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JOSS :
Journal of Social Science
MSME PROFITS BOOST: THE IMPACT OF REVITALIZATION USED
FINTECH, E-COMMERCE, AND DIGITAL PAYMENTS
Christine
1
, Wendi
2
, Shella Pratamawati
3
, Nizwan Zukhri
4
Universitas Bangka Belitung Indonesia
1,2,3,4
Email : [email protected]
KEYWORDS
MSMEs, Fintech,
E-Commerce,
Digital Payment,
Profit Growth
ABSTRACT
This study aims to examine the influence of financial technology, electronic
commerce, and digital payment variables on profit growth variables in
MSMEs in Pangkalpinang City, Bangka Belitung Islands Province. The
method that the researchers used in this study is an associative method with
a quantitative approach that uses primary data sources obtained through the
distribution of questionnaires. The sampling method is Nonprobability
Sampling with purposive sampling techniques which obtained 100
respondents who are MSME actors. This type of research is quantitative
research that is associative. Data analysis techniques use the help of
Statistical Package for the Social Sciences (SPSS) software version 25. The
results revealed that simultaneously fintech (X1), e-commerce (X2), and
digital payment (X3) had a positive and significant effect on profit growth
in MSMEs in Pangkalpinang City, while partially fintech variables had a
positive and significant effect on profit growth, while e-commerce and
digital payment variables did not have a positive and significant effect on
profit growth in MSMEs in Pangkalpinang City. The R Square value is
0.504 or 50.4%, so it is concluded that 50.4% of the variation in the
dependent variable can be explained by the independent variable. Then the
Adjusted R Square value is 0.488 or 48.8%. So it can be concluded that
fintech, e-commerce, and digital payments can affect profit growth by
48.8% while the remaining 51.2% is influenced by other variables that were
not studied in this study.
INTRODUCTION
In the business world, developments always accelerate quickly to create fierce business
competition. Technology is one of the fields that is growing rapidly in the current era of the
Industrial Revolution 4.0. The birth of the concept of the era of society 5.0 carried by Japan
since 2016 increasingly makes technology an important intake for humans. Both of these eras
have raised technology as a medium for elaborating various social challenges and problems,
including those in the business world (Tarantang et al., 2019).
Business is always about profit and has many types and criteria. One of the businesses
that is currently being occupied by many Indonesians is MSMEs. These MSMEs have been
regulated in Law Number 20 of 2008 concerning Micro, Small, and Medium Enterprises.
Micro, Small, and Medium Enterprises abbreviated as MSMEs are forms of small and
medium-scale economic businesses and/or activities carried out by individuals or business
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entities and managed productively to obtain profits (Simamora & Saputra, 2023).
In 2020, since the beginning of the emergence of COVID-19 in Indonesia, MSMEs have
been the sector most affected by this outbreak. The revival of MSMEs in Indonesia after the
COVID-19 pandemic is not easy, but the digital era can provoke MSMEs to rise. In this digital
era, MSME players are required to be able to master the ability of digital and internet experts
to be able to survive in tight business competition (Purwana et al., 2017).
The phenomenon of digital acceleration has been born throughout Indonesia, including
Pangkalpinang City which is located and is also the capital of the Bangka Belitung Islands
Province. The growth and development of MSMEs is supported by the utilization and
application of existing technology. The number of MSMEs in Pangkalpinang City has
increased significantly and in June 2022 it increased to 24,521 and now it is predicted to
increase due to technological provocations (Slamet et al., 2016).
The phenomenon of digital acceleration in the financial services industry has expertized
the financial services industry globally and given birth to a new phenomenon in the financial
sector, namely Financial Technology commonly called Fintech. The digital era also presents
a breakthrough in the field of trade, namely Electronic Commerce or E-commerce. Not only
that, the rapid development of technology has created changes to payment tools and systems
that have fast, precise, and safe criteria in every transaction, namely Digital Payment (Sapitri,
2018).
Figure 1.
