Jurnal Ekonomi dan Bisnis.
Volume 28 No.
2 Oktober 2025, 491 - 512 Journal homepage: w.
edu/jeb ISSN 1979-6471 E-ISSN 2528-0147 Digital financial literacy and consumer financial behavior in emerging markets: Evidence from Indonesia Ratih Kusumawardhania*.
Wijiasih Prihatinb.
Johannes Maysan Damanikc.
Saidah Mubarokahd Faculty of Economic.
Universitas Sarjanawiyata Tamansiswa.
Yogyakarta.
Indonesia.
ratihkusuma@ustjogja.
Faculty of Economic.
Universitas Sarjanawiyata Tamansiswa.
Yogyakarta.
Indonesia.
wijiasih550@gmail.
Faculty of Economic.
Universitas Sarjanawiyata Tamansiswa.
Yogyakarta.
Indonesia.
maysan@ustjogja.
Faculty of Economic.
Universitas Sarjanawiyata Tamansiswa.
Yogyakarta.
Indonesia.
saidah1528@gmail.
ARTICLE INFO
ABSTRAK
Article History:
Received 03-05-2025 Revised 05-05-2025 Accepted 07-07-2025 Penelitian ini menganalisis pengaruh literasi keuangan digital terhadap perilaku berbelanja dan menabung generasi milenial di Indonesia, baik dalam praktik saat ini maupun dalam perencanaan keuangan masa depan.
Studi ini menggunakan pendekatan kuantitatif dengan metode Structural Equation Modeling untuk menganalisis data survei yang dikumpulkan dari milenial Indonesia.
Penelitian ini menggunakan teknik pengambilan sampel purposive sampling.
Sampel dalam penelitian ini adalah 226 responden Generasi Z yang berdomisili di Yogyakarta dan secara aktif menggunakan platform keuangan digital.
Responden dievaluasi berdasarkan tingkat literasi keuangan digital serta kebiasaan berbelanja dan menabung.
Analisis data menggunakan LISREL 8.
8 dan SPSS 0.
Hasil penelitian menunjukkan literasi keuangan digital berkontribusi terhadap pengelolaan keuangan yang lebih strategis, mendorong perilaku konsumsi yang lebih bijak dan praktik menabung yang lebih disiplin, baik saat ini maupun di masa depan.
Studi menunjukkan pentingnya peningkatan literasi keuangan digital dalam membentuk keputusan finansial yang lebih Berbeda dengan penelitian sebelumnya yang fokus pada inklusi keuangan, penelitian ini secara langsung menghubungkan literasi keuangan digital dengan perilaku finansial spesifik, mengisi kesenjangan literatur di pasar negara berkembang.
Temuan ini memberikan wawasan strategis untuk pembuat kebijakan, pendidik, dan institusi keuangan dalam merancang program edukasi yang efektif guna meningkatkan kesejahteraan finansial generasi muda di Indonesia.
Kata Kunci:
Literasi keuangan digital, perilaku berbelanja, perilaku menabung, generasi milenial, perilaku berbelanja masa depan, perilaku menabung masa depan, yogyakarta, perilaku keuangan Keywords:
Digital financial literacy, shopping behavior, saving behavior, millennials indonesia, future shopping behavior, future saving behavior, yogyakarta, financial *Corresponding Author Digital Financial Literacy and Consumer Financial (Kusumawardhani.
Prihatin.
Damanik.
Mubaroka.
ABSTRACT This study examines the impact of digital financial literacy on the shopping and saving behaviors of Indonesian millennials, with a focus on both current practices and future financial intentions.
digital financial services proliferate in emerging markets, understanding their influence on consumer financial decisionmaking is increasingly vital.
A quantitative approach using Structural Equation Modeling was employed to analyze data collected from 226 purposively selected Generation Z respondents .
ged 17Ae.
residing in Yogyakarta.
All participants actively use e-wallets, mobile banking, and fintech applications, and have prior experience with digital shopping and saving.
The methodology ensured sample relevance and construct validity.
Data were processed using LISREL 8.
8 and SPSS 0.
The findings reveal that higher levels of digital financial literacy significantly enhance strategic financial behavior, promoting responsible spending and consistent saving in both present and future contexts.
Unlike prior research that emphasized financial inclusion, this study contributes to the literature by directly linking digital literacy with specific financial behaviors.
The results highlight the need for targeted financial education to strengthen digital capabilities among youth.
Practical implications are offered for educators, policymakers, and financial service providers to foster sustainable financial well-being among millennials in Indonesia and comparable developing economies.
INTRODUCTION
The rapid advancement of technology has transformed various aspects of modern life, particularly in the financial sector.
For digital native generations such as Millennials and Generation Z, financial decision making is increasingly shaped by digital tools (Ayuningtyas & Irawan, 2.
The emergence of financial technology .
, defined as the integration of digital innovations into financial services, has reshaped how individuals save, spend, and manage financial resources.
As fintech platforms become more accessible and widely adopted, financial literacy especially digital financial literacy (DFL) has become a crucial capability to navigate the increasingly complex financial environment (Normawati & Santoso, 2.
However, this digital inclusion brings both opportunities and vulnerabilities.
