Abstract
The ongoing COVID-19 pandemic has considerably promoted the usage of Digital Financial Services (DFS) in India. Therefore, exploring the various determinants influencing the DFS users is crucial for the DFS providers to understand their customers better. This study aims to identify, measure, and validate the determinants of Digital Financial Literacy (DFL) from the Indian adults who use Digital Financial Services. A sample of 384 adult DFS users from India was surveyed using a self-administered questionnaire in 2021. A multidimensional scale was developed to measure the Digital Financial Literacy in this study. The results exhibit that Digital Knowledge, Financial Knowledge, Knowledge of DFS, Awareness of Digital Finance Risk, Digital Finance Risk Control, Knowledge of Customer Right, Product Suitability, Product Quality, Gendered Social Norm, Practical Application of Knowledge and Skill, Self-determination to use the Knowledge and Skill and Decision Making are the determinants of DFL among the adults in India. Further, the users of DFS without DFL will face numerous challenges such as inability to complete the transaction, financial loss and privacy breach, etc. Hence, the study concludes that DFL is prerequisite to use DFS effectively.
1. Introduction
Technological disruptions and rapid digitization of financial services bring a vast number of revolutionary and innovative Digital Financial Services (DFS) into the market (Alliance for Financial Inclusion, DFSWG and CEMCWG, Citation2021). DFS implies access and use of financial services through the digital platform at any time (Pazarbasioglu et al., Citation2020). DFS uprising is a global phenomenon. In India, a wide variety of digital financial services have been designed and launched and they are being utilized by the customers, thanks to the technology adoption mindset of the customers and the efforts of the Government of India. “Unified Payment Interface” (UPI), “Bharat Interface for Money” (BHIM), Bharat Quick Response (QR) code, National Automated Clearing House (NACH), Rupay cards, National Electronic Toll Collection, and Aadhaar Enabled Payment Services (AePS) are a few digital financial services that are available in India. Digital financial services have changed how individuals, businesses, and households make payments, borrow money, settle transactions, buy financial products, make investments, and make remittances (Yang et al., Citation2021). Digital financial services improve the financial inclusion of the people who are excluded financially because DFS overcomes the impediments of serving the excluded people (Alliance for Financial Inclusion, DFSWG and CEMCWG, Citation2021). Digital financial services usage is on the rise. Around 70.3 billion real-time payment transactions were recorded globally in 2020 and this was a 41% increase when compared to 2019 (ACI Universal Payments, Citation2021). Out of 70.3 billion real-time transactions, 20.5 billion transactions came from India (ACI Universal Payments, Citation2021). Thus, digital financial services are widely used in recent times and DFS integrates the economy by introducing revolutionary digital financial products and services such as virtual banking, Application Programme Interfaces (APIs), alternative credit scoring mechanism, digital lending, and so on (OECD, Citation2018). Digital financial services pose many challenges to the users. The users are exposed to risk when they intend to use digital financial services. The risks and challenges include identity theft, privacy concerns, unregulated service providers, security concerns, low digital literacy, low financial literacy, and limited awareness of DFS (Alliance for Financial Inclusion, DFSWG and CEMCWG, Citation2021).
Digital Financial Literacy (DFL) is an essential requirement for the effective usage of Digital Financial Services (DFS) and DFL is an important component of education in this digital age (Morgan et al., Citation2020). It is to be noted that DFL is essential to use DFS when DFS are made available to the customers by the government. However, the government does not allow DFS as a part of its policy initiative; then, the prerequisite to use is DFS is the favourable policy decision to allow DFS by the government. Digital financial literacy is a combination of digital literacy, and financial literacy, and DFL is “financially literate on digital platforms” (Lyons & Kass-Hanna, Citation2021a). Digital financial literacy is measured by metrics of both financial literacy and digital literacy. Financial Literacy (FL) implies awareness about financial products and services and the ability to apply financial knowledge and skills to manage financial resources to achieve good financial health (Xiao et al., Citation2014). Simply put, digital literacy indicates proficiency to use digital technologies (Alliance for Financial Inclusion, DFSWG and CEMCWG, Citation2021). Digital literacy (DL) is defined as “the ability to define, access, manage, integrate, communicate, evaluate and create information safely and appropriately through digital technologies and networked devices for participation in economic and social life” (UNESCO, Citation2018).
