FACTORS
AFFECTING CONSUMER ACCEPTANCE OF
DIGITAL FINANCIAL INCLUSION; AN ANECDOTAL EVIDENCE FROM BOGOR CITY, INDONESIA
Nevvi
Wibella
Faculty
of Management and Business,
Bogor
Agricultural University, Indonesia
E-mail: nevvi.wibella@gmail.com
Idqan
Fahmi
Faculty
of Economics and Management,
Bogor
Agricultural University, Indonesia
E-mail: ifahmi@mb.ipb.ac.id
Imam
Teguh Saptono
Faculty
of Management and Business,
Bogor
Agricultural University, Indonesia
E-mail: itsaptono@yahoo.co.id
Submission: 18/04/2018
Revision: 25/04/2018
Accept: 17/05/2018
ABSTRACT
Various
surveys have been conducted over the past few years indicating financial access
in Indonesia is still not good. Digital Financial Inclusion (DFI) which is a
digital access to use of formal financial services by underserved populations
to solve those problems. The success of the DFI services were not only
determined by the service provider, but also by the acceptance of the users.
The TAM (Technology Acceptance Model) model offers a powerful and simple
explanation of the factors that affect user acceptance of a technology. The
main purpose of this research was to understanding consumer acceptance of using DFI which was investigated and measured
by several factors through the TAM model ie; perceived usefulness, perceived
ease of use, perceived credibility, intention to use, and actual use. The
population of this research was conducted in Bogor City because the level of
financial inclusion in Bogor City was low but the digital development was quite
good. And total sample was 134 respondent. The PLS SEM analysis showed that perceived
usefulness has no significant effect (p> 0.05) to intention to use DFI
services but perceived ease of use and credibility has significant (p <0.05)
and positive effect to consumer interest in using DFI services. Perceived
credibility is the most influencing consumer interest in using DFI service
because it has the highest coefficient value. The results of this research were
expected to improve the development and acceptance of DFI services.
Keywords: DFI services, credibility
perceptions, TAM,
usability, ease of use
1.
INTRODUCTION
The
survey conducted by the world bank shows that in the 2014 Global Inclusion
Database (Figure 1), the percentage of Indonesian population over 15 years who
have access to financial services was only 36%, far below Thailand (78%),
Malaysia (81% ), and Singapore (96%). To solve these problems, Indonesia
government has created programs and policies to improve financial access for
underserved communities, ie, inclusive financial policies. One of the inclusive
financial programs is Digital Financial Inclusion (DFI), which is digital
access to use of formal financial services by underserved populations. Examples
of DFI services in Indonesia are internet banking, mobile banking, and emoney.
Digitalization is
considered appropriate because of its high penetration rate, even among the
poor and vulnerable. Some research have suggested that technology has an
important role in improving the access of the poor to banks by providing
sustainable financial services (CLAESSENS, 2006). Based on a survey conducted
by InterMedia (2015), 79% of Indonesians have access to mobile phones. Another
interesting fact is based on a survey conducted by the Association of Internet
Network Providers Indonesia (APJII) which revealed that more than half of
Indonesia's population has been connected to the internet now. Based on the
results of the APJII (2017) survey, penetration of internet users in Indonesia
reached 51.8% ie 132.7 million of the total population 256.2 million. There is
a fairly rapid increase compared to 2014, the penetration of internet users of Indonesia
was only 34.9% ie 88.1 million out of total 252.4 million population in 2014. Therefore,
Indonesian is ready for digital financial inlcusion.
Based on Deloitte Consulting
(2015), West Java is the province with the highest unbanked population in
Indonesia. Based on the research of Ummah (2015), one of the factors that can
influence financial inclusion is income distribution. The growing inequality of
income (gini index) shows lower levels of financial inclusion. Bogor City’s
gini index has a big value compared to other cities in West Java in 2015, which
is equal to 0.47. Thus, the level of financial inclusion in Bogor City still
tends to be lower than others. On the other hand, the digital development in
the city of Bogor is quite good. Based on BPS (2017), the percentage of
population of Bogor City who have cellular phone (HP) are 69.30% and 40.49% of
the population has accessed internet. Thus, Bogor City is one of the potential market
targets of DFI services.
