Abstract
The rapid growth of data and the increasing strategic importance of analytics have positioned Big Data Analytics Capability (BDAC) as a critical organizational competence. Despite substantial interest in its antecedents and performance outcomes, the historical evolution and intellectual development of BDAC research remain underexplored. This study addresses this gap by applying Citation Sequence Analysis (CSA) to examine the longitudinal citation trajectories of BDAC-focused publications. Using a curated dataset of 119 peer-reviewed records from Scopus, CSA classifies cited references into three trajectory types: sleeping beauties, reflecting delayed recognition; hot papers, indicating immediate but short-lived impact; and constant performers, representing sustained scholarly influence. A transparent methodological protocol, including detailed search queries, inclusion/exclusion criteria, citation normalization, and reliability verification, is provided. Findings reveal foundational works, transitional studies, and emerging contributions and offer a trajectory-based framework for guiding future research. By integrating CSA with trajectory classification, this study advances cumulative knowledge building, provides a historically grounded understanding of BDAC, and informs theory development and strategic practice in analytics deployment.