Corresponding Author: Citra
Handayani
E-mail: [email protected]
INTRODUCTION
BPSDMP (Human
Resource Development Agency of
Transportation) is under the Ministry of Transportation of
the Republic of Indonesia with the Vision "The Realization of Professional
and Ethical Human Resources and Transportation Counseling in Organizing Reliable
and " zero accident " oriented transportation in order to realize
this vision BPSDMP has several missions in including:
a)
Manage Professional Transportation Education, Training and Counseling to
create the capacity and quality of Transportation Human Resources as needed.
b)
Build an
effective organization with competent human resources and a reliable
information system to meet the needs of stakeholders.
In this
modern era, it cannot be denied that information technology is one of the main
resources in an organization that plays an important role in increasing
competitiveness and optimal service. (Mirabolghasemi et al., 2019). Therefore, every government organization
tries to apply information technology to increase service effectiveness and
efficiency (Garc�a-Juan
et al., 2018). This is intended to provide added value,
namely in the form of competitive advantage to BPSDMP, which developments in
information technology that occur in the modern era play a very important role
in the administration of government organizations.
In the
future, information technology and telecommunications will be the most dominant
sectors (Mujahid &
Sukmadewi, 2021). Technology has many roles in fields
including education, and health, especially in the field of government that
serves the wider community (Abdillah et
al., 2020). Information technology will easily remove
the limitations of space and time, which have been an obstacle to growth in the
world of government (Dalimunthe
& Azhari, 2019).
In essence,
information technology is a facility that improves the quality of information
services that are easier for everyone (Iswanaji,
2019). Information systems, which are part of
information technology, are very important in using information technology (Naibaho, 2017). Utilizing the personnel information system
at BPSDMP can provide convenience for the work units under its auspices in
carrying out services.
The
evaluation of information systems is simply a test of controlling information
system infrastructure (Mulyadi &
Choliq, 2019). With this evaluation, the achievement of
activities or activities for the implementation of an information system can be
immediately known, and further actions can be planned to improve the
performance of its implementation (Sallehudin et
al., 2019). Evaluation is also carried out to determine
whether the information system is running well to support the process of
improving the quality of service within the organization (Ulfa Syafitri
Bulegalangi, 2021). Measuring the
success of the personnel information system application at the BPSDMP (Human
Resources Development Agency for Transportation) can use the Human Organization
Technology (HOT-Fit Model) method. This model was chosen because it can explain evaluation comprehensively
using the core components of an information system approach. The HOT-Fit model
components used in this scientific research are human (System Use, User
Satisfaction), organization (Organizational Structure), Technology (System
Quality, Information Quality, Service Quality) and the suitability of these
three factors affect the benefits (Net benefits).
Based on the
above, the purpose of this study was to find and analyze information technology
using the Human Organization Technology (HOT) fit model of the personnel
information system at BPSDMP (Human Resource Development Agency for
Transportation).
METHODS
This
research was conducted in all work units under the auspices of BPSDM from the main
level to the work unit level. This research takes about three months, from
October 2020 to January 2021. This research uses a research approach with a
model evaluation method (Divayana, 2017). The population of this study is employees who manage
the staffing of the Ministry of Transportation's BPSDMP. The data collection
method was carried out by distributing questionnaires to employees in each work
unit. The distribution of the questionnaire was carried out using the Google
Form tool. Dissemination using Google form tools and
electronic mail is done by sending an e-mail to each respondent's e-mail
address. The e-mail contains a link to the questionnaire that was created. The
data analysis technique used in this study is to use validity test, reliability
test, classic assumption test and research hypothesis test.
RESULTS AND DISCUSSION
A.
Instrument
Test
1. Test the Validity and Reliability of Net
Benefit Variables
The
results of testing the validity of the reliability of the net benefit variable can
be seen in table 1. below.
