Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 239 https://internationalpubls.com A study on Socio-Demographic perspective of Digital Financial Inclusion in Rural Odisha Nirmala Chandra Pattnayak1, Dr. Rashmita Sahoo2 1Research Scholar, Department of Business Administration, Utkal University 2Asst. Professor, Department of Business Administration, Utkal University Article History: Received: 01-06-2024 Revised: 03-07-2024 Accepted: 29-07-2024 Abstract: Digital financial inclusion carries transformative potential for unserved and under-served population as well as for small and micro businesses. The digital pathways to financial transactions are often seen challenged by various social, economic and demographic factors that pull down the desired penetration of financial services. This study aimed at analysing the impact of social and demographic variables of the customers on different aspects associated with digital financial services namely awareness, comfort, availability, present use and frequency of usage of the rural customers in the state of Odisha. The study was conducted based on primary data collected from eight districts of the state. Six socio-demographic factors viz. age, gender, education, social stratification, annual family income and occupation have been taken up for the study. The study revealed that age, education and annual income are important factors significantly related with all aspects of digital financial services and the other three factors were relatively weaker. Through the multiple regression analysis, an attempt was made to determine relative importance of these three factors. Keywords: digital financial inclusion, demographic factors, formal financial sector, aspects of digital financial services, multiple regression 1. Introduction: Universal Financial Inclusion has always been a national commitment and public policy priority in India (Chakrabarty 2011), aiming to ensure that all Individuals and businesses get access to formal financial services. The financial inclusion is regarded as a powerful and significant socio-economic tool that serves as catalyst for economic empowerment and poverty reduction. It brings in the hitherto underprivileged and unbanked population to the mainstream formal finances. In this context the digital financial inclusion has garnered much attention in India and also in other parts of the world, especially in last few years. Digital financial inclusion involves offering digital financial services to the financially excluded and underserved populations and using a mobile phone or other digital devices to increase access to digital financial services (Ozili, 2018). Digital financial inclusion involves providing access to affordable formal financial services to the excluded population using existing digital technologies (Ozili, 2021). Digital financial inclusion revolves around the objective of ensuring that all individuals and businesses, especially those in underserved or marginalised communities, get access to range of quality financial services by leveraging digital technology to provide affordable, convenient, transparent, safe and secure financial services and economic empowerment of people. https://www.elibrary.imf.org/display/book/9798400223525/CH007.xml#CH007ref05 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 240 https://internationalpubls.com Financial inclusion in India has been witnessing transformational shift especially in last decade. The initiative saw a paradigm shift since 2014 with the introduction of JAM Trinity encompassing Jan Dhan Yojana (PMJDY) for universal banking access for all, Aadhaar for unique biometric identification, and mobile services for direct transfers. In order to bring in the unbanked population which were lying at bottom of the social and economic pyramid into the mainstream economic system and formal financial sector, the Digital India campaign launched in 2015 provided a catalysing effect. The robust digital payment infrastructure which was put in place aided an ecosystem which attracted people from all sections of society and the businesses voluntary joining digital bandwagon. If we analyse the current trend of digital financial transactions, the retail digital payments in India have grown from 162 crore transactions in FY 2012-13 to over 14,726 crore transactions in 2023-24 (till February 2024) i.e., a staggering 90-fold increase over a period of 12 years. Today, India accounts for nearly 46% of the world’s digital transactions (as per 2022 data). The Reserve Bank's Digital Payment Index has risen four-fold during last five years proving the extraordinary growth in digital payments. The Unified Payments Interface registered a massive 11 billion transactions during a single month of October 2023, making India a global leader in digital payments. Since the launch of Pradhan Mantri Jan Dhan Yojana in 2014, 500 million bank accounts have been opened providing a gateway to a huge population entering formal financial space. This has enabled Government to push the social benefit transfers through Direct Benefit Transfer (DBT) mode. During last year as many as 723 million beneficiaries received direct benefit transfers (DBT) in their bank accounts. The Government of India itself has transferred to the tune of ₹ 32 Lakh Crores using the DBT system, which also resulted into a financial savings of over ₹ 2.7 Lakh Crores. These staggering figures only reemphasize