Big data and predictive analytics to optimise social and environmental performance of Islamic banks

Qaisar Ali, Hakimah Yaacob, Shazia Parveen and Zaki Zaini
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Hakimah Yaacob: Universiti Islam Sultan Sharif Ali
Shazia Parveen: Universiti Teknologi Malaysia
Zaki Zaini: Universiti Islam Sultan Sharif Ali

Environment Systems and Decisions, 2021, vol. 41, issue 4, 616-632

Abstract: Abstract Regardless of known as environment-friendly entities, Islamic banks indirectly impact the environment through their clients’ engagement and slow response to sustainability concepts. The usage of big data and predictive analytics (BDPA) is substantially grounded in the financial industry; however, there is little information on how BDPA influences social and environmental performance. This study investigates the impact of BDPA on social performance (SP) and environmental performance (EP) of these Islamic banks using dynamic capability view (DCV) and organisational culture as a moderator. The data were collected from 407 executives and managers from 20 Islamic banks in Malaysia. The data were analysed using the structural equation modelling (PLS) technique. The results show that BDPA has a significant impact on SP and EP, whereas organisational culture (flexibility-oriented and control-oriented culture) does not affect the nexus between BDPA and SP/EP. This study contributes to understanding the performance implications of BDPA as well as empirically analyses how and when to use BDPA to improve the social and environmental performance of Islamic banks.

Keywords: Big data, Predictive analytics, Dynamic capability view, Social performance; environmental performance, Islamic banks, Malaysia (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10669-021-09823-1

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