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An Invisible Leverage in the Adoption of Online Social Support Community
Unformatted Document Text:  Running Head: Invisible Leverage in Adoption of Online Social Support Community 9 1992) found that a higher proportion of the members of larger active and intimate networks provide most kinds of support. However, Stokes (as cited in Wellman, 1992) found a curvilinear relationship between the number of confidents and satisfied social support. There must be some trade-off between the quantity and the quality of social support. It is often assumed that more densely-knit networks would lead to greater internal communication, and in turn, to more social support. However, empirical findings diverge. Hirsh (1980) found that women were more likely to obtain satisfying support from low-density networks. Social support does not always increase with network density. People in densely- knit, integrated networks experienced less stress and receive more support (Wellman, 1992). Cohesiveness in densely-knit networks may increase emotional social support through perceived social connectedness to some degrees, but may also serve as normative pressure on members. At the same time, their tight boundaries limit the ability of members to acquire additional external resources such as information about newly developed medication and treatments. Different density levels may contribute to different types of social support. Wellman (1992) suggested that the effects of density are contingent on the kinds of support provided. This inference concurs with the finding of Muncer et al.(2000) that the diabetes network was more diffuse than the depression network since the former needed more informational support while the latter necessitated more social companionship. Clustering and blockmodeling (Wellman, 1981) are additional analyses beyond density. Clustering sorts network members into discrete clusters. Density is high within each cluster, but there exist relatively few ties connecting clusters. Blockmodeling discovers network members with similar network positions together. Network members in the same block display similar types of ties to others rather than to each other. They may have no ties among themselves. This is similar to Burt’s structural equivalence (1987). Structural equivalence posits that structurally equivalent members compete in adopting innovations for fear that they might be replaced with another structurally equivalent member for not adopting innovations. Clustering and blockmodeling, therefore, may help more clearly discern how and what types of social support flow through networks. Tie connects a pair of network members by one of more relations. Tie varies in its multiplexity as well as its strength (Garton, Haythornthwite, & Wellman, 1997). Multiplexity refers to the degree to which a tie carries more than one role relationship. That is, if two network

Authors: Yun, Haejin., Park, Songyi. and Kim, Hee-Jung.
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Running Head: Invisible Leverage in Adoption of Online Social Support Community
9
1992) found that a higher proportion of the members of larger active and intimate networks
provide most kinds of support. However, Stokes (as cited in Wellman, 1992) found a curvilinear
relationship between the number of confidents and satisfied social support. There must be some
trade-off between the quantity and the quality of social support.
It is often assumed that more densely-knit networks would lead to greater internal
communication, and in turn, to more social support. However, empirical findings diverge. Hirsh
(1980) found that women were more likely to obtain satisfying support from low-density
networks. Social support does not always increase with network density. People in densely-
knit, integrated networks experienced less stress and receive more support (Wellman, 1992).
Cohesiveness in densely-knit networks may increase emotional social support through perceived
social connectedness to some degrees, but may also serve as normative pressure on members. At
the same time, their tight boundaries limit the ability of members to acquire additional external
resources such as information about newly developed medication and treatments. Different
density levels may contribute to different types of social support. Wellman (1992) suggested
that the effects of density are contingent on the kinds of support provided. This inference
concurs with the finding of Muncer et al.(2000) that the diabetes network was more diffuse than
the depression network since the former needed more informational support while the latter
necessitated more social companionship.
Clustering and blockmodeling (Wellman, 1981) are additional analyses beyond density.
Clustering sorts network members into discrete clusters. Density is high within each cluster, but
there exist relatively few ties connecting clusters. Blockmodeling discovers network members
with similar network positions together. Network members in the same block display similar
types of ties to others rather than to each other. They may have no ties among themselves. This
is similar to Burt’s structural equivalence (1987). Structural equivalence posits that structurally
equivalent members compete in adopting innovations for fear that they might be replaced with
another structurally equivalent member for not adopting innovations. Clustering and
blockmodeling, therefore, may help more clearly discern how and what types of social support
flow through networks.
Tie connects a pair of network members by one of more relations. Tie varies in its
multiplexity as well as its strength (Garton, Haythornthwite, & Wellman, 1997). Multiplexity
refers to the degree to which a tie carries more than one role relationship. That is, if two network


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