Abstract
ABSTRACT Online reviews have become one of the most influential persuasive messages concerning decisions making. Researchers have explored different aspects of online reviews and the characteristics of reviewers using various methods; however, few studies have focused on consumers who do not post online reviews after purchasing, resulting in a gap in the research. This study aims to identify consumers’ characteristics and analyze the importance of consumers in different online review scenarios. We employed a fusion analysis framework, which used machine learning to determine the value of different types of consumers; in addition, we quantitatively estimate the relationship between variables and consumer types using econometrics modelling. This research used real consumer data from three data resources, solving problems related to the use of limited questionnaire datasets and single-source data. Through rigorous analysis, we demonstrate that lurkers are more valuable than posters because they can be easily served and create more profit; thus, researchers should consider those who do not post online reviews rather than focusing only on the influence of online reviews and posters. Our findings also provide managerial implications for precision marketing by indicating that e-marketers should employ various marketing strategies and pay attention to different types of consumers, especially lurkers.
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