<![CDATA[This study evaluates digital metrics used to capture initial brand awarenesss perceptions, compares their operational implementation across social media platforms, examines their underlying theoretical foundations, and assesses the interdisciplinary methodologies employed. Following a structured systematic literature review aligned with PRISMA guidelines, we screened studies published up to 2024 using predefined inclusion criteria, resulting in a final sample of fifty empirical studies and review articles. These studies encompass quantitative, qualitative, and mixed‑methods research and focus on platforms such as Facebook, Twitter, Instagram, TikTok, and selected regional networks. The findings indicate that advanced computational approaches particularly those integrating sentiment analysis, social network metrics, and machine learning techniques improve sentiment classification accuracy, strengthen construct validity, and enhance the predictive validity of digital brand awarenesss measures. However, the literature reveals a persistent lack of standardized cross‑platform operationalization and limited theoretical coherence. Although a wide range of theoretical models informs metric development, many studies rely on implicit, fragmented, or weakly articulated foundations, constraining causal interpretation and cumulative theory building. The study contributes to both theory and practice by clarifying these limitations and offering directions for the development of more consistent, comparable, and theoretically grounded measures of digital brand awarenesss in an increasingly complex social media landscape]]>
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