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THE IMPACT OF TRAINING DEVELOPMENT, WORK ENVIRONMENT,
MOTIVATION AND COMPETENCY ON EMPLOYEE PERFORMANCE:
MEDIATED BY JOB SATISFACTION A CASE STUDY OF PT ADHI KARYA
(PERSERO) TBK DPKA
Meidhy Panginda Saputra
1*
, Leonnard Ong
2
Sekolah Tinggi Manajemen IPMI, Jakarta, Indonesia
1,2
meidhy.saputra@ipmi.ac.id
1
, leonnard.ong@ipmi.ac.id
2
ABSTRACT
With job satisfaction acting as a mediating variable, this study examines how employee performance is
influenced by training development, work environment, motivation, and competence. Employees of PT.
Adhi Karya,Tbk DPKA were given questionnaires as part of the data collection process. This study's
sample consisted of 59 respondents who were selected using a proportionate and cluster sampling
technique. Convergent validity, discriminant validity, composite reliability, Average Variance Extracted
(AVE), Collinearity Statistics (VIF), path coefficient test, bootstrap test, and Structural Equation
Modelling (SEM) test were all used to examine the data after it was processed using Smart PLS. This
study indicates that motivation and training development have a positive and significant impact on job
satisfaction. Job satisfaction is negatively and not significantly impacted by the work environment and
skills. Additional findings show that while job satisfaction can influence employee motivation and
training development, it cannot influence the work environment or competence. This research is
expected to be an additional reference for company to improve quality in terms of employee
performance. This research is extraordinary, for the sake of improvement in the future.
Keywords : Training Development; Work Environment; Motivation; Competence; Job Satisfaction;
Performance
INTRODUCTION
The intensity of competition and price wars between the construction industry has forced
the Company to redefine its vision and mission: To become an innovative corporation with a
superior culture for sustainable growth. Because of its limited liability company character,
Company is encouraged to keep doing what's best for all parties involved as it develops and as
Indonesia's construction sector continues to advance.
Human Resources (HR) has a very important role in the company, because it is appropriate
when management provides more attention through policies taken to improve employee
performance (Brewster et al., 2016; Tessem et al., 2015). Now many problems arise related to
employment especially the performance of employees, therefore problems related to performance
need to be sought out the way out (Leonnard, 2018). Every employee in a company is required to
make a positive contribution through good performance, given the performance of the company
depending on the performance of its employees (Riwukore et al., 2022). Performance is the level
of the employees achieving the requirements of the work efficiently and effectively Simamora
(2006). Employee performance is a working achievement, which is a comparison of work that
can be seen in real life with the organization's predefined work standards (Rosalia et al., 2020).
Then Robbins (2008) defines the performance of an outcome achieved by the employee in his job
according to certain criteria that apply to a job. It is believed that teams are an essential part of
work performance and increased productivity (Du Plessis et al., 2015).
In order to pursue new projects, The number of employees required to manage the project.
Company is targeting multiple new initiatives, so it needs a number of employe that are qualified
in their respective fields. Contractors operating in Indonesia are mandated to have construction
experts/technicians in every discipline whose proficiency is validated through official
documentation in the form of SKK. Construction professionals and technicians need to pass an
evaluation process that evaluates their operational, technical, and cognitive skills based on their
work experience in order to receive this SKK. Taking into account the background data, this study
would like to suggest the following problems: 1) There is a gap between employee needs and
availability; 2) How to catch-up employee competency for assignment in the project; 3) Does
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training development, work environment, motivation and competency affect satisfaction.
Theoretical Conceptual Framework
In this research, there are 6 variables are analyzed: Training Development, Work
Environment, Motivation, Competency, Job Satisfaction and Employee Performance.
Based on explanation above, below is theoretical conceptual framework, illustrated as
Figure 1.
Figure 1 Proposed Research Model by researcher (2024)
Source: Author, 2024
Refer to the research framework, they are 6 hypotheses that going to be tested.
H1: Training Development has a positive effect on Job Satisfaction.
