Problem definition: Ride-hailing platforms face a supply-demand imbalance. During peak periods, the demand from passengers far exceeds the supply of drivers, whereas during off-peak periods, there is an abundance of supply but weak demand. The well-studied surge pricing can be challenging to implement in markets where prices are subject to regulation and are inflexible to adjust. We study an alternative operational approach that shifts the supply of drivers from off-peak to peak periods to address the supply-demand imbalance. Methodology/results: We propose two novel incentive schemes: the qualification scheme and the prioritization scheme. Specifically, the platform sets a work target for the peak period. Under the qualification scheme, the platform assigns off-peak service requests only to drivers who meet the peak period target. Under the prioritization scheme, the platform prioritizes off-peak requests for drivers who meet the peak period target. We analyze the effectiveness of these schemes, considering the openness of the platform’s supply system. For platforms with a closed system that only allows full-time drivers to provide service, the qualification scheme improves the total matching volume to a greater extent but hurts full-time drivers more than the prioritization scheme. For platforms with an open system that also allows the abundant part-time drivers to serve off-peak requests, the prioritization scheme outperforms the qualification scheme in improving total matching volume. Furthermore, the implementation of an incentive scheme in an open system may benefit both the platform and full-time drivers. Managerial implications: Our study suggests an alternative to surge pricing for on-demand platforms to address the supply-demand imbalance when prices are inflexible. It provides insights for platforms with varying levels of supply system openness in choosing appropriate incentive schemes. The welfare results offer guidance for platforms and policymakers regarding both matching volume and worker welfare. Funding: This work was supported by the National Natural Science Foundation of China [Grant 61771331]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0216 .

Action recognition of interactive entities' actions has gained widespread adoption. Among the methods relying on skeletal data sets, there are approaches utilizing Graph Convolution (GCN) to capture spatio-temporal action features. Such techniques necessitate a substantial number of weighted average neighbor node feature vectors. Moreover, if the graph convolution entails the computation of higher-order edge features, a significant number of calculations are required to derive the higher-order edge feature vectors. In this paper, a Window Segmentation method is employed to segment the input features based on time, joints, and interactive entities. Subsequently, feature interactions are computed. This method offers robust semantic feature expression and significantly reduces computational load. While sliding through the window, we employ the Adaptive Joints method to identify skeletal points that yield better segmentation results. To further reduce computations, we substitute the original Cross-Entropy Loss with NLL Loss during the model estimation process, resulting in improved recognition performance.

E-commerce enables suppliers to bypass traditional retailers and sell directly to consumers, but such encroachment intensifies channel competition. Although price matching commitments can mitigate this conflict, their strategic role has primarily been studied under the assumption of profit-maximizing retailers. We develop a game-theoretic model to examine how a retailer’s valuation of consumer surplus alongside accounting profit affects a supplier’s pricing choice and direct-channel sales. The supplier chooses whether to adopt an exact commitment to match the retailer’s price. We show that the supplier adopts price matching only when the retailer’s weight on consumer surplus is sufficiently large and the direct-selling cost falls within an intermediate range. Under supplier-preferred selection among retail-only pricing equilibria and without a fixed entry cost, the supplier opens the direct channel throughout the maintained cost range. Under this selection, the price matching option changes the pricing arrangement rather than the entry decision. Greater retailer altruism improves consumer surplus and social welfare within each conditional outcome yet can lower either measure in equilibrium by inducing the supplier to adopt matched pricing or stop direct sales while retaining unmatched pricing. This interaction can give rise to an altruism paradox. Numerical extensions show that welfare losses can persist with positive entry costs or asymmetric channel demand. These findings offer implications for retailers that value consumer surplus alongside profit and suppliers considering direct-to-consumer expansion.