The
Influence of Use of Technology, Flexitime, and Tele Working on
Employee Performance with Work-Life Balance as a Mediating Variable
Catherine Liana Suhandi1*, Kurnadi
Gularso2
Universitas Bunda Mulia, Tangerang, Indonesia1*2
Email: [email protected], [email protected]
In an era
characterized by rapid technological advancements and shifting work paradigms,
understanding how factors such as technology use, flexible working hours, and
teleworking impact employee performance is crucial. This study explores these
dynamics within the context of Generation Z employees, a demographic
increasingly prevalent in today's workforce. This study aims to examine the
influence of Use of Technology, Flexitime, and Tele
Working on the performance of Generation Z employees in the Tangerang and
Jakarta areas, with work-life balance as a mediating variable. Adopting a
quantitative approach, this study used questionnaires distributed to Gen Z
employees from various industrial sectors. Data analysis was conducted using
regression techniques and mediation analysis. The results showed that Use of
Technology, Flexitime, and Tele Working significantly
contributed to the performance of Gen Z employees in the region. In addition,
work-life balance was shown to mediate the relationship between these factors
and Gen Z employee performance. These findings provide important insights for
organizations and HR managers in designing policies and strategies that suit
the preferences and needs of the younger generation in a rapidly changing work
environment. This research also has the potential to contribute to the
literature on human resource management, especially in the context of youth
work and Use of Technology.
Keywords: Use of Technology, Flexitime, Tele Working, Work-Life Balance, Employee
Performance.
Human resources play a key role in the success of a
company. Company performance is very dependent on the potential and
capabilities of its human resources. Every company has a vision, mission and
goals to achieve, and to realize these goals, quality human resources are
needed. Effective human resource management is crucial in ensuring
organizational success. To achieve optimal results, there needs to be
productive efforts from all parties involved. High employee productivity will
result in progress for the company and ensure the achievement of quality and
maximum results (Pelealu 2022).
Humans are the most important resource in a company.
The importance of human resources in a company is because the mental and
physical abilities possessed by these human resources can be utilized as an
effort to achieve success for the company or organization. (Findriyani and Parmin 2021).
Factor that influences performance are
personal factors indicated by the individual's level of skill, competence,
motivation and commitment (Junaidi and Mildawati 2022);(Riwukore 2022).
After the COVID-19 pandemic turned into an endemic, economic conditions
experienced a significant improvement, where restrictions on work became
looser, especially after the government announced via television media that the
use of masks was prohibited. Again required.
However, companies are faced with increasing challenges with rapid
technological advances, so human resources in companies need to adapt so that
company operations remain smooth and achieve goals in accordance with
predetermined targets.
Every employee who feels satisfied with their work will improve their
expected performance. If employee job satisfaction is not met, then work will
decline, and it will even make employees angry, resulting in resistance.
Therefore, companies need to set work standards according to the time,
abilities and physical characteristics of employees. Workload is a worker's
assessment of their motivational capacity and the work demands given and is
also influenced by various activities (Wijayanti, Inggit Dyaning. 2023).
To achieve the targets set by the company, employees will be burdened
with workloads. Companies are expected to provide burden
work according to the portion or abilities of the employee. If the
workload is heavy it will have an impact on work results due to physical
fatigue and emotional instability (Wijayanti, Inggit Dyaning. 2023).
Generation Z, or what is known as Gen-Z, has become the focus of
attention in recent years in Indonesia. The Gen-Z population in Indonesia has
almost reached 75 million individuals, which is equivalent to around 27 percent
of the total national population. Generation Z, or Gen-Z, are those born
between 1997 and 2012. Thus, on Currently,
Generation Z in Indonesia consists of individuals aged between 12 and 27 years (Maimunah, Afiatin, and Febriani 2024).
Generation Z has many unique qualities that differentiate it from other
age groups or generations. They are often referred to as generations which very tech savvy, able to use technology very well in everyday life.
Apart from that, they also have high concern for social and environmental
issues. This uniqueness can also be seen in the career aspect, where Gen-Z is
predicted to become the largest productive age group in 2045.
The current
phenomenon is that Indonesia in 2045 will enter a demographic phase, namely a
phase where the number of productive age (aged 15-64 years) is greater than the
number of unproductive population (under 14 years or over 65 years). Predicted
by Indonesia own the
percentage of the population of productive age is 70% and 30% is the population
of non-productive age (Samad 2020), this can have an impact on two
possibilities, namely demographic advantage or demographic disadvantage.
Demographic advantages can be in the form of quality human resources so that
they can increase the country's economic growth, conversely, if demographic
disadvantages occur, the productive age generation does not have good quality,
which can increase the unemployment rate in Indonesia.
According to this
data, Generation Z, born between 1997 and 2012, dominates with around 74.93
million people, or 27.94% of the total population. This generation is still
young to early teens, show great potential for future progress
and change (Fitria 2023).
