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ANALYSIS OF EMPLOYEE PERFORMANCE WITH THE ROLE OF
ORGANIZATIONAL COMMITMENT AS A MODERATION VARIABLE IN
AMUSEMENT PARK TOURISM COMPANIES IN THE CITY OF SURABAYA
Muhammad Taufiqurrahman1*, Tri Kartika Pertiwi2, Dhani Ichsanuddin Nur3
Faculty of Economic and Business, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya,
Indonesia1,2,3
ABSTRACT
The research aims to understand the impact of work discipline and competence in part on the
performance of employees as well as the influence of working disciplines and competences on Employee
Performance with organizational commitment as a variable of moderation. This case study was
conducted on one of the tourism companies that moved in the area of amusement parks in the city of
Surabaya. The sample in this study consisted of 103 permanent employees with saturated/sensus
sampling techniques. The type of data used is quantitative data. Primary data collection techniques use
questionnaires distributed to permanent employees and secondary data of permanent employees from
the entertainment park tourism company in the city of Surabaya. This study uses Smart PLS data
analysis. The conclusions of this study showed that in part work discipline and competence contributed
to improved Employee Performance, while organizational commitment as moderation could not
contribute to the relationship of work disciplines and competencies in improving Employee
Performances.
Keywords: Work Discipline; Competence; Organizational Commitment; Employee Performance
INTRODUCTION
Human resource management cannot be separated from the employee factor which is
expected to work as well as possible to achieve organizational goals. Employees are the main
asset of the organization and have a strategic role in the organization. To be able to survive in
increasingly competitive conditions, companies currently need quality human resources and have
high Employee Performance. This is because human resources are one of the determining factors
for the success of a company. In producing goods or services efficiently, it takes human resources
who are trained, experienced and competent in every activity they carry out. The more trained
and competent in production activities every day will produce products or services that meet
standards and can satisfy consumers of course. According to Widodo (Widodo, 2017, p. 6),
training is a process of improving systematically and in accordance with employee needs by
improving skills, knowledge and understanding and self-motivation. So that every employee of a
company or organization can produce products or services that can satisfy customers and improve
Employee Performance. The effect of competence on Employee Performance from several studies
has positive and significant results according to research by (Hermawan, 2022; Riduawan & Kuncoro,
2019). Different results are shown through the results of research by Maizar (2017) and Emi
Muniarsih and Ketut Sudarma (2016), which state through their research that competence on
Employee Performance has a positive but insignificant effect.
Organizational commitment also has an important position in a company to ensure that the
company gets the maximum value of employee productivity. Based on the book Middle Range
Theory in HR Management Science (Wulandari, 2021) organizational commitment is a reason
that explains why employees are willing to commit themselves to certain actions, which are
difficult to explain why. Employees with feelings of compatibility with fellow employees build
strong ties with their group. Employees with a common identity collectively show more affective
ties. Employees' interest in the organization, identifying with the organization's goals.
Organizational commitment is defined as an attitude that reflects employee loyalty to the
organization and the ongoing process by which organizational members express their concern for
the organization and its continued success and progress (Kaswan, 2012). According to (Cahyani
Analysis of Employee Performance with The Role of Organizational Commitment as A Moderation
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et al., 2019; Nurandini, 2014; Rizal et al., 2018) stated in their research that organizational
commitment has a positive and significant effect on Employee Performance.
According to the book (Fauzi & Hidayat, 2020) performance or performance is the
achievement obtained by an employee every time he finishes doing his job, where he can use
knowledge, for competency-based workers, his performance is assessed in terms of attitude, skills
and abilities in carrying out each task. Employee Performance according to (Tristina & Widagdo,
2019, p. 33) is the result of the work or activities of an employee in quality, quantity and timeliness
in an organization to achieve goals in carrying out the tasks and work assigned to him. Every
employee in addition to being required to have knowledge, skills and abilities, must also have
experience, motivation, commitment and work discipline in order to achieve maximum Employee
Performance. So that if the company's Employee Performance is good, the company's
performance will also increase which will lead to the achievement of company goals (Amaliyah,
2022).
Surabaya City is the capital of East Java Province which is used as a benchmark for
economic development and growth as well as development in the East Java region. Surabaya City
has a strategic location and public interest infrastructure that is very supportive in the business
world. Business in the field of tourism is one of the fields that is currently also experiencing very
intense business competition, both small, medium and largescale businesses. In order for a
company to survive and thrive in this fierce competitive situation, according to researchers,
companies need strategies in dealing with business competition, improving human resource
management, and optimizing marketing.
