ANALYSIS OF FACTORS INFLUENCING STUDENTS' DECISION-MAKING IN CHOOSING
ISLAMIC BOARDING SCHOOL AT NAHDLATUL WATHAN ISLAMIC CENTRE
Syahrul Bahri1*, Wasiman2
Putera
Batam University, Batam, Indonesia
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INFO
ARTIKEL |
ABSTRACT |
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Kata Kunci: Decision Making, Price,� Place,
Promotion. |
This study employs a quantitative research
approach to investigate the factors influencing decision-making at Pondok Pesantren Islamic Centre
Nahdlatul Wathan. Quantitative research utilizes
structured tools such as surveys to gather numerical data, focusing on
quantifying relationships and behaviors and employing statistical techniques
to test hypotheses. Conducted during the 2023/2024 academic year, the
research involved 100 respondents selected through purposive sampling,
comprising 22 vocational school students and community members surrounding
the boarding school in Tanjung Riau, Batam. Data were collected using an
online questionnaire with a 5-point Likert scale and analyzed using SPSS 21
for multiple linear regression to explore the impact of independent variables
(Price, Location, Promotion) on the decision to choose the boarding school.
Findings reveal that Price significantly affects decision-making (regression
coefficient B = 0.299, t-value = 3.222), indicating that higher prices
increase the likelihood of choosing the boarding school. Location also has a
substantial impact (B = 0.657, t-value = 6.793), suggesting that a strategic
location enhances the probability of selection. Promotion shows a significant
effect (B = 0.306, t-value = 4.739), albeit slightly weaker than Location.
Overall, the multiple linear regression analysis demonstrates the model's
significance (F-value = 17.183), implying that Price, Location, and Promotion
collectively influence the decision to choose a boarding school. These insights
are crucial for educational institutions aiming to understand key
decision-making factors. |
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Islamic boarding schools play a pivotal role in Indonesian Islamic
education (Meliani et al., 2022), not only imparting religious teachings but also shaping students'
character and skills (Mujahidin, 2021). One prominent example in Batam is the Nahdlatul
Wathan Islamic Centre.
Figure 1. Islamic Centre Nahdlatul Wathan
Boarding School Batam
Source: (Pondok-pesantren-Islamic-Centre-Nahdlatul-Wathan-Batam, 2024)
Parents' concerns about high educational costs often deter enrollment in
such institutions (Prasetyo et al., 2024). Pricing and location, particularly the school's distance from Batam's
center and limited transport access, pose challenges for prospective students
from distant areas (Ashshidqi & Kusuma, 2023; ). Effective promotion is crucial but often inadequate in informing
potential students and parents about the school's offerings (Fauzi & Kartiko, 2023).
This study, "Analysis of Factors Influencing Student Decision-making
at Nahdlatul Wathan Islamic Centre Boarding
School," aims to explore these factors' impact on enrollment decisions,
providing insights into student decision-making behaviors and the school's
attractiveness.
This study employs a quantitative research approach.
Quantitative research is characterized by the use of structured tools, such as
questionnaires or surveys, to gather numerical data (Noviyadi et al., 2024). It focuses on quantifying relationships, behaviors, and other
phenomena, and often employs statistical techniques to test hypotheses and
analyze data. The research was conducted at Pondok Pesantren Islamic Centre Nahdlatul
Wathan, located at Jl. KH Ahmad Dahlan Kampung Bukit RT 01 RW 06, Tanjung Riau,
Kec. Sekupang, Kota Batam,
Prov. Kepulauan Riau. The study focused on the
2023/2024 academic year, during which the school had 77 students, according to
the official educational data from sekolah.data.kemdikbud.go.id in 2024.
The sampling technique used in this study is
purposive sampling. Purposive sampling is a non-probability sampling method
where the researcher selects participants based on specific characteristics or
criteria. In this case, the study involved 100 respondents, including all 22
vocational school (SMK) students from the boarding school and members of the
surrounding community. This approach ensures that the sample represents the
population relevant to the study's objectives.
Data analysis techniques involve the utilization of
SPSS 21 statistical software to conduct multiple linear regression analysis,
allowing the study to assess the relationship between independent variables
(price, location, and promotion) and the dependent variable (decision to choose
the boarding school), with the analysis process including data prepara-tion, statistical testing, and interpretation of
results to draw meaningful conclusions.
RESULTS
AND DISCUSSION
Normality Test
The
One-Sample Kolmogorov-Smirnov Test was conducted to assess whether the sample
of 100 unstandardized residuals follows a normal distribution. The test yielded
a test statistic of 0.059 with an asymptotic significance (2-tailed) of 0.200
and a Monte Carlo significance (2-tailed) of 0.519. Both p-values exceed the
typical significance level of 0.05, indicating that there is no significant
deviation from normality. Thus, based on these results, we fail to reject the
null hypothesis, suggesting that the unstandardized residuals are consistent
with being sampled from a normal distribution.
Table 1.
