RELATIONSHIP BETWEEN REMOTE
WORK, ORGANIZATIONAL CLIMATE, AND WORK STRESS ON EMPLOYEE PERFORMANCE
Savira Akmalia1, Budhi Prihartono2
Institut Teknologi Bandung, Jawa Barat, Indonesia
akmaliasavira43@gmail.com1, budhi.prihartono123@gmail.com2
|
KEYWORDS |
ABSTRACT |
|
employee
performance, remote work, organizational climate, work stress, multi-group
analysis. |
The remote work
system is developing to be a solution for increasing employee performance and
one of the challenges is
maintaining the organizational climate. Besides, remote work is expected to
reduce stress. Therefore, the main objective of this research is to develop
and empirically test a research model that includes these four factors. The
developed model is a reflective construct. This study used a quantitative
approach with the PLS-SEM technique with a total of 78 respondents. In
addition, a multi-group analysis was carried out in the study to determine differences
in results in gender. Data collection was carried out using a purposive
sampling technique. The survey was distributed to research respondents from
Tokopedia, Shopee, Bukalapak, Blibli, and Lazada. The results showed that
remote work had a significant positive effect on employee performance, the
organizational climate had a significant positive effect on remote work, and
the organizational climate had a significant effect on work stress, and found
a mediating role of remote work for the construct relationship between
organizational climate and employee performance. The results of the
multi-group analysis stated that in all the relationships studied, there was
not enough evidence to accept significant differences in the male and female groups. |
|
DOI: 10.58860/ijsh.v2i9.91 |
|
Corresponding Author: Savira Akmalia
E-mail: akmaliasavira43@gmail.com
INTRODUCTION
The digital world
is currently developing very quickly. It must be connected to the online world,
which continues to increase yearly (Fatahila,
2022). E-commerce companies that
rank in the top five based on the number of customer visits to websites and
applications are Shopee, Tokopedia, Lazada, Blibli, and Bukalapak (Similarweb,
2023). Companies compete to
maintain or increase their position to the top ranking (Surya
Wijaya et al., 2023). One important factor of a
competition is performance. Performance is the result of work achieved by a
person or a group in carrying out certain tasks to achieve the goals set (Gary
Dessler, 2015). With good performance in
all aspects of the organization, achieving goals is possible (Hasibuan,
2014).
Organizations
need to continue to strive to be able to produce increased employee performance
and provide comfort for employees to be able to work well. One solution that is
developing for this problem is a remote work system or remote work. Remote work
is a special arrangement for employees of an organization in which they are not
required to travel or commute to certain work locations, such as offices,
shops, or warehouses (Chatterjee
et al., 2022). According to a study, 98%
of remote workers want to work remotely for at least the rest of their careers (Buffer,
2021). According to previous
researchers, remote work can increase job satisfaction, performance, or
turnover intention (Suryaningtyas
et al., 2022). However, these benefits
also come with their own set of challenges that must be overcome. One of the
challenges of working remotely for organizations is maintaining an
organizational climate (Pradoto et al.,
2022).
The relationship
between remote work and organizational climate is becoming increasingly
important as remote work increases, especially during the COVID-19 pandemic (Pradoto
et al., 2022). Organizational climate is
an individual's view of aspects of work and values in the organization or each
individual's perception of organizational characteristics and situations that
influence a person's behaviour in his work (Meithiana,
2017). Previous research said that
there is a relationship between organizational climate and employee performance
(Jannah
et al., 2022). Based on a preliminary
study regarding the definition and benefits of organizational climate, it was
concluded that an individual's view of work and the work environment includes
values, norms, and policies within the organization and can influence employee
behaviour and performance.
An organizational
climate that has the effect of being tense and politically charged can have a
negative impact on employee productivity and disrupt formal organizational
structures (Kumar
& Mohan, 2014). While some individuals may
perform better under pressure, it ultimately depends on the employee's
attitude. The inability to cope with stress can lead to increased absenteeism
and employee turnover. Work stress was found to have a direct negative effect
on employee performance (Irawanto
et al., 2021); (Pradoto
et al., 2022); (Saranani,
2015).
