Innovation in technology as well as digitalization has become a challenge for the financial sector globally. Fintech is a term used against the application of the latest technology in financial services and services [1]. Technological advances and digital transformation show the movement of paradigms in the financial area [2]. The basis of this transformation (technological advancement and digital transformation) is innovation in a business model based on emerging technologies in customer service [3]. The added value of fintech is based on creativity and skills that focus primarily on customers with more flexible financial services [1,4].

The development of fintech has the following objectives:

• support can be directly channeled to users: smartphones, tablets, PCs, laptops, and smartwatches

• utilization of cloud technology (google drive, dropbox, etc.) has the purpose of centralized data / information storage and also financial services without the need for physical space

• utilization of cryptocurrency algorithms that can transact globally, few intermediaries and transparency in payments

• utilization of mobile payment with better security and speed, as well as innovation in doing business and also managing finance [5]

Digitalization and connection to electronic devices have provided an expansion of the global financial system and then the utilization and integration of banking and corporate websites devoted to electronic payments, electronic financial loans, and electronic financial transfers [6-8]. From this effectiveness, the term fintech refers to new technologies that have changed the financial area [9]. The author gives one example that we always see: just by downloading fintech applications on mobile phones, people can transition easily without having to go or spend time in the bank to borrow money.

So far, the presence of fintech has been able to make MSMEs grow and progress in a positive direction. Business development in MSMEs using fintech tends to be more cpeat and consistent [10]. Of course, Indonesia has competence in the field of fintech. The presence of fintech is expected to provide encouragement for the development of MSMEs in Indonesia. Often MSMEs face some problems such as capital [11], marketing aspects [12], and the preparation of financial statements [13]. The first problem in MSMEs is that capital limitations are a problem that is often encountered in MSMEs. MSMEs may have business ideas to grow their businesses but must be stopped because there is no additional capital [14].

The second problem is the limited ability to access the wider market. Business continuity will not last long if the market is limited. As a result, innovative marketing strategies are required. Although some MSMEs have used social media, marketplace sites, and other online marketing methods to promote their products. In practice, however, it is still not optimal. MSMEs can, in fact, use the internet as an effective marketing tool. Google, Facebook, and Instagram have all become popular online marketing platforms [13]. The problem of the three unresolved financial statements can be seen from the still mixing of personal and business money in the same account used for personal use. Each will pay the home electricity bill, pulse bill and other owners pay directly from the account that is also used for business [11]. MSMEs play an important role in the economy in Indonesia. MSMEs account for more than 75% of all companies located in Indonesia. Contributed 50 - 70% of jobs, 0 - 60% of gross domestic product [11].

The population of this study is the number of fintech users in the Micro Small and Medium Enterprises (MSMEs) sector from 2018 to 2020. The sample in this study is based on non probability (non random selection) on keusioner aimed at people who live as a buffer of the capital city of Jakarta (economic center) today: Depok, Bogor and Banten during the business year 2018-2020. The number of samples studied in the 2018 - 2020 observation year as many as 660 to the community involved in the MSME sector in the capital buffer area as the center of economic turnover in Indonesia.

This study was analyzed using Partial Least Square (PLS) method and assisted with SmartPLS 3.0 software. PLS is one of the alternative methods of Structural Equation Modeling (SEM) that can be done to solve problems in the relationship between variables that are very complex and have non-parametric assumptions, meaning that the data does not refer to any particular distibusi. In pls there are 3 types of testing

• outer model testing (measurement model)

• inner model testing (structural model)

• hypothesis testing. Outer model testing consists of convergent validity test, discriminant validity test, and composite reliability. After testing the outer model that has met, the next test is done inner model (structural model)

 The inner model can be evaluated by looking at the r-square score (indicator reliability) for dependent contracts and the t-statistical value of the path coefficient test. Hypothetical test is done with provisions, if the value of t- calculate < t-table, and the significance level of > 0.05 then the hypothesis is rejected. Whereas if the value of t- calculates the > t-table and the significance level of < 0.05 then the hypothesis is accepted [15-16].

