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Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature.

The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey.

The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions.

The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.

Most of the previous studies agree about the significance of user satisfaction in ensuring the endurance of information systems (ISs). Accordingly, it is crucial to investigate the effect of e-Government systems on individual end-user satisfaction as more and more countries adopt and deploy such Government Financial Management Information Systems (GFMIS) in the era of digital transformation. Because of this, the purpose of this study is to investigate the factors that contribute to the success of GFMIS in Jordan and ultimately the satisfaction of its users.

The IS success model developed by DeLone and McLean (2003) serves as the theoretical underpinning for the current research. Adding training quality as a new variable to the proposed model has been found to further increase the satisfaction of GFMIS users. A total of 104 GFMIS users in Jordan provided the data used to verify the model. The partial least squares-structural equation modelling was used to test the hypotheses.

The empirical findings indicated that GFMIS user satisfaction is significantly affected by information quality, service quality and perceived usefulness; meanwhile, system quality is only partially supported. The research also showed that the level of satisfaction among Jordanian GFMIS users was related to the quality of training they received.

This study fills a crucial literature gap by developing a research model that can help improve GFMIS usage towards attaining greater performance amongst government agencies in Jordan.

The purpose of this study is to examine the factors that may have an influence on the continuous intention to use mobile money as one of the Financial Technology (FinTech) products beyond the COVID-19 pandemic lockdown period.

This study has empirically tested the expanded post-acceptance model (Extend-PAM) and the expectation-confirmation model (ECM) to explain the mobile money adoption in Jordan. Data collected were analyzed through partial least squares–structural equation modeling (PLS-SEM).

The results mainly showed that the quality of administrative services and trust significantly impact confirmation and perceived usefulness. In addition, perceived security and knowledge of mobile money as one of the FinTech services significantly influence users’ confirmation and perceived usefulness. Also, usefulness and satisfaction influence continuous intention. Significant relationships were noted among confirmation, perceived usefulness, satisfaction and continuous intention to use mobile money.

This paper integrates two key theories: the expanded post-acceptance model (Extend-PAM) and the expectation-confirmation model (ECM) in the post-adoption behavior of mobile money. Therefore, this study attempts to fill a literature gap by examining the antecedent factors that influence the continuous intention to use mobile money services in the post-consumption stage.