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Leadership, Work Motivation, Work Environment, Competence,
And Job Satisfaction Towards Employee Performance at the Tanah
Abang Shopping Building Management Company
Timotius, Suryadi, Teddy Gama Ucok, R. Marsetio Hadi Kusuma Negara, Joyline D’Mello
Kazian School of Manajemen – India
DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0663
Received: 20 October 2025; Accepted: 28 October 2025; Published: 15 November 2025
ABSTRACT
This study aims to analyze the influence of leadership, work motivation, work environment, competence, and
job satisfaction on employee performance at PT. GKS, a shopping building management company in Tanah
Abang. Using a quantitative approach with an explanatory survey design and Structural Equation Modeling-
Partial Least Squares (SEM-PLS) analysis on PT. GKS employees, the results of the study indicate that the
five independent variables have a positive and significant influence on employee performance. Specifically,
job satisfaction is proven to be the most dominant variable that influences employee performance (coefficient
= 0.364; T = 2.789; P = 0.005), followed by work motivation (coefficient = 0.264; T = 2.222; P = 0.026) and
work environment (coefficient = 0.245; T = 1.968; P = 0.043). Meanwhile, leadership (coefficient = 0.071; T =
1.965; P = 0.013) and competence (coefficient = 0.042; T = 2.339; P = 0.034) have a positive but relatively
small influence. This research model shows substantial explanatory power with an R² value of 0.574 and
strong predictive power with a Q² value of 0.574. The novelty of this research lies in confirming the
dominance of job satisfaction in the building management sector as well as the methodological contribution in
highlighting the predictive value (Q²) of the model. Drawing from the conclusions, these findings underscore
that job satisfaction emerges as the primary driver, fostering fair compensation, harmonious relationships, and
recognition to boost performance amid routine operations. While leadership and competence play supportive
roles, the model's robust explanatory (R² = 0.574) and predictive (Q² = 0.574) capacities offer practical insights
for HR strategies, emphasizing targeted interventions to enhance overall employee outcomes in similar
sectors.
Keywords: Leadership, Work Motivation, Job Satisfaction, Employee Performance, SEM-PLS.
INTRODUCTION
Employee performance is a crucial factor in determining the success and sustainability of an organization,
particularly in the service sector, such as shopping mall management companies. In today's modern era,
shopping malls have evolved into lifestyle centers that demand operational efficiency and excellent service
quality. Therefore, effective human resource (HR) management is crucial to ensure employees can make
optimal contributions and create positive experiences for both visitors and tenants. (Tambunan & Pandiangan,
2024). However, challenges in retaining and improving employee performance are often encountered, which
can hinder the achievement of a company's strategic targets.
Various studies have shown that employee performance is influenced by a range of internal organizational
factors. Effective leadership, high work motivation, a conducive work environment, adequate competency, and
positive job satisfaction are key elements that synergistically shape individual performance levels (Susan
Febriantina et al., 2024) . If these factors are not optimally managed, the impact can include decreased
productivity, poor service quality, and increased employee turnover rates. Recent findings from 2020 to 2024
continue to confirm the significant relevance of these variables to employee performance across various
sectors (Wilhelmus Antonius Djula, Ruben Tuhumena, 2024).
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Given the importance of these factors and their impact on company operations, this study is highly relevant to
comprehensively analyze the influence of leadership, work motivation, work environment, competence, and
job satisfaction on employee performance at PT. GKS, a shopping mall management company in Tanah
Abang. Therefore, the main problem formulation of this study is how these five factors influence, both
partially and simultaneously, employee performance at PT. GKS. The novelty of this study lies in the specific
case study focus on the Tanah Abang shopping mall management company, namely PT. GKS, which has not
been widely studied holistically by considering all five independent variables simultaneously. This approach is
expected to produce more contextual and relevant findings for management in the dynamic commercial
property sector, as well as provide in-depth understanding and concrete managerial implications for improving
HR practices in similar companies and industries Preliminary insights from the analysis reveal a robust model
with substantial explanatory power (R² = 0.574), indicating that 57.4% of performance variability is accounted
for by these factors, alongside strong predictive relevance (Q² = 0.574). Notably, job satisfaction emerges as
the dominant driver (coefficient = 0.364), confirming that fair compensation, harmonious working
relationships, and recognition significantly boost performance in building management routines. Work
motivation follows closely (coefficient = 0.264), fueling enthusiasm and persistence, while the work
environment (coefficient = 0.245) ensures conducive conditions that enhance productivity and reduce turnover.
