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

Author

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  • Qaisar Ali

    (Universiti Islam Sultan Sharif Ali)

  • Hakimah Yaacob

    (Universiti Islam Sultan Sharif Ali)

  • Shazia Parveen

    (Universiti Teknologi Malaysia)

  • Zaki Zaini

    (Universiti Islam Sultan Sharif Ali)

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.

Suggested Citation

  • Qaisar Ali & Hakimah Yaacob & Shazia Parveen & Zaki Zaini, 2021. "Big data and predictive analytics to optimise social and environmental performance of Islamic banks," Environment Systems and Decisions, Springer, vol. 41(4), pages 616-632, December.
    • Handle: RePEc:spr:envsyd:v:41:y:2021:i:4:d:10.1007_s10669-021-09823-1
    DOI: 10.1007/s10669-021-09823-1
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    4. Tseng, Hsiao-Ting, 2023. "Customer-centered data power: Sensing and responding capability in big data analytics," Journal of Business Research, Elsevier, vol. 158(C).

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