Effect of Quality Management System and Employability Skills

American Journal of Humanities and Social Sciences Research (AJHSSR) 2023
A J H S S R J o u r n a l P a g e | 1
American Journal of Humanities and Social Sciences Research (AJHSSR)
e-ISSN : 2378-703X
Volume-07, Issue-06, pp-01-10
www.ajhssr.com
Research Paper Open Access
Effect of Quality Management System and Employability Skills
Erwin Susilo1
, Sri Mintarti1
, Irsan Tricahyadinata1*
Faculty of Economics and Business, Mulawarman University, Samarinda.
ABSTRACT : This study aims to determine the effect of quality management system and
employability skills on successful change management and organizational performance at coal mining
and construction companies in Indonesia. The sampling technique used is the survey method with a
questionnaire that is a sample of 118 managers and professional staff from the entire population of
170 people with a tenure ofmore than 5 years. This study used a quantitative - descriptive approach
and the method of hypothesis testing analysis using the SEM-PLS analysis tool. Based on the results
of calculations and data analysis, it is obtained that; l) Quality management system has a direct
positive and significant effect on successful of change management, 2) Employability skills directly
have a positive but not significant effect on successful ofchange management, 3) Quality management
system, employability skills and successful of change management directly have a positive and
significant effect on organizational performance, 4) Quality management system has a positive and
significant indirect effect on organizational performance through change management. 5)
Employability skills indirectly have a positive but not significant effect on organizational performance
through change management.
KEY WORDS: Quality Management System; Employability Skills; Change Management;
Organizational Performance
I. INTRODUCTION
The era of globalization with all its obstacles and challenges has had its own impact on the
economy at all levels of society and the business world, especially the coal mining and construction
industry in Indonesia. Currently, the national industrial sector is preparing to face challenges, the
presence of the Industrial Revolution 4.0. The mining industry sector which of course also feels a
considerable influence. The mining industry in entering the 4.0 industrial revolution faces at least four
new challenges. The challenges faced include Greenfield Exploration, increasing the added value of
minerals, increasing the added value of coal and also the transformation of mining 4.0.
These four challenges certainly add pressure on mining companies. Moreover, global
conditions are currently in uncertainty due to the Covid-19 pandemic outbreak: reported by
"duniatambang.co.id" (Fernando, 2020). Not to mention other obstacles due to the decline in world
coal prices which affected the decline in coal prices in Indonesia, this increased the competition that
occurred in the coal mining contractor industry in Indonesia. From this situation and conditions,
several small-scale coal mining companies are expected to go bankrupt or close down, following the
provision of coal selling prices for power plants set through the Decree of the Minister of Energy and
Mineral Resources (ESDM), which is below the cost of production. For large companies,
cumulatively there may be no loss, only a reduced profit margin because it is covered by export
revenues: reported by "economy.okezone.com" (Sindo, 2018). The unstable trend of coal price
movements is still a stumbling block for heavy equipment industry players. This in turn is expected to
affect demand and production of heavy equipment for the next year: reported by "kontan.co.id"
(Julian, 2020).

A Quality Management System (QMS) uses a quality assurance approach that focuses on
providing confidence that quality requirements will be met (ISO 9000, 2015). ISO standards are also
known to improve organizational performance, and are one of the most successful management
system standards in the world. Srivastav (2010) and Ochieng et al. (2015) have evaluated the impact
of ISO standards on the social and business performance of organizations. (Yadav et al., 2020).
Companies implement quality management systems as a satisfactory alternative in their efforts to
improve organizational performance. (Al-Dhaafri et al., 2016); (Corredor & Goñi, 2011);
(Kafetzopoulos et al., 2015); (Meftah Abusa & Gibson, 2013); (Miyagawa & Yoshida, 2010). A study
shows that the quality management system has a positive impact on company performance, including
the areas of cost, reliability, quality, innovation, efficiency, and business effectiveness. (Addae-
Korankye, 2013).. In addition, the implementation of QMS leads companies to change their business
behavior. According to other studies, it confirms that the implementation of a quality management
system involves strategic planning and resources that are aligned with supporting strategies (Alidrisi
& Mohamed, 2013). (Alidrisi & Mohamed, 2012)..