While access to online financial services grows, many users still lack the necessary digital financial literacy (DFL) to make sound financial decisions in increasingly complex digital environments (Adel, 2024.
Al-Afeef & Alsmadi, 2.
In Indonesia, fintech has experienced exponential growth, with the number of fintech firms increasing from only four in 2006 to 165 in 2016 (Nizar, 2.
This substantial growth has not only influenced the expansion of IndonesiaAos banking sector but also contributed to the development of e-commerce.
The rise of fintech has significantly impacted the profitability of Indonesian banks.
Research-based fintech Jurnal Ekonomi dan Bisnis.
Volume 28 No.
2 Oktober 2025, 491 - 512 innovations have introduced both positive and negative effects, as new technical systems have been developed that surpass the capabilities of traditional banks during the early stages of fintech development in Indonesia (Fadhilah, 2.
However, the rapid development of fintech also brings challenges, particularly in the legal and regulatory domains.
Financial issues exacerbated by the pandemic have posed additional challenges for Indonesian banks, with some negative impacts potentially leading to legal complications.
Fintech, a product of scientific and technological advancements, facilitates financial activities through digitalization processes, utilizing devices, the internet, and application services as the primary technological platforms (Stevani & Sudirman, 2.
Digital financial literacy, defined as the integration of financial knowledge, digital competency, and critical awareness, is now recognized as a cornerstone of responsible financial behavior (Rahayu, 2022.
Zaimovic et al.
, 2.
Unlike traditional financial literacy.
DFL enables users to evaluate fintech tools, understand risk, and engage responsibly in digital consumption and saving.
Recent studies emphasize that DFL not only improves individual outcomes like saving discipline but also mitigates adverse behaviors such as excessive online shopping or impulsive financial decisions (FrIczek & Klimontowicz, 2.
Digital financial literacy is a multidimensional construct that combines financial knowledge, digital skills, and critical awareness (Rahayu, 2022.
OECD, 2.
It includes familiarity with online financial services, the ability to assess risk, and awareness of consumer rights (Figures & Morgan, 2013.
Respati et al.
, 2.
Low levels of DFL have been linked to poor financial decision making, excessive online shopping, and inadequate savings, especially among younger demographics (Wardani & Lutfi, 2019.
Zulaihati et al.
Conversely, enhanced DFL is positively associated with responsible consumption, long-term financial planning, and improved financial well-being (Priyanto et al.
, 2021.
Sivaramakrishnan et al.
, 2.
While the relationship between general financial literacy and financial behavior has been well-documented (Allgood & Walstad, 2012.
Henager & Cude, 2.
, empirical research linking DFL to specific financial behaviors particularly in time-differentiated contexts such as current vs.
future shopping and saving remains Prior studies often generalize financial behavior without isolating its temporal dimensions, missing the opportunity to capture how DFL influences immediate consumption as well as long-term financial planning (Choung et al.
, 2023.
Yadav et , 2.
Moreover, despite Indonesia's status as one of the fastest-growing digital economies, limited research has focused on millennial financial behavior within this context (Yanto et al.
, 2.
Even though this generation is crucial to national financial inclusion plans, little is known about the subtleties of how DFL promotes logical decision-making over various time periods, both present and in the future (Menberu.
Mishra et al.
, 2.
This study aims to investigate the influence of digital financial literacy on four Digital Financial Literacy and Consumer Financial (Kusumawardhani.
Prihatin.
Damanik.
Mubaroka.
key aspects of financial behavior among Indonesian millennials: current shopping behavior, future shopping behavior, current saving behavior, future saving behavior.
By using a quantitative approach with Structural Equation Modeling (SEM), this research provides a robust analysis of the relationships between DFL and both present and future-oriented financial actions.
This research offers several important The study enriches the understanding of Behavioral Finance Theory and the Theory of Planned Behavior (Ajzen, 1.
by introducing DFL as a central variable influencing financial behavior in digital contexts.
By using data from 226 millennial respondents in Indonesia, this study provides new empirical evidence on how digital financial knowledge and skills shape daily financial decisions filling a gap in the literature for emerging markets.
In addition, the results offer strategic insights for policymakers, educators, and fintech developers to design targeted educational programs and user-friendly platforms that foster digital financial capability, especially for younger users.
While the relationship between general financial literacy and financial behavior has been extensively explored, this study introduces a significant novelty by focusing specifically on digital financial literacy (DFL) as a determinant of both shortterm and long-term financial behaviors.
Unlike previous studies that typically examine financial behavior in aggregate terms, this research distinctly separates current shopping behavior, future shopping behavior, current saving behavior, and future saving behavior as independent outcome constructs.
Furthermore, this study provides contextual novelty by investigating millennials in Indonesia, a cohort that plays a pivotal role in the rapid digitalization of emerging markets.
By applying a rigorous Structural Equation Modeling (SEM) approach, the study empirically demonstrates how DFL influences financial decision-making across different temporal dimensions, an area that remains underexplored in the existing literature.
Therefore, this research fills a critical gap by advancing the understanding of the dynamic relationship between digital financial literacy and time-differentiated financial behaviors, thereby enriching the behavioral finance framework within digital contexts.
