PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.
With the outbreak of COVID-19, the importance of rural areas has been gradually highlighted, and the importance of rural ecological livability has been gradually recognized. A growing body of literature recognizes the importance of building a rural ecological livability (REL) system. It is urgent that we clarify the status quo and spatial-temporal differences in and distributional characteristics of rural ecological livability and that we carry out targeted and differentiated construction to promote rural ecological livability in post-epidemic China. This study proposes a conceptual model that incorporates various economic, social and environmental factors and develops a comprehensive multifactor (production-living-ecology) evaluation system. Using Fujian Province as an example, the entropy weight method is used to measure the REL level of 55 counties and cities, which are comprehensively evaluated from 2015 to 2019. Moran's I and Getis-Ord Gi* are used to analyze the spatial and distributional characteristics of the REL level in Fujian. The results show that the level of REL in Fujian Province has been relatively flat over the past five years, with a slight downward trend. The overall value of the rural ecological livability index in 2015 was 0.345, and its overall value in 2019 was 0.334, with an average value of 0.343. The REL of Fujian Province is spatially correlated, with high levels of livability in the southeast and low levels in the northeast. The autocorrelation in the level of ecological livability in Fujian's counties and cities continues to increase.
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