Generation Z, often
referred to as Gen Z and colloquially known as zoomers,
refers to individuals born between 1997 and 2012, a demographic group that
follows the Millennials and precede Generation Alpha grew up with
technology, the internet and social media. Generation Z has several
characteristics, namely big ambitions to achieve success, likes simple,
practical and instant things, wants freedom, has high self-confidence, likes
details, wants recognition, likes information technology, is more realistic and
more tolerant and appreciative. differences (Fitria 2023).
Generation Z is more
optimistic in the world of professional work and is very enthusiastic and ready
to face all the challenges that will arise as well as Generation Z Already getting used to
technology has an impact on working faster and more efficiently (Sidorcuka and
Chesnovicka 2017).
Generation Z has
several special characteristics or is quite different from previous
generations. Generation Z likes to collaborate in doing work, is flexible,
responsive to challenges and can be motivated by achievement and likes to
explore new ways of solving problems, therefore, organizations should adapt
their leadership style to maximize the performance of generation Z employees (Widiyanti, 2021).
Data spread
throughout the world shows that resignations aresignificantfrom generation Z will still
happen. A recent survey conducted by Lever (2022) shows that around 40% of Gen
Z members want to leave their workplace within two years. The three main
reasons young employees leave their jobs are referred to as job dissatisfaction
or burnout (Deloitte 2022).
To increase the
productivity of Generation Z employees, Hanifah and Wardono
(2020) argue that a pleasant workplace will make employees remain loyal and
work better for the company. According to He (2019), work environment variables
influence employee work productivity, and Atik (2023) found that work discipline
influences work productivity.
GenVersionZ dominates today's workforce. Therefore, to maximize
organizational effectiveness, management must understand what influences the
performance of millennial and Z generation employees (WaVworuntu, Kainde, and Mandagi 2022).
Worker forced to use digital
communication media because of the rapid development of communication
technology. This allows them to complete tasks outside of work hours, resulting
in less time with family orintensitymaintaining
health (Putra and
Nugroho 2024).
Suitability between work
responsibilities and an employee's personal life is referred to as work-life
balance (Nurjanah and
Indawati 2021).
Recent research Wiradendi Wolor (2020) proves that work-life balance
has a positive and significant effect on employee performance. The authors then
further summarize related phenomena problem work-life balance in the Gen Z
generation in two important ways. First, this generation prefers work balance
and flexible working hours to suit their lifestyle (Waworuntu,
Kainde, and Mandagi 2022).
Job satisfaction is
another factor that can influence worker performance, besides the balance
between work and personal life. Other researchers (Ben-Othman,
J�udu, and Bhat 2020) agree that increasing job
satisfaction helps employees stay employed and stay motivated to achieve the
best work results for the company.
Tele Workingrequires Technology and will encourage
increased Flexitime which leads to increased Employee
Performance.According toExisting research on teamwork, effective team
technology is characterized by: �(1) sharing of unique information possessed by
team members in face-to-face environments and openness of information in
virtual environments as well as (2) implementation of closed technology
procedures that acknowledge receipt of information and clarify any differences
in interpretive information�(Salas,
Shuffler, Thayer, Bedwell, and Lazzara, 2022).
Use Of Technologyis an employee's overall perception
of sharing information, ideas, and emotions among individuals, team leaders,
and team members, to effectively and creatively integrate knowledge andinformationprofessionalism
between teams(He, Jia,
McCabe, Chen, and Sun, 2022).
Technology has an
impact on a person's abilities, namely the ability to use it socially and
personally(Trenholm and
Jensen, 2022). Therefore, technological
capabilities are very important for employee performance(Mar�n and
Roelofs, 2022). This may be used to see the
wide range of behaviors that each person has. These variations originate from
cognition, emotions, internal motivation and circumstances, culture, and
external interpersonal relationships.
Furthermore,
flexibility is defined as an illustrative idea behavior
necessary for job performance
and organizational results, as demonstrated by skills, character, qualities,
abilities, capacities, and capabilities(Alsabbah and Izwar Ibrahim, 2022).
This study will
consider this definition as an idea of competence describe behavioral
requirements necessary to do a job well. Technology effectiveness refers to the
way team members formally or informally exchange new and important information(Yang, Lee, and
Cheng, 2022).
Through their experience of exploring and
experimenting with new ideas, frontline employees can develop better
creativity, implying a positive moderating effect of the effectiveness of Use Of Technology on the relationship between Tele Working and
Use Of Technology Gen Z.