LITERATURE REVIEW AND HYPOTHESIS
Theory of Planned Behavior The theoretical foundation of this study is primarily grounded in Behavioral Finance Theory, which emphasizes that financial decisions are not always rational but are influenced by psychological and behavioral factors (Barberis & Thaler, 2.
addition, this study is informed by the Theory of Planned Behavior (TPB) proposed by Ajzen (Ajzen, 1.
, which posits that individual behavior is shaped by attitudes, subjective norms, and perceived behavioral control.
Within this framework.
DFL enhances perceived behavioral control by equipping individuals with the knowledge and skills necessary to navigate digital financial systems, thereby promoting more Jurnal Ekonomi dan Bisnis.
Volume 28 No.
2 Oktober 2025, 491 - 512 disciplined shopping and saving behaviors (Hung et al.
, 2.
This integration of digital capabilities into TPB offers a more comprehensive understanding of financial decision-making in the digital age.
Existing empirical studies provide robust support for the positive association between financial literacy and responsible financial For instance.
Allgood & Walstad .
found that both actual and perceived financial literacy significantly affect consumption and saving decisions.
In the context of digital platforms.
FrIczek & Klimontowicz .
Henager & Cude .
demonstrated that DFL not only improves financial outcomes but also mitigates excessive and impulsive online spending.
Furthermore.
Sivaramakrishnan et al.
identified that attitudinal factors and literacy levels are strong predictors of saving intentions and stock market participation.
The proposed model assumes that higher levels of DFL will positively influence both current and future financial behaviors, specifically shopping and saving.
While current behavior reflects immediate decisionmaking under the influence of digital access and awareness, future behavior captures planned financial activities, such as long-term savings and responsible consumption This forward-looking orientation provides a novel contribution to the literature by integrating anticipated behavioral outcomes into the literacy-behavior Digital Financial Literacy Digital financial literacy (DFL) has emerged as a critical antecedent in understanding modern consumer financial behavior, especially in the context of digital transformation in emerging economies.
DFL integrates financial knowledge and digital competencies, enabling individuals to make informed financial decisions through online platforms.
Combining financial knowledge with digital platforms is known as digital financial literacy (Rahayu, 2.
A digital platform is a hardware and software combo that uses internet and computer technology.
Conversely, assert that familiarity with online banking platforms, payment methods, and transactions is a prerequisite for digital financial literacy (Respati et al.
, 2.
Figures & Morgan .
proposed four dimensions in DFL, such as knowledge of risk management, awareness of threats to the digital financial domain, comprehension of digital financial products and services, and knowledge of consumer rights and loss processes.
Four characteristics are used to quantify digital financial literacy: comprehension, experience, awareness, and abilities (Alysa et al.
, 2.
According to Haryati .
, digital finance is a financial service in digital form that we can use as a means of payment without having to make physical contact.
This will later change the traditional transaction system in the community.
The benefits of digital finance are transparency and monitoring of digital and real money.
Financial literacy is defined as Auknowledge, skills, and beliefs that influence attitudes and behavior to improve the quality of decision-making and financial management to achieve prosperityAy by the financial services authority, as outlined in the Indonesian National Financial Literacy Strategy.
From a consumer standpoint.
Digital Financial Literacy and Consumer Financial (Kusumawardhani.
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financial literacy and education can help people make better purchasing decisions and get good and services for less money.
To prevent financially harmful transactions, prevent becoming a victim of fraud, and utilize their rights as consumers, financial education is also crucial.
People's ability to make critical financial decisions about how much to save and spend on digital platforms can be impacted by a lack of DFL.
Results from earlier studies demonstrate that financial behavior, such as saving and shopping habits, is significantly influenced by financial literacy (Allgood & Walstad, 2012.
Lone & Bhat, 2024.
Putri & Miharti, 2021.
Sivaramakrishnan et al.
, 2017.
Zulaihati et , 2.
Financial literacy in digital finance is crucial regarding how digital financial technology influences saving and shopping habits.
If consumers are aware of DFL, excessive shopping can be avoided.
Furthermore, when DFL gets better, further savings may become available (Wardani & Lutfi, 2.
discovered that inadequate financial knowledge can result in future savings and increased shopping.
Even if poor shopping and saving choices are harder to see now, they are more likely to happen later on and can have major effects on long-term financial security (Wardani & Lutfi.
Digital Financial Literacy and Shopping Behavior Several stages carried out by consumers before deciding to purchase a product (Kotler & Keller, 2.
Purchasing behavior encompasses both the decision-making process that leads to these actions and the actions that are immediately related to exchanging money for products and services.
Both physical and mental activity are involved in consumer purchase decisions, as demonstrated by the customers' actual activities throughout the decision-making process .
when they assess things based on personal standard.
Buyers purchasing decisions result from several wellorganized decisions combined.
According to Misnawati & Sumarni .
, there are seven main factors influence every purchasing choice.
These factors include the product type, form, brand, sales, amount, timing of the purchase, and payment method.
To sum it up, buying decisions are the results of a person's cognitive processes as they choose which goods to buy and how to persuade someone to buy them.
