Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F21%3A63527178" target="_blank" >RIV/70883521:28120/21:63527178 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0040162521001980?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0040162521001980?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.techfore.2021.120766" target="_blank" >10.1016/j.techfore.2021.120766</a>
Alternative languages
Result language
angličtina
Original language name
Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance
Original language description
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 sup porting 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Technological Forecasting & Social Change
ISSN
0040-1625
e-ISSN
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Volume of the periodical
168 (2021)
Issue of the periodical within the volume
120766
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
1-12
UT code for WoS article
000651337500003
EID of the result in the Scopus database
2-s2.0-85110265219