Adoption of Digital Technology and Financial Knowledge: Strategies for Achieving Sustainable Performance of MSMEs

Abstract

Micro, small and medium enterprises (MSMEs) contribute significantly to Indonesia’s economic growth. In an increasingly digitalised era, MSMEs face challenges and opportunities that affect their performance. Technology adoption will have an impact on operational efficiency and ease of transactions, providing added value for consumers. Meanwhile, good financial management depends on the level of financial literacy and inclusion of MSME players. This study aims to examine the factors that influence the sustainable performance of MSMEs from the aspects of technology adoption and financial knowledge. The independent variables include automation, digital payments, financial inclusion and financial literacy, and the dependent variable is MSME performance. This study uses primary data in the form of questionnaires, and data processing uses SEM-PLS. Statistical test results show that the variables of business automation and financial literacy have a positive effect, while the variables of digital payments and financial inclusion have no effect. The results of the study show that financial literacy is an important key to MSME performance and the importance of business automation that affects efficiency through technology. The results of this study are expected to provide useful recommendations for MSME actors and policymakers in formulating strategies to improve the competitiveness of MSMEs.

1. Introduction

Micro, small, and medium enterprises (MSMEs) play an important role in Indonesia’s economic activities, contributing 61.07% to the gross domestic product (GDP) and employing 97% of the total workforce (Kemenko Perekonomian, 2025). The number of MSMEs continues to increase, reaching 66 million units in 2023 (Ilham, 2025). However, MSME businesses face many challenges, including limited capital, access to finance, technology and financial management (Yaya et al., 2023). This has an impact on the very low sustainability rate of MSMEs, where 25% fail within the first two years, 45% fail after five years, and 65% fail after 10 years (Mariska, 2024).

According to Pratama et al. (2023), the adoption of digital technology will strengthen the competitiveness of MSMEs, as it has several advantages. Digital technology, for example, in the form of digital payments and automation, has advantages in terms of operational efficiency, ease of transactions and a higher level of security (Bakrie et al., 2024; Moreira et al., 2023). The development of digital payments such as QRIS, which now has 48.90 million users with a transaction value of 194.06% yoy in 2023 (Hidranto, 2024), opens up opportunities for MSMEs to take advantage of it to increase sales. Digital payments have a positive impact on improving the financial performance of MSMEs and encourage their sustainability (Daud et al., 2022). However, the use of this technology is still not optimal in MSMEs, so MSMEs must continue to be encouraged to utilise technological advances to support their business operations (Purba et al., 2024; Saputri, 2021).

On the other hand, the level of financial knowledge and management of MSMEs still needs to be improved, including in terms of financial inclusion and financial literacy. Financial inclusion refers to the opportunity to have access to formal financial services, while financial literacy is the ability to manage finances well (OJK, 2023). According to the OJK’s National Survey of Financial Literacy and Inclusion (SNLIK), the financial literacy index of the Indonesian people in 2022 is still low, at 49.6%, while financial inclusion has reached 85% (komdigi.go.id, 2024). However, financial literacy and access by MSMEs is still limited because not all MSMEs are able to meet the financing requirements set by banks (Nugraheni & Muhammad, 2024). In fact, with good financial literacy, MSMEs have a greater chance of surviving in the long term (OECD, 2024).

This study aims to examine the factors that influence MSME performance from the aspects of digital technology and financial management. These factors refer to studies (Daud et al., 2022; Pratama et al., 2023), which state that digital payments and financial literacy have a positive effect on MSME performance, and studies (Cohen & Nelson, 2011; Fomum & Opperman, 2023; Saskia et al., 2023), which find that financial literacy and financial inclusion are closely related to MSME performance. Financing from financial institutions greatly influences the progress of MSMEs (Fomum & Opperman, 2023). Therefore, technological advances will add value to business activities, not only facilitating production activities but also product marketing and customer service activities, including MSMEs. On the other hand, financial management is also key to business development (Darma & Handoyo, 2022), where well-managed capital and profits can improve MSME performance (Bakrie et al., 2024; Oussouadi & Cherkaoui, 2024; Saputri, 2021).