The
success of DFI services are not only determined by the service provider, but
also by the acceptance of the users (ORUC; TATARS, 2017). The success
of DFI services depend on how consumers receive the service. In other words, an
important issue for service providers when implementing DFI is to know what
factors affect consumer acceptance in using DFI. Because by knowing these
factors then the service providers can encourage the actual interest of
customers so willing to use DFI.
One model by Davis
(1989) that is often used to describe the level of technology acceptance is the
Technology Acceptance Model (TAM). The TAM model offers a powerful and simple
explanation for technology acceptance and user behavior (VANKATESH; MORRIS, 2000). In addition,
according to Chuttur (2009), MPT is a very popular model and is often used by
researchers to explain and estimate the acceptance of a system. Thus, this
reasearch used TAM as the model.
The main target of
DFI is to improve financial access to all levels of society in Indonesia.
Therefore, it is necessary to design a better application system from DFI to be
accepted by consumers. The main purpose of this research is to investigate the
importance of understanding consumer acceptance of using DFI. The impact of
several factors of consumer acceptance through the TAM model ie perceived
usefulness, perceived ease of use, perceived credibility, intention to use and actual
use of DFI services. The results of this research are expected to provide the
information needed to improve the development and acceptance of DFI services.
2.
FRAMEWORK AND LITERATURE REVIEW
2.1
Research Framework
This research
examines the impact of perceived usefulness, perceived ease of use, and
perceived credibility toward the intention to use by customer, reviews the
effect of perceived credibility to perceived usefulness and perceived
credibility, and also review the effect of intention to use on actual use of DFI
services. The research model can be seen in Figure 2.
2.2
Literature Review
2.2.1
Perceived Usefulness and Perceived Ease of Use
Perceived
usefulness and perceived ease of use come from TAM (DAVIS, 1989). Perceived
usefulness is the extent to which a person believes that using a particular
system will improve his performance and the perceived ease of use is defined as
the extent to which a person believes that using a particular system will be
free of effort (DAVIS, 1989). Results
from a number of studies Eze et al. (2011), Wang et al. (2003), Abadi & Nematizadeh (2012), Rusu & Shen (2012), Aderonke & Charles (2010), Liao &
Wong (2008), Afifah and Widyanesti (2017) and Widjana and Rachmat (2011) show
that the perceived usefulness and ease of use that makes customers have a
positive attitude to receive and adopt digital financial services. This result
is in harmony with Afifah and Widyanesti (2017) research, proving the perceived
ease of use usage proved significantly positive toward customer intention to
use mobile banking services at one of the banks in Jakarta. By applying it to
the context of DFI services, the first two hypotheses are:
H1: Perceived usefulness have a positive effect on intention
to use DFI services
H2: Perceived ease of use positively affects intention
to use DFI services
2.2.2
Perceived Credibility
Perceived
credibility are as far as safe individuals believe that using a system will be
free of individual equality and privacy issues (CHUTTUR, 2009). A number of
studies (ABADI; NEMATIZADEH,
2012; ADERONKE & CHARLES, 2010; RAHAYU,
2010; MUNIRUDDEEN, 2007) have tested and confirmed that perceived credibility
have a significant influence tp perceived usefulness, perceived ease of use and
actual use of ebanking. Therefore, to research the effect of perceived
credibility on user acceptance in DFI services, the following hypotheses are:
H3: Perceived credibility have a positive effect on intention to use DFI services
H4: Perceived credibility positively affects the perceived usefulness in using DFI services
H5: Perceived credibility positively affects the perceived ease of use in using DFI services
2.2.3
Intention to Use and Actual Use of DFI Services
Davis and
Vakentesh (1996) define intention to use as a trend of consumer behavior to use
a technology. The use of DFI in question is the actual use of DFI. Davis (1989)
defines the actual use of usage as a real and real condition for the use of
such a system. The results of Aderonke and Charles (2010) show that the construct of intention to
use in technology acceptance model has a positive effect on actual use. The
results of Rusu and Shen (2012) also show that the tendency of intention to use
to keep using technology has a significant positive effect on the real
condition of technology usage. With an understanding of the following
behavioral interests the hypothesis is:
H6: The hypothesis of the influence of behavior
interest has a positive effect on the use of DFI in using DFI services
3.
