Analysis of Factors Affecting MSME in Using Fintech Lending as Alternative Financing: Technology Acceptance Model Approach

Abstract

This study aims to determine factors impacting the intention of Micro, Small, and Medium Enterprises (MSMEs) in using fintech lending applications as an optional source of business financing using the technology acceptance model approach. The population in this study were MSMEs in Indonesia. Samples were taken by purposive sampling with the criteria of having used a licensed fintech lending application for business financing. The samples used were 171 samples. This study used structural equation model (SEM) as the analysis technique. The results of this study showed that Perceived Ease of Use had an impact on Perceived Usefulness but had no effect on Attitude Toward Using. The factor Perceived Usefulness had an effect on Attitude Toward Using, and Attitude Toward Using influences Behavioral Intention to Use. Fintech companies can play a role by providing education and empowerment to foster understanding of digital literacy for MSME stakeholders. The governments need to develop policy frameworks that can balance innovation and risk mitigation.

Keywords:
Behavior Finance; MSMEs; Fintech Lending; Technology Acceptance Model

RESUMO

Este estudo tem como objetivo determinar os fatores que impactam a intenção de Micro, Pequenas e Médias Empresas (MPMEs) em usar aplicativos de empréstimo fintech como uma fonte opcional de financiamento de negócios usando a abordagem do modelo de aceitação de tecnologia. A população neste estudo eram MPMEs na Indonésia. As amostras foram coletadas por amostragem intencional, selecionando indivíduos com base no critério de terem utilizado um aplicativo de empréstimo fintech licenciado para financiamento de negócios. A amostra consistiu de 171 indivíduos. Este estudo utilizou o modelo de equação estrutural (SEM) como técnica de análise. Os seus resultados mostram que a Facilidade de Uso Percebida tem impacto na Utilidade Percebida, mas não teve efeito na Atitude Face ao Uso. O fator de Utilidade Percebida tem um efeito na Atitude Face ao Uso, e Atitude Face ao Uso influenciam a Intenção Comportamental de Uso. A empresa fintech pode desempenhar um papel ao fornecer educação e capacitação para promover a compreensão da alfabetização digital para as partes interessadas das MPME. Os governos precisam desenvolver estruturas políticas que possam equilibrar inovação e mitigação de riscos.

Palavras-chave:
Finanças de Comportamento; MPME; Fintech de Financiamento; Modelo de Aceitação de Tecnologia

1. Introduction

Indonesia has many internet users. Based on the We Are Social data, Indonesia had around 175.4 million internet users in January 2020 (We Are Social, 2020). In 2019, at least 73.75% of households had internet access. This number is increasing from year to year. Moreover, social media users in Indonesia are among the largest in the world. In Indonesia there were 338.2 million mobile phone connections and 160 million users of social media by January 2020 (Badan Pusat Statistik, 2020). The growth of internet users in Indonesia is also very high (Kharisma et al., 2021). Internet technology has also brought many changes in the industry of finance. Digital technology is the main capital needed by industry in developing their business nowdays (Susilowati, 2020).

Financial technology (Fintech) has developed as an outcome of transformation in people's consumer lifestyles. This is the outcome of technology involvement in daily activities. Fintech has developed and become an innovative financial service transaction option for traditional banking, insurance, and asset management (Majid, 2021). Based on OJK statistical data, fintech lending companies as of March 2021 registered 147 total companies and 46 of them were licensed (OJK, 2021). Meanwhile, the balance of debit and credit disbursed by fintech reached IDR 15.3 trillion as of December 2020, and is projected to continue to grow in the near future. The development of fintech lending in Indonesia will accelerate, and target the Micro, Small and Medium Enterprises (MSMEs) traders. MSMEs plays a big role in the Indonesian economy, so that they become one of the leading sectors that grow and increase from year to year (Purbasari et al., 2021). Technological developments make it easier for MSMEs to sell goods, services, and help record transactions (Salim & Frederica, 2020).

Fintech lending provides loan services for those who need capital while facilitating capital development through funding. It can provide financing solutions for MSMEs to develop the business due to its payment flexibility facilities. Fintech is relatively easier to use in financial transaction processes. It also cuts costs incurred in transaction process. MSMEs undeniably played an important role in Indonesian economic growth, and in the employment problem in Indonesia (Yanny et al., 2020). Indonesia is one of the countries with the largest number of MSME industry players around 56.54 - 62.92 million units (Fajar et al., 2021). Because of the people's consumer behavior that changes over time, and the frequency of technology usability in society, fintech has potential to be the solution of the commonly found problems in MSMEs, especially in access to finance. Fintech provides new opportunities for businesspeople to enhance their economic activities due to its efficient and effective consideration (Darma et al., 2020). The technology-based financial services are prospected to provide support for increasing the financial inclusion (Rosyadah et al., 2021). Thus, fintech optimization in encouraging the ability of MSMEs in Indonesia must continue to improve because it has the potential to increase MSMEs capability (Suhartono et al., 2020). In the future, fintech is believed to be rapidly developing (Retno Rahadjeng & Hermawan, 2021).

