The Influence of Leadership Style on Employee Performance Mediated by Job Satisfaction and Moderated by Work Motivation
Muhammad Mirza Soetirto, Pudji Muldjono, Furqon Syarief Hidayatulloh
Science Management Study Program, IPB University, Bogor City, West Java, Indonesia
e-mail: [email protected], [email protected], [email protected]
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Keywords |
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ABSTRACT |
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leadership style, job satisfaction, work motivation, performance. |
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This study aims to examine the effect of work motivation on performance mediated by. The approach used in this research is a quantitative approach. The sample used in this study were employees of PT TTNT, totaling 77 respondents. The research data was obtained from the results of filling out the questionnaire and analyzed using the PLS SEM analysis technique with the help of the SmartPLS program. The results of this study indicate that (1) leadership style has a positive and significant effect on employee job satisfaction. The better the superior's leadership style, the higher the employee's job satisfaction, and vice versa the worse the leadership, the lower the employee's job satisfaction; (2) leadership style influences employee performance, the results of this study indicate that a good leadership style does not always produce high employee performance. The results of this study indicate that leadership style can improve employee performance if the leadership style can shape employee job satisfaction; (3) The results of this study succeeded in proving that job satisfaction affects performance. The higher the employee's job satisfaction, the higher the employee's performance, conversely, low employee satisfaction will reduce employee performance; (4) Job satisfaction can mediate the effect of leadership style on job satisfaction. A good leadership style can increase employee job satisfaction which will further improve employee performance; (5) Work motivation has a positive effect on performance. |
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INTRODUCTION
The automotive industry in Indonesia is currently growing rapidly. This development can be seen from the number of new model cars launched in the last 3 years (Sari, 2022). This new model car is actually still produced by automotive companies that have long existed in Indonesia including Toyota. According to the results of a survey conducted by katadata (2021), despite the Covid-19 pandemic, car sales remain and Toyota continues to lead the market with 21,826 units of car sales as of July 2021.
Although Toyota still leads the market, the actual trend of Toyota car sales fell during the pandemic. In fact, in 2022, Toyota's car sales trend continues to decline. Based on data from the Association of Motor Vehicle Industries (Gaikindo), retail car sales in February fell 10.9 percent month to month (mtm) to 69,989 compared to sales in January of 78,567 percent. Compared to last year, retail sales rose 49.1 percent. Retail car sales in February were still dominated by Toyota with 21,187 units, while sales this month decreased 7.5 percent compared to January.
The phenomenon of declining Toyota car sales in Indonesia is quite a concern for Toyota. Various strategies need to be carried out by Toyota so that the company's performance remains good even though the car market is experiencing a decline (Putra Pane, 2019). One strategy that Toyota can do to stay afloat when the number of car sales is declining is to optimize the logistics section, where Toyota's logistics partners are currently handled by 8 logistics companies, namely Dunex Logistic Solution, SQL, ARMAS, Hikari Logistic, Agung logistics services, Syncrum logistics, Puninar Logistics and TTLC Nasmoco Transport. Among the 8 Toyota logistics partners, TTNT is the Logistics Partner that contributes the most to TMMIN's Logistics delivery system for the supply of spare parts for production needs. Therefore, Toyota car logistics optimization can be started from the logistics partner company (Sinaga, 2020).
PT TTLC Nasmoco Transport or known as PT. TTNT is a logistics company engaged in the delivery of car and motorcycle manufacturing goods using milkrun delivery methods and also direct delivery (Rohman Hakim, 2022). PT TTNT has long been established since July 1995 as a company that provides manufacturing goods delivery services. The number of employees in this company is quite large, that is, as many as people. The head office of PT TTNT is located at Jl. Irian V Blok KK-8 Industrial Estate MM 2100 Cikarang Barat – Bekasi, West Java. In order to make the delivery of goods more efficient, this company has un branch in Karawang KIIC Industrial area, this branch office only functions as a pool truck.
Performance appraisal at PT. TTNT uses a Performance Appraisal Report Card called Employee Evaluation, this is intended to obtain objective assessment results. This performance report card has several elements of performance appraisal, namely attendance, reprimand, job knowledge, attitude / complience, accident / incident, loyalty, work attitude and teamwork. With this objective performance appraisal, each employee will have their own work report card which can then be used by the company as material for evaluating employee performance.
