Adoption of Central Bank Digital Currency in India: A Structural Model Using ISM
Vaibhav Dixit et al.
What the paper says
Purpose: This study aims to explore and structure the key determinants influencing the adoption of central bank digital currency (CBDC) in India, specifically focusing on the retail digital rupee (e-rupee). It employs the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) framework in conjunction with interpretive structural modeling (ISM) to identify, categorize, and model the adoption enablers. Design/Methodology/Approach: Seven constructs from UTAUT3—performance expectancy, effort expectancy, social influence, facilitating conditions, price value, hedonic motivation, and behavioral intention—were selected based on extensive literature review and expert validation. ISM was used to map interrelationships between these constructs. The study involved the creation of a structural self-interaction matrix, final reachability matrix, hierarchical ISM model, cross-impact matrix multiplication applied to classification analysis, and fuzzy logic to assess driving and dependence power. Findings: The results reveal that facilitating conditions and performance expectancy are the most influential constructs, forming the foundational layer in the adoption hierarchy. In contrast, hedonic motivation and behavioral intention act as dependent variables, influenced by preceding constructs. The findings underscore the importance of infrastructure readiness, user convenience, and institutional support in shaping public intent to adopt CBDC. Practical Implications: The study offers strategic insights for policymakers, financial institutions, and developers to enhance CBDC adoption by addressing structural enablers and barriers. It also provides a scalable framework applicable to other emerging economies exploring digital currency implementation. Originality/Value: This research uniquely integrates UTAUT3 with ISM to present a structured, empirically grounded model of CBDC adoption behavior, bridging theoretical and practical gaps in digital finance adoption literature.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.