This study was analyzed using Partial Least Square (PLS) method and assisted with SmartPLS 3.0 software. PLS is one of the alternative methods of Structural Equation Modeling (SEM) that can be done to solve problems in the relationship between variables that are very complex and have non-parametric assumptions, meaning that the data does not refer to any particular distibusi. In pls there are 3 types of testin

• Outer model testing (measurement model)

• Inner model testing (structural model)

• Hypothesis testing

Outer Model Testing (Measurement Model)

Outer model testing consists of convergent validity test, discriminant validity test, and composite reliability. Convergent validity refers to the degree of conformity between the attributes of the measuring instrument's measurement results and theoretical concepts that explain the existence of the attributes of the variable. The convergent validity test is performed by looking at the validity indicator item indicated by the loading factor value. The initial examination of the matrix loading factor is approximately 0.3 considered to have met the minimum level, and for loading factor approximately 0.4 is considered better, and for loading factor greater 0.5 in general considered significant (Lin, et. al, 2020 ; Juliandi, 2018). After data processing using SmartPLS 3.0 the loading factor results can be shown as in Table 1. Based on table 1, the loading factor score for all items is above 0.5 so it is declared valid, except for invalid X41 and X52 items because the score is still below 0.5.

The next validity test is discriminant validity. Discriminant validity refers to the degree of discrepancy between attributes that should not be measured by measuring instruments and theoretical concepts of the variable. Models have a better discriminant validity when the square root of the Average Varian Extracted (AVE) for each contract is greater than the correlation between the two contracts within the model. A good AVE value is required to have a value greater than 0.50. In this study, the AVE value for each contract can be shown in Table 2. Based on table 2, the AV E score for all variables I s above 0.5 so that it is declared valid again, except for the Price and Customs variables that are not yet valid because it is still below 0.5.

Table 1: Loading Factor Value In Convergent Validity Test

Table 2: AVE Value

Table 3: Composite Reliability and Cronbach's Alpha Values

Table 4: R Square Value On Dependent Variable

Outer models in addition to being measured by assessing convergent validity and discriminant validity can also be done by looking at the reliability of the contract or latent variables measured. Reliability test in PLS can use two methods namely cronbach's alpha and composite reliability. Cronbach's alpha measures the lower limit of a construct's reliability value while composite reliability measures the true reliability value of a construct. However, composite reliaility is considered better at estimating the internal consistency of a construct. Constructs are declared reliable if composite reliability has a value of >0.7. The smartpls output result for composite reliability value can be shown in Table 3. Based on table 3, the composite reliability and cronbach's alpha scores for all variables are above the standard norm so that they are declared reliable, except for price and custom variables that are not yet reliable because they are still below 0.7.

Figure 1: Structural Model Research

Table 5: Coeffiecient Path Values Between Variables

Table 6: Coefficient Path Values, Standard Deviation, T Statistics, And P Values For Total Effect

Inner Model Testing (Structural Model)

After testing the outer model that has met, the next test is done inner model (structural model). The inner model can be evaluated by looking at the r-square score (indicator reliability) for dependent contracts and the t-statistical value of the path coefficient test. The higher the r-square value means the better the predictive model of the proposed research model. The path value of coefficients indicates the degree of significance in hypothesis testing. The R square and coefficient path values can be seen in Table 4 and 5.

Based on the r-square value in Table 4 shows that 7 free variables X was able to explain the variability of the Intention contract by 74.5%, and the remaining 25.5% explained by other contracts beyond those studied in this study. While Intention was able to explain the variability of behavioral contracts by 80.7% and the remaining 19.3% explained by other contracts outside of those studied in this study.

Hypothesis Testing

Hypothesis testing is conducted to see if a hypothesis can be accepted or rejected among others by considering the value of significance between contracts, t-statistics, and p-values. The hypothesis testing of this study was conducted with the help of SmartPLS (Partial Least Square) 3.0 software. These values can be seen from the bootstrapping calculation results. The rules of thumb used in this study were t-statistics>1.65 (N = 207) with a p-value significance rate of <0.05 (5%) and a positive beta coefficient. The test value of this research hypothesis can be shown in Table 6.

The first hypothesis tests whether The Expectations of Fintech Users affect the Behavioral Intentions of Fintech Use. The test results showed a statistical T score of 4,119>1.65 and a p-Value score of 0.00<0.05 so it can be concluded that there is a significant influence. Based on this, hypothesis 1 (H1) which states that The Expectation of Fintech Users influences the behavioral intentions of fintech users is accepted. The second hypothesis tests whether the influence of Social Fintech affects the Behavioral Intentions of Fintech Use. Test results show a statistical T score of 1,641<1.65 and a p-Value score of 0.101>0.05 so that it can be concluded that there is no significant influence. Based on this, the hypothesis 2 (H2) which states that the influence of Social Fintech affects the intention of Fintech Use Behavior Intentions is rejected.

