Psikohumaniora: Jurnal Penelitian Psikologi Vol 10.
No 1 .
: 21-42.
DOI: 10.
21580/pjpp.
ISSN 2527-7456 .
2502-9363 .
The relationship between work-life conflict, workforce agility, and subjective well-being among remote employees:
The underlying mechanism of job stress Ega Yugesti Sari ,1 Arini Widyowati ,1A Najuwa Arendse Department of Psychology.
Faculty of Psychology.
Universitas Ahmad Dahlan.
Yogyakarta Ae Indonesia.
2Institute for Social and Health Sciences.
University of South Africa.
Pretoria Ae South Africa Abstract: While remote working conditions became more prominent post COVID-19, they have influenced a shift in the subjective well-being (SWB) of remote employees.
The impact of the associated job stress has been underexplored in Indonesia.
Drawing on the job demands-resources model, this research investigates the underlying mechanisms of job stress in explaining the relationships between work-life conflict, workforce agility, and SWB among remote employees.
A total of 350 permanent employees who work fulltime remotely in several organization in Indonesia .
7% women.
M-age = 19 years.
SD = 3.
participated in the study.
Data were collected using scales measuring work-life conflict, workforce agility.
SWB and job stress, distributed online via Google Forms.
The data were analyzed using structural equation modeling (SEM) techniques with the IBM SPSS AMOS program.
The results showed that work-life conflict and workforce agility were associated with subjective well-being ( = -.
32 and .
79, p < .
Furthermore, job stress fully explains the relationship between work-life conflict and subjective well-being (CI -.
46 Ae -.
and partially explains the association between workforce agility and subjective well-being (CI .
62 Ae .
The findings have implications for how organizations and counsellors can assist remote employees in enhancing their well-being by managing their stress, promoting workforce agility, and overcoming work-life conflicts.
Keywords: JD-R model.
job stress.
remote working.
subjective well-being.
workforce agility.
work-life conflict Copyright A 2025 Psikohumaniora: Jurnal Penelitian Psikologi This is an open access article under the terms and conditions of the Creative Commons Attribution-NonCommercialShareAlike 4.
0 International License.
To cite this article (APA Styl.
Sari.
Widyowati.
, & Arendse.
The relationship between work-life conflict, workforce agility, and subjective well-being among remote employees: The underlying mechanism of job stress.
Psikohumaniora: Jurnal Penelitian Psikologi, 10.
, 21-42.
https://doi.
org/10.
21580/pjpp.
__________
Corresponding Author: Arini Widyowati .
widyowati@psy.
Faculty of Psychology.
Universitas Ahmad Dahlan.
Jl.
Kapas 9.
Umbulharjo.
Yogyakarta 60293 Ae Indonesia.
https://journal.
id/index.
php/Psikohumaniora Submitted: 28 Sep 2024.
Received in revised form: 28 Apr 2025.
Accepted: 9 May 2025.
Published regularly: May 2025 iC 21 E.
Sari et al.
Introduction While information and communication technologies (ICT) have supported remote working conditions across organizations for over 20 years, their practice was limited prior to the COVID-19 pandemic, compared to the traditional working conditions (Daniels et al.
, 2001.
Delfino & van der Kolk, 2021.
Nambisan, 2.
With the onset of the COVID-19 pandemic, however, governments worldwide restricted population movement, partly with stay-at-home requirements to curb the spread of infection, thus necessitating remote working practices for nonessential employees (Al-Habaibeh et al.
, 2021.
Pan et al.
, 2.
During the post COVID-19 period, most organizations have continued to apply the concept of remote working arrangements to provide employees with more flexibility in time and work space .
, at home, in a co-working space, a private office, or any other location apart from a traditional office building or campus (Krajsyk et al.
, 2023.
Smite et al.
, 2.
In this case, remote work refers to work that can be performed from any location using increasingly advanced technology (Charalampous et al.
, 2.
Remote workspaces are affected by both positive and negative factors, impacting employees and organizations.
For employees, remote work is associated with time efficiency and cost savings related to travel to the workplace, demonstrating organizationsAo resilience in maintaining future competitiveness (Kiwert & Walecka, 2022.
Krajsyk et al.
, 2.
The policy also increases future remote recruitment plans (Ozimek, 2.
Conversely, negative impacts include employeesAo feelings of social isolation, loneliness, unhappiness at work, and lower wellbeing, together with enhanced stress due to the possible conflict between work and personal life (Ingusci et al.
, 2021.
Nemueanu & Dabija, 2.
These feelings of loneliness and unhappiness at work have also been associated with lower 22 iC subjective well-being (SWB), which refers to an individual's cognitive and affective evaluation of their overall life (Diener et al.
, 2018.
Lucas & Diener.
Straus et al.
, 2.
