific pay-off from implementing the technology?
IF NOT, energize first the social networks in the community
Make them the anchors of the new computer network
The road from here…
Study of Lexington modeled after the Los Angeles study
how does the specific spatial location of each respondent in a specific geographic location influence their social ties on and off-line?
Yahoo! study follow-up:
How does the Internet/media interact with our social contexts?
The Internet as other media plays an important role in the process of social integration
It facilitates emergence of “ties the bind”
It serves as a “magnifying glass” – strengthens pre-existing propensities for social action
Do you talk with other people about your neighborhood? (1-10 scale, median split)
Are you a member of any community organization?
Do you primarily use community media or
Mainstream media for
community information, entertainment or shopping
TV, newspapers, radio
Do you have Internet access from home, work or anywhere else?
Have you ever met someone on-line you consider a personal friend?
Coleman: multiplexity and strength of ties between social actors (manifested as obligations and expectations), social norms related to trust and access/density of information channels
Putnam: those features of social life—social networks, norms and trust—that enable collective action
Belonging and new/old media connectedness: a communication infrastructure model Connections to Community Organizations Local/Community Media Connections Participation in Interpersonal Storytelling BELONGING Internet connection Mainstream Mass Media Connections 1.8 1.7 5.6 1.4 1.6 Metamorphosis study: English-speaking samples 1.4
On-line sociability predicted by belonging ( dv: “Have you ever met someone on-line you consider a personal friend?”) Variable B S.E. Wald Sig Exp(B) BELONGING .0639 .0296 4.6612 .0309 1.0660 GENDER .5391 .3013 3.2019 .0736 1.7144 AGE -.0095 .0143 .4344 .5098 .9906 EDUC .1811 .1156 2.4549 .1172 1.1985 INCOME -.1030 .0863 1.4238 .2328 .9021 IMMIG.GEN. -.1362 .1296 1.1045 .2933 .8727 KOREATOWN 3.2065 1.3314 5.7996 .0160 24.6915 KOREAN/BELONG.-.1231 .0702 3.0737 .0796 .8842 CRENSHAW .2197 .5752 .1459 .7025 1.2457 ELA -1.2143 .8942 1.8441 .1745 .2969 MONTEREY PARK .5827 .5824 1.0010 .3171 1.7908 WESTSIDE .1334 .5405 .0609 .8051 1.1427 PICO UNION -.5566 .8220 .4586 .4983 .5731
Operationalizing “Intensity of on-line activity”
Starting Point: How much activity is generated by a typical club in any given state?
First instinct: average number of messages/member for each club, then average the averages
Problem: ignores the fact that some clubs are larger or older, had more chances to facilitate activity
Solution: “Adjusted” measure of “average number of messages” per club
MEASURE CONCEPTUALLY: What would the number of messages sent to a typical club in any given state be if the influence of number of members and club age would be constant (the same)?
OLS-procedure: DV: Number of Messages; IVs: # of members; club age in months; R-square = .65 Predict number of messages for each club using group size (# of members) and club age (longevity in months)
MEASURE OPERATIONALLY: Average predicted number of messages for each state: SUM of predicted number of messages / Number of clubs in each specific state
Results Curvilinear relationship between trust and weighted average group activity Sparser populated states generate more active clubs Adjusted R 2 =.21 .012 -2.637 -2.634 .000 -.0007 % Answered Yes “Most people can be trusted” squared .028 2.292 2.277 .023 0.05 % Answered Yes “Most people can be trusted” .065 -1.905 -.315 .000 -0.0003 Population density Beta Std. Error B Sig. t Standardized Coefficients Unstandardized Coefficients
Curvilinear relationship between trust and on-line activity
Curvilinearity of relationship between trust and activity mirrored by that between off-line involvement and on-line involvement