User Behaviors, Engagement, and Consequences

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Paper Number

1468

Paper Type

short

Description

Understanding what makes a review helpful is important for consumers and online retailers. However, empirical research has shown mixed findings regarding the effect of review length on review helpfulness. Additionally, review helpfulness has not been studied adequately in terms of review-related features (syntactic complexity, cohesion, review quantity). Drawing on the elaboration likelihood model, this study developed a research model that incorporates four review-related features and product type. Specifically, we posit that four review-related features (i.e., syntactic complexity, cohesion, review length, and review quantity) impact review helpfulness differentially. These relationships vary depending on product type. To validate the research model, we collected a dataset from Amazon.com to conduct data analysis. This research will not only contribute to a better understanding of review-related features that affect review helpfulness but also provides practical insights to online platforms and consumers regarding how to select and write a helpful review.

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Dec 12th, 12:00 AM

Online Review Helpfulness: The Role of Review-related Features and Product Type

Understanding what makes a review helpful is important for consumers and online retailers. However, empirical research has shown mixed findings regarding the effect of review length on review helpfulness. Additionally, review helpfulness has not been studied adequately in terms of review-related features (syntactic complexity, cohesion, review quantity). Drawing on the elaboration likelihood model, this study developed a research model that incorporates four review-related features and product type. Specifically, we posit that four review-related features (i.e., syntactic complexity, cohesion, review length, and review quantity) impact review helpfulness differentially. These relationships vary depending on product type. To validate the research model, we collected a dataset from Amazon.com to conduct data analysis. This research will not only contribute to a better understanding of review-related features that affect review helpfulness but also provides practical insights to online platforms and consumers regarding how to select and write a helpful review.

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