This research is important for several reasons: first, the government is aggressively promoting financial inclusion and literacy to increase productivity and develop MSMEs. MSMEs are a priority target in the government’s financial literacy and inclusion programme (Kemenko Perekonomian, 2025) because they can promote more stable business performance. Second, the adoption of technology can increase transactions (i.e., digital payment methods) and business efficiency (i.e., automation). Thus, the results of this study are expected to provide a comprehensive analysis of the key factors that can affect MSME performance in the digital era, so that MSMEs can increase transactions, operational efficiency, access to formal financial services, and improve financial management capabilities. Understanding the relationship between the research variables and MSME performance can also provide input for stakeholders (government, regulators, and financial institutions) to take strategic steps that can encourage productivity, competitiveness, and more stable MSME business performance. For example, the government can create training programmes related to digitalisation for MSMEs, develop low-cost digital-based MSME business models, and create MSME-specific financial products by banks, thereby strengthening the MSME ecosystem in Indonesia. Therefore, this study aims to answer the following primary research question: What is the impact of digitalization (through digital payments and automation) and financial knowledge (through financial inclusion and financial literacy) on the performance of MSMEs in Indonesia?

2. Literature Review

2.1. Resource-Based View (RBV) Theory

Resource-Based View (RBV) theory argues that superior company performance is determined by the ownership of resources and capabilities (Darcy et al., 2014). The adoption of technology in the form of digital payments and automation can be seen as a technological capability that provides a competitive advantage, for example, by providing faster processes and better services than competitors (Maskuri et al., 2024; Miran et al., 2025). On the other hand, access to finance (inclusion) is a critical financial resource for growth, while financial literacy demonstrates human capital capabilities that enable the effective management of financial and technological resources (Frimpong et al., 2022). Thus, the combination of technological, financial, and human capabilities can together build the core competency of MSMEs to achieve superior and sustainable performance.

2.2. Micro, Small and Medium Enterprises (MSMEs)

In general, Micro, Small and Medium Enterprises (MSMEs) in Indonesia are defined based on net wealth and annual turnover criteria. According to Government Regulation No. 7 of 2021, Micro Enterprises are productive business units owned by individuals with a maximum net wealth of IDR 50 million and an annual turnover of up to IDR 300 million. Small Enterprises are independent economic entities with a net worth of between IDR 50 million and IDR 500 million and an annual turnover of IDR 300 million to IDR 2.5 billion, while Medium Enterprises are productive economic enterprises that are not subsidiaries or branches of large companies with a net worth of IDR 500 million to IDR 10 billion and an annual turnover of IDR 2.5 billion to IDR 50 billion. MSMEs play a strategic and multifunctional role as the main pillar of the national economy, not only as the largest contributor to Gross Domestic Product (GDP) and creator of open employment, but also as a massive absorber of labour, a driver of income equality, and a forum for the development of entrepreneurship and innovation at the grassroots level (Ariyanti, 2023; Gameti & Morrish, 2025; Kemenkeu, 2023). However, in their journey, MSMEs face various complex classic challenges, such as limited access to capital and formal financial institutions, limited managerial and technical capacity, difficulties in marketing and expanding distribution networks, and low capacity for technology and innovation adoption, all of which have the potential to hamper their productivity and competitiveness.

The existing literature on the influence of these variables on MSME performance presents mixed results, with some studies demonstrating a positive relationship and others showing no significant effect. The impact of digitalization and financial access on performance has been widely studied, yet findings are not always consistent. In the banking sector, Kaddumi et al. (2023) found that financial inclusion, automation, and alternative payment methods positively affect financial performance. This positive effect is also observed among MSMEs; for instance, Musyaffi et al. (2024) concluded that digital payments improve financial performance and encourage sustainability. Supporting this, Bapat et al. (2023) states that technology use can enhance MSME productivity, efficiency, and competitiveness. The role of financial literacy is also critical, as it empowers individuals to apply financial knowledge in decision-making (Amnas et al., 2024).

However, a contrasting perspective suggests these relationships are not so straightforward. Some studies argued that the direct causal link between digital adoption and performance is often ambiguous, as benefits can be negated by high costs or a lack of strategic integration (Damane & Ho, 2024; Wiranatakusuma & Latief, 2024). Similarly, concerning financial inclusion, Banerjee et al. (2015) contends that mere access to financial services is insufficient for improving performance if MSMEs lack the necessary literacy to utilize funds effectively.

