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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 20 In general, simultaneous third-order polynomial regression analyses of all three network size measures found that the contribution of the number of adopters to R-square change was the highest, the number of posters the next highest, and the number of registered members was the lowest. Hypothesis 3: The network threshold of physical illness OSSCs tends to be larger than that of mental illness OSSCs. Hypothesis 3 was not supported. When separate third-order polynomial regressions were performed by illness type, no significant curvilinear relationship was found in any measure of adoption on both illness types at the same time. Therefore, no comparison of network thresholds by illness type was possible. Hypothesis 4: There will a curvilinear relationship between the participation of the major contributor and adoption. Hypothesis 4-1 was partially supported. The major contributor’s frequency activity showed curvilinear as well as positive linear relationships with four adoption measures – the frequency of postings per adopter, the frequency of postings per adopter excluding the major contributor, the proportion of adopters among posters, and network activeness. Total variances in all these four measures were better explained by curvilinear regression than by linear regression (See Table 4). The major contributor’s frequency activity accounted for 30.6% of the total variance (p<.01) in terms of the frequency of postings per adopter, for 18.0% (p<.01) in terms of the frequency of postings per adopter excluding the major contributor, for 8.1% (p<.01) in terms of the proportion of adopters among posters, and for 35.1% (p<.01) in terms of network activeness (all curvilinear). Hypothesis 4-2 was also partially supported. The major contributor’s duration activity showed curvilinear as well as positive linear relationships with the average duration and the average duration excluding the major contributor. Total variances in the two measures were better explained by curvilinear regression than by linear regression. Only a curvilinear relationship was found with the proportion of adopters among posters. However, unlike the findings of the previous hypotheses, standardized coefficients for quadratic terms were negative,

Authors: Yun, Haejin., Park, Songyi. and Kim, Hee-Jung.
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Running Head: Invisible Leverage in Adoption of Online Social Support Community
20
In general, simultaneous third-order polynomial regression analyses of all three network
size measures found that the contribution of the number of adopters to R-square change was the
highest, the number of posters the next highest, and the number of registered members was the
lowest.
Hypothesis 3: The network threshold of physical illness OSSCs tends to be larger than that of
mental illness OSSCs.
Hypothesis 3 was not supported. When separate third-order polynomial regressions were
performed by illness type, no significant curvilinear relationship was found in any measure of
adoption on both illness types at the same time. Therefore, no comparison of network thresholds
by illness type was possible.
Hypothesis 4: There will a curvilinear relationship between the participation of the major
contributor and adoption.
Hypothesis 4-1 was partially supported. The major contributor’s frequency activity
showed curvilinear as well as positive linear relationships with four adoption measures – the
frequency of postings per adopter, the frequency of postings per adopter excluding the major
contributor, the proportion of adopters among posters, and network activeness. Total variances
in all these four measures were better explained by curvilinear regression than by linear
regression (See Table 4).
The major contributor’s frequency activity accounted for 30.6% of the total variance
(p<.01) in terms of the frequency of postings per adopter, for 18.0% (p<.01) in terms of the
frequency of postings per adopter excluding the major contributor, for 8.1% (p<.01) in terms of
the proportion of adopters among posters, and for 35.1% (p<.01) in terms of network activeness
(all curvilinear).
Hypothesis 4-2 was also partially supported. The major contributor’s duration activity
showed curvilinear as well as positive linear relationships with the average duration and the
average duration excluding the major contributor. Total variances in the two measures were
better explained by curvilinear regression than by linear regression. Only a curvilinear
relationship was found with the proportion of adopters among posters. However, unlike the
findings of the previous hypotheses, standardized coefficients for quadratic terms were negative,


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