Green Innovation Strategies for Enhancing Sustainable Performance: A Bibliometric Analysis and Research Avenues

Background. Amidst an increasingly dynamic global economic landscape, understanding how green innovation contributes to sustainable performance remains a crucial research agenda. Despite its growing popularity, existing studies continue to report fragmented and divergent outcomes regarding the effec...

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Збережено в:
Бібліографічні деталі
Дата:2026
Автори: Widanti, Ni Putu Kusuma, Aristana, I Nengah, Landra, Nengah, Puspitawati, Ni Made Dwi
Формат: Стаття
Мова:Англійська
Опубліковано: Dr. Viktor Koval 2026
Теми:
Онлайн доступ:https://ees-journal.com/index.php/journal/article/view/332
Теги: Додати тег
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Назва журналу:Economics Ecology Socium

Репозитарії

Economics Ecology Socium
Опис
Резюме:Background. Amidst an increasingly dynamic global economic landscape, understanding how green innovation contributes to sustainable performance remains a crucial research agenda. Despite its growing popularity, existing studies continue to report fragmented and divergent outcomes regarding the effects of green innovation on sustainable performance. Purpose. This study presents a systematic literature review and bibliometric analysis of 211 peer-reviewed articles published between 2019 and 2025, sourced from the Scopus and ScienceDirect databases. Findings. Utilising VOS Viewer software, this research draws on the epistemological construction of the disciplines. It identifies six thematic clusters: (1) eco-environmental innovation and sustainable finance, (2) CSR and green supply chains for business performance,             (3) circular economy and sustainable supply chain development, (4) digital transformation and ESG-oriented sustainability, (5) green and sustainable manufacturing research landscape, and (6) AI-based innovation and ESG performance. For the keyword co-occurrence analysis, a complete counting method was applied with a minimum keyword occurrence threshold of 5 to reduce analytical noise and enhance map interpretability, which was considered appropriate given the final dataset size. Network normalisation and visualisation were performed using the association strength method, following the standard VOSviewer procedures to ensure transparency and reproducibility. Implications. This study shows a shift from a fundamental conceptualisation of green innovation toward its strategic alignment with dynamic capabilities, absorptive capacity, and digital technologies, such as artificial intelligence, that enable green, digital, and resilient economic transitions. This study provides a holistic picture of the scope, geographic distribution, and temporal evolution of green innovation research and reveals gaps in geographic and sectoral coverage, particularly in emerging economies.
DOI:10.61954/2616-7107/2026.10.1-8