The nexus of digital financial inclusion, digital financial literacy and demographic factors: lesson from Indonesia

Abstract

This study aims to answer the impact of digital financial literacy (DFL) and the demographic factors on digital financial inclusion (DFI). This model employed the independent variables that consist of digital financial literacy and demographic factors including gender, age, occupation, marital status, income. By applying purposive sampling, this study collected the data from 160 households who have minimum one banks’ accounts. To answer the research hypotheses, this study analyzed the data using binary logistic regression that categorized sample into DFI or otherwise. Based on the model fit test, the findings showed that DFL and demographic factors affect DFI. The effect of gender and marital status showed an insignificant effect on DFI. Conversely, DFL and other demographic factors including age, income, occupation significantly explain DFI in Indonesian household. The model can predict 84.4% respondents in a correct classification as DFI or otherwise.

IMPACT STATEMENT

This study highlights the impact of digital financial literacy (DFL) and demographic factors on digital financial inclusion (DFI) among Indonesian households. It contributes in filling the research gap which is conducted using entrepreneurial households. DFI has become a crucial factor to accelerate the economic growth especially for society who are financially excluded. Therefore, DFI is needed to support entrepreneurial household business activities. These findings underscore the importance of tailored financial literacy initiatives and inclusive policies to promote broader access to digital financial services, driving socioeconomic development in Indonesia and beyond.

Keywords:

• Digital financial inclusion
• digital financial literacy
• demographic factors
• binary logistic regression

Reviewing Editor:

• Wright Len Tiu, De Montfort University Faculty of Business and Law, United Kingdom

Subjects:

• Asian Studies
• Finance
• Business, Management and Accounting

1. Introduction

Recently, almost all the countries in the world have a commitment to support the first agenda of Sustainable Development Goals (SDGs) by eliminating poverty that could be achieved through financial inclusion (Sha’ban et al., 2020) and digital financial inclusion, especially in Asian countries (Tay et al., 2022). As one of the developing countries, Indonesia must concentrate on its policy to reduce the rate of poverty and narrow the gap of society between lower level and upper level of income. Sangmi (2013) emphasized that financial inclusion is a condition to ensure that all element of society have accessibility and utilize the legal financial system. Financial inclusion had been considered as an important aspect that needed to promote the national economic growth rapidly (Mbutor & Uba, 2013).

Based on the survey from Financial Service Authority, in Indonesian we called Otoritas Jasa Keuangan (OJK), the Indonesian index of financial inclusion in 2019 reached 76.19%. It was higher than the former survey in 2016 that was 67.8% (OJK, 2019). Therefore, the government concerned to enhance the index of financial inclusion by designing the regulation to give the wider financial access through the policy in enhancing financial inclusion especially for their society which is categorized in the lowest income.

Previous research measured financial inclusion by observing some dimensions including financial accessibility (Demirguc-Kunt et al., 2018; Gammage et al., 2017; Widyastuti et al., 2019), usage (Gammage et al., 2017; Widyastuti et al., 2019) and barriers (Widyastuti et al., 2019). One of the indicators that reflects accessibility is bank account ownership. Solo (2008) explained that people with lower income and lower education were often known as unbanked because they did not have any bank account. The society who was categorized as unbanked dominantly came from the bottom of the pyramid including fisherman, farmer, women, worker of informal sector and immigrant.

Along with technological advances and the COVID disruptions (Tay et al., 2022), accessibility of financial services has shifted towards financial digitalization by utilizing technology. Nowadays, emerging of digital financial services have encouraging people to adopt many digital platforms to meet their needs. This condition leads financial institution to enhance their services by providing digital financial inclusion. It enables to make the financial system more accessible to support financial transactions including digital payment, digital investment platforms, internet-based money transfer systems by utilizing mobile phone as one of tools in financial technology. Daily financial transactions can be influenced and supported by the adoption and utilization of digital financial services, then, significantly contributed to the nations’ economic growth.

Previous studies on DFI have been conducted in many countries, including Ukraine (Naumenkova et al., 2019), Nigeria (David-West, 2016), China (Lai et al., 2020; Wang & He, 2020; Yang & Zhang, 2020), Bahrain (Sadayan & Rao, 2017), Bangladesh (Aziz & Naima, 2021) and China (Ahmad et al., 2021), as well as many countries in Southeast Asia (Koh et al., 2018) consisting of Myanmar, Thailand, Singapore, Malaysia, Indonesia. These studies examined DFI using various units of analysis, including adults (Koh et al., 2018), households (Lai et al., 2020), farmers (Wang & He, 2020) and compare the level of DFI based on gender (Gammage et al., 2017).