the transformational impact of digital financial inclusion and therefore, how imperative it is for the Governments to lay focus on it’s furtherance. The world witnessed severe stress induced by COVID 19 pandemic in the form of lock downs and restricted mobility. The pandemic, however, pushed close to 80 million adults adopting digital financial services (World Bank, 2021). Despite a significant advancement in the area of digital payments, the country continues face several hurdles in achieving universal financial inclusion. Some of these challenges are absence of adequate infrastructure, especially in rural areas, low level of digital financial awareness, low penetration of smart phones and mobile technology, lower usage of financial products, lack of confidence and trust in the digital systems, digital divide and most importantly socio-economic barriers due to which some people are still out of formal financial systems. There needs a multi-pronged approach by Government(s), regulators, financial service providers, private sector, and also community involvement to address these challenges effectively. Odisha, being one of the poorest states of the Country needs significant efforts to uplift it’s population from clutches of poverty and backwardness. The state continues to be plagued by lack of access to financial infrastructure, low digital penetrations and social and economical challenges. An attempt has been made through this study to examine various facets of impact of socio-demographic factors on aspects of digital financial inclusion in rural areas. https://www.financialexpress.com/india-news/ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 241 https://internationalpubls.com 2. Literature Review: Ali, J. and Ghildiyal, A.K.(2023) carried out a study on ‘Socio-economic characteristics, mobile phone ownership and banking behaviour of individuals as determinants of digital financial inclusion in India’ . The findings of the study revealed that there is a significant association between the socio-economic profiles of individuals with the adoption of digital financial services in terms of gender, age, education, occupation and income. The researchers have further mentioned that the marginal effect indicated that socio-economic factors, mobile phone ownership and individual’s behaviour from banking perspective towards borrowings and savings have had significant influence on digital financial inclusion. The researchers mention that factors like age, education, working status and higher income are more likely to impact adoption of digital financial services. Ljumovic, I., Jaksic, K. and Trajkovic, S. (2021) in their study titled ‘Socio-Demographic Characteristics of Digital Financial Services users: Evidence from Serbia’ examined the socio- demographic characteristics of users of digital financial services in the Republic of Serbia. The authors examined the impact of age, education, employment and income on adoption of digital financial services. The results of the study revealed that socio-demographic factors, apart from gender and income level, were statistically significant. Education also emerged as a strong factor influencing the behaviours of users towards digital financial inclusion. Vinitha, K. and Vasanta, S.(2017) in their study titled “Factors Influencing Consumer’s Intention to Adopt Digital Payment” examined various perceptions of users on adoption of digital financial services. The factors like perceived risk, perceived usefulness, perceived ease of use and trust and intention to adopt e-payment was tested by the researchers in their study. The study recommends that to achieve the desired penetration of digital financial services, there needs to be concerted efforts and intensive work to increase digital financial awareness, increase convenience and ease of use, make it easily accessible and available and strengthen the statutory framework. Kandari, P., Bahuguna, U. and Salgotra, A.K.(2021) in their study titled ‘Socio-Economic and Demographic Determinants of Financial Inclusion in Underdeveloped Regions: A Case Study in India’ examined the relationship between financial inclusion and socio-economic and demographic factors. The findings of the study revealed that significant association of socio-economic variables exist with financial inclusion. The study further reveals that financial literacy play a very important role in determining the likelihood of having bank account, usage of mobile banking facility and availing credit from formal financial sources. Further, the study also indicates the gender does play an important factor and women are found more vulnerability than men in terms of mobile usage and availing credit. Lohana, S., & Roy, D. (2023) conducted a study on ‘Impact of Demographic Factors on Consumer’s Usage of Digital Payments’. The study reveals a significant impact of age, education, occupation and income of respondents on the digital financial services usage level, however, the gender and marital status came out with no significant impact on consumers’ usage. Dwivedi, Y.K., et. al.(2016) in their study on "Common Services Centres (CSCs) as an approach to bridge the digital divide: Reflecting on challenges and obstacles" have mentioned that there are certain key issues that is creating hurdle in digital payments adoption viz. connectivity problems, lack of or delayed rollout of government to citizen (G2C) services, low computer literacy, lack of digital financial https://www.emerald.com/insight/search?q=Yogesh%20K.