This study will focus on digital platforms, namely the fintech lending business model as an alternative source of financing. The thing that most distinguishes fintechs from banks is fintech lending only acts as an intermediary, connecting fund owners and those who need funds, so fintech lending only gets a fee based on each transactions made (OJK, 2021) Fintech lending does not require a large core capital as in banking. The traditional retail banking industry has conducted its business with customers by direct interactions (Asmarani & Wijaya, 2020). Over the past decade, traditional banks have changed, and new financial service concepts have emerged (Lestari et al., 2020). Fintech comes in tandem with the growth of information technology, including smartphones, the internet, and big data analytics, which can make the distribution of financial services faster and cheaper (Batunanggar, 2019).

MSMEs have a major problem to get financing in the form of credit disbursement in order to have business capital (Ardiansyah, 2019). Fintech lending is believed to be an alternative financing for MSMEs to increase financial inclusion. Financial accessibility of fintech-based MSMEs seems to be faced with many obstacles at the level of MSME business actors (Erwin & Anwar, 2021). This is in line with the level of penetration of internet and smartphone network usage in Indonesia which is quite significant. However, the 2019 Financial Literacy National Survey by the OJK stated that it was 38.03%, while the level of financial inclusion based on a survey conducted by the National Financial Inclusion Council (DNKI) in 2020 reached 81.4%. This fact shows that almost most users of financial services, both banks and fintech, do not understand the benefits of the product and its risks.

To understand and evaluate alternatives to financial markets, financial knowledge and literacy are important (Effendi et al., 2021). Information technology in business plays an important role to improving an entrepreneur’s digital literacy (Daud et al., 2022). The low level of financial literacy, including among MSME actors, can pose potential risks such as cybercrime and misuse of personal data. All parties involved need financial literacy skills to avoid the mentioned bad situations. Financial literacy is knowledge and skill set that bring a person to make decisions effectively with all their financial resources (Safitri, 2020). If these conditions are not properly mitigated, the financial inclusion target may not be achieved because consumers lose confidence in financial instruments such as fintech. An increase in the fintech literacy index, which influences the financial inclusion index, will be reflected in an increase in users (Pambudianti et al., 2020).

In today’s world of globalization, technological advances are a driver of economic growth. Several shifts in the business world, towards the digital economy, involve a lot of online or virtual activities (Abdillah et al., 2021). Taking into account the high amount of internet use and the momentum of digitization during the COVID-19 pandemic, fintech lending has the potential to be an alternative source of financing for MSME actors. MSMEs are one of the industries that can survive in the pandemic era, and develop into a new source of strength for a nation's economy (Anggarini, 2022). The Covid-19 pandemic has encouraged MSMEs to adapt their sales by using digital platforms media (Giharis, 2022). On one hand, there is limited access to MSME finance in the banking sector, but on the other hand, low financial literacy can be an obstacle for MSME actors in accessing financial services, including fintech lending, which is relatively new and requires an adequate level of digital literacy. This is confirmed by the results of a survey by the National Council for Inclusive Finance (DNKI) in 2020 which showed that most telephone users (68.8%) had not been able to use their devices for digital financial transactions. Therefore, MSMEs actors inevitably must keep up with increasingly sophisticated technological developments (Dwijayanti et al., 2022). Indeed, MSMEs need access to capital to maintain their business continuity even during the pandemic (Kartiko & Rachmi, 2021).

The research on technology usage behavior can be approached with the Technology Acceptance Model (TAM) invented by Davis (Davis, 1989). TAM is an applicable theory which is broadly used to describe acceptance of the use of information technology systems. The theory predicts that individual acceptance of information technology can be affected by two main variables, namely Perceived Ease of Use and Perceived Usefulness. The Perceived Ease of Use and Perceived Usefulness are benchmarks in measuring the TAM. Perceived Ease of Use is a condition when there is a belief that, by using a certain system, an individual does not need to make any effort. The ease of use can be demonstrated through the intensity of use and relationship between the user and the system. Meanwhile, Perceived Usefulness shows individual confidence levels when using certain applications that will increase their productivity. The TAM concerns itself on developing individual attitudes towards the use of information technology-based perceptions of convenience and benefits.

In addition, for a description of the study of this phenomenon, there are research gaps that need to be considered in recent studies, Purnamasari’s research, concludes that there is an impact of Perceived Ease of Use on user attitudes (Purnamasari et al., 2020). However, the results of this study differ with the results of Joshua Tandiono's which concludes that Perceived Ease of Use has no direct effect on the perception of fintech lending users (Tandiono et al., 2020). According to Darmansyah et al. (2020) and Taufan (2019), Perceived Usefulness affects MSME attitudes on the use of fintech lending. Meanwhile, Chandra's research shows that Perceived Usefulness is not impacted by the attitude of MSME actors to use fintech lending (Candra et al., 2020).

