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@article{206790,
        author = {Dr. P. KUMAR},
        title = {AI-Driven Digital Payment Adoption among MSMEs: Examining the Roles of Technological Readiness, Perceived Intelligence, Trust, and Business Performance Using PLS-SEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2814-2829},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206790},
        abstract = {Artificial intelligence (AI) is reshaping digital payment systems by enhancing transaction efficiency, fraud detection, personalized financial services, and payment security. These advancements present significant opportunities for Micro, Small, and Medium Enterprises (MSMEs) to improve operational efficiency and business competitiveness. However, despite the increasing availability of AI-enabled digital payment solutions, their adoption among MSMEs remains inconsistent due to technological, organizational, and behavioural challenges. Existing studies have primarily examined conventional digital payment adoption, with limited emphasis on AI-driven payment technologies and the mechanisms through which they influence business performance. Addressing this gap, the present study proposes a conceptual framework to investigate the determinants of AI-driven digital payment adoption among MSMEs by examining the roles of technological readiness, perceived intelligence, trust, and business performance. Specifically, the study evaluates the influence of technological readiness and perceived intelligence on trust, the effect of trust on AI-driven digital payment adoption, and the subsequent impact of adoption on business performance. Trust is further proposed as a mediating variable linking the antecedent constructs with adoption behaviour. The study adopts a quantitative, cross-sectional research design using a census approach involving 133 MSMEs. Primary data will be collected through a structured questionnaire employing a seven-point Likert scale and analysed using IBM SPSS Statistics and SmartPLS 4 through Partial Least Squares Structural Equation Modelling (PLS-SEM). The proposed research is expected to contribute to the FinTech and digital payment literature by providing a comprehensive understanding of AI-driven payment adoption in MSMEs while offering practical implications for financial institutions, fintech providers, and policymakers to promote digital transformation and sustainable business performance.},
        keywords = {Artificial Intelligence, Digital Payment Adoption, MSMEs, FinTech, Technological Readiness, Perceived Intelligence, Trust, Business Performance, PLS-SEM.},
        month = {July},
        }