Employee performance must continue to be evaluated because the progress of a company is certainly determined by the quality of human resources working in the company. Through increased employee performance, the effectiveness and productivity of the company will increase. However, to get optimal performance from employees is not easy, it takes high awareness and sense of responsibility from employees so that employees can have high performance.
Based on the results of initial observations of employee performance scores at PT. TTNT from 2018 – 2021 in Figure 1, there is an indication of a decrease in employee performance in the company. In 2019, the performance score increased, but in the following years, namely 2020 and 2021, the performance score of employees at the company continued to decline. In line with the phenomenon of increasingly fierce car market competition in the automotive industry, companies need to re-evaluate employee performance so that employee performance can be improved through effective and efficient ways.
Improving employee performance can be achieved in several ways. Robbin (1996) suggests that performance is influenced by the ability factors of the employees themselves such as the level of education, knowledge, experience where the higher the level of ability will have higher performance. Thus a low level of education, knowledge and experience will have a negative impact on employee performance. While Jackson et al (2001) suggest that employee performance is influenced by ability, motivation, support received, the existence of the work they do and their relationship with the organization (Nurlaili, 2020).
In this study, the author feels that secondary data from companies related to employee performance scores cannot be used as a basis to find out what factors affect employee performance, therefore researchers conducted a pre-survey of 50 employees of PT TTNT (Chi, 2008).
Figure 1 Pre-survey results of 50 respondents
Based on the results of the pre-survey in Figure 1, it was found that of the 10 performance factors given to respondents, it turned out that the 3 most dominant factors affecting respondents' performance were motivation factors (score 31.33%), job satisfaction factors (score 19.33%) and leadership factors (score 20.67%). Based on the results of the pre-survey, the variables to be studied in this study are performance variables as the main variable, job satisfaction variables, motivation variables and leadership variables as variables that influence it (Sardarmayanti., 2011). Based on the phenomenon that occurred at PT. TTNT and pre-survey results along with research gaps, researchers are interested in conducting research entitled "The Influence of Leadership Style on Employee Performance mediated by Job Satisfaction and moderated by Employee Motivation of PT TTLC Nasmoco Transport".
METHODS
This research was conducted at PT TTLC Nasmoco Transport (PT. TTNT) which is located at Industrial Estate MM2100 Cibitung Jl. Irian V Block KK-8 MM2100 Industrial Town, Kec. Cikarang Bar., Bekasi Regency, West Java. This research will be carried out in August - October 2022.
The type of data used in this study is primary data obtained from the results of filling out questionnaires. In addition, research data was also obtained from interviews with HRD PT TTNT and observations of employee performance scores for the past 4 years along with observations of performance appraisal standards used by the company.
The population in this study is all employees of PT TTNT which amounts to 324 people. The sampling technique used in this study was purposive sampling technique. The minimum number of study samples is calculated using the Slovin formula because the number of study populations is known. With a population of 324 employees, with a significance level of 10%, the results of calculating the minimum number of samples are as follows:
Based on the results of calculating the minimum number of samples with the Slovin formula above, the number of samples in this study was 77 respondents. The data in the study was obtained from the results of filling out questionnaires by respondents which were then analyzed using the SEM PLS analysis technique.
RESULTS AND DISCUSSION
Respondent Demographic
This study involved 77 respondents, all of whom were employees working at PT TTNT. Based on the results of data collection in this study, the following is an overview of respondents' characteristics according to age, gender, occupation, length of service. Based on the results of the analysis in Table 1 above, it was obtained that according to the age range, most respondents in this study were employees aged between 45 – 55 years (27.3%), as many as 27.3% aged 25 – 35 years, while the remaining 18.2% aged <25 years, as many as 18.2% aged 35-45 years, and as many as 9.1% aged >55 years. According to the gender of the respondents, most of the respondents were male employees (90.9%), While the rest are female employees as much as 9.1% (Kusumawati, 2021). According to the education level of respondents, most respondents had an S1 education of 54.5%, while the remaining 27.3% had a diploma education, as many as 18.2% had a high school education and as many as 0.1% had an S2 education. According to respondents' tenure, most respondents have worked at PT TTNT for >10 years (45.5%), while the remaining 36.4% have a working period of 1-5 years and as many as 18.2% have a working period of 5-10 years.