Lack of DFL is a constraint for the rational and effective usage of digital financial services. Even a person who has a fair amount of financial literacy cannot use digital financial services effectively when he/she does not have digital literacy. So, both digital literacy and financial literacy are required to deal with digital financial services. In other words, digital financial literacy is a prerequisite for access and usage of digital financial services. Research studies revealed that the users have low financial literacy, limited awareness of DFS, low or nil digital literacy, and distrust of DFS (Alliance for Financial Inclusion, DFSWG and CEMCWG, Citation2021; Azeez & Akhtar, Citation2021; Bansal, Citation2019; Lyons & Kass-Hanna, Citation2021a; Prasad et al., Citation2018). However, in addition to these, there are several other determinants that influence the Digital financial literacy among adults. Therefore, this study aims to identify, measure, and validate the determinants of DFL among the adult DFS users in the Indian context. Eventually, the determinants of DFL, as the outcome of this study, will enable the digital financial service providers to understand their customers better.
2. Review of the extant literature
Digital financial inclusion has become a policy initiative for the government of India and so, focus has been given on constructing digital infrastructure in India (RBI, Citation2021). Digital India has been a flagship programme towards digitisation of India and creating an awareness about utilisation of digital services (Department of Electronics and Information Technology Citation2014). Usage of digital financial services promotes digital financial inclusion (Shen et al., Citation2018). Digital technologies and Fintech brought radical changes in the financial services sector and digital financial services have become accessible and affordable across the globe (Lyons & Kass-Hanna, Citation2021a). Digital technologies integrate the economy and largely impact the financial industry (OECD, Citation2018). Digital financial services provide “concrete benefits” to consumers and entrepreneurs (OECD, Citation2018). Digital financial services provide a greater opportunity to promote financial inclusion further (Alliance for Financial Inclusion, Citation2021).
Digital financial services are defined as “financial operations using digital technology, including electronic money, mobile financial services, online financial services, i-teller, and branchless banking, whether through a bank or non-bank institutions” (OECD, Citation2018). Digital financial literacy is a prerequisite for the effective usage of digital financial services (Morgan et al., Citation2020). Digital financial services provide opportunities as well as challenges to financial consumers. Opportunities include customized financial products and services, faster transactions, and convenient access to the financial products and the challenges are adoption of complex technology-based financial products, and handling of sophistication (Alliance for Financial Inclusion, Citation2021). The pandemic mandated development of innovative digital financial services and products and many countries founded regulatory sandboxes to develop innovative DFS (Global Partnership for Financial Inclusion, Citation2021). Financial literacy and digital literacy are pre-requisites of DFL (Toronto Centre, Citation2022). Financial literacy is a basic literacy that persons need (Hayati & Syofyan, Citation2021). Financial education is to be customized one based on the learners Anita, Citation2019. Financial literacy facilitates better financial decision-making (Shen et al., Citation2018). Financial literacy is at low level among Indian adults (Tony & Desai, Citation2020). Young individuals are less financially literate (Fanta & Mutsonziwa, Citation2021). There is a need for enhancing financial literacy rate and digital literacy rate in India (Stephen, Citation2022). Digital literacy improves financial learning experience of the online users (Tiwari et al., Citation2020).
