Li, LanLemke, Fred2025-03-052025-03-0520250167-454410.1007/s10551-025-05969-zhttps://repository.vlerick.com/handle/20.500.12127/7652This article offers a comprehensive overview of Business Sustainability (BuS), and directly addresses the lack of consensus around this important concept. Through a mixed-methods approach, we conduct the first systematic literature review of BuS employing Latent Dirichlet Allocation (LDA) topic modeling to uncover hidden thematic structures, Narrative Synthesis to refine and extend BuS definitions within different contexts, and the LDA-HSIM method to classify topics and design a new framework. We analyzed an extensive dataset comprising 92,311 articles sourced from 11,579 journal outlets. From this dataset, we identified 9,561 articles suitable for LDA topic modeling by applying funnel criteria, focusing on articles with clear theoretical underpinnings. A text extraction technique enabled us to identify and analyze theories used in BuS studies. This analysis revealed 150 underlying theories that advance the BuS concept across different research topics. The study contributes to BuS theory development with great potential to improve ethical decision-making by establishing meaningful, context-specific definitions and providing clear guidance for future researchers in selecting appropriate theoretical perspectives for their work. We identify research gaps, propose a prioritized research agenda focused on theory development, and formulate key implications for practitioners and policymakers. This study demonstrates the effectiveness of machine learning methods in conducting large-scale literature reviews to accelerate theoretical advancements and generate research agendas.enBusiness Sustainability (BuS)Systematic Literature ReviewLDA Topic ModelingJournal of Business Ethics1573-0697293193186039