Table 1 Description of Respondent Characteristics.
|
Characteristic |
Category |
Frequency |
Percentase (%) |
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Age |
<25 years |
21 |
18.2 |
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25-35 years |
21 |
27.3 |
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35-45 years |
14 |
18.2 |
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45-55 years |
21 |
27.3 |
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>55 years |
7 |
9.1 |
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Sex |
Man |
70 |
90.9 |
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Woman |
7 |
9.1 |
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Education |
High school |
14 |
18.2 |
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Diploma |
21 |
27.3 |
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S1 |
41 |
54.5 |
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S2 |
1 |
0.1 |
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Working |
1-5 years |
28 |
36.4 |
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5-10 years |
14 |
18.2 |
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>10 years |
35 |
45.5 |
Analysis SEM PLS
The stages in SEM PLS analysis include the outer model testing stage and the inner model testing stage. In the outer model testing phase, the validity and reliability of the construct are tested, while in the iner model, the research hypothesis is to be tested.
Outer Model Testing
Validity Konvergen
Convergent validity testing is carried out to determine the degree of validity of each relationship between indicators and their latent constructs (Sahlan, 2020). In this test, the indicator is valid if it has a loading fator value of > 0.7 and each construct has an AVE value of > 0.5. The outer model test results in Table 2 show that all indicators in the PLS model are valid in measuring their constructs, because they already have a loading factor value of > 0.7 and the analysis results in Table 5 show that each construct has an AVE value of > 0.5.
Table 2 Convergent Validity
|
Variabel |
Indicator |
Loading factor |
Cut Value |
AVE |
Validitity Convergen |
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GK |
GK1 |
0,884 |
0,7 |
0,806 |
valid |
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GK10 |
0,919 |
0,7 |
valid |
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GK11 |
0,879 |
0,7 |
valid |
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GK12 |
0,839 |
0,7 |
valid |
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GK13 |
0,909 |
0,7 |
valid |
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GK14 |
0,908 |
0,7 |
valid |
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GK15 |
0,874 |
0,7 |
valid |
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GK16 |
0,909 |
0,7 |
valid |
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GK17 |
0,875 |
0,7 |
valid |
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GK18 |
0,912 |
0,7 |
valid |
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GK19 |
0,879 |
0,7 |
valid |
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GK2 |
0,897 |
0,7 |
valid |
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GK3 |
0,915 |
0,7 |
valid |
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GK4 |
0,900 |
0,7 |
valid |
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GK5 |
0,926 |
0,7 |
valid |
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GK6 |
0,876 |
0,7 |
valid |
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GK7 |
0,916 |
0,7 |
valid |
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GK8 |
0,916 |
0,7 |
valid |
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GK9 |
0,920 |
0,7 |
valid |
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GK * MOT |
GK * MOT |
1,055 |
0,7 |
1,000 |
valid |
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KEP |
KEP1 |
0,838 |
0,7 |
0,769 |
valid |
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KEP10 |
0,791 |
0,7 |
valid |
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KEP11 |
0,851 |
0,7 |
valid |
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KEP12 |
0,941 |
0,7 |
valid |
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KEP13 |
0,931 |
0,7 |
valid |
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KEP14 |
0,923 |
0,7 |
valid |
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KEP15 |
0,907 |
0,7 |
valid |
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KEP2 |
0,858 |
0,7 |
valid |
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KEP3 |
0,895 |
0,7 |
valid |
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KEP4 |
0,879 |
0,7 |
valid |
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KEP5 |
0,882 |
0,7 |
valid |
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KEP6 |
0,752 |
0,7 |
valid |
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KEP7 |
0,856 |
0,7 |
valid |
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KEP8 |
0,939 |
0,7 |
valid |
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KEP9 |
0,889 |
0,7 |
valid |
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KIN |
KIN1 |
0,932 |
0,7 |
0,769 |
valid |
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KIN10 |
0,937 |
0,7 |
valid |
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KIN11 |
0,936 |
0,7 |
valid |
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KIN12 |
0,922 |
0,7 |
valid |
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KIN13 |
0,934 |
0,7 |
valid |