Effective usage of DFS demands higher levels of digital financial literacy (Morgan et al., Citation2020). Digital Financial Literacy (DFL) is a construct that combines financial literacy, financial capability, and digital literacy (Alliance for Financial Inclusion, Citation2021). Digital financial literacy is defined as “acquiring the knowledge, skills, confidence, and competencies to safely use digitally delivered financial products and services, to make informed financial decisions and act in one’s best financial interest per individual’s economic and social circumstance” (Alliance for Financial Inclusion, DFSWG and CEMCWG, Citation2021). Digital financial literacy is a new concept, and it is to be focused on by the regulatory bodies and the governments (Alliance for Financial Inclusion, DFSWG and CEMCWG, Citation2021). Digital financial literacy lies at a point where digital literacy and financial literacy intersects and digital financial literacy enables the users to have full benefits of digital financial services (Melinda Gates Foundation, Citation2021). Digital financial capability refers to “the knowledge, attitudes, and skills that enable an individual to use digital financial services actively” (Dimova et al., Citation2021). Access to digital financial services demands a fair amount of digital financial literacy (Morgan et al., Citation2020). Financial knowledge, digital financial knowledge, and financial attitude impact financial behaviour (Normawati et al., Citation2021). Digital financial literacy is measured by the researchers from different perspectives. Knowledge of DFS, awareness of the risk associated with DFS, knowledge on controlling digital financial risk, and knowledge on consumer rights and issue redressal are the measures of DFL (Morgan et al., Citation2020). A more detailed and comprehensive measurement of DFL was advocated by Lyons and Kass-Hanna (Citation2021a). They specified five core dimensions each for FL and DL such as basic knowledge and skill, awareness, practical know-how, decision-making, and self-protection (Lyons & Kass-Hanna, Citation2021a). Literacy, numeracy, access, consumer awareness, and design are the enablers of DFL (Melinda Gates Foundation, Citation2021). From the works of literature examined, it is understood that DFL is a recent concept and still evolving. A first detailed definition for DFL was given by Alliance for Financial Inclusion (Citation2021). DFL is a multidimensional construct that combines FL and DL. The metrics proposed by Morgan et al. (Citation2020), Alliance for Financial Inclusion (Citation2021), and Lyons and Kass-Hanna (Citation2021a), and Melinda Gates Foundation (Citation2021) have considered both financial literacy and digital literacy to measure DFL. In the Indian context, few studies focused on DFL in India (Azeez & Akhtar, Citation2021; Bansal, Citation2019; Rajdev et al., Citation2020), but they adopted the scale of Morgan et al. (Citation2020). So, this tries to identify, measure, and validate the determinants of DFL of the individual users of DFS in the Indian context.
3. Research methods and materials
3.1. Research framework
This descriptive research aims to identify, measure, and validate the determinants of digital financial literacy of DFS users. The correlational investigation is adopted, and primary data is collected using the survey method from the adults who use digital financial services. The target population of the study is digital financial services users in Bangalore, India. The study was conducted in the natural environment and the study environment was not influenced by the researchers. So, it is a non-contrived field study. The study was conducted in one shot from July 2021 to December 2021 and thus it is a cross-sectional study. The sample consists of both male and female respondents belonging to various age generation groups. Financial literacy and digital literacy depend on gender (OECD, Citation2018) and age (Klapper et al., Citation2015; Morgan et al., Citation2020; Rajdev et al., Citation2020).
3.2. Sample and study procedure
A well-structured questionnaire was prepared and circulated online using google forms with adults who use DFS. An online questionnaire was circulated with 828 adults and 391 questionnaires were filled out by the respondents. Complete information of the questionnaires was checked, and 387 questionnaires had the complete information. The sample size was determined as 384 using Krejcie and Morgon’s (Citation1970) formula. Out of 391 questionnaires received, the first 384 questionnaires that have complete information were used for the analysis.
3.3. Measures
This study has measured digital financial literacy by developing a multidimensional scale that is customized to the Indian context. The existing research works conveyed that DFL is a construct that reflects both digital literacy and financial literacy. This research work accepts this view on DFL. Based on the literature survey, it was found that there is no consensus on metrics of DFL. Important metrics of DFL proposed by various researchers (Morgan et al., Citation2020; Alliance for Financial Inclusion, DFSWG and CEMCWG, Citation2021; Lyons & Kass-Hanna, Citation2021b; Melinda Gates Foundation, Citation2021) are presented below.