Based on the previous description, the author is
interested in examining the influence of flexitime,
teleworking, and the use of technology on employee performance which is
mediated by work-life balance in Generation Z. This research identifies several
problems, such as Generation Z's challenges in adapting to rapidly changing
technology in the workplace. , the need for balance
between work responsibilities and personal life, as well as the effectiveness
of work flexibility in improving their performance. The scope of this research
is focused on Generation Z in companies in Tangerang and Jakarta, with
independent variables including the use of technology, flexitime
and teleworking, as well as a mediator variable, namely work-life balance. This
research aims to examine the direct influence of the three independent
variables on employee performance and how work-life balance mediates this
relationship. It is hoped that this research will provide benefits for theory
developers, researchers, academics, government and further authors as a
reference for developing better research and policies regarding the performance
of Generation Z.
The research design functions as a guide for the
entire research process, from planning to implementation, and serves as a
reference for parties involved in the research. According to Sekaran and Bougie
(2019), research design includes the process of collecting, measuring and
analyzing data that is planned based on the research statement, with the sample
data collection method following the variables and measures determined in
hypothesis testing. This research uses a quantitative method with a survey approach,
which is based on the philosophy of positivism and analyzes data through
research instruments using statistical techniques(Sugiyono 2023). The research subjects were Gen Z employees in Jakarta and Tangerang,
with research objects including the direct and indirect influence between the
use of technology, flexitime and teleworking on
employee performance, with work-life balance as a mediating variable. The
variables used include the dependent variable (Employee Performance), the
independent variable (Use of Technology, Flexitime,
Teleworking), and the mediating variable (Work-Life Balance), with measurements
using ordinal and Likert scales.(Sugiyono 2023). The research population includes workers in DKI Jakarta and Tangerang,
with a purposive sampling technique in accordance with research criteria
(Martono, 2020). Data was collected through questionnaires and analyzed using SmartPLS version 4.0 to test hypotheses descriptively and
inferentially, with this method being able to handle latent variables and
untested theoretical analysis (Ghozali, 2021).
Validity and reliability tests were carried out to ensure the quality of the
research instrument, with validity measured based on the holding factor value
and reliability through the Cronbach's Alpha value (Hair et al., 2019).
This research consists
of the variables Use of Technology, Flexitime, Tele
Working, Work-Life Balance and Employee Performance. To find out respondents'
responses, descriptive statistical analysis of all variables is needed.
Descriptive statistical analysis of research variables is used to determine the
tendency of answers to the questionnaire or the extent to which respondents'
responses match the answer choice categories using a Likert scale from 1
(strongly disagree) to 5 (strongly agree) to the statements for each variable.
The collected data is then tabulated to determine the distribution of
respondents' answers to each indicator for each research variable.
Determining
qualifications is used for each variable, so what must be determined first is
the width of the class interval. According to Hadi in Sholichah
(2008:32) says that, "to determine the width of class interval (i) is the measurement distance (R) divided by the number of
class intervals (K). Thus, the formula used to determine the length of interval
class (i)� is as follows:
i=
The questionnaire uses
a multilevel measurement type with a Likert Scale. The range used to measure
the degree of very poor or very good for variable indicators in this study is 1
(one) to 5 (five), namely with the following weighting levels:
1. Answers strongly disagree with a weight of 1;
2. Disagree answer, weight value 2;
3. If you disagree, the score is 3;
4. Agree answer, weight value 4;
5. The answer is strongly agree, a score of 5
Based on the formula
above, the scale range can be obtained with the following calculations:
Highest answer score = 5
Lowest answer score = 1
So
the length of interval class (i) is:
i=
These value weights are then interpreted using an interval scale.
Furthermore, the criteria interval is 0.80 so that from these provisions, the
assessment criteria are:
1. If the value is between 1.00 � 1.80, it means the criteria are very low
2. If the value is between 1.81 � 2.60, it means the criteria are low
3. If the value is between 2.61 � 3.40, it means the criteria are
sufficient
4. If the value is between 3.41 � 4.20, it means the criteria are high
5. If the value is between 4.21 � 5.00, it means the criteria are very high
The descriptive statistical analysis provides a foundational
understanding of respondents' perceptions regarding the variables of Use of
Technology, Flexitime, Tele Working, Work-Life
Balance, and Employee Performance. By employing a Likert scale ranging from 1
(strongly disagree) to 5 (strongly agree), the study captures the degree of
agreement among respondents with statements related to these variables.
The calculation of the class interval width, using a formula based on
the highest and lowest scores, results in an interval class of 0.8. This
interval scale helps categorize respondents' responses into meaningful ranges,
which are essential for interpreting the data effectively.
The interval criteria are designed to translate numerical scores into
qualitative assessments, ranging from very low to very high. For instance,
scores between 1.00 and 1.80 indicate a very low perception, while scores from
4.21 to 5.00 suggest a very high perception of the variables in question. This
approach allows for a nuanced understanding of how respondents view each factor
and their overall impact on employee performance.
