Sage Journals: Discover world-class research

Abstract

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.

Keywords

CBDC e-rupee UTAUT3 ISM digital currency adoption technology acceptance India FinTech Fuzzy

Introduction

The rapid digitization of financial systems has led central banks to explore central bank digital currencies (CBDCs). These currencies aim to modernize currency issuance, enhance payment efficiency, and improve financial inclusion. India, with its expanding digital infrastructure and high smartphone penetration, stands at the forefront of this transformation with the introduction of its CBDC—the e-rupee. Being among the first major emerging nations to conduct a large-scale CBDC trial, India’s initiatives mark a turning point in global financial innovation. The e-rupee’s success depends on user adoption. This adoption is influenced by complex socio-technical factors that go beyond simple access or utility.

Understanding the adoption of the e-rupee requires an in-depth study of individual attitudes, perceived value, and environmental factors that impact behavioral intention (BI). Previous studies have repeatedly demonstrated that factors including social influence (SI), perceived ease of use, performance expectancy (PE), and trust are important to user adoption of digital financial services. According to a study on the adoption of digital currency in India, BI to adopt CBDC is strongly predicted by PE, SI, and government support, especially when combined with financial literacy and perceived technological fit (Desai, 2024). Similarly, despite occasional mistrust over privacy and control, it has been discovered that trust and perceived security have a major impact on younger generations’ openness to CBDC adoption (Kaur et al., 2024).

These findings align with the Unified Theory of Acceptance and Use of Technology (UTAUT). UTAUT is being applied in the context of digital banking, e-payments, and now CBDC adoption studies. The UTAUT framework identifies key constructs such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FCs), and behavioral intention (BI) as critical drivers of user acceptance. UTAUT2 and UTAUT3 extensions have demonstrated strong predictive power across various fintech services in India. These services include Unified Payments Interface (UPI) platforms and mobile wallets (Chauhan et al., 2021). The need to contextualize these ideas is enhanced by India’s distinct social and technical environment. For example, the impact of SI on technology adoption is elevated by collectivist attitudes and intergenerational financial decision-making. According to research on UPI adoption, BI and use behavior were substantially impacted by trust, attitude, and SI. This suggests that CBDC adoption strategies might be successful (Razi-ur-Rahim et al., 2024).

This study aims to identify and analyze the key factors influencing the adoption of the e-rupee in India through the lens of the UTAUT3 framework, using interpretive structural modeling (ISM). The ISM methodology maps the relationships among constructs. It reveals hierarchical linkages between foundational drivers, such as infrastructure and perceived ease, and outcome-oriented variables, like hedonic motivation (HM) and behavioral intent.

The research contributes to the academic literature by applying a structured modeling approach (ISM) to UTAUT3 in the specific and underexplored context of CBDC adoption in India. It also offers practical insights for policymakers, financial institutions, and fintech developers aiming to enhance user engagement and trust in digital currency platforms. Recent research studies also reinforce the need for a strong regulatory and institutional framework to complement digital infrastructure in emerging markets. Regulatory quality, not just technical readiness, is a decisive factor in the success of CBDC rollouts in developing economies (Corbet et al., 2024). This study will develop a hierarchical ISM-based model to identify, organize, and analyze the key UTAUT3 constructs influencing the adoption of the e-rupee in India.

Literature Review

Central Bank Digital Currencies

Central bank digital currencies, or CBDCs, are a transformative development in modern monetary systems. The central bank of a nation issues and regulates a digital version of fiat money called a CBDC, which is intended to be a safe, effective, and widely available means of trade (Raju, 2021). CBDCs provide the benefits of programmable, digital financial infrastructure while preserving monetary sovereignty, in contrast to decentralized cryptocurrencies (Surgentova, 2023).

Due to the demand for inclusive financial services, the rise in digital payment use, and the decline in cash use, CBDCs have become increasingly relevant. CBDCs serve as instruments to improve cross-border settlements, modernize payment systems, lower transaction costs, and strengthen central banks’ authority over monetary policy (Batra et al., 2024). They also counterbalance the emergence of private digital currencies and stablecoins, promoting confidence and stability in national payment ecosystems (Das et al., 2023).

With nations such as China and India spearheading extensive implementation trials, more than 100 central banks are studying or testing CBDCs (Chikobava, 2023). CBDCs have the potential to improve the durability of digital public infrastructure and encourage financial inclusion in poor nations (Tekdoğan & Güney, 2024).

Digital Currency Adoption

The Reserve Bank of India (RBI) is piloting the digital rupee in India, and a growing body of empirical research is trying to identify the variables influencing the adoption of CBDCs. These studies demonstrate the complex relationships between infrastructure, psychology, and technology that influence user intents and actions.

The adoption of CBDC is greatly impacted by trust, openness, and data security. According to a recent survey-based study, consumers’ willingness to accept the digital rupee was highly impacted by their perceptions of transactional transparency and security and their trust in the issuing authority. These results support the idea that the acceptability of state-backed digital currencies is predicated on institutional trust.

However, there are still a number of obstacles preventing wider use. According to Krishnamoorthy and Aggarwal (2024), there is still a lack of general understanding of CBDC, and many consumers are unable to tell it apart from other digital payment systems like the UPI. The study found that the retail CBDC pilot’s poor acceptance is a result of this lack of distinction as well as a lack of outreach and education (Krishnamoorthy & Aggarwal, 2024).

Research with a demographic focus shows that, although EE has little impact, Generation Z in Northern India exhibits a strong behavioral intent to adopt CBDC due to PE and perceived security, indicating a tech-savvy and innovation-receptive segment (Kaur et al., 2024). SI and performance anticipation significantly impact adoption intentions, but the effect is moderated by the perception of less additional value in CBDC among regular UPI users. This suggests that past experience with digital systems like UPI may dampen the excitement surrounding CBDC adoption (Gupta et al., 2023). Trust partially mediates the association between perceived risks and adoption readiness, whereas perceived risks diminish trust. Perceived usefulness did not significantly predict adoption behavior, highlighting the importance of emotional and trust-based elements (Gupta et al., 2023).