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KIN14 |
0,906 |
0,7 |
valid |
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KIN15 |
0,855 |
0,7 |
valid |
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KIN16 |
0,889 |
0,7 |
valid |
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KIN17 |
0,969 |
0,7 |
valid |
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KIN18 |
0,924 |
0,7 |
valid |
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KIN19 |
0,921 |
0,7 |
valid |
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KIN2 |
0,922 |
0,7 |
valid |
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KIN20 |
0,831 |
0,7 |
valid |
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KIN21 |
0,918 |
0,7 |
valid |
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KIN22 |
0,915 |
0,7 |
valid |
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KIN23 |
0,935 |
0,7 |
valid |
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KIN3 |
0,842 |
0,7 |
valid |
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KIN4 |
0,952 |
0,7 |
valid |
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KIN5 |
0,937 |
0,7 |
valid |
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KIN6 |
0,985 |
0,7 |
valid |
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KIN7 |
0,868 |
0,7 |
valid |
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KIN8 |
0,967 |
0,7 |
valid |
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KIN9 |
0,958 |
0,7 |
valid |
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MOT |
MOT1 |
0,935 |
0,7 |
0,867 |
valid |
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MOT10 |
0,908 |
0,7 |
valid |
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MOT11 |
0,911 |
0,7 |
valid |
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MOT12 |
0,926 |
0,7 |
valid |
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MOT13 |
0,948 |
0,7 |
valid |
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MOT14 |
0,934 |
0,7 |
valid |
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MOT15 |
0,921 |
0,7 |
valid |
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MOT16 |
0,996 |
0,7 |
valid |
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MOT17 |
0,935 |
0,7 |
valid |
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MOT18 |
0,923 |
0,7 |
valid |
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MOT19 |
0,889 |
0,7 |
valid |
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MOT2 |
0,932 |
0,7 |
valid |
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MOT20 |
0,964 |
0,7 |
valid |
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MOT3 |
0,908 |
0,7 |
valid |
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MOT4 |
0,937 |
0,7 |
valid |
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MOT5 |
0,894 |
0,7 |
valid |
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MOT6 |
0,933 |
0,7 |
valid |
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MOT7 |
0,900 |
0,7 |
valid |
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MOT8 |
0,936 |
0,7 |
valid |
||
|
MOT9 |
0,986 |
0,7 |
valid |
Validity Description
Discriminant validity is done to ensure that each concept of each latent variable model is different from other variables (Prayatna, 2016). In this test, the indicator is declared to have met the required description validity criteria if the HTMT between constructs is below 0.9. The results of the discriminant validity test in Table 4 show that the HTMT value between constructs has been below 0.9 which means that the discriminant validity has been met by each construct. The results of the description validity test in the Table show that all indicators and constructs have met the required criteria for description validity, HTMT between constructs < 0.9.
Table 3 Descriminant Validity - HTMT
|
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GK |
GK_MOT |
KIN |
KK |
MOT |
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GK |
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GK_MOT |
0.130 |
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KIN |
0.370 |
0.701 |
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KK |
0.832 |
0.184 |
0.351 |
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MOT |
0.086 |
0.064 |
0.398 |
0.149 |
Composite Reliability and Cronbach Alpha
Composite Reliability measures the true reliability value of a variable, while Crombach Alppha measures the lowest value (lowder bound) reliability of a variable. In construct reliability measurement, the required cronbachs alpha value is > 0.7, as well as the required composite reliability value is > 0.7 (Kusumawati, Erna, 2022). The results of the construct reliability test in Table 3 show the alpha cronbachs value of all constructs > 0.7 as well as the composite reliability value of all constructs > 0.7 which means that all constructs in this PLS SEM model are reliable.
Table 4 Composite Reliability
|
|
Cronbach's Alpha |
rho_A |
Composite Reliability |
|
GK |
0.987 |
0.989 |
0.987 |
|
GK_MOT |
1.000 |
1.000 |
1.000 |
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KIN |
0.992 |
0.994 |
0.992 |
|
KK |
0.978 |
0.981 |
0.980 |
|
MOT |
0.992 |
0.994 |
0.992 |
Inner Model Testing
Inner model testing includes assessment of the goodness of fit of structural models, assessment of path coefficients, significance test of partial influence of exogenous variables on endogenous variables and calculation of coefficient of determination. The test results at this stage can be used to test the research hypothesis.