Projected Value of Fintech Transactions for 2016-2022
Source: Statista, 2022
Based on data in Figure 1, it shows that Indonesia is the country with the highest
projected value of fintech transactions throughout 2016-2022 with the value of fintech
transactions in Indonesia projected to grow by an average of 15.5 per year throughout 2018-
2022. Fintech was born to be a fillip for the growth of MSMEs in Indonesia through Peer to
peer-to-peer lending, Equity Crowdfunding, E-Wallet, and Personal Finance features
(Suryanto & Dai, 2020).
2016 2017 2018 2019 2020 2021 2022
Indonesian
14,2 17,67 21,61 25,86 30,23 34,51 38,51
Malaysia
3,7 4,7 5,9 7,3 8,8 10,3 11,66
Philippines
3,74 4,64 5,69 6,84 8,05 9,28 10,46
0
5
10
15
20
25
30
35
40
45
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Figure 2.
E-Commerce Revenue in Indonesia 2017-2023
Based on the data in Figure 2, it shows that e-commerce revenue has increased
significantly from 2017-2023. This is one of the reasons we want to make e-commerce one
of the variables of our research because we want to analyze and find out whether many
MSMEs in Pangkalpinang City have used e-commerce to market their products and whether
its application can increase profits for their business (Raharjo & Yuliana, 2022).
In the financial sector, technology not only brings changes to financial services, but
technology that is growing very rapidly has created changes to payment tools and systems
that have fast, precise, and safe criteria in every transaction, one of which is digital payment
(Abidin, 2015).
Source: Bank Indonesia, 2021
Figure 3.
Total Digital Payment Transaction Value in 2017-2021
It is clear in Figure 3 that the total value of digital payment transactions from 2017 to
2021 continues to experience a significant increase and is predicted to continue to increase
due to the encouragement of digitalization factors that make all people prefer something
practical. There are very potential opportunities from e-commerce for MSME players to
expand and develop and allow MSME players to market in the global market and potentially
penetrate exports.
12.375
47.198
145.165
204.909
305.435
2017 2018 2019 2020 2021
122,89%
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Several studies related to the influence of fintech, e-commerce, and digital payments on
MSMEs have been conducted by several researchers. According to (Abbasi et al., 2021) stated
that fintech has a positive influence on the efficiency of MSMEs. According to (Puspita, 2019)
the application of e-commerce in MSMEs has not shown an increase in company profits. The
implementation of a digital payment system for MSMEs plays a significant role in
encouraging the regeneration of MSMEs (Handayani & Soeparan, 2022).
Based on the phenomena that have been described and some previous research results
that show that there is an influence between fintech, e-commerce, and digital payments on
MSME profits in some regions, there are still some shortcomings and weaknesses in their
application, so Researchers are interested in research to analyze and find out whether fintech,
e-commerce, and digital payments have a positive and significant effect on profit growth in
MSMEs in the Pangkalpinang City.
METHOD RESEARCH
This research method is an associative method with a quantitative approach because it
aims to determine the influence or relationship between two or more variables with data in
the form of numbers which are then analyzed using statistical methods on the variables used
in this study, namely independent variables (fintech variables (X1), e-commerce variables
(X2) and digital payment variables (X3)) on dependent variables (profit growth (Y)) on
MSMEs in Pangkalpinang City, Bangka Belitung Islands.
Place and Time of Research
Researchers researched MSMEs in Pangkalpinang City, where data was first collected
which was sampled through the Micro, Small and Medium Enterprises Cooperative Office of
Bangka Belitung Islands Province, followed by the Integrated Business Service Center
(PLUT) of Bangka Belitung Islands Province. This research activity began with the
ratification of the research proposal and research permit, namely from July to August 2023.
Object and Subject of Research
The object of research in this study is MSMEs in Pangkalpinang City. The subjects in
this study are MSME actors in Pangkalpinang City, equipped by officials at the Micro, Small,
and Medium Enterprises Cooperative Office and PLUT of Bangka Belitung Islands Province.
Population and Research Sample
The population used in this study is all Micro, Small, and Medium Enterprises in
Pangkalpinang City. Of the population of 9,125 MSME units in Pangkalpinang City, 100
MSME units have met the representative sample criteria and become samples in this study.
