دانلود رایگان مقاله انگلیسی تحلیل کلان داده و عملکرد شرکت: اثر قابلیت های پویا - الزویر 2017

abstract

Drawing on the resource-based view and the literature on big data analytics (BDA), information system (IS) success and the business value of information technology (IT), this study proposes a big data analytics capability (BDAC) model. The study extends the above research streams by examining the direct effects of BDAC on firm performance (FPER), as well as the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between BDAC and FPER. To test our proposed research model, we used an online survey to collect data from 297 Chinese IT managers and business analysts with big data and business analytic experience. The findings confirm the value of the entanglement conceptualization of the hierarchical BDAC model, which has both direct and indirect impacts on FPER. The results also confirm the strong mediating role of PODC in improving insights and enhancing FPER. Finally, implications for practice and research are discussed.

 Conclusions

The primary objective of this study was to examine the direct impact of BDAC on FPER, as well as the mediating effects of PODC on the relationship between BDAC and FPER. The results show that all the causal links posited by our model are supported. More specifically, both BDAC and PODC explain 65% of the variance of FPER in which 30% of the variance is explained by the mediator. The study estimated the size of the indirect effect using variance accounted for (VAF) value, which indicates the ratio of the indirect effect to the total effect (0.84 ∗ 0.28/0.84 ∗ 0.28 + 0.56). The findings show that the higherorder BDAC construct has a stronger effect on FPER than the PODC. However, PODC appears to be a significant partial mediator, which suggests improving both BDAC and PODC in order to enhance FPER. Among all the dimensions of BDAC, infrastructure and personnel capabilities (β = 0.96) were relatively more important than management capability (β = 0.93). Although we identified these differences in measuring the importance of BDAC dimensions, we note that differences are very small, thus all the dimensions should be given equal importance in building BDAC. The findings also show that second-order constructs have significant positive association with their corresponding first order components. For instance, infrastructure capability was reflected by connectivity (β = 0.90), compatibility (β = 0.90) and modularity (β = 0.92) in which modularity reflects the highest variance (85%) of infrastructure capability. Accordingly, variance of management capability and personnel capability were calculated to reflect their corresponding components (Fig. 2). Overall, the nomological validity of the study was ensured as the findings show that BDAC has a significant positive impact on both PODC (R2 = 0.70) and FPER (R2 = 0.65) in which PODC was recognized as a strong mediator.