Goodness of Fit Model PLS
The goodness of fit of the SEM PLS model can be seen from the value of R Square and Q Square (Irfan, 2021). The R square value indicates the strength of the model in predicting endogenous variables. This R Square value ranges from 0-1 and is categorized into 3 categories, namely strong, moderate, and weak. According to Chin (1998), the R square value of >0.67 indicates the PLS model in the strong category, the R Square value between 0.33 – 0.67 indicates that the PLS model is in the moderate category and the R Square value between 0.19 – 0.33 indicates that the PLS model is in the weak category (Hair, 2019). While the Q Square value of the model indicates the level of predictive relevance of the model. Q square values are categorized into 3 categories, namely small, medium and large, Q square values of 0.02 – 0.15 are declared small, Q square values of 0.15 – 0.35 are declared medium and Q square values of >0.35 are declared large.
Table 5 Goodness of Fit Model
|
Endogen Construct |
R2 |
Adjusted R2 |
Criteria |
Q2 |
Predictive Relevance |
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Work Satisfaction |
0,689 |
0,517 |
Moderate |
0.442 |
big |
|
Performance |
0,885 |
0,746 |
Strong |
0.355 |
big |
The results of the analysis in Table 5 show that the SEM PLS model estimated fit with the analyzed data, because it has the strength of the model in the moderate category (strong enough), predictive relevance is large and the SRMR value of the model is in the fit criteria. Therefore, this model can be considered feasible to be used to test research hypotheses.
Figure 2 PLS Bootstrapping model estimation result 500 sample
Direct Influence
Direct influence or often referred to as dirrect effect is the influence of exogenous variables directly on endogenous variables (Agustin, 2021). In the PLS SEM analysis, the significance and direction of direct influence are seen from the value of p value, statistical t and the coefficient of the pathway path connecting endogenous to exogenous. If the p value is obtained < 0.05 and the statistical T > 1.96 (t value two tail, α 5%), it is concluded that the exogenous variable has a significant effect on endogenous with the direction of influence according to the sign attached to the path coefficient. Furthermore, if the p value is obtained > 0.05 and the statistical T < 1.96 (t value two tail, α 5%), then it is concluded that the exogenous variable has no significant effect on endogenous (Adnan Hakim, 2021).
Table 6 Direct Influence Test Results
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Original Sample (O) |
Sample Mean (M) |
Standard Deviation (STDEV) |
T Statistics (|O/STDEV|) |
P Values |
|
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GK -> KIN |
0,147 |
0,152 |
0,127 |
1,156 |
0,124 |
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GK -> KK |
0,832 |
0,835 |
0,024 |
35,073 |
0,000 |
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KK -> KIN |
0,399 |
0,403 |
0,114 |
3,502 |
0,000 |
|
MOT -> KIN |
0,405 |
0,404 |
0,116 |
3,484 |
0,000 |
Based on the test results of Table 6, the following results were obtained (1) Leadership style has no effect on KIN, indicated by sig = 0.124 > 0.05 and statistical T 1.156 < 1.96, this means that the high and low GK does not affect the high and low KIN. (2) Leadership style has a positive and significant effect on job satisfaction, indicated by sig. = 0.000 <0.05, T statistic 35.073 > 1.96 and positive path coefficient of 0.832 means that the higher the leadership style, the higher the job satisfaction, vice versa the lower the leadership style, the lower the job satisfaction; (3) Job satisfaction has a positive and significant effect on employee performance, indicated by SIG. = 0.000 <0.05, T statistic 3.502 > 1.96 and positive path coefficient of 0.399 means that the higher the job satisfaction, the higher the employee performance, vice versa the lower the job satisfaction, the lower the employee performance; (4) Work motivation has a positive and significant effect on employee performance, indicated by sig. = 0.000 <0.05, T statistic 3.484 > 1.96 and a positive path coefficient of 0.405 means that the higher the work motivation, the higher the employee's performance, vice versa the lower the work motivation, the lower the employee's performance.
Moderation effect testing
In this study, work motivation acts as a moderation on the influence of leadership style on performance (Rahwani, 2020). To test the role of motivation in moderating the influence of leadership style on performance, a moderation test was conducted by looking at the p value of the interaction between motivation and leadership style on performance, if the p value < 0.05 and T > 1.96 then it was concluded that motivation can moderate the influence of leadership style on performance, while if p value > 0.05 and T < 1.96 then it was concluded that motivation cannot moderate the influence of leadership style on performance (Paracha, 2012).