These elements synergize to create a supportive framework, where leadership and competence provide
foundational stability, ultimately guiding targeted HR interventions for sustained operational excellence at PT.
GKS (Rifka Alkhilyatul Ma’rifat, I Made Suraharta, 2024).
LITERATURE REVIEW
This study presents a relevant theoretical foundation for understanding the various factors that influence
employee performance, with a focus on leadership, work motivation, work environment, competence, and job
satisfaction. The study is systematically structured, starting from universal grand theories to more applicable
middle-range theories, thus establishing a robust and focused conceptual framework for this research. Each
theory is elaborated in depth through conceptual definitions from leading experts, while also analyzing its
relevance to the operational context of a shopping mall management company. With this approach, the study
not only presents a solid theoretical understanding but also confirms its relevance to the practical realities of
the workplace. In the context of PT. GKS, these theories align with empirical insights showing job satisfaction
as the dominant influencer (coefficient 0.364), where factors like fair compensation and harmonious relations
echoed in (Triastuti & Sanusi, 2025) drive performance amid routine operations. Similarly, work motivation
(0.264) and environment (0.245) synergize to foster productivity, as per (Susan Febriantina et al., 2024), while
leadership and competence provide stability. This integration yields a model with strong explanatory power
(R²=0.574), offering actionable HR strategies for dynamic sectors like Tanah Abang's commercial properties
(Tambunan & Pandiangan, 2024).
A. Human Resource Theory (Grand Theory)
As a grand theory, Human Resource Theory (HRM) asserts that people are an organization's most valuable
asset, not merely a production cost. Strategic HR management in the contemporary era integrates technologies
such as data analytics and artificial intelligence to design HR processes. This theory underpins the need for HR
to align strategy with organizational goals to achieve competitive advantage and sustainability (Tambunan &
Pandiangan, 2024). In the context of PT. GKS, this theory underpins the company's efforts to strategically
develop and manage employees for optimal performance.
B. Performance Theory (Middle-Range Theory)
Performance Theory, as a middle-range theory, focuses on the results of individual job functions. Employee
performance is defined as tangible behavior or work achievements that can be observed and evaluated (Leni
Wijaya, 2025) . Performance is also the manifestation of work done by employees as a basis for assessment,
the essence of which is still relevant as quoted in the article (Umniyyah et al., 2023). The modern performance
management approach emphasizes continuous feedback, collaborative goal setting, and real-time performance
monitoring to encourage continuous improvement and employee engagement (Ndlovu et al., 2024). This
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theory is the main framework for measuring and understanding the impact of independent variables on
employee performance at PT. GKS.
C. Motivation Theory (Middle-Range Theory)
Motivation theory explains the internal and external drives that drive individuals to work. Motivation is a
psychological factor that plays a significant role in triggering enthusiasm, directing energy, and determining
the effectiveness of a person's work. A high level of motivation is usually reflected in an individual's
enthusiasm, perseverance, and consistency in carrying out tasks, thus positively impacting performance
achievement (Cikita Fadila, Harsono, 2025). Expectancy theory, although developed in 1964, remains
essentially relevant and is cited in recent studies discussing motivation, emphasizing that motivation is
influenced by expectations of outcomes and the value placed on those outcomes (Kholilah et al., 2024).
Understanding motivation helps PT. GKS design an incentive system that encourages employee performance
in a shopping mall, considering that motivation indicators include development needs and opportunities.
D. Leadership Theory (Middle-Range Theory)
Leadership theory highlights how leaders influence subordinates to achieve organizational goals. Leadership is
the ability to persuade people to work persistently toward achieving goals with passion (Artaya et al., 2021).
Transformational leadership theory, for example, is associated with increased job satisfaction and performance,
focusing on employee development and creating a stimulating work environment. An effective leadership style
has a positive and significant impact on employee performance (Sokolic et al., 2024); (Wilhelmus Antonius
Djula, Ruben Tuhumena, 2024). This theory is important for analyzing how leadership styles at PT. GKS
affect performance.