The phenomenon that occurs in one of the amusement park tourism companies in Surabaya
City based on pre-research interviews conducted by researchers found that there is still a high rate
of employee indiscipline related to being present on time. Table 1 shows the percentage of
tardiness of permanent employees of amusement park tourism companies in Surabaya City.
Table 1 Employee Delay Data
Month
Number of Late Employees
Percentage
November 2022
38
17%
December 2022
55
25%
January 2023
43
20%
February 2023
45
21%
March 2023
49
22%
Total
230
105%
Average
46
21%
Source : HRD amusement park tourism company in Surabaya City,
Data processed 2023.
Based on Table 1, it shows fluctuations in the number of employees who are late every
month from 17% to 25%. According to Sri Purnama (2018) work discipline is a management
activity to carry out standard procedures that have been set, that every employee must have the
awareness to carry out discipline in carrying out their work.
From the explanation above, this study takes the title "Analysis of Employee Performance
with the Role of Organizational Commitment as a Moderating Variable at Amusement Park
Tourism Companies in Surabaya City".
RESEARCH METHOD
This type of research is quantitative research which is a study of the causal relationship
between the independent variable (independent) and the dependent variable (dependent). The data
used in this study are primary data and secondary data. Primary data obtained through direct
measurement of Work Discipline, Competence, and Organizational Commitment variables on
research subjects by distributing questionnaires made using a linkert scale as a measurement.
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Meanwhile, secondary data is used to support the research results in the form of permanent
employee data at one of the amusement park tourism companies in Surabaya City. This research
was conducted at one of the amusement park tourism companies in Surabaya City. The research
time required is 6 (six) weeks by considering the willingness of correspondents and research
design. The estimated research will start from week 3 (three) in May 2023 to week 1 in July 2023.
While the population in this study were permanent employees in one of the amusement park
tourism businesses in Surabaya City with a total of 103 permanent employees.
Data Analysis Techniques
The analysis technique in this study uses an inference statistical test approach. Inference
statistics itself is part of a statistical method that studies inference techniques as well as
explanation and meaning with results displayed openly from existing data (Boediono & Koster,
2004). In this study, existing data is grouped based on variables, types of respondents, and
tabulation of existing data to carry out hypotheses according to existing data. In this study, data
analysis used SmartPLS (Smart Patrial Least Square) software assistance. In processing using
PLS there are two (2) stages of measurement evaluation, the first is the outer model, which can
be called a measurement model that will be used in analyzing a model, while the second is the
inner model, which can be called a structural model used for the validity and reliability of a model
(Hamid & Anwar, 2019).
1. Uji Outer Model
The measurement model or outer model shows how each indicator block relates to its latent
variable. Evaluation of the measurement model through confirmatory factor analysis is to
use the MTMM (MultiTrait-MultiMethod) approach by testing convergent and discriminant
validity. While the reliability test is carried out in two ways, namely with Cronbach's Alpha
and Composite Reliability (Ghozali & Latan, 2015).
a. Convergent Validity; Convergent validity of the measurement model with reflexive
indicators can be seen from the correlation between the item score/indicator and the
construct score. An individual reflective measure is said to be high if it correlates more
than 0.70 with the construct to be measured. However, in the research stage of scale
development, a loading of 0.50 to 0.60 is still acceptable (Ghozali & Latan, 2015).
b. Discriminant Validity; Discriminant validity of indicators can be seen in the cross
loading between indicators and their constructs. If the correlation of the construct with
its indicator is higher than the correlation of the indicator with other constructs, it
indicates that the latent constructs predict the indicators in their block better than the
indicators in other blocks. Another method to assess discriminant validity is to compare
the square root of the average variance extracted (√AVE) for each construct with
correlation between the construct and other constructs with the model. The model is said
to havei discriminant validity is quite good if the AVE root for each construct is greater
than the correlation between the construct and the other constructs (Fornell & Larcker,
1981 dalam Ghozali, 2011).