RESEARCH METHODOLOGY
This
research was conducted from January to April 2018. . The object of this
research were DFI services which wee well known by consumer, i.e. internet
banking, mobile banking, and e-money. Methods of collecting data and
information was survey methods with instruments include observation,
questionnaires, interviews and literature research. The sampling technique was
used purposive sampling (judgmental sampling) which the sample was determined
by certain criteria. The criteria of respondents were DFI consumers, living in
Bogor City (already settled ≥5 years), and age more than 15 years old. The
question type in this questionnaire were multiple choice and scale rating
(likert scale). The number of respondents in this research was 134 peopleCriteria of
respondents in this research were (1) residents of Bogor City who have settled
for at least 5 years, (2) over the age of 15 years and; (3) consumer of DFI
services.Processing
and data analysis used SmartPLS 3 program.
PLS SEM structural model of this
research can be seen in Figure 3. This research illustrates the factors that determine
consumer acceptance using DFI services. Factors analyzed consisted of percived
usefulness, perceived ease of use, perceived credibility, intention to use and
actual use of DFI services. In this research, perceived usefulness had 3
variable indicators, perceived ease of use had 4 variable indicators, perceived
credibility had 3 variable indicators, intention to use had 4 variable
indicators and actual use of DFI services had 3 variable indicators.
4.
RESULT AND DISCUSSION
4.1
Characteristics of Respondents
Table 1 shows the
profile of the 134 respondents. The respondents in this research are 61% women
and 39% men. It was not obtained intentionally, because in the acquisition of
respondents there is no difference of gender . Age group of respondents in this
research is dominated by young age is 20-24 years and 25-29 years. This may be
due to the younger age group having better financial independence compared to
youth groups (15-19 years) and more early adopters than adult groups (≥35 years).
Table 1: Sample profile
|
Variable |
Description |
Frequncy |
Percent (%) |
|
Gender |
Male |
52 |
39% |
|
Female |
82 |
61% |
|
|
Age |
15-19
years |
5 |
4% |
|
20-24
years |
76 |
57% |
|
|
25-29
years |
32 |
24% |
|
|
30-34
years |
4 |
3% |
|
|
≥35
years |
17 |
13% |
|
|
Education Level |
Primary
school |
3 |
2% |
|
Junior
high school |
1 |
1% |
|
|
Senior
high school |
15 |
11% |
|
|
Undergraduate |
106 |
79% |
|
|
Postgraduate |
9 |
7% |
|
|
Pekerjaan |
Non-employed |
4 |
3% |
|
Entrepreneur |
15 |
11% |
|
|
Student |
31 |
23% |
|
|
Employee |
62 |
46% |
|
|
Government
employee |
4 |
3% |
|
|
Etc |
18 |
12% |
|
|
Income |
<
Rp 1 million |
17 |
13% |
|
Rp
1 - 2.5 million |
28 |
21% |
|
|
Rp
2.6 - 5 million |
38 |
28% |
|
|
>
Rp 5 million |
51 |
38% |
The respondents at
the last level of undergraduate education dominate about 79%. Respondents with
recent senior high school education level followed by a percentage of 11%. And
the respondents with the last education finished primary school and Junior High
School were only 3%. This suggests that the level of education affects
consumers' adaptability in choosing the financial products and services they
use.
Table 1 shows the
distribution of respondents by profession dominated by employee groups then
students and entrepreneurs. This can be due to the ease and practicality
required to transact demanded more in these three professions. Based on income
level, respondents with income >Rp 5 million dominate with percentage 38%.
