The Effect of Transformational Leadership Style and Change Management on
the Performance of the Continuous Improvement Program
Afif
Muliana1*, Laura Lahindah2
Sekolah Tinggi Ilmu Ekonomi Harapan Bangsa,
Indonesia
Emails: mm-23104@students.ithb.ac.id1, laura@ithb.ac.id2
|
Abstract |
Continuous
Improvement Programs (CIP) are pivotal in enhancing operational efficiency
and fostering innovation across industries. At PT PSG South Sumatra, the
hydrate cleanup program, a key CIP initiative, addresses refinery hydrate
challenges and aims to increase LPG supply. This research investigates the
influence of transformational leadership and change management on the
performance of CIP at PT PSG. A quantitative approach was adopted, employing
multiple regression analysis to evaluate the effects of these variables. Data
were collected through questionnaires completed by 106 respondents and
analyzed using SPSS software version 25. The results indicate that
transformational leadership significantly impacts CIP performance, with a tcalculated value of 4.052 (ttable
= 1.983) and a p-value of 0.001 (α < 0.05). Similarly, change
management also exerts a significant positive effect, with a tcalculated value of 6.231 and a p-value of 0.001. The
combined effect of these variables shows a strong correlation (r = 0.823) and
explains 67% of the variance in CIP performance (R˛ = 0.677). These findings
emphasize the critical role of visionary leadership and adaptive change
management in driving the success and sustainability of CIP initiatives. PT
PSG is encouraged to strengthen these aspects to achieve greater operational
performance and innovation. This research contributes to the understanding of
how leadership and change management practices can optimize continuous
improvement efforts in industrial settings. |
|
Keywords: |
transformational leadership,
change management, continuous improvement program, hydrate. |
Introduction
Continuous Improvement Program, a
philosophy that emphasizes the need for gradual and consistent progress in
business processes, is integral to maintaining competitiveness in today's
dynamic marketplace.
The company's commitment to
supporting the national energy program and reducing dependence on fuel
subsidies is commendable.
PT PSG, a joint-venture
company between Pertagas and Samtan Co., Ltd. is actively engaged in continuous
improvement programs (CIP) to improve operational efficiency and innovation.
These programs are designed to foster creativity and problem solving among
employees, encouraging them to explore and exploit every possibility to improve
business processes and results. For
example, in 2021, PT PSG (Pertagas), organized its
11th CIP event with the theme "Creativity in You, Explore & Exploit
Every Possibilities". This
event resulted in significant value creation, amounting to IDR 401 billion
The program encourages
employees to develop innovative solutions to everyday challenges, leading to
increased productivity and profitability.
In summary, the continuous
improvement program at PT PSG is a multifaceted endeavor. It requires a
concerted effort from all levels of the organization, with leadership playing a
critical role. By fostering a culture of excellence, investing in technology,
and maintaining a forward-looking approach, the company can ensure the
stability of domestic LPG stocks and contribute significantly to the national
energy landscape.
As the business environment
becomes increasingly complex, the principles of continuous improvement will
remain a guideline for companies like PT PSG, enabling them to navigate the
challenges and seize the opportunities that lie ahead.
The volume of LPG consumption
in the Indonesian household sector during the period 2012-2022 showed an
increasing trend, in line with the kerosene to LPG conversion policy. In 2012,
consumption began to increase, then continued to jump significantly in 2013-2015,
driven by government policies that expanded the distribution of subsidized 3 kg
LPG to new areas. The peak of the consumption surge occurred in 2016 as prices
stabilized and subsidized LPG users increased. Although distribution control
policies were implemented in 2017-2019, consumption continued to grow. During
the COVID-19 pandemic in 2020, LPG consumption increased, driven by home
cooking habits. The high consumption trend continued in 2021 and peaked in
2022, influenced by economic recovery and increased household energy needs.
The continuous increase in
demand causes Indonesia to import LPG from abroad. LPG imports can increase the
risk of LPG shortages and LPG price fluctuations. Therefore, it is important to
increase domestic LPG production capacity by building new LPG refineries and
improving the operating efficiency of existing LPG refineries.