E. Work Environment Theory (Middle-Range Theory)
Work Environment Theory discusses the conditions surrounding an individual that influence work
effectiveness, comfort, and motivation (Sitompul et al., 2022). The work environment includes physical
aspects such as facilities and non-physical aspects such as organizational culture and relationships between
employees (Sunarto & Anjani, 2022). A positive work environment can inspire employees and improve their
performance (Sudarsono & Syaiful Arif, 2024; Widowati et al., 2025), and even significantly influence
productivity. This theory is relevant to examining how environmental conditions at PT. GKS affect its
employees.
F. Competency Theory (Middle-Range Theory)
Competency Theory explains that individuals who possess a combination of job-relevant knowledge, skills,
and abilities will demonstrate superior performance. The definition of competency continues to evolve, even
linked to curriculum standards, and miscompetence between graduates and the job market is a concern
(Herbert et al., 2020 ). Competency has a positive and significant influence on employee performance (Jalil &
Kristiawati, 2024). This theory is crucial for analyzing the alignment of PT. GKS employee competencies with
job demands and how this affects their effectiveness in managing a shopping mall.
G. Job Satisfaction Theory (Middle-Range Theory)
Job Satisfaction Theory views job satisfaction as an individual's positive attitude toward their job, which is
influenced by various factors. Job satisfaction reflects how much an employee likes their job, resulting from an
evaluation of the fit between the individual and the work environment (Hutagalung & Asbari, 2020;
Wicaksono & Gazali, 2021). Factors such as motivation and compensation are often associated with job
satisfaction , and increased job satisfaction correlates with high discipline and can reduce absenteeism and the
desire for turnover. (Triastuti & Sanusi, 2025). This theory helps PT. GKS understand how employee
satisfaction impacts performance.
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RESEARCH METHODOLOGY
This study adopted a quantitative approach with an explanatory survey design, aiming to test the causal
relationship between independent and dependent variables (Muin, 2023; Purnawansa et al., 2022). The
quantitative approach was chosen because of its ability to statistically test hypotheses and generalize findings
to a wider population, providing objectivity to the study (Joaquim Pinto, 2024). The research location is PT.
GKS in Tanah Abang, Jakarta, and will be conducted in the period July 2025 - August 2025. The research
subjects are PT. GKS employees who are directly involved in the operational management of shopping
buildings. The selection of participants was carried out using a purposive sampling technique, with inclusion
criteria including permanent employees of PT. GKS with at least one year of work experience, having work
interactions, and being willing to be respondents, ensuring the relevance of the collected data, this study
involved 66 respondents (Yoon et al., 2012). Primary data collection was conducted through a closed-ended
questionnaire measuring variables of leadership, work motivation, work environment, competence, job
satisfaction, and employee performance. The questionnaire instrument will be validated through a validity test,
ensuring measurement accuracy, using a Likert scale (1-5) (Rahman, 2020). Data analysis will use Structural
Equation Modeling - Partial Least Squares (SEM-PLS) to test the relationship model between variables
simultaneously (Purwanto & Sudargini, 2021). SEM-PLS was chosen because of its effectiveness in handling
complex models with latent variables, does not require strict normality assumptions, and focuses on prediction,
making it flexible for various data scales and small samples. The entire analysis process will be assisted by
SmartPLS software.
Analysis of Research Results
The results of this study present an in-depth analysis using the Structural Equation Modeling-Partial Least
Squares (SEM-PLS) approach , which includes two main parts, namely the evaluation of the measurement
model (outer model) to assess the validity and reliability of the research instrument, and the analysis of the
structural model (inner model) to test the hypothesis of the relationship between variables (Muin, 2023).
A. Evaluation of Measurement Model (Outer Model)
Measurement model analysis aims to ensure that the indicators used to measure each variable (construct) are
valid and reliable. This assessment is based on three main criteria: convergent validity, discriminant validity,
and composite reliability.
Picture 1 Outer Model Schematic After Testing
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Convergent Validity
Convergent validity indicates the extent to which the indicators of a construct actually measure that construct.
This is evaluated through outer loadings and Average Variance Extracted (AVE) values.