Regulatory readiness and infrastructure are also crucial for the adoption. The digital rupee pilot has demonstrated backend readiness by including large financial institutions and focusing on use cases such as retail payments and government securities. However, research warns that without comparable efforts in ecosystem integration, user education, and public engagement, technology infrastructure alone cannot ensure adoption (Kumar & Gupta, 2024).

Empirical research shows that India’s digital and financial infrastructure is suitable for CBDC implementation. However, widespread adoption depends on building trust, raising public awareness, and clearly explaining the benefits of CBDC over existing digital payment options.

Applications of UTAUT3 in Digital Technology Adoption

UTAUT is one of the most influential models in technology acceptance research. Developed by Venkatesh et al. (2003) and later extended into UTAUT2 and UTAUT3, the framework integrates multiple behavioral theories to explain user adoption of new technologies. UTAUT3 adds constructs such as price value (PV) and hedonic motivation (HM) to reflect evolving consumer behaviors in digital ecosystems.

UTAUT3 includes seven core constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FCs), price value (PV), hedonic motivation (HM), and behavioral intention (BI) (Figure 1). These constructs have been widely validated in studies involving digital banking, mobile wallets, cryptocurrency, and FinTech adoption (Amnas et al., 2023).

Figure 1.

UTAUT3 Model.

UTAUT3 is particularly effective in explaining technology adoption because it captures both utilitarian and affective motivations behind user behavior. It has demonstrated high predictive power in digital finance studies, with R² values exceeding 70% in models of FinTech and e-wallet adoption (Raihan, 2019).

UTAUT3 has been widely applied across digital finance domains. It shows a strong predictive power for user adoption. In mobile wallet studies in Malaysia and Indonesia, constructs such as PE, EE, and SI were significant determinants of BI (Leong, 2020). In the Indian banking sector, PE and FCs significantly influenced the intention to adopt blockchain technologies (Jena, 2022). Cross-cultural research has also shown that UTAUT3 constructs effectively explain adoption behavior across diverse generational cohorts and national contexts, especially in FinTech payment systems (Urus et al., 2022).

The following are the constructs of the model:

Performance expectancy: PE refers to the degree to which individuals believe that using technology will enhance their performance or productivity. Studies consistently show it as a dominant predictor of BI in digital banking and mobile services adoption. Its importance holds across financial, educational, and mobility contexts (Amnas et al., 2023; Maharjan & Kc, 2023; Zainordin et al., 2022).

Effort expectancy (EE): EE captures the perceived ease of using a system. It significantly impacts adoption when technologies are new or complex, especially for first-time or older users. Though occasionally found insignificant, it remains crucial in usability-sensitive environments (Boison et al., 2022; Nguyen et al., 2020; Xie et al., 2021).

Social influence (SI): SI refers to how much an individual’s decision to adopt technology is shaped by others’ opinions—family, friends, or societal norms. In collectivist cultures, SI can strongly drive adoption, especially when endorsed by influencers or institutions (Arisona et al., 2023; Escobar-Rodríguez et al., 2014; Zainordin et al., 2022).

Facilitating conditions: FC measures the perception that adequate resources and support (e.g., tech infrastructure, help desks) are available for effective use. It often directly impacts both BI and actual use behavior, especially in resource-variable environments (Dhingra & Gupta, 2020; Indah et al., 2023; Nguyen et al., 2020).

Price value: PV is the user’s assessment of the trade-off between the cost of using technology and the benefits received. It strongly predicts BI in contexts where price sensitivity is high, such as mobile payments or CBDCs (Amnas et al., 2023; Salsabila et al., 2024; Utami & Irwansyah, 2022).

Hedonic motivation: HM refers to the pleasure or enjoyment derived from using a system. It is especially important in consumer-oriented digital tools and has shown mixed effects—critical in gamified or incentive-driven apps but less in utilitarian platforms (Maharjan & Kc, 2023; Sari et al., 2024; Zarlis, 2024).

Behavioral intention: BI reflects the user’s willingness or readiness to adopt technology. It serves as the central outcome variable in the UTAUT framework and is strongly influenced by all preceding constructs. Studies show that it reliably predicts actual usage across banking, e-commerce, and smart mobility domains (Ali & Warraich, 2023; Boison et al., 2022; Dhingra & Gupta, 2020).

This study focuses on the following research questions:

RQ1. Which UTAUT3 constructs significantly influence the adoption of the e-rupee in India, and how can they be systematically identified using the ISM approach?

RQ2. How are the identified adoption factors interrelated, and which variables act as key drivers or dependent elements in the hierarchical structure of e-rupee adoption?

Research Objectives

To identify and analyze the key UTAUT3-based factors influencing the adoption of India’s e-rupee using ISM and fuzzy Cross-Impact Matrix Multiplication Applied to Classification (MICMAC).

To develop a hierarchical model that highlights the interrelationship and driving–dependence power of the factors affecting CBDC adoption in the Indian context.

Research Methodology

Interpretive Structural Modelling

The study uses the UTAUT3 framework, specifically applied to the context of CBDCs like the e-rupee. The digital rupee (e-rupee), introduced by the RBI, aims to promote financial inclusion, lower operating expenses related to physical currency, and improve payment efficiency. The adoption of CBDC is expected to be facilitated by India’s advanced digital infrastructure, UPI ecosystem, and growing public confidence in mobile-based transactions (Gupta et al., 2023; Krishnamoorthy & Aggarwal, 2024; Mahesh et al., 2024). Empirical research has shown that customer support, trust, and transparency are important factors in determining how Indian consumers will act while using the e-rupee (Sandhu et al., 2023). These contextual factors offer a strong foundation for using the UTAUT3 model to understand adoption dynamics specific to the digital currency environment in India.

ISM is used to explore the contextual relationships between the UTAUT3 framework constructs that are believed to impact the adoption of the E-rupee. These constructs include PE, EE, PV, HM, SI, and FC. ISM uses literature support, logical reasoning, and expert judgment to help identify and organize key aspects hierarchically.

The structural self-interaction matrix (SSIM) is constructed based on a review of technology adoption literature. The review used the UTAUT3 framework, specifically applied to the context of CBDCs like the e-rupee.

In the ecosystem of digital financial technology adoption, the relationships between PE, EE, SI, FC, HM, and PV are not isolated. These relationships are contextually shaped and interdependent.