Therefore, based on the background described above, this work will focus on analyzing factors that influence the attitudes and decisions of MSME actors in Indonesia to use fintech lending as an option source of financing their businesses, especially after the COVID-19 pandemic.

2. Literature Review

2.1. Micro, Small, Medium Enterprises (MSME)

MSME in this study is defined by Government Regulation Number 7 of 2021 (Presiden Republik Indonesia, 2021) based on the amount of capital and sales, as shown in the following Table 1.

Table 1
MSME Criteria by Capital and Sales

Business Scale	Capital (IDR)	Sales (IDR)
Micro	Up to 1 billion	Up to 2 billion
Small	1 - 5 billion	2 - 15 billion
Medium	5 - 10 billion	15 - 50 billion

• Source: Prepared by the authors
• Note: *in Indonesian Rupiah (IDR)

Difference between MSMEs and large-scale businesses can be known from turnover and total wealth up to annual sales results (Jenita et al., 2022). The criteria for micro-enterprises are ownerships with a maximum net worth of IDR 50 million and annual sales up to IDR 300 million rupiah. Whereas a small business is a business unit with more than IDR 50 million to IDR 500 million net worth and has reached sales of more than IDR 300 million to IDR 2.5 billion per year. Medium Enterprises are companies with more than IDR 500 million up to IDR 10 billion net worth and have annual sales results of more than IDR 2.5 billion up to IDR 50 billion. A principle of MSMEs is the growth of independence, togetherness for work on their own initiative, for the sake of transparent, accountable, and fair (Putu Julianto et al., 2021).

2.2. Financial Technology

Fintech is a fusion of financial technology and services, transforming a variety of businesses from a traditional business model to a modern one, where selling, buying, borrowing, and other transactions that do not have to be done directly. Fintech can introduce financial services to users that were previously unreachable by traditional institutions or existing services (Candraningrat et al., 2021). The existence of the fintech industry is expected to facilitate impetus for the development of micro, small, and medium enterprises (MSMEs) in Indonesia (Suryanto et al., 2020).

In Indonesia, fintech is divided into categories based on their services. Those categories are lending and payments. Any financial technology in lending and borrowing activities or fintech lending is organized by the Financial Services Authority (Otoritas Jasa Keuangan/OJK). Whereas financial technology in payments services or fintech payments in Indonesia is organized by the Central Bank of Indonesia (Bank Indonesia/BI). Most of the MSMEs use financial services for payment transaction (Wening Perwitasari, 2022). This research focus only the fintech lending that are registered and licensed in Indonesia.

2.3. Technology Acceptance Model (TAM) and Previous Work

TAM is a model created from Theory of Reasoned Action (TRA). TAM predicts Perceived Ease of Use and Perceived Usefulness as the principal factors which influence individual acknowledgment of data information technology.

The Perceived Usefulness (PU) and Perceived Ease of Use (PEU) influence an individual's Attitude Toward Using (ATU) in utilizing innovation. Thus, the instruments that constructs Perceived Ease of Use affects the expansion in Perceived Usefulness. Since the framework activity is easy to use and clients don't need to take most of the day to learn it, execution can run effectively (Davis et al., 1989). The Attitude Toward Using in this construct is a concept of an attitude of acceptance and rejection as an effect if an individual uses a technology in carrying out his activities. The Behavioral Intention to Use (BI) is a behavioral tendency to persist in using certain technologies. The intensity of the use of a technology can be noticed from the user's attitude towards the technology, both the desire to maintain the use and the motivation to promote it to others. The actual system usage is the real state of using a conceptualized system by measuring the duration and frequency of technology use.

Commonly, TAM is used to estimate the level of user acceptance based on perceptions of the ease of use and benefits of information technology. The acceptance level can be seen by examining the relationship between the acceptance of information technology and its impact on individual users.

TAM aims to continue measurement in predicting and describing applications. The research here focused on theoretical constructs, Perceived Usefulness, and perceived convenience. These constructs are theorized as factors that are fundamental to the application of the system (Davis et.al, 1989).

There are studies that show that PEU and PU have an effect on intention. From the study by Khan, et. al., it can be concluded that PEU and PU have an effect on the intention to use P2P (Peer-to-Peer Lending) (Khan et al., 2021). From Abbasi, et. al, PU has an effect on BI and PEU has an effect on BI(Abbasi et al., 2021). Chen, C. shows that PU has an effect on BI, PEU has an effect on BI, PU has an effect on actual use through BI, and PEU has an effect on actual use through BI(Chen et al., 2017). Manis, and Choi shows that PEU has an effect on the perception of use (POU) (Manis & Choi, 2019). POU has a positive effect on ATU, PEU has no effect on ATU. ATU affects BI, but BI has no effect on Actual Use (AU). The research of Sheng, which shows the results that PU, PEU, Compatibility and risk have a significant effect on interest in using P2P Lending (Sheng, 2021). Akhnes Noviyanti and Teguh Erawati shows the results that PEU has an effect on interest in using fintech, trust has a negative effect on interest, and effectiveness has a positive effect on interest (Noviyanti & Erawati, 2021). Research by Tun-Pin shows that interest in adopting fintech is influenced by following variables: PEU, PU, ​​ personal innovativeness, social influence, perceived enjoyment, security concerns, and demographic profile (Tun-Pin et al., 2019).