Table 1. Validity Test Y (Net
Benefit)
|
Statement
|
r count
|
Information
|
Cronbach's
Alpha
|
Reliability
|
|
NB1
|
0.577
|
Valid
|
0.727
|
Reliable
|
|
NB2
|
0.769
|
Valid
|
|
NB3
|
0.606
|
Valid
|
|
NB4
|
0.604
|
Valid
|
|
NB5
|
0.726
|
Valid
|
|
NB6
|
0.556
|
Valid
|
|
NB7
|
0.516
|
Valid
|
The seven
statements above from the net benefit variable (Y) are declared valid because
all rcounts obtained using SPSS show greater than the rtable,
namely 0.250, and can be continued in the next test. The statistical
reliability test data processing results showed a Cronbach's alpha value of
0.727, meaning that all statement items for the net benefit variable were
reliable because Cronbach's alpha value was 0.727 > 0.70, according to what
was required in this study.
2. Test the Validity and Reliability of Human
Factor Variables
The
results of testing the reliability validity of the human factor variable can be
seen in table 2. below.
Table 2. Validity
Test X1 (Human Factor)
|
Statement
|
rcount
|
Information
|
Cronbach's
Alpha
|
Reliability
|
|
SU1
|
0.499
|
Valid
|
0.731
|
Reliable
|
|
SU2
|
0.562
|
Valid
|
|
SU3
|
0.491
|
Valid
|
|
SU4
|
0.582
|
Valid
|
|
SU5
|
0.533
|
Valid
|
|
SU6
|
0.720
|
Valid
|
|
US1
|
0.717
|
Valid
|
|
US2
|
0.682
|
Valid
|
|
US3
|
0.335
|
Valid
|
The nine statements above from the
human factor variable (X1) are valid because all rcounts obtained
using SPSS are greater than the rtable, namely 0.250, and can be
continued in the next test. The statistical reliability test data processing
results showed a Cronbach's alpha value of 0.731, meaning that all statement
items for the human factor variable were reliable because Cronbach's alpha value
was 0.731 > 0.70, according to what was required in this study.
3. Test the Validity and Reliability of Organizational Factor Variables
The results of testing the reliability
validity of the organizational factor variables can be seen in Table 3 below.
Table 3. Validity Test X2 (Organizational
Factors)
|
Statement
|
r count
|
Information
|
Cronbach's
Alpha
|
Reliability
|
|
STR1
|
0.626
|
Valid
|
0.701
|
Reliable
|
|
STR2
|
0.534
|
Valid
|
|
STR3
|
0.634
|
Valid
|
|
STR4
|
0.459
|
Valid
|
|
STR5
|
0.505
|
Valid
|
|
STR6
|
0.528
|
Valid
|
|
EVR1
|
0.633
|
Valid
|
|
EVR2
|
0.418
|
Valid
|
|
EVR3
|
0.551
|
Valid
|
The nine statements above from
organizational factor variables (X2) are declared valid because all
rcounts obtained using SPSS show greater than the rtable,
namely 0.250, and can be continued in the next test. The statistical
reliability test data processing results showed a Cronbach's alpha value of
0.701, meaning that all item statement items for organizational factor
variables were reliable because Cronbach's alpha value was 0.701 > 0.70,
according to what was required in this study.
4. Test the Validity and Reliability of Technology Factor Variables
The results of testing the reliability validity of the technology factor
variables can be seen in Table 4 below.
Table 4. Validity Test X3 (Technology Factor)
|
Statement
|
r count
|
Information
|
Cronbach's
Alpha
|
Reliability
|
|
SQ1
|
0.632
|
Valid
|
0.859
|
Reliable
|
|
SQ2
|
0.642
|
Valid
|
|
SQ3
|
0.601
|
Valid
|
|
SQ4
|
0.547
|
Valid
|
|
SQ5
|
0.515
|
Valid
|
|
SQ6
|
0.513
|
Valid
|
|
IQ1
|
0.594
|
Valid
|
|
IQ2
|
0.653
|
Valid
|
|
IQ3
|
0.575
|
Valid
|
|
IQ4
|
0.568
|
Valid
|
|
IQ5
|
0.571
|
Valid
|
0.859
|
Reliable
|
|
SEQ1
|
0.472
|
Valid
|
|
SEQ2
|
0.553
|
Valid
|
|
SEQ3
|
0.607
|
Valid
|
|
SD1
|
0.418
|
Valid
|
|
SD2
|
0.331
|
Valid
|
|
SD3
|
0.475
|
Valid
|
|
SD4
|
0.498
|
Valid
|
The eighteen statements above from
the technological factor variable (X3) are declared valid because
all rcounts obtained using SPSS are greater than the rtable,
0.250, and can be continued in the next test. The statistical reliability test
data processing results showed a Cronbach's alpha value of 0.859, meaning that
all statement items for the technological factor variable were reliable because
Cronbach's alpha value was 0.859 > 0.70, according to what was required in
this study.