The third hypothesis tests whether Fintech Facilities affect the Behavioral Intentions of Fintech Use. The test results showed a statistical T score of 2,899>1.65 and a p-Value score of 0.00<0.05 so it can be concluded that there is a significant influence. Based on this, hypothesis 3 (H3) which states that Fintech Facilities affect the behavioral intentions of fintech users is accepted. The fourth hypothesis tests whether Fintech Prices affect the Behavioral Intentions of Fintech Use. Test results show a statistical T score of 1,205<1.65 and a P Value score of 0.229>0.05 so it can be concluded there is no significant influence. Based on this, the hypothesis 4 (H4) which states that Fintech Facilities affect the behavior intentions of fintech users is rejected.

The fifth hypothesis tests whether Fintech User Habits affect Fintech Usage Behavior The test results show a statistical T score of 2,959>1.65 and a p-Value score of 0.003<0.05 so that it can be concluded there is a significant influence. Based on this, the hypothesis 5 (H5) which states that Fintech User Habits affect the Behavior of fintech users is accepted.

The sixth hypothesis tests whether Fintech Risk affects the Behavioral Intentions of Fintech Use. The test results show a statistical T score of 2.192>1.65 and P Value score 0.029<0.05 so it can be concluded there is a significant influence. Based on this, hypothesis 6 (H6) which states that Fintech Risk affects the behavioral intentions of fintech users is accepted. The seventh hypothesis tests whether Fintech Trust affects the Behavioral Intentions of Fintech Use. Test results show a statistical T score of 9,389 >1.65 and a p-Value score of 0.000<0.05 so that it can be concluded that there is a significant influence. Based on this, the hypothesis 7 (H7) which states that Fintech Trust affects the behavior intentions of fintech users is accepted.

The eighth hypothesis tests whether the Intention of Fintech Use Behavior affects Fintech Usage Behavior. The test results showed a statistical T score of 9,268>1.65 and a P Value score of 0.000<0.05 so it can be concluded that there is a significant influence. Based on this, hypothesis 8 (H8) which states that the Intention of Fintech Use Behavior affects the Behavior of fintech users is accepted. The ninth hypothesis tests whether Fintech Facilities affect the Behavior of Fintech Use. Test results show a statistical T score of 11,427>1.65 and a P Value score of 0.000<0.05 so that it can be concluded that there is a significant influence. Based on this, the hypothesis 9 (H9) which states that Fintech Facilities affect the behavior of fintech users is accepted.

• Hypothesis 1 (H1) Fintech User Expectations affect the behavioral intentions of fintech users accepted, so it can be concluded that the greater the expectations of fintech users the greater the influence on the behavioral intentions of fintech use

• Hypothesis 2 (H2) which states that the social influence of fintech affects the intention of Fintech Use Behavior is rejected, so it can be concluded that the greater the social influence of fintech users does not have an influence on the behavioral intentions of fintech use

• Hypothesis 3 (H3) which states that fintech facilities affect the behavioral intentions of fintech users accepted, so it can be concluded that the greater the influence of Fintech Facilities has an influence on the behavioral intentions of fintech use

• Hypothesis 4 (H4) which states that Fintech Prices affect the Intention of Fintech Use Behavior is rejected so it can be concluded that the greater the Price of Fintech has no influence on the Behavioral Intentions of Fintech Use

• Hypothesis 5 (H5) which mentions that the habits of Fintech Users affect the behavior of fintech users accepted. so it can be concluded that the greater the habits of fintech users have an influence on the behavioral intentions of the use of fintech

• Hypothesis 6 (H6) which states that the risk of fintech affects the behavioral intentions of fintech users accepted, so it can be concluded that the greater the risk of fintech users have an influence on the behavioral intentions of fintech use

• Hypothesis 7 (H7) which states that fintech trust affects the behavioral intentions of fintech users is accepted, so it can be concluded that the greater the trust of fintech users has an influence on the behavioral intentions of fintech use

• Hypothesis 8 (H8) which states that the intention of fintech use behavior affects the behavior of fintech users is accepted so that it can be concluded that the greater influence of behavioral intentions of fintech use has an influence on the behavior of fintech use

• Hypothesis 9 (H9) which states that Fintech Facilities affect the behavior of fintech users is accepted so that it can be concluded that the greater the influence of Fintech Facilities has an influence on the behavior of fintech use. Thus, it is the finding of this study that the role of financial technology for small and medium-sized micro enterprises is needed to improve business continuity.

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