Sustainable Environmental Performance: A Cross-Country Fuzzy Set Qualitative Comparative Analysis Empirical Study of Big Data Analytics and Contextual Factors

The rise of big data analytics has become crucial in aiding firms facing sustainability challenges, prompting researchers and practitioners to explore how this technology can contribute to environmental sustainability performance under specific circumstances. Based on the resource-based and dynamic capabilities view theory lens, it uses partial least square structural equation modeling and qualitative comparative analysis to explore the contribution of big data analytics-driven dynamic capabilities in innovation on environmental performance under enterprise factors and combinations of conditions. The empirical study gathered data from 319 Indian and American enterprises. The results demonstrate seven solutions with very high environmental performance, depicting core presence for big data analytics-driven dynamic capabilities in sensing, seizing, and transforming in an uncertain environment of dynamism and hostility in India and American firms. The synergy of big data analytics-enabled dynamic capabilities in sensing, seizing, and transforming shows an essential role in enhancing sustainable environmental performance for enterprises in the USA compared to those in India. Based on the configuration analyses, big data analytics significantly mitigates environmental dynamism and hostility challenges enterprises encounter. It consequently exerts a more pronounced influence on green performance, particularly within the service sector and small enterprises in the USA, through radical process innovation. Conversely, this impact is observed primarily among large product firms in India by incremental innovation strategies. This indicates that this emerging technology is essential to attend to the necessary aspects of the circular economy in developing and developed economies through specific configuration conditions.