The implementation of Quality Management System (QMS) does not fully guarantee the
overall business performance of the company. For example, a recent study of 148 manufacturing
companies in China provides evidence that quality certification cannot guarantee a company’s
competitive advantage. It also enlightens managers regarding the existence of barriers from quality
management systems to business performance (Liu et al., 2020). Another study showed that there are
two groups of barriers, one has high driving force and low dependency that requires maximum
attention and strategic importance (such as lack of top management commitment, lack of
interdepartmental coordination) and the other has high dependency and low driving force and its
resultant effects (such as high turnover rate at management level, lack of continuous improvement
culture, employee resistance to change), (Talib et al., 2011). QMS has practical implications in the
proposed framework for improving firm performance so more cases in other economic sectors should
be analyzed, (Pereira et al., 2018).
Economic and business changes that are increasingly dynamic in the era of globalization make
these changes must be balanced with the readiness of the company in managing changes in all fields
in the company environment. An ancient Greek philosopher named Heraclitus once said that in this
world there is nothing permanent, except change. This statement is still proven by the fact that in the
current globalization period changes occur so quickly and continuously.
According to previous research (Beer & Nohria, 2000); (Kotler, 1997), 70% of all major
change projects fail to fulfill their original proposals. A study by (Buckingham et al., 2009) that
surveyed over 1,500 change practitioners found that 59% of change projects failed or were
problematic. Many studies over the past few years (Burnes, 2004)has reported similar results of poor
success rates of organizational change initiatives.
At its core, change management is the act of proactively managing change and minimizing
resistance to organizational change through a series of structured processes or approaches to transition
employees, teams, and/or the entire organization to a desired future state in accordance with the global
changes taking place. Unfortunately, although change management is a mature discipline in many
ways, organizations continue to struggle with effective change to improve the effectiveness of
organizational performance through its human resources.
II. METHODS
This research uses a quantitative approach, then two research methods are chosen, namely
descriptive and hypothesis testing. Where in this study is an organization or company, descriptive
research can describe the characteristics of respondents such as age, gender, tenure and various other
characteristics to be studied, including the results of the description of respondents' answers to the
questionnaires distributed. The measuring instrument used for data collection in this study is a
structured questionnaire or questionnaire with closed questions in the form of a rating scale.
The data in this study are internal and external data. Internal data was obtained from staffing
data and questionnaire scores obtained through distributing questionnaires to respondents. External
data was obtained through various external reports of the organization, including various publication
reports on topics similar to this research.

In this study, the population is the leaders or managers and professional staff of coal mining
and construction companies from active customers of PT Altrak 1978 including managers and
professional staff from PT Altrak 1978, totaling 170 people with a work period of 5 years.
The types of individual respondents who have been directed and determined to be researched
are as follows:
Position as a company leader or manager and professional staff in the field of mining and construction
company operation management; and
Must have 5 years of service.
The following is the sample size of a certain population developed from Isaac and Michael in
(Sugiyono, 2016), for an error rate of 1%, 5%, and 10%. The formula for calculating the sample size
of a known population is as follows:
𝑆 =
𝜆². 𝑁. 𝑃. 𝑄
𝑑2 𝑁 − 1 + 𝜆2 𝑃. 𝑄
Where:
λ² with dk = 1, the error rate can be 1%, 5%, 10%.
P = Q = 0.5
d = 0,05
s = number of samples
𝑆𝑎𝑚𝑝𝑒𝑙 =
3,841 𝑥 170 𝑥 0,5 𝑥 0,5
0,0025 𝑥 170 − 1 + 3,841 𝑥 0,5 𝑥 0,5
=
163,24
1,38
= 118,06
Using Isaac and Michael's calculation above, if the error rate is 5%, the sample size in this study
is 118 people from a population of 170 managers and professional staff in coal mining and
construction companies at PT Altrak 1978 and its customers in Indonesia.