PE represents the users’ perceptions that utilizing the e-rupee would improve their financial results and transactional efficiency. It has an impact on PV, HM, and BI. Nguyen et al. (2020) showed that customers’ inclination to use digital banking services in Vietnam was highly influenced by their expectations of better performance. In cryptocurrency investment performance expectancy moderated the impact of other factors on BI (Alzyoud et al., 2024). A study by Putranta et al. (2020), also confirmed that PE significantly influenced mobile payment adoption behavior.

It was suggested that EE affected PV, HM, PE, and BI. Users’ perceived value and expected performance rise when they believe a system is easy to use, which strengthens their intent to use the technology. EE has a considerable impact on motivational outcomes and cognitive assessments of usefulness in digital banking environments (Meiranto et al., 2024).

SI was identified as a significant component influencing several variables, including PE, EE, BI, HM, and PV. Adoption choices are frequently influenced by institutional trust, peer pressure, and professional advice in culturally collectivist nations like India. Salsabila et al. (2024) found that SI has a substantial impact on digital bank adoption by influencing both intrinsic motivation and perceived system value. Study emphasized how it influences young users’ behavioral decisions in financial environments (Widyastuti & Layaman, 2023). Amany and Indrayani (2024) also found SI to be a driver of BI in mobile banking adoption.

HM refers to the satisfaction gained from using technology. It is regarded as an outcome of prior perceptions and a determinant of BI. One factor influencing customers’ willingness to accept an innovative financial instrument like the e-rupee is its emotional appeal. According to a study, among students using digital wallets, HM was a powerful predictor of BI (Kardoyo et al., 2022).

PV indicates the consumers’ trade-off between the perceived benefit and the monetary or cognitive cost of utilizing the e-rupee. It was associated with BI. According to research by Alzyoud et al. (2024), consumers’ desire to use digital financial technologies improves considerably when they believe that they are more beneficial or cost-effective than traditional techniques.

FCs were identified as a key construct that influences all others, including PE, EE, SI, HM, PV, and BI. A strong digital infrastructure, regulatory readiness, and user support mechanisms promote more trust and usage of innovations such as the e-rupee. Research has shown that the adoption of digital and mobile banking systems is facilitated by favorable circumstances (Amany & Indrayani, 2024; Nguyen et al., 2020; Widayani, 2022).

Structural Self-Interaction Matrix

SSIM is the first step in ISM. It examines and systematically maps the contextual relationships among key factors influencing the adoption of CBDCs such as the e-rupee. PE, EE, SI, HM, PV, FC, and BI are the constructs from the UTAUT3 that were used to create SSIM for this study.

These constructs are compared pairwise to create the SSIM, which is based on logical reasoning, and a study of the literature. The directional connection between each pair is shown by a symbolic representation:

V: Variable i influences variable j.

A: Variable j influences variable i.

X: Variables i and j influence each other.

O: No relation exists between variables.

Warfield (1974) first introduced this method, which works especially well for structuring complicated social and technical systems into hierarchical models. It makes it easy to see the elements that drive and are dependent on them.

In the context of digital financial services like CBDCs, prior research supports the interdependent nature of these constructs. FCs, for example, have been repeatedly identified as the most important adoption driver, impacting several other constructs, including PE, EE, SI, and BI (Albugami, 2022). PE and EE have been recognized as the major contributing factors to digital banking activity under a range of cultural and economic circumstances (Killian et al., 2017; Moorthy et al., 2019).

The SSIM matrix in this study was developed with the knowledge that collectivist societal norms and a rapidly growing digital infrastructure allow constructs such as SI and FCs to play enhanced roles in the Indian digital payment landscape (Mahesh et al., 2024; Sandhu et al., 2023). HM and PV are influenced by consumers’ emotional and cost-benefit evaluations. SI influences not just BI but also perceived value and ease of use.

This methodology has been successfully used in similar studies, such as those examining the structural adoption factors of digital banking in emerging economies, and in e-government, mobile payments, and financial technology environments (Albugami, 2022; Alzubaidi et al., 2020).

The UTAUT3 constructs developed an SSIM table (Table 1). This table reflects the directional influences on CBDC adoption. It aligns with established theoretical linkages and contextualizes these linkages within the unique digital, economic, and cultural environment of the Indian financial ecosystem.

Table 1.

Structural Self-interaction Matrix.

Variables	PE	EE	SI	HM	PV	FC	BI
Performance expectancy	–	A	A	V	V	A	V
Effort expectancy	V	–	A	V	V	A	V
Social influence	V	V	–	V	V	A	V
Hedonic motivation	A	A	A	–	A	A	A
Price value	A	A	A	V	–	A	V
Facilitating conditions	V	V	V	V	V	–	V
Behavioral intention	A	A	A	V	A	A	–

Note: PE = Performance expectancy; EE = Effort expectancy; SI = Social influence; HM = Hedonic motivation; PV = Price value; FC = Facilitating condition; BI = Behavioral intention.

Initial Reachability Matrix

SSIM is converted into binary values to show the direct effect between variables, which creates the initial reachability matrix (IRM). Every symbol in the SSIM—V, A, X, and O—is converted into 1s and 0s using the common ISM conversion rules. Within the CBDC adoption framework, this transformation enables us to systematically identify which constructs—such as PE, EE, SI, etc.—directly affect other constructs (Table 2). The final reachability matrix (FRM) (Table 3) and hierarchy levels, as well as the identification of transitive linkages, are developed based on the IRM (Albugami, 2022; Warfield, 1974).

Table 2.

Initial Reachability Matrix.

Variables ®	PE	EE	SI	HM	PV	FC	BI
PE	1	0	0	1	1	0	1
EE	1	1	0	1	1	0	1
SI	1	1	1	1	1	0	1
HM	0	0	0	1	0	0	0
PV	0	0	0	0	1	0	1
FC	1	1	1	1	1	1	1
BI	0	0	0	1	0	0	1

Note: PE = Performance expectancy; EE = Effort expectancy; SI = Social influence; HM = Hedonic motivation; PV = Price value; FC = Facilitating condition; BI = Behavioral intention.

