p–ISSN: 2723 - 6609 e-ISSN: 2745-5254

Vol. 5, No. 8 August 2024 http://jist.publikasiindonesia.id/

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3088

Evaluation of Fintech Use Using Methods Technology

Acceptance Model (TAM)

Demianus Softer Jefdy Bawala

, Andeka Rocky Tanaamah

Universitas Kristen Satya Wacana, Indonesia

Email: [email protected]

, [email protected]

*Correspondence

ABSTRACT

Keywords: fintech, digital

payments, OVO, gopay,

technology acceptance

model (TAM).

The rapid increase in the use of technology, especially

finance (fintech) in recent years has changed the way we

conduct financial transactions with digital payment systems.

However, the acceptance and adoption of this technology are

still a concern for researchers and practitioners in this field.

This study aims to evaluate the use of fintech in the context

of digital payments by applying the Technology Acceptance

Model (TAM) method version 2 where the TAM model will

be used to measure and analyze the factors that influence

user acceptance of the use of fintech in digital payments.

Through this research, it is hoped that a better understanding

can be obtained of the factors that influence user acceptance

of fintech in digital payments, especially for the benefit of

further development. The approach used in this research is

quantitative by using a questionnaire as a data collection

tool. Respondents for this study were selected from groups

of users of digital payment applications such as OVO and

Gopay.

Introduction

In this digital era, all activities have shifted to technology, including fintech

(financial technology) in the scope of digital payments. Fintech makes it easier to make

electronic transactions faster, safer, and more efficient (Arindy & Suzianti, 2020).

However, the use of fintech itself, especially in Excuse me, ladies and gentlemen journal

editors, we have corrected the manuscript. please process it again and please publish it

immediately (Narulita, Suhaji, & Ginanjar, 2022).

Thank you. According to the IMF report (Purwanto, Yandri, & Yoga, 2022), global

economic growth is projected to decline from 3.4% in 2022 to 2.9% in 2023, with the

projection rising again to 3.1% in 2024. Fintech adoption is key in helping to boost global

economic growth. In addition, Fintech also plays an important role in improving financial

access for the unbanked or underbanked (Hidayatullah, Ariyanto, Mubarok, & Yohannes,

2020).

However, the results of the research on "User Innovativeness and Fintech Adoption

in Indonesia" by Budi, et al. revealed that user attitudes have the most significant direct

Evaluation of Fintech Use Using Methods Technology Acceptance Model (TAM)

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3089

impact on individuals' intentions to use Fintech. Where the study proves that attitude

variables are the most significant determining factor, and conversely, user financial

literacy contributes the least to the adoption of Fintech (Naution, Hasibuan, & Prayoga,

2021).

The results of the research on "User Attitude and Intentions Towards FinTech in

Bangladesh" by Ayoungman, et al. who researched that consumers in Bangladesh are

generally inadequate to access FinTech services. This study determined the acceptance of

FinTech in Bangladesh. The attitude of local users is very positive towards FinTech

services and products (Ningsih, Jogianto, Jessica, & Tanesia, 2022). The results show that

perceived costs and perceived trust have a strong effect on consumers. They stated that if

FinTech companies provided them with easy accessibility, they would prefer to start using

these services and products (Aliyudin, 2020).

The authors chose the Technology Acceptance Model (TAM) Version 2 as the

theoretical framework because this model has proven relevant in predicting technology

adoption, including Fintech. According to the research of Budi, et al. (Nabilla, 2021), user

attitudes have the most significant direct impact on individuals' intentions to use Fintech.

Therefore, this research will expand or add new dimensions such as cultural factors and

geographical location of respondents to modify the theoretical framework (Ayoungman,

Chowdhury, Hussain, & Tanchangya, 2021). In addition, this research will focus on

Fintech services such as OVO and Gopay to evaluate consumer acceptance of digital

payments. The results of this research can be used as a reference for improving or

improving digital payment applications so that they can provide quality services to

customers (Mahardika, 2021).

This study aims to analyze the level of acceptance and use of digital payments in

the community. Through factors that affect the use of the application such as perception

of usability, perception of ease of use, and perception of security related to the perception

and attitude of users towards data security and privacy in the use of fintech, as well as the

perception of self-control over the user's intention to use fintech and evaluate fintech in

facilitating digital transactions and making it easier to manage financial transactions for

users. In this study, the number of research objects using digital payment applications

used by users in Indonesia.