3. Research Method

This study utilised primary data in the form of questionnaires related to the variables under investigation. The questions in the questionnaire consisted of respondent demographics and questions related to dependent and independent variables. The sample used purposive sampling, where the questionnaire was addressed to MSME actors who had used digital payments in Indonesia. The questionnaire was measured using a 1–5 Likert scale, where 1 indicates strongly disagree and 5 indicates strongly agree.

There are some steps in this study. First, questionnaire preparation and respondent selection. This step consists of first, preparing a questionnaire based on previous research and adapted to the topic and target respondents. The indicators for each latent variable (Digital Payments, Automation, Financial Inclusion, Financial Literacy, and MSME Performance) were developed through a rigorous process to ensure content validity. The constructs were operationalized based on previous studies (Baker et al., 2023; Febriansyah et al., 2024; Lontchi et al., 2023; Miran et al., 2025) which tailored to the context of Indonesian MSMEs. Indicators of digital payments focused on aspects of transaction ease, security, and customer reach. Automation measured the use of technology for automating tasks to improve the operational efficiency and manage transactions. Financial Inclusion captured access to and usage of formal credit, savings, and other financial services. Financial Literacy covered knowledge and behaviour in financial planning, record-keeping, and cash flow management. MSME Performance measured the perceived performance in terms of sales growth, profitability, and customer satisfaction. Second, this study identified MSMEs that have used digital payments in their business processes, and visited MSMEs to request permission to distribute the questionnaire. The questionnaire was distributed via Google Forms.

Second, this study conducts data testing and analysis using PLS-SEM, by testing the data validity and reliability, forming a structural model and testing the goodness of fit of the model. This study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) as the primary data analysis technique. The primary goal of this study is to predict the key factors influencing MSME performance and to explain the variance in the dependent variable. PLS-SEM is a variance-based method that is particularly powerful for prediction and theory development, making it highly suitable for our research objective (Hair et al., 2019).

The analysis began with the creation of a structural model (inner model) that described the hypothetical relationship between variables, in which MSME performance as a dependent variable was directly influenced by automation, digital payments, financial inclusion, and financial literacy. Next, a measurement model (outer model) was formed to determine how the questionnaire indicators loaded on each latent variable. Before testing the hypotheses, the measurement model was evaluated by examining its validity and reliability. Convergent validity is assessed based on a factor loading >0.7 and Average Variance Extracted (AVE) >0.5, while reliability is tested with a Composite Reliability and Cronbach’s Alpha value above 0.7. To ensure discriminant validity, the square root of AVE for each construct must be higher than the correlation with other constructs. After the measurement model was declared eligible, the structural model was evaluated by looking at the R-Square value to assess the predictive power of the model and the significance of the T-statistic value through the bootstrapping procedure. Based on these test results, research hypotheses can be accepted or rejected, as indicated by the findings that automation and financial literacy variables have a significant positive effect, while digital payments and financial inclusion are not significant for MSME performance.

3.1. Hypothesis Development

3.1.1. Digital Payments and MSME Performance

Digital payments (such as QRIS) offer operational efficiency, ease of transaction, and better security for SMEs (Bakrie et al., 2024; Moreira et al., 2024). The adoption of digital payments can increase transaction volume, expand market reach (as it can serve consumers who do not use cash), and enhance the appeal of businesses in the eyes of modern consumers. Previous studies of Daud et al. (2022) and Bhattarai et al. (2023) have proven that digital payments have a positive impact on the financial performance and sustainability of SMEs. Ledi et al. (2023) stated that payments using QR codes have a positive effect on SME performance. Musyaffi et al. (2022) also stated that secure digital payments increase consumer convenience in transactions and therefore will affect MSME performance. Therefore, this study developed the following hypothesis:

H1. 

Digital payments have a positive effect on MSME performance.