Final Reachability Matrix with Transitivity

The FRM is developed by applying the transitivity principle to the IRM. According to this principle, if variable A influences B and B influences C, then A is assumed to influence C as well (Albugami, 2022). This step ensures that all direct and indirect relationships among the UTAUT3 variables are captured. The FRM in Table 3 provides a complete foundation for structuring the hierarchical model of factors influencing e-rupee adoption in India

Table 3.

Final Reachability Matrix with Transitivity.

Variables ®	PE	EE	SI	HM	PV	FC	BI
PE	1	1	1	1	1	0	1
EE	1	1	1	1	1	0	1
SI	1	1	1	1	1	0	1
HM	0	0	0	1	0	0	0
PV	0	0	1	1	1	0	1
FC	1	1	1	1	1	1	1
BI	0	0	0	1	0	0	1

Note: PE = Performance expectancy; EE = Effort expectancy; SI = Social influence; HM = Hedonic motivation; PV = Price value; FC = Facilitating condition; BI = Behavioral intention.

Level Partitioning Process

The FRM is tested using the level partitioning procedure to determine the hierarchical structure of UTAUT3 components that impact the adoption of e-rupee. Each construct is recognized together with its antecedent set (variables that influence it) and reachability set (variables it may affect). The reachability set is then used to compare the intersection of these sets. The construct is given the current level and eliminated in the following iteration if both are the same. Repeating this process reveals various layers of impact, allowing us to see which elements are fundamental drivers (such as FCs) and which are more outcome-oriented (such as BI) in the context of CBDC adoption in India.

Reachability set (R): All variables it can reach (row-wise 1s)

Antecedent set (A): All variables that can reach it (column-wise 1s)

Intersection (R ∩ A): Common elements in both sets

If R = R ∩ A, that variable is placed at the top level for this iteration and is removed from the matrix in the next round.

HM is placed at the bottom level as it only influences itself and no other variables, indicating that it is the most outcome-oriented construct (Table 4).

Table 4.

Iteration 1.

Variable	Reachability Set (R)	Antecedent Set (A)	R ∩ A	Level
PE	PE, EE, SI, HM, PV, BI	PE, EE, SI, FC	PE, EE, SI
EE	PE, EE, SI, HM, PV, BI	PE, EE, SI, FC	PE, EE, SI
SI	PE, EE, SI, HM, PV, BI	PE, EE, SI, PV, FC	PE, EE, SI, PV
HM	HM	PE, EE, SI, HM, FC, PV, BI	HM	1
PV	SI, HM, PV, BI	PE, EE, SI, PV, FC	SI, PV
FC	PE, EE, SI, HM, PV, FC, BI	FC	FC
BI	HM, BI	PE, EE, SI, PV, FC, BI	BI

BI is assigned the next level since it can reach only itself and HM (already removed), showing that it is primarily influenced by other constructs (Table 5).

Table 5.

Iteration 2.

Variable	Reachability Set (R)	Antecedent Set (A)	R ∩ A	Level
PE	PE, EE, SI, PV, BI	PE, EE, SI, FC	PE, EE, SI
EE	PE, EE, SI, PV, BI	PE, EE, SI, FC	PE, EE, SI
SI	PE, EE, SI, PV, BI	PE, EE, SI, PV, FC	PE, EE, SI, PV
PV	SI, PV, BI	PE, EE, SI, PV, FC	SI, PV
FC	PE, EE, SI, PV, FC, BI	FC	FC
BI	BI	PE, EE, SI, PV, FC, BI	BI	2

PV and SI are placed at the third level from the bottom (Table 6).

Table 6.

Iteration 3.

Variable	Reachability Set (R)	Antecedent Set (A)	R ∩ A	Level
PE	PE, EE, SI, PV	PE, EE, SI, FC	PE, EE, SI
EE	PE, EE, SI, PV	PE, EE, SI, FC	PE, EE, SI
SI	PE, EE, SI, PV	PE, EE, SI, PV, FC	PE, EE, SI, PV	3
PV	SI, PV	PE, EE, SI, PV, FC	SI, PV	3
FC	PE, EE, SI, PV, FC	FC	FC

PE and EE meet the criteria where their reachability equals their intersection sets, so they are positioned as strong driving constructs (Table 7).

Table 7.

Iteration 4.

Variable	Reachability Set (R)	Antecedent Set (A)	R ∩ A	Level
PE	PE, EE	PE, EE, FC	PE, EE	4
EE	PE, EE	PE, EE, FC	PE, EE	4
FC	PE, EE, FC	FC	FC

FC remains the last variable, influencing all others but not influenced by any—making it the most foundational driver of e-rupee adoption (Table 8).

Table 8.

Iteration 5.

Variable	Reachability Set (R)	Antecedent Set (A)	R ∩ A	Level
FC	FC	FC	FC	5

ISM Diagram

The ISM hierarchical diagram (Figure 2) presents a structured visualization of the contextual relationships among UTAUT3 constructs influencing the adoption of the e-rupee (CBDC) in India. Based on the FRM and level partitioning process, constructs are positioned across six levels. Level 1 at the bottom represents the most dependent variables, and higher levels represent progressively more influential drivers.

Figure 2.

ISM Hierarchical Model for CBDC Adoption (e-Rupee).

At the base of the model (level 1) lies HM. This indicates that it is the most outcome-oriented construct—shaped by other variables but not influencing any. Above it, BI appears at level 2. This suggests that it is influenced by multiple constructs such as PE, PV, and EE.

PV and SI occupy level 3, reflecting its role as a mediator influenced by constructs such as PE and EE while also affecting BI. PE and EE are located at level 4, highlighting their dual role as influencers of value perceptions and user intentions.

At the top of the hierarchy (level 5) is FC, the most dominant driving construct. It affects almost all other variables but is not influenced by any, emphasizing its foundational importance in ensuring the infrastructure, accessibility, and support needed for the adoption of the e-rupee.

This hierarchical structure demonstrates how foundational elements such as digital infrastructure and perceived ease of use cascade through value perceptions and social context to influence end-user motivation and BI. It provides a clear pathway for policymakers and system designers to prioritize intervention areas that will have the most impact on adoption behavior.