Research Methods

The research methodology used in this study is quantitative research with the

research population being Fintech users in the context of digital payments in Indonesia,

especially UKSW students with expected respondents of around 200 respondents.

The scientific reason that encourages the selection of the population of fintech users

in Indonesia, especially students, as the subject of the study is because the majority of

fintech users in Indonesia are in the age range of 25-35 years. In addition, students are

considered a group that is active in using technology and has the potential to become

fintech users in the future. In addition, previous research has also shown that students

have a strong level of understanding and satisfaction with the use of fintech. Therefore,

Demianus Softer Jefdy Bawala, Andeka Rocky Tanaamah

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3090

the selection of the population of fintech users in Indonesia, especially students, as the

subject of the study is considered relevant and can provide important insights in the

context of fintech adoption in Indonesia.

In this study, the research sample was taken using the purposive sampling technique

and the data collection technique used was an online questionnaire through a survey

platform that could be accessed by respondents. The questionnaire used consisted of

questions related to the variables in the TAM version 2 model, as well as additional

factors such as financial literacy, user attitudes towards Fintech, cultural factors, and the

geographical location of respondents who use digital payment applications.

The data obtained will be analyzed using multiple regression analysis to determine

the influence of each variable on user intention in adopting Fintech digital payment

applications. Multiple regression analysis is a statistical technique used to evaluate the

relationship between two or more independent variables and one dependent variable. In

addition, this study uses a conceptual diagram in the form of the TAM Version 2 model

to clarify the relationship between the variables in this study. Additional factors such as

financial literacy, user attitudes, cultural factors, and geographic location are also

included in the analysis to provide deeper insights into how these factors affect Fintech

adoption in Indonesia.

The validity and reliability of the collected data are ensured by using relevant

statistical techniques. The validity in the study states the degree of accuracy of the

research measuring instrument to the actual content measured. The validity test is a test

that is carried out to ensure that the measuring tool used in this study can accurately

measure the variable in question. Meanwhile, reliability refers to an understanding that

the instruments used in research to obtain the information used can be trusted as a data

collection tool and can reveal actual information in the field. Reliability tests were

conducted to ensure that the measuring tools used in this study could produce consistent

and reliable data. The statistical tool used for this study is IBM SPSS Statistics.

Data Collection

The data collected in this study was obtained from respondents' answers distributed

online using Google Forms. Furthermore, in this study, 202 digital payment user (fintech)

respondents were obtained with the criteria of student users. By looking at the data

presented, it can be concluded that the majority of respondents are men as many as 60.9%,

and women as many as 39.1%. There is a variation in the number of respondents based

on the year of the batch, with the > class of 2023 having the highest number of 73

respondents (36.1%), followed by the class of 2020 with 63 respondents (31.7%).

Meanwhile, the distribution of respondents by faculty also varied, with Information

Technology having the highest number of respondents as many as 54 (27.7%), followed

by Teacher Training and Education with 33 respondents (16.3%). This information

provides a complete overview of the diversity and contribution of respondents in the

framework of this study. The following is Table 1 which is a breakdown of the

questionnaire respondents.

Evaluation of Fintech Use Using Methods Technology Acceptance Model (TAM)

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3091

Table 1

Detail Respondent

Results and Discussion

Initial Test Data Presentation

1. Validity Test

The Validity Test has a significant role in evaluating the strength of the conclusions

and the inference of the test results, which is the basis for confirming the existing facts

(Putra, 2022). The validity test procedure involves the application of correlation

techniques, specifically by examining the correlation value of the r-count. The validity of

the measuring tool is stated if the correlation value of the r-count exceeds the

predetermined r-table value. The validity assessment was carried out by comparing the r-

count (RH) value and the r-table (RT) value, and if the RH > RT, then the question item

was considered valid. The determination of the r-table value was carried out using a

degree of significance of 5% in a two-way test. This means that to consider an instrument

valid, the r-count correlation value must exceed the r-table value at a significance level

Category

Percentage of Respondents

Gender

Male

60.9%

Woman

39.1%

Force

> 2023

73 (36.1%)

2022

15 (7.4%)

2021

40 (19.8%)

2020

63 (31.7%)

< 2020

11 (6%)

Faculty

Language and Art

14 (6.9%)

Biology

2 (1%)

Economics and Business

30 (14.9%)

Law

4 (2%)