3.1.2. Business Automation and MSME Performance

Automation is part of the adoption of digital technology that can simplify and accelerate repetitive business processes (such as financial recording, inventory, or ordering). This directly contributes to increased operational efficiency, reduced costs, and reduced human error (Moreira et al., 2024). This efficiency can then be allocated to more strategic activities, ultimately increasing productivity and business performance. Moreira et al. (2024) state that automation in business processes plays an important role in saving time on routine activities and stimulating employee creativity. Meanwhile, Espeña et al. (2022) argue that automation will make businesses more competitive by increasing efficiency and productivity. Therefore, this study developed the following hypothesis:

H2. 

Business Automation has a positive effect on MSME performance.

3.1.3. Financial Inclusion and MSME Performance

Financial inclusion refers to MSMEs’ access to formal financial services such as capital loans, savings, or insurance (OJK, 2023). Access to formal financing (from banks or other financial institutions) is crucial for the advancement of MSMEs (Fomum & Opperman, 2023), as it enables them to expand their businesses, purchase equipment, or manage cash flow more effectively. Although Indonesia’s financial inclusion rate is high (85%), not all MSMEs are able to meet bank requirements (Nugraheni & Muhammad, 2024). MSMEs that successfully access formal financial services are assumed to have more resources to invest and grow. Study of Thathsarani and Jianguo (2022) in Sri Lanka shows that financial inclusion affects SME performance. Similarly, Rahadjeng et al. (2023) states that financial inclusion has a positive effect on SME performance in Malang, Indonesia. Therefore, this study developed the following hypothesis:

H3. 

Financial inclusion has a positive effect on MSME performance.

3.1.4. Financial Literacy and MSME Performance

Financial literacy is the ability to manage finances well, including planning, recording income and expenditure, and understanding financial products (OJK, 2023). MSMEs with good financial literacy are more likely to survive in the long term (OECD, 2024). The ability to manage capital and profits well is key to business development (Darma & Handoyo, 2022) and has a direct impact on the health and financial performance of MSMEs (Bakrie et al., 2024; Oussouadi & Cherkaoui, 2024; Saputri, 2021). Research by Rahadjeng et al. (2023) and Sari et al. (2023) found that financial literacy has a positive effect on the performance of MSMEs in Indonesia. Therefore, this study developed the following hypothesis:

H4. 

Financial literacy has a positive effect on MSME performance.

4. Results & Discussions

The respondents in this study were owners or managers of MSMEs who had adopted digital technology in their business activities. The questionnaire was distributed directly to MSMEs via Google Forms and distributed online. A total of 278 respondents were successfully collected and met the requirements for processing.

Table 1. Respondent demographics.

No.	Sex	Number	Percentage (%)
1	Male	134	48.20
2	Female	144	51.80
Total		278	100
No.	Age	Number	Percentage (%)
1	15–20 Years old	5	1.80
2	21–30 Years old	122	43.88
3	31–40 Years old	123	44.24
4	41–50 Years old	25	8.99
5	>50 Years old	3	1.08
Total		278	100
No.	Position	Number	Percentage (%)
1	Manager	39	14.03
2	Owner	239	85.97
Total		278	100
No.	Education Level	Number	Percentage (%)
1	Primary School	2	0.72
2	Lower Secondary School	9	3.24
3	Upper Secondary School	141	50.72
4	Diploma	29	10.43
5	Bachelor’s Degree	94	33.81
6	Master’s Degree	3	1.08
Total		278	100
No.	Years in Business	Number	Percentage (%)
1	<1 Year	26	9.35
2	1–3 Years	124	44.60
3	3–5 Years	89	32.01
4	5–10 Years	28	10.07
5	>10 Years	11	3.96
Total		278	100
No.	Employee	Number	Percentage (%)
1	None	59	21.22
2	1–5 employees	138	49.64
3	6–10 employees	43	15.47
4	11–20 employees	30	10.79
5	>20 employees	8	2.88
Total		278	100
No.	Profit	Number	Percentage (%)
1	<Rp 5,000,000	73	26.26
2	Rp 5,000,000–Rp 10,000,000	86	30.94
3	Rp 10,000,000–Rp 50,000,000	82	29.50
4	Rp 50,000,000–Rp 100,000,000	32	11.51
5	>Rp 100,000,000	5	1.80
Total		278	100
No.	Business Type	Number	Percentage (%)
1	Fashion/Clothing	39	14.03
2	Service (Salon, laundry, dll)	20	7.19
3	Culinary	151	54.32
4	Craft	17	6.12
5	Automotive	12	4.32
6	Others	14	5.04
7	More than 1 Business	25	8.99
Total		278	100

shows the demographic data of the respondents.