The scales presented in Table focused on financial literacy, financial capability, and digital literacy to measure DFL. However, these scales did not consider the quality aspect of DFS. The quality of financial products and services is an essential component of financial inclusion (Alliance of Financial Inclusion, Citation2016; Ravikumar, Citation2017). The users, whether they use traditional financial services or digital financial services, look for their quality. The quality of DFS is one of the factors that determine the financial behavior and financial decisions of the users (Alliance of Financial Inclusion, Citation2016; Ravikumar, Citation2017). So, this study has considered “Quality” as an explicit subdimension of DFL. Furthermore, theoretical digital financial literacy will not lead to rational financial decision-making. Suppose an individual has a good amount of DFL but does not put his/her theoretical digital financial literacy into action, DFL will not be a complete one. So, the practical application of DFL has been considered as one of the metrics of DFL in this study. Sometimes, there may be some distractions to take a rational financial decision-making. For instance, a non-regulated digital finance company offers credit to the customers at an affordable rate of interest, but the terms and conditions of the credit are not transparent and there are huge hidden costs. The customer who is good in DFL may also take credit from such a company due to his financial emergency. In this condition, DFL has become a futile literacy. So, self-determination (Zycinska and Januszek, Citation2021) to apply DFL is essential. It has been documented in the existing research that there is a gender disparity in access to financial services and so, the gendered social norm is considered as a metric in this study (Melinda Gates Foundation, Citation2021; Ravikumar et al., Citation2021). Therefore, determination to use DFL is one of the metrics of DFL. Dimensions and subdimensions used in this study to measure DFL are presented in Table .
Table 1. Existing measures of DFL source: authors Compilation
Table 2. Proposed measures of DFL
3.4. Construction of items
A questionnaire was prepared which had two sections. Section 1 deals with personal characteristics of the respondents such as gender, age, education, marital status, income, and place of living. Section 2 measured the DFL of the respondents. This section has 47 statements in a 5-point Likert Scale quantified from 1 (Strongly Disagree) to 5 (Strongly Agree) representing 12 dimensions stated in Table . Digital knowledge, financial knowledge, knowledge of DFS, awareness of digital financial risk, digital finance risk control, awareness of customer rights, product suitability, product quality, gendered social norm, practical application of knowledge and skill, decision-making, and self-determination are measured using 5, 6, 4, 4, 4, 2, 3, 4, 2, 6, 2, and 5 statements, respectively, in the questionnaire. Detailed descriptions of items of the DFL scale are presented in Table .
Table 3. Description of items of DFL scale
4. Results
First, the DFL scale and its customization were validated by the experts qualitatively. Three experts participated in qualitative validation. One expert was from behavioral finance, another was from the financial technology area, and the third expert was from the financial education area. Each expert evaluated the DFL scale dimensions, statements representing dimensions, adequacy of statements representing each dimension, and suitability of statements in the Indian context. Experts advised to rewrite a few statements and to increase the numbers of statements that represent few dimensions considered. The changes suggested were incorporated in the DFL scale. Then, a preliminary study was conducted, and Cron Bach’s alpha scores were computed for each dimension of the DFL scale and overall DFL scale. Alpha scores computed are presented in Table .
Table 4. Alpha scores
Alpha scores were satisfactory (α > 0.700) for all dimensions except awareness of digital finance risk which had an alpha score of 0.691 and the overall alpha score for the scale was 0.923. So, the main study was conducted to measure and validate the DFL scale quantitatively.
The main study conducted reveals certain characteristics of the sample population. Female adults are slightly more in the sample population (50.7%) than male adults (49.3%). Generation Z adults (19 years to 24 years) are dominant in the sample with 78.5% followed by Millennials (25 years to 40 years) with 14.8%. Generation X (41 years to 56 years) and baby boomers (more than 56 years) represent 5.3% and 1.4%, respectively, in the sample. Further, the predominant sample of adults are single and reside in an urban area with undergraduate education and income of not exceeding INR 1,00,000. Table exhibits descriptive statistics of the variables considered.
