The more, the not merrier? exploring information overload in the context of electronic word-of-mouth (eWOM)

Abstract

ABSTRACT The power of consumer-to-consumer recommendation has long been known to marketers. With so-called electronic word-of-mouth (eWOM) that reaches a much wider audience, consumers easily express opinions to and access others’ opinions on products or brands, and are frequently exposed to vast amounts of information. While past research separately focuses on the significance of eWOM for consumer opinion, and the effects of information overload on consumer opinion, the relationship between eWOM and information overload has been overlooked. Drawing from the Elaboration Likelihood Model, the present research investigates this relationship by comparing eWOM sentiment across different information levels. For the analysis, 8,213 tweets containing the keyword ‘Bitcoin’ are retrieved and scored for positive, and negative sentiments and polarity. The sentiment scores are then tested for their relationship with (1) the number of articles, and (2) the number of tweets on Bitcoins using the logistic regression models. We find strong evidence that information overload increases both eWOM negativity and polarity. The findings provide further insight for marketers in order to dedicate more resources to information management, and to prevent information overload on consumers.