H2: Work environment has a positive effect on Job Satisfaction.
H3: Motivation has a positive effect on Job Satisfaction.
H4: Competency has a positive effect on Job Satisfaction.
H5: Job Satisfaction has a positive effect on Employee Performance.
H6: Training & Development, Work Environment, Employee Motivation and Employee
Competence has a positive effect on Performance which is mediated Job Satisfaction
RESEARCH METHOD
This study will use a Survey strategy and will involve cluster and proposional sampling a
representative proportion of the population to observe contributing variables and permits the
collection of vast data will be used to answer the research question/statement. Primary data is data
directly obtained by the researcher from the questionnaire sent out and filled in for survey from
the original source.
Most of the questionnaire will use ordinal scale, according to Uma Sekaran (2016) an
ordinal scale not only categorizes the variables in such a way as to denote differences among the
various categories, it also rank orders the categories in some meaningful way. With any variable
for which the categories are to be ordered according to some preference, the ordinal scale would
be used. The ordinal scale used will be mostly Likert scale, which is designed to examined how
strongly subjects are agree or disagree with statements on a five points scale. A likert scale
Training Development
Paposa (2019), Wajidi (2023), Saiari (2020) H6
1 Training environment
2 Trainer quality
3 Learning experience
H1
H6
Work Environment
Pentury & Usmany (2023), Fahlevi et al (2019)
1 Work atmosphere
2 Good relationship H2
3 Facilities Job Satisfaction Performance
Robbins & Judge (2009) Fahlevi et al. (2019) Fahlevi et al. (2019)
1 The job itself 1 Quality
2 Salary H5 2 Quantity
Motivation H3 3 Promotion Opportunities 3 Timelineness
Mangkunegara (2009) adopted from Harzberg
1 Responsibility
2 Opportunity for advancement
3 Challenging work H6
H4
Competency
Spencer (1993), Pentury & Usmany (2023
1 Intelectual competence
2 Emotional competence H6
3 Social competence
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question is a type of question in which the answer to the question is in the form of a five-point
scale. This study uses cluster sampling as the sampling design. The population of the study is
employee at PT. Adhi Karya (Persero) Tbk DPKA.
To considering how many samples will be generated, there are some literature to take info
account. The number of samples can affect the validity of the study. That is because, the more
samples or respondents in this study, the data will become more normally distributed. The
required sample size for this research will be 48.
In accordance with this research title in this thesis: The impact of Training Development,
Work Environment, Motivation and Competency on Job satisfaction and Performance. In this
study, 6 (two) research variables ware used as follows: performance (P) is the dependent
variables, job satisfaction (JS) is the mediating variable, Training Development (TD), work
environment (WE), Motivation (M), Competency (C) is independent variable. The research
variables are measured in a five-point Likert scale from strongly disagree (1) to agree (5) for each
given statement strongly.
The indicator used to measure Training Development refers to Paposa & Kumar (2019),
Wajidi et al. (2023) and Saiari et al. (2020). The indicator used to measure Work Environment
refers to Fahlevi et al. (2019) and Pentury & Usmany (2023). The indicators used to measure
motivation refer to Mangkunegara (2009) adopted from Harzberg. The indicator used to measure
Competency refers to Pentury & Usmany (2023). The indicator used to measure job satisfaction
according to Mulyadi (2012) adopted from Robbins, Fahlevi et al. (2019). The indicators used to
measure employee performance according to Fahlevi et al. (2019).
RESULT AND DISCUSSION
Data analysis starting with data collection, and coding, then followed by data validity and
reliability check using SPSS (Statistical Package for Social Sciences) software version 29. Prior
to hypothesis checking, we need to analyze the model using SEM-PLS 4 software approach
utilizing Smart PLS software.
Measurement Model Evaluation
To determine how well the data suits the theory is by evaluating the path model. The
evaluation consists of two processes. The first evaluation is the measurement model and then
evaluation of the structural model. There are 3 (three) criteria for measurement model evaluation
i.e. internal consistency, convergent validity, and discriminant validity.
