Purely online channels are limited in conveying non-digital product attributes, such as fit, material, and usage experience, which creates product-fit uncertainty and utility loss for consumers. Physical showrooms can reduce such uncertainty, but consumers must incur hassle costs from travel, time, and store visits. This paper develops an asymmetric duopoly model with an incumbent online retailer and a market entrant, and compares four showroom deployment scenarios: NN, SN, NS, and SS. For each scenario, we derive equilibrium prices, demands, and profits, and characterize the showroom deployment equilibrium.The results show that showroom deployment is valuable only when the utility loss from online purchase exceeds the hassle cost of offline experience. In this case, showrooms can reshape demand allocation and competitive intensity. The incumbent may use showrooms to reinforce its first-mover advantage, while the entrant may use them for differentiated entry when platform recognition is sufficiently high. Moreover, mutual showroom deployment may arise as an equilibrium even when it lowers total industry profit, leading to a Prisoner’s Dilemma. These findings provide analytical insights for omnichannel deployment and pricing decisions by online retailers.
Shi-Tong Li, Zhen-Xing Xu, Wen-Yu Chen et al.· 2026 12th International Conf...· 0 citations
A key problem in language-guided UAV target search is how to transform a language-referred target in the current observation into an executable spatial goal. Existing methods either predict actions directly or introduce relatively heavy mapping, memory, or planning modules, making the intermediate link between semantic grounding and spatial execution difficult to examine in isolation. In this paper, we present a lightweight closed-loop framework for language-guided UAV target search and reaching. Given a natural-language instruction, an RGB image, a depth map, and the UAV pose, the system first localizes a 2D target with a vision-language model, then recovers a 3D search goal in the world coordinate system using depth cues and camera geometry, and finally executes point-to-point flight toward the recovered goal. Rather than addressing obstacle avoidance, global mapping, cooperative coverage, or complex trajectory optimization, we focus on validating whether semantic target grounding, explicit 3D search-goal recovery, and flight execution can form an effective perception-to-execution loop. Preliminary AirSim results show that observation-consistent 3D search-goal recovery yields more stable target-search execution than both an image-plane heuristic baseline and a fixed-depth recovery baseline. These results suggest that explicit 3D search goals provide a practical and interpretable bridge between semantic grounding and spatial execution.
Jun-Song Zhang, Yao-Hong Zhang, Rui Guan et al.· 2026 12th International Conf...· 0 citations
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