Figure 1 - from "Antecedents of Political Consumerism: Modeling Online, Social Media and WhatsApp News Use Effects Through Political Expression and Political Discussion"

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Figure |. Cross-sectional Structural Equation Model of Key Variables. Note. N = 1338. Maximum-likelihood estimation. Continuous path entries are standardized SEM coefficients (StdYX standardization): * p < 0.05; **p < 0.01; ***p < 0.001. The model controls for demographic variables (age, gender, education and income, and race), socio-political antecedents (political intertest, discussion network size, ideology) and traditional communication variables (traditional news use and offline political discussion) by residualizing all observed variables prior to model fitting. All W! variables were brought into the model by mentioning their variance in the MODEL command. The model includes indirect effects on Political Consumerism (W') and (W7) (see Table |). Model bootstrapped 1,000 iterations. Goodness of fit: x? = 20.824; df = 4; p = 0.000; RMSEA = 0.059, CFI = 0.991, TLI = 0.974, SRMR = 0.019. Explained variance of criterion variables: SM Political Expression (W') R? = 0.506; Political Discussion (W') R? = 0.589; and Political Consumerism (W') beyond the variance explained by all residualized controls R? = 0.027.

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Figure 2. Lagged Structural Equation Model of Key Variables. Note. N = 511. Maximum-likelihood estimation. Continuous path entries are standardized SEM coefficients (StdYX standardization): * p < 0.05; **p < 0.01; ***p < 0.001. The model controls for demographic variables (age, gender, education and income, and race), socio-political antecedents (political intertest, discussion network size, and ideology) and traditional communication variables (traditional news use and offline political discussion) by residualizing all observed variables prior to model fitting. All W' variables were brought into the model by mentioning their variance in the MODEL command. The model includes indirect effects on Political Consumerism (W') and (W7) (see Table |). Model bootstrapped 1,000 iterations. Goodness of fit: x7 = 6.689; df = 5; p = 0.245; RMSEA = 0.027, CFI = 0.997, TLI = 0.994, SRMR = 0.019. Explained variance of criterion variables: SM Political Expression (W') R? = 0.523; Political Discussion (W') R? = 0.520; and Political Consumerism (W') beyond the variance explained by all residualized controls R? = 0.018.

Figure 3. Autoregressive Structural Equation Model of Key Variables. Note. N = 511. Maximum-likelihood estimation. Continuous path entries are standardized SEM coefficients (StdYX standardization): * p < 0.05; **p < 0.01; ***p < 0.001. The model controls for demographic variables (age, gender, education and income, and race), socio-political antecedents (political intertest, discussion network size, and ideology), and traditional communication variables (traditional news use and offline political discussion) and the autoregressive term (Political Consumerism W') by residualizing all observed variables prior to model fitting. All W! variables were brought into the model by mentioning their variance in the MODEL command. The model includes indirect effects on Political Consumerism (W') and (W?) (see Table |). Model bootstrapped 1,000 iterations. Goodness of fit: y? = 5.64; df = 5; p = 0.343; RMSEA = 0.017, CFI = 0.999, TLI = 0.998, SRMR = 0.017. Explained variance of criterion variables: SM Political Expression (W') R* = 0.523; Political Discussion (W') R? = 0.519; and Political Consumerism (W') beyond the variance explained by all residualized controls R* = 0.012.

Zicheng Cheng

, Bingbing Zhang

Antecedents of Political Consumerism: Modeling Online, Social Media and WhatsApp News Use Effects Through Political Expression and Political Discussion

Abstract: According to lifestyle politics theory, social media platforms introduce new ways for people to engage in civic life. Based on the communication mediation model, prior scholarship laid out theoretical and empirical foundations for how media exposure to the news positively influences people's political participatory behavior through supplemental communicative processes. Building on this line of research, we rely on a two-wave panel survey of U.S. adults to examine how the different online and social media communicative patterns among U.S. citizens, such as news use, political expression, and discussion, predict political consumerism behavior-the purchase decision of consumers based on political or ethical reasons. Advancing diverse causal order structural equation models, this study highlights a positive influence of news consumption, social media political expression, and political discussion in explaining political consumerism (i.e., boycotting and buycotting). Specifically, results underscore the importance of political expression and discussion mediating the relationship between online,

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