Sahoo, Saumyaranjan, Upadhyay, Arvind and Kumar, Anil (2023) Circular economy practices and environmental performance: analyzing the role of big data analytics capability and responsible research & innovation. Business Strategy and the Environment. ISSN 0964-4733 (In Press)
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Abstract / Description
This study employs dynamic capability view theory to comprehend the interplay between big data analytics capability (BDAC), responsible research and innovation (RRI), and circular economy practices (CEPs) as an execution strategy for improving environmental performance. The study uses partial least square structural equation modelling to analyse primary survey data collected from 326 manufacturers. The results indicate that BDAC, RRI and CEPs favourably affect environmental performance. Notably, RRI emerges as the most influential factor among the three. Furthermore, the findings suggest that implementing CEPs serves as a partial mediator for the influence of BDAC and RRI on environmental performance. Surprisingly, the study finds that the moderating impact of resource commitment is not statistically significant in any of the three pairwise interactions involving BDAC, RRI, and CEPs with respect to environmental performance. The results have various intriguing implications for how manufacturers can enhance their circular economy strategies to achieve better environmental performance, representing a noteworthy contribution to the foundational theory of the dynamic capability view. Finally, these findings also provide valuable insights to managers, enabling a deeper understanding of the determinants that contribute to deploying CEPs and improving environmental performance within a manufacturing setting.
Item Type: | Article |
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Uncontrolled Keywords: | Big data analytics capability; circular economy practices; responsible research and innovation; environmental performance; manufacturing |
Subjects: | 600 Technology > 650 Management & auxiliary services |
Department: | Guildhall School of Business and Law |
Depositing User: | Anil Kumar |
Date Deposited: | 24 May 2023 08:37 |
Last Modified: | 24 May 2023 08:37 |
URI: | https://repository.londonmet.ac.uk/id/eprint/8548 |
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