Understanding enablers and inhibitors of digital rupee: a dual-factor theory perspective
Abstract
Purpose
Central banks globally are actively investigating the issuance of central bank digital currencies, a digital form of fiat money. In this light, this study aims to explore and empirically validate the factors that enable or inhibit user behavioral intentions to adopt the digital rupee in India.
Design/methodology/approach
The study employed dual-factor theory (DFT) to capture the users’ perceptions of both enablers and inhibitors of the digital rupee. The authors gathered survey data from 351 individuals in India through online questionnaires. The authors used partial least squares structural equation modeling and multigroup analysis (MGA) to evaluate the proposed conceptual model.
Findings
The findings reveal that enablers such as perceived government support, trialability and similarity positively influence users’ attitudes toward the digital rupee. In contrast, inhibitors such as usage, value and risk barriers increase users’ resistance. Attitude has a significant positive impact on the intention to use the digital rupee, while resistance significantly reduces the intention to adopt it. MGA results highlight the importance of gender and income status in understanding intention to use the digital rupee.
Originality/value
By applying DFT, the study identifies a set of enablers and inhibitors that influence the behavioral intention to use the digital rupee in India. It provides actionable insights for governments and central bankers to devise effective policies, design considerations and targeted interventions, ensuring a sustainable environment for the successful implementation of the digital rupee.
Keywords
Citation
Balasubramanian, S.A. and Thirumaran, P. (2024), "Understanding enablers and inhibitors of digital rupee: a dual-factor theory perspective", Digital Policy, Regulation and Governance, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/DPRG-04-2024-0069
Publisher
:Emerald Publishing Limited
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