While ESG goals are now central to corporate strategy, disclosures often fail to engage consumers. This study identifies which message elements in ESG goal advertisements best translate disclosures into consumer responses through social media. Using an S–O–R-based advertising value model, we test the effects of advertising value and flow experience on attitudes toward the advertisement and purchase intention. Based on CFA and SEM of survey data from 364 consumers who had previously read corporate sustainability reports, we found that (1) entertainment, credibility, and message relevance positively affect advertising value; (2) entertainment and message relevance positively affect flow experience; (3) advertising value positively affects attitude toward the advertisement; and (4) both flow experience and attitude toward the advertisement positively affect purchase intention. Mediation analyses reveal two routes to purchase intention: (i) an attitudinal path, in which message elements increase advertising value, thereby improving attitude toward the advertisement and, in turn, purchase intention; and (ii) an immersive path, in which message elements increase flow experience, thereby increasing purchase intention. Advertisements of ESG goals can translate disclosure into demand by prioritizing entertainment and message relevance, linking ESG communication to market responses that support sustainability-oriented operations.

With the rise of Industry 4.0, Augmented Reality (AR) has become pivotal for human-robot collaboration. However, most industrial AR systems still rely on pre-defined tracked images or markers, limiting adaptability in unmodeled or dynamic environments. This paper proposes a novel Interactive Semantic-Augmented Reality (ISAR) framework that synergizes Edge AI and Cloud Vision-Language Models (VLMs). To ensure real-time performance, we implement a Dual-Thread Asynchronous Architecture on the robotic edge, decoupling video streaming from AI inference. We introduce a Confidence-Based Triggering Mechanism, where a cloud-based VLM is invoked only when edge detection confidence falls below a specific threshold. Instead of traditional image cropping, we employ a Visual Prompting strategy—overlaying bounding boxes on full-frame images—to preserve spatial context for accurate VLM semantic analysis. Finally, the generated insights are anchored to the physical world via Screen-to-World Raycasting without fiducial markers. This framework realizes a semantic-aware 'Intelligent Agent' that enhances Human-in-the-Loop (HITL) decision-making in complex industrial settings.