ISSN: 2226-3624
Open access
In the era of digital transformation, the integration of Big Data Analytics Technology (BDAT) has become a key driving force for manufacturing enterprises to achieve sustainable development. Based on Resource-Based View (RBV) and Dynamic Capability Theory (DCT), this paper builds a conceptual framework to explore how BDAT can through green innovation The intermediary role of the Green Innovation Strategy (GIS) improves the Sustainable Firm Performance (SFP) of enterprises. By reviewing the existing literature and synthesizing Ertz et al. (2025) and Chatterjee et al. (2025) and other latest research results. This article points out that BDAT provides enterprises with the analytical ability to generate environmental and operational insights, so as to promote green innovation activities and improve economic, environmental and social performance. Unlike previous researches that rely on second-hand data or qualitative analysis, this study puts forward an empirical model based on questionnaires, which emphasizes the "Triple Bottom Line" concept of sustainable development for Chinese manufacturing enterprises. Finally, this article summarizes the theoretical revelation, management significance and future empirical research direction. Purpose: This study aims to explore how big data analysis technology (BDAT) drives the sustainable performance (SFP) of Chinese manufacturing enterprises, focusing on the intermediary role of green innovation strategies (GIS). Based on the resource-based concept (RBV) and the dynamic capability theory (DCT), the research strives to clarify how digital resources can be transformed into sustainable results in the economic, social and environmental dimensions. Design/methodology/approach: This paper builds a conceptual framework that integrates RBV and DCT to explain the indirect impact of BDAT on SFP through GIS. The model assumes that BDAT can improve the ability of enterprises to perceive, seize and reconfigure, thus promoting green innovation and improving multi-dimensional sustainable performance. Future research will carry out empirical testing based on questionnaires, with manufacturing enterprises as the research situation. Findings: The theoretical analysis shows that BDAT is not only an information technology, but also a strategic capability. It promotes the formation and implementation of green innovation strategies and transforms data-driven insights into sustainable results. As a key intermediary mechanism, GIS connects BDAT with economic, social and environmental performance, highlighting the strategic significance of the integration of digital transformation and sustainable development. Research limitations/implications: This research is a conceptual research and has not been empirically verified. In the future, the model can be tested based on first-hand data through Structural Equation Modeling (SEM). The research scope can also be expanded to other industries, and adjustment variables such as enterprise size, supply chain coordination or environmental dynamics can be introduced to further improve the theoretical framework. Practical implications: For managers, the research results emphasize the importance of building a data-driven culture and integrating green innovation into the core strategy of the enterprise. Manufacturing enterprises should use BDAT to identify green opportunities, optimize production processes, and establish a multi-dimensional performance evaluation system. Policymakers should provide institutional support, such as digital transformation subsidies and green innovation incentives, to promote the sustainable upgrading of the industry. Originality/value: The main contributions of this study include three aspects: first, integrate RBV and DCT to build a unified framework to explain how digital capabilities can be transformed into sustainable advantages; second, identify green innovation strategies as the key missing link between BDAT and sustainable performance; third, introduce the "triple bottom The perspective of Triple Bottom Line (TBL) provides a systematic understanding of digital transformation to promote the sustainable development of the manufacturing industry with operational SFP.
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