Decoding business analytics: discovering the hidden core through a novel taxonomy

Abstract

Purpose: This study develops a comprehensive taxonomy of the business analytics (BA) discipline, uncovering its intellectual core and revealing its evolution over the past 12 years. Design/methodology/approach: Using stakeholder-driven identity formation theory, this study explores how organizational identity emerges through stakeholder negotiations. It investigates how top scholarly journals shape the BA discipline’s image and influence perceptions. High-quality articles from top journals listed by the Australian Business Deans Council are analyzed using latent Dirichlet allocation (LDA), a natural language processing and topic modeling method. Findings: The study outlines key research areas identified as analytics methods, marketing, finance, operations and decision support analytics, along with 12 subareas. An analysis of the top 100 topics reveals prevalent research themes, showcasing the breadth of BA. A 12-year time-series review shows initial growth followed by maturation across most areas, except for decision support analytics, which maintained steady growth. These findings provide empirical evidence of BA’s development as a distinct discipline, highlighting its interdisciplinary nature and evolving research focus. Originality/value: This study presents the first comprehensive, data-driven taxonomy of BA research, distilling the intellectual core into five key areas and 12 subareas, while identifying 100 supporting themes. It extends the stakeholders’ approach to identity development theory in the context of BA, providing empirical support for discussions on the field’s identity and diversity. The findings offer valuable insights for scholars, industries, managers and professionals, guiding curriculum development, research directions and practical applications of BA.