Analyzing
the descriptive statistics in this manner provides insight into the general
trends and tendencies in respondents' perceptions. This information is crucial
for identifying areas where employees feel positively or negatively influenced
by the variables under study. Furthermore, it aids in tailoring strategies and
interventions to address specific needs and preferences of Generation Z
employees in the context of their work environment.
Data analysis
techniques using SmartPLS to assess the outer model
are Convergent Validity and Composite Reliability. Convergent validity of the
measurement model with reflexive indicators is assessed based on the
correlation between item scores/component scores estimated with PLS software.
An individual reflexive measure is said to be high if it correlates more than
0.70 with the construct being measured. In this research, a loading factor
limit of 0.70 will be used.
Table 1. Outer
Loadings
|
Variable |
Item Code |
Outer Loading |
Information |
|
Employee Performance |
EP1 |
0.866 |
Valid |
|
EP2 |
0.790 |
Valid |
|
|
EP3 |
0.813 |
Valid |
|
|
EP4 |
0.807 |
Valid |
|
|
EP5 |
0.790 |
Valid |
|
|
EP6 |
0.822 |
Valid |
|
|
Flexi-Time |
FT1 |
0.903 |
Valid |
|
FT2 |
0.842 |
Valid |
|
|
FT3 |
0.877 |
Valid |
|
|
FT4 |
0.848 |
Valid |
|
|
FT5 |
0.862 |
Valid |
|
|
FT6 |
0.757 |
Valid |
|
|
Tele-Working |
TW1 |
0.813 |
Valid |
|
TW2 |
0.871 |
Valid |
|
|
TW3 |
0.879 |
Valid |
|
|
TW4 |
0.865 |
Valid |
|
|
TW5 |
0.815 |
Valid |
|
|
TW6 |
0.850 |
Valid |
|
|
Use of Technology |
UT1 |
0.858 |
Valid |
|
UT2 |
0.878 |
Valid |
|
|
UT3 |
0.834 |
Valid |
|
|
UT4 |
0.723 |
Valid |
|
|
UT5 |
0.729 |
Valid |
|
|
Work-Life Balance |
WLB1 |
0.740 |
Valid |
|
WLB2 |
0.837 |
Valid |
|
|
WLB3 |
0.847 |
Valid |
|
|
WLB4 |
0.799 |
Valid |
|
|
WLB5 |
0.844 |
Valid |
|
|
WLB6 |
0.772 |
Valid |
Source: Data
Processing Results (2024)
Validity testing for
indicators uses the correlation between item scores and construct scores. The
indicator is said to be valid if the loading factor value is above the
recommended value, namely 0.7. From table 1, it can be seen that all indicators
have a loading factor value > 0.7, so it is said that the indicators are
valid, so that no constructs for all variables have been eliminated from the
model.
The assessment of the
outer model, or measurement model, using SmartPLS
focuses on evaluating Convergent Validity and Composite Reliability. Convergent
validity is a critical measure that ensures each reflexive indicator correlates
strongly with its respective construct. In this study, a threshold loading
factor of 0.70 was applied to determine the validity of the indicators.
Table 1 reveals that
all indicators for the constructs�Employee Performance, Flexi-Time,
Tele-Working, Use of Technology, and Work-Life Balance�exceed the loading
factor threshold of 0.70. This high level of correlation indicates that the
indicators effectively measure their respective constructs, confirming their
validity. For example, Employee Performance indicators (EP1 to EP6) all have
loading factors well above 0.70, with the lowest being 0.790, suggesting strong
measurement accuracy for this construct. Similarly, indicators for Flexi-Time,
Tele-Working, Use of Technology, and Work-Life Balance also demonstrate high
validity, with values consistently above the critical threshold.
The consistent
validity across all indicators means that the measurement model reliably
captures the constructs of interest without the need for eliminating any
constructs. This strong convergent validity is crucial for ensuring that the
data accurately reflects the theoretical constructs being studied and
reinforces the robustness of the research model.
Overall, the results
confirm that the measurement model used in this research is sound and
effectively operationalizes the key variables of interest. This reliability in
measurement paves the way for robust analysis and interpretation of the
relationships between Use of Technology, Flexitime,
Tele-Working, and Employee Performance, with Work-Life Balance as a mediating
variable.
Validity
and reliability criteria can also be seen from the reliability value of a
construct and the Average Variance Extracted (AVE) value of each construct. The
AVE value is at least 0.5, indicating good convergent validity. This means that
one latent variable can explain more than half of the average variance of the
indicators (Ghozali, 2021).
Table 2. Average Variance Extracted (AVE) Test Results
|
Variable |
Average variance extracted (AVE) |
Information |
|
Employee Performance |
0.664 |
Valid |
|
Flexi-Time |
0.721 |
Valid |
|
Tele-Working |
0.721 |
Valid |
|
Use of Technology |
0.651 |
Valid |
|
Work Life Balance |
0.652 |
Valid |
Source: Data Processing Results (2024)
Based
on tables 1 and 2, it shows that the outer loading value of all indicators that
meet the variable requirements is above 0.7, which indicates that all
constructs in the estimated model meet the criteria and are declared valid.