On the other hand, employees with high SWB are satisfied with their work, experience positive emotions such as happiness and joy, and rarely experience negative emotions such as sadness, anger or disappointment (Diener et al.
, 1999.
Moore & Diener.
SWB comprises three components: .
life satisfaction, which refers to an individual's overall sense of contentment and ability to enjoy life experiences such as social and family relationships, recreation and work.
positive affects, such as being excited, proud or happy.
negative affects, such as sadness, disappointment, depression or anxiety (Proctor, 2.
A theory that explains the complex relationship between individual and organizational factors and how these affect employeesAo well-being is the job demand-resources (JD-R) According to the model (Schaufeli, 2.
, every job includes demands and resources (Bakker & Demerouti, 2007.
Schaufeli, 2.
, with remote work being associated with increased workloads, and an indistinct line between work and personal life that oscillates with job demands and subsequently affects stress levels and psychological well-being.
Job demands .
workload, time pressure, conflicts with others, and future job insecurit.
refer to job aspects that require considerable effort over time and may drain employee resources .
, performance feedback, support from others, and job contro.
(Bakker & Demerouti, 2007.
Schaufeli, 2.
, which can impact both physical and mental wellbeing (Bakker & Geurts, 2.
Meanwhile, job resources refer to the physical, psychological, social, or organizational characteristics of work that facilitate goal achievement, stimulate growth and development, and reduce job-related problems, thus increasing employee motivation, engagement, and positive attitudes.
Psikohumaniora: Jurnal Penelitian Psikologi Ai Vol 10.
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The relationship between work-life conflict, workforce agility, and subjective well-being .
The JD-R model fundamentally integrates two psychological processes.
First, a stress process triggered by higher job demands and lack of resources, which can result in greater stress and burnout and in turn negatively impact employee attitudes and behavior .
, health problems, increased sickness absence, poor performance, reduced workability, and low organizational Second, a motivational process influenced by abundant job resources, which through work engagement may result in favorable results such as employee safety, extra-role behavior, organizational dedication, and higher work performance.
Job resources possess intrinsic motivational qualities by invigorating employees and enhancing their engagement, which leads to positive outcomes (Bakker & Demerouti, 2007.
Schaufeli, 2.
Further, the model emphasizes the importance of personal factors in shaping individual work attitudes and behaviors.
Personal resources are defined as affirmative self-assessments linked to resilience, reflecting individualsAo perception of their capacity to effectively manage and influence their The influence of personal resources depends on individual characteristics.
some stable personality traits, such as optimism, are likely to serve as antecedents of job demands and job resources, while malleable ones .
, self-efficac.
could act as mediators between job characteristics and outcomes (Schaufeli, 2.
The JD-R model is an empirically validated model that describes the associations between job .
nd persona.
characteristics, employee wellbeing, and outcomes.
Essentially, it affirms that reducing job demands while enhancing job .
nd persona.
resources reduces job stress and improves employee well-being.
This leads to less negative and more positive outcomes for both employees and organizations (Schaufeli, 2.
An important factor affecting employeesAo wellbeing is the conflict between work and personal This is a feeling of conflict that occurs when energy, time or work roles clash with family roles or personal life, causing disruption, or when personal demands generate pressure that disrupts one's professional life (Kossek & Lee, 2017.
Padmanabhan & Kumar, 2.
According to Greenhaus and Beutell .
work-life conflict includes three components: .
time-based conflict, which involves challenges in managing the demands of work and personal life.
strain-based conflict, characterized by negative emotional states such as anxiety, irritability, and fatigue resulting from these demands.
behaviorbased conflict, which pertains to the incompatibility of behaviors in professional and personal settings.
Work-life conflict is considered to relate to job demands that require physical and mental effort, which can lead to physical and psychological fatigue, ultimately impacting individuals .
decreased performance and declining physical and mental healt.
and organizations negatively (Bakker & Demerouti, 2007.
Schaufeli, 2.
Remote work has the potential to blur the boundaries between work life and personal life, thus triggering a work-life conflict (Elahi et al.
Previous research has shown that work-life conflict can lead to dissatisfaction in various aspects of an employeeAos life and can impact SWB, as employees find it challenging to allocate time and energy to fulfill all their roles .
, as friends, bosses, parents, etc.
) and related expectations, leading to lower performance due to pressure in one role interfering with other roles, and role ambiguity (Claes et al.
, 2023.
Huo & Jiang, 2023.
Russell et al.
, 2009.
Skurak et al.
, 2.
Another factor influencing employeesAo SWB are personal resources, which are considered as positive self-evaluation related to individual resilience, and refer to an individual's perception of their ability to successfully control and influence their environment (Schaufeli, 2.
Workforce Psikohumaniora: Jurnal Penelitian Psikologi Ai Vol 10.
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Sari et al.
agility, an important personal resource, denotes the ability of employees to adapt quickly and effectively to a changing work environment through proactive and adaptive knowledge, skills, behavior, and attitudes (Tessarini Jr.