PT PSG was established on May
7, 2008 with the aim of producing LPG to support the government's program in
providing energy for the community, while reducing the government's burden in
fuel subsidies. PT PSG is a subsidiary of PT Pertamina Gas (Pertagas) and
Samtan Co. Ltd. PT PSG has
two LPG processing plants in South Sumatra, namely an extraction plant in
Prabumulih which began construction in 2010 and a fractionation plant in Sungai
Gerong which was built in 2011. The two plants started commercial operations in
May 2013 and are able to contribute to the national LPG supply.
Hydrates, a problem
encountered in gas refineries, are solid crystals formed from a mixture of
water and gas at low temperatures and pressures.
For PT PSG alone, a full day
of refinery stoppage is estimated to result in a loss of around 600 MT of LPG.
We can imagine the burden the country will bear to meet the needs of that much
LPG stock. Based on industry selling prices alone, PT PSG is estimated to lose
up to 14 billion rupiah in revenue over a period of several years. This
requires the hydrate cleaning program to be as effective as possible so that
the frequency of cleaning can be reduced.
The success of CIP
implementation depends heavily on this factor. The role of leadership is one of
the factors that influence the program. Effective leadership in determining
every decision is very influential in determining the chosen business strategy,
and ensuring that the CIP runs according to the goals set by PT PSG.
PT PSG has experienced
significant regulatory changes, particularly in the production sector,
coinciding with each leadership transition. Since 2015, the company has
implemented several updates to its Continuous Improvement Program focusing on
refinery hydrate removal. These changes reflect the company's adaptive strategy
and the evolving vision of its leaders to improve operational efficiency and
address emerging challenges in the production process
In the context of PT PSG, the
role of leadership is not just limited to decision-making. It involves
developing a culture that values continuous learning and adaptability. Leaders
must inspire their teams to embrace change and seek opportunities for improvement.
This is especially important in an industry that is prone to rapid changes in
both market demands and technological advancements. The ability to anticipate
future trends and prepare accordingly is a trait that will set companies apart
in the long run.
Research on the influence of
leadership on Lean Management (LM) maturity and team performance has been
widely discussed in several studies. Hilvirda et al. (2023) from the University
of Groningen and Erasmus MC highlighted the role of 58 leadership activities in
nine themes that influence Continuous Improvement Program (CIP) maturity, and
encouraged the adoption of a hybrid leadership style with transformational
elements to improve LM maturity. In line with these findings,
Method
This research aims to analyze the continuous
improvement program for hydrate removal at PT PSG from a financial perspective,
with a focus on the effect of refinery shutdown on product loss experienced by
the company. The research only covers aspects of the hydrate removal program in
the production department, without addressing other aspects of refinery
operations. PT PSG, established on May 7, 2008, aims to produce LPG to support
government programs in energy supply and fuel subsidy reduction. In the LPG production
process, hydrates formed from the combination of hydrocarbon gas and water pose
a serious threat, as they can clog pipes, damage equipment, and disrupt gas
flow. Contributing factors include high pressure, low temperature and high
water content. Preventive measures include gas drying, hydrate inhibitor
injection and strict process control. This research quantifies the financial
impact of hydrate removal methods, particularly in terms of product loss.
Results and Discussion
Profile of Respondents Based on Employee
Age
The age of
respondents is divided into 4 groups, namely ≤ 26 years, 26-35 years,
36-45 years, 46-55 years, and ≥ 55 years. Based on the research that has been done, an
overview of 106 respondents based on age can be seen in Figure 4.3. The following is respondent data based on age.
Figure 1. Diagram of Respondents by Age
Based on Figure 1, it can be
seen that the most or majority of respondents are respondents aged between 36 -
45 years with a percentage of 43.3%. Respondents with an age range of 26 - 35
years only differ slightly in percentage, namely 41.3%, followed by respondents
aged 46 - 55 years with a percentage of 9.6%. Meanwhile, respondents with ages
that are in the minority are ages ranging from ≤ 26 years and ≥ 55
years.