While respondents with income <Rp 1 million has the smallest percentage that
is only about 13%. This can be due to the greater the income level of a person,
the higher the financial literacy (financial literacy), the more sensitive the
financial services available.
4.2
Performance of Indicator Variables
Factors determining acceptance of
the use of DFI services were analyzed using the TAM model consisting of perceived
usefulness, perceived ease of use, perceived credibility, intention to use and actual
use of DFI. Those factors have its own indicator variables. Perceived
credibility and usefulness has 3 indicator variables, whereas perceived ease of
use, intention to use, and actual use has 4 indicator variables. The performance
of the indicator variables in each factor was indicated through frequency
analysis from consumer appraisal result to the statement of indicator variable
on each factor in questionnare. Assessment is done by using likert scale 1-5 (1
= strongly disagree s.d. 5 = strongly agree). Table 2 shows performance of
indicator variables.
Table 2: Descriptive statistics of
indicator variables
|
Variable |
Indicator |
n |
Mean |
Med |
Min |
Max |
Standard Deviation |
|
Perceived Credibility (X1) |
X11 |
134 |
3.64 |
4 |
1 |
5 |
0.850 |
|
X12 |
134 |
3.93 |
4 |
1 |
5 |
0.755 |
|
|
X13 |
134 |
4.05 |
4 |
1 |
5 |
0.721 |
|
|
Perceived Usefulness (Y1) |
Y11 |
134 |
3.67 |
4 |
1 |
5 |
0.920 |
|
Y12 |
134 |
4.46 |
5 |
1 |
5 |
0.834 |
|
|
Y13 |
134 |
4.37 |
5 |
1 |
5 |
0.835 |
|
|
Perceived Ease of Use (Y2) |
Y21 |
134 |
4.28 |
4 |
1 |
5 |
0.878 |
|
Y22 |
134 |
4.33 |
5 |
1 |
5 |
0.862 |
|
|
Y23 |
134 |
4.20 |
4 |
1 |
5 |
0.937 |
|
|
Y24 |
134 |
3.77 |
4 |
1 |
5 |
0.977 |
|
|
Intention to Use (Y3) |
Y31 |
134 |
3.75 |
4 |
1 |
5 |
1.025 |
|
Y32 |
134 |
4.08 |
4 |
1 |
5 |
0.811 |
|
|
Y33 |
134 |
4.08 |
4 |
1 |
5 |
0.811 |
|
|
Y34 |
134 |
4.08 |
4 |
1 |
5 |
0.852 |
|
|
Actual Use (Y4) |
Y41 |
134 |
3.28 |
4 |
1 |
5 |
1.102 |
|
Y42 |
134 |
3.92 |
4 |
1 |
5 |
0.763 |
|
|
Y43 |
134 |
3.91 |
4 |
1 |
5 |
0.868 |
4.3
Evaluation of Model Results
4.3.1
Measurement Model Results (Outer Model)
Evaluation of the measurement
model (outer model) is based on three criteria to assess the outer model
through testing of convergent validity, discriminant validity, and reliability
using SmartPLS 3.2.4 software.
Table 3: The value of loading factor for each indicator
variables (convergent validity)
|
Variable |
Indicator |
Loading Factor |
Convergent Validity |
|
Perceived
Credibility (X1) |
X11 |
0.832 |
Valid |
|
X12 |
0.931 |
Valid |
|
|
X13 |
0.830 |
Valid |
|
|
Perceived
Usefulness (Y1) |
Y11 |
0.742 |
Valid |
|
Y12 |
0.930 |
Valid |
|
|
Y13 |
0.912 |
Valid |
|
|
Perceived
Ease of Use (Y2) |
Y21 |
0.902 |
Valid |
|
Y22 |
0.869 |
Valid |
|
|
Y23 |
0.926 |
Valid |
|
|
Y24 |
0.766 |
Valid |
|
|
Intention
to Use (Y3) |
Y31 |
0.828 |
Valid |
|
Y32 |
0.932 |
Valid |
|
|
Y33 |
0.958 |
Valid |
|
|
Y34 |
0.926 |
Valid |
|
|
Actual
Use (Y4) |
Y41 |
0.741 |
Valid |
|
Y42 |
0.907 |
Valid |
|
|
Y43 |
0.910 |
Valid |
Convergent validity is seen from loading factor value. Indicator
variable is reliable (valid) if it has loading factor value above 0.7. The
loading factor value <0.7 must be removed from the model and re-estimation
of the loading factor values. The result of loading factor in Table 3 shows that
all the indicators which was used in this research has value above 0.7 so it is declared reliable/valid.