B.
Classical
Assumption Testing
1.
Normality
test
Table 5. Normality Test Results for Multiple Regression Estimation
Errors
|
Tests of Normality
|
|
|
Kolmogorov-Smirnov a
|
Shapiro-Wilk
|
|
Statistics
|
df
|
Sig.
|
Statistics
|
df
|
Sig.
|
|
Unstandardized
Residuals
|
,077
|
62
|
,200 *
|
,934
|
62
|
,002
|
|
*.
This is a lower bound of the true significance.
|
|
a.
Lilliefors Significance Correction
|
Table 5 shows that the multiple regression
estimates error's probability values (Sig.) are 0.200. The probability value is
greater than the significant level (α) of 0.05, so the estimated error
data for multiple regression is normally distributed.
2.
Multicollinearity Test
Table 6. Multicollinearity test results
|
Coefficients a
|
|
Model
|
Collinearity Statistics
|
|
tolerance
|
VIF
|
|
1
|
Human
Factors (X1)
|
,678
|
1,474
|
|
Organizational
Factors (X2)
|
,642
|
1,557
|
|
Technology
Factor (X3)
|
,640
|
1,563
|
|
a.
Dependent Variable: Net benefit (Y)
|
The results of all multicollinearity tests
in table 4.25 show that all regression models have a tolerance value of >
0.10 and a VIF value of < 10. So, there are no symptoms of multicollinearity
in the regression model used.
3. Autocorrelation Test
Table 7. Autocorrelation Test
Results
|
Summary Model b
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
std. The error in the Estimate
|
Durbin-Watson
|
|
1
|
,751 a
|
,564
|
,542
|
2,300
|
1,712
|
|
a.
Predictors: (Constant), Technology Factors (X3), Human Factors (X1),
Organizational Factors (X2)
|
|
b.
Dependent Variable: Net benefit (Y)
|
Based on table 7, the
Durbin-Watson value is 1.712. This value lies between dU (1.692) and 4-dU
(2.308), so it can be concluded that there are no autocorrelation symptoms in
the regression model used.
4.
Heteroscedasticity
Test
Table 8. Heteroscedasticity Test
Results
|
Coefficients a
|
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
|
B
|
std. Error
|
Betas
|
|
1
|
(Constant)
|
2,794
|
2,512
|
|
1.112
|
,271
|
|
Human
Factors (X1)
|
-.036
|
,068
|
-.084
|
-.532
|
,597
|
|
Organizational
Factors (X2)
|
.066
|
.065
|
, 165
|
1.020
|
,312
|
|
Technology
Factor (X3)
|
-.031
|
.033
|
-,151
|
-,933
|
,355
|
|
a.
Dependent Variable: RESIDUAL Y
|
The results of the heteroscedasticity test
with the Glejser test in table 8 show that the regression model has a
significant value of more than 0.05. So, there is no heteroscedasticity problem
in the regression model used.
C.
Research
Hypothesis Testing
1.
First Hypothesis
Table 9. Simple Correlation Coefficient
Between X1 and Y
|
Correlations
|
|
|
Net Benefits (Y)
|
Human Factors (X1)
|
|
Net Benefits (Y)
|
Pearson Correlation
|
1
|
,636 **
|
|
Sig. (2-tailed)
|
|
,000
|
|
N
|
62
|
62
|
|
Human Factors (X1)
|
Pearson Correlation
|
,636 **
|
1
|
|
Sig. (2-tailed)
|
,000
|
|
|
N
|
62
|
62
|
|
**. Correlation is significant at the 0.01 level
(2-tailed).
|
Based on the calculation results, the
product-moment correlation coefficient between the human factor and the net
benefit (r1y) is 0.636 with a probability value of Sig. (0.000) <
significant level (0.05).
