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When Uses and Gratifications Meet the Knowledge Gap: The Impact of Media Motives and Demographics on Political Activity
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with age (p<.01), income (p<.01), and race (p<.05). Except for residence years, the
demographics (age, p<.01; income. P<.01; gender, p<.05); race, p<.10; race, p<.05) are correlated
with interest in politics. Interpersonal communication about politics is significantly associated
with education (p<.01), income (p<.01), and gender (p<.05). There are, thus, important
associations, especially in the case of income. These findings are consistent with Hypothesis 1.
Hypothesis 2 suggests that motivations for using media in the domain of politics will
have strong effects on indicators of political activity. To begin with, the correlation analysis (see
Table 2) shows social interaction is significantly correlated with voting (p<.05) and consumption
of media coverage (p<.01). Surveillance (p<.01) and social interaction (p<.01) are both
significantly correlated to interest in politics, and the surveillance (p<.01) and interaction (p<.01)
motives are both significantly associated with interpersonal communication about politics. These
findings offer support for the hypothesis, especially in terms of the social interaction motive
dimension.
The hierarchical regression analyses also case light on Hypothesis 2 (see Table 3). When
controlling for income, gender, residence and education, the social interaction motive still has a
main effect on voting (
β
= .099, p<.10;
β
= .151, p<.01;
β
= .125, p<.05;
β
= .115, p<.05,
respectively). When controlling for age, income, gender, race, residence, and education, social
interaction still has a main effect on consumption of media coverage (
β
=.416, p<.01;
β
= .505,
p<.01;
β
= .455, p<.01;
β
= .426, p<.01;
β
= .427, p<.01;
β
= .452, p<.01, respectively). When
controlling for age, income, gender, race, residence and education, interaction motive still has a
main effect on interest in politics (
β
=.350, p<.01;
β
= .313, p<.01;
β
= .352, p<.01;
β
= .384,
p<.01;
β
= .353, p<.01;
β
= .363, p<.01, respectively). Lastly, when controlling for age, income,
gender, race, residence and education, social interaction still has a main effect on interpersonal
communication about politics (
β
=.399, p<.01;
β
= .365, p<.01;
β
= .396, p<.01;
β
= .401, p<.01;
β
= .375, p<.01;
β
= .410, p<.01, respectively). Now, we look at the surveillance motive. When
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| | Authors: Thorson, Esther., Jin, Yan. and Beaudoin, Christopher. |
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with age (p<.01), income (p<.01), and race (p<.05). Except for residence years, the
demographics (age, p<.01; income. P<.01; gender, p<.05); race, p<.10; race, p<.05) are correlated
with interest in politics. Interpersonal communication about politics is significantly associated
with education (p<.01), income (p<.01), and gender (p<.05). There are, thus, important
associations, especially in the case of income. These findings are consistent with Hypothesis 1.
Hypothesis 2 suggests that motivations for using media in the domain of politics will
have strong effects on indicators of political activity. To begin with, the correlation analysis (see
Table 2) shows social interaction is significantly correlated with voting (p<.05) and consumption
of media coverage (p<.01). Surveillance (p<.01) and social interaction (p<.01) are both
significantly correlated to interest in politics, and the surveillance (p<.01) and interaction (p<.01)
motives are both significantly associated with interpersonal communication about politics. These
findings offer support for the hypothesis, especially in terms of the social interaction motive
dimension.
The hierarchical regression analyses also case light on Hypothesis 2 (see Table 3). When
controlling for income, gender, residence and education, the social interaction motive still has a
main effect on voting (
β
= .099, p<.10;
β
= .151, p<.01;
β
= .125, p<.05;
β
= .115, p<.05,
respectively). When controlling for age, income, gender, race, residence, and education, social
interaction still has a main effect on consumption of media coverage (
β
=.416, p<.01;
β
= .505,
p<.01;
β
= .455, p<.01;
β
= .426, p<.01;
β
= .427, p<.01;
β
= .452, p<.01, respectively). When
controlling for age, income, gender, race, residence and education, interaction motive still has a
main effect on interest in politics (
β
=.350, p<.01;
β
= .313, p<.01;
β
= .352, p<.01;
β
= .384,
p<.01;
β
= .353, p<.01;
β
= .363, p<.01, respectively). Lastly, when controlling for age, income,
gender, race, residence and education, social interaction still has a main effect on interpersonal
communication about politics (
β
=.399, p<.01;
β
= .365, p<.01;
β
= .396, p<.01;
β
= .401, p<.01;
β
= .375, p<.01;
β
= .410, p<.01, respectively). Now, we look at the surveillance motive. When
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