Apart from that, all Average Variance Extracted (AVE) values are
> 0.50. Based on table 4.8, it can be seen that the highest AVE value is for
the Tele-Working and Flexi-Time variables with a value of 0.721. Meanwhile, the
lowest AVE value is found in the Use of Technology variable with a value of
0.651. Thus the data from this research can be said to
meet the test requirements for convergent validity.
The Average Variance Extracted (AVE) test is a critical measure for
assessing the convergent validity of the constructs in the research model. The
AVE value indicates the extent to which a latent variable can explain the
variance of its indicators. For robust convergent validity, the AVE should be
at least 0.50, meaning the construct explains more than half of the variance in
the indicators.
Table 2 shows that all constructs in the study�Employee Performance,
Flexi-Time, Tele-Working, Use of Technology, and Work-Life Balance�exceed the
0.50 threshold for AVE. Specifically, Flexi-Time and Tele-Working have the
highest AVE values at 0.721, indicating that these constructs account for a
substantial portion of the variance in their respective indicators. Use of
Technology has the lowest AVE at 0.651, but still well above the acceptable
threshold.
The consistent performance of all constructs in meeting the AVE criteria
confirms that each latent variable demonstrates good convergent validity. This
validation supports the reliability of the measurement model and ensures that
the constructs effectively capture the variance in their indicators,
reinforcing the robustness of the research findings.
Composite
reliability measuring the true reliability
value of a construct. The composite reliability value must be greater than 0.7,
and the Cronbach's Alpha value must be greater than 0.7 (Ghozali,
2021). Composite reliability measures the true reliability value of a variable.
Therefore, every latent variable that has a Composite Reliability value >
0.7 means that it has been able to meet the Composite reliability requirements.
Table 3. Composite Reability Test Results
|
Variable |
Composite reliability |
Information |
|
Employee Performance |
0.922 |
Reliable |
|
Flexi-Time |
0.939 |
Reliable |
|
Tele-Working |
0.939 |
Reliable |
|
Use of Technology |
0.903 |
Reliable |
|
Work Life Balance |
0.918 |
Reliable |
Source: Data Processing Results (2024)
Based
on table 3, it shows that the composite reliability value for all variables in
the variables is above 0.7, which indicates that the variable is said to be
reliable because it meets the requirements for a composite reliability value
> 0.70.
Table 3 demonstrates that all constructs in this study�Employee
Performance, Flexi-Time, Tele-Working, Use of Technology, and Work-Life
Balance�achieve Composite Reliability values well above the 0.70 threshold.
Specifically, Flexi-Time and Tele-Working both have values of 0.939, while
Employee Performance, Use of Technology, and Work-Life Balance also show strong
reliability with values of 0.922, 0.903, and 0.918, respectively.
Composite Reliability is an essential metric for evaluating the
reliability of constructs in a research model. It assesses how consistently a
construct measures its intended concept. For a construct to be considered
reliable, its Composite Reliability value should exceed 0.70.
The high Composite Reliability values indicate that each construct
reliably measures the intended variables, meeting the necessary criteria for
reliability. This reinforces the validity of the measurement model and supports
the robustness of the research findings.
Cronbach's
Alpha and Composite Reliability are two ways that can be
used to test PLS. Composite reliability calculates the actual reliability value
of a construct by looking at the Cronbach's Alpha value of the indicator block
that measures the construct. The Cronbach's Alpha value of the construct is
declared reliable if the value is greater than 0.60. Reliability tests are
carried out to ensure that the instrument measures the precision, accuracy and
consistency of the construct. In this research, the reliability of the
instrument was tested by looking at the Cronbach's Alpha value. Research tools
are considered reliable if the value is more than 0.60 (Ghozali,
2021). Therefore, the standards for making reliability test decisions are as
follows:
a.
If the Cronbach's
Alpha value is > 0.60, then the question items in the questionnaire are
reliable.
b.
If the Cronbach's
Alpha value is <0.60, then the question items in the questionnaire are not
reliable.
Table 4. Cronbach's Alpha Test Results
|
Variable |
Cronbach's alpha |
Information |
|
Employee Performance |
0.899 |
Reliable |
|
Flexi-Time |
0.922 |
Reliable |
|
Tele-Working |
0.922 |
Reliable |
|
Use of Technology |
0.864 |
Reliable |
|
Work Life Balance |
0.893 |
Reliable |
Source: Data Processing Results (2024)
Based on Table 4, the Cronbach's
Alpha value of the employee performance variable is 0.899, the flexi-time and
tele-working variable is 0.922, the use of technology variable is 0.864 and
work-life balance is 0.893. All Cronbach's Alpha (α) values are
> 0.70 so all variables are said to be reliable.
