Perceptual, Attitudinal, and Behavioral Outcomes of Organization–Public Engagement on Corporate Social Networking Sites

Abstract

This study proposes and tests a conceptual model that not only measures public engagement with corporate pages on social networking sites, but also evaluates the influence of such engagement on important perceptual, relational, and behavioral outcomes. Study results provide empirical evidence of the positive effects of public engagement on perceived corporate authenticity, organizational transparency, organization–public relationships, and public advocacy. Findings underscore the importance of public engagement via social media on enhancing perceived corporate transparency and authenticity, and thereby cultivating strong relationships. Additionally, organization–public relationships emerged as a deciding factor driving the effects of public engagement on advocacy behaviors.

Notes

¹Before answering these questions, respondents were directed to their Facebook profile and were asked to locate the company that had the most recent post under “Page Feeds.”

²According to Kline (2005), SEM is a technique that can be applied to both non-experimental and experimental data to verify a priori models comprised of latent variables or a mix of latent and observable variables. Thus, in the present study, structural SEM was used as the primary statistical method to test the hypothesized model.

³According to Hu and Bentler (1999), a cutoff value close to 0.95 for CFI and Tucker-Lewis index (TLI); a cutoff value close to 0.08 for standardized root mean square residual (SRMR); and a cutoff value close to 0.06 for RMSEA indicate good fit between the hypothesized model and the observed data. Additionally, according to Hu and Bentler's (1999) joint cutoff criteria, an SEM model with CFI, TLI ≥ 0.95 and SRMR <0.10 or RMSEA ≤0.06 and SRMR ≤0.10 suggests that the fit between the data and the proposed model is reasonable.

⁴According to Kline (2005), a single-fit index reflects only a particular aspect of model fit and a favorable value of that index does not by itself indicate good fit. There is no single “magic index” that provides a gold standard for all models. The chi-square is the most commonly reported measure of model-data fit. However, it is strongly dependent on the sample size.

⁵The error covariance between trust and control mutuality was .39. The error covariance between control mutuality and commitment was .36. The error covariance between trust and commitment was .21. The error covariance between engagement-contributing and control mutuality was .20. The error covariance between contributing and trust was .18.

⁶According to Byrne (2010), bootstrapping is a procedure in which one takes repeated, smaller random samples of an existing sample to develop empirical estimates of standard errors of any parameter. Bootstrapping is a common procedure used to address multivariate non-normality issues.

⁷According to Keith (2006), a standardized coefficient (â) of less than 0.05 suggests a negligible effect; a standardized coefficient of 0.05–0.10 suggests a small but meaningful effect; a standardized coefficient of 0.10–0.25 suggests a moderate effect; and a standardized coefficient of above 0.25 suggests a large effect.