Social Sciences and Communication

Sciences

11 (5.4%)

Interdisciplinary

5 (2.5%)

Medicine and Health Sciences

17 (8.4%)

Teacher Training and Education

33 (16.3%)

Psychology

6 (3%)

Science and Mathematics

7 (3.5%)

Electronics and Computer

Engineering

6 (3%)

Agriculture and Business

10 (5%)

Information Technology

54 (27.7%)

Theology

3 (1.5%)

Demianus Softer Jefdy Bawala, Andeka Rocky Tanaamah

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3092

of 5%. The validity test results show that the correlation value of the r-count for each

variable is higher than the set r-table value. Therefore, all indicators on the measured

variable can be considered valid. So with high validity results, it can be relied on that the

instruments used in this study can measure these variables accurately.

Table 2

Validity Test

Variable

Indicator

Value

𝑟

ℎ𝑖𝑡𝑢𝑛𝑔

Value

𝑟

𝑡𝑎𝑏𝑒𝑙

Information

Financial

Literacy

X1.1

0.879

0.138

Valid

X1.2

0.878

0.138

Valid

User Attitude

X2.1

0.783

0.138

Valid

X2.2

0.742

0.138

Valid

X2.3

0.875

0.138

Valid

X2.4

0.826

0.138

Valid

X2.5

0.846

0.138

Valid

Cultural and

Geographical

Factors

X3.1

0.733

0.138

Valid

X3.2

0.847

0.138

Valid

X3.3

0.822

0.138

Valid

Perceived

Usefulness

X4.1

0.834

0.138

Valid

X4.2

0.820

0.138

Valid

X4.3

0.794

0.138

Valid

Perception of

Ease

X5.1

0.848

0.138

Valid

X5.2

0.841

0.138

Valid

X5.3

0.844

0.138

Valid

Intention To Use

X6.1

0.739

0.138

Valid

X6.2

0.783

0.138

Valid

Evaluation of Fintech Use Using Methods Technology Acceptance Model (TAM)

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3093

X6.3

0.744

0.138

Valid

X6.4

0.364

0.138

Valid

Actual Usage

Behavior

Y1.1

0.861

0.138

Valid

Y1.2

0.837

0.138

Valid

2. Reliability Test

The Reliability Test aims to evaluate the level of accuracy of the measuring

instrument. The reliability test process includes an assessment of the durability or

consistency of measurement results, especially if it is carried out at the same time

(Sarwono, 2011). In testing reliability, Cronbach's Alpha Value is used as an indicator,

with the comparison of the Alpha Value to the commonly used Limit Value, which is

0.60. Based on the questionnaire data collected. Reliability is considered met if the

Cronbach's Alpha (CA) value exceeds the Limit Value (NB).

Based on the data collected from the questionnaire, the results of the reliability test

showed that the Cronbach's Alpha value in each variable (Financial Literacy, User

Attitude, Cultural and Geographical Factors, Perceived Usefulness, Perception of Ease,

Intention to Use, and Actual Usage Behavior) exceeded the standard Limit Value of 0.60.

Based on these criteria, it can be concluded that all variables in this study are considered

reliable because the value of Cronbach's Alpha exceeds the predetermined Limit Value.

Thus, the results of the reliability test show that the measurement instruments used in this

study are reliable and provide consistent results, increasing the validity of the data

obtained from the respondents. In other words, the questions or indicators used in this

study have adequate relevance and accuracy, increasing confidence in the validity of the

data obtained from the respondents. The results of the reliability test are concluded

through calculations using Cronbach's Alpha formula with the help of the SPSS

application, as seen in Table 3.

Table 3

Reliability Test

Variable

Cronbach's

Alpha Values

Limit Value

Information

Financial Literacy

0.704

0.600

Reliable

User Attitude

0.873

0.600

Reliable

Cultural and

Geographical Factors

0.718

0.600

Reliable

Perceived Usefulness

0.747

0.600

Reliable

Demianus Softer Jefdy Bawala, Andeka Rocky Tanaamah

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3094

Perception of Ease

0.796

0.600

Reliable

Intention To Use

0.854

0.600

Reliable

Actual Usage Behavior

0.612

0.600

Reliable

3. Residual Normality Test

The Normality Test was carried out to evaluate the extent to which the distribution

of the data obtained reflected normal conditions. To identify this, a normality test was

carried out using One-Sample Kolmogorov-Smirnov with a significance level of 0.05 . In

the context of parameter normality, the research data with N=202 shows a mean of about

