Increasing the Acceptance of New Santri with Digital
Marketing and Service Innovation through ICT Literacy at Muhammadiyah Boarding
School Yogyakarta
Sultan Agung Islamic University Semarang, Semarang, Indonesia
Email: masodjie123@gmail.com
Abstract
The problem addressed in this research is the suboptimal use of
digital media for marketing educational services at Muhammadiyah Boarding
School (MBS) Yogyakarta, resulting in a less than significant increase in new
student admissions. This study aims to explore the influence of digital
marketing and service innovation on the acceptance of new students at MBS
Yogyakarta, with ICT literacy as a mediating variable. Despite advancements in
technology and the availability of various digital media for marketing, MBS
Yogyakarta primarily relies on traditional methods such as brochures and
word-of-mouth for new student admissions, leading to stagnation in growth. The
study seeks to answer how digital marketing affects the acceptance of new
students, its impact on ICT literacy, how service innovation influences
acceptance, and its impact on ICT literacy. Using an ex-post-facto qualitative
approach, the research involved 81 individuals, including school leaders,
foundation administrators, teachers, and new student acceptance committee
members, employing a saturated sampling technique. Data was collected via
questionnaires and analyzed using descriptive analysis and the Structural
Equation Model (PLS). The findings reveal that digital marketing significantly
impacts the acceptance of new students and ICT literacy and that service
innovation similarly positively affects both. Furthermore, ICT literacy
mediates the effectiveness of digital marketing and service innovation in
increasing new student admissions. The study concludes that digital marketing
and service innovation are crucial for increasing new student admissions, with
ICT literacy serving as a key mediating factor, suggesting that improving ICT
literacy can further enhance the effectiveness of these strategies.
Keywords: Digital Marketing, ICT
Literacy, Increasing acceptance of new students, Service innovation
Introduction
The development of technology over time
makes Islamic educational institutions Pondok Pesantren continue to innovate
aggressively, especially in terms of educational marketing
Educational institutions must overcome challenges to use digital media to
market their services effectively
Service innovation, including improving public
service quality, is essential for increasing student enrollment. This involves
using business sector concepts for competitive services
At Muhammadiyah Boarding School (MBS Yogyakarta),
new student enrollment has increased significantly, attributed to digital
marketing, service innovation, and ICT-based methods like online registration.
Student numbers rose from 850 in 2014 to 2,520 in 2023 due to these efforts.
Previous research shows mixed results regarding the impact of digital marketing
and service innovation on student enrollment and product sales. Some studies
Given this research gap, the study aims to confirm whether digital marketing and service innovation, supported by ICT literacy, increase new student enrollment at MBS Yogyakarta. The formulation of the problem in this study is how to increase the acceptance of new students with Digital Marketing and Service Innovation through ICT Literacy as a mediating variable at the Muhammadiyah Boarding School (MBS) Yogyakarta Islamic Boarding School.
This
study aims to determine the influence of Digital Marketing on increasing the
acceptance of new students and ICT Literacy, the influence of Service
Innovation on increasing the acceptance of new students and ICT Literacy, as
well as the mediating role of ICT Literacy in the relationship between Digital
Marketing and Service Innovation on increasing the acceptance of new students.
Theoretically, the results of the study are expected to contribute to
understanding the variables of Digital Marketing, Service Innovation, and ICT
Literacy.
Practically, this research is useful for researchers to develop knowledge and insight in the field of human resources, especially related to the acceptance of new students with factors of Digital Marketing, Service Innovation, and ICT Literacy. For Pondok Pesantren Muhammadiyah Boarding School Yogyakarta, this research can be a strategic input material. In addition, the results of this study are also expected to be useful for future researchers as a reference and comparison in related research.
Research Methods
This research uses quantitative methods,
which focus on testing theories by measuring variables with numbers and
analyzing data with statistical procedures. This quantitative research is
ex-post facto, where data is obtained from events that have occurred, and
researchers reveal facts based on measurements of symptoms that exist in
respondents. This study aims to determine the increase in acceptance of new
students based on Digital Marketing and Service Innovation with ICT Literacy as
a mediating variable at the Muhammadiyah Boarding School (MBS) Yogyakarta
Islamic Boarding School.