The research by Makina shows the results that planned behavior, acceptance model, and use of technology have an effect on BI (Makina, 2019). Scherer shows the results that PU, PEU, ATU, BI and actual use are above 50% strongly agree (Scherer et al., 2019). Chang shows the results that all TAM variables influence Behavioral Intention to Use (Chang et al., 2017).

Other studies have shown that PEU has no effect on intention. Granić and Marangunić research which results state that PEU has no effect on BI, PEU has no effect on actual use, PEU has no effect on BI as a mediating variable, effectiveness affects BI and actual use of fintech, risk has no effect on actual use (Granić & Marangunić, 2019). PEU, risk and effectiveness simultaneously affect the actual use of fintech. Research by Tandiono, shows that ease of use, self-efficacy, and trust are not significant in user perceptions (Tandiono et al., 2020).

3. Research Method

This research used quantitative methods to explain cause-and-effect relationships and hypothesis testing. This research is supported by surveys through collecting information from or about an individual to compare, illustrate, or describe the attitudes, knowledge, and behavior of respondents. The researcher collects quantitative and qualitative data on a variety of research questions which are compiled into a questionnaire that is filled out by the respondents.

The secondary data in this study was obtained from the literature that discusses the theory of financial behavior, financial inclusion, MSMEs, and the development of fintech lending. The literature sources are obtained in the form of articles, journals, and also data obtained from related institutions, such as BI, OJK, and international organizations that are concerned with issues of financial inclusion and digital finance.

Processing of quantitative data obtained from the results of questionnaires measured by an ordinal scale and a Likert scale to measure the opinions, attitudes, and perceptions of respondents related to the events studied (Bougie & Sekaran, 2019). In measuring the Likert scale, the researcher assigns a score to each question or statement instrument graded from very negative to very positive, with details (Table 2).

Table 2
Likert Measurement Scale

Statement	Value
Strongly Agree	5
Agree	4
Neutral	3
Disagree	2
Strongly Disagree	1

• Source: Bougie & Sekaran, 2019

As indicated by Sugiyono there are two varieties of examining strategies: Probability and Non-Probability Sampling (Sugiyono, 2017). Non-Probability Sampling is utilized in this examination. By utilizing this procedure, there is no equal opportunity for every member of the population to be selected as a sample. Purposive testing procedure is additionally utilized in this review. With certain considerations, this technique can determine the sample according to certain criteria on the basis of the characteristics of the subject who will be the sample in the study. The criteria used in the selection of the sample are:

1. Respondents are micro, small, and medium enterprises (MSMEs)

2. Respondents currently, or have previously, productive business financing from one of the licensed/registered fintech lending companies from the OJK.

To clarify the direction of the research, a framework is designed as shown in Figure 1. This framework shows the influence of PEU, PU on ATU and BI in the use of fintech lending applications by MSMEs in the context of business financing.

Figure 1.
Research Thinking Framework

4. Result

4.1. Descriptive Statistics

Respondent’s characteristics are used to determine their identity including gender, age, and education level. Table 3 shows the characteristics of the respondents.

Tabel 3
Respondents Characteristics

Indicator	Category	Frequency	Percentage (%)
Gender	Male	51	29,8
	Female	120	70,2
Birth Year	Baby Boomer (1944-1964)	3	1,8
	Gen X (1965-1980)	74	43,3
	Gen Y (1981-1995)	75	43,9
	Gen Z (1996 -2010)	19	11,1
Education	Primary	1	0,5
	Secondary	13	7,6
	High Secondary	119	69,6
	Diploma/Bachelor	36	21,1
	Postgraduate	2	1,2

• Source: Prepared by the authors

As shown in Table 4, it can be known that respondents are dominated from the Trading Business Sector, as many as 105 respondents (87.7%). Other respondents from the Service Business Sector is 17 (9.9%). The respondents from agricultural and other business sectors is only 2 respondents (1.2%).

Table 4
Business Profile

Indicator	Category	Frequency	Percentage (%)
Business Sector	Service	17	9,9
	Others	2	1,2
	Trade	150	87,7
	Farming	2	1,2
Location	Bali-Nusa Tenggara	38	22,2
	Jawa	105	61,4
	Kalimantan	18	10,5
	Sumatera	10	5,8
Operating Value	Up to Rp 2 miliar	169	98,8
	Rp 2 - 15 miliar	2	1,2
Marketing Line	Store/Physical outlet	123	71,9
	E-commerce	15	8,8
	Social media	85	49,7
	B2B	1	0,6
Financial products used other than online loans	Savings	171	100,0
	Current account	3	1,8
	Deposit	4	2,3
	E-money	73	42,7
	Insurance	32	18,7
	Non-online loans credit	38	22,2
	Credit card	1	0,6

• Source: Prepared by the authors
• Note: *in Indonesian Rupiah (IDR)

4.2. Normality Test

The results of the multivariate normality test found that the 0.05 or p-value < significant alpha 5%, as shown in Table 5. This causes the data distribution is not normal. In this study, the data were not normally distributed because the responses given by the respondents were not evenly distributed and mostly right (good). Due to the violation of the normality assumption, one way for this research can be proceed is to predict the model on the basis of Maximum Likelihood, and to correct the bias by using the asymptotic covariance matrix (Ghozali, 2016).