0.0000000 and a standard deviation of about 1.13807140. The results of the normality

test produced a statistical test of 0.040, with a significance value (asymp. Sig. 2-tailed) of

0.200d. Therefore, the normality test of this parameter provides an overview of the

distribution of data. Despite the extreme differences, significance values greater than the

commonly used significance level (0.05) indicate that the data are relatively normally

distributed. This indicates that the assumption of normality can be considered to meet the

requirements, and validates the reliability of the statistical analysis performed. The results

of the normality test are documented in Table 4. Based on the results of the Kolmogorov-

Smirnov normality test, the significance value was obtained at 0.200d, which exceeded

the value of 0.05. Thus, it can be concluded that the data has a normal distribution.

Table 4

Residual Normality Test

One-Sample Kolmogorov-Smirnov Test

Unstandardized Residual

202

Normal Parameters

Mean

.0000000

Std. Deviation

1.13807140

Most Extreme Differences

Absolute

0.040

Positive

0.021

Negative

- 0.040

Test Statistic

0.040

Asymp. Sig. (2-tailed)

0.200d

Multiple Linear Regression Test Data Presentation

a. T Test or Sig Test (Partial Influence)

Evaluation of Fintech Use Using Methods Technology Acceptance Model (TAM)

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3095

The T-test was carried out to evaluate the influence of each independent variable,

namely Financial Literacy, User Attitude, Cultural and Geographical Factors, Perceived

Usefulness, Perception of Ease, and Intention To Use, partially on the bound variable,

namely Actual Usage Behavior [21]. If the value of T Calculate > the value of Table T, it

can be concluded that there is an influence. Details of the results of the T-test can be

identified in Table 5.

Tabel 5

Uji T Coefficients

Model

Unstanda

rdized

Coefficient

Standardiz

Coefficients

Sig.

Std. Error

Beta

(Constant)

0.360

0.543

0.63

0.508

Financial

Literacy

(X1)

0.141

0.084

0.130

1.683

0.094

User

Attitude

(X2)

0.065

0.041

0.137

1.569

0.118

Cultural

and

Geographi

cal Factors

(X3)

-0.046

0.036

-0.071

-1.284

0.201

Perceived

Usefulness

(X4)

0.081

0.052

0.109

1.576

0.117

Perception

of Ease

(X5)

0.163

0.045

0.242

3.655

<0.001

Intention

To Use

(X6)

0.206

0.039

0.327

5.331

<0.001

b. Dependent Variable: Actual Usage Behavior

Based on the table, the multiple linear regression equation is obtained as follows:

Y = 0,360 + 0,141 X1 + 0,065 X2 + (-0,046 X3) + 0,081 X4 + 0,163 X5 + 0,206 X6

The constant value in the Unstandardized B column yields a positive number, which

is 0.360. All variable values X1-X6 in the Unstandardized B column have a combination

Demianus Softer Jefdy Bawala, Andeka Rocky Tanaamah

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3096

of positive and negative values. This illustrates that each variable, both positive and

negative, has an impact on the value of the Actual Usage Behavior variable. For example,

the Financial Literacy variable has an impact of 0.141 on Actual Usage Behavior. User

Attitude has an impact of 0.065. Cultural and Geographical Factors had an impact of -

0.046. Perceived Usefulness has an impact of 0.081. Perception of Ease had an impact of

0.163. and Intention to Use had an impact of 0.206.

In this test, the significance value used was 0.05. The t-table value obtained is 1.972.

Therefore, the results of the T-test can be concluded as follows:

1. Financial Literacy has a t-count value smaller than the t-table value, which is 1.683 <

1.972, and a significance value greater than 0.05, which is 0.094 > 0.05. This means

that the Financial Literacy variable is not proven to affect the Actual Usage Behavior

variable (H1 is rejected). Rejection of H1 indicates that the level of financial literacy

of respondents, although considered important in the theory of technology adoption,

does not directly affect fintech usage behavior in the context of this study. This is due

to 2 factors: first, respondents may have basic knowledge of finance but do not apply

it significantly in the decision to use fintech applications. Second, the fintech

applications used may be intuitive or user-friendly, making financial literacy less

relevant in influencing usage decisions.

