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<p>Digital currency adoption is a critical frontier for emerging-market financial systems. Given the paucity of large-scale empirical research on Central Bank Digital Currency (CBDC) adoption intentions in emerging economies, this study examined behavioural factors influencing CBDC usage intention among the cohort of existing fintech users in India. Drawing upon the modified Unified Theory of Acceptance and Use of Technology (UTAUT), Elaboration Likelihood Model (ELM), and network externalities, survey data from 1,816 users were analysed using Covariance-based Structural Equation Modelling (CB-SEM), followed by Artificial Neural Networks (ANN) to assess non-linear effects and predictor importance. Performance expectancy, effort expectancy, social influence, and facilitating conditions significantly predict behavioural intention to use CBDC, with facilitating conditions emerging as the most influential driver. Financial knowledge exhibited a counterintuitive negative mediation on the performance expectancy → intention and facilitating conditions → intention relationships, suggesting expertise-related mechanisms, such as overconfidence and cognitive saturation, among digitally proficient users, thereby confirming the ELM. Age, gender, and workplace activity do not moderate CBDC readiness, indicating that CBDC readiness transcends basic demographics within the fintech-active cohorts. The hybrid CB-- SEM–ANN approach achieves strong explanatory power (R² = 0.702) and high predictive accuracy (98.08%), demonstrating the value of combining structural and machine-learning techniques in technology-adoption research. The study offers novel evidence on CBDC adoption in an emerging market. It extends UTAUT by integrating financial knowledge and workplace activity as contextual variables, providing actionable insights for central banks and fintech stakeholders designing CBDC roll-outs.</p>

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Digital currency usage intention among existing fintech users: testing the modified UTAUT using hybrid SEM-ANN approach

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Sumita Jagdishprasad Shroff,
  • Rajkumari Nitin Soni,
  • Simran Rohira

摘要

Digital currency adoption is a critical frontier for emerging-market financial systems. Given the paucity of large-scale empirical research on Central Bank Digital Currency (CBDC) adoption intentions in emerging economies, this study examined behavioural factors influencing CBDC usage intention among the cohort of existing fintech users in India. Drawing upon the modified Unified Theory of Acceptance and Use of Technology (UTAUT), Elaboration Likelihood Model (ELM), and network externalities, survey data from 1,816 users were analysed using Covariance-based Structural Equation Modelling (CB-SEM), followed by Artificial Neural Networks (ANN) to assess non-linear effects and predictor importance. Performance expectancy, effort expectancy, social influence, and facilitating conditions significantly predict behavioural intention to use CBDC, with facilitating conditions emerging as the most influential driver. Financial knowledge exhibited a counterintuitive negative mediation on the performance expectancy → intention and facilitating conditions → intention relationships, suggesting expertise-related mechanisms, such as overconfidence and cognitive saturation, among digitally proficient users, thereby confirming the ELM. Age, gender, and workplace activity do not moderate CBDC readiness, indicating that CBDC readiness transcends basic demographics within the fintech-active cohorts. The hybrid CB-- SEM–ANN approach achieves strong explanatory power (R² = 0.702) and high predictive accuracy (98.08%), demonstrating the value of combining structural and machine-learning techniques in technology-adoption research. The study offers novel evidence on CBDC adoption in an emerging market. It extends UTAUT by integrating financial knowledge and workplace activity as contextual variables, providing actionable insights for central banks and fintech stakeholders designing CBDC roll-outs.