Normality Test
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One-Sample
Kolmogorov-Smirnov Test |
|||
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|
Unstandardized Residual |
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N |
100 |
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Normal Parametersa,b |
Mean |
.0000000 |
|
|
Std. Deviation |
5.13213148 |
||
|
Most Extreme Differences |
Absolute |
.059 |
|
|
Positive |
.059 |
||
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Negative |
-.048 |
||
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Test Statistic |
.059 |
||
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Asymp. Sig. (2-tailed)c |
.200d |
||
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Monte Carlo Sig. (2-tailed)e |
Sig. |
.519 |
|
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99% Confidence Interval |
Lower Bound |
.506 |
|
|
Upper Bound |
.532 |
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a. Test distribution is
Normal. |
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Multicollinearity
Test
The
multicollinearity among the predictor variables in the regression model was
assessed using tolerance and Variance Inflation Factor (VIF). It is evident
that all utilized independent variables exhibit tolerance values exceeding 0.10
and VIF values below 10 (Ramadanty & Trihantoyo, 2024; Wasiman, 2021).
Table 2.
Multicollinearity Test
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Coefficientsa |
|||
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Model |
Collinearity
Statistics |
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Tolerance |
VIF |
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1 |
(Constant) |
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Price |
0.857 |
1.167 |
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Place |
0.630 |
1.588 |
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Promotion |
0.687 |
1.455 |
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a. Dependent Variable:
Y |
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These results
indicate that each predictor retains a significant amount of independent
variance and experiences only mild inflation of variance due to collinearity.
Therefore, one can deduce that there is an absence of multicollinearity among
the predictor variables incorporated within the regression model.
Heteroskedasticity Test
The White test
results for the regression model indicate that the computed chi-square value is
4.7, calculated by multiplying the R square (0.047) by the sample size (n=100).
This result shows that the computed chi-square value (4.7) is lower than the critical
chi-square value (5.992) at the specified significance level. This suggests
that there is insufficient evidence to reject the null hypothesis in the White
test, implying that there is no significant heteroskedasticity in this
regression model.
Table 3. Heteroskedasticity
Test
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Model Summary |
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Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
1 |
.216a |
.047 |
.017 |
31.65595 |
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a. Predictors: (Constant),
X3, X1, X2 |
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Results
of the Coefficient of Determination R2 Test
The
results of the coefficient of determination test indicate that the regression
model used has an R value of 0.591. This value suggests a strong correlation
between the dependent variable and the independent variables (Promotion, Price,
Location). An R Square of 0.349 indicates that 34.9% of the variance in the
dependent variable can be explained by the variation in these three independent
variables. The Adjusted R Square value of 0.329 reflects a more conservative
adjustment for the number of predictors in the model, still indicating that
approximately 32.9% of the variance in the dependent variable is explained by
this model. The standard error of the estimate, at 5.212, signifies the level
of prediction error associated with using this regression model.
Table 4.
Coefficient of Determination R2 Test
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Model Summary |
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Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
1 |
.591a |
.349 |
.329 |
5.212 |
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a. Predictors: (Constant),
Price, Place, Promotion |
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Despite
some unavoidable margin of error, the obtained R Square and Adjusted R Square
values indicate that this model effectively explains the variance in the
dependent variable based on the chosen independent variables. Overall, the
model can be considered reasonably reliable for predicting the dependent
variable, taking into account Promotion, Price, and Place as determining
factors.
Results of the F Test
Multiple
linear regression using the F-test is a statistical method used to test the
simultaneous influence of several independent variables on a dependent
variable. In this analysis, the variables Price, Location, and Promotion are
examined to see their impact on the decision to choose a boarding school. In
the table, we observe that the computed F-value is 17.183. This value indicates
how much variance in the decision-making process can be explained by the
regression model with the included independent variables (Promotion, Price, and
Location). To determine the statistical significance of this F-value, we
compare it to the critical F-value.
Table 5.
Results of the F Test
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|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
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1 |
Regression |
1400.171 |
3 |
466.724 |
17.183 |
.000b |
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|
Residual |
2607.539 |
96 |
27.162 |
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Total |
4007.710 |
99 |
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a. Dependent
Variable: Keputusan Memilih |
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b. Predictors:
(Constant), Promosi, Harga, Lokasi |
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With
degrees of freedom between regression (3) and residual (96), and at a significance
level of 5%, the critical F-value from the table is approximately 2.70. Since
the computed F-value (17.183) is much larger than the critical F-value (2.70),
we can conclude that the regression model is statistically significant overall.
This means that at least one of the independent variables included in the model
(Promotion, Price, and Location) has a significant influence on the decision to
choose the boarding school. Therefore, in this scenario, the significant F-test
result supports the conclusion that factors such as Promotion, Price, and
Location indeed influence the decision-making process regarding choosing a
boarding school, confirming the alternative hypotheses posited in the
regression analysis.
CONCLUSION
Based
on the analysis of the influence of Price, Location, and Promotion on the
decision to choose a boarding school: Price (regression coefficient B = 0.299)
significantly affects decision-making, with a computed t-value of 3.222
surpassing the critical t-value (�1.984), indicating that higher prices tend to
increase the likelihood of choosing a boarding school, assuming other factors
remain constant. Location also has a highly significant impact, as evidenced by
a regression coefficient B = 0.657 and a computed t-value of 6.793, well above
the critical t-value, showing that a better or more strategic location
significantly increases the probability of choosing a boarding school.
Additionally, Promotion (regression coefficient B = 0.306) has a significant
effect, although slightly weaker than Location, with a computed t-value of
4.739 also exceeding the critical t-value. Overall, the multiple linear
regression analysis indicates the model's significance (computed F-value =
17.183, exceeding the critical F-value F 0.05,3,96 = 2.70), suggesting that at
least one of the independent variables (Price, Location, Promotion)
significantly influences the decision to choose a boarding school.
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