In Ghozali & Latan (2015) describe another test to assess the validity of the construct
by looking at the AVE value. The model is said to be good if the AVE of each construct
value is greater than 0.50.
c. Reliability; In addition to the validity test, model measurement is also carried out to test
the reliability of a construct. Reliability tests are carried out to prove the accuracy,
consistency and accuracy of the instrument in measuring constructs. In PLS-SEM using
the SmartPLS 3.0 program, to measure the reliability of a construct with reflexive
indicators can be done in two ways, namely with Cronbach's Alpha and Composite
Reliability. The construct is declared reliable if the composite reliability or Cronbach
alpha value is above 0.70 (Ghozali & Latan, 2015).
2. Inner Model Test (Model Structure)
The structural model or inner model shows the relationship or estimation strength between
latent variables or constructs based on substantive theory.
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a. R-Square; In assessing the structural model, first assess the R-Square for each
endogenous latent variable as the predictive power of the structural model. Testing of
the structural model is done by looking at the R-square value which is a goodness-fit
model test. Changes in the R-Square value can be used to explain the effect of certain
exogenous latent variables on endogenous latent variables whether they have a
substantive effect. R-Square values of 0.75, 0.50 and 0.25 can be concluded that the
model is strong, moderate and weak (Ghozali & Latan, 2015).
b. Estimate For Path Coefficients; The next test is to see the significance of the influence
between variables by looking at the parameter coefficient value and the significance
value of T statistics, namely through the bootstrapping method (Ghozali & Latan, 2015).
3. Test the Hypothesis
a. T-test; The t test is a statistical test that is often encountered in statistical practice
problems. This test is used to partially test the relationship of the independent variable
with the dependent variable (Hasan, 2003). The basis for taking using the first method
is as follows:
1. If the sig value < α 5% then H0 is rejected, meaning that the independent variable
has a statistically significant effect at α 5% on the dependent variable.
2. If the sig value> α 5% then H0 is accepted, meaning that the independent variable
has an effect but is not statistically significant at α 5% on the dependent variable.
b. F test; This test is used to test the relationship between the independent variable and the
dependent variable together. Can use the first guideline as follows:
1. If sig. < α 5% then H0 is rejected, which means that the independent variable has
a statistically significant effect at α 5% on the dependent variable.
2. If sig. > α 5% then H0 is accepted which means that the independent variable has
an effect but is not statistically significant at α 5% on the dependent variable.
RESULT AND DISCUSSION
Model Analysis Using PLS-SEM
Model analysis in this study uses the PLS-SEM method. Analysis using the PLS-SEM
method includes 2 stages, namely the reflective measurement model evaluation stage, and the
structural model evaluation stage. Evaluation of the reflective measurement model consists of
testing the validity and reliability of research indicators. This evaluation aims to measure the
relationship between variables and their constituent indicators, meaning how much the latent
variable is able to contain the diversity of data contained in each indicator and how much the
relationship between the latent variable and its indicators is related. In this case, there are three
aspects that are assessed, namely Convergent Validity, discriminant validity, and composite
reliability. Meanwhile, the evaluation of the structural model aims to test the research hypothesis.
Reflective Measurement Model Evaluation
Evaluation of the reflective measurement model is an evaluation of the relationship
between variables and their indicators where the depiction is shown by arrows from the construct
(ellipse-shaped) to several indicators (box-shaped). This evaluation includes two stages, namely
Convergent Validity and discriminant validity tests.
a. Stage 1: Convergent Validity test
Convergent Validity aims to measure the suitability between the indicators of variable
measurement results and the theoretical concepts that explain the existence of indicators of these
variables. The Convergent Validity test can be evaluated in three stages, namely by looking at
outer loadings, composite reliability, and Average Variance Extracted (AVE). Outer loadings is
a table containing loading factors to show the amount of correlation between indicators and latent
variables. The weakest loading factor that is acceptable for validity is 0.5. Outer loadings output
can be obtained from the PLS Algorithm Report SmartPLS. To make it easier to see the outer
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loadings of the indicator blocks that measure the construct, the path diagram (Inner Model) is
presented in Figure 1 below.
Figure 1 Output diagram jalur (Inner Model)
Source: smart-PLS output (2023).
From Figure 4.9, it can be seen the amount of loading factor estimated from each
indicator that measures the construct. The estimation results show that all indicators have met
good validity because they have a loading factor of 0.50 and / or more than 0.50. Because the
validity test with outer loadings has been fulfilled, the measurement model has the potential to
be tested further. Another form of presentation of the outer loadings output of the estimation
results is shown in table 2.
