Corresponding Author: Maria
Andreatea Ottemusu
E-mail: [email protected]
INTRODUCTION
The COVID-19 pandemic that has passed has changed
various aspects of daily life, including the way we interact, one of which is
the use of technology (Pandey & Pal,
2020). When the pandemic made technological progress faster
(Renu, 2021), one of the technologies developed to overcome the
problem of social distancing was self-service technology. SST is a vital tool
for improving operational efficiency and increasing profits (Chan & Petrikat,
2022). Several current literature reviews show that SST has
helped companies increase customer satisfaction (Djajanto et al., nd), (Purba et al., 2022), (Sedighimanesh et al.,
2017). The positive benefits provided by SST have made
several businesses apply this model, such as banking, e-commerce, restaurant
food delivery, and, most recently, services at gas stations (public fuel
filling stations). To use SST, a user-centred system is needed. However,
several external factors influence user perceptions of a system (Considine &
Cormican, 2016).
Many consumers have noticed the application of SST in
several industries. However, it is common for them to have doubts and need help
understanding how to use SST. This is due to the low average technological
literacy possessed by Indonesian citizens; this is evidenced by the
intermediate frequency of information-seeking activities for Indonesian
citizens, which is smaller than communicating or using social media (Kominfo, 2020). One example of a problem when using SST is its use
at gas stations. Based on media publications, Antara News (Arfani, 2022) and (Marshanda, 2022) state that the SST policy can train customers to be
more independent in obtaining gas station services, but the lack of consumer
knowledge results in long queues and wasted fuel due to misuse. The media (Bob, 2020) also shows that people still cannot use SST for
payments and petrol filling and need help to operate it (Bob, 2020). Some still choose not to use SST gas stations and
choose to look for other gas stations Kompas, 2023. Another phenomenon is the
uncertainty the public feels when filling fuel because they do not know the
limits. They are not aware of the operation of the filling lever so that fuel
drips or overflows (None, 2020); both of these things will be dangerous for the
public.
SST gas stations have only been adopted in Indonesia,
where from the above phenomenon, it is possible that consumers who have tried
it do not want to use self-service technology when filling their vehicles with
petrol, especially now that it is in the post-pandemic period. Research on the
use of SST at gas stations has been carried out using Theory Acceptance Model
3. However, there needs to be research examining the continuity of consumer
usage regarding SST adoption. To find out the components that influence the
intention to use SST at gas stations, you can use the TCT variable, where this
theory is a synthesis of three models (Technology Acceptance Model, Expectation
Confirmation Model and Cognitive Model) to detect user behaviour in accepting
the sustainability of Information Systems (Liao et al., 2009). The variables
used are perceived usefulness, perceived ease of use, confirmation, attitude,
and continuity of use. From previous research (J. Lee et al., 2019) using self-efficacy as an antecedent in TAM3, it was
found that self-efficacy has a positive relationship with perceived ease of
use. However, there is still not much research that combines self-efficacy as
an antecedent in TCT. One of the predecessor studies that used self-efficacy as
an antecedent in TCT was Daragmeh, 2021 where the research was conducted during
the Covid pandemic and in the context of e-wallet; the results of the research
showed that self-efficacy had a positive relationship with perceived ease of
use of e-wallet. Research (EM et al., 2022) and (Kumar, 2007), which examine self-efficacy,
suggest that this SST research can be carried out in business fields other than
QSR and banking and can be carried out in other countries besides Singapore and
Korea. Previous research (Cheng et al., 2019) using TCT found that attitude, perceived usefulness
and satisfaction had a significant effect on continuance intention. The
research aims to determine the influence of self-efficacy, perceived
usefulness, ease of use, confirmation, and attitude on the continuance
intention of self-service technology that already exists at gas stations.
When viewed from a business perspective, Self-Service
gas stations promise high profitability due to savings in labour costs.
However, if seen from the perspective of customers who are used to being served
by officers, there is a risk of causing service failure because consumers want
to avoid using the self-service service. Another factor that causes consumers
not to want to use Self-Service is the need for Self-Efficacy from customers so
that it is difficult or they do not want to use the machine. Finally, with this
adoption, satisfaction, attitude, and continuance may have an influence where
customers may prefer to look for another gas station that can serve them rather
than using the self-service kiosk.
