To test the hypothesis for this research, path analyses were applied. Independent variables included online political information use, Facebook political information use, and mobile phones political information use, which were all tested for in separate models due to their conceptual overlaps. These variables were used to test for direct and indirect
association, through the two expected mediators namely wider views exposure and credibility, and results were collected in 3 figures and two tables, which constitutes a total of 6 models.
Each model represents a different communications variable. Each model contained 4 blocks, with block 1 reflecting demographics, block 2 ideology, block 3 communication variables, and block 4 the two secondary orientations.
This section begins by mentioning some additional results that are noteworthy, but which are not tested for by the research questions. Table 1 shows that age is a consistent indicator for the role of online political information use (β = .075, p < .001), Facebook political information use (β = .140, p < .001), and mobile phone political information use (β
= .117, p < .001) in online political participation, which suggests that those who participation in online political participation are mostly older. Another finding for online political
participation hints towards a civic divide with regards to mobile phone political information use, with race (β = .072, p < .05), and education (β = .064, p < .05) also weakly significant.
This suggests that white educated and slightly older respondents are more likely to use mobile phones in online political participation.
The same is also seen in Table 2 for offline political participation, being very strong indicators in the model for online political information use (β = .927, p < .001), Facebook political use (β = .926, p < .001), and mobile phone political use (β = 1.003, p < .001), again indicating than an older respondent is more likely to participate in offline political
participation. Also relevant in offline political participation, is the consistent indicator of
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education for online political information use (β = .219, p < .05), Facebook political use (β
= .296, p < .01), and mobile phone political use (β = .258, p < .01), suggesting that those more likely to participate in offline political participation are also more educated. When combined with the above, signs of civic divide form.
This research then moves on to report the results from the three research questions:
Research question one asks: What are the direct and indirect associations of ideology, online political information use, wider view exposure, and credibility amongst each other and on online political participation within the context of the O-S-O-R model?
Table 2 about here.
Overall, this model is strongly supported, explaining 28% of the variance for online political participation, 1.7% by demographic variables, 2.3% by ideology, 24.1% political information mediums, and .6% by the second level orientations. The first step in the O-S-O-R model finds support for the role of the strength of political ideology on online political information use (β = .087, p < .001), resulting in an increase of use.
Figure 1 about here.
Next this model investigate the association between online political information use and wider view exposure, and finds strong support (β = .443, p < .001) leading to a very strong increase of wider view exposure. The same path is also tested through credibility is also strongly supported (β = .458, p < .001) leading to an increase in credibility. The next step is for this model to determine whether wider view exposure leads to online political participation. This path is found to be not supported (β = .030, p > .05) suggesting that wider view exposure does not lead to online political participation. Credibility is however
supported (β = .070, p < .05) leading to a small increase in online political participation. This model also tested for direct effects of the strength of political ideology and online political information use. The strength of ideology is found to be strongly supported (β = .087, p
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< .001) leading to a small direct increase in online political participation. Online political information use is also found to be strongly supported (β = .501, p < .001) leading to a strong increase in online political participation.
Research question 2 asked what differences are distinguishable between online and offline political participation?
Table 3 about here.
Again, overall support is found for offline political participation with 29.3% of the variance explained, 23.7% by demographic variables, .6% by ideology, 4.5% political information mediums, and .5% by second level orientations. Then this model follows the same steps through the O-S-O-R model, finding support for the strength of political ideology on online political information use (β = .087, p < .001), resulting in an increase of use. Next this model investigate the association between online political information use and wider view exposure, and finds strong support (β = .443, p < .001) leading to a very strong increase of wider view exposure. The same path is also tested through credibility and is also strongly supported (β = .458, p < .001) leading to an increase in credibility. The next step is for this model to determine whether wider view exposure leads to offline political participation. This path is found to be not supported (β = .146, p > .05) suggesting that wider view exposure does not lead to online political participation. Also, no support is found for credibility (β
= .114, p > .05). For the direct effects of the strength of political ideology no support is found (β = .137, p > .05). For the direct effect of online political information use strong support is found (β = .530, p < .001) leading to a strong increase in offline political participation.
Research question 3 asks what differences are visible between online political information use, and Facebook political information use, as well as and mobile phone political information use for both direct and indirect association on political participation.