Digital financial technology affects saving behavior in addition to purchasing (Moenjak et al.
, 2.
It is impossible to stop people's excessive consumption habits from growing because they can negatively impact money management, which can lead to a high frequency of use of mobile payment services (Agarwal et al.
, 2.
Additionally, his research revealed that Indian shopping increased as a result of the use of digital payments assisting with .
e Bassa Scheresberg, 2.
which states that fintech has a positive effect on financial behavior.
However, contrary to Haqiqi & Pertiwi .
that highlight the lack of any discernible impact of financial technology on financial conduct.
It is impossible to separate the importance of financial literacy from the process of managing finances, including finance (Herawati et al.
, 2.
However, it is different from Herawati et al.
that financial conduct is unaffected Jurnal Ekonomi dan Bisnis.
Volume 28 No.
2 Oktober 2025, 491 - 512 by financial knowledge.
According to a study, digital payments have increased extravagant shopping in Singapore (Agarwal & Zhang, 2.
Fintech, therefore, has the power to alter behavioral patterns.
Digital financial technology has the potential to influence saving behavior in addition to shopping behavior (Hamzah & Suhendar, 2.
The percentage of adults that save outside of financial institutions, including digital savings, and in financial institutions differs Savings in financial institutions are generally on the decline, whereas savings in cash are on the rise.
This could provide digital savings a chance to participate in the financial system.
Thus, regulators must introduce digital savings into the regulated financial industry.
Someone who actively engages in the decisionmaking process about the procurement and utilization of commodities, as well as the planning and execution of shopping activities, is said to exhibit shopping behavior.
Based on the analysis as mentioned above, the following hypotheses are proposed:
H1: Digital financial literacy has a positive effect on current shopping H2: Digital financial literacy has a positive effect on future shopping behavior.
Digital Financial Literacy and Saving Behavior Savings is the difference between net worth after a period and its beginning or the excess of income over consumption expenditures in a given period (Oppenheim et , 2.
Madhavapeddy & Minsky .
suggest that savings are a portion of income within a period that is not fully utilized.
Therefore, savings can also be interpreted as the remaining income not used for needs.
Saving behavior is what a person does daily to help him reach his future objective (Raszad & Purwanto, 2.
Saving behavior requires a person to be disciplined in managing financial affairs according to future needs.
Saving, as a frugal trait, can be a positive trait if it consistently leads to a better quality of life.
Saving also means an activity carried out by not shopping for money for the current period because it is for future use.
People tend to define saving more broadly as investment activities, putting money in bank accounts, speculating, and paying off mortgages (Madhavapeddy & Minsky, 2022.
Oppenheim et al.
, 2.
Meanwhile.
Sukirno .
defines saving as an activity related to storage, income distribution, and consumption.
The allowance is made with several objectives: storage or productive capital investment.
The definition of saving in Indonesian is described as an action taken by an individual to store money, either tangible or intangible, in a secure location such as a bank, post office, piggy bank, etc.
A person who practices saving behavior sets aside a portion of his money to be saved and utilized later.
The quantity of money received for consumption and the amount set aside for savings determine a person's saving behavior.
The desire and ability to save Digital Financial Literacy and Consumer Financial (Kusumawardhani.
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Mubaroka.
also arise when the two are combined- the willingness to save and the ability to savebecause of these two factors.
Good economic behavior will result in the well-being of each individual if both are sustainable.
The author concludes that saving behavior is defined as deferring consumption for purposes of future security based on the definition provided by experts.
Based on the analysis as mentioned above, the following hypotheses are proposed:
H3: Digital financial literacy has a positive effect on current saving behavior.
H4: Digital financial literacy has a positive effect on future saving behavior.
RESEARCH METHODS
This quantitative research study employs the Structural Equation Modeling (SEM) method with SPSS 0.
25 and LISREL 8.
8 software to analyze the impact of Digital Financial Literacy (DFL) on current Shopping Behavior (BSB), future Shopping Behavior (BSV), current Saving Behavior (FSB), and future Saving Behavior (FSV).
The millennial generation, which uses digital financial services in Indonesian cities, makes up the study's sample.
The I respondents in the millennial generation sample are between the ages of 18 and 40 and were born after 1980 (Moore et al.
, 2014.
) This work applied structural equation modeling (SEM) for data analysis.
It can be used to explain the relationship between simultaneous variations in variables and variables that are not changeable throughout time, which is structural equation According to Jyreskog & Syrbom .
SEM stands for structural equation model, a structural generation model of two multivariate analyses that gives researchers detail information about a specific model and the ability to investigate the link between complicated variables.
Tabri & Elliott .
claim that the number of indicators with a ratio of 5-8 can be used to modify the minimum sample size for SEM.
Accordingly, if this study contains forty indications .
efer to Appendix A), one can compute the minimal sample size necessary by multiplying 40 by 5 to get 200 This study complied with the minimal sample requirements because 226 samples were gathered According to Sugiyono .
, a research instrument is a tool used for gathering data for studies, as well as a means of measuring observed social and environmental events.
In order to provide correct data, research instruments are made theoretically and used as a base.
This study employed a Likert scale questionnaire as its tool.