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Figure 2 Path Coefficient Graphic
Source: PLS data process, 2024
Internal Consistency Reliability, the results obtained from the questionnaire data process
using PLS, based on table 4.4 below, show that all research variable indicators have Composite
Reliability and Cronbach Alpha varies above 0.7. A higher value indicating a higher level of
reliability. Based on the result, it can be concluded all the constructs above have a higher level of
internal consistency Reliability.
Convergent Validity, there are 2 (two) criteria to determine Convergent Validity i.e. Outer
Loading and Average Variance Extracted (AVE). From table 4.4 below, all the constructs have
the outer loading value above 0.7. the standardized outer loading should be ≥ 0.708 (F. J. Hair et
al., 2014). It can be concluded that all indicators have high outer loading. An AVE value above
0.5 indicates that the constructs have good validity (Ghozali, 2018). The result value of AVE on
table 4.4 are above of minimum level of 0.5, thus based on both criteria above, we can conclude
that all the construct measures have high levels of convergent validity.
Table 1 Construct Reliability and Validity Result
Variable
Code
Loading
Factor
Cronbach’s
Alpha
AVE
Training & Development
TD1
0.750
0.780
0.692
TD2
0.856
TD3
0.883
Work Environment
WE1
0.918
0.877
0.803
WE2
0.866
WE3
0.904
Motivation
M1
0.857
0.748
0.664
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M2
0.860
M3
0.720
Competency
C1
0.855
0,744
0.662
C2
0.708
C3
0.867
Job Satisfaction
JS1
0.765
0.755
0.671
JS2
0.852
JS3
0.837
Performance
P1
0.936
0.901
0.834
P2
0.895
P3
0.908
Source: PLS data process, 2024
Discriminant Validity, discriminant validity is the extent to which a research variable differs from
other variables. We use Cross Loading and Fornell-Larcker indicators to determine the
discriminant validity of the construct. The result of cross-loading is presented in table 4.5 below.
The value of outer loading indicators (C1, C2, C3) on the Competency construct is higher outer
loading than the cross-loading of (C1, C2 C3) on the construct of Job Satisfaction, Motivation,
Performance, Training & Development and Work Environment. It can be concluded that the
correlation of constructs is eligible for discriminant validity.
Table 2 Cross Loading Result
Competency
Job
Satisfaction
Motivation
Performance
Training &
Development
Work
Environment
C1
0.855
0.506
0.643
0.478
0.483
0.492
C2
0.708
0.341
0.454
0.372
0.360
0.682
C3
0.867
0.458
0.526
0.557
0.284
0.564
JS1
0.621
0.765
0.631
0.546
0.561
0.482
JS2
0.404
0.852
0.646
0.411
0.550
0.497
JS3
0.266
0.837
0.563
0.313
0.609
0.468
M1
0.640
0.665
0.857
0.725
0.467
0.542
M2
0.416
0.683
0.860
0.434
0.667
0.603
M3
0.631
0.470
0.720
0.272
0.490
0.540
P1
0.493
0.515
0.534
0.936
0.355
0.400
P2
0.677
0.467
0.606
0.895
0.343
0.541
P3
0.431
0.467
0.523
0.908
0.313
0.332
TD1
0.266
0.449
0.402
0.262
0.750
0.453
TD2
0.319
0.536
0.479
0.255
0.856
0.542
TD3
0.519
0.716
0.717
0.382
0.883
0.613
WE1
0.607
0.570
0.665
0.425
0.694
0.918
WE2
0.698
0.499
0.530
0.366
0.485
0.866
WE3
0.558
0.519
0.641
0.452
0.565
0.904
Source: PLS data process, 2024
The second criteria are the Fornell Larcker which compares the correlation of the variable
with the square root of the construct’s AVE. The result of the square roots of each construct’s
AVE must be greater than its highest correlations with any other constructs in the path model.
According to table 4.6 below, the construct Competency (C) has a value of 0.813 for the square
roots of its AVE which higher than all correlation values in the column Competency (C).