Sampling Techniques
The sampling technique in this study uses the Nonprobability Sampling method with
purposive sampling technique which is a method of determining respondents to be
respondents with certain predetermined criteria (Sugiyono, 2018). The criteria in this study
are: 1) The research will focus on MSMEs registered at the Cooperative Office and PLUT of
Pangkalpinang City; 2) The period studied is 2020-2023; 3) MSMEs that already use and
conduct transactions with fintech and digital payments; and 4) MSMEs that already use e-
commerce for their sales.
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Data Collection Methods
This study used primary data. The data collection method is carried out by conducting
field surveys (observations) and distributing questionnaires. Data collection is carried out
through three stages, namely: 1) Directly observing the data in the Micro, Small, and Medium
Enterprises Cooperative Office which is continued at the PLUT of Bangka Belitung Islands
Province; 2) Disseminating questionnaires to MSME actors offline (visiting MSMEs directly)
and online (using google form assistance) followed by interviews to add information; 3)
Collecting data from questionnaire results. The distribution of questionnaires is carried out by
applying the Likert scale as follows:
Table 1
Measurement Scale
Respondent's Answers
Code
Score
Agree
TA
5
Agree
A
4
Simply Agree
SA
3
Disagree
D
2
Strongly Disagree
SD
1
Source: Sugiyono, 2020
Data Analysis Techniques
By looking at the theoretical framework processed by the researcher, the data analysis
technique used in this study is quantitative analysis using the help of SPSS 25 software with
1) Descriptive Statistical Analysis; 2) Test validity; 3) Reliability test; 4) Test normality; 5)
Multicollinearity test; 6) Heteroscedasticity test; 7) Multiple linear regression analysis test; 8)
Partial test (t); 9) Simultaneous test (f); and 10) Determination coefficient test (R2).
RESULTS AND DISCUSSION
Results of Descriptive Statistical Analysis
Table 2
Results of Descriptive Statistical Analysis
Descriptive Statistics
N
Minimum
Maximum
Sum
Mean
Std. Deviation
Fintech
100
15
25
2167
21,67
2,878
E-commerce
100
12
25
1891
18,91
3,285
Digital payment
100
10
25
2056
20,56
3,154
Profit Growth
100
31
50
4138
41,38
3,792
Valid N
(listwise)
100
Source: Research Results, Data processed, 2023
Table 2 shows that the amount of data or N used in this study is 100 which shows the
description of fintech, e-commerce, digital payment, and profit growth variables. Each
variable in the study showed a mean value greater than the standard deviation value, so it can
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be concluded that the data deviation that occurred was low and the deviation of the value was
average for fintech, e-commerce, digital payment, and profit growth variables.
Validity Test Results
Table 3
Validity Test Results
Statement
R Calculate
R Table
Conclusion
X1.1
0,910
0,165
Valid
X1.2
0,921
0,165
Valid
X1.3
0,904
0,165
Valid
X1.4
0,916
0,165
Valid
X1.5
0,872
0,165
Valid
X2.1
0,812
0,165
Valid
X2.2
0,931
0,165
Valid
X2.3
0,879
0,165
Valid
X2.4
0,831
0,165
Valid
X2.5
0,868
0,165
Valid
X3.1
0,898
0,165
Valid
X3.2
0,884
0,165
Valid
X3.3
0,910
0,165
Valid
X3.4
0,897
0,165
Valid
X3.5
0,870
0,165
Valid
Y1
0,737
0,165
Valid
Y2
0,667
0,165
Valid
Y3
0,721
0,165
Valid
Y4
0,231
0,165
Valid
Y5
0,737
0,165
Valid
Y6
0,740
0,165
Valid
Y7
0,439
0,165
Valid
Y8
0,504
0,165
Valid
Y9
0,494
0,165
Valid
Y10
0,494
0,165
Valid
Source: Research Results, Data processed, 2023
The validity test is said to pass if the r value is calculated > r table. R table is obtained
by the formula df = N-2. Df is the number of respondents, then df = 100 - 2 or df = 98. Then,
obtained an r table at a significance of 0.05 or 5% with a one-sided test and obtained an r table
of 0.165. Based on the test results in Table 3 show that all statements of variables X1, X2,
X3, and Y are declared to pass the validity test because all r values are calculated > table r
values.