Discussing about digital financial inclusion in the digitalization era relate to a person’s understanding of financial services provided by financial institutions, both banks and non-banks. In this case, a person’s understanding and knowledge of financial services provided on a digital platform is the main factor that plays a role in determining whether a person will use the service or not. The study about the relationship between digital financial literacy and financial inclusion has been conducted by Hasan et al. (2022) and found that women entrepreneurs with higher DFL tend to engage more in using financial services through formal banking. However, refer to the best of our knowledge, there is less study on DFI which is related to DFL and demographic factors by emphasized in entrepreneurial households. With the development of financial technology, business activities carried out by entrepreneurial households are expected to increase through broadening the financial services along the COVID 19 pandemic. DFI has become a crucial factor to accelerate the economic growth especially for society who are financially excluded. Therefore, DFI is needed to support entrepreneurial household business activities.

1.1. Digital financial inclusion

According to World Bank (2014), financial inclusion was defined as access of individuals or companies in utilizing financial services. Basically, financial inclusion is a form of financial service deepening which aimed at the community with the lowest income and to take advantage of legal financial products and services in several manner including fund transfers, loans, savings and insurance. It was revealed by Sun and Siagian (2015), who emphasized that financial inclusion is a formal financial service provided to low-income communities. It covered various facilities such as credit, savings, insurance and payment services.

The measurement of DFI were developed based on various dimensions. Sarma (2008) focused on the access of financial services, bank penetration, the existence of financial services and usage of financial products. While, Aduda and Kalunda (2012) observed DFI from the dimensions including utilization and easiness to access financial services. The factors such as savings, loans, payments and managing risk were observed in measuring DFI (Kunt & Klapper, 2012), it also reflected the constraints faced (Gupta, 2015; Kunt & Klapper, 2013). Meanwhile, Naumenkova et al. (2019) explained that financial inclusion includes access, quality, use and impact, while Widyastuti et al. (2019) examined financial inclusion from the fishermens’ point of view referring to the dimensions of accessibility, barriers and utilization of financial services.

World Bank (2014) stated that financial inclusion and accessibility to financial products are different issues. People with the low intensity in using financial services could not be categorized as financially excluded and lack of access to the legal financial system. They do not choose to utilize it for any reason although they can easily access and afford it. Reversely, many others do not have access due to high service fees, irreguler income, residence legal documents, regulatory issue and other barriers.

Technological innovation enables to reduce the cost and inconvenience of accessing financial services. Digital payment technology, which is combined with mobile technology, are driving financial system re-engineering in eliminating 90% of transaction fees. The digital platform could be scaled up to tens of millions of transactions per day considering that the current government policy to support the usage of digital or cashless payments. It is one part of DFI. Chu (2018) defines DFI as providing access to and delivery of primary banking services, savings, loans, insurance and other financial services to each people in the population, especially those living below the poverty line.

Lai et al. (2020) described the aspects used to measure the DFI index, namely extensive and intensive utilization, as well as digital service provision. Extensive usage reflected the number of people who have ‘Alipay’ accounts, and the number of these accounts were linked to bank cards. Intensive usage measured people’s intensity in using Alipay accounts to make payments, to borrow money, to buy insurance, to spend their money and to invest in money markets.

Meanwhile, Naumenkova et al. (2019) described DFI as access to legal financial services for all society by utilizing digital technology, digital interaction between financial intermediaries and costumers, the availability of infrastructure for the use of digital financial products. The digital financial services referred to digital banks, mobile banks, digital payments via the internet, mobile wallets, e-wallets.

Chu (2018) explained that DFI cannot be separated from mobile technology, considering that cellular technology is able to expand digital financial inclusion. Regarding infrastructure in the use of digital financial products, Chu (2018) emphasized that accessibility is defined as a cellular network that can be accessed anywhere and anytime. It also means that the network must be standards-based so that it can operate with a variety of cellular network operators and cell phone models. In the era of industry 4.0, Fintech company also implements artificial intelligence (AI) as a tool to support their services to reach the wider society at the bottom of the pyramid (Mhlanga, 2020).

Yang and Zhang (2020) explain several advantages of digital financial inclusion, namely (1) increasing access to financial services, (2) improving information asymmetry, (3) improving financial service efficiency and (4) improving financial service coverage. Therefore, digital financial inclusion is a crucial thing to improve. Despite the advantage in facilitating and narrowing of the physical access gap to financial services by digital platforms, these services remain underutilized owing to insufficient connectivity, low level of financial literacy and social awareness (Aziz & Naima, 2021).