Because there was no problem with convergent validity
then the next tested issue was related to discriminant validity. Discriminant
validity can be tested by comparing the values of the square root of AVE with
the correlation value between the variables. From Table 4, it can be seen that
the square root value of AVE is greater than the correlation of each construct.
So, it can be concluded that there is no problem of discriminant validity.
Table 4: The values of the square root of AVE between the variables
(discriminant validity)
|
|
Actual Use |
Perceived
Usefulness |
Perceived
Ease of Use |
Perceived
Credibility |
Intention to Use |
|
Actual Use |
0.856 |
||||
|
Perceived Usefulness |
0.580 |
0.865 |
|||
|
Perceived Ease of Use |
0.586 |
0.783 |
0.868 |
||
|
Perceived Credibility |
0.622 |
0.561 |
0.637 |
0.866 |
|
|
Intention to Use |
0.659 |
0.572 |
0.609 |
0.601 |
0.912 |
The last outer model test after convergent validity and
discriminant validity, PLS SEM also performs relaibility test. Reability
test measure internal consistency of
measuring instrument. Reliability shows the accuracy, consistency and precision
of a measuring instrument. Reliability test in the PLS can be done with two
methods of Cronbach's alpha value must be greater than 0.7 and the value of
composite reliability must be greater than 0.7. Table 5 shows that all
constructs have composite reliability and Cronbach's alpha values above 0.7.
Therefore, there is no reliability/ unidimensionality problem in the
established model.
Table 5:The value of Cronbachs
Alpha and composite reliability
|
Variable |
Cronbachs Alpha |
Composite Reliability |
|
Perceived Credibility
(X1) |
0.831 |
0.899 |
|
Perceived Usefulness
(Y1) |
0.827 |
0.899 |
|
Perceived Ease of Use
(Y2) |
0.888 |
0.924 |
|
Intention to Use (Y3) |
0.932 |
0.952 |
|
Actual Use (Y4) |
0.816 |
0.891 |
4.3.2
Evaluation of Structural Model Results (Inner Model)
Inner model evaluation is tested by three way ie.
R-Square, Q2, and GoF value. The R-square value is used to measure the magnitude of
the relation of variable (exogenous) to the dependent variable (endogen). Table
6 shows the R-square value of perceived usefulness was 31.5%, perceived ease of
use was 40.6%, intention to use varible was 46.1%, and actual use variable was
43.4%. This explains the ability of independent variables to explain the
dependent variable of perceived usefulness was 31.5% and the rest was explained
by other independent variables that were not in this research. Furthermore, the
ability of independent variables to explain the dependent variable perceived ease
of use was 40.6% and the rest was explained by other independent variables that
are not in this research. Then, the ability of independent variable to explain
dependent variable of intention to use was 46.1% and the rest is explained by
other independent variable which was not in this research. Meanwhile, the
ability of independent variables to explain the dependent variable of actual
use was 43.4% and the rest is explained by other independent variables that are
not in this research.
Table 6: The value of R-Square
|
Variable |
R2 |
|
Perceived Usefulness
(Y1) |
0.315 |
|
Perceived Ease of Use
(Y2) |
0.406 |
|
Intention to Use (Y3) |
0.461 |
|
Actual Use (Y4) |
0.434 |
The following for inner model
testing can be done by looking at the value of Q2 (predictive
relevance) function to validate the model. The value of Q2 of this
study was 0.88 (quite large). This suggests that exogenous latent variables were
good (as appropriate) as explanatory variables that were able to predict their
endogenous variables. Next to measuring the inner model was to find the value
of Goodness of Fit (GoF). The GoF value was a single measure to validate the
combined performance between the measurement model and the structural model.
