Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance

Usama Awan, Saqib Shamim, Zaheer Khan, Najam Ul Zia, Syed Muhammad Shariq and Muhammad Naveed Khan

Technological Forecasting and Social Change, 2021, vol. 168, issue C

Abstract: Big data analytics (BDA) is a revolutionary approach for sound decision-making in organizations that can lead to remarkable changes in transforming and supporting the circular economy (CE). However, extant literature on BDA capability has paid limited attention to understanding the enabling role of data-driven insights for supporting decision-making and, consequently, enhancing CE performance. We argue that firms drive decision-making quality through data-driven insights, business intelligence and analytics (BI&A), and BDA capability. In this study, we empirically investigated the association of BDA capability with CE performance and examined the mediating role of data-driven insights in the relationship between BDA capability and decision-making. Data were collected from 109 Czech manufacturing firms, and partial least squares structural equation modeling was applied to analyze the data. The results reveal that BDA capability and BI&A are positively associated with decision-making quality. This effect is stronger when the manufacturer utilizes data-driven insights. The results demonstrate that BDA capability drives decision-making quality in organizations, and data-driven insights do not mediate this relationship. BI&A is associated with decision-making quality through data-driven insights. These findings offer important insights to managers, as they can act as a reference point for developing data-driven insights with the CE paradigm in organizations.

Keywords: Big data analytics; Data-driven insights; Big data analytics capabilities; Decision-making; Circular economy; Manufacturing firms (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (33)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:168:y:2021:i:c:s0040162521001980

DOI: 10.1016/j.techfore.2021.120766

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