Table 5
Multivariate Normality

Skewness			Kurtosis			Skewness and Kurtosis
Value	Z-Score	P-Value	Value	Z-Score	P-Value	Value	P-Value
61.575	20.577	0.000	313.411	9.800	0.000	519.462	0.000

• Source: Prepared by the authors

4.3. Construct Validity Test

Validity relates to the level of accuracy that is obtained by an indicator in assessing something. If an instrument can be used to measure things that should be measured, then the instrument is said to be valid (Sugiyono, 2017). The indicators are multidimensional, so after reviewing the relationship loading factor between each observed and the latent variable, each latent variable/construct can be tested. According to Table 6, all indicators are defined as valid. Based on the indicators, the loading factor value is 0.5. So, each indicator is defined as valid and can be used for further analysis.

Table 6
Construct Validity Test

Variable	Code	Factor Loading	Cut Off	Comment
Perceived Usefulness (PU)	PU1	0.71	>0.50	Valid
	PU2	0.72	>0.50	Valid
	PU3	0.60	>0.50	Valid
	PU4	0.74	>0.50	Valid
	PU5	0.70	>0.50	Valid
Perceived Ease of Use (PEU)	PEU1	0.81	>0.50	Valid
	PEU2	0.84	>0.50	Valid
	PEU3	0.69	>0.50	Valid
	PEU4	0.86	>0.50	Valid
Attitude Toward Using (A)	A1	0.72	>0.50	Valid
	A2	0.88	>0.50	Valid
	A3	0.91	>0.50	Valid
Behavioral Intention to Use (BI)	BI1	0.60	>0.50	Valid
	BI2	0.78	>0.50	Valid
	BI3	0.90	>0.50	Valid

• Source: Prepared by the authors

4.4. Reliability Test

In order to find out the size of a questionnaire which is an indicator of a construct or variable, a reliability test is needed as an instrument (Ghozali, 2016). The reliability test is a series of measuring instruments that are consistent even though the measurements are carried out many times at different times (Sugiyono, 2017). This test can also show the level of usefulness of a measuring instrument if the measurement is carried out more than twice at different times. Reliability tests can also show the extent to which the measurement results are relatively consistent (Kuncoro, 2004). In order to test the consistency of the questionnaire-which, when tested repeatedly, the same data was found-a reliability test was then necessary. In this study, the reliability test will use construct reliability based on the equation (Yamin & Kurniawan, 2011):

$$ConstructReliability=\mathrm{}\frac{{(\sum Std.Loading)}^2}{{(\sum Std.Loading)}^2+\sum {\epsilon }_j}$$ (1)

All instruments are reliable as shown in Table 7. The construct reliability value has met the limit, that is, if the value of construct reliability is greater than 0.7 it has reached an acceptable limit value.

Table 7
The Construct Reliability

Variable	Indicator	Standardized Factor Loading	SFL Squared (Perception)	Error [εj]	Construct Reliability
Perceived Usefulness (PU)	PU1	0.71	0.504	0.496	0.824
	PU2	0.72	0.518	0.482
	PU3	0.60	0.360	0.640
	PU4	0.74	0.548	0.452
	PU5	0.70	0.490	0.510
Total		3.470	2.420	2.580
Perceived Ease of Use (PEU)	PEU1	0.81	0.656	0.344	0.878
	PEU2	0.84	0.706	0.294
	PEU3	0.69	0.476	0.524
	PEU4	0.86	0.740	0.260
Total		3.200	2.577	1.423
Attitude Toward Using (A)	A1	0.72	0.518	0.482	0.878
	A2	0.88	0.774	0.226
	A3	0.91	0.828	0.172
Total		2.510	2.121	0.879
Behavioral Intention to Use (BI)	BI1	0.60	0.360	0.640	0.810
	BI2	0.78	0.608	0.392
	BI3	0.90	0.810	0.190
Total		2.280	1.778	1.222
Acceptable Limit					≥ 0,7

• Source: Prepared by the authors

4.5. Goodness of Fit Test

As shown in Table 8, the cut-off value appeared in more than five goodness of fit markets. This means that the evaluation shows an acceptable model. This shows that the model produces the level of estimation that has been excepted. Thus, this model is a feasible and categorize as a good model to describe the relationships between variables.

