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论文编号：	16169
作者编号：	1120211172
上传时间：	2026/6/15 16:27:21
中文题目：	AR技术赋能的零售商运营策略研究
英文题目：	Operational Strategies for Retailers Empowered by Augmented Reality Technology
指导老师：	李勇建
中文关键字：	增强现实（AR）技术；运营策略；服务延迟；技术采纳
英文关键字：	Augmented Reality (AR) Technology; Operational Strategy; Service Delay; Technology Adoption
中文摘要：	实体零售对推动我国经济发展发挥着重要作用。随着消费结构的不断升级，越来越多的消费者注重购物过程中的服务质量与互动体验。然而，当前消费环境仍存在服务质量不高、体验不佳等问题。增强现实（Augmented Reality，AR）技术凭借将虚拟内容叠加于实体环境的功能优势，能够有效增强交互体验，成为实体零售店提升服务体验的关键手段。对此，诸多零售商引进AR技术以解决实体店不同运营阶段的痛点问题，但同时也引发一系列新挑战。基于此，本文按照AR技术赋能实体零售店“内部提升（消费者进店前的需求激发→进店后的服务改善→长期运营下的系统升级）与外部竞争”的研究脉络层层递进，建立了综合考虑消费者多维异质性、AR技术双向效应以及易拥堵系统特性的博弈模型，以探究AR技术赋能下实体零售商的运营策略。具体研究内容与主要结论如下：首先，从零售商内部运营视角出发，聚焦消费者进店前的需求激发阶段，系统刻画消费者AR互动价值“双向+异质”特性，即不同消费者从AR互动中获得不同水平的正向或负向价值，基于此构建考虑AR互动价值与产品感知价值双重异质的博弈模型，探究零售商的AR互动渠道开设与优惠券发放策略。研究表明，对于高价值产品，零售商不应开设AR互动渠道；但对低价值产品应开设AR渠道，并发放无限量优惠券。然而，对于中等价值产品，则需要基于AR互动渠道发放限量优惠券。此外，进一步探究了使消费者福利最大化的优惠券数量，揭示了当产品价值较低（或较高）时，消费者的优惠券期望数量低于（或高于）零售商利益最大化的优惠券发行数量；只有在产品价值处于中等水平时，消费者期望数量与零售商最优发行量才会达到协同。其次，针对消费者进店后的服务改善阶段，考虑易拥堵的服务系统特征，建立了同时刻画AR技术的拥堵削弱、不匹配性、技术摩擦等正/负效应的排队博弈模型，探究AR技术的引进与定价策略。研究发现，当市场规模较大时，引进AR技术可以实现对零售商和消费者均有利的双赢局面；但当市场规模处于中等水平时，引进AR技术反而会降低零售商收益。更为重要的是，在价格内生情形下，AR技术的引进可能会引致对消费者和零售商均不利的双输局面，揭示了零售服务业中的Downs-Thomson悖论的存在。因此，零售商不应在中等规模市场中引进AR技术。此外，引进AR技术会对产品价格产生非单调影响，采用AR的零售商应在市场规模较小（或较大）时设定更低（或更高）的价格。进而，聚焦长期运营下的系统升级阶段，同时考虑AR技术与传统服务渠道间的替代与互补效应，构建基于AR 协同效率的排队博弈模型，探究不同效应下AR系统前端使用体验与后端匹配精度的提升策略。研究表明，随着AR协同效率的提升，更多消费者从替代转向互补。当AR的匹配精度尚未达到足够高的水平时，盲目提升系统的使用体验可能反而会降低零售商收益，因此AR系统的提升应遵循“先精度后体验”的升级路径。当仅存在替代效应时，在小规模市场中提高AR匹配精度可以实现帕累托改善，但当互补效应产生时，应注意升级程度不足可能引致收益下降。此外，在内生价格情形下，当市场规模处于中等水平时，系统升级可能会同时降低零售商收益和消费者福利，揭示了零售商通过策略性定价将系统升级负效应转嫁至消费者的现象。当互补效应产生时，应对AR 系统前端的使用体验进行积极改善以获得更高利益。最后，延伸至外部竞争阶段，建立考虑服务延迟的Hotelling模型，探究易拥堵服务系统中，短期内不同AR服务能力对价格竞争的影响，以及长期视角下的价格与AR服务联合竞争策略。研究发现，只有当产品价值较高时，具有更高AR服务能力的零售商才会制定更高的均衡价格；当产品价值较低时，不同AR服务能力的零售商均衡价格相同。然而，当产品价值处于中等水平时会产生多重均衡。随着AR服务能力的提高，零售商之间的竞争会依次经历“无竞争→消极竞争→激烈竞争”的三个阶段。此外，即使在无成本的情形下，盲目提高AR服务能力也可能会降低零售商收益或消费者剩余，零售商应制定中等水平的AR服务能力以最大化收益。最后，在长期视角下，随着AR服务能力边际成本的提高，均衡AR能力不断降低，但市场竞争呈现先削弱后增强的非单调趋势。综上所述，本文针对AR技术赋能实体零售店不同运营阶段所面临的问题，综合考虑消费者AR互动价值、产品价值、拥堵敏感度、企业偏好等多维异质性以及AR技术的拥堵削弱、不匹配性、技术摩擦、信息筛选等正、负效应，为零售商基于AR技术的需求激发、服务改善、效率提升以及市场竞争提供运营决策建议与理论支撑。
英文摘要：	Brick-and-mortar retail plays a vital role in driving economic development. As consumption structures continue to upgrade, consumers increasingly emphasize service quality and interactive experience, yet offline retail still faces persistent shortcomings in both dimensions. By overlaying virtual content onto physical environments, augmented reality (AR) enhances interactivity and has become a key tool for improving in-store experiences. Many retailers have adopted AR to address operational pain points across different stages, though this also introduces new challenges and trade-offs. Against this backdrop, this study develops a progressive framework—from internal enhancement (pre-visit demand stimulation → in-store service improvement → long-term system upgrading) to external competition—and constructs game-theoretic models that incorporate multidimensional consumer heterogeneity, bidirectional AR effects, and congestion characteristics to examine optimal retail strategies in a unified manner. The main findings are as follows. First, from an internal perspective, focusing on pre-visit