& Saltorato.
Aligned with the JD-R model, workforce agility motivates individuals to achieve goals, promoting job satisfaction and reducing the impact of pressure on employees' SWB.
Those with high workforce agility can maintain their expertise and empower themselves .
, their personal resource.
through control over their work and and are proactive in providing and responding positively to feedback, finding solutions to the problems they experience and subsequently prioritizing human values (Paul et , 2020.
Petermann & Zacher, 2.
For remote employees, workforce agility is essential to adapt to changes in the work environment effectively and quickly.
Remote work also provides opportunities for employees to receive direct feedback on their work, and encourages them to solve work-related issues independently due to the limited assistance from superiors or their organization (Muduli, 2.
The sense of autonomy, feedback and problem-solving abilities contribute to a sense of competence, which impacts satisfaction and SWB (Clausen et al.
, 2022.
Reis et , 2.
Previous studies have shown a positive relationship between workforce agility and SWB (Claes et al.
, 2023.
Petermann & Zacher, 2.
, as well as between psychological capital and SWB (Maulida & Shaleh, 2.
Based on the JD-R model, work-life conflict and workforce agility are two factors that are considered to influence job stress.
Such stress is defined as the primary source of anxiety for individuals due to unclear work expectations, time pressure, and noisy work conditions (Bell et al.
Shukla & Srivastava, 2.
, which lead to negative behavioral, physical, emotional and 24 iC cognitive reactions, such as fatigue, anxiety, unhappiness, headaches, weakness, nervousness, and increased use of cigarettes, alcohol or sedatives (Olusegun et al.
, 2014.
Rathi & Kumar.
Previous research has found that high job stress impacts physical and psychological wellbeing, decreasing performance and quality of work life (M.
Chen, 2019.
Rathi & Kumar, 2.
Schaufeli's JD-R model (Schaufeli, 2.
reiterates that work-life conflict places pressure on individuals, draining their physical and psychological energy due to the efforts made to fulfill responsibilities in work and personal life.
Individuals are required to expend a great deal of energy managing various roles that may clash, especially employees who work remotely, and they are also required to be able to adapt to rapid changes and technological developments.
The results of previous research have shown that work-life conflict influences job stress.
individuals encounter work-life conflict, allocating more resources to one role diminishes those available for other roles, leading to reduced performance, fatigue and work-related stress (Bell et al.
, 2.
Job stress is also influenced by individualsAo positive assessment of their resilience in facing pressure and their ability to adapt to environmental changes.
Petermann and Zacher .
noted that workforce agility facilitates responses to environmental feedback in an adaptive way, giving people the freedom to manage and control their work independently, and helping them to solve work problems they face.
Schaufeli .
also demonstrates that personal resources, including workforce agility, can reduce the stress experienced by individuals.
Previous research results have shown that workforce agility is related to low job stress amongst employees (Mastriani, 2.
Job stress is also closely related to an individualAos mental health and SWB (Ryu et al.
Numerous studies indicate a correlation Psikohumaniora: Jurnal Penelitian Psikologi Ai Vol 10.
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The relationship between work-life conflict, workforce agility, and subjective well-being .
between job stress and adverse health and mental well-being outcomes, such as elevated blood pressure, musculoskeletal disorders, cardiovascular disease, anxiety, depression, burnout, emotional exhaustion, and dissatisfaction (Hasin et , 2023.
Iacovides et al.
, 2003.
Schaufeli & Bakker.
For example, a study conducted in Norway indicates that job stress is a risk factor for poor mental well-being among medical students (Tyssen et al.
, 2.
Other studies found that job stress affects SWB.
the lower the job stress, the greater the employees' SWB (Ayadi et al.
, 2016.
Tsalasah et al.
, 2.
A systematic review of recent cross-sectional and longitudinal cohort studies indicates a strong association between job stress and employeesAo well-being (Hirschle & Gondim.
As has been demonstrated, job stress is crucial in explaining the relationship between work-life conflict, workforce agility and SWB.
The research referred to above has made three key findings: .
how the influence of remote work on work-life conflict, and the unclear boundaries between work and personal life, can result in stress (Bell et al.
how personal resources in the form of workforce agility aid individualsAo management of change and work pressure, reducing the impact of potential work-related stress (Mastriani, 2021.
Petermann & Zacher, 2.
Conversely, low workforce agility may result in increased job stress due to the inability to manage pressure.
Research has shown that employee stress can reduce SWB because of excessive workloads, imbalances between work and personal life, and emotional fatigue (Bell et al.
, 2.
In addition, the remote work pattern can lead to high work-life conflict, directly contributing to job stress.
, and the inability to adapt can lead to low workforce agility, further increasing job stress and reducing the SWB of remote workers.