Construct of Job Satisfaction (JS) 0.819, Motivation (M) 0.815, Performance (P) 0.913, Training
Development (TD) 0.832, and Work Environment (WE) 0.896 are higher than all correlation
values in both rows and columns. This is evidence of discriminant validity.
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Table 3 Fornell-Larcker Criterion
Competency
Performance
Job
Satisfaction
Motivation
Training &
Development
Work
Environment
Competency
0.813
Job
Satisfaction
0.543
0.819
Motivation
0.673
0.755
0.815
Performance
0.582
0.529
0.606
0.913
Training &
Development
0.464
0.701
0.666
0.369
0.832
Work
Environment
0.691
0.592
0.686
0.463
0.653
0.896
Source: PLS data process, 2024
Structural Model Evaluation
Evaluating the structural model consists of assessing for collinearity issues (VIF), path
coefficient (β), coefficient of determination (R2), the effect sizes (f2) (F. J. Hair et al., 2014). The
coefficient of determination - R Squared (R2) measures the dependent variable’s variance in
relation to the independent variable’s change. The R2 value ranges from 0 to 1 (Table 4), with a
higher score showing higher precision levels. R2 values of 0.25, 0.5, or 0.75 for an endogenous
variable can be portrayed as weak, moderate, or substantial (J. F. Hair et al., 2011). As can be
seen in Table 4.8 the R2 of Competitive Advantage, has moderate level (0.642), and for
Improvisational Competence is weak (0.280).
Table 4 Coefficient of Determination (R2)
R square
Job Satisfaction
0.643
Performance
0.280
Source: PLS data process, 2024
The second criteria on structural model evaluation are path coefficient, which shows the
correlation between two variables, ranging from - 0.00 to 1.00. A correlation of 0 shows no
relationship (Leonnard, 2019), a correlation of 1.0 indicates a perfect positive correlation, and a
value of -1.0 shows a perfect negative correlation. As shown in Table 4.9, the effect of Motivation
(M) on Job Satisfaction (JS) showing by path coefficient (β) (0.486), indicates a medium effect.
A medium effect was also shown on the effect of Training Development to Job Satisfaction
(0.362). A strong effect was shown from Job Satisfaction to Performance (0.529). The effect of
Work Environment (WE) to Job Satisfaction (JS) showing negative weak effect (-0.022), and a
weak effect has shown on the effect of Competency (C) to Job Satisfaction (0.063).
Table 5 Path coefficient, VIF, and f2
Effect
Path
Coefficient
VIF
F
2
Training & Development → Job Satisfaction
0.362
2.115
0.174
Work Environment → Job Satisfaction
-0.022
2.711
0.000
Motivation → Job Satisfaction
0.486
2.669
0.248
Competency → Job Satisfaction
0.063
2.283
0.005
Job Satisfaction → Performance
0.529
1.000
0.389
Source: PLS data process, 2024
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The third criterion in structural model evaluation is multicollinearity. The result in Table 5
indicates of no collinearity issues because all of the VIF value below 5 (F. J. Hair et al., 2014).
The fourth criterion in structural model evaluation is the f² values, which assesses a
predictor variable’s comparative influence on an independent variable (F. J. Hair et al., 2014).
The f
2
which ranging from 0.02, 0.15, and 0.35, correspondingly, indicate small, medium, and
large effect sizes (Cohen, 2016). The results in Table 4.9 shown for the current study that the
model has a small (0.000) effect of Work Environment (WE) to Job Satisfaction (JS) and (0.005)
effect of Competency (C) to Job Satisfaction (JS). A medium f
2
is on the effect Training
Development (TD) to Job Satisfaction (JS) (0.174) and the effect of Motivation (M) to Job
Satisfaction (JS) (0.248). Large f
2
is shown for Job Satisfaction to Performance (0.389).
Hypothesis Testing
This study employed critical values for one-tailed tests: 1.65 (significance level= 5%), (F.