By understanding
the factors that affect employee performance, organizations can take
appropriate action to improve it. Organizations are trying to answer the
challenge of remote work; there is a need for research to understand the
relationship between organizational climate (organizational climate), remote
work (remote work), work stress (work stress), and employee performance
(employee performance). This study identifies and analyses relationship between
remote work, organizational climate, and work stress on employee performance. This
research can be input for companies that want to improve employee performance
for the benefit of the organization in implementing remote working methods. This
research found there are 5 dimensions to reflect organizational climate, namely
trust, interpersonal dynamics, transformational leadership, organizational
culture and management structure. Apart from that, 3 dimensions were found to
reflect remote work, namely working time flexibility, place flexibility and
infrastructure flexibility.
METHODS
The method used
in this research is quantitative. Sampling was carried out in this study using
purposive sampling. This research determines the minimum sample size using R^2,
as Cohen (1992) stated in (Hair
Jr. et al., 2014). With a minimum R^2 of 0.5
and the maximum number of arrows pointing to one construct in the model is 3
(three), the minimum number of data samples is 38 samples. Data
collection was carried out to test and validate the relevance of the model to
real conditions in the field. The process of collecting data is done by
distributing questionnaires to company employees start on April until June 2023
while the research start from January until July 2023. Respondent criteria in
this study include (1) Working background in e-commerce companies such as
Shopee, Tokopedia, Lazada, Bukalapak, and Bblibli; (2) Doing work with a remote
work system (remote work).
The
number of validated respondents is expected to reach 76, and that collection of
as many as 78 respondents. The questionnaire was distributed online via the
Google form. After the data is collected, testing of measuring instruments will
be carried out. The questions in the questionnaire were adapted from previous
research, which were considered the most relevant and had the highest loading
factor. Data was collected in Indonesia from May to July 2023. Currently, only some
e-commerce companies are undergoing remote work. In the companies targeted in
this study, only a few sections implemented a hybrid system or a mixture of
work from the office and remote work. Respondent participation sequentially
from the companies from the five research objects in e-commerce companies,
namely Tokopedia, Shopee, Blibli, Bukalapak, and Lazada, was 15%, 23%, 22%,
30%, and 10%.
RESULTS AND DISCUSSION
It
was found that male respondents (58%) and female respondents (42%) had little
difference in numbers. This slight difference is due to the spread of the
questionnaire using a fairly even number of female and male workers in the
e-commerce company that is the object of research. Apart from gender, there is
an age difference found in 4 categories, with the largest percentage in the age
range 21-25 years (55%), age range 26-30 years (37%), age range 31-35 (5%), and
age range age> 35 years (3%). Respondents' job levels were dominated by
staff, with 63 respondents, 81%, and 20 supervisors/managers, or 19%.
Sequentially, the origin of the respondent companies from the five research
objects in e-commerce companies, namely Tokopedia, Shopee, Blibli, Bukalapak, and
Lazada, is 15%, 23%, 22%, 30%, and 10%. Finally, the length of time the
respondents worked was less than one year by 10%, the range of 1-3 years
dominated by 73%, and the remaining 4-6 years by 17%. These explanations can be
summarized in Table 1.
Table
1. Demographics of respondents
|
Characteristics
of Respondents |
Category |
Amount |
Percentage |
|
Gender |
Man |
45 |
58% |
|
Woman |
33 |
42% |
|
|
Age |
21-25 |
43 |
55% |
|
26-30 |
29 |
37% |
|
|
31-35 |
4 |
5% |
|
|
>35 |
2 |
3% |
|
|
Position |
staff |
63 |
81% |
|
Supervisors/Managers |
15 |
19% |
|
|
The
type of company |
Tokopedia |
12 |
15% |
|
Shopee |
18 |
23% |
|
|
Blibli |
17 |
22% |
|
|
Bukalapak |
23 |
30% |
|
|
Lazada |
8 |
10% |
|
|
Length
of working |
<1
Year |
13 |
10% |
|
1-3
Years |
57 |
73% |
|
|
4-6
Years |
8 |
17% |
In
testing the validity and reliability of the measurement model, SmartPLS4 was
used. The software is also used to test structural models and perform
multi-group analysis by gender. This study uses three independent variables:
remote work, organizational climate, and work stress. In addition, there is one
dependent variable, namely employee performance. The total indicators of the
measurement model are 40 indicators. After testing the validity and reliability
of the indicators used, eight indicators are removed so that there are 32
indicators ready for further testing.
