Literature Review on Conceptualizing the Journey from Algorithmic Exposure to Consumer Value: The Role of Short-Form Video Marketing in Shaping Brand Awareness, Engagement, and Purchase Intention

The rapid growth of algorithm-driven short-form video platforms has significantly transformed the digital marketing landscape. Unlike traditional social media platforms, these platforms employ artificial intelligence (AI) and machine learning algorithms to determine content visibility, personalize user experiences, and influence consumer decision-making. Consequently, brands increasingly rely on these platforms to enhance visibility, foster consumer engagement, and improve purchase intentions. Unlike traditional social media platforms that relied primarily on follower-based distribution, platforms such as TikTok, Instagram Reels, and YouTube Shorts employ sophisticated artificial intelligence (AI) and machine learning algorithms to personalize content recommendations based on user behavior, engagement history, and predicted preferences. Consequently, marketers have shifted significant advertising investments toward algorithm-driven short-form video marketing in pursuit of greater reach, engagement, and conversion. Despite this growing strategic emphasis, an important research gap remains regarding whether algorithmically generated visibility and high engagement metrics translate into meaningful business outcomes such as brand awareness, consumer trust, and purchase intention. This study proposes an integrated conceptual framework to investigate the influence of algorithm-driven short-form video marketing on brand awareness, consumer engagement, and purchase intention among digital-native consumers. The proposed model synthesizes the Stimulus–Organism–Response (S-O-R) framework, Technology Acceptance Model (TAM), Uses and Gratifications Theory, Social Influence Theory, and Elaboration Likelihood Model (ELM) to explain the psychological and behavioral mechanisms through which algorithmic content personalization shapes consumer decision-making. Specifically, algorithm-driven marketing capability is conceptualized as the primary external stimulus influencing consumers' perceptions of content relevance and algorithmic trust, which subsequently enhance cognitive, emotional, and behavioral engagement. Consumer engagement and brand awareness are proposed as sequential mediating mechanisms through which digital attention is transformed into purchase intention. The study adopts a quantitative research design employing Partial Least Squares Structural Equation Modeling (PLS-SEM) to empirically validate the proposed conceptual framework. Data will be collected from digital-native consumers who actively consume branded content on TikTok, Instagram Reels, and YouTube Shorts. The findings are expected to contribute to digital marketing theory by reconceptualizing recommendation algorithms as active marketing intermediaries that shape consumer cognition and purchasing behavior rather than functioning merely as technological distribution tools. From a managerial perspective, the study offers strategic insights for marketers seeking to optimize short-form video campaigns through effective algorithmic optimization, authentic storytelling, personalized consumer experiences, and trust-building initiatives.