The digitalisation of banking services has been undergoing a metamorphosis. The recent development in the digitalisation is the proposal to introduce Central Bank Digital Currency or e-currency. Since 2013 there has been continuous research carried in the feasibility of implementing CBDC. The studies could come out with major advantages of CBDC implementation. However, a few countries have withdrawn their proposal to implement CBDC. Though the overall benefits of applying CBDC is common to almost all the countries the difficulties in the implantation differs between the developed and developing countries (Piero Cipollone, 2022).

In the context of developing countries like, the major problem is the financial inclusion. Also, the acceptance of such new technology or the intention to adopt the technology depends on the level of awareness, the perceived benefits from the use of technology, the trust of the public on the agency providing such technology and the problems associated with the use of the technology. In the Indian context, with more than 20 per cent of the population are not having a bank account, creating awareness on the (Leora Klapper et.al.2021). hence, in the context India, the feasibility in the implementation of CBDC depends on the intention to use, the trust, the perceived benefits, awareness and the problems in the usage. In the present paper it is attempted to examine the views of the selected respondents in district of Coimbatore. 

Since banks are essential in people's daily lives, technology adoption in the banking business is making strides toward rapid advancement (Ashworth and Goodhart, 2020). Such technology-based digital metamorphosis is occurring not only in a selected countries but is unleashed to the world, India being no exception.

The digital economy has given birth to digital money called Central Bank Digital Currency (CBDC). In contrast to other digital innovations in payment systems, CBDC is the officially sanctioned concept of digital innovations in payments initiated and managed by the central bank.

Globally, central banks have been developing CBDCs with the intention of replacing or supplementing fiat currency. The introduction of Central Bank Digital Currency (CBDC) represents a landmark event in financial service history that acts as a gateway to more sophisticated financial services, while at the same time increasing the efficiency, security, and accessibility of financial services provided by the banks to the public. More importantly, it promotes financial inclusion in the country and reduces cash circulation costs.

In consideration of realization stated above, almost all countries of the world are now undertaking attempts to introduce a Central Bank Digital Currency. By 2023, 60 countries had either issued a CBDC or were at various stages of research and trials on it; about 20 countries completely launched CBDC. Several countries that issued CBDC are the Bahamas, Jamaica, Anguilla, Nigeria, and Grenada. Additionally, most of the European and Asian countries are in the development phase with regard to their own CDBCs, which include Australia, China, and Indonesia. In India, the introduction of CBDC is still in its infancy, and discussions are ongoing among technical and academic experts on the advantages and disadvantages thereof. Since the introduction of the concept for CBDC in the world, a lot of advantages and disadvantages associated with it have been researched by various scholars in the aspects of financial inclusion, its role and purpose, design, competition for commercial banks, and its contribution toward economic development and success of financial policy in adoption (Peterson K. Ozili, 2023).

The research carried out at the global context in both the developed and developing countries like India could come out with certain inferences. Bijlsma et al. (2021) viewed that the central bank of the Netherlands concluded in their survey that a better understanding of CBDCs increases the likelihood of their adoption, highlighting the importance of public awareness. The 2022 study by Abass on the public's perception of CBDCs in Ireland revealed that a lack of awareness and understanding of CBDCs are significant barriers to adoption, emphasizing the need for educational initiatives. Research by Carapella et al. (2020) showed that technological advancements have led to a growing array of electronic payment options, raising questions about how public knowledge affects CBDC acceptance.

Bijlsma et al. (2021) also noted that trust in banks and central banks significantly boosts the likelihood of adopting CBDCs, underlining the role of institutional trust. Zarifis et al. (2023) argued that building consumer trust is crucial for the successful development and acceptance of CBDCs, as trust can greatly affect usage patterns. Hossain et al. (2023) studied China's e-CNY and found that trust acts as a mediating factor in the adoption process, highlighting its importance for user acceptance. Regarding the anticipated challenges of using Central Bank Digital Currency (CBDC), Guo et al. (2022) pointed out that users' concerns about privacy and consumer protection are significant obstacles to CBDC adoption. The Federal Reserve's report (2022) noted that implementing a CBDC could raise concerns such as bank disintermediation and potential negative effects on financial stability. Additionally, the report emphasized that a CBDC could exacerbate the effective lower bound issue with nominal interest rates. On the potential benefits of CBDCs for consumers, the Federal Reserve's (2022) study suggested that CBDCs could improve welfare by reducing financial frictions in deposit markets and promoting financial inclusion.