1.2. Digital financial literacy

DFL is a crucial aspect to support the success of financial inclusion programs, therefore several countries in the world are focusing their policies on the two crucial issues. DFL consist of two basic concepts including digital literacy and financial literacy. It is also added with other aspects, namely product characteristics and awareness about potential risks that they faced. Morgan et al. (2019) emphasized that digital financial literacy has a multi-dimensional concept. Several previous literatures explained that DFL was defined in various constructs which were developed accordance to the purpose of their study.

Morgan and Long (2020) stated four dimensions that reflect DFL including knowledge of digital financial products and services, digital financial risks’ awareness, knowledge of digital financial risk control and understanding of consumer rights and compensation procedures.

According to Prasad et al. (2018), DFL is directly related to knowledge about online purchases, online payments and online banking systems. One of the studies on DFL conducted by Setiawan et al. (2020) examined DFL and its impact on the millennial generations’ saving behavior in Indonesia. They emphasized that DFL was measured using four aspects of digital financial products and services including knowledge, experience, risk awareness and skills to control and regulate digital financial activities.

2. Materials and method

This study aims to examine whether DFL and the demographic factors including age, income, occupation, marital status influence DFI. There is a need to incorporate more information on set of questions that are utilized to measure these two variables in the paper. The population were households in Indonesia, while the number of populations is unknown (infinite). Therefore, the sample were determined using purposive sampling by filtering the responden who has minimum one banks’ accounts. This study calculated the number of the samples based on Lemeshows’ formula which could be applied for unknown population (Lwanga et al., 1991). The formula was stated as follow: $$n=\frac{z^{2}P\left(1-P\right)}{d^2}$$

Notes:

n = the number of samples in this research

z = z score for 5% significance level (1.96 for 5% significance level)

P = probability of occurance (0.5)

d = accepted sampling error (we took 0.10 referring the literature)

Based on this formula, the minimum sample that needed for the binary logistic regression analysis was 96 samples, and this study used 160 households who have a banks’ accounts. The independent variables consist of DFL and the demographic factors including gender, age, occupation, marital status, income. The model employed digital financial inclusion as the dependent variable that was measured using the nominal scale which categorized sample into two categories namely 1 = digital financial inclusion, and 0 = otherwise (digital financial exclusion) (see Table 2). The binary categories for DFL determined based on the questionnaire that was adapted from Naumenkova et al. (2019), while the measurement of DFI was developed by Setiawan et al. (2020). The detail of the measurement of variables presented in Table 1. The instruments which are developed in this study previously had got any consent from the ethics commission in the Faculty of Economics, Universitas Negeri Jakarta.

Table 1. The instruments.

Variable	Indicators	Scale
Digital Financial Inclusion (Naumenkova et al., 2019)	Used a mobile phone or the internet to access an account Used e-debet, e-credit, mobile/internet banking, e-money, e-wallet and internet to pay bills or make a payment Used e-debet, e-credit, mobile/internet banking, e-money, e-wallet and internet to buy something online Used a debit card to withdraw fund Used fintech to make an investment Used fintech to make an insurance Used an account to receive income/wages/government payments Used an account to pay utility bills Used a mobile phone or the internet to fund transfer	1 = Yes 0 = No
Digital Financial Literacy (Setiawan et al., 2020)	I have a good understanding of digital payment products (such as E-Debit, E-Credit, E-Money, Mobile/Internet banking, E-wallet). I have a good understanding of product digital asset management (such as Tanamduit, Finansialku, Bareksa). I have a good understanding of digital alternatives (such as Investeree, Kreditku, etc.). I have a good understanding of digital insurance (such as W+, Cekpremi, Rajapremi). I have a good understanding of customer rights and protectionas well as the procedure to complain about the service from digital financial providers. I have experience in using the product and service of fintech for digital payment (such as OVO, Gopay, LinkAja). I have experience in using the product and service of fintech for financing (loan) and investment (such as CoinWorks, Investree, Modalku, Amarta). I have experience in using the product and service of fintech for asset management (such as Bareksa, tanamduit and finansialku). I aware about the potential of financial risk in using digital financial provider or fintech, such as the legality of the fintech provider, interest rate and transaction fee. I have a good capability in managing financial activities through a digital platform such as managing the cost of using digital financial transactions. I have a good control on financial activities using digital platforms by evaluating the spending in the platforms.	4 = Strongly Agree 3 = Agree 2 = Disagree 1 = Strongly Disagree

Table 2. The measurement of variables.