Table 2 Outer Loading
Indicato
r
Work
Disciplin
e
Employe
Performanc
e
Organizationa
l
Commitment
Compensatio
n
Moderatio
n -1
(X1*Z)
Moderatio
n -2
(X2*Z)
(X1*Z)
1.153
(X2*Z)
1.010
X1.1
0,858
X1.2
0,786
X1.3
0,912
X1.4
0,881
X2.1
0,724
X2.2
0,803
X2.3
0,835
X2.4
0,849
X2.5
0,907
X2.6
0,870
Y1.1
0,752
Y1.2
0,875
Y1.3
0,872
Y1.4
0,880
Y1.5
0,822
Z1.1
0,885
Z1.2
0,920
Z1.3
0,884
Source: smart-PLS output (2023)
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The next check of Convergent Validity is reliability. Reliability is defined as the ability of
instrument indicators to produce the same value repeatedly (consistency) in each research activity.
The level of reliability is measured by the Composite Reliability value, Cronbach Alpha and
Average Variance Extracted (AVE) value. The composite reliability and Cronbach alpha values
assume that all indicators have unequal assessment weights. The composite reliability and
Cronbach alpha values if greater than 0.6 indicate that the construct has reliable reliability. The
composite reliability and Cronbach alpha output obtained from the PLS Algorithm Report
SmartPLS is presented in table 3 below.
Table 3 Composite Reliability and Cronbach Alpha Test
Cronbach's Alpha
Composite Reliability
Work Discipline
0,883
0,919
Employee Performance
0,896
0,924
Organizational Commitment
0,878
0,925
Competence
0,911
0,931
Source: smart-PLS output (2023).
From table 3, the results of the composite reliability and Cronbach Alpha tests
show that all constructs are reliable or have acceptable composite reliability and Cronbach
Alpha values. This is because the composite reliability and Cronbach Alpha values for
each construct are greater than 0.7. Another measurement that is also used to test
reliability and validity is Average Variance Extracted (AVE). The AVE value aims to
measure the level of variance of a construct component collected from its indicators by
adjusting the error rate. Testing with AVE values is more critical than composite
reliability. The recommended minimum AVE value is 0.50. The AVE output obtained
from the PLS Algorithm Report SmartPLS which is presented in table 4.
Table 4 Average Variance Extracted (AVE) Value
Average Variance Extracted (AVE)
Work Discipline
0,740
Employee Performance
0,709
Organizational Commitment
0,804
Competence
0,694
Source: smart-PLS output (2023).
From table 4 the test results with the AVE value show that all constructs have potential
validity for further testing. This is because the AVE value on all constructs is greater than 0.50..
b. Stage 2: Discriminant Validity Test
Discriminant validity is the level of differentiation of an indicator in measuring instrument
constructs. To test discriminat validity, it can be done by examining cross loading, namely the
correlation coefficient of the indicator against its association construct (loading) compared to the
correlation coefficient with other constructs (cross loading). The correlation coefficient value of
the indicator must be greater against its associated construct than other constructs. This greater
value indicates the suitability of an indicator to explain its association construct compared to
explaining other constructs. Another discriminant validity test is to compare the correlation
between variables with the square root of the AVE (√). The measurement model has good
discriminant validity if the √𝐴𝑉𝐸 of each variable is greater than the correlation between other
variables. SmartPLS as a tool for PLS-SEM analysis has included a discriminant validity test. The
discriminant validity assessment produced by SmartPLS uses the Fornell-Lacker Criterion and
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cross loadings criteria. The following are the results of the cross loadings output obtained from
the PLS Algorithm Report SmartPLS presented in table 5.
Table 5 Cross Loading
Indicator
s
Work
Disciplin
e
Employee
Performanc
e
Organizationa
l Commitment
Competenc
e
Moderatio
n -1
(X1*Z)
Moderatio
n -2
(X2*Z)
(X1*Z)
0,155
0,171
-0,023
0,244
1.000
0,212
(X2*Z)
0,278
0,203
0,214
-0,071
0,212
1.000
X1.1
0,858
0,657
0,505
0,439
X1.2
0,786
0,466
0,573
0,264
X1.3
0,912
0,634
0,448
0,436
X1.4
0,881
0,608
0,513
0,416
X2.1
0,296
0,437
0,524
0,724
X2.2
0,416
0,546
0,348
0,803
X2.3
0,382
0,484
0,331
0,835
X2.4
0,383
0,547
0,387
0,849
X2.5
0,396
0,544
0,459
0,907
X2.6
0,413
0,633
0,566
0,870
Y1.1
0,528
0,752
0,834
0,551
Y1.2
0,627
0,875
0,573
0,580
Y1.3
0,612
0,872
0,542
0,482
Y1.4
0,618
0,880
0,517
0,611
Y1.5
0,532
0,822
0,620
0,468
Z1.1
0,532
0,587
0,885
0,406
Z1.2
0,502
0,664
0,920
0,471
Z1.3
0,538
0,734
0,884
0,521
Source: smart-PLS output (2023).