Based on the background above, this research aims to
assess the intention to use SST by customers at Jabodetabek gas stations. To
investigate the influence of customer self-efficacy on the continuance
intention of SST at gas stations. To assess the influence of satisfaction on
SST continuance intention at gas stations. Moreover, to assess customer
satisfaction and continuance intention after using SST at gas stations.
METHOD
Indonesian people,
especially at gas stations, need help using this self-service technology. Some
gas station customers are still unable to operate the petrol filling SST and
need help to use the machine. Apart from that, the adoption of SST at gas
stations is also faced with the problem of long queues due to some customers
who still choose not to use self-service and look for other gas stations that
still provide traditional services.
This research uses
quantitative methods, namely surveys, to collect data from gas station
customers in Indonesia. Questionnaires will be distributed via the Google Forms
(form) platform to assess the influence of customer self-efficacy on
continuance intention in SST. The survey has questions based on the TCT
theoretical framework model and a Likert scale to measure self-efficacy,
confirmation of expectations and satisfaction.
In this research,
some variables influence (repeat relate to new variables). The data collection
technique from the questionnaire is then measured using a Likert scale where
the indicators for each variable in the questionnaire have answers in the form
of a value scale. This research uses the PLS-SEM (Partial et al. Equation
Modeling) data analysis technique, with SmartPLS (v4.0) software as a data
analysis tool, which is used to carry out statistical tests consisting of
convergent validity tests, discriminant validity tests and reliability to test
the validity and reliability of the questionnaire, then to test data analysis
using tests such as Coefficient of Determination (R2), Predictive Relevance
(Q2), and Hypothesis Testing (Ghozali & Latan,
2015). After carrying out in-depth analysis through several
calculations, the final data results obtained will be interpreted by comparing
the findings with relevant literature references and in accordance with the
research objectives.
RESULTS AND DISCUSSION
Analysis of validity reliability
The analysis was carried out using PLS-SEM version 4
software. The variables for analysis were self-efficacy, perceived usefulness, perceived
ease of use, expectation confirmation theory, attitude toward technology, and
continuance intention. Invalidity and reliability analysis, they generally used
an outer loading greater than 0.5 with an average variance extracted (AVE)
greater than 0.5. There are several data, such as SE3 and CIN5, which have
values less than 0.5 (0.368, 0.282), so inappropriate data items are cleaned to
increase the AVE and Construct Reliability (CR) values. The requirements for
Construct Reliability or Cronbach Alpha can be seen in Table 2 below.
Table 2. Cronbach's alpha reliability range (Arof, 2018 )
|
Coefficient of Cronbach's Alpha
|
Reliability Level
|
|
More than 0.9
|
Excellent
|
|
0.8-0.9
|
Good
|
|
0.7-0.79
|
Acceptable
|
|
0.6-0.69
|
Questionable
|
|
0.5-0.59
|
Poor
|
|
Less than 0.59
|
Unacceptable
|
Table 3. Outer Loadings, Cronbach Alpha and AVE of the
variables studied
|
Construct
|
Items
|
Outer Loading
|
CR
|
AVE
|
|
Self
Efficacy
|
SE1. I have
confidence in using self-service at gas stations
|
0.634
|
|
|
|
SE2. I can
use self-service technology without learning about it first
|
0.647
|
|
|
|
SE4. I can
fulfil my needs using self-service technology at gas stations independently,
without help from people around me
|
0.798
|
|
|
|
SES. I can
effectively meet my needs by using self-service technology at gas stations
|
0.671
|
0.633
|
0.477
|
|
Perceived Usefulness
|
USE1. Gas
station self-service technology is very useful in meeting my needs
|
0.743
|
|
|
|
USE3. Using
gas station self-service technology will increase the effectiveness of
filling my vehicle with oil.
|
0.715
|
|
|
|
USE4. Using
gas station self-service technology will make it easier to fill my vehicle
with oil.
|
0.634
|
|
|
|
USES. I
prefer to use self-service gas stations, considering my time and energy.
|
0.784
|
0.690
|
0.520
|
|
Perceived Ease of Use
|
EOU4.
