Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations
By Rameshwar Dubey, Angappa Gunasekaran, Stephen J. Childe, David James Bryde, Mihalis Giannakis, Cyril R. H. Foropon, David Roubaud, Benjamin T. Hazen. International Journal of Production Economics, 2020
682 citations recorded by the source corpus.
The importance of big data analytics, artificial intelligence, and machine learning has been at the forefront of research for operations and supply chain management. Literature has reported the influence of big data analytics for improved operational performance, but there has been a paucity of research regarding the role of entrepreneurial orientation (EO) on the adoption of big data analytics. To address this gap, we draw on the dynamic capabilities view of firms and on contingency theory to develop and test a model that describes the role of EO on the adoption of big data analytics powered by artificial intelligence (BDA-AI) and operational performance (OP). We tested our research hypotheses using a survey of 256 responses gathered using a pre-tested questionnaire from manufacturing firms in India with the help of the National Association of Software and Services Companies (NASSCOM) and the Federation of Indian Chambers of Commerce and Industry (FICCI). The results from our analysis indicate that EO enables an organisation to exploit and further explore the BDA-AI capabilities to achieve superior OP. Further, our results provide empirical evidence based on data analysis that EO is strongly associated with higher order capabilities (such as BDA-AI) and OP under differential effects of environmental dynamism (ED). These findings extend the dynamic capability view and contingency theory to create better understanding of dynamic capabilities of the organisation while also providing theoretically grounded guidance to the managers to align their EO with their technological capabilities within their firms.
Subfields: Industrial and Manufacturing Engineering, Information Systems, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/3e333e592e245065ff0691b56a876cac88e3d1bc
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Citations in the corpus
- Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance (2021, background): “However, some researchers have proposed that increased BDA is likely to improve decision-making ability (Dubey, Gunasekaran, Childe, Bryde, et al., 2019).”
- An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance (2021, methodology): “WarpPLS-based analysis has been used previously by leading researchers in the field of management, and their work has been accepted in top ranked journals (e.g., Dubey, Gunasekaran, & Childe, 2019, Dubey, Gunasekaran, Childe, Blome, & Papadopoulos, 2019; Wamba et al., 2017).”
- Artificial intelligence in supply chain management: A systematic literature review (2021, neutral): “Several studies have mentioned this need (e.g. Dubey et al., 2020; Min, 2010; Vargas Florez et al., 2015).”