<![CDATA[Information asymmetry in the offline retail market imposes substantial "search costs" on purchasing professionals, who frequently lack visibility into real-time product availability across physical stores. Existing solutions, such as generic store locators, fail to provide inventory context, while traditional e-commerce platforms are unable to meet immediate, same-day procurement needs due to logistical delays. This research addresses this gap by developing a Real-Time Location-Aware mobile artifact aimed at optimizing offline procurement efficiency in Batam City. Grounded in Design Science Research (DSR), the system employs a short-polling architecture implemented via Expo (React Native), Express.js, and PostgreSQL to ensure data freshness. Technical performance testing validated the system's "Near Real-Time" capabilities, achieving an average API response time of 180 ms and a stable synchronization interval of 5 seconds under 4G network conditions. Furthermore, a usability evaluation involving 40 purchasing professionals yielded an average System Usability Scale (SUS) score of 71.81, categorizing the application as "Good." These results empirically demonstrate that lightweight polling architectures can effectively mitigate cognitive load and search latency, offering a scalable software engineering solution for the "Offline-to-Offline" (O2O) retail sector.]]>
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