Table 1 shows that, in terms of sex, respondents were divided almost equally, with 51.80% female and 48.20% male. In terms of age, the majority of respondents were in the productive age group, with 44.24% aged 31–40 and 43.88% aged 21–30, indicating that MSMEs are mostly run by young and middle-aged people. In terms of position, most respondents were business owners (85.97%), indicating that the data obtained came directly from key decision makers. The respondents’ education levels were dominated by high school graduates (50.72%) and bachelor’s degree holders (33.81%), reflecting that the educational literacy of MSME players is quite good. In terms of business experience, 44.60% of businesses have been operating for 1–3 years and 32.01% for 3–5 years, indicating that most businesses are in the early-growth stage. The scale of the businesses is dominated by micro businesses with 1–5 employees (49.64%), and 21.22% do not have any employees (sole proprietorships). Financially, most businesses have monthly profitability below IDR 50 million, with a relatively even distribution in the categories below IDR 5 million (26.26%), IDR 5–10 million (30.94%), and IDR 10–50 million (29.50%). The most dominant type of business is the culinary sector (54.32%), followed by fashion (14.03%), which is in line with the general MSME trend. Overall, the characteristics of these respondents provide a strong foundation for analysing the influence of technological and financial variables on MSME performance.

The results of descriptive statistical testing are shown in

Table 2. Descriptive statistics.

Descriptive Statistics
	Minimum	Maximum	Mean	Std. Dev.
Digital Payment	4	20	17.43	2.944
Business Automation	5	25	20.92	3.792
Financial Inclusion	8	25	18.88	3.309
Financial Literation	4	20	17.17	2.713
Performance in MSME	11	55	46.45	7.559

. The MSME performance variable, as the dependent variable, recorded the highest mean value of 46.45 on a scale of 55, with a standard deviation of 7.559. This indicates that, in general, MSME actors feel that their business performance is very good. Among the independent variables, business automation had the highest mean value (20.92), followed by financial inclusion (18.88), financial literacy (17.17), and digital payments (17.43).

Figure 1 shows the outer model output before any treatment. All indicators in the Digital Payment, Business Automation, Financial Literacy, and Performance constructs appear to have positive loadings. However, for the Financial Inclusion construct, the FI5 indicator is inconsistent, showing a negative loading. This results in a lower AVE value for that construct, necessitating model improvements by removing FI5.

As detailed in

Table 3. Outer loading (before–after treatment).

Latent Variables	Code	Before Treatment Loading Factor	Before AVE	After Treatment Loading Factor	After AVE
Business Automation	BO1	0.759	0.692	0.759	0.692
	BO2	0.873		0.873
	BO3	0.778		0.778
	BO4	0.894		0.894
	BO5	0.846		0.846
Digital Payment	DP1	0.898	0.717	0.898	0.717
	DP2	0.872		0.872
	DP3	0.858		0.858
	DP4	0.752		0.752
Financial Inclusion	FI1	0.875	0.678	0.875	0.796
	FI2	0.911		0.911
	FI3	0.915		0.915
	FI4	0.866		0.866
	FI5	−0.454
Financial Literacy	FL1	0.853	0.688	0.853	0.688
	FL2	0.863		0.863
	FL3	0.869		0.869
	FL4	0.723		0.723
MSMEs Performance	FP1	0.821	0.686	0.821	0.686
	FP2	0.843		0.843
	FP3	0.849		0.849
	FP4	0.804		0.804
	FP5	0.851		0.851
	NFP1	0.863		0.863
	NFP2	0.804		0.804
	NFP3	0.824		0.824
	NFP4	0.832		0.832
	NFP5	0.771		0.771
	NFP6	0.847		0.847

, the FI5 indicator was removed. Before treatment, the FI5 indicator in the Financial Inclusion construct had a negative loading (−0.454), thus reducing the model quality. After the FI5 indicator was removed, the AVE for Financial Inclusion increased from 0.678 to 0.796. All other indicators in each construct had loading values above 0.7, indicating that they were valid in measuring their respective constructs. Thus, convergent validity was met for all constructs. The outcome of this improvement is shown in Figure 2, which presents the outer model output after treatment.