The GoF value of this study was 0.587 which was large and good. Based on test
results of the value of R2, Q2 and GoF seen that the
model formed was robust. So that hypothesis testing can be done.
4.4
The Relationship between Variable
If the
statistical t-value is more than 1.64 (two-tiled) or 1.96 (one-tiled) and
probability value (p-value) less than 0.05 or 5%, so there is significant
effecf between variables. In Table 7 can be seen the T-Statistic value of the
SEM processed results in each variables. Based on T-statistics which shows
relationship between constructs, so: (1)
Y2àY3, (2) X1àY1, (3) X1àY2 , (4) X1àY3, and (5) Y3àY4 because it has a T value of statictics> 1.96 and
a value of P value <0.05. While the relationship between Y1àY3 proved to be insignificant because it has a static T
value of 1.590 (<1.96) and the value of P value of 0.112 (> 0.05). So,
perceived usefulness has no significant effect to intention to use. But,
perceived use of ease & credibility has positive and significant effect to
intention to use. Perceived credibility also has significant and positive
effect to perceived usedulness and ease of use. And intention to use has
significant and positif effect to actual use.
Table
7: The result of PLS SEM bootstraping
|
|
|
Original
Sample (O) |
Sample
Mean (M) |
Standard
Deviation (STDEV) |
T
Statistics (|O/STDEV|) |
P
Values |
Sig. |
|
H1 |
Y1 --> Y3 |
0.192 |
0.194 |
0.121 |
1.590 |
0.103 |
Tidak
Sig |
|
H2 |
Y2 --> Y3 |
0.243 |
0.237 |
0.094 |
2.581 |
0.007 |
Sig |
|
H3 |
X1 --> Y3 |
0.338 |
0.354 |
0.077 |
4.377 |
0.000 |
Sig |
|
H4 |
X1 --> Y1 |
0.561 |
0.556 |
0.090 |
6.203 |
0.000 |
Sig |
|
H5 |
X1 --> Y2 |
0.637 |
0.630 |
0.094 |
6.769 |
0.000 |
Sig |
|
H6 |
Y3 --> Y4 |
0.659 |
0.662 |
0.062 |
10.694 |
0.000 |
Sig |
Note: X1 (Perceived Credibility), Y1 (Perceived
Usefulness), Y2 (Perceived Ease of Use),
Y3 (Intention to Use), Y4 (Actual Use), Sig
(Significant)
The results of this research as shown in Table 7
indicate that there was no significant effect of the perceived usefulness to
intention to use. In other words, consumer confidence about the usefulness of
DFI services can not affect their intention to use the service. The results of
this research in line with the results of Hosein (2009) which shows that the
perception of the use of ebanking has no significant effect in determining
consumer behavior interest in using it in the Midwest. This may be because the
respondent has not considered that usability of DFI is important because it can
be obtained from various other services.
The results of this research as
shown in Table 7 indicate that there was significant effect of the perceived
ease of value to intention to use. The
results of this research in line with the results of Afifah and Widyanesti
(2017) which was describing the acceptance of mobile banking in Jakarta using
the MPT approach also indicates that the perceived of ease of use has a
significant positive effect to intention to use mobile banking services in
Jakarta. Each increase of 1 unit from the value of the perceived ease of use
(Y2) will increase 0.243 unit from the value of intention to use (Y3). In other
words, consumer trust that DFI services are easy to use can affect their intention
to use. Consumers think that using DFI services is a good and profitable idea.
The results of this research as
shown in Table 7 indicate that there was positive and significant effect of the
perceived credibility to intention to use. The results of this research in line with the
results of Rahayu (2010) and Jalal et al. (2011) which show that the perceived credibility
has a positive and significant effect on the acceptance of ebanking. Each
increase of 1 unit from the value of the perceived credibility (Y2) will
increase 0.338 unit from the value of intention to use (Y3). So, consumer
confidence about the credibility of the service provider may affect intention
to use. If consumers feel confident about the service provider's credibility
then they will like to use DFI and think that using it is a good idea.