In this study, data analysis used the Partial Least Square (PLS) approach. PLS (Partial Least
Square) is used to estimate partial least squares of regression models or known as projections on latent
structures. PLS is a predictive technique that is an alternative to Ordinary Least Square (OLS)
regression, or structural equation modeling (SEM).
Table 1. Results of Validity Testing of Research Instruments
Variables Item Correlation
Coefficient
Ket.
Quality Management System (QMS) 30 QMSI.I 0.735 Valid
30 QMS1.2 0.738 Valid
30 QMS2.1 0.763 Valid
30 QMS2.2 0.781 Valid
30 QMS3.1 0.867 Valid
30 QMS3.2 0.801 Valid
30 QMS4.1 0.839 Valid
30 QMS4.2 0.638 Valid
30 QMS5.1 0.629 Valid
30 QMS5.2 0.526 Valid
Employability Skills (ES) 30 ESLI 0.822 Valid
30 ESI.2 0.851 Valid
30 ES2.1 0.738 Valid
30 ES2.2 0.807 Valid
30 ES3.1 0.888 Valid
30 ES3.2 0.917 Valid
30 ES4.1 0.837 Valid

Variables Item Correlation
Coefficient
Ket.
30 ES4.2 0.830 Valid
30 ES5.1 0.729 Valid
30 ES5.2 0.714 Valid
Succesfull Change Management
(SCM)
30 SCMI.I 0.901 Valid
30 SCM1.2 0.919 Valid
30 SCM1.3 0.943 Valid
30 SCM2.1 0.951 Valid
30 SCM2.2 0.889 Valid
30 SCM3.1 0.764 Valid
30 SCM3.2 0.924 Valid
30 SCM4.1 0.761 Valid
30 SCM4.2 0.835 Valid
30 SCM4.3 0.875 Valid
Organizational Performance 30 OPI .1 0.715 Valid
30 OPI .2 0.737 Valid
30 OPI .3 0.827 Valid
30 OPI .4 0.643 Valid
30 OPI .5 0.785 Valid
30 OP2.1 0.692 Valid
30 OP2.2 0.788 Valid
30 OP2.3 0.768 Valid
30 OP2.4 0.719 Valid
30 OP2.5 0.662 Valid
30 OP3.1 0.806 Valid
30 OP3.2 0.709 Valid
30 OP3.3 0.760 Valid
30 OP3.4 0.700 Valid
30 OP3.5 0.659 Valid
30 OP4.1 0.716 Valid
30 OP4.2 0.652 Valid
30 OP4.3 0.751 Valid
30 0.854 Valid
30 OP4.5 0.765 Valid
30 OP5.1 0.787 Valid
30 OP5.2 0.532 Valid
30 OP5.3 0.742 Valid
30 OP5.4 0.831 Valid
30 OP5.5 0.666 Valid
Based on the table above, it can be seen that the latent variable indicators consisting of Quality
Management System, Employability Skills, Successful Change Management, and Organizational
Performance have met the test criteria with a value of r> 0.30 and a significance value of r colleration
< than 95% or a = ().05 which can be said that the research instrument is valid.
Table 2. Results of Research Instrument Reliability Testing
Variables Cronbach's Alpha Ket
Quality Management System 0,904 Reliable

Employability Skills 0,943 Reliable
Succesfull Change Management 0,966 Reliable
Organizational Performance 0,964 Reliable
Based on the table above, it can be seen that the reliability coefficient value of all research
instruments is> 0.60, it can be concluded that this research instrument is reliable.
III. RESULTS AND DISCUSSION
Outer Model Evaluation
The results of estimating the structural model with the entire PLS Algorithm estimation method
show the value of the path coefficient, namely through the t-statistic test (> 1.96) and p value (<0.05)
between construct variables,
Discriminant Validity
Discriminant validity is intended to test that a construct precisely measures only the construct to
be measured, not other constructs. The method of testing discriminant validity can use the Fornell
Larcker Criterion approach which is the root value of the AVE. If the square root value of the AVE of
each construct is greater than the correlation value between constructs and other constructs in the
model, then the model is said to have good discriminant validity value. (Fornell & Larcker, 1981) in
(Wong, 2013).