Learning to use gas station self-service technology is easy
|
0.808
|
|
|
|
EOUS. Using
gas station self-service technology is very simple
|
0.883
|
0.608
|
0.716
|
|
Expectation Confirmation Theory
|
C1. My
experience in using gas station self-service technology was better than I
expected
|
0.736
|
|
|
|
C2. The
service provided by the gas station's self-service technology was better than
I expected
|
0.676
|
|
|
|
C3. The gas
station's self-service technology suits my needs
|
0.722
|
|
|
|
C4. This gas station's
self-service technology meets my expectations
|
0.744
|
|
|
|
C5. Gas
station self-service technology meets my needs
|
0.705
|
0.763
|
0.514
|
|
Satisfaction
|
S1. I am
satisfied with the way the gas station's self-service technology works
|
0.722
|
|
|
|
$2. I am satisfied
with the service provided by self-service technology at gas stations.
|
0.684
|
|
|
|
$5. I am
satisfied with the use of self-service technology at gas stations.
|
0.718
|
|
|
|
S6. I feel
happy with the use of self-service technology at gas stations
|
0.702
|
|
|
|
$7. My
overall experience using self-service technology has been truly enjoyable.
|
0.709
|
0.750
|
0.500
|
|
Attitude toward technology
|
ATT1. I am
satisfied with my decision to use gas station self-service technology
|
0.745
|
|
|
|
ATT2. I am interested
in the services provided by gas station self-service technology.
|
0.707
|
0.708
|
0.533
|
|
ATT6. I like
the idea of using self-service technology for gas filling.
|
0.743
|
|
|
|
ATTZ. Using
gas station self-service technology will be a pleasant experience
|
0.727
|
|
|
|
Continuance
|
CIN1. I
intend to continue using gas station self-service technology rather than
discontinuing it.
|
0.749
|
|
|
|
CIN2. I
intend to continue using gas station self-service technology rather than
other alternative methods.
|
0.761
|
|
|
|
CIN3. I would
like to continue using gas station self-service technology as much as
possible.
|
0.744
|
|
|
|
CIN4. I will
advise my friends to use self-service technology for refuelling vehicles.
|
0.705
|
0.725
|
0.548
|
(*) reversed statement
Table 3 is a data item that has been cleaned, where the AVE values are
close to 0.5, so it is accepted because it has a Cronbach Alpha > 0.7, which
shows the data is reliable. For self-efficacy data with an AVE value of less
than 0.5, but because it has a Cronbach Alpha > 0.6, even though the data is
questionable, it is still acceptable, so the self-efficacy item is not
discarded.
Discriminant
Validity Fornell Larcker
|
Attitude
|
Confirmation
|
Continuance Intention
|
Perceived Ease of Use
|
Perceived Usefulness
|
Satisfaction
|
Self Efficacy
|
|
Attitude
|
0.730
|
|
|
|
|
|
|
|
Confirmation
|
0.750
|
0.717
|
|
|
|
|
|
|
Continuance
Intention
|
0.730
|
0.736
|
0.740
|
|
|
|
|
|
Perceived
Ease of Use
|
0.505
|
0.542
|
0.522
|
0.846
|
|
|
|
|
Perceived
Usefulness
|
0.658
|
0.737
|
0.735
|
0.452
|
0.721
|
|
|
|
Satisfaction
|
0.745
|
0.827
|
0.722
|
0.570
|
0.705
|
0.707
|
|
|
Self Efficacy
|
0.582
|
0.679
|
0.660
|
0.638
|
0.619
|
0.642
|
0.691
|
Discriminant Validity describes how different a latent variable is from
other variables; if the AVE value is higher than the squared correlation value,
the validity of the construct is confirmed. All correlations between factors
and AVE in this study show higher values than the squared correlation
coefficients of other factors, so reliability is confirmed.
Fit Models
Model fit is carried out first before proceeding to the hypothesis theory
verification stage. In the model fit item, SRMR (standardized root mean square
residual) determines whether the model can be said to be a good fit. Saturated
model is a model that measures the correlation between each construct.
Meanwhile, the estimated model is a model that is based on the total effect
scheme and considers the model structure.
SRMR must be less than 1 to be considered a fit model. The SRMR in the
saturated model is 0.068 and is smaller than 1, so it meets the model fit
requirements. The estimated model is 0.089, so it is a fit model. Then, the
model fit requirement for NFI must be greater than 0.9. Meanwhile, the NFI
saturated model is 0.697 < 0.9 and the NFI estimated model is 0.674 <
0.9. So, the fit model is based on SRMR, which meets the requirements for a fit
model for both the estimated model and the saturated model.
