This study aims to explore key factors influencing financial analytics technology (FAT) performance. The developed research model analyzes the interrelationships of research constructs, such as data quality, technology quality, talent quality, decision quality, perceived satisfaction and performance impact, using a resource-based view (RBV) and the DeLone and McLean model (DMM).
A cross-sectional survey was undertaken to gather quantitative data from financial firms in Jordan to uncover the relationships in the proposed model. A total of 173 responses were analyzed using structural equation modeling-partial least squares (PLS-SEM).
The findings demonstrate that perceived satisfaction was positively affected by technology quality and talent quality, while decision quality was influenced by data quality and talent quality. The results also indicate that both decision quality and perceived satisfaction positively influence the performance of FAT.
The findings of this study can help managers and policymakers in financial firms enhance the effectiveness of FAT, ultimately driving better overall firm performance.
This empirical research has contributed to the discourse regarding the pivotal role of big data analytics in the financial industry. It is also the first study to address the impact of analytical talent quality in the context of financial data analytics.