The object of this research is Pondok
Pesantren Muhammadiyah Boarding School (MBS) Yogyakarta, which is located at
Jl. Piyungan Km 2, Marangan, Bokoharjo, Prambanan, Sleman, Yogyakarta 55572.
The study population included pesantren leaders, foundation administrators,
teachers, pesantren administrators, and the new student admission committee,
totaling 81 people. Since the population size is below 100, the sampling
technique uses saturated samples, where all members of the population are
sampled for research.
Data collection techniques are carried out
using questionnaires, which can be closed- or open-ended questions or
statements. The questionnaire is given to respondents directly or sent over the
Internet using a Google Form. Respondents' answers were measured on the Likert
scale, which had five answer options from "Strongly Disagree" to
"Strongly Agree.”
For data analysis, this study used
SmartPLS software. Data analysis is carried out with two main stages:
descriptive analysis and data analysis with a Structural Equation Model (SEM)
based on Partial Least Square (PLS). Descriptive analysis provides an empirical
picture of the information obtained from respondents. Meanwhile, SEM-PLS
analysis is used for measurement model testing (validity and reliability) as
well as structural model testing (causality).
The validity of the instrument is tested
by confirmatory factor analysis to confirm the dominant factors in the variable,
while reliability is tested by composite reliability and variance extracted.
The hypothesis test in SEM-PLS is carried out by looking at the significance of
the relationship between variables through the value of critical ratio (c.r)
and significance probability. The model is evaluated using indicators such as
R-square and Predictive Relevance (Q2), with a Q2 value greater than 0
indicating the model has relevant predictive value.
Analisa Partial Least Square (PLS)
The Partial
Least Square (PLS) approach was used to continue the analysis of data for this
investigation. PLS-based Structural Equation Modeling (SEM) is an alternative
analytical technique. SmartPLS version 4 application is a tool used and created
specifically to calculate structural equations based on variance.
Data Quality Testing / Evaluation
Measurement (Outer) Model
Konrgen
Validity is one of three criteria used to evaluate out-of-the-box models in PLS
the other two criteria are Discriminant Validity, measured by the square root
of the mean-variance extracted (AVE), and Composite Reliability of SmartPLS
software, convergence validity for measurement models, along with Composite
Reliability and Discriminant Validity in the square root form of the extracted
mean-variance (AVE).
Analyze the
correlation between item value/component scores calculated using SmartPLS
software and convergence validity of scoring models using reflection of
evaluated dimensions. Cheung et al.
Figure
1.
Full
Model Structural Partial Least Square (Outer) Model
The majority of
validity indicators of each variable in this study have a loading factor value
greater than 0.70, so it is considered genuine, in accordance with the findings
of data processing by SmartPLS 4, as shown in the figure above. The Digital
Marketing variable has four indications, all of which are declared valid
because the outer loading value is more than 0.70. The service innovation
variable has five indications, all of which are declared valid because the
outer loading value is more than 0.70. The ICT Literacy variable has four
indications, all of which are declared valid because the outer loading value is
more than 0.70. The variable increase in new student admissions has three
indications, all of which are declared valid because the outer loading value is
more than 0.70.
Based on the
picture above, the following is presented outer loading table that has been
processed as follows:
Table
1. Outer Loading Score Table
|
Variable |
Indicator |
Score |
Information |
|
Digital
Marketing |
X1.01 X1.02 X1.03 X1.04 |
0,839 0,877 0,743 0,734 |
Valid Valid Valid Valid |
|
Service
Innovation |
X2.01 X2.02 X2.03 X2.04 X2.05 |
0,818 0,851 0,807 0,854 0,775 |
Valid Valid Valid Valid Valid |
|
ICT
Literacy |
Z.01 Z.02 Z.03 Z.04 |
0,868 0,764 0,867 0,792 |
Valid Valid Valid Valid |
|
Increased
Acceptance of New Students |
Y.01 Y.02 Y.03 |
0,864 0,908 0,811 |
Valid Valid Valid |
If all values
are more than 0.7, this data will be analyzed and used as primary data in this
study. In addition to outer loading, two more criteria are used with the
SmartPLS 4 data analysis method to evaluate the outer model, namely
discriminant validity (cross loading, AVE, Fornell larckelracted criterion) and
composite reliability.