After FI5 was removed, the outer model in Figure 2 showed that all indicators in the Financial Inclusion construct (FI1–FI4) had strong and consistent loadings. This confirmed that the Financial Inclusion construct met the convergent validity criteria. The post-treatment model performed better overall, with all constructs declared valid and reliable.

Table 4. Fornell–Larcker criterion.

	Business Automation	Digital Payment	Financial Inclusion	Financial Literacy	MSMEs Performance
Business Automation	0.832
Digital Payment	0.661	0.847
Financial Inclusion	0.673	0.541	0.892
Financial Literacy	0.765	0.722	0.67	0.829
MSMEs Performance	0.711	0.67	0.626	0.827	0.828

presents the assessment of discriminant validity using the Fornell-Larcker criterion. The diagonal values (√AVE) in the table, namely Business Automation (0.832), Digital Payment (0.847), Financial Inclusion (0.892), Financial Literacy (0.829), and MSMEs Performance (0.828), are all greater than the correlation values between constructs in their respective rows/columns. For example, the √AVE value for Business Automation is 0.832, higher than its correlation with Digital Payment (0.661), Financial Inclusion (0.673), Financial Literacy (0.765), and MSMEs Performance (0.711). This means that each construct has good discriminant validity, because the construct better represents its own indicators compared to other constructs.

Reliability evaluation can be performed by looking at the Composite Reliability and Cronbach’s Alpha values.

Table 5. Reliability test.

	Cronbach’s Alpha	Composite Reliability
Business Automation	0.887	0.918
Digital Payment	0.868	0.910
Financial Inclusion	0.914	0.940
Financial Literacy	0.847	0.898
MSMEs Performance	0.954	0.960

shows that All Cronbach’s Alpha and Composite Reliability values were greater than 0.7, indicating that the instrument was reliable. This indicates that the constructs used can produce consistent results when retested. The construct with the highest reliability was MSMEs Performance (α = 0.954; CR = 0.960), indicating that MSME performance indicators are highly stable.

Structural models (inner models) are used to test hypotheses about the relationships between variables. The reliability of measurement models ensures that any relationships found between independent variables such as Digital Payment, Business Automation, and MSME performance truly reflect the relationships between constructs, and can be seen in

Table 6. Structural model.

	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T Statistics (|O/STDEV|)	p Values
Digital Payment -> MSMEs Performance	0.112	0.116	0.074	1.516	0.065
Business Automation -> MSMEs Performance	0.127	0.125	0.062	2.05	0.020
Financial Inclusion -> MSMEs Performance	0.086	0.09	0.059	1.442	0.075
Financial Literacy -> MSMEs Performance	0.591	0.586	0.069	8.527	0.000
VIF	2.044–3.270
Adjusted R-Square	0.704
SRMR	<0.10
NFI	0.809

.

The results show that the Adjusted R² value of 0.704 indicates that 70.4% of the variation in MSME performance can be explained by the four exogenous variables. Standardized Root Mean Square Residual (SRMR) <0.10 indicates a good model fit. The closer NFI is to 1, the better the fit. The Normed Fit Index (NFI) value is 0.809 meaning that it indicates acceptable fit. The results of hypothesis are discussed below.

4.1. Digital Payment and MSME Performance

Digital payment has a t-statistic value of 1.516 less than 1.96 and a p-value of 0.065 greater than 0.05, indicating that this variable has no effect on MSME performance, thus rejecting hypothesis 1. The data from the sample is not strong enough to conclude that digital payments directly and significantly improve MSME performance. There are several possible reasons why this variable has no effect. First, many MSMEs adopt digital payments merely as an additional payment option to meet customer demand, without integrating it into their core business operations. This is in line with Giang et al. (2024) and Al-Qudah et al. (2024), who state that consumers prefer electronic payments because of their ease and convenience. As a result, it becomes a passive tool for receiving payments rather than an active tool for financial management, sales growth, or operational efficiency. Without this strategic integration, its impact on overall performance remains limited. Second, the fees associated with digital payment platforms (transaction fees, withdrawal fees, etc.) can eat into the already thin profit margins of MSMEs. Wiranatakusuma and Latief (2024) stated that high price perception is a consideration for MSMEs to prefer cash payments. If the cost of using the service outweighs the perceived benefits (like a slight increase in sales), the net effect on financial performance (profitability) can be neutral or even negative. Third, it is possible that the effect is indirect or moderated by other variables. For example, digital payments improve financial literacy (because transactions are neatly recorded), which in turn improves performance. Musyaffi et al. (2022) argue that security and safety of digital payment should be maintained to support the comfort of MSMEs.