Table 3. Fornell Larcker Criterion Test Results
Item, Indicator QMS (Xl) ES (X2) SCM (VI) OP (Y2)
QMS (Xl) 0.722
ES (X2) 0.739 0.727
SCM (VI) 0.679 0.601 0.750
OP (Y2) 0.691 0.691 0.739 0.711
QMSI 0.752 0.475 0.589 0.495
QMS2 0.660 0.455 0.409 0.463
QMS3 0.866 0.710 0.540 0.601
QMS4 0.866 0.650 0.589 0.611
ESI 0.550 0.808 0.431 0.488
ES2 0.572 0.782 0.534 0.637
ES3 0.633 0.854 0.433 0.492
ES4 0.695 0.827 0.550 0.594
ES5 0.536 0.780 0.491 0.599
SCMI 0.585 443 0.873 0.661
SCM2 0.600 0.531 0.886 0.656
SCM3 0.559 0.545 0.839 0.572
SCM4 0.613 0.587 0.883 0.665
OPI 0.701 0.665 0.661 0.876
OP2 0.597 0.574 0.695 0.843
OP3 0.548 0.582 0.617 0.850
OP4 0.606 0.597 0.666 0.914
OPS 0.547 0.579 0.566 0.838
Based on the table above, all the roots of the AVE (Fornell-Larcker Criterion) for each
construct are greater than the correlation with other variables.
Likewise with other latent variables, where the AVE Root value> Correlation with other
constructs. Because all latent variables AVE Root value> Correlation with other constructs, the
discriminant validity requirements in this model have been met, as listed in the table above.
The second assessment is through Average Variance Extracted (AVE). The convergent validity
of a construct with reflective indicators is evaluated by Average Variance Extracted (AVE). The AVE
value should be equal to ().5 or more.

An AVE value of 0.5 or more means that the construct can explain 50% or more of its item
variance.
Table 4. AVE and Root AVE
Latent Variable Average Variance Extracted (AVE) Root (AVE)
Quality Management System (QMS) 0.522 0.722
Employability Skills (ES) 0.529 0.727
Successful Change Management (SCM) 0.562 0.750
Organizational Performance (OP) 0.505 0.711
And based on the Average Variance Extracted (AVE) value to determine the achievement of
convergent validity requirements, all constructs have achieved convergent validity requirements
because the AVE values are all> 0.50. For example, the AVE of the QMS latent variable is 0.522>
0.50, so the QMS latent variable is convergently valid. Likewise with other variables where the value
is> 0.5 so that all of them are valid.
Construct Reliability
Construct Reliability, measures the reliability of latent variable constructs. The value that is
considered reliable must be > 0.70. Construct reliability is the same as Cronbach alpha.
Table 5. Reliability
Latent Variable Cronbach's Alpha Composite Reliability Ket.
Quality Management System (QMS) 0.845 0.883 Reliable
Employability Skills (ES) 0.900 0.918 Reliable
Successful Change Management (SCM) 0.913 0.928 Reliable
Organizational Performance (OP) 0.951 0.955 Reliable
Based on the table above, it can be seen that all constructs have a Cronbach's alpha value of >
0.7, so it can be said that all constructs are reliable. For example, Cronbach's alpha of the latent
variable QMS (XI) is 0.845> 0.7, so the latent variable QMS (XI) is reliable. Likewise with other
variables where the value is> 0.7 so that everything is reliable.
R-Square (R2) Value
R2 values of 0.75, 0.50, and 0.25 indicate that the model is strong, moderate, and weak
(Sarstedt et al., 2017). Meanwhile, Chin provides R2 value criteria of 0.67, 0.33 and 0.19 as strong,
moderate, and weak (Chin, 1998 in Ghozali and Latan, 2015).