%20Dwivedi Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 242 https://internationalpubls.com literacy, absence of availability of facilities, convenience, poor infrastructure and lack of governmental and regulatory support. Dasgupta et.al. (2011) carried out a study on “Factors Affecting Behavioural Intentions towards Mobile Banking Usage: Empirical Evidence from India”. The study reveals that that consumer behaviour plays a significant role in determining the mobile banking usage and also usage of other digital financial products. Perceptions with regard to usability, reputation, value it would bring about, self-efficacy, convenience and credibility are critical factors which impact the use of technology. 3. Elements of Digital Financial Inclusion: According to the Committee on Medium-Term Path to Financial Inclusion (Chairman: Shri Deepak Mohanty, RBI, 2015) the financial inclusion can be envisioned as, “convenient access to a basket of basic formal financial products and services that should include savings, remittance, credit, government-supported insurance and pension products to small and marginal farmers and low income households at reasonable cost with adequate protection progressively supplemented by social cash transfers, besides increasing the access of small and marginal enterprises to formal finance with a greater reliance on technology to cut costs and improve service delivery, ….”. To take this forward, when we use digital means for these financial products and services, we call it digital financial inclusion. The three key elements of digital financial inclusion are access, usage and quality. Access signify access to physical and digital infrastructure; usage denotes level of adoption and quality indicates the purpose(s) for which it is being used. The factors that influence these elements are availability, affordability, education, utilisation and awareness etc. According to Ozili, P.(2022), the components of digital financial inclusion are: (a) Digital Devices: An instrument e.g. mobile phone, computer, laptop etc. that enables transmission of financial data electronically (b) Retail Agents: They are agents or vendors which are connected to communication networks to receive and transmit the electronic data and convert the cash to electronically stored value and vice versa. (c) Additional Financial Services: They are added financial products and services being used by customers like savings, credit, insurance, investments, mortgage and risk management products. (d) A digital transactional platform: An interface that enables customers to exchange financial data electronically (e) Customers: The customers may be individuals as well as business entities and also governments. Adoption of digital financial services are dependent upon multiple factors. As this study focuses on rural population, the social, economic and demographic factors play a major role determining the aptitude for adoption. This study, therefore, focuses on product awareness, usage convenience and service availability driving level of usage. 4. Objectives of the Study: The main objectives of the study are to find the following: Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 243 https://internationalpubls.com (i) To determine important social and demographic factors significantly influencing adoption of digital financial services (ii) To understand the strength and direction of relationship between select socio-demographic factors and various aspects of digital financial inclusion (iii) To identify the socio-demographic factors which significantly predict levels of awareness, comfort, availability, present use and frequency of use of digital financial services and to ascertain relative importance of each of the factors. (iv) To quantify strength and direction of relationships between select socio-demographic factors and certain aspects of digital financial inclusion. 5. Research Methodology: This study has been carried out based on the primary data collected through a purposive random survey conducted in rural areas across eight districts of Odisha. The survey methodology was based on a questionnaire containing two parts, the first being the demographic profiles and the second containing the banking attitude of the rural customers. A sample size of 350 has been considered for this study. In this study, six demographic variables have been considered viz. age, gender, education, occupation, social stratification and annual family income. The association of these variables on the various aspects of digital financial service adoption in rural areas was studied. 