Profile of Respondents Based on Education Level
This
research examines the continuos improvement program in the company which is of
course related to several levels of education. The criteria for employee
education levels will provide an overview of the educational background of the
employees in this research. The education level of the respondents is divided
into 4 groups of 106 respondents, namely SMA, Diploma, S1, S2 and others. Based
on the research that has been conducted, it is found that the description of respondents based on the level of education in this research can be seen in Figure 2:
Figure 2. Diagram of Respondents by
Education Level
Based on Figure 2, it can be
seen that the most respondents or the majority are respondents in the S1
education level category with a percentage of 64.8%, while the minority are
respondents with high school and other education level categories. The second
highest percentage is at the Diploma education level which shows a percentage
of 20%. In addition, the S2 education level shows a percentage of 8.6%.
Profile of Respondents Based on Tenure
The categories of
employee tenure who are respondents are divided into 4 groups, namely ≤ 2
years, 2-5 years, 6-10 years, 11-15 years and ≥ 15 years. Based on the research that has been done,
a description of 106 respondents based on their length of service in this research
can be seen in the following figure of respondent data based on employee
tenure.
Figure 3. Diagram of Respondents by Length of Service
Based on Figure 3,
it can be seen that the most or majority of respondents' tenure is 6 - 10 years
and 11 - 15 years which shows the same percentage value of 41.9%, while the
minority of employees' tenure is ≤ 2 years and ≥ 15 years.
Employees who have worked for 2 to 5 years have a percentage of 8.6%.
Respondents' Responses Based on Research Variables
Based on the calculation
results of the SPSS program, with the independent variables of transformational
leadership (X1) and change management (X2) and the
dependent variable, namely the continuous improvement program (Y), the
frequency of customer responses to the indicator items of these variables will
be explained as follows:
Respondents' Responses Based on
Transformational Leadership Variables (X1)
Respondents' responses
regarding the transformational leadership variable will provide an overview of
the extent to which the concept of continuos improvement program at PT PSG. The
transformational leadership variable is represented by eight question items.
The respondents' opinions on the items of the transformational leadership
variable can be explained in Table 1 below:
Table 1. Respondents' Responses Regarding
the Transformational Leadership Variable (X1)
|
Variables |
Statement |
SS (5) |
S (4) |
N (3) |
TS (2) |
STS (1) |
Total |
|
X1.1 |
My boss fosters my confidence in doing my job. |
50 |
4 |
12 |
0 |
0 |
106 |
|
X1.2 |
My boss gives me confidence that the company's goals will be
achieved. |
46 |
0 |
10 |
0 |
0 |
106 |
|
X1.3 |
My boss is my role model in the company. |
31 |
0 |
19 |
18 |
1 |
106 |
|
X1.4 |
My boss instills a sense of pride in me during
my time with him. |
31 |
0 |
19 |
18 |
1 |
106 |
|
X1.5 |
My boss encourages me to use my creativity to
get the job done. |
38 |
4 |
13 |
1 |
0 |
106 |
|
X1.6 |
Superiors listen well to subordinates' ideas and
thoughts. |
40 |
1 |
12 |
1 |
1 |
106 |
|
X1.7 |
My boss is working to improve my personal
development. |
43 |
9 |
12 |
1 |
1 |
106 |
|
X1.8 |
My boss treats me as a private individual, not
just as a member of a work group. |
31 |
4 |
17 |
2 |
1 |
106 |
|
|
Total Respondents |
106 |
|||||
Respondents' Responses Based on Organizational Change Variables (X2)
The
organizational change variable in this research, which is also an independent
variable, will certainly provide an overview of the extent to which the concept
of continuous improvement program at PT PSG. Similar to the previous variable,
the organizational change variable is also represented by eight question items.