Table 10. The
coefficient of determination between X 1 and Y
|
Summary models
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
std. The error in the Estimate
|
|
1
|
, 636 a
|
,404
|
,394
|
2,645
|
|
a.
Predictors: (Constant), Human Factors (X1)
|
Based on the
results in table 10. it can be concluded that H0 is rejected and
H1 is accepted. In other words, there is a significant positive influence
between the human factor and the net benefit. The coefficient of determination
(r1y)2 is 0.404, which means that human factors can
explain 40.4% of the proportion of the net benefit variance. The results of this study are supported by previous research (Mawarni & Dharminto, 2021), which states that whether or not the human factor is good in using
the SKIP application has a relationship with the SIKP net benefits.
When other variables are controlled, the
relationship between human factors and net benefits is done by partial
correlation analysis. The partial correlation coefficient obtained and the test
results are presented in Table 11.
Table 11. The
partial correlation coefficient between X1 and Y
|
et al.
|
Partial Correlation Coefficient
|
Sig.
|
α
|
Information
|
|
59
|
r 1y.2 = 0.482
|
0.000
|
0.05
|
Significant
|
|
59
|
r 1y.3 = 0.488
|
0.000
|
0.05
|
Significant
|
Based on the results of table 11. it can be concluded that the
partial correlation coefficient between human factors and net benefits when
organizational factors are controlled is very significant (significant), so it
can be interpreted that if organizational factors are controlled consistently,
human factors provide a stable, meaningful contribution to net benefits. The
partial correlation coefficient between the human factor and the net benefit when
the technological factor is controlled is very significant (significant), so it
can be interpreted that if the technological factor is controlled consistently,
the human factor provides a stable, meaningful contribution to the net benefit.
2. Second
Hypothesis
Table 12. The
simple correlation coefficient between X 2 and Y
|
Correlations
|
|
|
Net Benefits (Y)
|
Organizational Factors (X2)
|
|
Net
Benefits (Y)
|
Pearson
Correlation
|
1
|
,619 **
|
|
Sig.
(2-tailed)
|
|
,000
|
|
N
|
62
|
62
|
|
Organizational
Factors (X2)
|
Pearson
Correlation
|
,619 **
|
1
|
|
Sig.
(2-tailed)
|
,000
|
|
|
N
|
62
|
62
|
|
**.
Correlation is significant at the 0.01 level (2-tailed).
|
Based on the calculation results,
the product-moment correlation coefficient between organizational factors and
net benefits (r2y) is 0.619 with a probability value of Sig. (0.000)
< significant level (0.05).
Table 13. The
coefficient of determination between X 2 and Y
|
Summary models
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
std. The error in the Estimate
|
|
1
|
, 619 a
|
,383
|
,373
|
2,691
|
|
a.
Predictors: (Constant), Organizational Factors (X2)
|
Based on the results in table 13, it can be concluded
that H0 is rejected and H1 is accepted. In other words, organizational factors
have a significant positive influence on net benefits. The coefficient of
determination (r2y)2 is 0.383, which means that organizational
factors can explain 38.3% of the proportion of net benefit variance.
When other variables are controlled, the relationship between
organizational factors and net benefits is done by partial correlation
analysis. The partial correlation coefficient obtained and the test results are
presented in Table 14.
Table 14. The
partial correlation coefficient between X 2 and Y
|
et al.
|
Partial
Correlation Coefficient
|
Sig.
|
α
|
Information
|
|
59
|
r 2y.1
= 0.453
|
0.000
|
0.05
|
Significant
|
|
59
|
r 2y.3
= 0.444
|
0.000
|
0.05
|
Significant
|
Based on the results of table 14,
it can be concluded that the partial correlation coefficient between
organizational factors and net benefits when human factors are controlled is
very significant (significant), so it can be interpreted that if human factors
are controlled consistently, organizational factors provide a stable,
meaningful contribution to net benefits. The partial correlation coefficient
between organizational factors and net benefits when technological factors are
controlled is significant (significant), so it can be interpreted that if
technological factors are controlled consistently, they provide a stable,
meaningful contribution to net benefits.