Table
2. Average Variant Extracted (AVE)
|
No |
Variable |
AVE |
Information |
|
1 |
Digital
Marketing |
0,641 |
Valid |
|
2 |
Service
Innovation |
0,675 |
Valid |
|
3 |
Increased
Acceptance of New Students |
0,743 |
Valid |
|
4 |
ICT
Literacy |
0,679 |
Valid |
All study
variables had AVE values greater than 0.5, according to the information in the
table above. As a result, each variable has strong discriminant validity.
Discriminant Validity
Cross-loading
values are used in discriminant validity tests. If the cross-loading value of
an indicator on one variable is higher than other variables, it is said to have
Discriminant Validity. The cross-load values for each indication are as
follows:
Table
3. Discriminant Validity Value (Cross Loading)
|
Indicator |
Variable |
Conclusion |
|||
|
X1 |
X2 |
Z |
Y |
|
|
|
X1.01 |
0,839 |
0,624 |
0,620 |
0,337 |
X1.01 (X1 > X2, Z, Y = valid) |
|
X1.02 |
0,877 |
0,843 |
0,697 |
0,435 |
X1.02 (X1 > X2, Z, Y = valid) |
|
X1.03 |
0,743 |
0,639 |
0,568 |
0,207 |
X1.03 (X1 > X2, Z, Y = valid) |
|
X1.04 |
0,734 |
0,590 |
0,486 |
0,352 |
X1.04 (X1 > X2, Z, Y = valid) |
|
X2.01 |
0,670 |
0,818 |
0,838 |
0,387 |
X2.01 (X2 > X1, Z, Y = valid) |
|
X2.02 |
0,785 |
0,851 |
0,690 |
0,433 |
X2.02 (X2 > X1, Z, Y = valid) |
|
X2.03 |
0,688 |
0,807 |
0,611 |
0,480 |
X2.03 (X2 > X1, Z, Y = valid) |
|
X2.04 |
0,745 |
0,854 |
0,702 |
0,331 |
X2.04 (X2 > X1, Z, Y = valid) |
|
X2.05 |
0,601 |
0,775 |
0,519 |
0,481 |
X2.05 (X2 > X1, Z, Y = valid) |
|
Z.01 |
0,700 |
0,739 |
0,868 |
0,488 |
Z.01 (Z > X1, X2, Y = valid) |
|
Z.02 |
0,555 |
0,624 |
0,764 |
0,276 |
Z.02 (Z > X1, X2, Y = valid) |
|
Z.03 |
0,672 |
0,767 |
0,867 |
0,349 |
Z.03 (Z > X1, X2, Y = valid) |
|
Z.04 |
0,504 |
0,572 |
0,792 |
0,273 |
Z.04 (Z > X1, X2, Y = valid) |
|
Y.01 |
0,404 |
0,458 |
0,438 |
0,864 |
Y.01 (Y > X1, X2, Y = valid) |
|
Y.02 |
0,299 |
0,371 |
0,337 |
0,908 |
Y.02 (Y > X1, X2, Z = valid) |
|
Y.03 |
0,373 |
0,472 |
0,330 |
0,811 |
Y.03 (Y > X1, X2, Z = valid) |
The table above
shows that when compared with the cross-loading value of other variables, each
indication of the research variable has the highest cross-loading value of the
variable it creates. Based on these results, it can be concluded that the
indicators used in this study show strong discriminant validity when collecting
data. Another method of assessing the information in the table above shows that
when compared with the cross-loading value of other variables, then each
indication of the research variable has the highest cross-loading value of the
variable it creates.