While R2 Ajdusted is the corrected R2 value based on the standard error value. The Adjusted
R2 value provides a stronger picture than R2 in assessing the ability of an exogenous construct to
explain endogenous constructs.
Table 6.2
R Square R Square Adjusted
SCM (VI) 0.483 0.474
OP (Y2) 0.652 0.643
Based on the results of the coefficient of determination analysis above, it can be concluded as
follows:
The R2
value of the joint or simultaneous influence of XI and X2 on Yl is 0.483 with an Adjusted R2
value of 0.474. So, it can be explained that all exogenous constructs (XI and X2) simultaneously
affect Y 1 by 0.474 or 47%. Because the R2
Adjusted value is > 33% but < 67%, the influence of all
exogenous constructs XI and X2 on Yl is moderate; and
The R2
value of the joint or simultaneous influence of XI, X2 and Y 1 on Y2 is 0.652 with an
Adjusted R2
value of 0.643. So, it can be explained that all exogenous constructs (XI, X2 and Y1)
simultaneously affect Y2 by 0.643 or 64%. Because R2
Adjusted R Square > 33% but < 67%, the
influence of all exogenous constructs XI, X2 and Yl on Y2 is moderate.

Q-Square (Q2) Value and Q2 Predictive Relevance
Based on the R2 value contained in the table above, the Q2 predictive relevance value using the
Stone-Geisser Q Square Test formula is as follows:
1-0.483) (1-0.652)
Q2 = 1-(0.517) ( 0.348) 1-0.180
Q2 = 0.820 = 82%
The results of the calculation of Q2
predictive relevance in this study amounted to 0.820 or
82%, thus it can be concluded that the model in this study has a relevant predictive value, where the
model used can explain the information in the research data by 82%.
The following is the Q value2
on the dependent variable (endogenous) through the calculation
of Blindfolding SmartsPLS 3.2.9, which can be seen in the table below:
Table 7. Predictive Relevance Q2
Endo en Variable Q2
(-1 -
SSE/SSO)
SCM (Yl) 0.265
OP (Y2) 0.322
Based on the data presented in the table above, it can be seen that the Q2 value for each
dependent variable (endogenous) is 0.265 for Yl and 0.322 for Y2. By looking at this value, it can be
concluded that this study has a good observation value because the Q2 value> 0 (zero), namely 0.265
& 0.322 (Chin, 1998).
Hypothesis Testing of Direct Influence
Hypothesis testing of the direct effect between exogenous and endogenous variables can be
seen in the test results between research variables in addition to being shown by the path coefficient
and t-statistics and P-value, which can also be seen in the PLS Algorithm and Bootstrapping path
diagram.
Table 8. Direct Effect
Variables
Coef.
Path
T Stat. P Values
Effect Description
Exogen
ous
Endogeno
us
> 1.96 < 0.05
QMS
SCM (VI) 0.517 5.201 0.000 Positive Significant
OP (Y2) 0.167 2.194 0.029 Positive Significant
ES (X2)
SCM (VI) 0.219 1.892 0.059 Positive Not Significant
OP (Y2) 0.300 3.666 0.000 Positive Significant
SCM OP (Y2) 5.694 0.000 Positive Significant
Based on the analysis of the path coefficient parameters, t-statistic testing, and p-value, it shows
that there are four path coefficients that have a significant effect and there is one path coefficient that
has an insignificant effect between the research variables. Hypothesis test parameters use a
comparison of t values, namely if the t-statistic value is> t table (1.96) or P-value (<0.05), then HO is
rejected and HI is accepted. The results of hypothesis testing are further explained as follows:
The Effect of Quality Management System on Successful Change Management
The parameter coefficient for the QMS (Xl) variable on SCM (Y1) is 0.517, which means that
there is a positive influence of QMS (X 1) on SCM (Y1). Or it can be interpreted that the higher the
value of QMS (X 1), the more SCM (Y l) will increase. An increase of one unit of QMS (X l) will
increase SCM (Y 1) by 51.7%. Based on the calculation using bootstrap or resampling, where the t-
statistic value is 5.201 > 1.96 and the P-value is 0.000 < 0.05 so that HI is accepted or which means
that the direct effect of QMS (Xl) on SCM (Y1) is meaningful or statistically significant.
