Impact of multidimensional self-disclosure on review helpfulness from the perspective of the elaboration likelihood model

Purpose This paper employed the elaboration likelihood model (ELM) to investigate the effects of multidimensional self-disclosure on review helpfulness, focusing on three forms of self-disclosure: identity, picture and text self-disclosure. Moreover, the moderating effects of review sentiment considering the effect of potential negativity bias were included in the model. Design/methodology/approach A substantial dataset was collected from Ctrip.com, comprising 2,671,744 reviews of 6,862 distinct hotels in Beijing. Tobit regression models were employed in the empirical analysis. Findings The results revealed that, after controlling for the other factors, reviewer identity and picture disclosure, which had been categorized as peripheral cues, had significant positive impacts on review helpfulness. By contrast, there was an inverted U-shaped relationship between the depth of textual self-disclosure, a central cue and review helpfulness. Furthermore, the results indicated that negative sentiments in reviews could strengthen the influence of multidimensional self-disclosure on review helpfulness, outweighing the impact of positive sentiments. Originality/value This study is the first attempt to examine review helpfulness from the perspective of consumer multidimensional self-disclosure, providing a distinctive viewpoint. It advances the literature by utilizing the elaboration likelihood model to explicate the mechanism behind the impact of multidimensional self-disclosure on review helpfulness and expands the literature on negativity bias by revealing how review sentiment moderates the relationship between self-disclosure and review helpfulness.