Composite Reliability
The Cronbach
alpha value is used to strengthen the reliability test and the composite
reliability value. A variable is said to be highly reliable if the Composite Reliability value is more
than 0.7. If the Cronbach alpha value of a variable is more than 0.6, it can be
considered reliable or fits the Cronbach alpha criterion. The Composite
Reliability values of each variable and the Cronbach Alpha alpha for this study
are listed below.
Table
4. Composite Reliability dan Cronbach Alpha
|
Variable |
Composite Reliability |
Cronbach Alpha |
Information |
|
Digital
Marketing |
0,833 |
0,812 |
Reliable |
|
Service
Innovation |
0,885 |
0,880 |
Reliable |
|
Increased
Acceptance of New Students |
0,829 |
0,827 |
Reliable |
|
ICT Literacy |
0,860 |
0,842 |
Reliable |
Based on the
information in the table above, all research variables have a composite
reliability value of > 0.7 and have a Cronbach alpha value of > 0.6.
These findings show that each variable satisfies composite reliability, leading
to the conclusion that each variable has a high degree of dependence.
R-Squares Test
Results of
SmartPLS 4 All variants in the construct described by the model are represented
by R-squares. The output of determining the value of R-squares is shown below:
Table
5. R-Square value
|
No |
Variable |
R-Squares |
|
1 |
ICT
Literacy |
0,693 |
|
2 |
Increased Acceptance of New Students |
0,262 |
The table above
shows that 0.693 and 0.262, respectively, are R-Square values for the ICT
Literacy variable and the increase in new student admissions. This finding
shows that Digital Marketing and service innovation affect ICT Literacy by
69.3% and increase the acceptance of new students by 26.2%
The structural
model of PLS R-Square is applied to the dependent variable, and the value of
the coefficient for the independent variable, then the significance of each
route is determined using t-statistical values. We can examine the
t-statistical relationship between dependent and independent variables for path
coefficients in SmartPLS Output 4 (shown below) to determine the relevance of
model predictions to the structural model context.
F-Square Test
Researchers
will look at the substantive influence of endogenous conception influenced by
exogenous conception through the value of F2. The magnitude of the substantive
effect is clarified to 3, which is 0.02, 0,15, and 0.35, sequentially falling
into the category of small, medium, and large influences. The F2 value data can
be seen in the following table:
Table
6. F2 value
|
No |
Relationship |
F2 |
Magnitude of Influence |
|
1 |
Digital
Marketing towards ICT Literacy |
0,021 |
Small |
|
2 |
Digital
Marketing to Increase New Santri Acceptance |
0,039 |
Small |
|
3 |
ICT
Literacy to Increase the Acceptance of New Students |
0,023 |
Small |
|
4 |
Service
Innovation for ICT Literacy |
0,438 |
Big |
|
5 |
Service
Innovation to Increase New Student Acceptance |
0,071 |
Small |
Large substantive
influences occur on variables X2 on Z (0.438), while small substantive
influences occur on variables X1 on Y (0.039), X1 on Z (0.021), X2 on Y (0.071),
and Z on Y (0.023).
Goodness of Fit Model Test
The goodness of
fit model test can be seen from the model's SRMR values. The PLS model is
declared to have met the goodness of fit model criteria if the SRMR value is
< 0.10, and the model is declared perfect fit if the SRMR value is <
0.08.
Table
7. The Goodness of Fit Model Test Results
|
No |
Struktural Model |
Estimated |
Cut-Off Value |
Information |
|
1 |
SRMR |
0,086 |
< 0,10 |
Fit |
|
2 |
d_ULS |
1,008 |
>
0,05 |
Fit |
|
3 |
d_G |
0,692 |
>
0,05 |
Fit |
|
4 |
Chi-Square |
271,859 |
0,05 |
Fit |
|
5 |
NFI |
0,719 |
Close
to 1 |
Fit |
Based on the
results of the goodness of fit test of the PLS model, the table above shows
that the SRMR value of the PLS model is 0.086, which means it is lower than
0.10, so it shows a good model. For the output result, d_ULS, which is 1.008,
indicates a result higher than 0.05, which indicates a good model. The output
result d_G, which is 0.692, indicates a result higher than 0.05, which
indicates a good model. The Chi-square result is 271,859, which means that the
model is good. The NFI Output result is 0.719, meaning that the model was well
received.