Table 1 Outer Model
Based on the table above, it can be seen that all indicators have excellent outer loading values. The lowest
value is 0.779 (X4-1) and the highest is 0.872 (X2-3). All of these values are well above the recommended
threshold of 0.70. which shows that each indicator has a strong correlation with its latent construct, which
means that the indicators are valid in measuring the variables they represent. The AVE value measures how
much of the variance of an indicator can be explained by its latent construct. The recommended AVE value is
above 0.50. Based on the data listed in the table above, all constructs meet this criterion well. Because all AVE
values are greater than 0.50, it can be concluded that convergent validity for all constructs in this study has
been met. (Sitompul et al., 2022).
Discriminant Validity
Discriminant validity in this study was tested using the Fornell-Larcker Criterion, HTMT, and Collinearity
(VIF) approaches to ensure that each construct is unique and free from excessive overlap. (Sitompul et al.,
2022). Based on the results of the Fornell-Larcker Criterion, the AVE root value on the diagonal is higher than
the correlation between constructs, for example X1 (0.860), X2 (0.860), X3 (0.830), X4 (0.825), X5 (0.812),
and Y (0.807), all of which exceed the highest correlation values between variables, such as X5–Y (0.654) and
X2–Y (0.559). These results indicate that each construct is better able to explain its own indicators compared
to indicators of other constructs. Furthermore, the HTMT test also strengthens these results, where all values
are below the threshold of 0.90, with the lowest value in the X1–X3 relationship (0.437) and the highest values
in X3–X4 (0.884) and X5–Y (0.872). Although the last two values are close to the threshold, both are still
acceptable so they do not cause serious problems in construct discrimination. In addition, the results of the
collinearity test (VIF) show that all indicators are below the critical value of 5.00, with the lowest values in
Outer
Loadings
Cronbach's
Alpha
Composite
Reliability
( rho_a )
Composite Reliability
(rho_c )
AVE
X1-3
0.866
0.747
0.648
0.850
0.739
X1-4
0.853
X2-1
0.847
0.747
0.650
0.850
0.739
X2-3
0.872
X3-3
0.837
0.748
0.548
0.816
0.689
X3-5
0.823
X4-1
0.779
0.735
0.554
0.809
0.681
X4-2
0.868
X5-1
0.862
0.741
0.742
0.853
0.660
X5-3
0.780
X5-5
0.79 2
Y2
0.822
0.734
0.738
0.848
0.651
Y3
0.802
Y4
0.798
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X4-1 and X4-2 (1.154) and the highest value in X5-1 (1.765). All are still within a reasonable range so it can
be ascertained that there are no symptoms of multicollinearity (Ghozali, 2018) in (Widowati et al., 2025).
Thus, through these three approaches, it can be concluded that the research instrument meets the requirements
of discriminant validity and is suitable for use in further structural model analysis.
1. Internal Consistency Reliability
Reliability measures the internal consistency of indicators within a construct. This is evaluated using
Cronbach's Alpha and Composite Reliability. Cronbach's Alpha, n this value measures the lower limit of
reliability, The acceptable upper limit is > 0.70. The table above shows that all constructs have good
Cronbach's Alpha values, ranging from 0.734 to 0.748. Composite Reliability (rho_c), this metric is often
considered a better measure of reliability than Cronbach's Alpha in the context of PLS-SEM and the
recommended value is >0.70. The research data in the table above shows that the composite reliability value is
very high for all constructs, ranging from 0.809 to 0.853. Based on these two measurements, it can be
concluded that all instruments used in this study have a high and consistent level of reliability as stated by Hair
in (Widowati et al., 2025).
So, overall, the measurement model in this study is very strong , where all the instruments found are proven
valid and reliable, both in terms of convergent validity, discriminant validity, and internal consistency.
Therefore, further analysis of the relationships between variables (structural model) can be conducted with
high confidence. (Widowati et al., 2025).
B. Structural Model Analysis (Inner Model) and Hypothesis Testing
This analysis aims to test the research hypothesis regarding the influence of independent variables (X1, X2,
X3, X4, X5) on the dependent variable (Y). The test is conducted by looking at the path coefficient values
(Original Sample), T-statistics, and P-values presented in the table below, where the hypothesis is considered
statistically significant if the T-statistics value is > 1.96 and P-values < 0.05. (Sitompul et al., 2022).
Table 2 Hypothesis testing
Hypothesis Results:
1. The effect of X1 on Y, The hypothesis is accepted because there is a positive and significant influence
of X1 on Y (coefficient = 0.071; T = 1.965; P = 0.013). Although statistically significant, the strength
of the influence is very small.