Table 7 Moderation Test Results
|
|
Original Sample (O) |
Sample Mean (M) |
Standard Deviation (STDEV) |
T Statistics (|O/STDEV|) |
P Values |
|||||
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GK_MOT -> KIN |
0,719 |
0,703 |
0,120 |
5,996 |
0,000 |
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Based on the results of the moderation test in Table 7, obtained the p value of the interaction of leadership style and motivation on performance of 0.000, statistical T of 5.996 and positive path coefficient of 0.719, because of the p value of < 0.05, T > 1.96 and positive flow coefficient, it is concluded that motivation can moderate the influence of leadership style on performance.
Mediation Effect Testing
In this study, job satisfaction acts as a mediating variable for the influence of leadership style on performance (Vermeeren, 2014). To test the role of employee engagement and job satisfaction in mediating the influence of employer brand on employee retention, mediation tests were conducted as follows:
Table 8 Indirect Effect Test
|
Original Sample (O) |
Sample Mean (M) |
Standard Deviation (STDEV) |
T Statistics (|O/STDEV|) |
P Values |
|
|
GK -> KK -> KIN |
0,332 |
0,336 |
0,094 |
3,515 |
0,000 |
Source: data process (2022)
Based on the results of the analysis in Table 8 above, it was found that in the indirect path of the influence of leadership style on employee performance through job satisfaction, a p value of 0.000 was obtained with a statistical T of 3.515 with a positive indirect path coefficient of 0.332 because the p value obtained < 0.05 and the statistical T > 1.96, it was concluded that leadership style can indirectly affect employee performance by mediated by job satisfaction. In this PLS model, job satisfaction is shown to mediate the indirect influence of leadership style on employee performance.
Coeffisient of determination
The coefficient of determination shows the magnitude of the contribution of all exogenous to endogenous. The coefficient of determination can be seen from the value of Adjusted R Square. This value ranges from 0 – 1 or can also be interpreted in percent (0 – 100%) (Aswad., 2022). The greater the coefficient of determination, the greater the endogenous variance described by the exogenous exogenous, while the small coefficient of determination indicates the low influence of exogens on the endogenous, this is because there are still quite a lot of factors outside the exogenous exogens that can affect endogenous.
Figure 3 Adjusted R Square Value
Based on the results of the analysis in Figure 3 above, it was found that the R square of job satisfaction was 0.689 which means that 68.9% of job satisfaction variables were influenced by leadership style while the remaining 31.1% of job satisfaction variance was influenced by other factors outside leadership style (Putra, 2021).
Furthermore, the R square value of performance is 0.885 which means 88.5% of performance variables are influenced by motivation, leadership style and job satisfaction, while the remaining 11.5% of performance variance is influenced by other factors outside motivation, leadership style and job satisfaction.
Hypothesis Testing
Table 9 Testing Hypothesis
|
Path Coefficient |
t |
Sig. |
Conclussion |
||
|
1 |
Leadership Style Affects Job Satisfaction |
0.832 |
35.073 |
0.000 |
Approved |
|
2 |
Leadership Style affects Employee Performance |
0.147 |
1.156 |
0.124 |
Reject |
|
3 |
Job Satisfaction affects Employee Performance |
0.399 |
3.502 |
0.000 |
Approved |
|
4 |
Job Satisfaction mediates the influence of leadership style on Employee Performance |
0.332 |
3.515 |
0.000 |
Approved |
|
5 |
Work motivation affects performance |
0.405 |
3.484 |
0.000 |
Approved |
|
6 |
Work motivation reinforces the influence of leadership style on employee performance |
0.719 |
5.996 |
0.000 |
Approved |
CONCLUSION
The results of this study succeeded in proving that (1) leadership style has a positive and significant effect on employee job satisfaction; (2) leadership style affects employee performance; (3) Job satisfaction affects performance. The higher the employee's job satisfaction, the higher the employee's performance, conversely, low employee satisfaction will be able to reduce employee performance; (4) job satisfaction can mediate the influence of leadership style on job satisfaction; (5) work motivation affects performance and (6) work motivation can moderate the influence of leadership style on performance.
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