The respondents' opinions on the items of this variable are described in Table
2 below:
Table 2. Respondents' Responses Regarding
the Organizational Change Variable (X2)
|
Variables |
Statement |
SS (5) |
S (4) |
N (3) |
TS (2) |
STS (1) |
Total |
|
X2.1 |
I am able to adapt to
changes that occur in the organizational system. |
54 |
45 |
7 |
0 |
0 |
106 |
|
X2.2 |
I support any changes
that occur in the organizational system. |
40 |
54 |
10 |
2 |
0 |
106 |
|
X2.3 |
I have the skills to
reliably operate a new set of machinery at the refinery. |
36 |
56 |
14 |
0 |
0 |
106 |
|
X2.4 |
I am able to integrate
knowledge and skills about new machines in my daily work activities. |
45 |
51 |
10 |
0 |
0 |
106 |
|
X2.5 |
Employees can accept
when a new management system is implemented. |
38 |
55 |
12 |
1 |
0 |
106 |
|
X2.6 |
Employees follow every
change process in management from year to year. |
42 |
55 |
8 |
0 |
0 |
106 |
|
X2.7 |
The organization where
I work has a work culture that provides opportunities for employees to gain knowledge
to improve work skills. |
43 |
52 |
9 |
1 |
1 |
106 |
|
X2.8 |
The organization I work
for has a work culture that requires employees to disseminate the knowledge
they acquire. |
39 |
52 |
15 |
1 |
0 |
106 |
|
|
Total Respondents |
106 |
|||||
Respondents' Responses
Based on the Continuous Improvement Program Variable (Y)
The continuous
improvement program variable is the dependent variable in research at PT Perta-Samtan Gas. Respondents' responses were recorded the same
as the independent variables. Similar to the independent variable, the organizational change variable is
also represented by eight question items. The respondents' opinions on the
items of this variable are described in Table 3 below:
Table 3. Respondents' Responses Regarding
the Continuous
Improvement Program Variable
(Y)
|
Variables |
Statement |
SS (5) |
S (4) |
N (3) |
TS (2) |
STS (1) |
Total |
|
Y.1 |
Good quality standard setting is implemented in my
department or team. |
53 |
45 |
6 |
1 |
1 |
106 |
|
Y.2 |
I believe that any planning done in my department
or team will support the company's vision and mission. |
40 |
57 |
8 |
1 |
0 |
106 |
|
Y.3 |
I go through step by step the implementation of the
program implemented in my department or team. |
40 |
56 |
0 |
0 |
0 |
106 |
|
Y.4 |
I was able to follow the program implementation
well and without any major obstacles. |
49 |
50 |
8 |
0 |
0 |
106 |
|
Y.5 |
Employees are involved in the review of every
program conducted at the refinery. |
32 |
51 |
9 |
3 |
1 |
106 |
|
Y.6 |
Employees follow every inspection process
related to the continuous improvement program. |
32 |
61 |
3 |
0 |
0 |
106 |
|
Y.7 |
I can implement every work program given. |
40 |
58 |
8 |
1 |
1 |
106 |
|
Y.8 |
I was involved in every evaluation related to
the work program at the refinery. |
40 |
43 |
8 |
4 |
1 |
106 |
|
|
Total Respondents |
106 |
|||||
Research Instrument Test
Validity Test
The validity test is intended
to determine how much accuracy and accuracy of a measuring instrument in
performing its measuring function. The validity test as a measuring tool in
this research, namely using Pearson's product moment correlation, namely by
correlating each question with the total score, then the correlation results
are compared with the critical number at a significant level of 5%
The validity of the instrument
is sought by comparing the Pearson Product Moment correlation value found in
the data processing results with the help of the SPSS program seen in the CITS
(Corrected Item-Total Correlation) column with the r value in the PMM (Person
Product Moment) table. The critical value of the correlation table (r-table)
with n as many as 105 respondents at a significance level of (α) 5% is 0.1927 in the Pearson Product Moment r table. The decision-making criteria:
a. If the value of
> 
(0.1927), it is declared valid,
b. If the value of < (0.1927), then it is declared invalid
After further data processing,
the results obtained can be seen in the Validity Test Table (attached in the
appendix). Based on this table, it is known that each indicator (item) on each
transformational leadership variable and change management as an independent
variable and the Continuous Improvement Program as the dependent variable has a
Pearson's Product Moment value result with a significance value of 0.000
<0.05, so that the indicators (items) used in this research variable can be
declared appropriate or relevant and can be used as items in data collection.
Reliability Test
This test is carried out to
show the extent to which a measurement result is relatively consistent. A good
question or statement is a statement or question that is clear, easy to
understand, and has the same interpretation even though it is submitted to different
respondents and at different times. The reliability test uses Cronbach's Alpha.