Shahzad et.al. (2018) viewed that due to the complexity in the digital currency developing countries could not adopt CBDC and a lion’s share of the people in these developed countries do not possess financial literacy and awareness. The data base of Global Findex (2021) pointed out that 22 per cent of adults in India do not have a bank account. The earlier report by Global findex (2008) pointed out that 61 per cent of rural adults and 40 per cent of urban adults do not have bank accounts. Also, the All-India Debt and Investment Survey (AIDIS) data indicated that with regard to Indian women there is no much difference between the rural women and urban women in accessing a bank account as 80.7% of rural women and 81.3% of urban women had deposit accounts. This indicates that nearly one-fifths of the women in both rural and urban areas do not have a bank account. These data highlight the difficulty in financial inclusion in India which helps to understand the influence of gender, location, and socioeconomic status on the access to banking.

From the above studies it could be concluded that the adoption intention on CBDC depends on the trust, the perceived benefits from CBDC and the expected problems.   However, it is the awareness level that influences the knowledge on the perceived benefits and the trust on the CBDC agency.

Thus, under the latent construct, Awareness, Trust, and Expected Problems are categorised as independent variables (exogenous constructs) that influence the perception of CBDCs. The Perceived Benefits acts as a mediating variable, influenced by awareness, trust, and expected problems.Adoption Intent is the dependent variable (endogenous construct), affected by perceived benefits and other factors. The Path analysis under the structural equation modelling is designed as:

• Awareness → Perceived Benefits → Adoption Intent
• Trust → Perceived Benefits → Adoption Intent
• Expected Problems (potential negative effect) → Perceived Benefits & Adoption Intent

OBJECTIVES OF THE STUDY

Based on the above discussion, the objectives formulated for the present study are:

1. To identify the socio-economic conditions of the sample respondents
2. To trace out the level of awareness on CBDC
3. To identify the factors determining the adoption and intention to use CBDC

METHODOLOGY AND RESEARCH DESIGN

A direct interview method was adopted to collect data from 300 respondents across the district of Coimbatore.

SOURCE OF DATA

A total of 300 sample respondents were selected by the method of convenience. The samples include the financial analysts, bank employees who are branch managers, educationists, stock market practitioners. In the course of data collection, it could be identified that even among the educated, a higher share of the public were not aware of even what is CBDC. The share was found to be higher among female than their counterparts. Hence, those respondents who were not aware of CBDC were ignored and replace. The sample selection is constrained or limited by the availability or the interest of the respondents in responding to the questions by the researcher.

DATA COLLECTION

A pre tested questionnaire was used which included the basic information on their socio-economic conditions and questions relating to awareness, benefits, intention of usage, trust and problems in the use of CBDC. Under these five constructs 19 factors were identified from the extensive literature survey.Likert scale was used as the measurement tool.

TOOLS USED

Apart from simple percentage method, Exploratory Factor Analysis (EFA) was used to identify the key latent variables influencing CBDC adoption. Confirmatory Factor Analysis (CFA) was used to validate these constructs.

As provided in Table 1, among the sample respondents, 87 per cent are male and the remaining 13 per cent are females. A highest share of the sample respondents lives in urban areas. As high as 41 per cent of the sample respondents are in the age category of 45-55 years. The respondents who have General Degree like, graduation or post-graduation in arts, science, commerce formed the highest share with 40.33 per cent. Among the sample respondents, the bank managers identified from the various branches of public and private sector banks formed the highest with 32.33 per cent.

As it could be seen in the table 2, the highest share of the respondents highly agreed to the following views: "I am aware of Digital Rupee (CBDC) " (23.00 per cent), "I am very much familiar with the concept of CBDC? " (24.67 per cent), " Social Media is the major source of awareness on CBDC?" (22.67 per cent), and " I am highly aware of the difference between CBDC and UPI payments?" (23.00 per cent). In the case of the following factors, a majority moderately agreed to it. These include: "Digital Payments are More Secured in CBDC" (60.67 per cent), "CBDC will improve financial inclusion in rural areas" (59.00 per cent), "Dependency on physical cash is reduced due to the introduction of CBDC" (60.33 per cent), "The cross-border transaction would be made easier due to the introduction of CBDC" (58.67 per cent), "I trust RBI in managing CBDC effectively, if introduced " (50.33 per cent), " CBDC would help to maintain privacy and security" (49.00 per cent), "CBDC is more safer than all other methods of digital transactions" (50.67 per cent), "If CBDC is launched, I will continue to use " (54.00 per cent), "My usage would depend on the usage of my friends and families" (49.67 per cent), "I would prefer using CBDC over cash or any other method of transactions " (52.00 per cent), and " I would recommend CBDC to friends and family " (50.67 per cent), .