Table 4. Model Fit Results
|
|
Saturated
Model
|
Estimated
Model
|
|
SUMMER
|
0.068
|
0.089
|
|
d_ULS
|
1,853
|
3,216
|
|
d_G
|
0.677
|
0.778
|
|
Chi-square
|
1010.061
|
1089.17
|
|
NFI
|
0.697
|
0.674
|
Hypothesis
Verification
Hypothesis verification using path analysis in bootstrap in the PLS-SEM 4
application, results can be seen in Table 5 and Figure 2. Standard deviation
(STdev) shows the stability and accuracy of the parameters studied. To adopt a
hypothesis, it can be seen from the t statistics, where the t statistics must
have a value above 1.96 with a p-value of less than 0.05.
Regarding the relationship between attitude and continuance intention,
the standard deviation is 0.055, and the mean is 0.32. This shows that there is
no data deviation. In research with a significance level of 0.05, the t table
is 1.96. The t statistic is 5.893, which is bigger than the t table. The P
value is 0, smaller than 0.05, so it is significant. Thus, the hypothesis can
be accepted. Attitude has a significant positive relationship with continuance
intention.
In the relationship between confirmation and perceived usefulness, the
standard deviation is 0.072, and the mean is 0.688. This shows that there is no
data deviation. In research with a significance level of 0.05, the t table is
1.96. The t statistic is 9.706, which is bigger than the t table. The P value
is 0, smaller than 0.05, so it is significant. Thus, the hypothesis can be
accepted. Confirmation has a significant positive relationship with perceived
usefulness.
In the relationship between confirmation and satisfaction, the standard
deviation is 0.059, and the mean is 0.665. This shows that there is no data
deviation. In research with a significance level of 0.05, the t table is 1.96.
The t statistic is 11.321, which is bigger than the t table. The P value is 0,
smaller than 0.05, so it is significant. Thus, the hypothesis can be accepted.
Confirmation has a significant positive relationship with satisfaction.
Then, in the relationship between perceived ease of use and attitude, the
standard deviation is 0.045, and the mean is 0.262. This shows that there is no
data deviation. In research with a significance level of 0.05, the t table is
1.96. The t statistic is 5.77, which is bigger than the t table. The P value is
0, greater than 0.05, so it is significant. Thus, the hypothesis can be
accepted. Perceived ease of use has a significant positive relationship with
attitude.
Then, in the relationship between perceived ease of use and perceived
usefulness, the standard deviation is 0.068, and the mean is 0.08. This shows
that there is no data deviation. In research with a significance level of 0.05,
the t table is 1.96. The t statistic is 1.091, which is smaller than the t
table. The P value is 0.138, greater than 0.05, so it is insignificant. Thus,
the hypothesis cannot be accepted. Perceived ease of use has an insignificant
relationship with perceived usefulness.
Then, regarding the relationship between perceived usefulness and
attitude, the standard deviation is 0.061, and the mean is 0.537. This shows
that there is no data deviation. In research with a significance level of 0.05,
the t table is 1.96. The t statistic is 8.891, which is bigger than the t
table. The P value is 0, greater than 0.05, so it is significant. Thus, the
hypothesis can be accepted. Perceived usefulness has a significant positive
relationship with attitude.
In the relationship between perceived usefulness and continuance
intention, the standard deviation is 0.062, and the mean is 0.367. This shows
that there is no data deviation. In research with a significance level of 0.05,
the t table is 1.96. The t statistic is 5.87, which is bigger than the t table.
The P value is 0, smaller than 0.05, so it is significant. Thus, the hypothesis
can be accepted. Perceived usefulness has a significant positive relationship
with continuance intention.
In the relationship between perceived usefulness and satisfaction, the
standard deviation is 0.054, and the mean is 0.212. This shows that there is no
data deviation. In research with a significance level of 0.05, the t table is
1.96. The t statistic is 3.871, which is bigger than the t table. The P value
is 0, smaller than 0.05, so it is significant. Thus, the hypothesis can be
accepted. Perceived usefulness has a significant positive relationship with
satisfaction.