Variables	Category	Coding	n
Gender	Male	1	69
	Female	0	91
Age	20- to 29-year old	1	15
	30- to 39-year old	2	25
	40- to 49-year old	3	43
	50- to 59-year old	4	69
	More than 60	5	8
Occupation	Entrepreneur	1	22
	Other occupations	0	138
Marital status	Married	1	24
	Single/divorce	0	136
Income	Less than or equal to 5 million rupiah	1	24
	More than 5 to 10 million rupiah	2	70
	More than 10 to 15 million rupiah	3	28
	More than 15 to 20 million rupiah	4	14
	More than 20 million rupiah	5	24
DFL	High DFL	1 if total score is equal to or more than median	77
	Low DFL	0 if total score is less than median	83
DFI	Digital financial inclusion	1 if total score is equal to or more than median	25
	Digital financial exclusion	0 if total score is less than median	135

3. Results and discussions

Based on the data analysis using binary logistic regression, we conducted the first step which is intended to test the model fit by referring to the first criterion namely the initial value of –2 Log Likelihood. The value of –2 Log Likelihood at the beginning step, when block number = 0, was compared to the final value of –2 Log Likelihood at the block number = 1. The initial model only included the constant in the regression model, while the time block number = 1, model included the constant and independent variables. The result shows that the initial value of –2 Log Likelihood = 138.688 (see Table 3), it is greater than the final value of –2 Log of Likelihood = 104.666 (see Table 5). It indicates that the hypothesized model is in line with the data. The decrease in –2 Log Likelihood expresses a fit regression model.

Table 3. The value of -2 log likelihood block number = 0.

Iteration	−2 Log likelihood	Coefficient
		Constant
Step 0 1	140.882	1.375
2	138.705	1.658
3	138.688	1.686
4	138.688	1.686

Source: authors analysis.

Table 5. Model summary.

Step	−2 Log likelihood	Cox & Snell R square	Nagelkerke R square
1	104.666	0.192	0.330

Source: authors analysis.

Table 4 represented the Hosmer and Lemeshow test, as the second criterion in the model fit test, which results the significance value of chi-square. Refer to the significance value of chi-square (0.272), it can be concluded that the null hypothesis is accepted, and it could be interpreted that the overall regression model is fit.

Table 4. Hosmer and Lemeshow test.

	Chi-square	df	Sig	Result
Step 1	9.908	8	0.272	The model is fit

Source: authors analysis.

The contribution of all independent variables in explaining the digital financial inclusion was indicated from the Nagelkerke R Square (see Table 5). These results found that the independent variables have a contribution of 33% in affecting DFI. The test for partial effect of each independent variable is carried out by looking at the significance value of each partial effect hypothesis which is shown in the Exp (B) value that reflected the coefficient of the logistic regression (see Table 6). Based on the results, it described that demographic factors influence DFI. This finding was in line with the study conducted by Park and Mercado (2015). The first demographic factor is gender which did not have a significant impact on DFI. It is contrary with Kandari et al. (2021) who found that women are relatively vulnerable than men in the context of mobile phone usage and access to credit.

Table 6. The partial effect of independent variables on DFI.

Step 1	B	SE	Wald	df	Sig	Exp(B)	95% C.I.for EXP(B)
							Lower	Upper
Gender	0.556	0.531	1.096	1	0.295	1.744	0.616	4.939
Age	−1.348	0.383	12.395	1	0.000	0.260	0.123	0.550
Marital status	−0.049	0.977	0.003	1	0.960	0.952	0.140	6.464
Income	0.501	0.235	4.540	1	0.033	1.651	1.041	2.617
Occupation	−1.208	0.677	3.184	1	0.074	0.299	0.079	1.126
DFL	1.051	0.544	3.738	1	0.053	2.861	0.986	8.306
Constant	4.788	1.693	7.994	1	0.005	120.064

Source: authors analysis.