Table 4 shows that all variables
in this study�Employee Performance, Flexi-Time, Tele-Working, Use of
Technology, and Work-Life Balance�have Cronbach's Alpha values exceeding 0.60.
Specifically, the values are 0.899 for Employee Performance, 0.922 for both
Flexi-Time and Tele-Working, 0.864 for Use of Technology, and 0.893 for
Work-Life Balance. These high values indicate that the questionnaire items are
reliable and consistently measure their respective constructs
Cronbach's Alpha is a key
indicator for assessing the internal consistency and reliability of measurement
instruments. It measures how well a set of items on a questionnaire correlates
to each other, indicating the degree to which they reliably measure the same
construct. A Cronbach's Alpha value greater than 0.60 is considered acceptable
for ensuring that the instrument accurately and consistently measures the
intended constructs.
The results confirm that the
measurement instrument used in this research is robust, providing precise and
accurate assessments of the variables under study. This reliability enhances
the credibility of the research findings and supports the validity of the
overall measurement model.
Validity
is defined as a measure that should be measured (Ghozali,
2021). The validity of the indicators for each research variable can also be
carried out by testing discriminant validity, namely by checking the cross-loading
value, namely the correlation coefficient of the indicator with its construct
compared with the correlation coefficient with other constructs. Validity
testing is carried out to ensure that the measuring instrument performs its
measurement function correctly (Ghozali, 2016). In
SMART-PLS, the Fornell-Larcker criterion and cross loading test can be used to
assess the validity of discriminant testing. In the Fornell-Larcker test, the
construct indicator value must be higher than the construct correlation with
other latent variables, while the cross-loading test must show that the root
AVE of the construct is higher than the construct correlation with other latent
variables (Sekaran & Bougie, 2019). In this validity test, the decision-making
criterion is that the statement item in the questionnaire is valid if the
r-count is greater than the r-table, and conversely if the r-count is smaller
than the r-table, then the statement item in the questionnaire is invalid.
Table 5. Discriminant Validity Test Results
|
CODE |
E.P |
FT |
TW |
UT |
WLB |
|
EP1 |
0.866 |
0.563 |
0.633 |
0.525 |
0.665 |
|
EP2 |
0.790 |
0.461 |
0.515 |
0.469 |
0.498 |
|
EP3 |
0.813 |
0.571 |
0.592 |
0.535 |
0.545 |
|
EP4 |
0.807 |
0.635 |
0.612 |
0.478 |
0.653 |
|
EP5 |
0.790 |
0.614 |
0.705 |
0.477 |
0.651 |
|
EP6 |
0.822 |
0.468 |
0.573 |
0.484 |
0.578 |
|
FT1 |
0.607 |
0.903 |
0.630 |
0.441 |
0.604 |
|
FT2 |
0.585 |
0.842 |
0.557 |
0.501 |
0.598 |
|
FT3 |
0.606 |
0.877 |
0.652 |
0.431 |
0.606 |
|
FT4 |
0.547 |
0.848 |
0.571 |
0.408 |
0.603 |
|
FT5 |
0.640 |
0.862 |
0.569 |
0.480 |
0.603 |
|
FT6 |
0.481 |
0.757 |
0.384 |
0.414 |
0.461 |
|
TW1 |
0.560 |
0.543 |
0.813 |
0.468 |
0.525 |
|
TW2 |
0.706 |
0.598 |
0.871 |
0.487 |
0.613 |
|
TW3 |
0.624 |
0.593 |
0.879 |
0.473 |
0.558 |
|
TW4 |
0.622 |
0.541 |
0.865 |
0.436 |
0.610 |
|
TW5 |
0.625 |
0.489 |
0.815 |
0.444 |
0.596 |
|
TW6 |
0.659 |
0.623 |
0.850 |
0.460 |
0.646 |
|
UT1 |
0.512 |
0.422 |
0.416 |
0.858 |
0.464 |
|
UT2 |
0.527 |
0.452 |
0.508 |
0.878 |
0.519 |
|
UT3 |
0.517 |
0.498 |
0.428 |
0.834 |
0.463 |
|
UT4 |
0.419 |
0.321 |
0.339 |
0.723 |
0.289 |
|
UT5 |
0.465 |
0.407 |
0.483 |
0.729 |
0.440 |
|
WLB1 |
0.628 |
0.491 |
0.560 |
0.459 |
0.740 |
|
WLB2 |
0.584 |
0.521 |
0.510 |
0.388 |
0.837 |
|
WLB3 |
0.642 |
0.611 |
0.613 |
0.515 |
0.847 |
|
WLB4 |
0.525 |
0.512 |
0.550 |
0.393 |
0.799 |
|
WLB5 |
0.688 |
0.657 |
0.634 |
0.506 |
0.844 |
|
WLB6 |
0.482 |
0.499 |
0.493 |
0.363 |
0.772 |
Source: Data Processing Results (2024)
Based
on table 5, the results of the discriminant validity test after modifying the
model as seen above, show that all indicators have cross loading values
for their constructs that are greater than cross loading values
for other constructs so they are declared valid. It can be
concluded that all constructs have good discriminant validity.