6. Data Analysis: The primary data collected from rural areas of eight districts of Odisha has been subjected for statistical analysis as indicated above with the results as follows: Table-1: Correlation of Socio-Demographic Factors with Aspects of Digital Transaction Platforms in Case of Rural Customers. Socio-Demographic Factors Awareness Comfort Availability Present Use Frequency of Use Age 0.687* 0.764* 0.634* 0.679* 0.717* Gender -0.003 0.085 0.098 0.079 0.051 Education 0.718* 0.712* 0.806* 0.765* 0.795* Social Stratification 0.075 0.080 0.019 0.083 0.016 Occupation 0.032 0.023 0.066 0.011 0.036 Annual Family Income 0.704* 0.683* 0.733* 0.716* 0.742* N.B:- * – Significant at 5% Level (P<0.05) for DF=349. Source : Survey by the Researcher From the above correlation table, the following was interpreted: The above table shows the correlation of socio-demographic factors with five aspects of digital financial services adoption. It is observed that age, education and annual family income with values of 0.687, 0.718 and 0.704 exhibited strong positive correlation with digital financial awareness. Gender, with a correlation coefficient of -0.003, indicates a negligible correlation with awareness. Social Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 244 https://internationalpubls.com stratification and occupation although showed positive correlation, the coefficients values of 0.075 and 0.032, respectively, show relatively weaker relationship. The above trend was observed for the comfort factor as well. Age(0.634), education(0.806) and Annual Family Income(0.683) exhibited strong correlation with users comfortability. However, gender, social stratification and occupation displayed a weaker correlation. W.r.t. availability of digital financial services, the factors age, education and annual family income exhibited strong correlation with coefficients of 0.634, 0.806 and 0.733 respectively. However, like for the other aspects, gender, social stratification and occupation were found to be weakly correlated with the digital financial services being available. With regard to usage parameters of present use as well as of frequency of usage, age, education and annual family income found to be strongly correlated whereas gender and social stratification even though with positive coefficient values, were found be relatively weakly correlated. Occupation with a negligible correlation coefficient of 0.011 and social stratification with a coefficient of 0.016, which are near to zero values, were found to have negligible impact on present use and frequency of use, respectively. Table-2: Multiple Regression of Socio-Demographic Factors with Awareness on Digital Transaction Platforms in Case of Rural Customers. Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) -0.051 0.009 -5.578 0.000 Age 0.072 0.007 0.377 10.171 0.000 Education 1.702 0.035 0.216 49.005 0.000 Annual Family Income 0.147 0.013 0.457 11.508 0.000 N.B:- r = 0.741, R2 = 0.549 Source : Researcher’s Survey From the above table the regression model would be: Awareness=(-)0.051+0.072×Age+1.702×Education+0.147×Annual Family Income The constant at (-)0.051 represents level of digital financial awareness when all the independent variables(Age, Education, and Annual Family Income) are zero. Practically, however, these variables are unlikely to be zero. W.r.t. age, the coefficient is 0.072, which indicates increase of digital financial awareness by approx.. 0.072 units with each additional year of age. Further, the standardized coefficient (Beta) is 0.377 suggests that age as a variable has a strong positive impact on awareness, but not as strong as annual family income. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 245 https://internationalpubls.com The coefficient for education at 1.702 indicates awareness of digital transaction platforms to increase by approximately 1.702 units for each additional unit increase in education level (e.g., from high school to college). The standardized coefficient (Beta) is 0.216, suggesting education has a positive impact on awareness but a weak variable compared to age or annual family income. W.r.t. annual family income, the coefficient at 0.147 indicates that for each additional unit increase in family income, awareness of digital financial services would increase by approximately 0.147 units. The standardized coefficient (Beta) is 0.457 indicates that the variable income has the strongest positive impact on awareness. The t-values and associated p-values (Sig.) indicate the significance of each coefficient. All coefficients were found statistically significant (p < 0.05). Table-3: Multiple Regression of Socio-Demographic Factors with Comfort in Using Digital Transaction Platforms in Case of Rural Customers. Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 0.013 0.006 2.309 0.022 Age 2.056 0.027 0.099 74.845 0.000 Education 0.096 0.010 0.437 9.528 0.000 Annual Family Income 0.047 0.007 0.295 6.592 0.000 N.B:- r = 0.732, R2 = 0.534 Source : Researcher’s Survey A multiple regression was run to analyse the comfort or convenience in using digital financial services and the following regression model emerged: Comfort (Ease of use) = 0.013+2.056×Age+0.096×Education+0.047×Annual Family Income The coefficient for Age is 2.056 indicates that for each additional year of age, comfort on digital transaction platforms increases by approximately 2.056 units. The standardized coefficient (Beta) at 0.099 suggests that age has a positive impact on comfort of usage but not as strong as the other variables in the model. The coefficient for education is 0.096 which indicates that for each additional unit increase in education level, comfortability of using digital financial services increases by approximately 0.066 units. The standardized coefficient (Beta) at 0.437 suggests that education has a relatively strong positive impact on usage comfortability aspect compared to other variables in the model. The coefficient for annual family income is 0.047 indicating increase in comfort factor by approximately 0.047 units with increase in each additional unit increase in annual family income. The standardized coefficient (Beta) is 0.295, suggesting income has a positive impact on comfort but not as strong as education. