Description
Social media has triggered change in word of mouth (WOM) literature and transformed it from traditional offline communication to real time, online buzz. Computer mediated negative WOM becomes viral and adversely impacts service provider’s reputation. In this context, we now explore three questions (i) How non-interpersonal characteristics of sender impacts receivers WOM propagation (ii) How interpersonal characteristics of sender-receiver dyad impacts receiver WOM propagation (iii) Finally, how can companies respond to these communication to advertise their products and services positively. Using econometric modelling, our findings suggest that anonymity, dispersion, valence, and homophily are strong predictors of social-WOM propagation after online service failure. Secondly, characteristics like tie strength play little role. Thirdly, service provider responsiveness plays an important role in curbing negative effect of positive WOM after dispersion.
Recommended Citation
Vakeel, Khadija Ali and Dey, Shubhamoy, "Motivations for Word-of-Mouth Propagation post-Online Service Failure: Moderating Role of Responsiveness" (2017). AMCIS 2017 Proceedings. 14.
https://aisel.aisnet.org/amcis2017/eBusiness/Presentations/14
Motivations for Word-of-Mouth Propagation post-Online Service Failure: Moderating Role of Responsiveness
Social media has triggered change in word of mouth (WOM) literature and transformed it from traditional offline communication to real time, online buzz. Computer mediated negative WOM becomes viral and adversely impacts service provider’s reputation. In this context, we now explore three questions (i) How non-interpersonal characteristics of sender impacts receivers WOM propagation (ii) How interpersonal characteristics of sender-receiver dyad impacts receiver WOM propagation (iii) Finally, how can companies respond to these communication to advertise their products and services positively. Using econometric modelling, our findings suggest that anonymity, dispersion, valence, and homophily are strong predictors of social-WOM propagation after online service failure. Secondly, characteristics like tie strength play little role. Thirdly, service provider responsiveness plays an important role in curbing negative effect of positive WOM after dispersion.