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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 19 the major contributor, and network activeness (See Table 1) 10 . The number of registered members accounted for 5.5% of the total variance (p< .01) in terms of the frequency of postings per adopter, for 8.6% (p< .01) in terms of the frequency of postings per adopter excluding the major contributor, for 4.6% (p< .01) in terms of the average duration excluding the major contributor, and for 41.6% (p< .01) in terms of network activeness (all linear). Hypothesis 2-2 was partially supported. The number of posters showed positive linear as well as curvilinear relationships with the frequency of postings per adopter excluding the major contributor and network activeness. Total variances in the two measures were better explained by curvilinear regression than by linear regression. Positive linear relationships were found with the frequency of postings per adopter, the average duration of adopter excluding the major contributor, and the proportion of adopters among posters (See Table 2). The number of posters accounted for 22.3% of the total variance (p<.01) in terms of the frequency of postings per adopter excluding the major contributor, for 58.4% (p<.05) in terms of network activeness (all curvilinear), for 7.4% (p<.01) in terms of the frequency of postings per adopter, for 2.6% (p<.01) in terms of the average duration of adopter excluding the major contributor, and for 3.8% (p<.01) in terms of the proportion of adopters among posters (all linear). Hypothesis 2-3 was also partially supported. The number of adopters showed positive linear as well as curvilinear relationships with the frequency of postings per adopter excluding the major contributor and the proportion of adopters among posters. Total variances in the two measures were better explained by curvilinear regression than by linear regression. Positive linear relationships were found with the frequency of postings per adopter, the average duration of adopter excluding the major contributor, and network activeness (See Table 3). The number of adopters accounted for 27.5 % of the total variance (p<.01) in terms of the frequency of postings per adopter excluding the major contributor, for 11.1% (p<.05) in terms of the proportion of adopters among posters (all curvilinear), for 11.1% (p<.01) in terms of the frequency of postings per adopter, for 3.8% (p<.01) in terms of the average duration of adopter excluding the major contributor, and for 66.2% (p<.05) in terms of network activeness (all linear). 10 A cubic relationship found with network activeness was considered inappropriate because the tolerance level is close to ‘0’. In all later analyses, cubic relationships with low tolerances were considered inappropriate.

Authors: Yun, Haejin., Park, Songyi. and Kim, Hee-Jung.
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Running Head: Invisible Leverage in Adoption of Online Social Support Community
19
the major contributor, and network activeness (See Table 1)
10
. The number of registered
members accounted for 5.5% of the total variance (p< .01) in terms of the frequency of postings
per adopter, for 8.6% (p< .01) in terms of the frequency of postings per adopter excluding the
major contributor, for 4.6% (p< .01) in terms of the average duration excluding the major
contributor, and for 41.6% (p< .01) in terms of network activeness (all linear).
Hypothesis 2-2 was partially supported. The number of posters showed positive linear as
well as curvilinear relationships with the frequency of postings per adopter excluding the major
contributor and network activeness. Total variances in the two measures were better explained
by curvilinear regression than by linear regression. Positive linear relationships were found with
the frequency of postings per adopter, the average duration of adopter excluding the major
contributor, and the proportion of adopters among posters (See Table 2).
The number of posters accounted for 22.3% of the total variance (p<.01) in terms of the
frequency of postings per adopter excluding the major contributor, for 58.4% (p<.05) in terms of
network activeness (all curvilinear), for 7.4% (p<.01) in terms of the frequency of postings per
adopter, for 2.6% (p<.01) in terms of the average duration of adopter excluding the major
contributor, and for 3.8% (p<.01) in terms of the proportion of adopters among posters (all
linear).
Hypothesis 2-3 was also partially supported. The number of adopters showed positive
linear as well as curvilinear relationships with the frequency of postings per adopter excluding
the major contributor and the proportion of adopters among posters. Total variances in the two
measures were better explained by curvilinear regression than by linear regression. Positive
linear relationships were found with the frequency of postings per adopter, the average duration
of adopter excluding the major contributor, and network activeness (See Table 3).
The number of adopters accounted for 27.5 % of the total variance (p<.01) in terms of the
frequency of postings per adopter excluding the major contributor, for 11.1% (p<.05) in terms of
the proportion of adopters among posters (all curvilinear), for 11.1% (p<.01) in terms of the
frequency of postings per adopter, for 3.8% (p<.01) in terms of the average duration of adopter
excluding the major contributor, and for 66.2% (p<.05) in terms of network activeness (all
linear).
10
A cubic relationship found with network activeness was considered inappropriate because the tolerance level is
close to ‘0’. In all later analyses, cubic relationships with low tolerances were considered inappropriate.


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