Table 5. Descriptive statistics
Differences between components of DFL and personal and economic characteristics of the respondents are analysed. No variances exist between components of DFL and gender, marital status, and place of residence of the respondents. However, financial knowledge and practical application of knowledge and skill significantly vary based on age of the respondents. All other components of DFS do not significantly base on age. Mean scores convey that Baby boomers have more financial knowledge and practical application of knowledge and skill. Similarly, financial knowledge and practical application of knowledge and skill significantly vary based on education of the respondents. The respondents who have completed master’s degree possess more financial knowledge and practical application of knowledge and skill. Financial knowledge and digital financial risk control differ significantly based on income of the respondents. Financial knowledge is more among the respondents who belong to middle-income group (more than Rs1,00,000, but less than Rs 4,00,000). Digital financial risk control is high among the respondents whose income is lower (Up to Rs 1,00,000 per year).
Among the variables chosen to measure DFL in this study, digital knowledge and financial knowledge may closely relate to knowledge of digital financial services variable. In other words, digital knowledge, financial knowledge, and knowledge of digital financial services have a potential to be autocorrelated. Autocorrelation is statistically checked and measured using Durbin-Watson statistic. Durbin-Watson test identifies and measures autocorrelation in the residuals of regression analysis. Durbin-Watson test statistic ranges from 0 to 4. If Durbin-Watson test statistic is around 2, it indicates that autocorrelation does not exist between the chosen variables. In this study, autocorrelation is checked by Durbin-Watson test in the regression analysis. Durbin-Watson test score for digital knowledge and knowledge of digital financial services is 2.095 and the score for financial knowledge and knowledge of digital financial services is 1.960. So, autocorrelation between these variables does not exist in this study.
“Factor analysis is a multivariate statistical method for data reduction and for determination of common factors through correlations” (Hayton et al., Citation2004). Exploratory Factor Analysis (EFA) is more appropriate for scale development. EFA has been applied to investigate the inter-correlation of the variables considered and the results are presented here.
Since the KMO test (Table ) score is 0.838, the data is meritorious for factor analysis. Further, Bartlett’s test p-value indicates rejection of the existence of the identity matrix. So, the variables considered are related and are suitable for factor analysis.
Table 6. KMO and bartlett’s test
In this study, 47 statements of twelve variables are loaded in the factor analysis. The correlation technique is employed to understand the interrelationship among the variables and to find common factors. Twelve factors extracted and their eigenvalues and proportions of variance are presented in Table . These twelve factors extracted significantly account for 70.87% of the variance in the scale and all other factors are insignificant. Factor one accounts for 29.62% variance in the scale, the second factor explains 9.09% variance in the scale, and the third factor contributes 5.17% variance.
Table 7. Total variance explained by extracted factors
Generally, Kaiser greater than 1 (K1) criterion and Scree test are used to decide the number of factors that are to be retained in the factor analysis (Hayton et al., Citation2004). K1 criterion has certain issues and it sometimes overestimates the number of factors (Horn, Citation1965). Scree test experiences subjectivity and ambiguity (Hayton et al., Citation2004). Horn’s Parallel Analysis (PA) is another method used to determine factors to be retained (Dinno, Citation2013). PA overcomes the K1 criterion problem of overestimation of factors due to sampling error (Horn, Citation1965). Horn’s parallel analysis helps to decide the number of factors to be retained in principal component analysis in EFA (Dinno, Citation2013). The parallel analysis uses randomly generated eigenvalues to determine the number of factors to be considered (Cokluk & Kocak, Citation2016). The parallel analysis provides good results in an accurate number of factors to be retained (Cokluk & Kocak, Citation2016). Real eigenvalues are to be compared with random eigenvalues to decide on factor retention. Those factors that have higher real eigenvalues than average random eigenvalues and 95 percentiles of random eigenvalues are retained (Hayton et al., Citation2004).