The reading of cross loadings in table 5 is by column. It can be seen that indicators X1.1,
X1.2, X1.3, and X1.4 have a higher correlation to their associated construct, namely Work
Discipline with a correlation coefficient of 0.858, 0.786, 912, and 0.881. The correlation
coefficient value of the indicator block has a greater value for its association construct than other
constructs.
Indicators Y1.1, Y1.2, Y1.3, Y1.4 and Y1.5, also have a higher correlation with their
associated construct, namely Employee Performance. Likewise, the same is the case with other
construct indicators which correlate higher with their associated constructs than other constructs,
so it can be said to have good discriminant validity.
The next check is to compare the correlation between variables with √𝐴𝑉𝐸 . The
measurement model has good discriminant validity if the √𝐴𝑉𝐸 of each variable is greater than
the correlation between variables. The √𝐴𝑉𝐸 value can be seen from the Fornell-Larcker
Criterion SmartPLS output presented in table 6.
Table 6 Fornell-Larcker Criterion
Work Discipline
Employee
Performance
Organizational
Commitment
Competence
Work Discipline
0,860
Employee Performance
0,695
0,842
Organizational Commitment
0,585
0,744
0,897
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Competence
0,460
0,644
0,525
0,833
Source: smart-PLS output (2023)
The way to read the Fornell-Larcker Criterion table in table 6 is based on the intersection
of rows and columns. It can be seen that the √𝐴𝑉𝐸 value of the Work Discipline variable is 0.860,
while the highest correlation value of the Work Discipline variable with other variables is only
0.695, thus the √𝐴𝑉𝐸 of the Work Discipline variable is greater than the correlation of Work
Discipline with other variables. Similarly, other variables show √𝐴𝑉𝐸 greater than the correlation
between variables. So that the requirements for discriminant validity with √𝐴𝑉𝐸have been met.
Structural Model Evaluation
Structural model evaluation aims to test the presence or absence of influence between
constructs, R Square, and the effect of indirect relationships between constructs. The structural
model is evaluated using the p-value to determine the significance of the structural path parameter
coefficient and R Square to determine the effect of the independent latent variable on the
dependent latent variable whether it has a substantive effect.
a) Evaluate R Square Value
The R Square value is used to explain the effect of exogenous variables on
endogenous variables. The R Square value is obtained from the PLS Algorithm Report
SmartPLS and can be seen in table 7.
Table 7 R Square
R Square
Employee Performance
0,716
Source: smart-PLS output (2023).
The R Square value of the Employee Performance variable is 0.716, meaning that the Work
Discipline, Organizational Commitment, and Competence variables are simultaneously able to
explain their influence on the Employee Performance variable by 71.6% while the remaining
28.4% is explained by other variables outside the model studied.
Then for the assessment of goodness of fit in this study, it can be seen from the Q-Square
value. The Q-Square value has the same meaning as the coefficient determination (R-Square) in
regression analysis, where the higher the Q-Square, the better or more fit the model is with the
data. The results of the calculation of the QSquare value are as follows:
Q-Square = 1 – [(1 – R21) x (1 – R22)]
= 1 – (1 – 0,716)
= 0,716
Based on the results of the above calculations, the Q-Square value is 0.716. This shows that
the amount of diversity of research data that can be explained by the research model is 71.6%.
While the remaining 28.4% is explained by other factors that are outside this research model.
Thus, from these results, this research model can be declared to have very good goodness of fit..
b) Evaluation of the significance of the path relationship on the research hypothesis
To conclude whether the hypothesis is accepted or rejected, the p-value price at a
significance of α = 5% or 0.05 is used. If the p-value <0.05 then H0 is rejected, meaning there
is an effect. Conversely, if the p-value> 0.05 then H0 is accepted, meaning that there is no effect.
The following are the results of the structural model evaluation obtained from the SmartPLS
Bootstrapping Report presented in table 8.