2. User Attitude had a t-count value smaller than the t-table value, which was 1.569 <

1.972, and a significance value greater than 0.05, which was 0.118 > 0.05. This means

that the User Attitude variable is not proven to affect the Actual Usage Behavior

variable (H2 is denied). Rejection of H2 indicates that although positive or negative

attitudes toward fintech applications may exist, these attitudes are not strong enough

to influence the decision to use fintech applications significantly. Factors that cause

this rejection include user attitudes that are not always present when using the

application. In addition, other factors, such as ease of use and intention to use the

application, have more influence on user decisions than their attitudes.

3. Cultural and Geographical Factors have a t-count value smaller than the t-table value,

which is -1.284 < 1.972, and a significance value greater than 0.05, which is 0.201 >

0.05. This means that the Cultural and Geographical Factors variable is not proven to

influence the Actual Usage Behavior variable (H3 was rejected). The study shows

that cultural factors and geographic location do not significantly affect the use of

fintech applications. This means that cultural and residential differences among

respondents do not affect the way they use the application because fintech applications

are already designed to address these differences well.

4. Perceived Usefulness has a t-count value smaller than the t-table value, which is 1.576

< 1.972, and a significance value greater than 0.05, which is 0.117 > 0.05. This means

that the Perceived Usefulness variable is not proven to affect the Actual Usage

Behavior variable (H4 rejected). Rejection of H4 indicates that while users may

perceive fintech apps as useful, this perception is not strong enough to influence their

decision to use the app. This is because even though something is perceived as useful,

it does not necessarily mean that people will actively use it if other factors, such as

ease of use or intention, are more influential in their decision.

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Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3097

5. Perception of Ease has a t-count value greater than the t-table, which is 3.655 > 1.972,

and a significance value of less than 0.05, which is <0.001, which is smaller than 0.05.

This means that the Perception of Ease variable is proven to influence the Actual Usage

Behavior variable (H5 is accepted). Acceptance of H5 confirms that the ease of use

of fintech applications is a key factor in influencing whether users will use the

application. Respondents indicated that the fintech application is easy to use, and they

are more likely to adopt it. Factors that support this acceptance include an intuitive

interface, simple processes, and minimal technical barriers that make users feel

comfortable using the application.

Intention To Use has a t-count value greater than the t-table, which is 5.331 > 1.972,

and a significance value of less than 0.05, which is <0.001, which is smaller than 0.05.

This means that the Intention To Use variable is proven to influence the Actual Usage

Behavior variable (H6 accepted). Acceptance of H6 indicates that users' intention to use

a fintech application greatly influences their decision to actually use it. This suggests that

when users have a strong intention to use an application, they are more likely to do so.

Factors such as effective marketing campaigns, promotions, or attractive features can

increase user intention and, in turn, influence application usage behavior.

c. F Test or Sig Test (Simultaneous Influence)

The F test was used to evaluate the extent to which all independent variables,

namely Financial Literacy, User Attitude, Cultural and Geographical Factors, Perceived

Usefulness, Perception of Ease, and Intention to Use, collectively affected the bound

variable, namely Actual Usage Behavior [22]. Detailed F-test results can be seen in Table

6 below.

Table 6.

Uji F ANOVA

Model

Sum of

Squares

Mean

Square

Sig.

Regression

313.168

52.195

39.095

<0.00

Residual

260.337

195

1.335

Total

573.505

201

a. Dependent Variable: Actual Usage Behavior

b. Predictors: (Constant), Financial Literacy, User Attitude, Cultural and

Geographical Factors, Perceived Usefulness, Perception of Ease, Intention To

Use

Based on the above test data, the F value of Table was obtained at 2.145. The

significance value used is 0.05. Based on the F test table, the F value of Calculate for all

Demianus Softer Jefdy Bawala, Andeka Rocky Tanaamah

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3098

independent variables is greater than the F value of the Table, which is 39.095 > 2.145,

and the significance value is <0.001b. This means that the variables Financial Literacy,

User Attitude, Cultural and Geographical Factors, Perceived Usefulness, Perception of

Ease, and Intention To Use all have a significant effect on the Actual Usage Behavior

variable (H7 accepted). Acceptance of H7 means that although each factor, such as

Financial Literacy, User Attitude, Cultural and Geographical Factors, Perceived

Usefulness, Perception of Ease, and Intention To Use, have different effects, the

combination of all these factors as a whole significantly affects the behavior of fintech

application users. This shows that when designing and developing fintech applications, it

is important to consider all these factors together to increase the adoption and usage of

the application.

d. Coefficient of Determination (Adjusted R Square)

Table 7 provides an explanation of the analysis carried out to understand the

relationship between two or more independent variables (X) and bound variables (Y)

simultaneously or simultaneously.