According to parallel analysis results (Table ), 12 components are to be retained out of 47 items in this study as unadjusted (real) eigenvalues of twelve factors are more than average random eigenvalues and 95 percentiles of random eigenvalues. Item loadings are presented in Table . Items are entered under the component where they have the highest loading. Items are expressed in terms of their respective codes followed by the statement numbers as per Table .
Table 8. Parallel analysis
Table 9. Rotated component loading
Twelve components extracted are named considering items loaded in the component. Component 1 is termed as “Quality of DFS” because this component is filled with DFS Product Suitability and Product Quality statements. Component 2 is “Knowledge of DFS and Digital Financial Service Providers (DSPs). Component 3 is named “Awareness of Digital Finance Risk and Risk Control”. Component 4 is labeled as “Digital Knowledge”. Component 5 is described as “Practical Application of Knowledge and Skill”. Component 6 is identified as “Digital-Savvy”. Component 7 is classed as “Self-determination to use the knowledge and skill”. Component 8 is characterized with “Digital Security Awareness”. Component 9 is identified as “Positive Financial Attitude”. Component 10 relates to “Gendered Financial Knowledge”. Component 11 deals with “Rational Financial Behavior”, and Component 12 is termed as “Financial Knowledge”.
Reliability values indicate the replicability of the components. Factor determinacy index is a “measure of variance or the correlation of factor score predictor with the corresponding factor (Beauducel & Hilger, Citation2017)”. Factor determinacy value facilitates evaluation of the validity of factor score predictor (Beauducel & Hilger, Citation2017). Factor determinacy values and reliability values presented in Table for the extracted components are appropriate and good. So, the extracted components are expected to be reliable, and the extracted components contribute to the variance in the corresponding factor. Thus, the extracted twelve components are statistically strong.
Table 10. Explained variance and reliability of rotated components
New components are identified based on item loadings in EFA, and the final model is built for the measurement of DFL among adults in India. The final model is tested for its goodness-of-fit. Confirmatory Factor Analysis (CFA) is applied to find the goodness-of-fit of the model. The Goodness-of-fit results () indicate an overall acceptable fit of the model. provides a pictorial representation of latent variables and their observed variables.
Figure 1. Confirmatory factor analysis: digital financial literacy model.
Table 11. Goodness of fit
Thus, Quality of DFS, Knowledge of DFS and Digital Financial Service Providers, Awareness of Digital Finance Risk and Risk Control, Digital Knowledge, Practical Application of Knowledge and Skill, Digital-Savvy, Self-determination to use the knowledge and skill, Digital Security Awareness, Positive Financial Attitude, Gendered Financial Knowledge, Rational Financial Behavior, and Financial Knowledge are the determinants of DFL among the adults in India.
Further, with the twelve standardized factors that measure DFL, this study measured the differences in DFL of the adults based on the personal characteristics of the respondents. Table presents the results (p-values) of the differences measured by the Mann-Whitney test and Kruskal Wallis Test. These tests are applied as the factors are not normally distributed.
