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The online retailing environment has grown more complex with the myriad information items available for customers to peruse. Two such information items include the trending lists of currently popular products, and online customer reviews and ratings of products. A trending product is one that relatively large groups of people are currently browsing, purchasing, or discussing. In this paper, we analyze the pricing implications of both types of information items. Our main goal is to test whether, acting as signals of desirability and popularity, a trending status or high ratings and reviews can lead to price cuts on homogeneous products. We provide empirical support for a model that demonstrates these relationships using data on books from twenty-four categories of Amazon.com. An important theoretical contribution of our model is that even in the absence of strategic considerations or demand dependencies, our model suggests lower prices for trending and positively reviewed products. Our work shows that pricing systems using popularity and average customer ratings as inputs can help achieve increased profitability.
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Stored only when both papers are in the corpus — 13 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.