If a model shows that almost all
the criteria showing its suitability are already on the matching criteria (good
fit), then the model can be continued for hypothesis testing. In this study, it
is known that of the five goodness of fit model tests, five tests were met, so the model in this study can be continued for hypothesis tests.
Structural Model Testing (Inner Model)
The proportion
of variance described is R2 for the dependent variable with the structural path
coefficient, used to evaluate the structural model or as an inner model. The
bootstrapping method is used to assess the stability of estimates.
Figure
2.
Full
Model Structural Partial Least Square (Inner) Model
Structural
model testing (inner model) is carried out to ensure the structural model built
is robust and accurate. Because the SRMR value of the model is below 0.10, the
PLS model is declared fit, so it is feasible to be used to test the research
hypothesis.
Hyphotesis Test
Path Coefficient (Direct Influence)
The PLS
structural model is applied to the dependent variable, and the value of the
coefficient for the independent variable, then the significance of each route
is determined using t-statistical values. We can examine the t-statistical
relationship between dependent and independent variables for path coefficients in
SmartPLS Output four (shown below) to determine the relevance of model
predictions to the structural model context
Table
8. Path Coefficient in Model Testing
|
Hipotesis |
Relationship |
Path koefisien |
P-values |
Information |
|
H1 |
Digital
Marketing to Increase New Santri Acceptance |
0,612 |
0,023 |
Influential |
|
H2 |
Digital
Marketing towards ICT Literacy |
0,642 |
0,010 |
Influential |
|
H3 |
Service
Innovation to Increase New Student Acceptance |
0,622 |
0,020 |
Influential |
|
H4 |
Service
Innovation for ICT Literacy |
0,718 |
0,000 |
Influential |
|
H5 |
ICT
Literacy to Increase the Acceptance of New Students |
0,594 |
0,037 |
Influential |
1)
Hypothesis Testing 1
This study's first hypothesis is that Digital Marketing has an
effect on increasing the acceptance of new students, where the value is
determined by calculations using the SmartPLS software program version 4. A P-value
of 0.023 < 0.05 was obtained at a significance level of 5%. This indicates
that H1 is acceptable.
2)
Hypothesis Testing 2
This study's second hypothesis is that Digital Marketing affects ICT
Literacy, where the value is determined by calculations using the SmartPLS
software program version 4. P-value 0.010 < 0.05 was obtained at a
significance level of 5%. This indicates that H2 is acceptable.
3)
Hypothesis Testing 3
The third hypothesis, this study shows that service innovation has
an effect on increasing the acceptance of new students, where the value is
determined by calculations using the SmartPLS software program version 4. P-value
0.020 < 0.05 at a significance level of 5%. This indicates that H3 is
acceptable.
4)
Hypothesis 4 Testing
This study's fourth hypothesis is that service innovation affects
ICT Literacy, where the value is determined by calculations using the SmartPLS
software program version 4. P-value 0.000 < 0.05 was obtained at a
significance level of 5%. This indicates that H4 is acceptable.
5)
Hypothesis Testing 5
This study's fifth hypothesis shows that ICT Literacy has an effect
on increasing the acceptance of new students, where the value is determined by
calculations using the SmartPLS software program version 4. P-values of 0.037
< 0.05 were obtained at a significance level of 5%. This indicates that H5
is acceptable.
Proof of Mediation Variables
Mediation can
be seen by comparing the value of direct effect with indirect effect. The
mediation variable indirectly influences the relationship between the two
variables. Here is a table showing the mediation between variables.
