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
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.
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?
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
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