Previous studies have found that job stress explains the relationship between personal and organizational factors and outcomes.
for example, research by Khattak et al.
found that job stress explains the relationship between role ambiguity and job satisfaction.
Job stress also explains the relationship between job satisfaction and turnover intention (Dodanwala & Santoso, 2.
, and the relationship between quality work-life and work-life balance (Aruldoss et al.
, 2.
Studies on SWB among remote employees predominantly focus on those working from home during the COVID-19 pandemic, rather than the post-pandemic period (Anindita & Korompis.
Costin et al.
, 2023.
Fan & Moen, 2023.
Myhring et al.
, 2021.
Safira et al.
, 2023.
Saragih et , 2.
Research in the post-pandemic context remains limited.
Moreover, while these studies have specifically explored the impact of workrelated factors, such as perceived organizational support (Iman et al.
, 2.
work and family satisfaction (Myhring et al.
, 2.
and job stress and burnout (Costin et al.
, 2.
on the SWB of remote employees, there is little focus on the situational .
, work-life conflic.
and personal .
, workforce agilit.
factors impacting remote employees in Indonesia, and how job stress affects the numerous interacting relationships (Kismono et al.
, 2.
Even though remote work provides the flexibility and opportunity for work-life balance, employees need to effectively complete their work with limited technical support, and it may be challenging to untangle the separation between personal and emotional issues when exposed to the same environment (Ahrendt et al.
Baert et al.
, 2.
In the case of Indonesia, a developing country located in Asia that is experiencing rapid economic growth, with more than 70% of the population of reproductive age (Jin & Kim, 2022.
Wisesa, 2.
, during the pandemic remote working faced challenges regarding the work-from-home arrangement due to work-family conflicts, with low productivity resulting from limited resources (PWC Malta.
Following the theoretical JD-R model (Schaufeli, 2.
, remote work has been Psikohumaniora: Jurnal Penelitian Psikologi Ai Vol 10.
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associated with increased workloads and an indistinct line between job demands and personal life, which may affect stress and well-being.
This research aims to contribute to advancing current knowledge on subjective well-being among remote employees, particularly in the postpandemic period, by investigating the relationship between work-life conflict, workforce agility, and subjective well-being, and by investigating how job stress can explain these relationships by taking a novel approach.
In addition, the study will benefit organizations by helping them understand and enhance SWB among remote employees by managing job stress.
Figure 1 provides a conceptual research framework.
Based on the discussion above, the following hypotheses are proposed:
H1: Work-life conflict is directly associated with SWB.
H2: Workforce agility is directly associated with SWB.
H3: Work-life conflict is directly associated with job stress.
H4: Workforce agility is directly associated with job stress.
H5: Job stress is directly associated with SWB.
H6: Work-life conflict is directly related to SWB through job stress.
H7: Workforce agility is indirectly related to SWB through job stress.
Methods The study employed a cross-sectional quantitative method, with surveys used for data Participants In this study, 350 participants aged 19 to 49 years (M-age = 26.
19 years.
SD = 3.
were The inclusion criteria were: .
those who had worked remotely for at least one year.
local residents drawn from three organizations in Indonesia that had applied the remote working model.
The participants 7% .
= .
females and 34.
3% .
= .
Measurements Data were collected using scales.
All scales were distributed to participants through an online Google Form.
Scale responses were structured using a 5-point Likert format .
alues ranging from 1 = strongly disagree to 5 = strongly agre.
, unless otherwise specified.
The items were added together to obtain a total score, with a higher score indicating a higher level for each construct.
Figure 1 Conceptual Framework 26 iC Psikohumaniora: Jurnal Penelitian Psikologi Ai Vol 10.
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The relationship between work-life conflict, workforce agility, and subjective well-being .
SWB Scale SWB was measured using the SWB scale (Diener, 1984.
Diener et al.
, 1.
, which was modified for suitability in the local context of employees working remotely and translated into Indonesian using the back translation method by Fitri .
This measuring tool included dimensions of the Satisfaction With Life Scale (SWLS), example item "I am satisfied with my lifeAy, and positive and negative affect: the Scale for Positive and Negative Experience [SPANE]), example item: AuI feel afraidAy.
The SWB score was obtained by adding together each participant's responses to all the SWLS items, plus the score on SPANE-Balance.
The SPANE-Balance score was obtained by adding together all the positive affect scores (SPANE-Positiv.
and subtracting the negative affect scores (SPANE-Negativ.
The score values could range from six to 30.
The negative scores were deducted from the positive ones, with the balance score constituting the range.
The original scale has a Cronbach's coefficient of .
87 for the SWLS.
84 for the positive affect.
80 for the negative affect.
88 for the balanced affect (Diener et al.
, 1.
In the Indonesian version, the scale has a Cronbach's of .
75 for SWLS and .
74 for positive affect.
76 for negative affect, and .
80 for balanced affect (Fitri, 2.
In this study.
