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Beyond Access: Digital divide, Internet Use and Gratifications Gained
Unformatted Document Text:  R ETHINKING THE D IGITAL D IVIDE 14 = .11, p < .001). These two types of Internet use explained 12 percent of variance in acquisition gratification with demographics and basic pattern of Internet use controlled. This pattern in the whole sample analysis resonated across all four subgroups. For the subgroup representing low socio-economic and young people, consumptive ( β = .33, p < .001) and surveillance use of the Internet ( β = .13, p < .001) were positive predictors of acquisition gratification. In this model, 14 percent of total variance in acquisition gratification was uniquely explained by the two types of Internet use. For the low socio-economic and old subgroup, acquisition gratification was explained by again both consumptive use ( β = .22, p < .001) and surveillance use of the Internet ( β = .13, p < .001) with 8 percent of total variance explained. Similarly, for the subgroup for high socio-economic and young respondents, both consumptive ( β = .35, p < .001) and surveillance use of the Internet ( β = .11, p < .001) explain acquisition gratification. In this model, 15 percent of variance in acquisition gratification was explained by these two factors. For the high socio-economic and old subgroup, acquisition gratification was explained by consumptive use ( β = .31, p < .001) and surveillance uses ( β = .09, p < .001) as well. A total of 12 percent of variance in acquisition gratification was explained by these two factors. In sum, acquisitive gratification was explained by two factors, consumption and surveillance over the Internet, consistently across all subgroups. Worth noting here is that acquisitive gratification was explained most in two younger subgroups, high SES and low SES, and least in low socio-economic and old subgroup. Discussion The results of this study highlight the fact that even when examining patterns of Internet uses and gratifications gained among Web users, there are notable differences across

Authors: Cho, Jaeho., Zuniga, Homero Gil de., Nah, Seungahn., Humane, Abhiyan., Hwang, Hyunseo., Rojas, Hernando. and Shah, Dhavan.
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background image
R
ETHINKING THE
D
IGITAL
D
IVIDE
14
= .11, p < .001). These two types of Internet use explained 12 percent of variance in acquisition
gratification with demographics and basic pattern of Internet use controlled. This pattern in the
whole sample analysis resonated across all four subgroups.
For the subgroup representing low socio-economic and young people, consumptive (
β
= .33, p < .001) and surveillance use of the Internet (
β
= .13, p < .001) were positive predictors of
acquisition gratification. In this model, 14 percent of total variance in acquisition gratification was
uniquely explained by the two types of Internet use. For the low socio-economic and old subgroup,
acquisition gratification was explained by again both consumptive use (
β
= .22, p < .001) and
surveillance use of the Internet (
β
= .13, p < .001) with 8 percent of total variance explained.
Similarly, for the subgroup for high socio-economic and young respondents, both consumptive (
β
= .35, p < .001) and surveillance use of the Internet (
β
= .11, p < .001) explain acquisition
gratification. In this model, 15 percent of variance in acquisition gratification was explained by
these two factors. For the high socio-economic and old subgroup, acquisition gratification was
explained by consumptive use (
β
= .31, p < .001) and surveillance uses (
β
= .09, p < .001) as well.
A total of 12 percent of variance in acquisition gratification was explained by these two factors.
In sum, acquisitive gratification was explained by two factors, consumption and
surveillance over the Internet, consistently across all subgroups. Worth noting here is that
acquisitive gratification was explained most in two younger subgroups, high SES and low SES,
and least in low socio-economic and old subgroup.
Discussion
The results of this study highlight the fact that even when examining patterns of Internet
uses and gratifications gained among Web users, there are notable differences across


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