3. Third Hypothesis
Table 15. Simple Correlation
Coefficient Between X 3 and Y
|
correlations
|
|
|
Net Benefits (Y)
|
Technology Factor (X3)
|
|
Net Benefits (Y)
|
Pearson Correlation
|
1
|
,589 **
|
|
Sig. (2-tailed)
|
|
,000
|
|
N
|
62
|
62
|
|
Technology Factor (X3)
|
Pearson Correlation
|
,589 **
|
1
|
|
Sig. (2-tailed)
|
,000
|
|
|
N
|
62
|
62
|
|
**. Correlation is significant at the 0.01 level
(2-tailed).
|
Based
on the calculation results, the product-moment correlation coefficient between
technological factors and net benefits (r3y) is 0.589 with a probability
value of Sig. (0.000) < significant level (0.05). Based on the results in
table 4.34, it can be concluded that H0 is rejected and H1 is accepted. In
other words, technological factors have a significant positive influence on net
benefits.
Table 16. The coefficient of determination between X 3
and Y
|
Summary models
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
std. The error in the Estimate
|
|
1
|
,589 a
|
,347
|
,336
|
2,769
|
|
a. Predictors: (Constant), Technology Factor (X3)
|
The
coefficient of determination (r3y )2 is 0.347, which
means that technological factors can explain 34.7% of the proportion of the net
benefit variance.
If other
variables are controlled, the relationship between
technological factors and net benefits is done by partial correlation analysis.
The partial correlation coefficient obtained and the test results are presented
in Table 17.
Table 17. Partial Correlation
Coefficient Between X 3 and Y
|
et al.
|
Partial Correlation Coefficient
|
Sig.
|
α
|
Information
|
|
59
|
r 3y.1 = 0.407
|
0.001
|
0.05
|
Significant
|
|
59
|
r 3y.2 = 0.387
|
0.002
|
0.05
|
Significant
|
Based on the results of table 17, it can be
concluded that the partial correlation coefficient between technological
factors and net benefits when the human factor is controlled is very
significant (significant), so it can be interpreted that if the human factor is
controlled consistently, then the technological factor provides a stable,
meaningful contribution to the net benefit. When organizational factors are
controlled, the partial correlation coefficient between technological factors
and net benefits is very significant (significant), so it can be interpreted
that if organizational factors are controlled consistently, then technological
factors provide a stable, meaningful contribution to net benefits.
4.
Fourth
Hypothesis
Table 18. Multiple Correlation
Coefficient
|
Summary models
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
std. The error in the Estimate
|
|
1
|
,751 a
|
,564
|
,542
|
2,300
|
|
a.
Predictors: (Constant), X3 intervals, X1 intervals, X2 intervals
|
The coefficient of determination (Ry.123)2
of 0.564 can be interpreted that 56.4% of the net benefit variance can be
explained jointly by human, organizational, and technological factors. Based on
the results of advanced calculations, the contribution of the dependent
variable to the human factor is 40.4%, the organizational factor is 38.3%,
technology factor is 34.7%.
Table 19.
Multiple Regression Significance Test Results
|
ANOVA a
|
|
Model
|
Sum of Squares
|
df
|
MeanSquare
|
F
|
Sig.
|
|
1
|
Regression
|
397,551
|
3
|
132,517
|
25,043
|
,000 b
|
|
residual
|
306,917
|
58
|
5,292
|
|
|
|
Total
|
704,468
|
61
|
|
|
|
|
a.
Dependent Variable: Net Benefit (Y)
|
|
b.
Predictors: (Constant), Technology Factors (X3), Human Factors (X1),
Organizational Factors (X2)
|
The double
correlation coefficient of the two independent variables on the net benefit (R y.123
) = 0.7581. The significance test results obtained an Fcount of
25.043 and the probability value Sig. (0.000) < significant level (0.05).
Based on these results, there is a positive influence on net benefits between
human, organizational, and technological factors.
A summary of the correlational analysis model can be seen in
Figure 1 as follows:
Figure 1. Empirical Model Between Variables
Several discussions
and interpretations of the above research results are described in more detail
below.
1.
The Effect of Human Factors on Net
Benefits
The results
of testing the first hypothesis can be concluded that there is a positive
influence between the human factor on the net benefit, where the correlation
coefficient is 0.636 resulting in a probability of Sig. (0.000) < significant level (0.05). This conclusion shows that
the higher the human factor, the higher the net benefit.
