AI, Social Commerce, and Engagement: Internet Memes in the Ecommerce 5.0 Ecosystem

Internet memes have emerged as strategic communicative assets within the emerging E commerce 5.0 paradigm, where AI-driven personalization, big data analytics, and al converge to create human-centric, hyper-personalized digital marketplaces. Positioned at the intersection of participatory culture and digital marketing, meme marketing leverages humor, relatability, and culturally situated symbolism to enhance brand awareness, perceived authenticity, and emotional connection, particularly among Generation Z and Millennial consumers who natively use memes as a primary mode of online expression. By exploiting virality and network effects across social platforms, memes generate disproportionately high levels of organic reach and engagement, often outperforming traditional branded content in driving brand recall and facilitating favorable attitudes that translate into purchase intention and, potentially, loyalty. Within E-commerce 5.0 ecosystems, meme-based content can be algorithmically targeted and optimized using AI and big data, integrated seamlessly into social commerce journeys, and amplified via user-generated content that further strengthens social proof and community co-creation around brands. This study aims to examine how Internet Memes can drive E-commerce, investigating its potential in E-commerce 5.0 context. It also outlines peculiar characteristics of e-commerce 5.0 era, highlighting the role Internet Memes play and its impact on consumer and business outcomes. Furthermore, this study proposes Integrative Framework explaining how internet memes function as strategic Communication asset within the human-centric, AI-driven, community-focused ecosystem of E-commerce 5.0. However, their effectiveness is contingent on cultural fluency, strategic alignment with brand identity, product involvement level, and demographic characteristics, as poorly calibrated or inauthentic meme usage can undermine engagement and credibility, indicating the need for nuanced, context-sensitive deployment and continued empirical investigation