MICMAC Analysis Diagram

The MICMAC analysis categorizes UTAUT3 constructs based on their driving power and dependence to understand their influence on e-rupee adoption (Figure 3).

Figure 3.

MICMAC Analysis Diagram for CBDC Adoption.

FCs emerged as the most powerful independent driver, influencing all other constructs with minimal dependence. PE, EE, and SI fall into the linkage category, showing both high influence and high dependence, indicating their dynamic and central role. BI and HM are highly dependent variables, positioned as outcomes in the adoption process. This classification supports the prioritization of foundational enablers such as infrastructure and perceived usefulness to enhance CBDC adoption.

Fuzzy MICMAC for Enhanced Construct Classification

To enhance the robustness of the ISM model and better reflect the intensity of interrelationships among factors, a fuzzy MICMAC analysis was conducted based on relationships derived from literature. This extension builds upon the hierarchical structure already developed using ISM and MICMAC. Fuzzy logic allows assigning degrees of influence, thereby providing richer insights into the driving and dependence power of variables. This approach allows for graded relationships using scores between 0.0 and 1.0 (Table 9), offering a more realistic modeling of complex socio-technical adoption factors.

Table 9.

The Fuzzy Scoring Scale.

Linguistic Term	Fuzzy Score Range
No influence	0.0
Low influence	0.1–0.3
Moderate influence	0.4–0.6
Strong influence	0.7–0.9
Very strong	1.0

Source: Adapted from Zeng et al. (2025); Wu and Lee (2007).

Given the conceptual nature of this study, and in the absence of expert interviews or empirical data collection, fuzzy values were assigned based on a simple review of the existing empirical literature on UTAUT3 and digital financial technology adoption. Relationships among constructs were evaluated by analyzing 0the frequency, direction, and consistency of findings in prior studies.

Facilitating Conditions → Performance Expectancy (Fuzzy Score: 0.8): Numerous studies in digital finance adoption emphasize that FCs—such as infrastructure readiness, mobile access, and digital literacy support—significantly improve users’ expectations of system performance. For instance, Bere et al. (2024) showed that well-established infrastructure directly enhanced the perceived usefulness of Indonesia’s CBDC (digital rupiah). Similarly, Kamajaya and Mimba (2024) found that technical and institutional support among MSMEs led to higher expectations of system efficiency. This recurring evidence supports a fuzzy score of 0.8 (Table 10).

Performance Expectancy → Behavioral Intention (Fuzzy Score: 0.9): PE consistently appears as the most influential construct across UTAUT3-based digital adoption research. Kaur et al. (2024) identified it as the dominant driver of e-rupee adoption in India, linking PE to perceptions of time savings and operational efficiency. Desai (2024) also highlighted the impact of performance benefits such as transparency and speed on BI, justifying a high fuzzy influence score of 0.9 (Table 10).

Table 10.

Fuzzy Influence Score.

Influencing Factor	Influenced Factor	Fuzzy Influence Score	Supporting Literature
Facilitating conditions	Performance expectancy	0.8	(Bere et al., 2024; Kamajaya & Mimba, 2024)
Performance expectancy	Behavioral intention	0.9	(Desai, 2024; Kaur et al., 2024)
Social influence	Behavioral intention	0.6	(Kaur et al., 2024; Ong et al., 2023)
Effort expectancy	Behavioral intention	0.5	(Bere et al., 2024)
Habit	Use behavior	0.7	(Bere et al., 2024)
Trust	Behavioral intention	0.9	(Desai, 2024; Kaur et al., 2024)
Price value	Behavioral intention	0.3	(Kamajaya & Mimba, 2024)

Social Influence → Behavioral Intention (Fuzzy Score: 0.6): Social norms and peer pressure have a moderate but relevant role in CBDC and digital payment adoption, especially in collectivist cultures. Ong et al. (2023) found that users’ intentions were shaped by family and peer endorsements in Malaysia’s QR-payment landscape. Similarly, Kaur et al. (2024) reported that public and social cues influenced adoption of India’s e-rupee to a measurable degree, supporting a score of 0.6 (Table 10).

Effort Expectancy → Behavioral Intention (Fuzzy Score: 0.5): EE, or perceived ease of use, was found to influence BI moderately, especially for users with lower digital familiarity. Bere et al. (2024) confirmed this in the context of the digital rupiah, noting that users who found the platform easier to use were more likely to adopt it. However, EE was less influential than PE or trust, leading to a moderate fuzzy score of 0.5 (Table 10).

Habit → Use Behavior (Fuzzy Score: 0.7): The habitual use of related technologies (e.g., mobile payments) has been shown to increase the actual usage of new platforms. Bere et al. (2024) observed that users already familiar with fintech tools were more likely to adopt CBDC, making habit a strong predictor of use behavior, meriting a fuzzy score of 0.7 (Table 10).

Trust → Behavioral Intention (Fuzzy Score: 0.9): Trust in the institution issuing the CBDC, as well as trust in technology and privacy protections, plays a critical role in shaping BI. Desai (2024) found that lack of trust was a key deterrent for users considering digital financial services. Kaur et al. (2024) reinforced this, showing that institutional trust in the RBI significantly shaped user intention. A fuzzy score of 0.9 is appropriate here (Table 10).

Price Value → Behavioral Intention (Fuzzy Score: 0.3): PV, representing perceived cost-benefit, had a limited effect on BI in many studies. Kamajaya and Mimba (2024) noted that where digital payment systems were free or subsidized, the perception of cost had little bearing on the adoption decision. Thus, a low fuzzy score of 0.3 was assigned (Table 10).

Integrating fuzzy scores derived from literature into the reachability matrix allows for a more granular mapping of UTAUT3 constructs. Constructs such as FCs, PE, and trust emerged as strong drivers, while BI and PV were more dependent. This improved modeling depth not only addresses reviewer suggestions for analytical enhancement but also provides a more realistic reflection of the complex socio-technical environment surrounding CBDC adoption.

Findings and Conclusion

This study used the UTAUT3 framework and ISM to study the contextual connections between important components affecting the adoption of CBDC, particularly the e-rupee in India. A hierarchical structure that gives priority to the relative relevance of adoption determinants was revealed by the study through the integration of literature synthesis and structured modeling.