The correlation between human
factors and net benefits shows its significance through product-moment and
partial correlations. The results of this analysis indicate that the human
factor is one of the variables contributing to the net benefit. From these
results, an increase in human factors will significantly contribute to the net
benefit of 40.4%.
In the formation of human factor variables consist of two
dimensions, namely: (1) system use and (2) use satisfaction. The calculation results are as
follows:
Table 20. Coefficient
of Determination of Human Factor Dimensions
|
Summary models
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
std. The error in the Estimate
|
|
1
|
1,000 a.m
|
1,000
|
1,000
|
,000
|
|
a.
Predictors: (Constant), Use Satisfaction Dimensions, System Use Dimensions
|
Based on table 20 on the R Square column,
which consists of two dimensions of 1,000. This means that these two dimensions
form the human factor variable 100%, and no other dimensions form these
variables. The
calculation results for each dimension can be seen in table 21 below.
Table 21. The Dimensions of Human Factors
|
Coefficients a
|
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
|
B
|
std. Error
|
Betas
|
|
1
|
(Constant)
|
-3,553E-15
|
,000
|
|
.
|
.
|
|
System Use Dimension
|
1,000
|
,000
|
,701
|
.
|
.
|
|
The Use Satisfaction Dimension
|
1,000
|
,000
|
,467
|
.
|
.
|
|
a. Dependent Variable: Human Factors (X1)
|
Based on the
calculation results for each dimension in the formation of the human factor variable, the
following values are obtained:
a. The first dimension is
the system use of 0.701;
b.
The second dimension is using
satisfaction of 0.467.
So it can be
found that the highest value is the first dimension, namely " system use ".
2. The Effect of
Organizational Factors on Net Benefit
The results
of testing the second hypothesis can be concluded that there is a positive
influence between organizational factors on net benefits, where the correlation
coefficient is 0.619 resulting in a probability of Sig. (0.000) < significant level (0.05). This conclusion shows that
the higher the organizational factor, the higher the net benefit.
The correlation between
organizational factors and net benefits shows its significance through product-moment
and partial correlations. The results of this analysis provide clues that
organizational factors are one of the variables contributing to net benefits.
From these results, an increase in organizational factors will significantly
contribute to the net benefit of 38.3%.
In the formation of organizational
factors, variables consist of two dimensions: (1) organizational structure and (2)
organizational environment. The calculation results are as follows:
Table 22. Coefficient of
Determination of Organizational Factor Dimensions
|
Summary models
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
std. The error in the Estimate
|
|
1
|
1,000 a.m
|
1,000
|
1,000
|
,000
|
|
a.
Predictors: (Constant), Organizational Environment Dimensions, Organization
Structure Dimensions
|
Based on
table 22 in the R Square column, which consists of two dimensions of 1,000.
This means that these two dimensions form the organizational factor variable
100%, and no other dimensions form these variables. The calculation results for
each dimension can be seen in table 23 below.
Table 23.
Organizational Factor Dimensions
|
Coefficients a
|
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
|
B
|
std. Error
|
Betas
|
|
1
|
(Constant)
|
3.553E-15
|
,000
|
|
.
|
.
|
|
Organizational
Structure Dimensions
|
1,000
|
,000
|
,729
|
.
|
.
|
|
Organizational
Environment Dimensions
|
1,000
|
,000
|
,392
|
.
|
.
|
|
a.
Dependent Variable: Organizational Factors (X2)
|
Based on the
calculation results for each dimension in the formation of organizational factor variables,
the following values are obtained:
a.
The first dimension is the organization
structure of 0.729;
b. The second dimension is the organizational environment of 0.392.
So it can be
found that the highest value is the first dimension, " organization structure ".
3. Effect of
Technological Factors on Net Benefit
The results
of testing the third hypothesis can be concluded that there is a positive
influence between technological factors on net benefits, where the correlation
coefficient is 0.589 resulting in a probability of Sig. (0.000) < significant level (0.05). This conclusion shows that
the higher the technological factor, the higher the net benefit. The results of
the research are in line with the results of research from (Kadarsih & Arafat, 2017), which states that there is a relationship between technological
factors and the net benefit of the digital library AMIK AKMI Baturaja (p-value
= 0.001, r = 0.751).
