What determines data analytics systems performance in financial engineering? A user perspective

Purpose Even though the end-user satisfaction construct has gained prominence as a surrogate measure of information systems performance assessment, it has received scant formal treatment and empirical examination in the data analytics systems field. In this respect, this study aims to examine the vital role of user satisfaction as a proxy measure of data analytics system performance in the financial engineering context. Design/methodology/approach This study empirically validated the proposed model using primary quantitative data obtained from financial managers, engineers and analysts who are working at Jordanian financial institutions. The quantitative data were tested using partial least squares-based structural equation modeling. Findings The quantitative data analysis results identified that technology quality, information quality, knowledge quality and decision quality are key factors that enhance user satisfaction in a data analytics environment with an explained variance of around 69%. Originality/value This empirical research has contributed to the discourse regarding the pivotal role of user satisfaction in data analytics performance in the financial engineering context of developing countries such as Jordan, which lays a firm foundation for future research.