Systematic review analysis through an overview applying methodologies text mining and coding: big data analytics for sustainability accounting
Wahid Wachyu Adi Winarto & Syaiful Ali
What the paper says
Purpose This study aims to critically examine the integration of big data analytics (BDA) into sustainability accounting, identifying thematic developments, methodological patterns and gaps that shape future research and practice. Design/methodology/approach A systematic literature review was conducted on 70 peer-reviewed articles published between 2017 and 2024. The study uses a structured analytical framework, text mining techniques and thematic coding to synthesize findings and identify research gaps. Findings The review reveals five key thematic clusters: supply chain and circular economy, artificial intelligence-enabled sustainability practices, climate change and sustainability accounting standards, stock returns and corporate transformation and environmental, social and governance (ESG) interactions. Significant research gaps are identified, with implications for academic inquiry, professional practice and regulatory policy. The study highlights the need to address fragmented reporting standards and technological barriers, emphasizing the urgency of aligned and data-driven ESG policies, robust assurance mechanisms and adaptive regulation. Originality/value This research seeks to provide methodological insights for interdisciplinary studies in sustainability accounting, integrating BDA. It explores the transformative potential of BDA to reshape sustainability reporting, assurance and policy development.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.