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Can Big Data and Predictive Analytics Improve Social and Environmental Sustainability?

Dubey, Rameshwar, Gunasekaran, Angappa, Childe, Stephen J., Papadopoulos, Thanos, Luo, Zongwei, Wamba, Samuel Fosso, Roubaud, David (2017) Can Big Data and Predictive Analytics Improve Social and Environmental Sustainability? Technological Forecasting and Social Change, 144 . pp. 534-545. ISSN 0040-1625. (doi:10.1016/j.techfore.2017.06.020) (KAR id:62113)

Abstract

Although literature indicates that big data and predictive analytics (BDPA) convey a distinct organisational capability, little is known about their performance effects in particular contextual conditions. Grounding our investigation in the dynamic capability views and organisational culture and based on sample of 205 Indian manufacturing organisations, we empirically investigate the effects of BDPA on social performance (SP) and environmental performance (EP) using variance based structural equation modelling (i.e. PLS). We find that BDPA has significant impact on SP/EP. However, we did not find evidence for moderating role of flexible orientation and control orientation in the links between BDPA and SP/EP. Our findings offer a more nuanced understanding of the performance implications of BDPA, thereby addressing the crucial questions of how and when BDPA can enhance social/environmental sustainability in supply chains.

Item Type: Article
DOI/Identification number: 10.1016/j.techfore.2017.06.020
Uncontrolled keywords: Big Data; Predictive Analytics; Dynamic Capability View; Supply Chains; Social Sustainability; Environmental Sustainability
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Thanos Papadopoulos
Date Deposited: 20 Jun 2017 10:07 UTC
Last Modified: 04 Mar 2024 18:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/62113 (The current URI for this page, for reference purposes)

University of Kent Author Information

Papadopoulos, Thanos.

Creator's ORCID: https://orcid.org/0000-0001-6821-1136
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