2. The effect of X2 on Y, the hypothesis can be accepted because There is a positive and significant
influence of X2 on Y (coefficient = 0.264; T = 2.222; P = 0.026). This variable makes a significant
contribution to Y.
3. The effect of X3 on Y, This hypothesis is acceptable, where there is a positive and significant
influence of X3 on Y (coefficient = 0.042; T = 2.339; P = 0.034). Similar to X1, the influence is
significant but very weak from a practical perspective.
Hypothesis
Coefficient Line (O)
T Statistics
P Values
Decision
X1 → Y
0.071
1,965
0.013
Significant
X2 → Y
0.264
2,222
0.026
Significant
X3 → Y
0.042
2,339
0.034
Significant
X4 → Y
0.245
1,968
0.043
Significant
X5 → Y
0.364
2,789
0.005
Significant
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4. The effect of X4 on Y, This hypothesis is accepted, because there is a positive and significant
influence of X4 on Y (coefficient = 0.245; T = 1.968; P = 0.043). The influence of this variable is
almost as strong as X2.
5. The effect of X5 on Y, The hypothesis is accepted because there is a positive and highly significant
influence of X5 on Y (coefficient = 0.364; T = 2.789; P = 0.005). Variable X5 is the strongest predictor
for variable Y in this model.
Based on the results of the analysis above, the results of the hypothesis testing show that all independent
variables (X1, X2, X3, X4, and X5) have a positive and significant influence on the dependent variable (Y),
although the strength varies. Variable X5 is proven to be the strongest predictor with a path coefficient of
0.364 (T = 2.789; P = 0.005) and an effect value of f² = 0.177 which is classified as moderate, thus providing a
dominant contribution to the increase in Y. Furthermore, X2 also has a significant effect with a coefficient of
0.264 (T = 2.222; P = 0.026) and f² = 0.112 which indicates a moderate influence, followed by X4 with a
coefficient of 0.245 (T = 1.968; P = 0.043) and f² = 0.095 which is classified as small-moderate. Meanwhile,
X1 (coefficient 0.071; T = 1.965; P = 0.013; f² = 0.008) and X3 (coefficient 0.042; T = 2.339; P = 0.034; f² =
0.003) have a statistically significant influence, but their strength is very small in a practical context. Overall,
this research model is considered strong with an R² value of 0.574 and an adjusted R² of 0.538, which means
that approximately 57.4% of the variability in Y can be explained by the five independent variables, while the
rest is influenced by other factors outside the model. In addition, the Q² test (predictive relevance) produces a
positive value (> 0), which indicates that this model has good predictive power for the dependent variable.
(Rifka Alkhilyatul Ma’rifat, I Made Suraharta, 2024). Thus, the strategy for increasing Y should be prioritized
on strengthening factor X5, accompanied by intervention support on X2 and X4, while X1 and X3 are still
considered even though their influence is more limited.
Tabel 3 R-square Table
Q² (predictive relevance)
Q2=1 − ( 1−R2)
Because in the model there is only 1 dependent variable (Y), then obtained:
Q2 = 1 − (1−0.574)
= 1 − (0.426)Q²
= 0.574
The Q² value = 0.574 > 0, which means the model has excellent predictive power for the dependent variable
(Y). According to Chin (Rifka Alkhilyatul Ma'rifat, I Made Suraharta, 2024), a Q² value > 0.35 is considered
strong, so this result indicates that the research model is not only statistically significant but also predictively
relevant.
DISCUSSION OF RESEARCH RESULTS
The Influence of X1 on Y (Leadership on Performance)
The results of this study indicate that leadership has a positive and significant influence on employee
performance, indicated by a regression coefficient of 0.071 with a T value of 1.965 and a significance value of
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P of 0.013. However, it is important to note that the contribution of leadership to performance is relatively
small, with an f² value of 0.008. These findings show an interesting difference compared to previous research.
As in the study (Artaya et al., 2021)and (Sokolic et al., 2024) They found that transformational leadership had
a very strong influence on performance. This difference in results is likely explained by the specific context of
PT. GKS, where work systems tend to be routine and structured. In this work environment, variations in
leadership style may not significantly impact employee performance.