J. Hair et al., 2014). The hypothesis was tested using the bootstrapping test that obtains the
significance of path coefficients by calculating empirical t values, which are larger than the
critical value (t distribution values). The coefficient is considered significant at a particular
probability of error. Hair et al. (2014) recommend that the bootstrap samples are 5.000.
Hypotheses testing was carried out using the bootstrapping technique in SmartPLS4 to assess
path coefficients’ significance and t values. Using one tails t-value is 1.65, and p-value 0.05 (at α
= 5%) (F. J. Hair et al., 2014). The result is reported in Table 4.10.
Table 6 Hypothesis Testing Result
Hypothesis
Path
Coefficient
T Statistics
p-
value
Result
H1. TD → JS
0.362
3.369
0.001
H1 supported
H2. WE → JS
-0.022
0.167
0.867
H2 not supported
H3. M → JS
0.486
2.804
0.005
H3 supported
H4. C → JS
0.063
0.298
0.766
H4 not supported
H5. JS → P
0.529
5.631
0.000
H5 supported
H6a. TD → JS → P
0.192
2.784
0.005
H6a supported
H6b. WE →JS → P
-0.012
0.163
0.870
H6b not supported
H6c. M → JS → P
0.258
2.295
0.022
H6c supported
H6d. C → JS → P
0.033
0.296
0.767
H6c not supported
Source: PLS data process, 2024
Discussion on Findings
a. The effect of Training Development (TD) on Job Satisfaction (JS)
In the H1 hypothesis, it is said that Training Development (TD) has a significant positive
effect on Job Satisfaction. This can be seen from the T-Value which showed a value of 3.369
which was greater than the T-Table 1.65. The effect given by the training development variable
was positive, which means that if the training development variable increased, the job satisfaction
variable would also increase. Training Development has a 2
nd
impact to job satisfaction after
Motication. Therefore, in theory, better training and development opportunity is associated with
more job satisfaction and furthermore has a significant impact on it (Paposa & Kumar, 2019).
Training contains aspects of knowledge, skills and abilities of personality characteristics that
affect job satisfaction. Employee who has high work skills from the results of finding of this
study support research of Sumarni et al. (2021) and Ullah et al. (2020), where the findings from
the study indicate that there is a strong correlation between training and job satisfaction.
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b. The effect of Work Environment (WE) on Job Satisfaction (JS)
In the H2 hypothesis, based on the test of the work environment variable on job
satisfaction, a P-value of 0.867 > 0, it can be concluded that the work environment has a negative
and insignificant effect on job satisfaction, so H2 is rejected. Thus, it is not in line with previous
research conducted by Kharisma & Rosia (2022) concluded that work environment affects job
satisfaction. Based on the findings of this study and previous research, the researcher argues that
work environment do not correlated on job satisfaction (Agbozo, 2017). From here work comfort
caused by the work environment can also affect the satisfaction of workers. A comfortable work
environment will certainly affect the comfort of employees completing their work. From the
respondent age profile, the majority age of respondents is between 41 – 50 years old and 31 – 40
years old. And also respondents work between more than 6 years is 62.7%. This mean that
respondent already working for a long time in one job. They need different ambient for working
so management needs to make job rotations for the employee, so it can increase a conducive
workplace environmental then employee will be encouraged to work with the better.
c. The effect of Motivation (M) on Job Satisfaction (JS)
In the H3 hypothesis, it was found that the results of the analysis supported the hypothesis
that the Motivation had a significant positive effect on job satisfaction. This can be seen from the
T-Value which showed a value of 2.804 which was greater than the T-Table 1.65. The effect given
by the motivation variable was positive, which means that if the motivation variable increased,
the job satisfaction variable would also increase. The motivation has the most effect on employee
job satisfaction (Nurjuha et al., 2022). Work is improved, so job satisfaction variables will also
increase. The aspect of motivation, desire to complete the work and obedience the rules, company
pay attention with education and promotion and desire working better than before, give a positive
response to aspects of motivation. The result of this study is in accordance with the supporting
theories, while in this study there are significant similarities in results with previous research
conducted (Natsir & D’Ornay, 2022; Ng et al., 2023; Sinaga et al., 2023).