The findings indicate that FCs are the most influential driver in CBDC adoption. It has an important role in digital infrastructure, regulatory support, and user readiness in fostering public confidence and technological acceptance (Mahesh et al., 2024; Nguyen et al., 2020). PE and EE emerged as mid-level influencers, suggesting that users’ perceptions of usefulness and ease of use significantly shape their intent to adopt the e-rupee (Meiranto et al., 2024; Putranta et al., 2020).

SI also played a notable role in shaping value perceptions, aligning with previous research that emphasizes the impact of peer recommendations and cultural context in technology adoption, particularly in collectivist societies like India (Salsabila et al., 2024; Widyastuti & Layaman, 2023). On the dependent side, BI and HM were identified as outcome constructs. This indicated that emotional appeal and willingness to use the e-rupee are shaped by upstream perceptions of value, effort, and trust.

These findings are consistent with previous ISM-based technology adoption studies in digital finance and banking contexts (Albugami, 2022; Moorthy et al., 2019). It reinforces the applicability of structured modeling in digital currency policy design. The fuzzy MICMAC analysis revealed that FCs, PE, and trust are the strongest driving constructs influencing CBDC adoption. Conversely, BI and PV emerged as highly dependent factors in the adoption framework. Integrating fuzzy logic into the ISM model enhanced the conceptual clarity of inter-construct relationships and highlighted the layered influence structure driving e-rupee adoption in India.

Implications and Future Scope

The findings of this study can help policymakers, banks, and technology developers focus on the most important factors—such as infrastructure and ease of use—to increase public adoption of the e-rupee. The ISM model provides a structured roadmap to design targeted awareness, usability, and trust-building strategies. Future research can build on this by using real user data and advanced methods like structural equation modeling (SEM) to validate and strengthen these insights. This can further support the development of more inclusive and effective digital currency systems in India and beyond.

This study has limitations as it relies exclusively on literature-based reasoning without empirical confirmation, and the ISM technique finds only the presence of correlations rather than their strength. Future research can build on this model by incorporating empirical validation using statistical techniques such as SEM or PLS, or refining influence scores using fuzzy Delphi methods or expert consensus to strengthen generalizability and policy relevance. Empirical validation through large-scale surveys and SEM would also strengthen the generalizability and predictive accuracy of the proposed hierarchy.

Footnotes

Acknowledgements

The authors express sincere gratitude to academic experts and industry professionals for their invaluable insights and feedback throughout the development of this research.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval and Informed Consent Statements

This article does not contain any studies involving human participants or animals performed by the authors. Informed consent was not required.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

References

Albugami

M. A.

(2022). Developing a hierarchical model for the drivers of digital banking—an interpretive structural modelling approach. Zagreb International Review of Economics and Business, 25(2), 85–96. https://doi.org/10.2478/zireb-2022-0016

Ali

, & Warraich

N. F.

(2023). Use and acceptance of technology with academic and digital libraries context: A meta-analysis of UTAUT model and future direction. Journal of Librarianship and Information Science, 56(4), 965–977. https://doi.org/10.1177/09610006231179716

Alzubaidi

, Slade

E. L.

, & Dwivedi

Y. K.

(2020). Examining antecedents of consumers’ pro-environmental behaviours: TPB extended with materialism and innovativeness. Journal of Business Research, 122, 685–699. https://doi.org/10.1016/j.jbusres.2020.01.017

Alzyoud

, Alshurafat

, & Khatatbeh

I. N.

(2024). Understanding cryptocurrency investment behaviour in Jordan: An examination of motivational drivers through the lens of the UTAUT2 model. Studies in Economics and Finance. https://doi.org/10.1108/sef-02-2024-0088

Amany

I. A. D.

, & Indrayani

(2024). Exploring mobile banking adoption in Indonesia using UTAUT2: A consumer perspective approach. Journal of Economics Finance and Management Studies, 07(07). https://doi.org/10.47191/jefms/v7-i7-78

Amnas

M. B.

, Selvam

, Raja

, Santhoshkumar

, & Parayitam

(2023). Understanding the determinants of FinTech adoption: Integrating UTAUT2 with trust theoretic model. Journal of Risk and Financial Management, 16(12), 505. https://doi.org/10.3390/jrfm16120505

Arisona

, Rofianto

, & Putriya

A. R.

(2023). UTAUT 2 (unified theory of acceptance and use of technology-2) and trust integration model towards behavioral intention to continue, willingness to recommend, and level of use. Studies And Scientific Researches: Economics Edition, 38. https://doi.org/10.29358/sceco.v0i38.558

Batra

, Aggarwal

, Dhingra

, Yadav

, & Kumar

(2024). Central bank digital currency: A bibliometric and content analysis [Conference session, pp. 1549–1554]. 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent, Systems (ICETSIS), Manama, Bahrain. https://doi.org/10.1109/icetsis61505.2024.10459631

Bere

A. B. M.

, Putra

R. W.

, & Wedari

L. K.

(2024). Investigation of digital rupiah acceptance using UTAUT-3 model. Indonesian Journal of Electrical Engineering and Computer Science, 35(3), 1710. https://doi.org/10.11591/ijeecs.v35.i3.pp1710-1721

10.

Boison

D. K.

, Malcalm

, Antwi-Boampong

, Doumbia

N. M. O.

, & Hiran

K. K.

(2022). Assessing factors affecting the blockchain adoption in public procurement delivery in Ghana. International Journal of Ambient Computing and Intelligence, 13(1), 1–13. https://doi.org/10.4018/ijaci.314568

11.

Chauhan

, Yadav

, & Choudhary

(2021). Adoption of electronic banking services in India: An extension of UTAUT2 model. Journal of Financial Services Marketing, 27(1), 27–40. https://doi.org/10.1057/s41264-021-00095-z

12.

Chikobava

(2023). CBDC - Digital Currency of Central Banks: Advantages and Disadvantages. 5th National Scientific Conference of Georgian Economists Positioning of Georgian economy in the conditions of confrontational globalization, Grigol Robakidze University.

13.

Corbet

, Cumming

, Glatzer

, & Johan

(2024). Understanding the rapid development of CBDC in emerging economies. Finance Research Letters, 70, 106226. https://doi.org/10.1016/j.frl.2024.106226

14.