Table 5 illustrates that all indicators exhibit higher cross-loading
values with their own constructs compared to their values with other
constructs, confirming their discriminant validity. For example, indicators for
Employee Performance (EP1 to EP6) show strong correlations with the Employee
Performance construct and lower correlations with Flexi-Time, Tele-Working, Use
of Technology, and Work-Life Balance. Similarly, indicators for other
constructs follow the same pattern, showing that they are distinct from one
another.
Discriminant Validity testing ensures that a construct is distinct from
other constructs in the model, meaning that each construct measures a unique
concept. This is assessed by comparing the cross-loading values of indicators
with their respective constructs versus other constructs. Additionally, the
Fornell-Larcker criterion is used, where each construct�s AVE root should be
higher than its correlations with other constructs.
The
results confirm that the constructs in the study possess good discriminant
validity. Each construct is effectively differentiated from the others,
validating the precision and accuracy of the measurement model. This
distinction is crucial for ensuring that the research findings accurately
reflect the unique contributions of each variable.
Causal
relationships (cause-effect relationships) between latent variables or
variables that cannot be measured directly can be predicted using structural
models, also known as inner models. This model is built based on theoretical
substance and is used to predict causal relationships between latent variables.
Inner model or structural model testing is carried out to see the relationship
between constructs, significance values and R-square of the
research model. The structural model was evaluated using R-square for the t-test
dependent construct as well as the significance of the structural path
parameter coefficients.
Structural Model Testing, or inner model testing, evaluates the causal
relationships between latent variables and assesses how well the theoretical
model fits the data. This involves predicting the effects of one construct on
another and examining the strength and significance of these relationships.
In this study, the structural model was assessed by examining the
R-squared values and the significance of the path coefficients. The R-squared
value indicates the proportion of variance in the dependent construct that is
explained by the independent variables. Higher R-squared values suggest a
better explanatory power of the model.
The significance of the path coefficients reveals whether the
relationships between constructs are statistically significant. Significant
path coefficients validate the hypothesized causal relationships, confirming
that the model�s predictions align with the data.
Overall,
evaluating the structural model helps to understand the effectiveness of the
theoretical framework in explaining the relationships between variables. It
ensures that the model accurately represents the data and supports robust
conclusions about the causal effects within the research context.
The
significance of the estimated parameters provides very useful information about
the relationship between the research variables. The results of hypothesis
testing are as follows:
Table 6. Hypothesis Testing
Results
|
Original sample (O) |
Sample mean (M) |
Standard deviation (STDEV) |
T statistics (|O/STDEV|) |
P values |
|
|
FT -> EP |
0.163 |
0.162 |
0.068 |
2,386 |
0.017 |
|
FT -> WLB |
0.348 |
0.346 |
0.078 |
4,463 |
0,000 |
|
TW -> EP |
0.336 |
0.332 |
0.079 |
4,259 |
0,000 |
|
TW -> WLB |
0.381 |
0.385 |
0.089 |
4,265 |
0,000 |
|
UT -> EP |
0.177 |
0.180 |
0.059 |
2,981 |
0.003 |
|
UT -> WLB |
0.158 |
0.158 |
0.064 |
2,488 |
0.013 |
|
WLB -> EP |
0.297 |
0.301 |
0.081 |
3,685 |
0,000 |
Source: Data Processing Results (2024)
Table 7. Hypothesis Testing
Results
|
Description |
Decision |
|
|
H1 |
Use of Technology
has a significant influence on Work-Life Balance |
Accepted |
|
H2 |
Flexi-Time
has a significant influence on Work-Life Balance |
Accepted |
|
H3 |
Tele
Working has a significant influence on Work-Life Balance |
Accepted |
|
H4 |
Use of
Technology has a significant influence on Employee Performance |
Accepted |
|
H5 |
Flexi-Time
has a significant influence on Employee Performance |
Accepted |
|
H6 |
Tele
Working has a significant influence on Employee Performance |
Accepted |
|
H7 |
Work-Life
Balance has a significant influence on Employee Performance |
Accepted |
Source: Data Processing Results
(2024)
Based on tables 6 and 7, the results of hypothesis testing show that
hypothesis 1, which states that the use of technology (Use of Technology) has a
significant influence on work-life balance, is accepted with a p-value <0.05
of 0.013 and the original sample estimate value is 0.158, indicating a positive
direction of influence. Hypothesis 2, which tests the effect of flexi-time on
Work-Life Balance, is also accepted with a p-value < 0.05 of 0.000 and an
original sample estimate value of 0.348, which shows a significant positive
effect. Hypothesis 3, which states that teleworking has a significant effect on
Work-Life Balance, is accepted with a p-value <0.05 of 0.000 and an original
sample estimate value of 0.381, indicating a positive direction of influence.