Table 12. Differences in determinants of DFL and DFL
5. Discussions
This study has measured digital financial literacy of the adults and its determinants. Among the determinants of DFL, rational financial behavior significantly differs based on gender. Female adults possess more rational financial behavior (Mean score: 3.346) than male adults (Mean score: 3.143). This result confirms the result of Rai and Sharma (Citation2019). Financial knowledge and practical application of knowledge and skill significantly vary based on the age of the adults. Mean scores convey that the adults who are aged more than 56 years (Baby boomers) have more financial knowledge (Mean score: 4.500) and more practical application of knowledge and skill (Mean score: 4.222) than Generation X, Millennials, and Generation Z adults. Practical application of knowledge and skill, digital security awareness, rational financial behavior, and financial knowledge differ significantly based on education. Adults who have completed master’s degrees possess more practical application of knowledge and skill (Mean score: 3.628), digital security awareness (Mean score: 3.726), and rational financial behavior (Mean score: 4.897). Financial knowledge is more among undergraduate qualified adults (Mean score: 3.371). Practical application of knowledge and skill and digital security awareness significantly vary based on the marital status of the adults. Married respondents apply their knowledge and skill more (Mean score: 3.546) and they possess more awareness of digital security (Mean score: 4.900). Quality of DFS, awareness of digital finance risk and risk control, digital savvy, positive financial attitude, and financial knowledge significantly differ based on income. More income (more than Rs 6,00,000), more consciousness for quality of DFS (Mean score: 3.774), awareness of digital finance risk, and risk control (Mean score: 4.250), and digital savvy (Mean score: 3.833). However, financial knowledge is more among those adults who have an annual income of Rs 1,00,001 to 4,00,000 (Mean score: 3.923). All other differences between dimensions of DFL and the personal characteristics of the adults are insignificant. Overall, digital financial literacy differs only according to education. Those who qualified for masters have more DFL than other adults in the sample. Thus, this study measured digital financial literacy among Indian adults and validated the determinants of DFL. Based on the study results, twelve components were identified through EFA, and those components were confirmed using CFA. Further, this study analyzed whether DFL and its determinants vary from adult to adult or not.
6. Conclusions
This study aims at measuring digital financial literacy and its determinants among the adults. As there is no consensus on determinants of DFL in the existing literature and there are limited studies on the measurement of DFL in India. Few researchers identified certain variables such as digital knowledge, financial knowledge, digital finance risk, digital finance risk control, knowledge of consumer rights, decision-making, and self-protection. This study has employed a few additional variables such as knowledge of DFS, quality of DFS, practical application of knowledge and skill, and self-determination to apply knowledge and skill. Forty-seven statements covering 12 variables were measured in the questionnaire. EFA reveals the existence of twelve factors that are named as Quality of DFS, Knowledge of DFS and Digital Financial Service Providers, Awareness of Digital Finance Risk and Risk Control, Digital Knowledge, Practical Application of Knowledge and Skill, Digital-Savvy, Self-determination to use the knowledge and skill, Digital Security Awareness, Positive Financial Attitude, Gendered Financial Knowledge, Rational Financial Behavior, and Financial Knowledge. Further, the results of EFA are confirmed through CFA. So, these twelve factors are determinants of the DFL of adults in India. It is found that the DFL of the adults varies based on education, and no other personal characteristics of the adults account for the variance in DFL.
Data Availability
The data that support the findings of this study are available from the corresponding author(s) upon reasonable request.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
Notes on contributors
T Ravikumar
Ravikumar T, Ph.D. is an Associate Professor of Finance in the School of Business and Management, CHRIST (Deemed University), Karnataka, India. He completed his Ph.D. at Bharathiar University, Tamil Nadu, India. He has vast experience in academics and research. He published numerous articles in the indexed journals of national, and international repute. Furthermore, he published two books and presented research articles at many conferences. This article is a part of Digital Financial Services Adoption and Digital Financial Inclusion Project.
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Appendix
Questionnaire
Section – A
Gender Male Female
Age
19 years to 24 years
25 years to 40 years
41 years to 56 years
More than 56 years
(3) Marital status Married Single
(4) Education
School education
Bachelor’s degree
Master’s degree
Others
(5) Annual income
Up to Rs 1,00,000
Rs 1,00,001 to Rs 4,00,000
Rs 4,00,001 to 6,00,000
More than Rs 6,00,000
(6) Place of business Rural Urban
(7) I have smartphone Yes No
(8) I have access to internet Yes No
(9) I own a personal computeror a laptop Yes No
Section – B
(10) Choose the most suitable choice given against each statement
SDA – Strongly Disagree, DA – Disagree, N – Neutral, A – Agree, SA – Strongly Agree
Thank you
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