Das

, Kumar

M. S. C. G.

, Kumar

, & Kulkarni

(2023). Central bank digital currency (CBDC) and application development. International Journal for Research in Applied Science and Engineering Technology, 11(12), 1658–1663. https://doi.org/10.22214/ijraset.2023.57703

15.

Desai

(2024). Analysing consumer switching behaviour and financial antecedents to determine the intention to use central bank digital currency: Evidence from the Indian economy. Asia Pacific Journal of Marketing and Logistics. https://doi.org/10.1108/apjml-05-2024-0669

16.

Dhingra

, & Gupta

(2020). Behavioural intention to use mobile banking. International Journal of Mobile Human Computer Interaction, 12(3), 1–20. https://doi.org/10.4018/ijmhci.2020070101

17.

Escobar-Rodríguez

, Carvajal-Trujillo

, & Monge-Lozano

(2014). Factors that influence the perceived advantages and relevance of Facebook as a learning tool: An extension of the UTAUT. Australasian Journal of Educational Technology, 30(2). https://doi.org/10.14742/ajet.585

18.

Gupta

, Pandey

D. K.

, Ammari

A. E.

, & Sahu

G. P.

(2023). Do perceived risks and benefits impact trust and willingness to adopt CBDCs? Research in International Business and Finance, 66, 101993. https://doi.org/10.1016/j.ribaf.2023.101993

19.

Gupta

, Taneja

, Sharma

, Singh

, Rupeika-Apoga

, & Jangir

(2023). Does previous experience with the unified payments interface (UPI) affect the usage of central bank digital currency (CBDC)? Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286

20.

Indah

D. R.

, Putra

A. P.

, & Firdaus

M. A.

(2023). Analysis of user acceptance using UTAUT2 model in KAI access application. Jurnal Teknologi Informasi Dan Pendidikan [Journal of Information Technology and Education], 15(2), 85–95. https://doi.org/10.24036/jtip.v15i2.553

21.

Jena

R. K.

(2022). Examining the factors affecting the adoption of blockchain technology in the banking sector: An extended UTAUT model. International Journal of Financial Studies, 10(4), 90. https://doi.org/10.3390/ijfs10040090

22.

Kamajaya

C. I. S.

., & Mimba

N. P. S. H.

(2024). Adoption of financial technology payments by MSMEs in Denpasar City: An analysis from the UTAUT2 perspective. E-Jurnal Akuntansi [E-Journal Accountancy], 34(10). https://doi.org/10.24843/eja.2024.v34.i10.p19

23.

Kardoyo

, Nurkhin

, Kusumantoro

, Mukhibad

, & Utami

(2022). An analysis of student’s behavioral intention to use digital wallet using UTAUT model [Conference session]. Proceedings of the 1st International Conference on Economic and Education, ICON 2021, 14–15 December 2021, Padang-West Sumatra, Indonesia. https://doi.org/10.4108/eai.14-12-2021.2318330

24.

Kaur

, Kaur

, Mago

, Singh

, & Mehta

(2024). Exploring factors affecting central bank digital currency adoption: a perspective from generation Z. Metamorphosis. https://doi.org/10.1177/09726225241286932

25.

Killian

, Kabanda

, & Kilian

(2017, July 16–20). Mobile payments in South Africa: Middle income earners’ perspective [Conference session]. PACIS Conference Proceedings. Langkawi Island, Malaysia https://aisel.aisnet.org/pacis2017/53

26.

Krishnamoorthy

, & Aggarwal

(2024). Digital rupee for retail adoption and challenges [Conference session, pp. 171–175]. ICSCC: 10th International Conference on Smart Computing and Communication. https://doi.org/10.1109/icscc62041.2024.10690822

27.

Kumar

, & Gupta

L. S.

(2024). Revolutionizing payment ease: Unleashing the power of Central Bank Digital currency (CBDC) in India. International Journal of Innovative Research in Engineering, 133–139. https://doi.org/10.59256/ijire.20240503016

28.

Leong

, Kwan

, & Lai

(2020). Integrating TRI and UTAUT in E-wallet adoption: A validity and reliability study. In Ahmad

(Ed.), Progressing beyond and better: Leading businesses for a sustainable future: Vol 88. European proceedings of social and behavioural sciences (pp. 1068-1077). European Publisher. https://doi.org/10.15405/epsbs.2020.10.100

29.

Maharjan

, & Kc

(2023). Using UTAUT2 to examine mobile payment usage in Kirtipur. Shahid Kirti Multidisciplinary Journal, 135–154. https://doi.org/10.3126/skmj.v1i1.60508

30.

Mahesh

K. M.

, Aithal

P. S.

, & Sharma

K. R. S.

(2024). Impact of centralized blockchain digital currency (CBDC): For financial inclusion and sustainability. International Journal of Management, Technology, and Social, Sciences (IJMTS), 9(2), 156–172. https://doi.org/10.5281/zenodo.11380056

31.

Meiranto

, Faisal

, & Yuyetta

E. N. A.

(2024). The mediating role of effort expectation on digital banking behavior intention in the Indonesian bank industry: An integration of UGT-UTAUT. International Journal of Data and Network Science, 8(4), 2547–2562. https://doi.org/10.5267/j.ijdns.2024.5.003

32.

Moorthy

, T’ing

L. C.

, Yee

K. C.

, Huey

A. W.

, In

L. J.

, Feng

P. C.

, & Yi

T. J.

(2019). What drives the adoption of mobile payment? A Malaysian perspective. International Journal of Finance & Economics, 25(3), 349–364. https://doi.org/10.1002/ijfe.1756

33.

Nguyen

T. T.

, Nguyen

H. T.

, Mai

H. T.

, & Tran

T. T. M.

(2020). Determinants of digital banking services in Vietnam: Applying UTAUT2 model. Asian Economic and Financial Review, 10(6), 680–697. https://doi.org/10.18488/journal.aefr.2020.106.680.697

34.

Ong

M. H. A.

, Yusri

M. Y.

, & Ibrahim

N. S.

(2023). Use and behavioural intention using digital payment systems among rural residents: Extending the UTAUT-2 model. Technology in Society, 74, 102305. https://doi.org/10.1016/j.techsoc.2023.102305

35.

