Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
50204 - Business and management
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Technological Forecasting & Social Change
ISSN
0040-1625
e-ISSN
—
Svazek periodika
168 (2021)
Číslo periodika v rámci svazku
120766
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
12
Strana od-do
1-12
Kód UT WoS článku
000651337500003
EID výsledku v databázi Scopus
2-s2.0-85110265219