This research examines customer engagement in social media (CESM) using a meta-analytic model of 814 effect sizes across 97 studies involving 161,059 respondents. Findings reveal that customer engagement is driven by satisfaction, positive emotions, and trust, but not by commitment. Satisfaction is a stronger predictor of customer engagement in high (vs. low) convenience, B2B (vs. B2C), and Twitter (vs. Facebook and Blogs). Twitter appears twice as likely as other social media platforms to improve customer engagement via satisfaction and positive emotions. Customer engagement is also found to have substantial value for companies, directly impacting firm performance, behavioral intention, and word-of-mouth. Moreover, hedonic consumption yields nearly three times stronger customer engagement to firm performance effects vis-à-vis utilitarian consumption. However, contrary to conventional managerial wisdom, word-of-mouth does not improve firm performance nor does it mediate customer engagement effects on firm performance. Contributions to customer engagement theory, including an embellishment of the customer engagement mechanics definition, and practical implications for managers are discussed.
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We did examine an alternative model allowing direct effects of trust and commitment on firm performance, behavioral intention and WOM. The chi-square difference between the CESM model and the alternative is 19.9 with 4 df (p = .00052). The CFI suggests a slight improvement in fit to 0.98 versus 0.97. The improvement in fit is due largely due to a positive, significant, and nontrivial trust-performance relationship. More importantly, the addition of the direct paths does not affect the parameter estimates to any large degree as the correlation between the CESM estimates and the alternative model is r = 0.922. The parameter stability further provides evidence of a lack of bias due to interpretational confounding.
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de Oliveira Santini, F., Ladeira, W.J., Pinto, D. et al. Customer engagement in social media: a framework and meta-analysis. J. of the Acad. Mark. Sci. 48, 1211–1228 (2020). https://doi.org/10.1007/s11747-020-00731-5