Resale platforms such as Swappa and ThredUP, which provide a channel for product‐holders to sell used products, have become common. Interestingly, in the presence of resale platforms, some firms, such as Apple, set lower rebates for the trade‐in‐for‐upgrade (TU) mode instead of implementing the trade‐in‐for‐upgrade‐and‐cash (TUC) mode as Huawei does. In this paper, we build game‐theoretical models to explore how a firm should adjust its trade‐in strategy (e.g., choose pricing and mode selection between TU and TUC) in reaction to the emergence of third‐party resale platforms. We derive several insights. First, we find that using the TU mode helps to encourage consumer repurchases, whereas the TUC mode may have a greater promotion effect on consumers’ first purchases. Second, we show that in the TUC mode, the amount of the trade‐in rebate is not affected by the presence of the resale platform. Differently, in the TU mode, whether the firm should provide a more generous trade‐in rebate depends on the unit product cost when the resale platform is present. Third, in response to the resale platform, the firm should choose the TU mode to take advantage of the platform's promotion effect if the unit product cost is high and choose the TUC mode to avoid the platform's cannibalization effect if the unit product cost is low. To verify the robustness of our findings, we consider the effects of reduced consumer uncertainty and the dynamic pricing mechanism in the extended models. Our main findings concerning trade‐in rebate and mode selection remain valid.

As brick-and-mortar retail rebounds, congestion in fitting rooms has reemerged as a critical operational challenge. Augmented reality (AR) fitting applications offer a scalable solution by enabling rapid virtual trials and reducing in-store delays, yet imperfect assessments may increase product mismatches. This study provides actionable guidance for retailers on AR adoption and pricing strategies by clarifying the economic roles of the substitution and complementarity effects. Our findings underscore the importance of a market-contingent approach to AR adoption and system upgrades. Rather than presuming that improvements in accuracy or usability will uniformly enhance performance, retailers and IT managers should rigorously evaluate how technological capabilities interact with market size, price fluctuations, and congestion dynamics. We also inform targeted marketing by demonstrating how heterogeneity in consumer technology preferences shapes channel selection and effective transaction outcomes. More broadly, our results caution policymakers that emerging digital technologies—while intended to reduce friction—may generate unintended welfare losses if mismatch risks and strategic responses are overlooked. These insights provide actionable guidance for digital transformation initiatives in retail and other capacity-constrained service systems characterized by heterogeneous consumers.