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 246 https://internationalpubls.com All coefficients were found statistically significant (p < 0.05), suggesting that age, education, and annual family income are all significantly predictors of ease of use on digital transaction platforms among rural customers. Table-4: Multiple Regression of Socio-Demographic Factors with Availability of Digital Transaction Platforms in Case of Rural Customers. Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) -0.049 0.007 -6.796 0.000 Age 0.033 0.006 0.251 5.806 0.000 Education 1.951 0.028 0.306 69.732 0.000 Annual Family Income 0.076 0.01 0.341 7.381 0.000 N.B:- r = 0.724, R2 = 0.524. Source: Researcher’s Survey In analysis of availability factor of digital transaction platforms, the regression model emerged as below: Y=(-)0.049+0.033×Age+1.951×Education+0.076×Annual Family Income The multiple regression run for the availability factor brought out coefficients for age, education and annual income at 0.033, 1.951 and 0.076, respectively. This suggests that the availability of digital financial services increases by these many units with additional year of age, additional unit of education and additional unit increase in family income respectively. The age, education and annual family income being important socio-demographic factors emerged with standardised coefficients at 0.251, 0.306 and 0.341 respectively that influence the availability of digital financial services among rural customers. All the coefficients were found to be statistically significant (p < 0.05), suggesting that age, education, and annual family income are all significant predictors of availability of digital financial services among rural consumers. Table-5: Multiple Regression of Socio-Demographic Factors with Present Use of Digital Transaction Platforms in Case of Rural Customers. Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 0.016 0.006 2.724 0.007 Age 0.051 0.007 0.305 6.891 0.000 Education 2.051 0.028 0.116 72.576 0.000 Annual Family Income 0.098 0.012 0.431 9.461 0.000 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 247 https://internationalpubls.com N.B:- r = 0.740, R2 = 0.548. Source : Researcher’s Survey Multiple regression run to analyse socio-demographic factors with the present usage of digital transaction platforms among rural customers showed up with the following regression model: Present Use=0.016+0.051×Age+2.051×Education+0.098×Annual Family Income The coefficients for age, education and annual income at 0.051, 2.051 and 0.098, respectively, indicates that the present use of digital financial services increases by these many units with additional year of age, additional unit of education and additional unit increase in annual income respectively. The standardised coefficients at 0.305, 0.116 and 0.431 for age, education and income respectively indicates age, education, and annual family income are important factors which influence present usage of digital financial services. The t-values and associated p-values (Sig.) are all statistically significant (p < 0.05), suggesting that age, education, and annual family income are all significant predictors of the present use of digital transaction platforms among urban customers. Table-6: Multiple Regression of Socio-Demographic Factors with Frequency of Using Digital Transaction Platforms in Case of Rural Customers. Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 0.111 0.036 3.103 0.002 Age 0.091 0.025 0.189 3.622 0.000 Education 1.462 0.097 0.166 15.108 0.000 Annual Family Income 0.109 0.021 0.276 5.535 0.000 N.B:- r = 0.729, R2 = 0.531. Source : Researcher’s Survey From the above analysis of socio-demographic factors with the frequency of using digital financial services, the regression model is as follows: Frequency of Use=0.111+0.091×Age+1.462×Education+0.109×Annual Family Income The coefficients for age, education and annual family income at 0.091, 1.462 and 0.109 indicates that the frequency of usage increases by these levels with additional year of age, additional unit of education and additional unit increase in family income respectively. The standardised coefficients at 0.189, 0.166 and 0.276 for age, education and income respectively indicates that that age, education, and annual family income are important socio-demographic factors predicting frequency of use of digital transaction platforms among rural customers, with income having the strongest impact, followed by age and education. The intercept and all coefficients are statistically significant (p < 0.05). 