Optimal pricing strategies of e‐commerce supply chain considering consumers’ anticipated regrets under the background of blockchain anti‐counterfeiting technology

Abstract

In this study, we investigate pricing decisions with different decision sequences in an e‐commerce supply chain. The e‐commerce platform sells blockchain‐compatible products through a self‐operated model, while the retailer operating on the platform sells general products. Furthermore, we take into account the impact of counterfeits infiltrating the general product market. This can trigger consumers’ anticipated regrets, including high‐price regret and fake purchase regret. We design three scenarios: Scenario ES (the e‐commerce platform makes the decision first), Scenario RS (the retailer makes the decision first), and Scenario VN (they make decisions simultaneously). We study the impact of consumers’ anticipated regrets and the decision sequence on pricing strategies, as well as the profits of the e‐commerce platform and the retailer. Our results show that the optimal retail prices of the two versions of the products and the profits of the e‐commerce platform and the retailer are negatively correlated with the relative regret intensity. The profits of the e‐commerce platform and the retailer are the lowest under Scenario VN due to the fiercest competition. The e‐commerce platform prefers Scenario RS to Scenario ES. If the counterfeit penetration rate is small and the commission rate is large, the retailer prefers Scenario RS to Scenario ES. However, if the counterfeit penetration rate is high, or if both the counterfeit penetration rate and the commission rate are low, the retailer prefers Scenario ES to Scenario RS. Furthermore, if government penalties (or consumer claims) exist, the retailer should combat counterfeiting appropriately for better financial performance.