An instrument is said to be reliable if Cronbach's Alpha is greater than 0.60
If the
Realibility Coefficient (Cronbach's Alpha) value is> 0.60, the measured
variable can be said to be reliable.
Table
4. Results of Instrument Reliability Test Research
Variables
|
Variables |
Cronbach's
Alpha |
Cutt Off |
N of Items |
Description |
|
Transformational
Leadership (X1) |
0,876 |
> 0,60 |
10 |
Reliable |
|
Organizational
Change (X2) |
0,861 |
> 0,60 |
10 |
Reliable |
|
Continuous
Improvement Program (Y) |
0,866 |
> 0,60 |
10 |
Reliable |
Source: Processed from Questionnaire, 2024
Based on the results of the
reliability test of the research variable instruments in Table 4 above, the
results of the reliability test of the independent variables of
transformational leadership (X (1)) and change management (X 2)
show that the data obtained are reliable because the Cronbach's Alpha value is
0.876 and 0.861 while the results of the reliability test of the dependent
variable continuous improvement program (Y) show that the data obtained are
reliable because the Cronbach's Alpha value is 0.866.
Classical Assumption Test
After obtaining the model, the next
step is to test whether the model developed is BLUE (Best Linear Unbiased
Estimator)
Normality Test
The data
normality test is carried out to determine whether the data obtained is
normally distributed or not. The normality test carried out on the sample was
carried out using the Kolmogrov-smirnov Test by setting the degree of
confidence (α) at 5% (Sarwono, 2006). The test results
can be presented as follows:
Table 5. Normality Test Results
|
Test of Normality |
Kolmogrov-Smirnov |
|||
|
Sig. |
|
Cutt Off |
Description |
|
|
Transformational Leadership (X2) |
0,200 |
> |
0,05 |
Normal |
|
Organizational Change (X2) |
0,132 |
> |
0,05 |
Normal |
|
Continuous Improvement Program (Y) |
0,145 |
> |
0,05 |
Normal |
Source: Processed from Questionnaire, 2024
Based on Table 5, it can be seen
that the probability or significance value for each variable is greater than
0.05, so it can be stated that the data in this research are normally
distributed. The normality test also aims to test whether in a regression
model, the independent variables or both have a normal distribution or absolute
good regression is normal or near normal data distribution. Detect normality by looking at the spread
of data points on the diagonal axis of the graph
a. If the data spreads around the diagonal line and follows the direction of the diagonal line, the regression model fulfills the assumption of normality;
b. If the data spreads far from the diagonal
line and or does not follow the direction of the diagonal line, the regression
model does not fulfill the assumption of normality.
Based on
Figure 4, it shows that the data is normally distributed, because the data
spreads around the diagonal line and follows the direction of the diagonal
line, so it can be stated that the regression model fulfills the assumption of
normality.
Figure 4. Normality Test Results
Multicollinearity Test
Multicollinearity test aims to test
whether the regression model found a correlation between independent variables.
A good regression model
should not have a correlation between the independent variables. A common way
to detect multicollinearity in this model is to look at
,
or based on the tolerance and VIF values.
Multicollinearity test can be done for the regression results for both
models to be estimated. The trick is to find the tolerance number, where
tolerance is the value of
.
After the tolerance number is obtained, then look for the VIF number. VIF
(Variance Inflation Factor) number which is the reciprocal of tolerance.
Thus, the
higher the tolerance value, the lower the degree of collinearity that occurs.
As for VIF, the lower the VIF, the lower the degree of collinearity that
occurs. The maximum VIF value limit commonly used to justify the presence of
collinearity is 10. The assumption of multicollinearity is a situation where
there is a perfect or near perfect linear relationship between the independent
variables in the model.
Table 5.
