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With the growing popularity of online user-generated reviews, research has emerged to understand the mechanism of how a review is voted helpful, focusing on the central-route influences of review content and quality, yet little research has studied the roles of peripheral cues such as reviewer credibility and contextual factors. Drawing on the theories of elaboration likelihood model and source credibility model, this study developed an integrative model of online review helpfulness, focusing on the direct influence of reviewer credibility and the moderating effects of service price and rating extremity. An econometrics regression analysis of 16,265 hotel reviews on Yelp showed that reviewer expertise in terms of the number of “Elite” badges, and reviewer online attractiveness in terms of the number of friends both helped a review receive helpfulness votes. The findings further revealed that a review written by an opinion leader (i.e., a reviewer with more Elite badges and more online friends) did not necessarily receive more helpfulness votes. Hotel price weakened the enhancing effect of reviewer expertise. Rating extremity also diluted the influence of reviewer credibility. These findings contribute to the knowledge of online review helpfulness, and offer practical implications on how to position valuable reviews.
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Stored only when both papers are in the corpus — 173 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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