Measurement Model
They are based on
the results of the assessment of the measurement model using three measurement
tools: convergent validity, discriminant validity, and composite reliability
(CR). All indicators that pass are in accordance with the standard, namely
having outer loading > 0.708. Each construct has a composite reliability
value of > 0.7 and AVE > 0.5. The measurement results can be seen in
Table 2. In addition, discriminant validity was carried out to evaluate how
much a construct differs from others. In this study, testing the discriminant
validity was carried out by testing the HTMT. All model improvements by
eliminating problematic indicators all HTMT values are below the recommended
threshold of 0.9 according to research of (Hair
Jr. et al., 2014). The HTMT test shows that
all variables have an HTMT value <0.9. The constructs meet the HTMT
criteria, are valid, and are quite different from one another. The results of
the HTMT test can be seen in Table 3.
Table
2 Measurement Model
|
Construct |
Indicator |
Outer Loadings |
Cronbach's Alpha |
Composite Reliability (CR) |
AVE |
|
Employee Performance |
EP1 |
0.864 |
0936 |
0.949 |
0.756 |
|
EP2 |
0910 |
||||
|
EP3 |
0.822 |
||||
|
EP4 |
0.843 |
||||
|
EP5 |
0891 |
||||
|
EP6 |
0.859 |
||||
|
Organizational Climate |
OC1 |
0.854 |
0.950 |
0.961 |
0.638 |
|
OC2 |
0.827 |
||||
|
OC3 |
0.831 |
||||
|
OC4 |
0.732 |
||||
|
OC5 |
0.823 |
||||
|
OC6 |
0.716 |
||||
|
OC11 |
0.734 |
||||
|
OC12 |
0.805 |
||||
|
OC13 |
0.878 |
||||
|
OC14 |
0.833 |
||||
|
OC15 |
0.804 |
||||
|
OC16 |
0.761 |
||||
|
OC17 |
0.724 |
||||
|
OC18 |
0.840 |
||||
|
Remote Work |
RW1 |
0.707 |
0.809 |
0.858 |
0.504 |
|
RW2 |
0.763 |
||||
|
RW3 |
0.708 |
||||
|
RW4 |
0.800 |
||||
|
RW8 |
0.708 |
||||
|
RW10 |
0.720 |
||||
|
Work Stress |
WS1 |
0.799 |
0897 |
0.920 |
0.659 |
|
WS2 |
0.738 |
||||
|
WS3 |
0.848 |
||||
|
WS4 |
0.802 |
||||
|
WS5 |
0811 |
||||
|
WS6 |
0.866 |
Table
3. Discriminant validity evaluation of the measurement model using HTMT
|
|
Employee Performance |
Organizational Climate |
Remote Work |
Work Stress |
|
Employee Performance |
|
|
|
|
|
Organizational Climate |
0.175 |
|
|
|
|
Remote Work |
0.349 |
0.394 |
|
|
|
Work Stress |
0.101 |
0.416 |
0.289 |
|
Structural Model Assessment
Bootstrapping is
done on the model to test significance. In bootstrapping, there are testing
rules, including the following (Hair
Jr. et al., 2014).
1. The minimum number of
bootstrap samples must be at least equal to the number of valid observations or
up to 5,000.
2. The critical values for the
two-tailed test are 1.65 (for a 10% significance level), 1.96 (for a 5%
significance level), and 2.57 (for a 1% significance level). The standard error
rate was 5% (p-value <0.05) at the 95% confidence level.
The results of
the significance test of the structural model path coefficient and the total
effect using the bootstrapping method follow the rules (Hair
Jr. et al., 2014), which can be seen in Table
4. The constructs that have a significant direct effect include (a)
organizational climate on remote work, (b) organizational climate on work
stress, and (c) remote work on employee performance. Constructs that have
direct effects that are not significant include (a) organizational climate on
employee performance, (b) remote work on work stress, and (c) work stress on
employee performance. There are differences in the results between the path
coefficient and the total effect. This also means that the mediating role of
work stress on the relationship between organizational climate and employee
performance and remote work and employee performance does not occur. The
research found the mediating role of remote work on the relationship between
organizational climate and employee performance.