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Table 8 Path Coefficients T-Values, P-Values
Path
Coefficients
T Statistics
P Values
Description
Work Discipline -> Employee
Perfo
0,307
3,439
0,001
Ada
pengaruh
Competence -> Employee
Performance
0,274
3,199
0,001
Ada
Pengaruh
Moderating Effect 1 ->
Employee Performance
0,050
0,951
0,342
Tidak ada
Pengaruh
Moderating Effect 2 ->
Employee Performance
0,036
0,602
0,548
Tidak ada
Pengaruh
Source: smart-PLS output (2023).
Bootstraping output with P-values on direct effects and moderation effects is presented in
Figure 2 below:
Figure 2 Output Bootstraping with P-Value
Source: smart-PLS output (2023).
Hypothesis Test
From the results of table 8, it can be concluded that the hypothesis that states:
H1 : It is suspected that Work Discipline has a positive effect on Employee Performance can be
accepted, with a path coeficients of 0.307 with a T-Value of 3.439 or a P-Value of 0.001
smaller than 0.05, so there is an influence or significant (positive).
H2 : It is suspected that Competence has a positive effect on Employee Performance can be
accepted, with path coefficients of 0.274 with a T-Value of 3.199 or a P-Value of 0.001
smaller than 0.05, so there is an influence or Significant (positive).
H3 : It is suspected that Work Discipline has a positive effect on Employee Performance with
Organizational Commitment as a moderating variable cannot be accepted, with path
coefficients of 0.050 with a T-Value of 0.951 or a P-Value of 0.342 greater than 0.05, so
there is no moderating influence or Non Significant.
H4 : It is suspected that Competence has a positive effect on Employee Performance with
Organizational Commitment as a moderating variable cannot be accepted, with path
coefficients of 0.036 with a T-Value of 0.602 or a P-Value of 0.548 greater than 0.05, so
there is no moderating influence or Non Significant..
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Discussion
Discussion of each variable relationship in detail against the objectives to be achieved in
this study as follows
.
Effect of Work Discipline on Employee Performance
From the results of testing the effect of work discipline on Employee Performance, it means
that the higher the value of Employee Performance discipline, it will greatly affect the high
Employee Performance felt by all employees. The results of this study are in line with the theory
conveyed by Wau, Samalua Waoma, Ferdinand T. Fau (2021), which states that discipline is the
sixth operative function of the most important human resource management that companies need
to pay attention to, because the better the employee's discipline, the better their performance. This
is in line with what Sastrohadiwirjo (2002: 295) in (Putri et al., 2019) has explained, that there is
a high correlation between work discipline and the performance a person achieves. This means
that the better the work discipline, the better the performance a person achieves. This is supported
by research conducted by (Mardiana & Paryanti, 2012), which states that work discipline variables
have a positive and significant effect on Employee Performance.
Work discipline greatly affects the achievement of company performance against
predetermined targets. If employees are not disciplined at work, the results obtained will not be
optimal. The impact of indiscipline at work is not only detrimental to the company but also reflects
the quality of the human resources of the company concerned. The results of this study indicate
that employees are responsible and obedient to the duties that are obligatory in each section, so
that the planning and division of tasks that have been set by the company can be ensured to be
carried out. Completion of tasks according to the provisions can make the company's goals
achieved according to plan, whether it is related to profit, operations, and the continuity of
company services to the community. The majority of employees of this amusement park company
have come and gone on time, but there are still employees who often come late for no apparent
reason. This usually happens to employees who have positions and have a long tenure or are
commonly called seniors in their work. In the future, the company must continue to ensure that
employees carry out their duties responsibly, obey the provisions, increase accuracy in work and
ensure that the number of late employees decreases every month so that the achievement of
company performance is much better.
Effect of Competence on Employe Performance
From the test results on the effect of employee competence, it is able to contribute to
Employee Performance, meaning that the higher the value of employee competence, it will greatly
affect the high Employee Performance felt by all employees. The results of this study are in line
with research conducted by (Hermawan, 2022; Maizar, 2017; Riduawan & Kuncoro, 2019),
which states that competence has a significant effect on performance. This is also in accordance
with Sutrisno's statement in (Ajabar, 2020) that the competency models that have been determined
by the company must be implemented in order to answer about the skills, knowledge, and
characteristics needed in a job. With competence, the company can find out what kind of
competency model can affect the improvement of company performance.