This study revealed that age and income have a significant influence on DFI at the 5% level of significance. By classified respondent into five categories of age, this study predicted the older people tend to be financially excluded than the younger person. It reflected from the negative significant influence between age and DFI. By investigating a case of financial inclusion, it was also found in Tunisia (Amari & Anis, 2021), Asia (Park & Mercado, 2015), Zimbabwe (Mhlanga, 2022). In addition, the family with higher income intensively used mobile phone to access their accounts, to make a payment, to tranfer their funds to another account, to buy something online, and to invest in financial instrument. The result also proved a significant impact of occupation towards DFI at 10% level of significance. In accordance with Febriana and Damayanti (2017), the head of family’s occupation will encourage the usage of financial system. Conversely, gender and marital status shown an insignificant effect on DFI, it is indicated by the significance value of each partial effect which is more than the significance level, both 5% and 10%. Reversely, Kandari et al. (2021) emphasized that gender has a significant role in determining financial inclusion. The overall model emphasize that demographic factors have contribution in explaining DFI.

Meanwhile, DFL has a significant influence on DFI at the 10% level of significance. In the case of financial inclusion, Febriana and Damayanti (2017) explored the contribution of financial literacy towards financial inclusion and found a significant influence between the two variables. People with better financial literacy will lead intensively to use and to get the access of financial system. Hasan et al. (2022) also highlighted that digital financial literacy on women entrepreneur could enhance the usage of financial services, specifically in supporting their banks’ transactions. Another research showed that the nexus of financial literacy and financial inclusion was mediated by financial self-efficacy (Noor et al., 2022).

The classification of the respondents consists of two groups including they who are categorized digital financial inclusion and another group who are financially excluded. Refer to Table 7, it describes 130 of 135 observed respondents correctly predicted as digitally financial inclusion. It means that 96.3% of the classification of respondents were correct that they are having an access and use the digital financial system. Meanwhile, 5 of 25 observation who were categorized as digital financial exclusion were correctly predicted, thus the correct rate is only 20%. Therefore, the overall percentage correct of respondent’s classification is 84.4%, there were 135 respondents who correctly categorized as DFI or otherwise.

Table 7. Classification matrix.

Step 1	Observed	Predicted
		DFI		Percentage correct
		Digital financial exclusion	Digital financial inclusion
DFI	Digital financial exclusion	5	20	20
	Digital financial inclusion	5	130	96.3
	Overall percentage			84.4

Source: authors analysis.

4. Conclusion

This study empirically proved that DFL and demographic factors have a contribution in explaining DFI. The finding showed that age, income, occupation and DFL influence DFI, but gender and marital status have an insignificant effect on DFI in Indonesian household. Overall model correctly classified 84.4% respondent into the categories of DFI or otherwise. This study filled the research gap about the influence of DFL and demographic factors toward DFI. For better prediction in the future research, we suggest the larger number of observations that use to predict DFI referring to another case. The study implies that the government, especially Indonesia, should be considered to enhance the usage of fintech DFI especially for the bottom of the pyramid. In line with these findings, the government should concern to educate community about the risk of the usage of the digital platforms, and the awareness to control their financial behavior to minimizing the propensity toward indebtedness due to the compulsive and impulsive buying.

Acknowledgement

The authors would like to thank the anonymous reviewers and editors for their excellent comments and significantly improved the quality of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Umi Widyastuti

Umi Widyastuti Head of Master of Management Study Program, Faculty of Economics, Universitas Negeri Jakarta, Indonesia. She is a professor of finance with some topic of research interests in behavioral finance and personal finance including financial behavior, financial literacy, financial inclusion. Many articles have been published on international journal and could be explored from her Scopus ID 57202750342.

Dwi Kismayanti Respati

Dwi Kismayanti Respati is a lecturer at Universitas Negeri Jakarta, Indonesia. She teaches accounting, finance and education subject. She is a researcher in the field of accounting, behavioral finance and entrepreneurship. Scopus ID 57322883600.

Vera Intanie Dewi

Vera Intanie Dewi Head of the Magister and Doctoral Program in the Faculty of Economics, Universitas Katolik Parahyangan (UNPAR), Bandung, Indonesia. Her interest in writing cases, books and research papers includes Digital Finance, Personal Finance, Behavioral Finance, Banking and financial markets. She teaches in the areas of Financial Management strategy and Research Methods for Business and Management. She has received several research grants and awards in research and writing papers.

Abdul Mukti Soma

Abdul Mukti Soma Head of Magister Management, Universitas Teknologi Muhammadiyah Jakarta and Lecturer, Master of Management, Telkom University, Indonesia. He is also a Senior Partner & Lead Researcher at PT. Sigap Tangkas Mandiri, Indonesia. He has some skills and Areas of Expertise: Financial Planning, Financial Literacy & Market Discipline, Financial Risk Management, Performance Management System & Knowledge Management – Learning Organizations, Digital Marketing.