The effects of signaling blockchain‐based track and trace on consumer purchases: Insights from a quasi‐natural experiment

Abstract Blockchain‐based track and trace (BCT) is increasingly adopted in the retail supply chain. However, there is little rigorous empirical evidence quantifying the effects of BCT on consumer purchases or examining the heterogeneity of these effects with varying product‐related characteristics. Employing transactional data from a leading global e‐retailer that contains 540 stock keeping units (SKUs), we design a quasi‐natural experiment spanning 80 weeks to estimate the signaling effect of BCT (i.e., disclosure of the BCT to consumers) on consumer purchases. Drawing on the signaling theory, we propose that BCT can serve as an effective and reliable signal of the product quality and trustworthiness of the retailer. Our research uncovers significant positive effects of BCT on the average purchase quantity per buyer, the total number of buyers, the number of new buyers, and the number of unique visitors to the traced products. We also find nuanced moderation effects for two product‐related characteristics—namely, consumer review inconsistency and product origins—on the influence of BCT on consumer purchases. Specifically, the signal effectiveness of BCT is stronger for products with more inconsistent customer reviews that indicate greater information asymmetry. The effect of BCT for products sourced globally is magnified because of the high BCT signal reliability attributed to the unique properties of the blockchain. The heterogeneous effects of BCT by varying product‐related characteristics can inform managers in selecting the right products to implement BCT.