Thus, while leadership has been shown to be significant, its role is not as strong or dominant in the building
management sector as it is in other contexts. This highlights a potential research gap, where the quality of
leadership style implementation in building management companies needs further study to understand the
mechanisms of its influence more deeply.
The Effect of X2 on Y (Motivation on Performance)
This study shows that motivation has a positive and significant influence on employee performance. This is
supported by a regression coefficient of 0.264, a T-value of 2.222, and a significance level of 0.026. The
influence of motivation is also classified as moderate, indicated by an f² effect value of 0.112. This finding is
consistent with previous research, such as that conducted by (Kholilah et al., 2024)and (Cikita Fadila, Harsono,
2025), which also confirmed that motivation plays a crucial role in increasing productivity. Specifically, at PT.
GKS, motivation appears to significantly boost employee performance. However, interestingly, this influence
is not as significant as job satisfaction on performance at the company.
This situation indicates a relevant research gap. Further studies are needed to better understand how
motivation works, both directly and indirectly through job satisfaction as a mediating variable, in efforts to
improve employee performance in similar contexts.
The Effect of X3 on Y (Competence on Performance)
The results of this study indicate that employee competence has a positive and significant influence on
performance, with a coefficient value of 0.042, a T value of 2.339, and a significance level of P of 0.034.
However, the strength of the influence of competence on performance was found to be very small, as indicated
by the f² effect value of 0.003. This finding contrasts with previous studies, such as (Herbert et al., 2020)and
(Jalil & Kristiawati, 2024)which found that competence has a strong influence on productivity. This difference
in results may be explained by the specific characteristics of PT. GKS, where employees tend to have
relatively uniform competency standards. Consequently, variations in competency among employees may not
be large enough to significantly explain differences in performance levels.
This condition highlights an important research gap, There is a need to test more specific competency
dimensions. In particular, soft skills competencies are considered highly relevant and warrant further research,
particularly given the nature of the shopping mall industry, which relies heavily on the quality of customer
service.
The Influence of X4 on Y (Work Environment on Performance)
This study revealed that the work environment has a positive and significant influence on employee
performance. This is supported by a regression coefficient of 0.245, a T-value of 1.968, and a significance
level of P of 0.043. The resulting effect level is classified as small to moderate, indicated by an f² value of
0.095. These findings are consistent with previous studies, such as those conducted by (Sitompul et al., 2022)
and (Widowati et al., 2025). Both studies also emphasize the importance of the work environment, both
physical and psychosocial, in driving increased productivity. At PT. GKS, it is clear that working conditions
and the quality of social relationships in the workplace contribute significantly to employee performance.
However, these results also highlight a crucial research gap, there is a need for more detailed research to
specifically differentiate the influence of the physical environment (such as facilities and comfort) and the
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psychological environment (including work relationships and organizational culture) on employee
performance.
The Effect of X5 on Y (Job Satisfaction on Performance)
Of all the variables studied, job satisfaction proved to be the most dominant variable in influencing employee
performance. This is indicated by the highest regression coefficient of 0.364, a T-value of 2.789, and a very
low P-significance level of 0.005. The strength of its influence is classified as moderate to strong, as indicated
by the f² effect value of 0.177. These findings significantly reinforce the findings of previous research , such as
that conducted by Wicaksono & Gazali (2021), which also stated that job satisfaction is a key factor in
increasing productivity. In the specific context of PT. GKS, job satisfaction appears to function as a key driver
of performance. This is understandable because job satisfaction encompasses aspects directly perceived and
valued by employees, such as fair compensation, harmonious working relationships, and recognition for their
contributions.
The novelty of this study lies in the assertion that job satisfaction has a greater influence than leadership or
competence in influencing performance in the building management sector. Thus, these results underscore that
job satisfaction should be a primary focus of intervention in human resource management strategies in similar
work environments.
Table 4 SRMR Model
Overall Model Quality
This research model demonstrates substantial explanatory power and predictive power, This is proven by the
coefficient of determination (R²) value of 0.574, which indicates that 57.4% of the total variation in employee
performance can be explained jointly by the five independent variables studied, namely Leadership,
Motivation, Competence, Work Environment, and Job Satisfaction. In addition to its ability to explain the
phenomena that occur, this model also has strong predictive power , as indicated by the predictive relevance (
Q² ) value which is also 0.574. This high Q² value firmly confirms that the model is not only able to explain
the relationship between existing variables, but also has a good ability to predict employee performance in the
future.