Cronbach's was .
80 for SWLS.
74 for positive 80 for negative affect.
80 for balanced Workforce Agility Workforce agility was measured using the Workforce Agility Scale developed by Sherehiy and Karwowski .
, which assesses three dimensions of workforce agility, namely proactivity, example item AuI fix something if it doesn't suit meAy.
adaptivity example item: AuI communicate well with people who have different back- groundsAy.
and resilience, example item: AuI do the work according to ordersAy.
For this research, the scale was translated into Indonesian using the back translation method.
Sherehiy and Karwowski .
report that the original scale has a Cronbach's coefficient of .
85 for proactivity.
for adaptivity.
71 for resilience, supporting validity by finding expected relationships with autonomy and agility strategy.
Based on findings from psychometric analysis using confirmatory factor analysis (CFA).
Viranda et al.
loaded the items into one factor, with loading factors starting from .
40 Ae .
Cronbach's coefficient 89, while in this current study it was .
Work-life Conflict Work-life conflict was measured using the Work-life Conflict Scale developed by Nichols and Swanberg .
, which aims to measure the interference of work with personal life, and that of personal life with work.
This scale consists of ten items, which are arranged based in two dimensions, namely five items in the Work Interference with Life (WIL) dimension (AuMy work prevents me from doing work at homeA.
and five items in the Life Interference with Work (LIW) dimension (AuMy family or my personal life prevents me from concentrating at workA.
The original scale had Cronbach's coefficients of .
for WIL and .
79 for LIW.
Since an Indonesia version was not available, the scale was adapted into Bahasa Indonesia using the back translation method by an expert translator fluent in both English and Bahasa, and the items were adjusted for the remote worker context.
The adapted scale was then analyzed using CFA.
Based on the CFA analysis, it was found that numerous indices denoted that the construct was a good model fit, with factor loadings ranging from .
50 to .
84, and demonstrating its validity through negative correlations with career satisfaction.
In this study.
Cronbach's was .
86 for WIL and .
87 for LIW.
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Job Stress Job stress was measured using a 20-item job stress scale developed by Wu et al.
Example items include: AuMy job is so difficult that I am Ay The scale was adapted for the Indonesian context by Rusni .
, who obtained 16 items that were rated as good, with a differential power index ranging between .
32 and .
Wu et al.
reported that the scale has a Cronbach's coefficient of .
73, and supported validity by finding expected relationships with safety behavior.
Rusni .
reported that the Indonesian version has a Cronbach's coefficient of .
88, and supports validity by finding negative associations with job We retested this measuring tool with CFA and obtained factor loadings of .
43 and .
The Cronbach's coefficient in this study was .
Procedure As indicated, the researchers use Google Forms to distribute the questionnaires.
Participation was voluntary, and participants were recruited through advertisement via social media platforms .
Instagram and WhatsApp group.
within the respective organizations.
A request was made for formal consent to participate, and the ethics principles were communicated.
Participants were informed that: .
their participation was voluntary.
they were free to withdraw at any point should they wish to discontinue their participation.
all identifiable information would be kept confidential and anonymized, with only their initials used on the distributed scales and accordingly coded on the data collection sheet.
all information would be securely protected by a password protected application and a secure com-puter.
the information would only be accessible to the research team for analysis purposes.
Analysis Approach Before testing the hypotheses, we performed a bivariate correlation analysis using Pearson 28 iC correlation analysis to evaluate the correlations between the demographic and latent variables of the conceptual model, which is an important initial step to determine the strongest and weakest variable associations.
Pearson correlation assesses the existence .
-valu.
and strength .
oefficient r ranging from -1 to .
of a linear relationship between two variables.
An absolute value of r of .
is classified as weak, .
3 as medium, and .
5 as strong (Schober et al.
, 2.
The IBM Statistical Package for the Social Sciences (SPSS) Analysis of Moment Structures (AMOS) Version 25 was used to run the structural equation modeling (SEM) to test the hypotheses.
For the initial stage, we created multi-item parcels to represent all the latent variables as multi-item parcels to produce fewer and more stable increase measurement reliability.
reduce the risk of violating normality assumptions.
and produce simpler model interpretations (Hau & Marsh, 2.
In creating parcels, exploratory factor analysis was performed on each scale by sorting items based on loading factors and distributing them to all parcels using an item-toconstruct balance approach, namely an item distribution method aimed at dividing items equally in terms of level of difficulty and discrimination (Hau & Marsh, 2.
Each latent variable was represented by three parcels, apart from the SWB variable, for which a particular scoring procedure was used.
After creating the parcels, we performed a measurement model analysis to ensure that each parcel would represent all the latent variables and would be different from one another.
Measurement model analysis is important to assess the validity of the theory of construct measurement and examine the complex relationship between a latent variable and its observable indicators, which is essential for ensuring the accuracy and validity of the measurement process.