Table 8
Goodness of Fit Test

Criteria	Goodness of Fit	Cut-off Value	Model Evaluation
CMIN/DF	1.98	≤ 2,00	Good Fit
GFI	0.89	≥ 0,9	Marginal Fit
AGFI	0.84	≥ 0,9	Marginal Fit
NFI	0.96	≥ 0,9	Good Fit
IFI	0.98	≥ 0,9	Good Fit
CFI	0.98	≥ 0,9	Good Fit
RFI	0.95	≥ 0,9	Good Fit
RMSEA	0.060	< 0.08	Good Fit
TLI/NNFI	0.98	≥ 0,9	Good Fit

• Source: Prepared by the authors

4.6. Structural Model Fit Test

This test is conducted to test the relationship between variables that were previously hypothesized. The structural equations resulting from data management are shown in Figure 2 as follows.

Figure 2.
Structural equation results

Based on the structural equation above (Figure 2), the following information can be explained:

1. Perceived Ease of Use (X2) has a positive effect of Perceived Usefulness (X1) with a coefficient value of 0.50. This shows that if Perceived Ease of Use (X2) increases by 1 and other independent variables are constant, then Perceived Usefulness (X1) will increase by 0.50.

2. Perceived Usefulness (X1) has a positive effect on Attitude Toward Using (Y1) with a coefficient value of 0.79. This shows that if Perceived Usefulness (X1) increases by 1 and the other independent variables are constant, then Attitude Toward Using (Y1) will increase by 0.79.

3. Perceived Ease of Use (X2) has a negative effect on Y2 with a coefficient value of -0.055. This shows that if the Perceived Ease of Use (X2) increases by 1 and the other independent variables are constant, then Y1 will decrease by 0.055.

4. Attitude Toward Using (Y1) has a positive influence on Behavioral Intention to Use (Y2) with a coefficient value of 0.90. This shows that if Attitude Toward Using (Y1) increases by 1 and other independent variables are constant, then Behavioral Intention to Use (Y2) will increase by 0.90.

5. Discussion

The first hypothesis shows that there is positive and significant influence in Perceived Ease of Use towards Perceived Usefulness. The t-value resulting from the effect of Perceived Ease of Use on Perceived Usefulness is 5.79. The t-value is higher than 1.96. The resulting coefficient is 0.50 (positive) which means the more the Perceived Ease of Use, the more the Perceived Usefulness tends to be. These results are aligned with the research of Suyanto and Kurniawan, and Putranto and Sobari which state that Perceived Ease of Use has a positive and significant impact variable on Perceived Usefulness (Putranto & Sobari, 2021; Suyanto, 2019).

The second hypothesis states that Perceived Usefulness has a significant impact on Attitude Toward Using. This is indicated by t-value of 7.06, which is greater than 1.96. The coefficient is 0.79 (positive), which means the higher the Perceived Usefulness; the Attitude Toward Using tends to increase. The result is aligned with the research of Legris et al. in Letchumanan who reported that about 12 out of 14 studies that have researched attitudes towards the use of technology, found that Perceived Ease of Use and Perceived Usefulness were significant forecaster of attitude towards the use of a technology product (Letchumanan & Muniandy, 2013; Uppal et al., 2003).

Then, the third hypothesis about the effect of Perceived Ease of Use on Perceived Usefulness, there is no significant impact. It is shown that the t-value is -0.79 less than 1.96. Different research results were stated by Adhiputra (Adhiputra, 2015). This study shows the presence of a positive impact between Perceived Ease of Use and Attitude Toward Using internet banking. Nugraha and Laksito and Wida, also suggest that Perceived Ease of Use has a positive and significant effect on Attitude Toward Using (Nugraha & Laksito, 2014; Wida et al., 2016).

The fourth hypothesis about the effect of Attitude Toward Using on Behavioral Intention to Use, it shows a significant influence. The t-value of 6.05 is greater than 1.96. Coefficient of 0.90 (positive), indicating the higher the Attitude Toward Using, the Behavioral Intention to Use tends to increase. This conclusion is in line with previous research by Suyanto & Kurniawan and Kurniawan who showed a positive and significant influence of the Attitude Toward Using variable on Behavioral Intention to Use (Kurniawan et al., 2019; Suyanto, 2019).

Table 9
Hypothesis Results

Hypothesis		Estimate	t-Value	Cut-off	Comment
1	Perceived Ease of Use ( Perceived Usefulness	0.50	5.79	>1,96	Significant
2	Perceived Usefulness ( Attitude Toward Using	0.79	7.06	>1,96	Significant
3	Perceived Ease of Use ( Attitude Toward Using	-0.055	-0.79	>1,96	No Significant
4	Attitude Toward Using ( Behavioral Intention to Use	0.90	6.05	>1,96	Significant

• ^Source:
  Prepared by the authors

Using TAM framework, this study has shown the benefits and opportunities for optimizing the use of fintech lending. For MSME actors, this opportunity can be used-however, it must be followed by a good understanding of digital literacy. To increase this understanding, fintech lending companies can play a role by providing education and empowerment. Companies can also take advantage of this opportunity by expanding their regional reach. For the regulators (government), it is necessary to develop a policy framework that can balance innovation and risk mitigation.