Putranta

Y. E.

, Alamsyah

, Tan

, & Tamara

(2020). The determinant factors of mobile payment adoption. Eurasian Journal of Social Sciences, 8(3), 134–147. https://doi.org/10.15604/ejss.2020.08.03.005

36.

Raihan

(2019). Analyzing factors influencing continuance intention of e-wallet adoption using UTAUT 2 model (A case study of Dana in Indonesia). e-Proceeding of Management, 6(2), 3717–3724.

37.

Raju

A. B. A.

(2021). Digital currency issuance by central Banks: CBDCs and monetary policy. International Journal of Science and Research, 10(11), 1587–1589. https://doi.org/10.21275/sr24914160743

38.

Razi-Ur-Rahim

, Rabbani

M. R.

, Uddin

, & Shaikh

Z. H.

(2024). Adoption of UPI among Indian users: Using extended meta-UTAUT model. Digital Business, 4(2), 100093. https://doi.org/10.1016/j.digbus.2024.100093

39.

Salsabila

Z. G.

, Kirani

B. A.

, & Sari

(2024). Analysis of factors influencing interest in the use of digital banks using UTAUT 2 model approach [Conference session, pp. 201–206]. ICIMTech: 2022 International Conference on Information Management and Technology. https://doi.org/10.1109/icimtech63123.2024.10780862

40.

Sandhu

, Dayanandan

, & Kuntluru

(2023). India’s CBDC for digital public infrastructure. Economics Letters, 231, 111302. https://doi.org/10.1016/j.econlet.2023.111302

41.

Sari

A. P.

, Sukardi

, & Abadi

M. K. R.

(2024). Adoption of user satisfaction with the UTAUT2 model in using Indonesia sharia mobile banking. FINANSIA: Jurnal Akuntansi Dan Perbankan Syariah [Journal of Accountancy and Perbanking Syariah], 7(1), 21–40. https://doi.org/10.32332/finansia.v7i1.8165

42.

Surgentova

(2023). Adoption and implications of central Bank Digital Currencies (CBDCs) and cryptocurrencies. ICRRD Quality Index Research Journal, 4(3). https://doi.org/10.53272/icrrd.v4i3.1

43.

Tajul Urus

, Kurniasari

, Syed Mustapha Nazri

S. N. F.

, Utomo

, Othman

I. W.

, Jimmy

S. Y.

, & Abd Hamid

(2022). A comparative study of fintech payment services adoption among Malaysian and Indonesian fresh graduates: Through the lens of UTAUT theory. Eastern-European Journal of Enterprise Technologies, 5(13(119)), 73–88. https://doi.org/10.15587/1729-4061.2022.265662

44.

Tekdoğan

Ö. F.

, & Güney

(2024). Central bank digital currencies (CBDCs): An evaluation from Islamic law and Islamic economics perspectives. International Journal of Islamic Economics and Finance Studies. https://doi.org/10.54427/ijisef.1455932

45.

Utami

R. A.

, & Irwansyah

(2022). Faktor-Faktor Yang Mempengaruhi Penggunaan Aplikasi E-Wallet Dana Di Kota Samarinda. Jurnal Bisnis Dan Pembangunan, 11(2), 60. https://doi.org/10.20527/jbp.v11i2.14994

46.

Venkatesh

, Morris

M. G.

, Davis

G. B.

, & Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

47.

Warfield

J. N.

(1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems Man and Cybernetics, SMC-4(1), 81–87. https://doi.org/10.1109/tsmc.1974.5408524

48.

Widayani

(2022). Adoption of mobile payment approach extended the UTAUT2. RELEVANCE Journal of Management and Business, 4(2), 156–175. https://doi.org/10.22515/relevance.v4i2.4019

49.

Widyastuti

, & Layaman

(2023). Millennial generation’s intention to use E-wallet through the UTAUT2 model. Al-Amwal Jurnal Ekonomi Dan Perbankan Syari Ah, 15(2), 244. https://doi.org/10.24235/amwal.v15i2.11941

50.

W. W.

, & Lee

Y. T.

(2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507. https://doi.org/10.1016/j.eswa.2005.12.005

51.

Xie

, Ye

, Huang

, & Ye

(2021). Understanding FinTech platform adoption: Impacts of perceived value and perceived risk. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1893–1911. https://doi.org/10.3390/jtaer16050106

52.

Zainordin

N. A.

, Engku Ali

E. R. A.

, & Haron

(2022). Factors influencing behavioral intention to participate in family takaful in Malaysia: An extension of UTAUT2 theory. Journal of Islamic Finance, 11(2), 68–83. https://doi.org/10.31436/jif.v11i2.691

53.

Zarlis

(2024). Determinants of autonomous vehicle adoption intention in Jakarta: Extending the UTAUT model. Journal of Logistics, Informatics and Service Science. https://doi.org/10.33168/jliss.2024.1024

54.

Zeng

, Li

, & Tho

S. W.

(2025). The application of fuzzy Delphi method for the development of STEM teaching model. International Journal of Evaluation and Research in Education (IJERE), 14(3), 2061. https://doi.org/10.11591/ijere.v14i3.29780

Adoption of Central Bank Digital Currency in India: A Structural Model Using ISM

Abstract

Purpose:

Design/Methodology/Approach:

Findings:

Practical Implications:

Originality/Value:

Keywords

Introduction

Literature Review

Central Bank Digital Currencies

Digital Currency Adoption

Applications of UTAUT3 in Digital Technology Adoption

UTAUT3 Model.

Research Objectives

Research Methodology

Interpretive Structural Modelling

Structural Self-Interaction Matrix

Initial Reachability Matrix

Final Reachability Matrix with Transitivity

Level Partitioning Process

ISM Diagram

ISM Hierarchical Model for CBDC Adoption (e-Rupee).

MICMAC Analysis Diagram

MICMAC Analysis Diagram for CBDC Adoption.

Fuzzy MICMAC for Enhanced Construct Classification

Findings and Conclusion

Implications and Future Scope

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Ethical Approval and Informed Consent Statements

Funding

References