7. Findings: The study considered six socio-demographic factors and it emerged that: Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 248 https://internationalpubls.com (i) Three factors viz. age, education and annual family income emerged to have strong positive correlation with all the aspects of digital financial inclusion i.e. awareness, comfort, availability, present use and frequency of use. (ii) Gender, as a factor, with correlation coefficients close to zero in most of the cases, showed weak or negligible correlation with all the aspects of digital financial services. (iii) Social stratification and occupation showed positive corelations with all the aspects of digital financial services, however, not as strong as age, education and family income. This indicates minor relationships exist between social strata of the user and the occupation with adoption of digital financial services. (iv) Multiple regression analyses further revealed the combined effects of age, education, and annual family income on various aspects of digital financial inclusion. The regression models underscore the significant predictive power of these socio-demographic factors, with age, education, and income emerging as strong determinants across all analysed aspects. (v) The multiple regression analysis revealed that age, education, and annual family income are important socio-demographic factors which influence awareness of digital transaction platforms among rural customers, with age and annual family income showing stronger effects in comparison to education. (vi) With regard to comfort or ease of use of digital financial services, analysis suggests that all the three factors i.e. age, education and annual family income positively influence ease of use of on digital transaction platforms, while the relationship with education is more pronounced compared to annual family income and age. (vii) As far as availability of digital financial services are concerned, all three variables emerged strong predictors of the adoption of digital financial services, with annual income showing the strongest impact, followed by education and age. (viii) With regard to present usage and frequency of usage, the results indicate that annual family income to be the strongest predictors of the usage. This suggests that a person’s earning propels usage of digital platforms for transactions. Education and age also found to be positively influencing the usage, however not as strong as income. (ix) The intercept and all coefficients are all statistically significant (p < 0.05) for all the aspects of digital financial services, suggesting that age, education, and annual family income are all significantly associated with all the aspects of digital financial services among rural customers. 8. Conclusion & Recommendations: The digital financial inclusion is the need of the hour considering it’s transformative impact to bridge the digital divide and economic upliftment. A country like India, still reeling under a substantial chunk of people being outside the formal financial services, needs to look at barriers that hinder the growth of digital financial services and aim to remove them effectively. This study, carried out in the rural areas of Odisha, throwed up some interesting facts. The study revealed that it’s the age, education and annual family income which drive a user’s propensity to use digital financial services. It’s therefore imperative that in order to achieve the desirable digital penetration, Government, regulators and financial institutions should have targeted policies aiming these factors which can propel the digital financial inclusion in a faster speed. At the same time, the study underscored gender being a weak parameter and social strata and occupation being relatively weaker than age, income and education. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 31 No. 7s (2024) 249 https://internationalpubls.com However, since this study was confined to only eight districts of Odisha, further study is recommended for pan Odisha and also at the country level to further substantiate the findings and tune the governmental policies accordingly. References: [1] Ali, J., Ghildiyal, A.K. (2023) , Socio-economic characteristics, mobile phone ownership and banking behaviour of individuals as determinants of digital financial inclusion in India, International Journal of Social Economics, 2023. [2] Lohana, S., & Roy, D. (2023). Impact of Demographic Factors on Consumer’s Usage of Digital Payments. FIIB Business Review, 12(4), 459-473 [3] Ozili, P. K. (2022), Research Publication on “Digital Financial Inclusion” [4] Ljumović, I., Jakšić, K., Trajković, S. (2021), Socio-Demographic characteristics of Digital Financial Services Users: Evidence from Serbia, ECONOMIKA [5] Kandari, P., Bahuguna, U., Salgotra, A.K. (2021), Socio-Economic and Demographic Determinants of Financial Inclusion in Underdeveloped Regions: A Case Study in India, Journal of Asian Finance, Economics and Business Vol 8 No 3 (2021) 1045–1052 [6] Ozili, P. K. (2021). Digital Finance, Green Finance and Social Finance: Is there a Link? Financial Internet Quarterly, 17(1), 1-7. [7] The World Bank(2021), COVID-19 Drives Global Surge in use of Digital Payments, Press Release, Washington [8] Ozili, Peterson K (2018), “Impact of Digital Finance on Financial Inclusion and Stability”, Essex Business School [9] Vinitha, K. and Vasantha, S.(2017), Factors Influencing Consumer’s Intention to Adopt Digital Payment - Conceptual Model’’, Indian Journal of Public Health Research & Development [10] Dwivedi, Y.K., Sahu, G.P., Rana, N.P., Singh, M. and Chandwani, R.K. (2016), "Common Services Centres (CSCs) as an approach to bridge the digital divide: Reflecting on challenges and obstacles", Transforming Government: People, Process and Policy, Vol. 10 No. 4, pp. 511-525 [11] Mohanty, D.(2015), Committee on Medium-term Path for Financial Inclusion, Reserve Bank of India [12] Dasgupta, S., Paul, RIK and Fuloria, S. (2011), “Factors Affecting Behavioural Intentions towards Mobile Banking Usage: Empirical Evidence from India”, Romanian Journal of Marketing. https://www.emerald.com/insight/search?q=Jabir%20Ali