Thus, the results of this study have twofold implications, including not only providing empirical evidence
regarding the significance of the influence of these variables, but also demonstrating strong predictive
relevance. One of the main methodological contributions of this study is the explicit emphasis on the
presentation and interpretation of predictive values (Q²). This aspect often receives less attention or is rarely
addressed in previous studies, thus this study successfully fills a methodological research gap. Thus , overall,
this study not only contributes to empirical understanding in the field of human resource management through
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XXVI October 2025 | Special Issue on Education
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Page 8773
its significant findings, but also provides valuable methodological contributions in the analysis and
interpretation of the model.
CONCLUSION
Based on comprehensive data analysis, this study concludes that leadership, work motivation, work
environment, competence, and job satisfaction significantly influence the performance of PT. GKS employees,
where the key findings are is:
1. Job satisfaction is the most dominant predictor of employee performance at PT. GKS, with a moderate
to strong influence (coefficient = 0.364). This confirms that factors such as fair compensation,
harmonious working relationships, and recognition contribute significantly to employee performance at
the building management company.
2. Work motivation also has a positive and significant influence on employee performance (coefficient =
0.264).
3. Work Environment was found to have a positive and significant effect on performance (coefficient =
0.245), underscoring the importance of conducive working conditions.
4. Leadership and Competence, although having a positive and significant influence, are relatively small
in the context of PT. GKS. Variations in leadership styles may be less dominant because the routine
work system and uniform competency standards limit the impact on performance differences.
5. This research model demonstrates substantial explanatory power (R² = 0.574), meaning 57.4 % of the
performance variation can be explained by these five variables. The model also has strong predictive
power (Q² = 0.574), indicating its relevance for predicting future performance.
6. This study makes a methodological contribution by emphasizing the interpretation of predictive
relevance (Q²) values, an aspect that is often underexplored.
Implications
The implications of this research can be divided into managerial implications and implications for further
research:
Managerial Implications
1. Prioritize Job Satisfaction: Given its dominance, PT. GKS management must make job satisfaction a
key focus of its HR management strategy. This could mean reevaluating the compensation system,
improving employee relationships, and ensuring proper recognition for employee contributions.
2. Maintain and Improve Motivation & Work Environment: Relevant incentive programs and efforts
to create a positive work environment (both physical and psychological) need to be continuously
maintained and improved to support employee performance.
3. Evaluation of Leadership Strategy and Competency Development: Although the effects were
small, leadership and competency remained significant. Management may consider leadership training
focused on effectiveness in routine contexts, as well as developing soft skills competencies more
relevant to customer service in the shopping industry, which may not have been optimally measured in
this study.
4. Focus on Targeted Interventions: Instead of conducting generic interventions, PT. GKS is advised to
design programs that specifically target improving job satisfaction, motivation, and the work
environment as key levers of performance.
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Implications for Further Research (Research Gaps)
1. Differentiation of Work Environment: More in-depth research is needed to specifically differentiate
the influence of the physical environment (e.g., facilities, comfort) and the psychological environment
(e.g., organizational culture, employee relations) on employee performance.
2. Specific Competency Dimensions: Future research could explore more specific competency
dimensions, particularly soft skills relevant to customer interactions in the shopping mall management
industry.
3. Motivation and Job Satisfaction Mediation: Further studies could investigate how motivation works,
either directly or indirectly through job satisfaction as a mediating variable, in improving employee
performance in similar contexts.
4. Quality of Leadership Style Implementation: Considering the non-dominant role of leadership at PT.
GKS, research can further examine the quality of leadership style implementation and its influence
mechanisms in a routine and structured work system.
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Attachment
Table: Path Coefficients and Significance
Path
Original Sample (O)
Sample Mean (M)
Standard Deviation (STDEV)
T Statistics
P Values
X1 → Y
0.071
0.077
0.108
1.965
0.013
X2 → Y
0.264
0.273
0.119
2.222
0.026
X3 → Y
0.042
0.038
0.125
2.339
0.034
X4 → Y
0.245
0.235
0.127
1.968
0.043
X5 → Y
0.364
0.373
0.131
2.789
0.005