Table
9. Spesific Indirect Effect
|
|
Coefficient Value |
P values |
Information |
|
Digital
Marketing → ICT Literacy → Increase in New Santri Acceptance |
0,613 |
0,005 |
ICT Literacy
is able to mediate the influence of Digital Marketing on Increasing the
Acceptance of New Students |
|
ICT Literacy
→ Service Innovation → Increase in New Student Acceptance |
0,660 |
0,001 |
ICT Literacy
is able to mediate the influence of Service Innovation on Increasing the
Acceptance of New Students |
1)
Hypothesis Testing 6
The sixth hypothesis this study shows that ICT Literacy is able to
mediate the influence of Digital Marketing on increasing the acceptance of new
students, this is evidenced by the specific indirect effect with a p-value of 0.005.
This indicates that H6 is acceptable.
2)
Hypothesis 7 Testing
The seventh hypothesis, this study shows that ICT Literacy is able
to mediate the effect of service innovation on increasing the acceptance of new
students, this is evidenced by a specific indirect effect with a p-value of
0.001. This indicates that H7 is acceptable.
Discussion
Analysis of the influence of Digital
Marketing on increasing the acceptance of new students at SBM Yogyakarta.
The first
hypothesis of this study is that there is a substantial positive relationship
between digital marketing and the increase in the energy of new students. This is in accordance with indicators
including:
1) The fulfillment website design shows that information can be
accessed easily by prospective new student registrants.
2) The website design of MBS Yogyakarta is easy to understand, thus
increasing information knowledge for prospective registrants.
3) There is two-way communication between prospective registrants and
the management of the pesantren website so as to facilitate registration.
4) Data information and information are kept confidential, thus
increasing the trust of registrants.
The existence
of this positive relationship influence is determined by calculations using the
SmartPLS software program version 4. Tstatistik (4.955) > Ttable (1.96) and
sig values of 0.023 < 0.05 at a significance level of 5%. This shows that H1 is acceptable, which is
shown if Digital Marketing at SBM Yogyakarta has a positive and significant
effect on increasing the acceptance of new students. This supports the results of Ria Eka Permatasari's
research
Analysis of the Influence of Digital
Marketing on ICT Literacy at SBM Yogyakarta
The second
hypothesis of this study shows that there is a substantial positive
relationship between Digital Marketing and ICT Literacy. Indicators that show
the relationship between the influence of digital marketing and ICT Literacy
are:
1) Fulfillment website design makes it easy to disburse information on
the Internet, improving registrants' abilities.
2) The design of SBM Yogyakarta is easy to understand, thus increasing
information knowledge for prospective applicants.
3) Two-way communication will help understand navigation and compile
knowledge about the content.
Its value is
determined by calculations using the SmartPLS software program version 4.
Tstatistik (5.016) > Ttabel (1.96) and sig values of 0.010 < 0.05 at a
significance level of 5%. This shows that H2 is acceptable, which is shown if
Digital Marketing at SBM Yogyakarta has a positive and significant effect on
ICT Literacy. This is in line with the research of Alam et al.
Analysis of the effect of service
innovation on increasing the acceptance of new students at SBM Yogyakarta
The third
hypothesis is that this study shows a substantial positive relationship between
service innovation and increased acceptance of new students. Indicators that
support the effect of services on the admission of new students include:
1) New innovations are superior to those before
2) Innovation in accordance with the vision and mission of Pesantren
3) The latest innovations are easier to understand and monitor
The value is
determined by calculation using the SmartPLS software program version 4.
Tstatistik (5.003) > Ttabel (1.96) and sig values of 0.020 < 0.05 at a
significance level of 5%. This shows that H3 is acceptable, which is shown if
service innovation at SBM Yogyakarta has a positive and significant effect on
increasing the acceptance of new students. This result supports the previous
study, namely Hasna
Analysis of the effect of service
innovation on ICT Literacy at SBM Yogyakarta
The fourth
hypothesis is that this study shows a substantial positive relationship between
service innovation and ICT literacy. Its value is determined by calculations
using the SmartPLS software program version 4. T-statistics (5.709) > Ttabel
(1.96) and sig values of 0.000 < 0.05 at a significance level of 5%. Some indicators that influence service
innovation towards ICT Literacy include service innovation, which has a better
advantage than before and can spur managers' ability to understand more about digital
literacy. In addition, online registration services use internet usage
guidelines that are easy to understand.