demand stimulation, we characterize the heterogeneous and bidirectional nature of AR interaction value and develop a model with dual heterogeneity in AR interaction value and product value to study AR channel adoption and coupon strategies. Retailers should avoid AR for high-value products, adopt AR with unlimited coupons for low-value products, and issue limited coupons for medium-value products. Moreover, consumer-preferred coupon levels are lower (higher) than the profit-maximizing level when product value is low (high), with alignment occurring only at intermediate levels, highlighting the importance of coordinating promotional intensity with product positioning. Second, at the in-store service stage, we develop a queueing game that captures both the positive and negative effects of AR, including congestion mitigation, mismatch, and technology friction. AR adoption yields a win–win outcome in large markets but may reduce retailer profit in moderate markets. Under endogenous pricing, it can even lead to a Pareto loss, revealing a Downs–Thomson-type paradox in retail service systems. In addition, AR has a non-monotonic effect on pricing: retailers should set lower (higher) prices in small (large) markets, underscoring the interaction between technology adoption and pricing decisions. Third, in the long-term system upgrading stage, we consider substitution and complementarity between AR and traditional channels. As AR synergy improves, consumers gradually shift toward complementarity. When matching accuracy is low, improving user experience alone may reduce profit, implying a “precision-first, experience-second” upgrade path. Under substitution, improving accuracy yields Pareto gains in small markets, whereas under complementarity, insufficient upgrading may reduce profits. Furthermore, in moderate markets with endogenous pricing, system upgrades may reduce both retailer profit and consumer welfare, while stronger complementarity makes front-end experience improvements more beneficial, highlighting the importance of balanced system design. Finally, in the external competition stage, a Hotelling model with service delay shows that higher AR capability leads to higher prices only for high-value products, while prices converge for low-value products and may exhibit multiple equilibria at intermediate levels. As AR capability increases, competition evolves from no competition to mild and then intense competition. Notably, excessive investment in AR capability—even without cost—may reduce retailer profit or consumer surplus, implying that a moderate level is optimal. In the long run, higher marginal costs reduce equilibrium AR capability, while competition follows a non-monotonic pattern, first weakening and then intensifying, reflecting the dynamic nature of technology-driven competition. In summary, this study provides a structured, stage-wise, and integrated analysis of AR-enabled retail operations. By jointly considering consumer heterogeneity, firm decision-making, and the dual effects of AR technology, it offers both rigorous theoretical foundations and actionable managerial insights for demand stimulation, service improvement, system upgrading, and competitive strategy in the digital transformation of brick-and-mortar retail.
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