Psikohumaniora: Jurnal Penelitian Psikologi Ai Vol 10.
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Once the measurement model analysis was complete and the results had revealed that the model was categorized as fit, we conducted a structural model test that aimed to investigate the causal relationship among latent and observable variables, facilitating the testing of the theoretical hypotheses and assessing the underlying mechanisms that drive the observed data.
Comparative Fit Index (CFI > .
and Root Mean Square Error of Approximation (RMSEA < .
Meanwhile, in the indirect effect model, we used AMOS bootstrapping (N = 2.
to obtain biascorrected 95th percentile confidence intervals (CI).
An indirect effect occurs if the CI does not contain Results Direct and indirect analysis was performed to test the direct and indirect relationships between predictors .
and outcome variables.
The direct effect is the pathway from predictor .
variables to the outcome while controlling for the mediator.
The indirect effect indicates the path from the predictor .
variables to the outcome through the mediator.
Bivariate Correlation Test Based on the bivariate correlation analysis results, it was found that work-life conflict was positively related to job stress, while workforce agility was negatively related.
Additionally, job stress was found to have a negative relationship with SWB.
The analysis also revealed that demographic variables such as age, length of service, and gender showed a low correlation with SWB .
= .
15 to .
therefore, these variables were not controlled.
The results of the bivariate correlation analysis between the latent variables are presented in Table 1.
To define the goodness fit of all models, we referred to the fit index parameters by Hair et al.
, in which if the sample is >250 >12 observed variables.
a good fit is indicated by N2 significant p-value expecte.
normed chi-square (N2/df< .
Goodness of Fit Index (GFI > .
Table 1 Summary of Bivariate Correlation Data (Shown on the Lower Diagona.
and Latent Variables (Uppe.
N=350
Variable SWB Work-life conflict Workforce Agility Job Stress Age (Yea.
Year of Service 49*** 84*** 87*** 27*** 62*** 59*** Gender Education *p < .
05, **p < .
01, ***p < .
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Measurement Model Testing The results of the measurement model test produced a p < .
N2/df = 2.
RMSEA = .
GFI =
AGFI = .
CFI = .
TLI = .
NFI = .
According to Hair et al.
, if there are four to five goodness of fit parameters that meet the requirements, then this is sufficient to assess the suitability of a model.
Therefore, it can be said that the model has a good fit.
Hair et al.
also state that for samples >200, the loading factor value must be above .
Our model obtained factor loadings ranging from .
43 Ae .
Hypothesis Testing (Structural Mode.
To test the hypotheses, we first tested the structural relationships as hypothesized in the introduction, as shown in Figure 1.
We then tested a series of direct and indirect relationships in the path model to determine the indirect effect from the predictor to the outcome variable.
The results of the data analysis show that the structural model had acceptable goodness of fit: p < .
05 N2/df = 2.
RMSEA = .
GFI = .
AGFI = .
CFI = .
TLI =
NFI = .
ee Figure .
On the path from work-life conflict and workforce agility to job stress, significant results were obtained (A = .
and -.
55, p < .
, supporting H3 and H4.
The path from job stress to SWB (A = -.
57, p < .
was very significant, supporting H5.
The model explains 58.
3% of the variance in job stress 8% of the variance in SWB.
Direct and Indirect Effects Model Test To determine the indirect effect, we assess a direct effect model first .
, a path from work-life conflict and workforce agility to SWB).
The results show that the model was fit, with p < .
N2/df = 70.
RMSEA = .
GFI = .
AGFI = .
CFI = .
TLI = .
NFI = .
Work-life conflict and workforce agility were directly related to SWB ( = 32 and .
79, p < .
, thus supporting H1 and H2 .
ee Figure .
The results also reveal that RMSEA which is considered an acceptable level of power achieved with reasonable sample sizes .
(Kenny et al.
, 2.
Figure 2 Direct and Indirect Effects Models.
N= 350.
Standardized Beta Weights Note: For direct paths are shown in bold numbers, while indirect paths are shown in non-bold.
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The results of the indirect effects test show that the model was fit, with p< .
N2/df = 2.
RMSEA = .
GFI = .
AGFI = .
CFI = .
TLI =
NFI = .
The results of the analysis also show that work-life conflict was associated with SWB indirectly through job stress (CI -.
46 Ae -.
, which supports H6.
Meanwhile, the direct path from work-life conflict to SWB was not significant (A = 001, p = .
99 > .
This shows that job stress fully explains the association between work-life conflict and SWB .
otal effects -.
28, direct -.
00, indirect .
The results of the data analysis on workforce agility show that it was associated with SWB directly and indirectly through job stress.
The indirect association (CI .
62 Ae .
supports H7, while the direct role .
irect pat.
from workforce agility to SWB (A = .
50, p <.
, remains This shows that job stress partially explains the relationship between workforce agility and SWB .
otal effects .