Table
4. Structural model assessment
|
hypothesis |
Relationships |
Path
coefficient |
t values |
p-values |
|
|
H1 |
OC ->
EP |
0.077 |
0.507 |
0.613 |
0.005 |
|
H2 |
RW ->
EP |
0.336 |
3,499 |
0.001 |
0.108 |
|
H3 |
WS ->
EP |
0.087 |
0.808 |
0.421 |
0.007 |
|
H4 |
OC ->
RW |
0.389 |
5.214 |
0.000 |
0.179 |
|
H5 |
RW ->
WS |
-0.102 |
0.733 |
0.466 |
0.011 |
|
H6 |
OC ->
WS |
-0.372 |
3,241 |
0.002 |
0.143 |
|
H7 |
RW -> WS -> EP |
-0.009 |
0.608 |
0.359 |
|
|
H8 |
OC -> WS -> EP |
-0.033 |
0.757 |
0.511 |
|
|
Finding |
OC -> RW -> EP |
0.131 |
2,469 |
0.015 |
|
The
coefficient of determination measures the accuracy of the model's predictions
and the square of the correlation between the actual and predicted values of
the endogenous construct. The coefficient of determination represents the
combined effect of exogenous latent variables on endogenous latent variables.
The coefficient of determination for the endogenous construct of 0.75 is
generally considered strong/large, 0.5 is considered quite strong/moderate, and
0.25 is considered weak. Meanwhile, Q^2 is an indicator of the predictive
reliability of the model. While PLS-SEM demonstrates predictive relevance, it
accurately predicts indicator data points in reflective measurement models from
endogenous and single indicator constructs. In the structural model, if the
value of Q^2 > 0 indicates that the exogenous construct has predictive
relevance for the endogenous construct studied. In testing, the Q^2 value can
be applied using a blindfolding procedure to measure the cross-validated
redundancy of each endogenous construct—the test results of the coefficient
of determination (R^2) and stone-Geisser's value (Q^2). Using SmartPLS software can be seen
in Table 5.
Table
5. Test results for R^2 and Q^2
|
Construct |
|
|
|
Employee Performance |
0.127 |
0.083 |
|
Organizational
Climate |
- |
|
|
Remote Work |
0.152 |
0.063 |
|
Work
Stress |
0.178 |
0.104 |
Multi-group Analysis
At this stage,
the purpose of the test is to test whether the difference in path coefficients
between male and female sexes is significant. SmartPLS4 software is used to
find out the permutation test output. If the p-value is ≤ 0.05, gender
significantly moderates the hypothesized path relationship. Based on Table 6,
it can be concluded that there is no significant difference between male and
female gender in each path relationship tested. This is obtained based on the
permutation p-value > 0.05.
Table
6. PLS-MGA Analysis
|
Connection |
Path Coef. Origin (L) |
Path Coef. Origin (F) |
Path Coeff. Ori difference |
Path Coef. Permutation Mean Differences |
2.5 % |
97.5 % |
p-Values |
|
|
OC->EP |
0.111 |
0.169 |
-0.058 |
-0.001 |
-0.615 |
0.543 |
0.900 |
|
|
OC -> RW |
0.443 |
0.284 |
0.159 |
-0.019 |
-0.305 |
0.309 |
0.430 |
|
|
OC -> WS |
-0.259 |
-0.589 |
0.330 |
-0.032 |
-0.432 |
0.264 |
0.100 |
|
|
RW -> EP |
0.386 |
0.284 |
0.102 |
-0.018 |
-0.518 |
0.355 |
0.760 |
|
|
RW -> WS |
-0.212 |
-0.009 |
-0.203 |
0.035 |
-0.452 |
0.495 |
0.520 |
|
|
WS -> EP |
0.089 |
0.141 |
-0.052 |
-0.038 |
-0.488 |
0.423 |
0.850 |
|
|
OC->WS->EP |
-0.023 |
-0.061 |
0.038 |
0.003 |
-0.162 |
0.174 |
0.710 |
|
|
RW->WS->EP |
-0.019 |
-0.001 |
-0.018 |
-0.001 |
-0.070 |
0.080 |
0.587 |
|
|
OC->RW-> EP |
0.171 |
0.079 |
0.092 |
-0.002 |
-0.237 |
0.206 |
0.430 |
|
|
Notes: *significant
at the 0.01 level (2-tailed), **significant at the 0.05 level (2-tailed) EP:
Employee Performance, OC: Organizational climate, RW: Remote Work, WS: Work
Stress |
|
|||||||
This
study aims to identify the relationship between remote work, organizational
climate, work stress, and employee performance in e-commerce companies in
Indonesia. Research results contribute to developing or expanding research on
the relationships of these variables. Based on preliminary studies, it was
found that the implementation of remote work increased employee performance (Irawanto
et al., 2021). Following the preliminary
study, the research proved that this way of working can have a positive impact
on companies to improve employee performance. In addition, a preliminary study
found that organizational climate plays an important role in improving
performance and supporting remote work systems (Lebopo
et al., 2020); (Screwdriver
et al., 2021) ; (Pradoto
et al., 2022).
