Online Word of Mouth and Product Review Disagreement

Studies of online word of mouth have frequently posited―but never systematically conceptualized and explored―that the level of disagreement between existing product reviews can impact the volume and the valence of future reviews. In this study we develop a theoretical framework of disagreement in online WOM and test our predictions in a dataset of nearly 300,000 online reviews for 425 movies over three years. This framework highlights that rather than thinking of disagreement as dispersion of opinions around a mean, high levels of disagreement can be better conceptualized as opposing opinion poles. Such a conceptualization has important implications for how disagreement can be measured and how results can be interpreted. We theoretically develop, validate, and apply a novel statistical measure of disagreement that can be used alongside existing alternative approaches such as standard deviation. We find that only high levels of disagreement―with opposing opinion poles―influence future reviews while simple dispersion does not. We show that high levels of disagreement among previously posted reviews lead to more future product reviews, a relationship that is moderated by informational content such that higher informational content amplifies the effect. Further, we show that increased disagreement leads to future reviews of lower valence. Our findings highlight that an important role for research on big data in information systems is to examine how existing measurement approaches and interpretations can be improved by fully leveraging the richness that digital trace data offers.