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Reliability Test Results
Table 4
Reliability Test Results
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance
if Item Deleted
Corrected Item-
Total
Correlation
Cronbach's Alpha if
Item Deleted
Fintech
80,85
62,149
,688
,652
E-commerce
83,61
64,362
,503
,740
Digital payment
81,96
64,928
,527
,727
Profit Growth
61,14
55,293
,567
,713
Source: Research Results, Data processed, 2023
The reliability test is declared accepted or passed if Cronbach's Alpha score > 0.6
(Sugiyono, 2012). According to Fraenkel (2012), Cronbach's Alpha value of 0.51 ñ 0.70 is
categorized as moderate reliability and Cronbach's Alpha value of 0.71 ñ 0.90 is categorized
as high reliability. Based on the results of the reliability test in Table 4, it can be concluded
that all variables are declared to pass the reliability test because the value of Cronbach's Alpha
on each variable is> 0.6. So reliability is accepted, with categories for fintech variables having
moderate reliability and e-commerce, digital payment, and profit growth variables having
high reliability.
Normality Test Results
In this study, researchers used normality tests with the Normal Probability Plot method
and the Kolmogorov-Smirnov method. Decision-making in the Normal Probability Plot test
is carried out based on criteria if the data spreads around the diagonal line and follows the
direction of the diagonal line or histogram graph, showing a normal distribution pattern, then
the regression model satisfies the normality assumption.
Figure 5.
Histogram Graph of Normality
Source: Research Results, Data processed, 2023
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Figure 6
Test Results of Normal Probability Plot
Source: Research Results, Data processed, 2023
Based on Figure 5 which shows the histogram graph, it can be seen that the histogram
does not deviate to the right and left, the curve of the histogram graph has followed a normally
distributed chart pattern. Thus, it can be concluded that the tested data is normally distributed.
Based on Figure 6, it can also be seen that the points are scattered and follow the direction of
the diagonal line so it can be stated that the regression model used in this study already meets
the assumption of normality. In addition to using graphic analysis, the results of the normality
test in this study were also strengthened using the Kolmogorov-Smirnov method. This is
because the normal probability plot method tends to contain relative interpretations.
Therefore, in this study, researchers also tested the normality of data using the Kolmogorov-
Smirnov statistical method so that interpretations related to research results were further
strengthened (Sutarmin & Susanto, 2017).
Table 5
Kolmogorov-Smirnov Method Normality Test Results
One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual
N
100
Normal Parameters
a,b
Mean
,0000000
Std. Deviation
2,67156180
Most Extreme
Differences
Absolute
,075
Positive
,052
Negative
-,075
Test Statistic
,075
Asymp. Sig. (2-tailed)
,185
c
a. Test distribution is Normal.
b. Calculated from data.
c. Lilliefors Significance Correction.
Source: Research Results, Data processed, 2023
In the normality test, if the significance > 0.05, then the data are normally distributed.
Based on Table 5, it can be seen that the magnitude of the significance value of the
Kolmogorov-Smirnov test is 0.185 which means that fintech, e-commerce, and digital
payment variables on profit growth have normally distributed data because the significance
value of 0.185 is greater than 0.05. Thus, it can be concluded that the residual regression
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model in this study is normally distributed. So that the residual normality assumption has been
fulfilled.
Multicollinearity Test Results
Table 6
Multicollinearity Test Results
Coefficients
Type
Unstandardize
d Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std.