6. Conclusion

It can be concluded that there is evidence of the factors that influence the attitudes and behavior motivation of MSMEs to use fintech lending as follows. According to the first hypothesis test, Perceived Ease of Use shows a positive and significant influence on Perceived Usefulness. This means that, the easier the fintech lending application can be to use, the more benefits users will get. Perceived Usefulness shows a positive and significant influence on Attitude Toward Using. This shows that, the more useful fintech lending, the more users will have a pleasant experience using the application. Then Perceived Ease of Use is not affected Attitude Toward Using. This means that there are other causes that affect the attitudes and experiences of fintech lending application users besides the perception of convenience. The fourth hypothesis test shows Attitude Toward Using have a positive and significant impact on Behavioral Intention to Use. The proof shows that the better the attitude and experience of users of fintech lending applications, the more likely the users will continue to use the application and recommend it to the others.

Thus, the results of this study stated that three of the four hypotheses showed a significant effect. TAM is a good model to prove the existence of influencing factors of the attitudes and motivations of MSME actors in using fintech lending. This study does not discuss the cost of funds, social factors, economic conditions that affect MSMEs accepting fintech technology. It was recommended for further research work can be applied by using a Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), social influences, conditions, or other factors that can affect interest.

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Appendix QUESTIONNAIRE

Dear Respondents, We aim to conduct a research titled "Analysis of Factors Affecting MSME in Using Fintech Lending as Alternative Financing: Technology Acceptance Model Approach". In matters of that, I invite you to participate in this research as Respondent. These are important points before you fill in this survey:

1. There is no wrong or correct answer, so you are expected to give an honest answer with full awareness.

2. It will take approximately 15-20 minutes to finish this questionnaire.

3. The author is aware of the importance of data confidentiality, so all forms of the information you give will only use for research matters.

4. Thank you for your willingness in this survey. If you have any questions about the survey, you can contact the author via the following e-mail:

[e-mail]

No Question Answer Description 1 Are you a Micro, Small and Medium Enterprises (MSMEs)? Yes / No If “ Yes ”, Continue If “No”, Finish 2 Do you ever use fintech lending application for business/productive financing? Yes / No If “ Yes ”, Continue If “No”, Finish 3 Fintech lending application that you use: Amartha Investree 4 How long have you been using the fintech lending application? Less than 1 year 1-3 years More than 3 years 5 How do you access credit/financing from fintech lending company? Through Mobile Apps Through the website on the computer Offline _ 6 Where did you get the information about the fintech lending application? Electronic/print media advertising Internet Telemarketing Socialization Family/friends Others ….

No	Question	Answer	Description
1	Are you a Micro, Small and Medium Enterprises (MSMEs)?	Yes / No	If “ Yes ”, Continue If “No”, Finish
2	Do you ever use fintech lending application for business/productive financing?	Yes / No	If “ Yes ”, Continue If “No”, Finish
3	Fintech lending application that you use:	Amartha Investree
4	How long have you been using the fintech lending application?	Less than 1 year 1-3 years More than 3 years
5	How do you access credit/financing from fintech lending company?	Through Mobile Apps Through the website on the computer Offline _
6	Where did you get the information about the fintech lending application?	Electronic/print media advertising Internet Telemarketing Socialization Family/friends Others ….

Respondent’s Demographics Profile

No Question Answer Description 7 Sex Male Female 8 Year of Birth Before 1944 (Traditionalist) 1944-1964 (Baby Boomer) 1965-1980 (Gen X) 1981-1995 (Gen Y) 1996 -2010 (Gen Z) 9 Education Not attending education Primary School Junior high school Senior high school Diploma/ Bachelor Postgraduate

No	Question	Answer	Description
7	Sex	Male Female
8	Year of Birth	Before 1944 (Traditionalist) 1944-1964 (Baby Boomer) 1965-1980 (Gen X) 1981-1995 (Gen Y) 1996 -2010 (Gen Z)
9	Education	Not attending education Primary School Junior high school Senior high school Diploma/ Bachelor Postgraduate

Business Profile

No Question Answer Description 10 Business Sector Service Trading Agriculture Manufacture Others 11 Business location according to region (if the business has any branch in different region, choose the main office location) Sumatra Java Borneo Sulawesi Bali - Nusa Tenggara Maluku - Papua Regional 12 How many Sales Value per year? Up to IDR 2 billion IDR 2 - 15 billion IDR 15 - 50 billion more than IDR 50 billion To measure MSME scale 13 What kind of marketing tools that used in the business? Shop/tangible shop E-commerce (such as Tokopedia, Bukalapak, Shopee, etc.) Social media (WhatsApp, Instagram, Facebook, etc.) Company/aggregator/ importer (B2B) Choose the most suitable 14 What kind of Financial Product that used besides fintech lending? Savings Current Account Deposit E-Money/Digital savings (such as Gopay, OVO, Dana, Shopeepay, LinkAja, etc.) Insurance (includes Unitlink, BPJS) Investments: stocks, mutual funds, bonds Bank/BPR/Leasing/Cooperative /LKM Credit (not include online loans) Credit Card Choose the most suitable