76, direct .
Therefore, the results show that work-life conflict and workforce agility were directly and indirectly associated with SWB through job stress.
Job stress fully explains the association between work-life conflict and SWB, and partially explains the relationship between workforce agility and SWB.
Discussion The study aimed to investigate how work-life conflict and workforce agility were related to the SWB of a sample of Indonesian employees who had worked remotely in the post-pandemic period, together with the influence of job stress.
The findings suggest that work-life conflict was indirectly associated with SWB through job stress, while workforce agility was directly and indirectly associated with SWB through job stress.
The study contributes to related knowledge by affirming that job stress significantly explains how organizational demands and personal resources influence SWB under remote post-pandemic working conditions.
First, in line with the JD-R model, the findings indicate a direct negative association between work-life conflict and SWB, thereby confirming the first hypothesis.
It suggests that individuals who struggle to balance their personal and work lives tend to experience a decline in SWB.
Remote employees are particularly susceptible to work-life conflict due to the blurred boundaries between work and personal life, which subsequently influence individualsAo experience of a sense of inadequacy as they struggle to allocate quality time to their families, primarily due to the demands of managing professional expectations, and vice versa, thus resulting in lower SWB (Elahi et al.
It aligns with previous studies that have demonstrated a negative relationship between work-life conflict and SWB, with remote employees finding it challenging to manage their time and energy effectively, often leading to dissatisfaction and lower well-being (Claes et al.
Huo & Jiang, 2023.
Skurak et al.
, 2.
Second, it was found that workforce agility was directly associated with SWB, thus supporting the second hypothesis.
It suggests that individuals with high agility are likely to have higher SWB.
Working remotely allows individuals with flexibility, adaptability, and the ability to handle unexpected situations, and provides them with better means to cope with pressure and overcome challenges, subsequently leading to increased satisfaction and psychological well-being (Srivastava & Gupta, 2.
The findings align with previous research, which also found a positive relationship between workforce agility and SWB, with workforce agility involving the active engagement of emotions such as feedback, social support, and autonomy, which are vital for enhancing employee well-being (Rietze & Zacher.
Tuomivaara et al.
, 2.
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Sari et al.
Third, the results confirm the third hypothesis, which is aligned with the JD-R model (Schaufeli.
Work-life conflict, as a job demand, influences the pressure experienced by remote employees, ultimately having a negative impact on It was found that work-life conflict was directly associated with job stress (Rathi & Kumar.
Specifically, remote employees are susceptible to conflicts between their personal and work lives, leading to job ambiguity (Shukla & Srivastava, 2.
It generates pressures as one role interferes with others, potentially causing physical, psychological, social, and organizational fatigue, ultimately resulting in higher job stress (Chen et al.
, 2.
Other research has found that work-life conflict is linked to stress due to difficulties in time management and work fatigue (Bell et al.
, 2012.
Lambert et al.
, 2.
Fourth, we also found that workforce agility was directly associated with job stress, which supports the fourth hypothesis.
It suggests that individuals with high workforce agility exhibit greater resilience, enabling them to navigate challenges across various situations, thereby enhancing sustained productivity and reducing work-related stress (Sherehiy & Karwowski.
Tampombebu & Wijono, 2.
Remote employees who possess high workforce agility exhibit greater adaptability, flexibility and resilience in confronting job-related challenges (Muduli, 2.
, which aids in the management of their stress (Schaufeli, 2.
The results of this study are in line with previous research which has found that workforce agility plays a role in achieving work-related goals and reducing the physical and mental burden of work due to individualsAo ability to quickly adapt to changes (Mastriani, 2021.
Rietze & Zacher, 2022.
Wang & Chen, 2.
In support of the fifth hypothesis, it was found that job stress was directly related to SWB, which indicates that high job stress is associated with 32 iC lower positive affect and life satisfaction.
Working remotely was associated with high stress due to the high demands, such as workload and technology overload (Ingusci et al.
, 2.
This situation can deplete an individuals' physical and mental energy, possibly leading to health problems, thus affecting employees' lives and wellbeing (Chong et al.
, 2.
It corresponds to previous research that has shown that job stress may lead to negative thoughts and psychological issues, ultimately reducing employee well-being (Ayadi et al.
, 2016.
Tsalasah et al.
, 2019.
Zhao et al.
Our findings support the JD-R model by demonstrating that job stress explains the relationship between work-life conflict and SWB, which supports hypothesis 6.
Individuals who experience work-life conflict tend to experience job stress, which in turn reduces SWB, and vice In remote working models, work-life conflict arises from a lack of clarity between work and personal life.
Individuals facing this situation must effectively manage personal and professional work demands, which can drain physical and psychological energy and cause high levels of job stress (Chen et al.
, 2.
It triggers lower SWB (Claes et al.
, 2.
