The main motivation for this research derives from the absence of knowledge and empirical research on how big data analytics can help firms cultivate their financial decision-making performance. This research aims to explore the relationships between big data analytics, data diagnosticity, data-driven insights and financial decision-making performance through the lens of resource-based theory and dynamic capabilities view.
The proposed research model tested the interrelations between four constructs including big data analytics, data diagnosticity, data-driven insights and financial decision performance drawing on quantitative data from Jordanian financial firms using a cross-sectional survey approach. The data were analyzed using the structural equation modeling (PLS) software.
The findings revealed that big data analytics has a positive and direct influence on data diagnosticity, data-driven insights and financial decision performance. The results indicate that data diagnosticity has a positive influence on financial decision performance, whereas data-driven insights do not have a significant impact on financial decision performance within the context of financial firms in Jordan.
This empirical research filled an important gap in big data research by providing theoretical evidence of a statistical nexus between big data analytics, data diagnosticity, data-driven insights and financial decision-making performance in an emerging financial market.