Error
Beta
Tolerance
VIF
1
(Constant)
20,61
2,326
8,863
0
Fintech
0,899
0,109
0,682
8,223
0
0,751
1,332
E-
commerce
0,113
0,098
0,098
1,153
0,252
0,715
1,399
Digital
payment
-0,042
0,106
-0,035
-0,39
0,695
0,662
1,51
a. Dependent Variable: Profit Growth
Source: Research Results, Data processed, 2023
The multicollinearity test is declared passed if the tolerance value > 0.1 and the VIF
value < 10, which means that there is no multicollinearity or regression model in this study
free from multicollinearity. Based on Table 6, it can be seen that the tolerance value of each
independent variable is greater than 0.10 for fintech (0.751 > 0.10), e-commerce (0.715 >
0.10), and digital payment (0.662 > 0.10) and the VIF value is smaller than 10 for fintech
(1.332 < 10), e-commerce (1.399 < 10), and digital payments (1.51 < 10). It can be concluded
that all independent variables in this study did not occur as multicollinearity between
independent variables because the independent variables in this study had a tolerance value
of > 0.1 and a VIF of < 10. Thus, it can be said that the assumption of non-multicollinearity
for regression models has been fulfilled.
Heteroscedasticity Test Results
This study analyzed the heteroscedasticity test using a scatterplot graph by looking at
patterns on the graph if there is no certain pattern and does not spread above or below zero on
the y-axis, then there is no heteroscedasticity in the study.
Figure 7
Heteroscedasticity Test Results (Scatterplot Graph)
Source: Research Results, Data processed, 2023
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Figure 7 shows that on the scatterplot graph, the points do not overlap each other and
spread randomly or irregularly above or below the number 0 on the Y axis. So it can be
concluded that there is no heteroscedasticity problem in the regression model so the regression
model is feasible to use.
Multiple Linear Regression Analysis Test Results
Table 7
Multiple Linear Regression Analysis Test Results
Coefficients
Type
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
20,612
2,326
8,863
,000
Fintech
,899
,109
,682
8,223
,000
E-commerce
,113
,098
,098
1,153
,252
Digital payment
-,042
,106
-,035
-,393
,695
a. Dependent Variable: Profit Growth
Source: Research Results, Data processed, 2023
Based on the test results listed in Table 7, the multiple linear regression equation can be
concluded as follows:
Y = 20,612 + 0,899 X
1
+ 0,113 X
2
- 0,042 X
3
The constant of 20.612 explains that if the fintech (X
1
), e-commerce (X
2
), and digital
payment (X
3
) value is 0, then profit growth (Y) is 20.612. The regression coefficient of the
fintech variable (X
1
) of 0.899 explains that if the fintech variable (X
1
) increases by 1%, then
the value of profit growth (Y) will increase or increase by 0.899% assuming other independent
variables remain. The regression coefficient of the e-commerce variable (X
2
) of 0.113
explains that if the e-commerce variable (X
2
) increases by 1%, then the value of profit growth
(Y) will increase or increase by 0.113% assuming other independent variables remain. The
regression coefficient of the digital payment variable (X
3
) of -0.042 explains that if the digital
payment variable (X
3
) increases by 1%, then the value of profit growth (Y) will decrease or
decrease by 0.042% assuming other independent variables remain.
Partial Test Results (t)
Table 8
Partial Test Result (t)
Coefficients
Type
Unstandardized
Coefficients
Standardized
Coefficients
T
Sig.
Collinearity
Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
20,612
2,326
8,863
0,000
Fintech
0,899
0,109
0,682
8,223
0,000
0,751
1,33
2
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E-
commerce
0,113
0,098
0,098
1,153
0,252
0,715
1,39
9
Digital
payment
-0,042
0,106
-0,035
-0,393
0,695
0,662
1,51
a. Dependent Variable: Profit Growth
Source: Research Results, Data processed, 2023
Based on Table 8, it can be concluded that the results of the statistical test t against the
hypotheses in this study are as follows:
H
1
= Financial Technology (Fintech) has a positive and significant effect on Profit
Growth in MSMEs in Pangkalpinang City. The results of the t-test calculation obtained
calculate for the fintech variable (X
1
) of 8.223 greater than table 1.661 and with a significance
value of 0.000 smaller than 0.05. This means that H
1
is accepted which shows that fintech
variables have a positive and significant effect on profit growth in MSMEs in Pangkalpinang
City.