Effect of Quality Management System on Organizational Performance
The parameter coefficient for the QMS variable (X 1) on OP (Y2) is 0.167, which means that
there is a positive influence of QMS (X 1) on OP (Y2). Or it can be interpreted that the higher the
value of QMS (X l), the more OP (Y2) will increase. An increase of one unit of QMS (X 1) will

increase OP (Y2) by 16.7%. Based on calculations using bootstrap or resampling, where the t-statistic
value is 2.194 > 1.96 and the P-value is 0.029 < 0.05 so that HI is accepted or which means that the
direct effect of QMS (XI) on OP (Y2) is meaningful or statistically significant.
The Effect of Employability Skills on Successful Change Management
The parameter coefficient for the ES (X2) variable on SCM (Y 1) is 0.219, which means that
there is a positive influence of ES (X2) on SCM (Y l). Or it can be interpreted that the higher the
value of ES (X2), the more SCM (Y 1) will increase. An increase of one unit of ES (X2) will increase
SCM (Y 1) by 21.9%. Based on calculations using bootstrap or resampling, where the t-statistic value
is 1.892 < 1.96 and the P-value is 0.059 > 0.05 so that HO is accepted (HI is rejected) or which means
that the direct effect of ES (X2) on SCM (Y l) is not meaningful or statistically significant.
Effect of Employability Skills on Organizational Performance
The parameter coefficient for the ES (X2) variable on OP (Y2) is 0.300, which means that there
is a positive influence of ES (X2) on OP (Y2). Or it can be interpreted that the higher the value of ES
(X2), the more OP (Y2) will increase. An increase of one unit of ES (X2) will increase OP (Y2) by
30%. Based on calculations using bootstrap or resampling, where the t-statistic value is 3.666> 1.96
and the P-value is 0.000 <0.05 so that HI is accepted or which means that the direct effect of ES (X2)
on OP (Y2) is meaningful or statistically significant.
The Effect of Successful Change Management on Organizational Performance
The parameter coefficient for the SCM (Y 1) variable on OP (Y2) is 0.445, which means that
there is a positive influence of SCM (Y 1) on OP (Y2). Or it can be interpreted that the higher the
value of SCM (Y 1), the more OP (Y2) will increase. An increase of one unit of SCM (Y 1) will
increase OP (Y2) by 44.5%. Based on calculations with
using bootstrap or resampling, where the t-statistic value is 5.694 > 1.96 and the P-value is 0.000 <
0.05 so that HI is accepted or which means that the direct effect of SCM (Y 1) on OP (Y2) is
meaningful or statistically significant.
Results of Indirect Effect Analysis (Mediation)
Indirect hypothesis testing (mediation) can be seen in the table that has been presented. Testing
indirect effects (mediation) aims to detect the position of the mediating variable in the model.
Mediation testing is carried out to determine the nature of the relationship between variables either as
a complete mediation variable, partial mediation and not a mediating variable.
The results of testing the indirect effect between research variables are shown by the path
coefficient and t-statistic and P-value, which can be seen as follows:
Table 9: Indirect Effect Table
Variables
Coef. Path
T Stat P Value
Ket.
Ekso en Mediation Endo en 1.96 0.05
QMS (X2) SCM (YD OP (Y2) 0.230 4.478 0.000 Significant
ES (X2) SCM (YD OP (Y2) 0.097 1.578 0.115 Not Significant
Hypothesis testing parameters use a comparison of t values, namely the t-count value> from
the t-table (1.96), and significance or P-value <0.05.