4.2. Business Automation and MSME Performance

The variable of automation has a t-statistic value of 2.050 > 1.96 and a p-value of 0.020 < 0.05, indicating that hypothesis 2 is accepted. The higher the level of automation technology utilisation in the business operations of an MSME, the higher the performance of MSME. The adoption of business automation has been proven to have a significant positive impact on improving MSME performance, possibly through increased operational efficiency. This result is in line with the study of Espeña et al. (2022) who argue that the usage of technology will give advantage to the MSME because of better productivity and efficiency.

4.3. Financial Inclusion and MSME Performance

Financial inclusion has a t-statistic value of 1.442 < 1.96 and a p-value of 0.075 > 0.05, indicating that hypothesis 3 is rejected, meaning that financial inclusion does not affect MSME performance. When MSMEs obtain formal credit, they may not have the financial literacy or managerial capacity to allocate the funds properly. There is even a possibility that the financing funds were used for consumption rather than business development. Banerjee et al. (2015) found that while microcredit increased access to formal finance, it did not lead to significant increases in average household income or consumption for the majority of borrowers. They found some effects on business investment and the starting of new businesses, but not a transformative impact on overall poverty or business performance as often assumed.

4.4. Financial Literacy and MSME Performance

The t-statistic value shows a value of (8.527) > 1.96 and a p-value of 0.000 smaller than 0.05, meaning that hypothesis 4 is accepted. The higher the level of financial literacy possessed by MSME managers, the better the performance of the MSME. The increased financial literacy means that the business owner or manager has ability to manage finances and thus, can significantly improve MSME performance. This finding is consistent with (Rahadjeng et al., 2023) who found that financial literacy has a positive influence on the business performance of MSME in Malang city, Indonesia. Good financial management is the key to MSME sustainability; therefore, financial literacy will increase the creativity of MSME (Hasan et al., 2024).

5. Conclusions

This study aims to examine the effect of technology adoption and financial behaviour on the performance of MSMEs. Statistical tests show that financial literacy and business automation have a positive effect on MSME performance, while digital payments and financial inclusion have no effect on MSME performance. These results indicate that financial literacy is the most important driver of SME performance. This reinforces the argument that the ability to manage finances (literacy) is more crucial than simply having access (inclusion). Business automation has also been proven to contribute significantly, emphasising the importance of operational efficiency through technology.

5.1. Implications

The research results have implications for several stakeholders. First, the government can create priority programmes in which MSME assistance programmes must prioritise financial literacy training and business process digitalisation (automation) rather than simply encouraging the adoption of digital payments or opening up financial access without adequate assistance. Second, banks/financial institutions can offer financial products for MSMEs that are equipped with financial management education programmes, as good literacy is the foundation for stable performance.

5.2. Limitations and Suggestions for Future Studies

This study has several limitations. First, it only focuses on two main aspects, namely technology adoption (automation and digital payments) and financial behaviour (financial inclusion and literacy). Other factors that also greatly influence SME performance, such as marketing strategies, human resource quality, government regulatory support, or macroeconomic conditions, are not included in the research model. Future research is recommended to add other relevant variables. This will provide a more holistic and comprehensive understanding of the factors that determine the sustainability performance of MSMEs. Second, this study uses a questionnaire in which respondents only select from the answers provided. Data collection can be expanded with interviews to explore deeper contexts or direct observation to see the application of technology and financial management. If necessary, the research can also be supplemented with secondary data from MSME financial reports. Third, future research can further explore insignificant findings by identifying the factors causing the lack of influence of digital payment and financial inclusion variables. Qualitative research can be very useful in answering the ‘why’ behind these statistical results.