Multicollinearity Test Results
|
Coefficientsa |
|
||||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
Collinearity Statistics |
|
|||
|
B |
Std. Error |
Beta |
Tolerance |
VIF |
|
||||
|
1 |
(Constant) |
.555 |
.308 |
|
1.800 |
.075 |
|
|
|
|
X1.1 |
.141 |
.071 |
.192 |
1.989 |
.050 |
.384 |
2.605 |
|
|
|
X1.2 |
.022 |
.068 |
.028 |
.317 |
.752 |
.475 |
2.106 |
|
|
|
X1.3 |
-.046 |
.045 |
-.079 |
-1.027 |
.307 |
.603 |
1.658 |
|
|
|
X1.4 |
.172 |
.063 |
.234 |
2.738 |
.007 |
.491 |
2.036 |
|
|
|
X1.5 |
.012 |
.055 |
.018 |
.218 |
.828 |
.530 |
1.888 |
|
|
|
X1.6 |
.157 |
.065 |
.235 |
2.404 |
.018 |
.377 |
2.650 |
|
|
|
X1.7 |
-.092 |
.070 |
-.139 |
-1.318 |
.191 |
.323 |
3.096 |
|
|
|
X1.8 |
.025 |
.051 |
.040 |
.499 |
.619 |
.569 |
1.758 |
|
|
|
X2.1 |
.126 |
.064 |
.155 |
1.982 |
.051 |
.590 |
1.694 |
|
|
|
X2.2 |
.175 |
.055 |
.243 |
3.213 |
.002 |
.629 |
1.591 |
|
|
|
X2.3 |
.022 |
.068 |
.029 |
.322 |
.748 |
.455 |
2.198 |
|
|
|
X2.4 |
.083 |
.070 |
.105 |
1.176 |
.243 |
.449 |
2.228 |
|
|
|
X2.5 |
-.049 |
.064 |
-.066 |
-.764 |
.447 |
.482 |
2.074 |
|
|
|
X2.6 |
-.053 |
.067 |
-.068 |
-.788 |
.433 |
.478 |
2.091 |
|
|
|
X2.7 |
.095 |
.070 |
.138 |
1.349 |
.181 |
.341 |
2.929 |
|
|
|
X2.8 |
.073 |
.063 |
.103 |
1.159 |
.250 |
.453 |
2.206 |
|
|
|
a. Dependent Variable: Y_COMPOSITE |
|||||||||
Source: Processed from
Questionnaire, 2024
Heteroscedasticity Test
The heteroscedasticity test aims to
test whether in a regression model there is an inequality of variance of
residuals from one observation to another. How to predict the presence or absence of
heteroscedasticity in a model can be seen from the pattern of the model's
Scatterplot image. The basis for
decision making includes:
a. If there is a certain pattern, such as
existing points forming a certain regular pattern (wavy, widening, then
narrowing) then heteroscedasticity has occurred.
b. If there is no clear pattern and the dots spread above and below the number 0 on the Y axis, then there is no heteroscedasticity. The test results are presented in Figure 7 as follows:
Figure 6. Heteroscedasticity Test Results
Conclusion
The conclusion in this research
shows that Transformational Leadership and Change Management have a significant
effect on the Continuous Improvement Program (CIP) on the hydrate removal
program at the PT PSG South Sumatra refinery. The results of multiple linear
regression analysis show that Transformational Leadership has a positive and
significant effect on CIP, as indicated by the calculated t value of 4.052
(greater than the t table value of 1.983) and a significance level of 0.001
(α < 0.05). Similarly, Change Management also has a positive and
significant effect on CIP, with a t-value of 6.231 (greater than the t-table
value of 1.983) and a significance level of 0.001 (α < 0.05).
Furthermore, the correlation coefficient (r = 0.823) indicates a very strong
relationship between the independent variables (Transformational Leadership and
Change Management) and the dependent variable (CIP), while the coefficient of
determination (R˛ = 0.677) indicates that 67% of the variation in CIP is
explained by these two factors. These findings confirm the synergistic impact
of Transformational Leadership and Change Management in driving the success of
the Continuous Improvement Program.
For future research, these
findings contribute to the understanding of leadership and management
strategies in organizational improvement initiatives. Future research can
explore other factors that influence CIP, such as organizational culture,
employee engagement, or technological innovation, to provide a more
comprehensive model. In addition, expanding the scope to include different
industries or geographic locations could further validate and generalize the
results of this research, providing valuable insights into how leadership and
change management practices can be adapted to diverse operational environments.
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