Then, regarding the relationship between satisfaction and continuance
intention, the standard deviation is 0.063, and the mean is 0.222. This shows
that there is no data deviation. In research with a significance level of 0.05,
the t table is 1.96. The t statistic is 3.541, which is bigger than the t
table. The P value is 0, smaller than 0.05, so it is significant. Thus, the
hypothesis can be accepted. Satisfaction has a significant relationship with
continuance intention.
Then, regarding the relationship between self-efficacy and perceived ease
of use, the standard deviation is 0.047, and the mean is 0.641. This shows that
there is no data deviation. In research with a significance level of 0.05, the
t table is 1.96. The t statistic is 13.488, which is bigger than the t table.
The P value is 0, smaller than 0.05, so it is significant. Thus, the hypothesis
can be accepted.
Self-efficacy has a significant relationship with perceived ease of use.
This illustrates that if customers who have a positive evaluation of gas
station self-service technology view that gas station technology can increase
effectiveness in filling oil and feel satisfied after using it, they will form
a positive intention to continue using self-service gas stations.
Table 5. Path analysis
|
Original sample (O)
|
Sample mean (M)
|
Standard deviation (STDEV)
|
T statistics (|O/STDEV|)
|
P values
|
|
Attitude
-> Continuance Intention
|
0.323
|
0.32
|
0.055
|
5,893
|
0
|
|
Confirmation
-> Perceived Usefulness
|
0.697
|
0.688
|
0.072
|
9.706
|
0
|
|
Confirmation
-> Satisfaction
|
0.673
|
0.665
|
0.059
|
11.321
|
0
|
|
Perceived
Ease of Use -> Attitude
|
0.262
|
0.262
|
0.045
|
5.77
|
0
|
|
Perceived
Ease of Use -> Perceived Usefulness
|
0.074
|
0,08
|
0.068
|
1.091
|
0.138
|
|
Perceived
usefulness -> Attitude
|
0.54
|
0.537
|
0.061
|
8.891
|
0
|
|
Perceived
usefulness -> Continuance Intention
|
0.366
|
0.367
|
0.062
|
5.87
|
0
|
|
Perceived
usefulness -> Satisfaction
|
0.21
|
0.212
|
0.054
|
3.871
|
0
|
|
Satisfaction
-> Continuance Intention
|
0.223
|
0.222
|
0.063
|
3.541
|
0
|
|
Self Efficacy
-> Perceived Ease of Use
|
0.638
|
0.641
|
0.047
|
13.488
|
0
|
P value <0.05 Sig , P value
> 0.05 not Sig
Figure 2. Hypothesis Test Results
P value <0.05 Sig , P value > 0.05 not Sig
CONCLUSION
The conclusions from this research are
as follows: 1) Enhancing Customer Trust: The findings indicate that customers
exhibit a positive inclination toward using supermarket fuel stations (SPBU
swalayan). The implication is the importance of reinforcing and maintaining
customer trust by delivering consistent and high-quality services. 2) Appropriate
Marketing Strategies: This data can be utilized to steer more effective
marketing strategies. Focusing on the strengths and benefits of supermarket
fuel stations in marketing campaigns can help alleviate customer concerns and
drive increased service usage. 3) Expansion and Service Improvement:
Considering the high interest from customers, expanding the network of
supermarket fuel stations or enhancing services at existing stations can be a
strategic choice. This could enhance the options and comfort for customers
using supermarket fuel stations. 4) Customer Referral Program Development: The
presence of customers willing to recommend supermarket fuel stations to their
friends signifies high satisfaction. Building incentive programs or rewards for
customers who recommend the service can strengthen customer loyalty and expand
business reach. 5) Innovation and Service Enhancement: This data can also serve
as a basis for innovating products or refining services at supermarket fuel
stations. Understanding customer preferences and needs can assist in refining
the offered services. 6) Boosting Competitiveness: With this research,
supermarket fuel stations can use it as a competitive advantage in the
industry. This could help enhance the competitiveness of these stations in an
increasingly competitive market. 7) Partnership and Collaboration Development:
Considering the enthusiasm of customers towards supermarket fuel stations,
initiatives to collaborate with other brands or entities to enhance services or
create joint promotional packages can be a strategic choice..
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