The results show that perceived
credibility has significant and positive effect to perceived usedulness and
ease of use. The results of this research in line with the results of Aderonke
and Charles (2010) and Rahayu (2010). Aderonke and Charles (2010) show that
perceived credibility has significant and positive effect to perceived
usedulness and ease of use of ebanking in Negeria. And Rahayu (2010) also show
that perceived credibility has significant and positive effect to perceived
usedulness and ease of use of ebanking in Indonesia. Each increase of 1 unit
from the value of the perceived credibility (Y2) will increase 0.637 unit from the
value of perceived usefulness (Y1) and 0.561 unit from the value of perceived
ease of use (Y2). So, the consumer's trust in the credibility of the service
provider can affect his belief in the usefulness and ease of use of the services.
The more consumers feel secure and trust the DFI service provider, the consumer
will like to use it and think that using the DFI service is a good and
profitable idea as it facilitates financial transactions.
The last relation in this research
was between intention to use and actual use of DFI services. From the results
in Table 7, intention to use has positive and significant effect to actual use
of DFI services. Every increase of 1 unit from the value of the perceived credibility
(Y2) will increase 0.659 unit from the value of perceived usefulness (Y1). So, changes
in intention to use DFI services will affect the actual usage of the DFI
services. The more consumers feel happy /satisfied (positive) of the using DFI
services, so it will increase the actual use of the DFI service. Conversely, if
the DFI services can not make consumers happy/satisfied then consumers will
reduce/stop using the DFI.
There are several variables that
are suspected to be mediator in this research that were perceived usefulness
and ease of use. Based on the results of statistical analysis using PLS SEM
which can be seen in Table 8, shows that the perceived usefulness (Y1) was not
a mediating variable between perceived credibility (X1) and intention to use
(Y3) because it has the value of T Statistics 1.668 (<1.96) and P values
0.096 (> 0.05). While the perceived credibility (X1) has a positive and
significant effect on intention to use (Y3) through perceived ease of use (Y2)
because it has the value of T Statistics 2.472 (> 1.96) and P values 0.014
(<0.05). Based on Table 7, perceived credibility (X1) has a significant
relationship to behavioral interest (Y3). So, it can be concluded that this
mediation is quasi-mediating. This means that independent variables (perceived
credibility) can directly influence the dependent variable (intention to use)
without going through/involving the mediator variable (perceived ease of use).
Table 8: Total Indirect
effects
|
|
Original Sample (O) |
Sample Mean (M) |
Standard Deviation (STDEV) |
T Statistics (|O/STDEV|) |
P Values |
|
X1 -> Y1> Y3 |
0.108 |
0.109 |
0.065 |
1.668 |
0.096 |
|
X1 -> Y2 -> Y3 |
0.155 |
0.148 |
0.063 |
2.472 |
0.014 |
Note: X1 (Perceived Credibility), Y1 (Perceived
Usefulness), Y2 (Perceived Ease of Use),
Y3 (Intention to Use)
5.
CONCLUSION
The
aims of this research examined the factors that influence the acceptance of DFI
services in Bogor, Indonesia. Based on the results of this research, perceived
usefulness have no significant effect (p> 0.05) to intention to use DFI
services. While perceived ease of use and credibility significantly positive (p
<0.05) affect intention to use DFI services. The results of this research
also proves that there were positive and significant effect from perceived
credibility to perceived usefulness and ease of use at 5% significance level. ‘Intention
to use’ also proved that have a significant positive effect (p <0.05) to the
actual use of DFI services. The perceived ease of use in this research was a
pseudo-mediator variable between perceived credibility and intetion to use DFI
services. So, we recommend that DFI service providers pay more attention to perceived
credibility aspects that most influence consumer intention to use and not to focus
too much on perceived usefulness of DFI services because it did not have
significant effect to consumer intention to use.
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