The correlation between
technological factors and net benefits shows its significance through product-moment
and partial correlations. The results of this analysis indicate that the
technological factor is one of the variables contributing to the net benefit.
From these results, an increase in technological factors will significantly
contribute to the net benefit of 34.7%.
The formation of technological factor variables consists of four
dimensions, namely: (1) system quality, (2) information quality, (3) service quality, and (4) system
development (Medyawati
& Hegarini, 2012). The calculation results are as follows:
Table 24. Coefficient
of Determination of Technology Factor Dimensions
|
Summary models
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
std. The error in the Estimate
|
|
1
|
1,000 a.m
|
1,000
|
1,000
|
,000
|
|
a.
Predictors: (Constant), System Development Dimensions, Service Quality Dimensions,
System Quality Dimensions, Information Quality Dimensions
|
Based on
table 24 in the R Square column, which consists of four dimensions of 1,000. It
means that the four dimensions form the technology factor variable 100%, and no
other dimensions form these variables. The calculation results for each
dimension can be seen in table 25 below.
Table 25. The Dimensions of Technology Factors
|
Coefficients a
|
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
|
B
|
std. Error
|
Betas
|
|
1
|
(Constant)
|
-3,553E-14
|
,000
|
|
.
|
.
|
|
System
Quality Dimensions
|
1,000
|
,000
|
,373
|
.
|
.
|
|
Information
Quality Dimensions
|
1,000
|
,000
|
,363
|
.
|
.
|
|
Service
Quality Dimension
|
1,000
|
,000
|
,359
|
.
|
.
|
|
System
Development Dimension
|
1,000
|
,000
|
,233
|
.
|
.
|
|
a.
Dependent Variable: Technology Factor (X3)
|
Based on the
calculation results for each dimension in the formation of the human factor variable, the
following values are obtained:
a. The first dimension is system quality of 0.373;
b.
The second dimension is information
quality of 0.363;
c. The third dimension is service quality of 0.359;
d.
The fourth dimension is system
development of 0.233.
So it can be
found that the highest value is the first dimension, namely " system quality ".
4.
The Influence of Human Factors,
Organizational Factors, and Technological Factors Together on Net Benefit
The results of testing the fourth hypothesis
can be concluded that human, organizational, and technological factors together
positively influence net benefits. The multiple correlation coefficient between
the three independent variables on the dependent variable Ry.123 is
0.751 resulting in a probability of Sig. (0.000) < significant level
(0.05). Coefficient of determination (Ry.123 )2 of 0.564
means that 56.4% of the net benefit variance can be explained jointly by human,
organizational, and technological factors. This is in accordance with research from (Dewi &
Syaifullah, 2017) which states that the Human,
Organizational, and Technology variables together have a significant effect on
Net Benefit by Fcount > Ftable (6,203 > 2,947) and the
value is significantly smaller than the error rate (0.002 < 0.05).
CONCLUSION
This study analyzed the
factors that influence the net benefit of the personnel information system
program at the Transportation Human Resources Development Agency (BPSDMP). The
analysis was carried out using the HOT Fit Model, which found several things, including
1) The results of human factors research affected a net benefit of 63.6%,
meaning that the staffing information system model at the Human Resource
Development Agency of Transportation (BPSDMP) was formed by the employees
themselves who involved in this system in order to maximize the performance of
the staffing system. 2) The results of research on organizational factors affect
the net benefit by 61.9% such as organizational structure, leadership,
teamwork, strategy, staffing, and staff turnover have a role in forming the Net
benefit model of the personnel information system at the Human Resource
Development Agency of Transportation (BPSDMP). 3)
The research results on the technology factor affect the
net benefit by 58.9%, such as the staffing information system model at the Human
Resource Development Agency of Transportation (BPSDMP). 4)
Human, organizational, and technological factors together
for Net benefits have a determination coefficient value of 0.564, which means
that humans, organizations, and technology can influence 56.4% of Net benefits,
and there are still 43.6% of other factors that can affect information systems
employment at the Human Resource Development Agency of Transportation (BPSDMP).
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