TABLE:2 FACTOR WISE OPINION ON CBDC HA-Highly Agree MA- Moderately Agree   N-Neutral   MD-Moderately Disagree HD-Highly Disagree

The Cronbach’s Alpha coefficient, an indicator of the reliability or the consistency of the data. The Cronbach alpha coefficient is estimated as 0.854. The estimated Cronbach’s Alpha coefficient it is higher than the minimum desirable value of threshold value of 0.70 indicating the consistency of the data.   An item wise alpha coefficient provided in table 3 also indicate that they are well above the value of 0.70. Hence each variable is consistent. 

The Kaiser Meyer – Oklin (KMO) measure of sample adequacy shows that the value is 0.887. this estimate suggests the adequacy of the data as it is higher than the threshold value of 0.80. The significant value of Bartlett's Test of Sphericity, implies the significant correlation among the variables included. An attempt made to understand the major factors that could be extracted from the 19 variables, as seen in Table 5, from the total variance explained, the component matrix it could be identified that the 19 variables have extracted into five components. The rotation indicated that the five extracted components could explain almost 91 per cent, as it is also seen in the table, the first component alone could explain more than 20 per cent of the variance.

The rotated component matrix, called as the factor loadings, shows the correlations between each of the variables and the estimated components. It can be seen that the first component has higher and almost equal correlation value with all the factors under dimension on Awareness. Interestingly none of the factors have registered value below 0.90 which implies that all the variables are highly relevant.

The regression model run in SPSS AMOS indicate that among the latent variable, the influence of Trust on Adoption intention is significant. A factor wise examination on the influence would show that all the factors under awareness, perceived benefits and trust are significantly influencing their related variables. This implies that in the context of implementation of CBDC, the respondents viewed that awareness, benefits from CBDC, the trust on the implementing agency are the major determinants. In the case of expected problems, the hidden fees and the internet availability in rural areas are the factors determining CBDC implementation. 

Given the discussion on the regression coefficients, the model summary indicate that the model is best fit in examining the impact of the latent variables on the CBDC implementation.

Thus, the included variable is all found to be relevant in explaining the opinion on the implementation aspect of CBDC. 

Given the influence of the socio-economic factors on the opinion on the CBDC implementation, it is also attempted to examine the differences in the opinion on the 19 variables by gender and by rural urban residence. To do this the discriminant analysis has been used.

As provided in Table 9, in the case of rural urban residence, the test of equality indicate that all the variables are significant and hence, the group means are significantly different. 

Thesmaller the Wilks's lambda, the more important is the independent variable to the discriminant function. Since the Wilks's lambda are less for all the variables, these variables are important in explaining the discriminant function. Among the five variables, awareness, trust and adoption are the variables of higher discrimination as these variables carry a smaller wilks’ lambda.  The significant value of Box M indicate that the data are not normally distributed. The higher eigen value of 2.49 indicate the greater power of discrimination. A higher value of canonical correlation indicates the higher discriminating ability between the two groups namely rural and urban. The canonical function coefficient is the discriminatory coefficient and higher value of awareness of 3.292 indicates the highest level of discrimination between rural and urban on the awareness on CBDC implementation. Similarly, the next highest discriminating coefficient is Trust. 

As provided in Table 10, in the case of gender difference, the test of equality indicate that all the variables are significant and hence, the group means are significantly different.  However, the Wilks's lambda values are found higher indicating the less importance of the independent variable in the discriminant function.  The significant value of Box M indicate that the data are not normally distributed.  The higher eigen value of 2.49 indicate the greater power of discrimination. A higher value of canonical correlation indicates the higher discriminating ability between the two groups namely male and female. The canonical function coefficient is the discriminatory coefficient and higher value of awareness of 2.592 indicates the highest level of discrimination between male and female on CBDC implementation. Similarly, the next highest discriminating coefficient is Trust.  The other factors indicate the positive discrimination.

CBDCs hold potential for financial transformation in India. The Governments should focus on awareness programmes and regulatory frameworks to enhance trust. These would help to ease implementation process of CBDC adoption.

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