| Summary: | Effective use of online consumer reviews is hampered by uncertainty about their helpfulness. Despite a growing body of knowledge on indicators of review helpfulness, previous studies have overlooked rich semantic information embedded in review content. Following design science principles, this study introduces a semantic hierarchy of product features by probing the review text. Using the hierarchical framework as a guide, we develop a research model of review helpfulness assessment. In the model, we propose and conceptualize three new factors—breadth, depth, and redundancy, by building on and/or extending product uncertainty, information quality, signaling, and encoding variability theories. The model-testing results lend strong support to the proposed effects of those factors on review helpfulness. They also reveal interesting differences in the effects of redundancy and readability between different types of products. This study embodies knowledge moments of multiple genres of inquiry in design science research, which have multifold research and practical implications. © 2019 Association for Computing Machinery.
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