No	Question	Answer	Description
10	Business Sector	Service Trading Agriculture Manufacture Others
11	Business location according to region (if the business has any branch in different region, choose the main office location)	Sumatra Java Borneo Sulawesi Bali - Nusa Tenggara Maluku - Papua	Regional
12	How many Sales Value per year?	Up to IDR 2 billion IDR 2 - 15 billion IDR 15 - 50 billion more than IDR 50 billion	To measure MSME scale
13	What kind of marketing tools that used in the business?	Shop/tangible shop E-commerce (such as Tokopedia, Bukalapak, Shopee, etc.) Social media (WhatsApp, Instagram, Facebook, etc.) Company/aggregator/ importer (B2B)	Choose the most suitable
14	What kind of Financial Product that used besides fintech lending?	Savings Current Account Deposit E-Money/Digital savings (such as Gopay, OVO, Dana, Shopeepay, LinkAja, etc.) Insurance (includes Unitlink, BPJS) Investments: stocks, mutual funds, bonds Bank/BPR/Leasing/Cooperative /LKM Credit (not include online loans) Credit Card	Choose the most suitable

Description

SD : Strongly Disagree

D : Disagree

N : Neutral

A : Agree

SA : Strongly Agree

No Perceived Ease of Use (PEU) Perception of the Fintech Lending Application convenience SD D N A SA PEU1 The structure and content of the fintech lending application is easy to use and understand. PEU2 I feel easy to become proficient in using the fintech lending application. PEU3 The fintech lending application is easy to use. PEU4 The whole instructions on the application are easy to follow.

No	Perceived Ease of Use (PEU) Perception of the Fintech Lending Application convenience	SD	D	N	A	SA
PEU1	The structure and content of the fintech lending application is easy to use and understand.
PEU2	I feel easy to become proficient in using the fintech lending application.
PEU3	The fintech lending application is easy to use.
PEU4	The whole instructions on the application are easy to follow.

No Perceived Usefulness (PU) Perception Usefulness of the Fintech Lending Application SD D N A SA PU1 The performance of my business is increase after using the fintech lending application. PU2 The productivity of my business is increase after using the fintech lending application. PU3 The management of my business is getting more effective after using the fintech lending application. PU4 The application is very useful for my business. PU5 In general, the fintech lending application makes me easy to get business financing.

No	Perceived Usefulness (PU) Perception Usefulness of the Fintech Lending Application	SD	D	N	A	SA
PU1	The performance of my business is increase after using the fintech lending application.
PU2	The productivity of my business is increase after using the fintech lending application.
PU3	The management of my business is getting more effective after using the fintech lending application.
PU4	The application is very useful for my business.
PU5	In general, the fintech lending application makes me easy to get business financing.

No Attitude Toward Using (A) Attitude in Using the fintech lending application SD D N A SA A1 I really want to use fintech lending application. A2 Using a fintech lending application is a satisfying experience for me. A3 Using a fintech lending application is a good idea or the right decision.

No	Attitude Toward Using (A) Attitude in Using the fintech lending application	SD	D	N	A	SA
A1	I really want to use fintech lending application.
A2	Using a fintech lending application is a satisfying experience for me.
A3	Using a fintech lending application is a good idea or the right decision.

No Behavioral Intention to Use (BI) Interest to Use fintech lending application SD D N A SA BI1 I want to use fintech lending application to obtain business financing BI2 If possible, I will use a fintech lending application for my business BI3 I will suggest others to use a fintech lending application

No	Behavioral Intention to Use (BI) Interest to Use fintech lending application	SD	D	N	A	SA
BI1	I want to use fintech lending application to obtain business financing
BI2	If possible, I will use a fintech lending application for my business
BI3	I will suggest others to use a fintech lending application

Miscellaneous Questions :

Question Answer Description Is the fintech lending application you use fulfilled your expectation? Yes, as expected Not really as expected Not as expected What feature do you want to add to the application to fulfill your expectation/needs? … Open answer After using the application, is it affect the cost/load in your business? The cost became higher Not affected The cost became lower (more efficient) Just if you consent, please input your contact. Based on consent

Question	Answer	Description
Is the fintech lending application you use fulfilled your expectation?	Yes, as expected Not really as expected Not as expected
What feature do you want to add to the application to fulfill your expectation/needs?	…	Open answer
After using the application, is it affect the cost/load in your business?	The cost became higher Not affected The cost became lower (more efficient)
Just if you consent, please input your contact.		Based on consent

Publication Dates

• Publication in this collection
  05 June 2023
• Date of issue
  May-Jun 2023

History

• Received
  09 Aug 2022
• Reviewed
  29 Dec 2022
• Accepted
  23 Jan 2023