This shows that
H4 is acceptable, which is shown if service innovation at SBM Yogyakarta has a
positive and significant effect on ICT Literacy.
Analysis of the effect of ICT Literacy
on Increasing the Acceptance of New Students at SBM Yogyakarta
This study's
fifth hypothesis shows a substantial positive relationship between ICT Literacy
and increased acceptance of new students. Indicators of influence relations
include the following:
1) Acceptance of new students through Internet Search can increase the
acceptance of new students to more
2) The distribution of the origin of students who register is wider
because of registration with easy access to the pesantren website.
The value of
influence is determined by calculation using the SmartPLS software program
version 4. Tstatistik (4.487) > Ttable (1.96) and sig values of 0.037 <
0.05 at a significance level of 5%. This shows that H5 is acceptable, which is
shown if ICT Literacy at SBM Yogyakarta has a positive and significant effect
on increasing the acceptance of new students.
This is supported by previous research, namely Eka Khuzniatus Z (2020),
which states that digital literacy can increase operating profit among small
businesses in the city of Surabaya.
Analysis of the influence of Digital
Marketing on increasing the acceptance of new students at SBM Yogyakarta
mediated by ICT Literacy
The sixth
hypothesis this study shows is that ICT literacy is able to mediate the
influence of digital marketing on increasing the acceptance of new students.
This is evidenced by the specific indirect effect, which has a p-value of
0.005. This indicates that H6 is acceptable. This is as indicators that state
the influence include:
1. Digital marketing through online-based registration on the internet
can be easily accessed by all people so as to increase the number of new
student registrants
2. The acceptance of students based on digital marketing provides wider
access, so the distribution of new students becomes wider not only in the area
around the pesantren but also in reaching areas outside the pesantren area.
Analysis of the effect of service
innovation on increasing the acceptance of new students at SBM Yogyakarta
mediating by ICT Literacy
The seventh
hypothesis is that this study shows that ICT Literacy is able to mediate the
effect of service innovation on increasing the acceptance of new students. This
is evidenced by a specific indirect effect with a p-value of 0.001. This
indicates that H7 is acceptable.
Indicators that
support the influence of service innovation on the admission of new students
mediated by ICT Literacy are that new service innovations are easier to
understand and accessible so as to increase understanding for those who want to
access both the new student admission committee and prospective registrants.
This increases the number of registrants, both in number and in the
distribution of students. In addition,
new service innovations with applications that have been tested and are easy to
supervise will also improve the quality of new student admissions.
This study aims to determine the influence of Digital Marketing and
service innovation on increasing the acceptance of new students with ICT
Literacy as a mediating variable in SBM Yogyakarta. This study's findings show
a significant influence between Digital Marketing on increasing the acceptance
of new students with a sig value of 0.023 < 0.05, as well as a significant
influence between Digital Marketing and ICT Literacy with a sig value of 0.010
< 0.05. Service innovation also has a significant effect on increasing the
acceptance of new students (sig value 0.020 < 0.05) and ICT Literacy (sig
value 0.000 < 0.05). In addition, ICT Literacy itself has a significant
effect on increasing the acceptance of new students (sig value 0.037 <
0.05). This study also found that ICT Literacy mediates the influence of
Digital Marketing and service innovation on increasing the acceptance of new
students with p-values of 0.005 and 0.001, respectively. The suggestions of
this study include: first, SBM Yogyakarta is advised to improve ICT Literacy
further because its effect on increasing the acceptance of new students is the
lowest; second, students are expected to be able to accept the methods applied
by Islamic boarding schools to increase the acceptance of new students; Third,
researchers are then expected to examine other variables that affect the
acceptance of new students and use a larger number of samples.
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Odjie Samroji (2024) |
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First publication right: Advances in Social Humanities Research |
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