The results of the analysis of the Quality Management System and Employability Skills on
Organizational Performance through Successful Change Management can be seen from the results of
the path coefficient analysis of each variable in table 9 The results of testing indirect effects can be
explained as follows:
Indirect Effect of QMS on OP through SCM
The indirect effect of QMS variables on OP through SCM can be seen from the Path
Coefficient value of 0.230, which means that there is a positive indirect effect or it can be interpreted
that the higher the value of QMS (X l), the more OP (Y2) through SCM (Y l) will increase. An
increase of one unit of QMS (X 1) will increase OP (Y2) through SCM (Y 1) by 23%. The t-Statistic
test result is 4.478> 1.96 and the P-value or significance with a value of 0.000 < 0.05. These results
indicate that QMS (X 1) on OP (Y2) through SCM (Y l) has a positive and significant effect. This

means that the Successful Change Management / SCM (Y 1) variable can mediate the Quality
Management System / QMS (X l) on Organizational Performance / OP (Y2).
Based on the results in table 8 (direct effect) and table 9 (indirect effect), it is known that the
value of the direct effect of QMS on OP is 0.167 or 16.7% and the result of the indirect effect of
QMS through SCM on OP is 0.230 or 23%. From the comparison of the value of direct influence
and indirect influence, the result is that the value of direct influence is smaller (K) than the value of
indirect influence, this result shows that the SCM variable (Y1) strengthens the relationship between
QMS (Xl) and OP (Y2) in coal mining and construction companies in Indonesia.
Indirect Effect of ES on OP Through SCM
The indirect effect of the ES (X2) variable on OP (Y2) through SCM (Y l) can be seen from the
Path Coefficient value of 0.097, which means that there is a positive indirect effect or it can be
interpreted that the higher the value of ES (X2), the more OP (Y2) through SCM (Y 1) will increase.
An increase of one unit of ES (X2) will increase OP (Y2) through SCM (Y l) by 9.7%. The t-Statistic
test result is 1.578 < 1.96 and Pvalue or significance with a value of 0.115 > 0.05. These results
indicate that ES (X2) on OP (Y2) through SCM (Y 1) has a positive but insignificant effect. This
means that the Successful Change Management / SCM (Y1) variable cannot mediate the Quality
Management System / QMS (X1) on Organizational Performance / OP (Y2).
Based on the results in the direct effect table and the indirect effect table, it is known that the
value of the direct effect of ES on OP is 0.300 or 30% and the result of the indirect effect of QMS
through SCM on OP is 0.097 or 9.7%. From the comparison of the value of direct influence and
indirect influence, the result is that the value of direct influence is greater (Y) than the value of
indirect influence, this result indicates that the SCM variable (Y1) weakens the relationship of ES
(X2) to OP (Y2) in coal mining and construction companies in Indonesia.
IV. CONCLUSION
Based on the results of the analysis and hypothesis testing and discussion, several conclusions
can be presented, namely as follows:
The results of this study indicate that the quality management system implemented and
employability skills possessed by employees in coal mining and construction companies in Indonesia
have a very strong impact on improving company performance. Furthermore, the change management
program is one of the successful programs in strengthening the influence of the quality management
system on company performance and directly has a very good impact on company performance. Coal
mining & construction companies have been successful with their change management programs, so
that until now these companies can contribute very well to the economy in Indonesia.
Some of the most important strategies in implementing a quality management system to support
the success of change management programs and improve organizational performance in coal &
construction companies in Indonesia are customer focus and top management leadership, such as: 1)
the company has an established mechanism for feedback from customers and customers know about
it; 2) there are frequent meetings with customers so that the relationship with customers will be more
harmonious; 3) top management support for its human resources, especially in quality problem
solving training; 4) top management periodically & continuously assesses quality performance.
Human Resources is one of the important factors in increasing effective and efficient
organizational performance in coal mining and construction companies in Indonesia, one of which is
human resources who have employability skills. The following types of employability skills affect
organizational performance from the highest to the lowest score, namely: 1) Teamwork, 2) Personal
values, 3) Problem solving skills, 4) Communication and information skills, and 5) Leadership skills.
Employability skills refer to attitudinal, and behavioral skills, in addition to technical abilities, to
enable a person to engage and advance in the demands of an ever-changing work environment and
remain as an asset to the employer or company.

LITERATURE
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