Table 7 Coefficient of Determination

Model Summary

Model

R Square

Adjusted R

Square

Std. Error

of the

Estimate

0.739

0.546

0.532

1.15545

Predictors: (Constant), Financial Literacy, User Attitude, Cultural and

Geographical Factors, Perceived Usefulness, Perception of Ease, Intention To Use

Based on the data in the table above, information can be obtained that the Adjusted

R-Square value reaches 0.532. This indicates that together, the variables Financial

Literacy, User Attitude, Cultural and Geographical Factors, Perceived Usefulness,

Perception of Ease, and Intention To Use have an influence of 53.2% on the Actual Usage

Behavior variable.

The results of the analysis show that from the whole study, there are several

findings, namely the following.

1. From the results of the analysis of the Evaluation of Fintech Use, it can be concluded

that based on the results of multiple linear regression analysis, an equation is found:

Y = 0,360 + 0,141 X1 + 0,065 X2 + (-0,046 X3) + 0,081 X4 + 0,163 X5 + 0,206 X6

The above equation provides a mathematical model that explains the relationship

between the free variable and the bound variable. The coefficient values in the equation

contribute each variable to the bound variable.

2. Based on the Adjusted R-Square value of 0.532, the findings indicate that around

53.2% of variations in Fintech usage behavior can be explained by the independent

variables studied (Financial Literacy, User Attitude, Cultural and Geographical

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Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3099

Factors, Perceived Usefulness, Perception of Ease, Intention To Use). Meanwhile, the

remaining 46.8% was influenced by other factors that were not included in the model.

These results highlight the significant contribution of independent variables to Fintech

usage behavior but also acknowledge the complexity of the phenomenon that may be

affected by the variability of external factors that have not been considered in detail in

the study.

3. The results of the analysis showed that the Financial Literacy variable had t-count

values < t-table (1,683 < 1,972), User Attitude t-count > t-table (1,569 < 1,972), and

Cultural and Geographical Factors t-count > t-table (-1,284 < 1,972). For the Perceived

Usefulness variable, t-count < t-table (1.576 < 1.972), while the Perception of Ease, t-

count > t-table (3.655 > 1.972), and Intention To Use t-count > t-table (5.331 > 1.972).

Therefore, H1 is rejected, H2 is rejected, H3 is rejected, H4 is rejected, H5 is accepted,

and H6 is accepted. The rejection of the H1, H2, H3, and H4 hypotheses indicates that

these variables have no significant influence. On the contrary, the acceptance of the

H5 and H6 hypotheses indicates that the variables Perceived Usefulness and Intention

To Use have a significant impact on the behavior of Fintech users. These results

provide important insights into the factors that critically affect the adoption of Fintech,

becoming the basis for the development of more appropriate strategies or policies in

encouraging the adoption and use of such financial technologies.

4. Through the F-test linearity test, an F-count value of 39.095 with a probability of

<0.001 was obtained, indicating that the regression model can be used for Fintech Use

Evaluation. Furthermore, it can be concluded that together, all independent variables

X (Financial Literacy, User Attitude, Cultural and Geographical Factors, Perceived

Usefulness, Perception of Ease, Intention To Use) have a significant influence on the

bound variable Y (Actual Usage Behavior). This means that these factors collectively

play an important role in shaping Fintech usage behavior, and these findings provide

valuable guidance for the development of more focused policies and strategies in

increasing the adoption of these financial technologies.

Conclusion

This study consistently reveals that the variables Perception of Ease and Intention

to Use have a significant role in shaping the actual usage behavior of an application or

service. These findings have profound implications for the design and development of

products or services, highlighting the need for improved user experience, especially in

terms of ease of use and intent to use. Although factors such as Financial Literacy, User

Attitude, Cultural and Geographical Factors, and Perceived Usefulness have often been

considered important in the literature, the results of this study show that these variables

do not have a significant impact on user behavior. The recommendation to further focus

on improving the Perception of Ease and Intention to Use provides clear guidelines for

developers to detail product or service development strategies as it underscores the

importance of these factors in guiding digital product development decisions. The

recommendations given emphasize the need to adjust product or service development

Demianus Softer Jefdy Bawala, Andeka Rocky Tanaamah

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3100

strategies to strengthen the perception of ease of use and increase user intent to adopt. In

this context, marketing strategies and product design approaches need to be tailored to

the findings that these aspects have a significant impact on user behavior.