It is in line with previous research findings, which have shown that worklife conflict influences job stress (Bell et al.
, 2.
and that job stress influences SWB (Ayadi et al.
Furthermore, work-life conflict affects employeesAo professional and personal lives, impairing physical, psychological, social, and family aspects, thereby increasing job stress and decreasing employee well-being (Ayadi et al.
Our findings demonstrate that job stress accounts for the relationship between workforce agility and SWB.
IT suggests that remote workers with high workforce agility are less likely to experience job stress, increasing their SWB.
Therefore.
Workforce agility is an important Psikohumaniora: Jurnal Penelitian Psikologi Ai Vol 10.
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The relationship between work-life conflict, workforce agility, and subjective well-being .
personal resource for remote workers (Aggarwal & Stanley, 2.
Agility allows individuals to enhance their decisions and effectively manage organizational crises (Kanwal et al.
, 2.
Moreover, those with higher agility are more adaptable, proactive, and resilient, qualities which help them better navigate the challenges of remote work (Kanwal et al.
, 2024.
Kohont & IgnjatoviN.
It supports previous research that indicated that workforce agility plays an important role in maintaining mental resilience and the balance between work time and rest time, thus reducing stress (Kocot et al.
, 2.
It in turn, affects SWB (Tsalasah et al.
, 2.
The findings reveal that job stress is a significant factor in understanding the impact of work-life conflict and workforce agility on SWB.
implies that management, organizations, and counsellors should assist employees in enhancing their SWB by managing their work-related stress.
Various studies have shown that techniques such as appreciative inquiry coaching (Cahyono & Koentjoro, 2.
stress management (Harahap & Susilawati, 2.
and mindfulness training (Chin et al.
, 2.
are effective in helping cope with job stress and improve individual agility.
Counsellors and management or organizations can also help improve employees' wellbeing by preventing work-life conflicts and enhancing their ability to adapt quickly and effectively to work .
orkforce agilit.
in the first Interventions such as involving family members in completing household tasks and regulating diet and exercise have been shown to reduce work-life conflict (Wilson et al.
, 2.
Other research has found that increasing family support can reduce such conflict in employees (Ayman & Antani, 2008.
Pluut et al.
, 2.
Consultants or management can also help increase workforce agility through employee training and development (Braun et al.
, 2.
Previous research has shown that training to increase information technology competency boosts employee agility (Lai et al.
, 2.
Additionally, psychological safety training can foster learning-oriented behavior, such as innovative behavior and advice seeking, which in turn can increase employee agility (Mulyadi et al.
This research has several limitations, including the unequal composition of the sample between men and women.
Nearly two-thirds of the participants were female, which may introduce bias due to the potential impact of work and different family responsibilities for women and men, especially in Eastern cultures or where homes are headed by men (Amstad et al.
, 2.
Despite the analysis showing that gender is not significantly related to the research variables, future research should aim for a more balanced sample of men and women.
It could also develop this study by examining the influence of gender and culture on work-life conflict and its influence on job stress and well-being.
Furthermore, evaluation of the other factors such as support from family, coworker or supervisors as well as organizational support that may buffer the effect of work-life conflict and workforce agility on job stress and the effect of job stress on well-being is required.
In line with the JDR model, personal resources may buffer the effect of stress on outcome variables.
In this case, workforce agility as a personal resource may moderate the relationships between work-life conflict and job stress, and between job stress and well-being.
Further research could also examine other factors that may moderate these It may provide information on factors that can help employees improve their SWB.
Future studies should also confirm the direction of those relationships using a crosslagged longitudinal study method, which allows alternative models, such as the reciprocal causation model, to be tested.
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Conclusion This research demonstrates that the JD-R model can be applied to explain subjective wellbeing (SWB) in remote employees, especially in the post-COVID-19 pandemic era, where remote work is often optional.
While the findings demonstrate that increased work-family conflict can raise job stress and subsequently weaken employee well-being, employers should implement mechanisms to support the SWB of remote This move may enhance organizational While the findings suggest that higher workforce agility may directly affect well-being and potentially reduce stress, which enhances well-being, they also provide insights for management to call on counsellors to help improve the SWB of remote employees and assist with coping strategies when support is required.
Counsellors and management can help employees improve their well-being by managing job stress, preventing work-life conflict, and increasing workforce agility.
Acknowledgments The research received financial support from the Directorate General of Higher Education.
Ministry of Education and Culture of the Republic of Indonesia (DRTPM) for the Master's Thesis Research (PTM) Author Contribution Statement Ega Yugesti Sari: Conceptualization.
Data Curation.
Formal Analysis.
Investigation.
Methodology.
Project Administration.
Writing Original Draft.
Arini Widyowati: Conceptualization.
Funding Acquisition.
Methodology.
Validation.
Writing.
Review & Editing.
Najuwa Arendse: Writing.
Review & Editing.
References