This study shows that employees have a high interest in new knowledge and training
organized by the company. This high interest from employees should be welcomed by the
company. So far, employees will get additional knowledge through several ways, including:
training from internal parties, sharing from internship students, bringing in expert speakers in
their fields and several years ago participating in seminars held for free. In addition, companies
can also conduct training activities and add the latest knowledge to support the improvement of
employee competence in carrying out their duties. Training can start with internal activities such
as coffee morning programs between sections to explain the characteristics of the work and how
to do the work in each section. From this activity, not only will other departments gain new
knowledge but it is possible to get input and suggestions for the better.
Analysis of Employee Performance with The Role of Organizational Commitment as A Moderation
Variable in Amusement Park Tourism Companies In The City of Surabaya
842 Return: Study of Management, Economic And Bussines, Vol 2 (8), August 2023
The Effect of Work Discipline on Employee Performance with Organizational Commitment
as a Moderating Variable
Based on this research, organizational commitment has not been able to have an effect on
strengthening the relationship between the effect of work discipline on Employee Performance,
in other words, the higher the organizational commitment does not strengthen the effect of high
work discipline in improving Employee Performance. The results of this study contradict the
results of research by (Sherly et al., 2022) where work discipline can strengthen organizational
commitment to Employee Performance.
Employees who have a high organizational commitment within themselves will create a
high sense of belonging to the organization as well, so they should not disappoint the
leadership/organization with their performance. In contrast to the results shown through this study
where employees with very good organizational commitment to their organization will not always
have good discipline. This is shown by the high level of employee tardiness every month. Based
on the observations of researchers, it was found that employee tardiness that occurred was
dominated by employees who had more than 20 years of service who had no doubt about their
loyalty to the organization. According to the researcher, the factor that can affect the lack of
employee discipline is that the company has not imposed strict sanctions or conducted intense
employee coaching for employees regarding attendance and other violations that are considered
not in accordance with the guidelines.
The Effect of Competence on Employee Performance with Organizational Commitment as
a Moderating Variable
Based on this research, it is found that organizational commitment has not been able to
have an effect on strengthening the relationship between employee competence in improving
Employee Performance in other words, the higher the organizational commitment owned by
employees may not necessarily support the influence of employee competence in improving
Employee Performance. The results of this study are in line with research conducted by (Afrina
et al., 2021) and Adlina (2014) who in their research stated that organizational commitment does
not moderate the effect of competence on Employee Performance.
High organizational commitment is needed in an organization, because the creation of high
commitment is expected to affect the professional work climate. In this study, it was found that
employees felt that they had not received additional new knowledge when carrying out tasks or
work. Meanwhile, the actual interest of employees in gaining new knowledge is very high. With
the high employee interest in knowledge or competency improvement, it is hoped that the
company can provide training to improve employee competence both internally and externally.
Research Limitations
Based on the research results and data interpretation above. The limitations of this study
are as follows:
• In this study, Organizational Commitment as a moderator has not been able to contribute
directly to work discipline, competence, and Employee Performance. Because in field
practice it is found that organizational commitment cannot always be imposed according to
existing rules because under certain conditions organizational commitment can be
generalized or specific so that it can be categorized into certain fields to improve work
discipline, competence, and Employee Performance.
• This research only focuses on improving Employee Performance that has been carried out
over the past 5 months with data showing Employee Performance that continues to
experience continuous evaluation stages.
Analysis of Employee Performance with The Role of Organizational Commitment as A Moderation
Variable in Amusement Park Tourism Companies In The City of Surabaya
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CONCLUSION
Based on the results and discussion of the variables of work discipline, competence,
Employee Performance and organizational commitment as moderators, the analysis can be
concluded as follows: 1) Work discipline is able to contribute to improving Employee
Performance. Work discipline greatly contributes to improving Employee Performance so that
making work discipline a habit will not feel heavy for employees in carrying out work discipline,
thus improving Employee Performance will always be good. 2) Competence is able to contribute
to improving Employee Performance. Competent employees make it easier to complete work
which can improve performance. 3) Organizational commitment as a moderator has not been able
to strengthen the relationship between work discipline in improving Employee Performance. The
high level of organizational commitment needs to be balanced with the application of sanctions
in order to further improve work discipline and can further improve Employee Performance. 4)
Organizational commitment as a moderator has not been able to strengthen the relationship
between competence in improving Employee Performance. High commitment needs
organizational support in providing increased employee competence in improving Employee
Performance.
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