Abstract:
Alongside technological advancements, one of the extensively studied areas of technological transformation is retail financial services, which includes e-banking. E-banking enables customers to engage in a wide range of financial services through official bank websites and mobile banking applications. Correspondingly, the present study was executed as relational research within the purview of deductive methodology, with the intention of determining the relationship between perceived ease of use (PEOU), perceived customer innovation (INNO), and perceived trust of e-services (PTES) on the adoption of e-banking (AEB). Moreover, the study analyzed whether there is an effect by the selected moderator, technology self-efficacy (TSE). In the current research, self-completed online Google Form questionnaires were distributed via email and social media among e-banking customers of six systemically important banks. These customers come from a wide range of backgrounds, aimed at minimizing the potential bias of non-probability sampling, with the convenience sample technique being followed. The minimum sample size, as per Cochran's formula, was determined to be 384. The questionnaires utilized a 7-point Likert scale. Additionally, several modifications were made to the questionnaires following the pilot study. Finally, 463 responses were used for data analysis, which involved both descriptive and structural equation modeling methods using IBM SPSS and AMOS software packages. The analysis revealed that the majority of e-banking customers commonly use "SMS alerts" as their most preferred and important product, as indicated by descriptive statistics. Furthermore, exploratory factor analysis unveiled that user innovation is formed by two factors, as indicated in the pattern matrix, which is a novel finding of this study. Subsequently, hypothesis testing indicated that all three main direct effects (PEOU, INNO, PTES towards AEB) are significant. However, the moderating effect of TSE is significant only in the relationship between INNO and AEB in the current research context, as per the interaction effect size. Consequently, an alternative path model was presented, aligning with findings of the hypotheses tested on direct and indirect relationships. In conclusion, this study presented implications and recommendations, along with suggestions.