Identifying the most helpful online customer reviews (OCRs) is crucial for online shopping sites aiming to support consumer purchase decisions. Equally important is understanding how OCR helpfulness varies across different types of goods. By focusing on the most informative part of OCRs – the OCR text – and applying a novel methodological approach, we provide this knowledge without relying on potentially biased, yet widely utilized, helpfulness votes. Grounded in the Elaboration Likelihood Model (ELM) of persuasion, we hypothesize that only selected thematic categories of OCR text are helpful, and that the type of goods moderates this helpfulness. Our findings reveal that product-related content (e.g., functionality or quality) is less helpful for experience goods than for search goods. Conversely, customer-related content (e.g., emotional attitudes or recommendations) is more helpful for experience goods than for search goods. Our contribution is threefold. First, we present an approach that allows the investigation of OCR helpfulness independent of potentially biased helpfulness votes in a generalizable, domain-independent setting. Second, using this approach, we provide insights into the helpfulness of OCR texts across thematic categories and types of goods. Third, we extend the application of the ELM by providing theoretically grounded explanations for the observed effects. From a practical perspective, our findings inform the design of OCR systems for online shopping sites that aim to provide consumers with the most helpful OCRs.
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