H
2
= Electronic Commerce (E-commerce) has a positive and significant effect on Profit
Growth in MSMEs in Pangkalpinang City. The calculation results of the t-test obtained
calculated
for the e-commerce variable (X
2
) of 1.153 smaller than table 1.661 and with a
significance value of 0.252 greater than 0.05. This means that H
2
is rejected which shows
that e-commerce variables do not have a positive and significant effect on profit growth in
MSMEs in Pangkalpinang City.
H
3
= Digital payment has a positive and significant effect on Profit Growth for MSMEs
in Pangkalpinang City. The calculation results of the t-test were calculated for the digital
payment variable (X
3
) of -0.393 smaller than table 1.661 and with a significance value of
0.695 greater than 0.05. This means that H
3
is rejected which shows that the digital payment
variable does not have a positive and significant effect on profit growth for MSMEs in
Pangkalpinang City.
Simultaneous Test Results (f)
Table 9
Simultaneous Test Results (f)
ANOVA
a
Type
Sum of Squares
Df
Mean Square
F
Sig.
1
Regression
716,973
3
238,991
32,470
,000
b
Residual
706,587
96
7,360
Total
1423,560
99
Source: Research Results, Data processed, 2023
Simultaneous tests are carried out by comparing the values of F
calculate
and F
tabel
with a
significance level of 5%. If profitability < 0.05, then the independent variable partially has a
significant effect on the dependent variable. To find F
table
, df
1
= k – 1 = 4 – 1 = 3 and df
2
= n
– k = 100 – 4 = 96 with a significance level of 0.05. So, the value of F
table
is 2.699. If F
calculate
> F
table
then it can be said that the hypothesis is accepted and if the significance level < 0.05
then it can be said that the independent variable affects the dependent variable. This F test is
used to test one hypothesis that tests the effect of three independent variables simultaneously.
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The hypothesis in question is: H
4
= Fintech, E-commerce, and Digital payment have a positive
and significant effect on Profit Growth in MSMEs in Pangkalpinang City. Based on Table 9
it can be seen that F
calculate
worth 32.470 is greater than F
table
with a value of 2.699 and the
significance value is 0.000 less than the significance level of 0.05. Thus, it can be concluded
that fintech, e-commerce, and digital payments together (simultaneously) have a positive and
significant effect on profit growth.
Determination Coefficient Test Results (R2)
Table 10
Determination Coefficient Test Results (R2)
Model Summary
Type
R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-Watson
1
,710
a
,504
,488
2,713
1,858
a. Predictors: (Constant), Digital payment, Fintech, E-commerce
b. Dependent Variable: Profit Growth
Source: Research Results, Data processed, 2023
In the determination coefficient test, if the value of R2 is closer to number 1, the
influence of the independent variable on the dependent variable is stronger. Based on Table
10 it can be seen that the R Square value is 0.504 or 50.4%. It can be concluded that 50.4%
of the variation in the dependent variable can be explained by the independent variable. Then
it can also be seen that the value of Adjusted R Square (R2) is 0.488 or 48.8%. So it can be
concluded that fintech, e-commerce, and digital payments can affect profit growth by 48.8%
while the remaining 51.2% is influenced by other variables that were not studied in this study.
Furthermore, we also see a Standard Error of the Estimate (SEE) of 2.713. The smaller the
SEE value will make the regression capital more precise in predicting the dependent variable
or it can be interpreted if the SEE value < the standard deviation of the independent variable,
the regression model is better at predicting the value of the independent variable. The standard
deviation value of the independent variable is profit growth of 3.792 (can be seen in Table 2).
So it can be concluded that the SEE value < the standard deviation of the independent variable
(2.713 < 3.792) which means that regression capital is more precise in predicting the variable
of profit growth.
