The effect of social media marketing activities dimensions on value co-creation behavior: An application of the commitment-trust theory
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DOIhttp://dx.doi.org/10.21511/im.20(3).2024.05
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Article InfoVolume 20 2024, Issue #3, pp. 56-69
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Previous studies have not paid much attention to the effect of social media marketing activities on value co-creation behavior. Especially, up to now, no one has studied the effects of social media marketing activities dimensions on value co-creation behavior. This study applies the commitment-trust theory to develop and estimate this relationship through brand trust and brand commitment on social media in Vietnam. The snowball sampling technique was applied to gather 504 social media users through social media platforms in Vietnam. The proposed research model was tested through PLS-SEM using SmartPLS 4. The results highlighted that most social media marketing activities dimensions (including trendiness, electronic word of mouth, interaction, and customization) affect brand trust (pc are 0.42, 0.152, 0.112, 0.097, respectively, and p-values are all less than 0.05). Simultaneously, brand trust was found to have a positive effect on brand commitment (pc =0.405, p = 0.000). Furthermore, calculation results revealed that brand commitment contributed significantly and strongly to their co-value behavior towards brands (pc = 0.531, p = 0.000). On the contrary, the data do not support a direct impact of entertainment on brand trust (pc = 0.001, p = 0.990) and brand trust on co-value behavior (pc = 0.025, p = 0.466) at a significance level of less than 5%. Ultimately, the findings also suggest a guide to social media marketers to drive customer value co-creation behavior for brands.
Acknowledgment
This work was supported by the National Foundation for Science & Technology Development of Vietnam under Decision No. 131/QD-HDQL-NAFOSTED dated September 21, 2020.
- Keywords
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JEL Classification (Paper profile tab)M31, M37
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References62
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Tables6
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Figures2
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- Figure 1. Research model
- Figure 2. Results of PLS-SEM (displaying outer loadings and p-value for the outer model, path coefficients and p-value for the inner model, R-squared in the constructs)
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- Table 1. Estimate of loadings and significance
- Table 2. Reliability of the constructs
- Table 3. Discriminant validity – HTMT matrix
- Table 4. PLS results for collinearity statistic – inner models
- Table 5. Hypotheses testing results
- Table 6. Indirect effects
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