A questionnaire is a tool used in data-collecting procedures where a form with questions directed at an individual or group of individuals is used to gather the information the researcher needs.
In this research, the questionnaire was created using the Google Form service.
The questions asked in the questionnaire are closed questions.
Closed questions refer to questions with answer choices.
The questions in the questionnaire or research Jurnal Ekonomi dan Bisnis.
Volume 28 No.
2 Oktober 2025, 491 - 512 instrument validity test were carried out five times, namely the first validity test on the digital financial literacy variable, the second on the current shopping behavior variable, the third on the future shopping behavior variable, the fourth on the current saving behavior variable, and the fifth on the future saving behavior variable using a Likert scale on 226 respondents.
To test the validity of researchers using SPSS 25.
using the correlation coefficient person significant level of 5%.
The acceptable validity value is if the r count exceeds the r table.
While the acceptable reliability test value is if the reliability coefficient (Cronbach's Alph.
is > 0.
6 (Sugiyono, 2.
The operational definition of research variables displayed Table 1.
No.
Table 1 Operational Definition of Research Variables Variable Indicator Digital Financial Literacy Understanding of digital payments Digital Financial Literacy Understanding of digital assets Digital Financial Literacy Understanding of alternative digital Digital Financial Literacy Understanding of digital insurance Digital Financial Literacy Experience with financial technology for payments Digital Financial Literacy Experience with fintech for financing and investment Digital Financial Literacy Experience using fintech products Digital Financial Literacy Awareness of digital financial risk Digital Financial Literacy Ability to manage financial activities on digital platforms Digital Financial Literacy Control over digital financial activities Current Saving Behavior Saving for transaction purposes Current Saving Behavior Saving to accumulate wealth Current Saving Behavior Saving for precautionary reasons Current Saving Behavior Saving for retirement planning Current Saving Behavior Saving for legacy purposes Current Saving Behavior Perceived financial independence via digital platforms Current Saving Behavior Perceived security of saving digitally Current Saving Behavior Satisfaction in saving digitally Current Saving Behavior Routine digital saving habit Current Saving Behavior Expected future digital spending Current Shopping Frequent online shopping via eBehavior Current Shopping Preference for digital platform Behavior Current Shopping Higher spending via digital platforms Behavior Current Shopping Buying domestic products via digital Behavior Current Shopping Purchasing personal items digitally Behavior Current Shopping Shopping for entertainment via digital Behavior Current Shopping Shopping on digital platforms for Behavior Future Financial Expected future spending via digital Perspectives Future Financial Future digital lifestyle adoption Measurement Scale Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 Likert Scale 1Ae5 No.
Digital Financial Literacy and Consumer Financial (Kusumawardhani.
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Variable Perspectives Future Financial Perspectives Future Financial Perspectives Future Financial Perspectives Future Financial Perspectives Future Financial Perspectives Future Financial Perspectives Future Financial Perspectives Future Financial Perspectives Indicator Measurement Scale Digital enjoyment usage in the future Likert Scale 1Ae5 Future digital comfort usage Likert Scale 1Ae5 Perceived freedom to save digitally in the future Perceived future transaction security Likert Scale 1Ae5 Future satisfaction in digital finance Likert Scale 1Ae5 Precautionary future digital saving Likert Scale 1Ae5 Speculative future digital usage Likert Scale 1Ae5 Digital saving for inheritance purposes Likert Scale 1Ae5 Likert Scale 1Ae5 Hypothesis testing is carried out to determine if the researcher's hypothesis is accepted or rejected.
Analysis employing structural equation modeling, or SEM, is used for hypothesis testing.
SEM is not only used to approve or reject a hypothesis but it may also be used to assess how well the researcher's model fits the data.
The model's appropriateness is assessed using the model fit test.
Three steps include the overall model fit test, measurement model fit test, and structural model fit test in the model fit test procedure.
To determine the degree of fit or Goodness of Fit Indices (GOFI).
overall model fit test is conducted.
By comparing the GOFI measure calculated from the model with the established GOFI measure criteria, the GOFI measure is used to assess the model's overall fit.
Using specific metrics, the GOFI measure indicates if a model is excellent.
RESULTS
This study displays the overall model specification together with 40 observed indicators and 5 latent variables.
All of the data derived from observed variables are handled as continuous data.
After the data is formed into an asymptotic covariance matrix, it is analyzed using LISREL 8.
8 software utilizing a data system file as input data Figure 1 shows statistical test results.
Jurnal Ekonomi dan Bisnis.
Volume 28 No.
2 Oktober 2025, 491 - 512 Figure 1 Structural Equation Modelling Results Figure 1 explains which of the 11 indicators, the three strongest indicators can explain the digital financial literacy variable, namely DFL1 with an influence value of 40.
DFL6 of 1.
27, and DFL10 of 0.
Meanwhile, out of 7 indicators, 4 most vital indicators can explain the current shopping behavior variable, namely BSB1 with an influence value of 0.
BSB4 of 0.
BSB5 of 1.
03, and BSB7 of 1.
Furthermore, out of 10 indicators, seven indicators can explain the future shopping behavior variable, namely BSV1 with an influence value of 1.