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First is Facebook political information use. Overall, this model is strongly supported, explaining 40.5% of the variance for online political participation, 3% by demographic variables, 3% by ideology, 33.3% by political information mediums, and 1.3% by the second level orientations, as can be seen in Figure 2.
The first step in the O-S-O-R model finds strong support for the role of the strength of political ideology on Facebook political information use (β = .099, p < .01), resulting in an increase of use. Next this model investigate the association between Facebook political information use and wider view exposure, and finds some support (β = .213, p < .05) leading to a strong increase of wider view exposure. The same path is also tested through credibility, some supported is found (β = .154, p < .05) leading to an increase in credibility. The next step is for this model to determine whether wider view exposure leads to online political participation. This path is found to be not supported (β = .048, p > .05) suggesting that wider view exposure does not lead to online political participation. Credibility is however
supported (β = .102, p < .001) leading to an increase in online political participation. This model also tested for direct effects of the strength of political ideology and online political information use. The strength of ideology is found to be strongly supported (β = .103, p
< .001) leading to a small increase in online political participation. Facebook political information use is also found to be strongly supported (β = .565, p < .001) leading to a strong increase in online political participation.
For offline political participation overall model support is also found with 26.5% of -the total variance explained, 23.2% by demographic variables, .2% by ideology, 2.1%
political information mediums, and 1% by second level orientations.
Then this model follows the same steps through the O-S-O-R model, finding support for the strength of political ideology on Facebook political information use (β = .099, p
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< .001), resulting in an increase of use. Next this model investigate the association between Facebook political information use and wider view exposure, and finds some support (β
= .213, p < .05) leading to a strong increase of wider view exposure. The same path is also tested through credibility is also supported (β = .154, p < .05) leading to an increase in credibility. The next step is for this model to determine whether wider view exposure leads to offline political participation. This path is found to be not supported (β = .134, p > .05) suggesting that wider view exposure does not lead to offline political participation. Some support is however found for credibility (β = .206, p < .05) leading to a strong increase in offline political participation. For the direct effects of the strength of political ideology no support is found (β = .073, p > .05). For the direct effect of Facebook political information use strong support is found (β = .266, p < .01) leading to an increase in offline political participation.
Figure 3 about here.
Overall, this model is strongly supported, explaining 24.3% of the variance for online political participation, 1.8% by demographic variables, 2.3% by ideology, 17.8% by political information mediums, and 2.4% by the second level orientations, as can be seen in Figure 3.
The first step in the O-S-O-R model finds some support for the role of the strength of political ideology on mobile phone political information use (β = .059, p < .01), resulting in a small increase of use. Next this model investigate the association between mobile phone political information use and wider view exposure, and finds no support (β = .109, p > .05).
The same path is also tested through credibility finding strong support (β = .252, p < .001) leading to a big increase in credibility. The next step is for this model to determine whether wider view exposure leads to online political participation. This path is found to be strongly supported (β = .097, p < .001) leading to a small increase in online political participation.
Strong support is also found for credibility (β = .116, p < .001) leading to an increase in
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online political participation. This model also tested for direct effects of the strength of political ideology and online political information use. The strength of ideology is found to be strongly supported (β = .112, p < .001) leading to a small increase in online political participation. Mobile phone political information use is also found to be strongly supported (β = .416, p < .001) leading to a strong increase in online political participation.
For offline political participation overall model support is also found, with 32.9% of the total variance explained, 23.9% by demographic variables, 1% by ideology, 7.6% by political information mediums, and .4% by second level orientations. Then this model follows the same steps through the O-S-O-R model, finding support for the strength of political ideology on mobile phone political information use (β = .059, p < .05), resulting in an increase of use. Next this model investigate the association between mobile phone political information use and wider view exposure, but finds no support (β = .109, p > .05).
The same path is also tested through credibility and is strongly supported (β = .252, p < .001) leading to an increase in credibility.
The next step is for this model to determine whether wider view exposure leads to offline political participation. This path is found to be not supported (β = .114, p > .05) suggesting that wider view exposure does not lead to offline political participation. The same is also true for credibility (β = .112, p > .05) suggesting that this path also does not lead to offline political participation. For the direct effects of the strength of political ideology, some support is found (β = .213, p < .05) with ideology leading to an increase in offline political participation. For the direct effect of mobile phone political information use strong support is also found (β = .706, p < .001) leading to a very big increase in offline political participation.