d. The effect of Competency (C) on Job Satisfaction (JS)
Based on the results H4 of the analysis presented in this study, it is known that the
competency variable has a t-statistic value (0.298) that is smaller than the t-table 1.65 and a p-
value greater than 5% (0.050 ≥ 0.05), so that the H4 hypothesis is rejected, which indicates that
it does not exist the influence of competence on employee job satisfaction. The results of testing
this hypothesis indicate that when employees work according to their competence, it will increase
their sense of satisfaction at work, but on the other hand, employees feel not challenged because
the work they are doing is considered too easy for them.
Based on the findings of this study, the researcher argues that competent employees and
being able to apply them to the business world will give them a sense of satisfaction, where
employees will feel that the characteristics that exist in them can be applied or applied to the
world of work so that employees will have a sense of to look for new experiences by trying to do
experiments that are considered better than what they currently have.
e. The effect of Job Satisfaction (JS) on Performance (P)
On the results of testing H5 hypothesis, it was found that the results of the analysis
supported the hypothesis that job satisfaction had a significant positive effect on performance.
This can be seen from the T-Value which showed a value of 5.631 which was greater than the T-
Table 1.65. The effect given by the job satisfaction variable was positive, which means if the job
satisfaction variable increased then the performance variable would also increase.
f. The effect of Training Development (TD), Work Environment (WE), Motivation (M),
Competency (C) on Performance (P), with mediating Job Satisfaction (JS) on the results of
testing H6 hypothesis, Job Satisfaction as a mediating variable can strengthen the effect of
The Impact of Training Development, Work Environment, Motivation and Competency on Employee
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146 Return: Study of Management, Economic And Business, Vol 3 (2), February 2024
the Training Development on Performance. To find out the direct effect between the training
development and job satisfaction. The test results can be concluded that the hypothesis H6a
which states that the training development has a significant effect on performance through
job satisfaction. Because of Training Development has a significant positive effect on job
satisfaction and job satisfaction has a significant positive effect on performance. As a
mediating variable, job satisfaction strengthens the influence of the training development on
performance, this is evidenced by adding up the direct effect of the training development on
job satisfaction with the direct influence of the training on performance.
It is the same with Job Satisfaction as a mediating variable the effect of the motivation on
performance. The test results can be concluded that the Hypothesis H6c which states that the
motivation has significant effects on performance through job satisfaction. Motivation has a
significant positive effect on job satisfaction and job satisfaction has a significant positive effect
on performance. As a mediating variable, job satisfaction strengthens the influence of motivation
on performance.
Meanwhile work environment has a negative and insignificant effect on job satisfaction
and job satisfaction as mediating variable on performance. This means that job satisfaction does
not mediate of work environment on performance, employees can not be satisfied also with their
workplace environment.
And also competency has a negative and insignificant effect on job satisfaction and job
satisfaction as mediating variable on performance. This means that job satisfaction does not
mediate of competency on performance, employees cannot be satisfied also with their
competency.
CONCLUSION
The result of the research questions as follows: 1) Training Development has positive effect on Job
satisfaction at employee PT. Adhi Karya DPKA, it can be considered that good training development will
create high job satisfaction; 2) Work Environment has no significant effect on job satisfaction at employee
PT. Adhi Karya DPKA; 3) Motivation has positive effect on job satisfaction at employee PT. Adhi Karya
DPKA, in can be said that high motivation can support high job satisfaction; 4) Competency have no
significant effect on job satisfaction of employee PT. Adhi Karya DPKA; 5) Job satisfaction has positive
effect on Performance at employee PT. Adhi Karya DPKA, it can be considered that employee who are
satisfied with their work will be able to show better performance in accordance with the needs of
organization; 6) Job satisfaction has mediating effect of training development and motivation on
performance, and job satisfaction has no mediating effect of work environment and competency on
performance.
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