BSV3 of 0.
BSV4 of 0.
BSV6 of 1.
BSV7 of 0.
BSV8 of 1.
10, and BSV10 of 0.
The strongest indicator that can explain the current saving behavior variable is FSB1, with an influence value of 0.
FSB2 of 1.
67, and FSB4 of 1.
74, while in the future saving behavior variable, all indicators can strongly explain the variable because the influence value of all indicators > 0.
A comprehensive model display with non-standardized estimated numbers is what the combination of Basic model estimates recurrence.
can be seen in Figure 4.
8, the model has 43 degrees of freedom .
f = .
With a pvalue of 0.
000 less than 0.
05, the resulting Satorra-Bentler scale value is 1039.
Because RMSEA < 0.
05, this investigation likewise concluded that all models are (Hair et al.
, 2.
state that various steps are involved in determining how well the data fit the model.
The model fit metrics used in this study are displayed in Table Digital Financial Literacy and Consumer Financial (Kusumawardhani.
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GOF measure Statistic Chi-Square (X.
Non-centrality parameter (NCP) The goodness of fit index
Root mean square of residuals (RMR) Root Mean square error of approximation (RMSEA) Adjusted Goodness of Fit Index
(AGFI)
Trucker- Lewis Index or NonNormed fit Index (TLI or NNFI) Normed Fit Index (NFI) Relative of Index (RFI) Parsimonious Normed Fit Index
(PNFI)
Parsimonious Goodness fit Normed Chi-Square Table 2 Model Fit Measures Acceptable Suitability Level Absolute-Fit Measures Following statistical tests relating to significance requirements.
The smaller the better.
They are expressed as a re-specification of chi-square.
The assessment is based on a comparison between models.
The smaller, the better Values range from 0-1, with higher values being better.
GFI >
90 is good-fit, while 0.
80 < GFI
90 is a marginal fit.
The mean residual between the observed and estimated .
orrelation or covarianc.
Standardized RMR < 0.
05 is a good fit.
The average difference per degree of freedom is expected to occur in the population and not in the sample.
RMSEA < 0.
08 is a good fit, while RMSEA < 0.
05 is a close fit.
Incremental fit measures Values range from 0-1, with higher values being better.
AGFI >
90 is a good fit, while 0.
80 < AGFI < 0.
90 is a marginal fit.
Values range from 0-1, with higher values being better.
TLI >
90 is good fit, while 0.
80 < TLI < 0.
90 is a marginal fit.
Values range from 0-1, with higher values being better.
NFI >
90 is a good fit, while 0.
80 < NFI < 0.
90 is a marginal fit.
Values range from 0-1, with higher values being better.
RFI >
90 is a good fit, while 0.
80 < RFI < 0.
90 is a marginal fit.
Parsimonious fit measures High values indicate a better fit.
they are only used for comparisons between alternative models A re-specification of the GFI, where higher values indicate greater parsimony.
This measure is used to compare models.
The ratio of the chi-square is divided by the degree of freedom.
Suggested values: lower limit: 1.
0, upper limit: 2.
0 or 3.
0, and more loosely, 5.
Prior to hypothesis testing, model fit was evaluated using multiple goodness-of-fit (GOF) indices to ensure structural model adequacy.
As shown in Table 3, most indices meet recommended thresholds, indicating an acceptable to good model fit.
Table 3 Fit Test Results Small value Estimation Results X= 1039.
Satorra-Bentler Scale P-value >0.
P= 0.
Not good NCP Interval Small value Narrow interval RMSEA < 0.
Good fit RMSEA P .
lose P-value > 0.
Good fit
ECVI
Values that are small and closer to saturated ECVI Good fit GOF measure Target match rate Suitability Level Jurnal Ekonomi dan Bisnis.
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GOF measure CAIC
NFI
NNFI
CFI
IFI
RFI
RMR
GFI
AGFI
Target match rate Values that are small and closer to the saturated CAIC NFI > 0.
NNFI > 0.
CFI > 0.
IFI > 0.
RFI > 0.
CN > 200
Standardize RMR < 0.
GFI > 0.
AGFI > 0.
Estimation Results Suitability Level Good fit Good fit Good fit Good fit Good fit Good fit Good fit Not so good Marginal fit Good fit Based on Table 3.
Since the NCP value produced, which is 303.
59, is within the width of the 90% confidence range of the NCP, which is .
, it can be said that the model fits the data well overall.
The estimation findings RMSEA value 04 < 0.
08, indicating a robust overall fit for the model, and the value of the RSMEA 064, supporting this conclusion.
Other GOFI measurements that show the model's overall fit include NFI.
NNFI.
CFI.
IFI, and RFI.
The RFI > 0.
90 and the predicted values of the NFI.
NNFI.
CFI, and IFI measurements.
The model's overall fit size is good, as indicated by the NFI's value of 0.
92, the NNFI's measure of 0.
99, the CFI's assessment of 0.
99, the IFI's measure of 0.
99, and the RFI's measure of 0.
With an expected value of CN > 200, the critical number of samples (CN) indicates whether or not the study's sample size is adequate to create a well-fitting model using the chi-square statistical test.