From an empirical perspective, this study reveals consistent findings, highlighting

the important role of the Perception of Ease and Intention To Use variables in shaping the

actual usage behavior of a digital payment application or service. These results provide a

concrete foundation for product or service developers to prioritize improving user

experience, especially in terms of ease of use and intent to use. The recommendations

given for adjusting the product or service development strategy based on these empirical

findings offer concrete guidance. Emphasizing the need to improve ease of use and user

intent to adopt, marketing strategies and product design can be adjusted to better suit the

dynamics of user behavior.

Bibliography

Aliyudin, Rizal Sukma. (2020). Peran Financial Technology Dalam Meningkatkan

Penerimaan Pajak Di Indonesia. J-Aksi: Jurnal Akuntansi Dan Sistem Informasi,

1(1), 56–67.

Arindy, & Suzianti, Amalia. (2020). Developing Digital Wallet Services in Indonesia: A

Multigeneration Perspective. Proceedings of the 2020 International Conference on

Engineering and Information Technology for Sustainable Industry, 1–6.

Ayoungman, Fairtown Zhou, Chowdhury, Nazmul Hasan, Hussain, Nida, & Tanchangya,

Papel. (2021). User attitude and intentions towards fintech in Bangladesh.

International Journal of Asian Business and Information Management (IJABIM),

12(3), 1–19.

Hidayatullah, Rachmad Syarifuddin, Ariyanto, Sudirman Rizki, Mubarok, Husni, &

Yohannes, Abebayehu. (2020). Collaborative problem-based Learning: An analysis

of problem-solving skills in vocational schools. IJORER: International Journal of

Recent Educational Research, 1(3), 209–217.

Mahardika, Maulida Swara. (2021). Pengaruh Kemudahan Penggunaan, Keamanan Dan

Persepsi Risiko Terhadap Minat Penggunaan Financial Technology (Fintech)

Payment Linkaja Syariah Siswa Smk Islam Pb Soedirman 2. Universitas Negeri

Jakarta.

Nabilla, Audi Ghea. (2021). Pengaruh Literasi Keuangan, Financial Technology, dan

Perceived Ease of Use terhadap Perilaku Keuangan (Studi Kasus pada Profesi

Akuntansi di Semarang Jawa Tengah). UniversitasMuhammadiyahSemarang.

Narulita, Siska, Suhaji, S., & Ginanjar, R. (2022). Analisis pengaruh implementasi

teknologi informasi terhadap kinerja karyawan (studi kasus pada perusahaan

Telekomunikasi di kota Semarang). Jurnal Hummansi (Humaniora, 5(1), 23–41.

Evaluation of Fintech Use Using Methods Technology Acceptance Model (TAM)

Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 3101

Naution, Doly Anwar, Hasibuan, Ria Reni Armayani, & Prayoga, Robi. (2021). Tingkat

Perkembangan Fintech (Financial Technology), Pemahaman Fintech (Financial

Technology) dan Minat Mahasiswa UIN Sumatera Utara. Jurnal Pendidikan

Tambusai, 5(3), 9080–9090.

Ningsih, Lakshmi Jaya, Jogianto, Shannon Eleanore, Jessica, Jessica, & Tanesia, Cindy

Yoel. (2022). Analisis Pengetahuan dan Tingkat Kepuasan Mahasiswa STIE

Ciputra Makassar Terhadap Penggunaan Fintech. Jurnal Mirai Management, 7(1),

256–268.

Purwanto, Hadi, Yandri, Delfi, & Yoga, Maulana Prawira. (2022). Perkembangan dan

dampak financial technology (fintech) terhadap perilaku manajemen keuangan di

masyarakat. Kompleksitas: Jurnal Ilmiah Manajemen, Organisasi Dan Bisnis,

11(1), 80–91.

Putra, Aquinaldo Sistanto. (2022). Identifikasi Faktor-Faktor Pendorong Adopsi Fintech

pada UMKM di Priangan Timur. JPEK (Jurnal Pendidikan Ekonomi Dan

Kewirausahaan), 6(2), 435–449.