Discussion
The results showed that simultaneously fintech, e-commerce, and digital payment
variables had a positive and significant effect on profit growth in MSMEs in Pangkalpinang
City. This is in line with research by (Hasyim & Hasibuan, 2022) that the results of the use of
fintech and e-commerce are very important for MSME players to use to increase sales and
financial performance of their businesses. However, partial research results show that fintech
has a positive and significant effect on profit growth, while e-commerce and digital payments
do not have a positive and significant effect on MSME profit growth in Pangkalpinang City.
Partially, financial technology or fintech variables that have been applied by MSMEs
in Pangkalpinang City have an influence on profit growth in MSMEs in Pangkalpinang City,
meaning that if MSMEs in Pangkalpinang City apply or use fintech, profits in MSMEs will
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grow or in other words profit growth. This is because the majority of people in Pangkalpinang
City have applied to fintech because of the ease of access and transactions. These results
support research by (Astari et al., 2022) which states that fintech has a positive and significant
effect on financial performance and is in line with (Ningsih, 2020) research which states that
the use of fintech has a positive and significant effect on increasing MSME income.
The partial e-commerce variable also does not have a positive and significant effect on
profit growth in MSMEs in Pangkalpinang City, this is due to the lack of empowerment of
MSME actors in Pangkalpinang City towards e-commerce so its utilization has not been
evenly distributed and many of them still find it difficult to access and utilize e-commerce,
especially Gen X MSME players. The results of this study are in line with research (Triandra
et al., 2019) that e-commerce does not affect increasing MSME income. In contrast to research
(Mahliza, 2019) and (Odoom et al., 2017) which concluded that e-commerce affects
increasing MSME income.
The partial digital payment variable also does not have a positive and significant effect
on profit growth for MSMEs in Pangkalpinang City, this is because neither MSME players
nor the people of Pangkalpinang City have not fully utilized digital payments for transactions
so they have not been able to maximize the use of digital payments. This result is in line with
research (Ningsih, 2020) where providing education to the public, especially on how to utilize
financial transaction technology, is very helpful for business actors later so that they will get
more benefits than before (Costa & Castro, 2021).
CONCLUSIONS
Based on the results of research and discussion in this study, it can be concluded that
partially the results of the study show that fintech has a positive and significant effect on profit
growth in MSMEs in Pangkalpinang City, while e-commerce and digital payments do not
have a positive and significant effect on profit growth in MSMEs in Pangkalpinang City.
Simultaneously, fintech,
e-commerce, and digital payments have a positive and significant
effect on profit growth for MSMEs in Pangkalpinang City. This is because both MSME actors
and the people of Pangkalpinang City have not fully utilized existing technology optimally,
especially in the use of e-commerce and digital payments, as well as the lack of empowerment
by authorities including the government towards MSME actors in Pangkalpinang City which
has an impact on the lack of understanding, expertise and skills possessed by MSME actors
in Pangkalpinang City so that they have not been able to maximize utilization existing
technology in carrying out their business activities.
Suggestions for future research to be able to add other variables that may affect profit
growth such as social media variables, celebrity endorsements, and digital marketing. The
limitation of this study is that the research was only conducted on business actors in
Pangkalpinang City so it cannot prove whether fintech, e-commerce, and digital payments
have a positive and significant effect on the profit growth of MSME players in other regions,
so it is expected that there will be research in other regions to expand the scope of research to
produce a comprehensive research comparison. It is hoped that the Micro, Small, and Medium
Enterprises Cooperative Office and PLUT of Bangka Belitung Islands Province can pay more
attention to MSMEs, especially those in Pangkalpinang City by providing education to
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MSME players regarding technological developments to help MSME players in
Pangkalpinang City to maximize the use of fintech, e-commerce, and digital payments in
increasing profits and business development. It is also expected for MSME players to be able
to utilize and maximize the application of technology consistently to expand market share and
be able to maximize the benefits obtained.
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Copyright holders:
Christine
1
, Wendi
2
, Shella Pratamawati
3
, Nizwan Zukhri
4
(2023)
First publication right:
JoSS - Journal of Social Science
This article is licensed under a Creative Commons Attribution-ShareAlike 4.0
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