According to the estimation findings, the CN achieved is 235.
31, indicating that there is enough sample data to support the proposed model and make it suitable .
it mode.
RMR.
GFI, and AGFI are additional metrics that show how well the model fits the data overall.
There is a 0.
064 RMR
The expected value < 0.
05 is used to calculate the RMS measure of the model's overall fit and thus leads to the conclusion that the model fit is not excellent.
GFI's estimated fit.
size is 0.
81, while AGFI's is 0.
Fit size criteria for GFI and AGFI are > 0.
80 marginal fit > 0.
90 good fit.
Thus, it may be said that the model's overall fit is only marginal.
Further details regarding the overall model fit are provided in Figure 2.
Figure 2 Overall Model Fit Digital Financial Literacy and Consumer Financial (Kusumawardhani.
Prihatin.
Damanik.
Mubaroka.
The results of the structural model analysis, including path coefficients and their significance levels, are presented in Table 4.
Path DFL Ie BSB DFL Ie BSV DFL Ie FSB DFL Ie FSV Table 4 Hypothesis Testing Results T-values Criteria Hypothesis > 1.
> 1.
> 1.
> 1.
Conclusion Accepted Accepted Accepted Accepted It is evident from Table 4's findings of the hypothesis testing that all of the research team's hypotheses are accepted with a T-statistical value greater than 1.
This investigation used a 95% significant value .
lpha = 0.
for hypothesis testing, and the result was 1.
The results are significant if the t-value is higher than the ttable value (Amiruddin et al.
, 2.
DISCUSSION
We applied the Structural Equation Modelling (SEM) technique for data analysis in this study using LISREL 8.
8 software.
The results in Table 3 show that knowing about digital finances has a big effect on how people shop now.
The t-statistic value of 6.
18 is higher than the important level of 1.
96, which proves that the effect is statistically significant.
The study's indicators assess an individual's familiarity with a diverse range of digital financial services and products, including mobile banking and e-money, and their experience with fintech platforms for financing, asset management, payments, and investments.
The data further suggests that respondents exhibit a high level of awareness regarding the potential risks associated with the use of fintech This heightened awareness enables individuals to utilize fintech products and services more effectively and efficiently while also managing risks in a more prudent Consequently, the findings of this study suggest that individuals with higher levels of digital financial literacy are more likely to make informed and judicious purchasing decisions in the current marketplace.
The study's conclusions align with prior research, including the work of Respati et al.
FrIczek & Klimontowicz .
, which also identified a positive correlation between financial literacy and consumer behavior.
Furthermore.
FrIczek & Klimontowicz .
observed that insufficient financial literacy may contribute to higher levels of over-shopping among younger consumers.
A t-statistic score of 9.
35 indicates that digital financial literacy exerts a significant impact on future purchasing behavior, as the t-statistic value exceeds the critical threshold of 1.
This finding is based on study variables that represent individuals who frequently engage in shopping on e-commerce platforms such as Shopee and Tokopedia, demonstrating a strong preference for digital platforms over traditional methods, such as shopping at conventional markets or supermarkets.
The Jurnal Ekonomi dan Bisnis.
Volume 28 No.
2 Oktober 2025, 491 - 512 data reveals that consumers are increasingly inclined to conduct transactions online due to the perceived ease and efficiency of searching for products, as well as the appeal of various promotions and discounts offered by these platforms.
Additionally, some consumers engage in online shopping as a means of relaxation and stress relief, indicating a psychological connection to the convenience afforded by digital Digital financial literacy plays a crucial role in helping these individuals manage their finances effectively, ensuring that their shopping behaviors remain within budgetary constraints and align with their broader financial goals.
The test results in this study support H2, confirming that digital financial literacy positively influences future purchasing behavior.
These findings are consistent with prior research, such as Peng et al.
, who discovered that individuals with greater financial awareness are more likely to make prudent financial decisions.
Similarly.
Chen & Volpe .
suggest that as financial literacy increases, consumption patterns tend to moderate, thereby reducing the likelihood of impulsive or unwise financial decisions.
Furthermore.
Chen & Volpe .
assert that individuals with a higher degree of financial literacy are more likely to save for their future well-being.
The study also shows that knowing how to use technology to manage money has a big effect on how people save money now, as shown by the fact that the t-statistic value is higher than the important level of 1.
The indicators used in this study reflect an individual's decision to save money through digital financial products, such as for speculative purposes, in anticipation of future needs, or in preparation for retirement, indicating that digital financial platforms have become the preferred method for individuals to manage their savings effectively.
The regular utilization of digital financial platforms for savings and daily transactions indicates a positive trend in the adoption of digital financial technology.
This study's significant findings support Hypothesis H3, which states that current saving behavior is positively impacted by digital financial literacy.
This conclusion is consistent with earlier studies by Henager & Cude .
, who discovered similar positive correlations between current saving behavior and financial literacy.
Furthermore, a t-statistic value of 8.
17, exceeding the critical threshold of 1.
indicates that digital financial literacy has a considerable impact on future saving behavior, according to the data analysis results.
The study's indicators refer to individuals having the autonomy to save through digital platforms.