Contrary to
preliminary studies, organizational climate cannot directly impact employee
performance but can have a significant effect if mediated by remote work. It
can be concluded that the influence of the new organizational climate has a
significant positive impact if the company implements good remote work methods
by taking into account the three main dimensions found in this study, namely
worktime flexibility, workplace flexibility, and infrastructure flexibility
(Chatterjee et al., 2022). However, these results are supported by other studies which
state that the relationship between organizational climate and employee
performance is not significant for the research object. Other researchers also
state that organizational climate variables do not affect employee performance
variables with employee respondents (Happy et al., 2013). In addition to the mediation relationship, the research
states a significant direct relationship between organizational climate and
remote work. Support from organizations can support the success of implementing
remote work (Lebopo
et al., 2020). In addition to the
organizational climate variable, one variable is examined, namely work stress.
Contrary to the
preliminary study, the study found that the relationship between remote work
and work stress was insignificant. This is supported by research that remote
work is one of the main drivers of employee anxiety and stress. The main
reasons are peer interactions, enjoyable rest routines, and conflicts between
work and family (Prasad
et al., 2023). On the other hand, it was
found that organizational climate and work stress had a positive and
significant relationship, in accordance with previous research, which found
that organizational climate had a negative and significant effect on work
stress (Pradoto
et al., 2022).
The relationship
between work stress was also tested for its significance directly with employee
performance, and the study's results stated that there was no significant
relationship. The mediating role of job stress has also yet to be proven. So,
in the implementation of remote work, the priority is to improve the
organizational climate so that it can impact employee performance. The stress
variable can be ignored because, based on research, it does not determine
employee performance. Finally, in multi-group analysis, an interpretation of
the results of the permutation test is carried out by looking at the p-value
> 0.05 so that it can be concluded that gender does not moderate the
relationship between the hypothesized constructs or, in other words, the
different path coefficient values do not indicate that there is a different
effect either.
CONCLUSION
This research
developed an employee performance model for implementing remote work in
e-commerce companies. The developed model consists of 4 latent variables:
employee performance, organizational climate, remote work and work stress.
There are five dimensions to reflect organizational climate, namely trust,
interpersonal dynamics, transformational leadership, organizational culture and
management structure. In addition, three dimensions were found to reflect
remote work, namely flexibility of working time, flexibility of place, and
flexibility of infrastructure.
Based on testing
the structural model using the entire sample, the relationship between remote
work and employee performance is significant. However, when a multi-group
analysis was performed, the male gender sample was significant. In contrast,
the female gender sample was not significant. Second, based on the results of
testing the structural model using all samples, a significant relationship was
found in the relationship between organizational climate and work stress.
However, when a multi-group analysis was performed, the male gender was
insignificant, while the female gender sample was significant. Third, based on
the results of testing the structural model using all samples, a significant
relationship was found between organizational climate and remote work. However,
when a multi-group analysis was performed, the male gender sample was
significant. In contrast, the female gender sample was not significant.
The multi-group analysis
results show no significant difference between male and female gender in terms
of both the overall measurement model and the entire structural model—employee
performance. However, when a multi-group analysis was performed, the male
gender sample was significant. In contrast, the female gender sample was not
significant.
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