For hypothesis 4, the results show that the use of technology has a significant
effect on employee performance (Employee Performance) with a p-value <0.05
of 0.003 and the original sample estimate value of 0.177. Hypothesis 5, which
tests the effect of flexi-time on Employee Performance, is accepted with a
p-value <0.05 of 0.017 and an original sample estimate value of 0.163,
indicating a significant positive effect. Hypothesis 6, regarding the effect of
teleworking on Employee Performance, is accepted with a p-value <0.05 of
0.000 and an original sample estimate value of 0.336, indicating a significant
positive effect. Finally, hypothesis 7, which tests the effect of Work-Life
Balance on Employee Performance, is also accepted with a p-value < 0.05 of
0.000 and an original sample estimate value of 0.297, indicating that Work-Life
Balance has a significant positive effect on Employee Performance.
The hypothesis testing results in Table 6 and Table 7 offer
comprehensive insights into the relationships among the variables under study.
Each hypothesis has been supported, reflecting significant interactions between
the variables of Use of Technology (UT), Flexi-Time (FT), Tele-Working (TW),
Work-Life Balance (WLB), and Employee Performance (EP). Here�s a deeper look
into these findings:
Use of Technology and Work-Life Balance (H1): The significant positive
effect of Use of Technology on Work-Life Balance (p-value = 0.013) suggests
that technological tools facilitate better management of work and personal
responsibilities. This aligns with the notion that technology can provide
greater flexibility and support, helping employees juggle their professional
and personal lives more effectively.
Flexi-Time and Work-Life Balance (H2): The strong positive impact of
Flexi-Time on Work-Life Balance (p-value = 0.000) indicates that flexible work
schedules allow employees to better align their work hours with their personal
needs. This flexibility can reduce stress and increase job satisfaction,
contributing to a more balanced life.
Tele-Working and Work-Life Balance (H3): The significant effect of
Tele-Working on Work-Life Balance (p-value = 0.000) highlights how remote
working arrangements can offer employees more control over their work
environment and schedules. This can lead to improved work-life integration,
reducing commute stress and providing a more comfortable working setting.
Use of Technology and Employee Performance (H4): The positive
relationship between Use of Technology and Employee Performance (p-value =
0.003) demonstrates that technological advancements can enhance productivity
and efficiency. Technology provides tools that streamline tasks and improve
work processes, leading to better performance outcomes.
Flexi-Time and Employee Performance (H5): The significant effect of
Flexi-Time on Employee Performance (p-value = 0.017) suggests that flexible
working hours can boost performance by allowing employees to work during their
most productive times. This flexibility can lead to increased motivation and
output.
Tele-Working and Employee Performance (H6): The strong positive
influence of Tele-Working on Employee Performance (p-value = 0.000) indicates
that remote work can enhance job performance by providing a more focused and
comfortable working environment, free from typical office distractions.
Work-Life Balance and Employee Performance (H7): The significant impact
of Work-Life Balance on Employee Performance (p-value = 0.000) reinforces the
idea that employees who achieve a healthy work-life balance are likely to
perform better. A balanced life reduces burnout and stress, which can improve
overall job performance and satisfaction.
In summary, the findings highlight the critical role that modern work
arrangements and technological tools play in improving both work-life balance
and employee performance. Organizations can leverage these insights to design
more effective work policies and environments that not only support employees'
personal and professional needs but also enhance overall productivity and job
satisfaction.
Based
on the research results, it can be concluded that the effective use of
technology, flexi-time policies, and the opportunity to work remotely
(teleworking) contribute significantly to improving the work and personal life
balance of workers in Tangerang and Jakarta, and have a direct positive impact
on performance Gen Z work in the area. Work-life balance acts as a mediator
that strengthens the relationship between technology use, flexi-time,
teleworking, and work performance. This research shows that the use of
technology, flexi-time, and teleworking each have a significant influence on
the work performance of Gen Z. In addition, this research provides a
theoretical contribution by adding a simultaneous study of three independent
variables that have not been studied previously and suggests that further
research should be carried out. Consider other variables such as social
support, organizational culture, and leadership style. For the government, it
is recommended to create policies that support the application of technology,
flexi-time and teleworking, as well as increasing public awareness about the
importance of work-life balance. This research also has limitations, such as
limited theoretical coverage to five variables, limited area and time, and a focus
on Gen Z. Future research agendas could involve additional variables such as
work environment and employee attitudes as well as expanding the research
objects and samples to cover various generations.
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Copyright holder: Catherine Liana Suhandi, Kurnadi
Gularso (2024) |
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