Mobile Banking Adoption: A Multi-Factorial Study on Social Influence, Compatibility, Digital Self-Efficacy, and Perceived Cost among Generation Z Consumers in the United States

The introduction of mobile banking is essential in today's financial sector, where technological innovation is critical. To remain competitive in today's market, businesses must analyze client attitudes and perspectives as they have an impact on long-term demand and company profitability. Despite previous studies exploring general adoption behaviors, not much is known yet on how some individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost can drive the uptake of mobile banking in this specific generational group. The current study addressed this knowledge gap by providing insights into these factors, contributing to the growing body of work on mobile banking adoption, and generating actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, the researcher gathered responses from mobile banking users and non-users among the Gen Z population in the United States. The study findings demonstrated that the strongest predictor of mobile banking adoption is the cost of usage. Mobile banking uptake is significantly affected by high transaction fees, internet charges, and hidden charges. In order to improve the adoption, financial institutions should implement transparent pricing and cost-reduction strategies. Similarly, social influence has a positive impact on mobile banking adoption. Adoption is driven by peer recommendations, institutional endorsements, and social acceptance. Low self-efficacy had a negative effect on the adoption. However, compatibility is not a major contributor to mobile banking adoption. Although mobile banking is in line with users’ lifestyles and financial habits, this does not have a strong impact on adoption decisions. Based on the study findings, financial institutions could focus on and adjust cost transparency and affordability to increase mobile banking adoption. Given that social influence was identified as a robust predictor, financial institutions should focus on implementing referral programs, leveraging peer endorsements, and creating social media marketing campaigns to facilitate word-of-mouth adoption. Future research can use longitudinal research or real-life transaction data to understand user behavior more objectively. Additionally, other factors that may significantly influence mobile banking adoption decisions, such as trust,  perceived risk, and regulatory policies, were not included. Incorporating these extra variables into the model would give a much more thorough analysis.