The belief that the security of digital transactions is well-protected and will continue to improve demonstrates confidence in the enhancement of security infrastructure in online Future trends suggest that as digital technology becomes more widely integrated into various facets of life, the use of digital platforms for transactions will also increase.
This reflects confidence in the benefits and practicality of using digital platforms in daily activities.
The growing use of digital platforms for prudential purposes such as retirement planning or long-term investments indicates that Digital Financial Literacy and Consumer Financial (Kusumawardhani.
Prihatin.
Damanik.
Mubaroka.
individuals are becoming more conscious of the benefits and potential of these technologies for prudent financial management.
The study's noteworthy findings lead to the acceptance of hypothesis H4, which states that digital financial literacy influences future saving behavior.
According to the research by Hung et al.
although poor financial choices may not be immediately apparent, they can have significant effects on long-term financial security.
The acceptance of the fourth hypothesis indicates a notable correlation between digital financial literacy and the following behaviors: current shopping, future shopping, current saving, and future The findings of this investigation serve as a foundation for future research, emphasizing the importance of digital financial literacy in influencing both present and future financial behaviors.
Future studies could build upon these results by incorporating additional variables to generate more comprehensive conclusions.
This would deepen the understanding of how digital financial literacy impacts financial decision-making among different demographic groups, thereby contributing valuable insights to the field of financial management.
CONCLUSIONS.
LIMITATIONS AND SUGGESTIONS
The primary goal of this research is to assess the influence of digital financial literacy on current and future shopping behaviors, as well as current and future saving The study investigates four hypotheses, with the findings indicating that financial literacy significantly impacts the examined variables.
Specifically, the study demonstrates that individuals with higher digital financial literacy are better equipped to manage their financial activities via digital platforms, leading to more informed decision-making regarding expenditures and savings.
The research highlights that high levels of digital financial literacy foster more strategic and planned shopping The respondents' preference to allocate savings to digital financial products for purposes such as speculating, anticipating future needs, preparing for retirement, and participating in inheritance fund programs serves as evidence.
Digital platforms have increasingly become the preferred method for managing personal savings, underscoring the role of digital financial literacy in creating opportunities for more effective and secure financial management in the future.
The findings suggest that fintech is not only revolutionizing daily transactional processes but is also providing a platform for speculative investments and more complex financial management activities.
The study forecasts that the use of digital platforms for investment purposes and retirement programs will continue to grow, further demonstrating how these technologies can offer more integrated and efficient solutions for long-term financial planning and asset management.
This transformation reflects how fintech is not merely altering the way individuals transact but also reshaping the broader landscape of financial planning.
In conclusion, the research suggests that individuals with higher levels of digital financial literacy will behave Jurnal Ekonomi dan Bisnis.
Volume 28 No.
2 Oktober 2025, 491 - 512 more rationally and strategically in terms of both saving and shopping, particularly as they plan for future investments.
This enhanced financial literacy enables individuals to better navigate digital financial products and services, thereby contributing to more sustainable financial decision-making in the long term.
This study contributes theoretically by enhancing the debate on digital financial literacy (DFL) as a multifaceted construct affecting financial behavior.
The study enhances the comprehension of how digital capabilities influence both immediate and prospective financial decisions by incorporating DFL within the frameworks of Behavioral Finance Theory and the Theory of Planned Behavior (TPB).
The categorization of financial behavior into present and prospective shopping and saving offers a fresh viewpoint that fills existing gaps in the literature, especially within emerging market environments.
This paradigm may provide a basis for future models examining dynamic decision-making processes affected by technology The study offers significant insights for stakeholders, including financial educators, fintech innovators, and legislators.
The results indicate that improving DFL among millennials may foster more responsible consumption and disciplined saving As digital platforms proliferate, it is essential to equip consumers with the appropriate knowledge and skills.
Educational institutions ought to integrate DFL into curriculum development, while fintech enterprises should prioritize the creation of user-friendly, transparent, and secure interfaces.
Policymakers may formulate specific initiatives or rules that promote digital financial inclusion while mitigating hazards linked to low literacy levels.
These activities can jointly enhance financial resilience and promote long-term well-being, particularly among younger, technologically adept The objective of this study is to examine whether digital financial literacy significantly influences decisions related to current and future shopping, saving behaviors, and shopping habits.
We collected data through a survey that targeted millennials, defined in this study as individuals aged 18 to 40.
Despite the survey's design ensuring confidentiality and anonymity, a number of potential respondents declined to participate due to concerns about disclosing their financial information.
Additionally, some respondents indicated difficulty in comprehending certain aspects of the questionnaire, which further contributed to non-participation.
Subsequent studies should increase the sample size by including respondents from a broader range of geographic areas within Indonesia to improve the robustness of future research.
Expanding the study to encompass more diverse regions would likely enhance the generalizability of the findings.
Furthermore, incorporating additional variables into the analysis could provide a more comprehensive understanding of how digital financial literacy impacts financial decision-making.
By increasing both the number of respondents and the complexity of the variables under study, future research could yield more nuanced and conclusive insights.
Digital Financial Literacy and Consumer Financial (Kusumawardhani.
Prihatin.
Damanik.
Mubaroka.
REFERENCES