Abstract
Orientation: This study examines the phenomenon of blue-collar workers’ struggles in organisations in various sectors in developing countries, such as Indonesia, to achieve happiness at work (HAW) with the challenges of a tiring work life.
Research purpose: This study aims to investigate the role of work-life integration (WLI) in promoting HAW and moderation of work stress. The study population consists of blue-collar workers in various sectors in big cities in Indonesia.
Motivation for the study: The study was carried out to supplement research in the field of WLI and HAW in blue-collar workers and the importance of understanding how WLI impacts HAW.
Research approach/design and method: The research sample technique is purposive sampling, obtaining a sample size of 1885. Research data collection is done using a questionnaire instrument. Data analysis uses partial least squares.
Main findings: The study’s results indicate that WLI, with its main dimension of WLI, has a positive impact on HAW on blue-collar workers in big cities in Indonesia. Furthermore, work stress can weaken the positive impact of WLI on HAW.
Practical/managerial implications: This study contributes to the insight of employers to form employee-friendly policies by considering the integrated work focus between work life and personal life, which will have an impact on increasing work happiness, especially for blue-collar workers.
Contribution/value-add: Work-life integration for blue-collar workers is a very significant transformation, where work life and personal life are not contradictory but mutually reinforcing, giving rise to a positive psychological effect that has an impact on higher happiness.
Keywords: work-life integration; work stress; happiness at work; blue-collar workers; well-being.
Introduction
Blue-collar workers in Indonesia are often trapped in a tiring routine of waking up at 5 a.m. to get ready for work and spending hours in traffic to reach their office. Consequently, workers go home in the evening and reach home exhausted (CNN, 2022). This daily cycle causes various negative impacts on workers, such as work stress, unhappiness, burnout and job dissatisfaction (Aditya et al., 2023). According to a survey by Gallup Consulting (2024), Southeast Asian workers experience a significant increase in work stress because of long working hours, inflexible work, messy work-life balance (WLB) and limited family-friendly policies. The phenomenon of work stress in workers is a challenge for organisations today because it will have an impact on decreasing job satisfaction, productivity and employee performance (Marlapa & Endri, 2024; Saleem et al., 2021). Blue-collar workers in organisations in Indonesia are those who are directly involved in day-to-day operations and often do not have a managerial role or strategic decision-making (Harumi et al., 2022). They act as implementers of technical and administrative tasks that ensure the organisation’s smooth operation (Gilson et al., 2019).
These workers are vital to their respective organisations, yet their contribution is less noticed as their role does not directly affect the organisational performance. Although considered a less noticed part, they are the backbone of many industries and economic sectors; however, without these workers, many companies would not be able to operate (Irawanto et al., 2021). Work stress in employees is currently still an important problem that must be addressed by organisations. Thus, it is important for organisations to first explore the root of the problem and its implications for employee well-being (Hsu et al., 2019; Julianto et al., 2024; Kismono et al., 2023). However, in current developments, the WLB concept has transformed into a dynamic and inclusive approach known as work-life integration (WLI). This shift occurs because WLB will never occur in workers’ lives, so integrating work and personal life into one unit in daily routines is inevitable (Johari et al., 2018). The WLI concept combines WLB by integrating various aspects of life, creating a work atmosphere that supports success in all walks of life (Busold & Husten, 2020). One of the significant impacts of WLI that is now increasingly recognised is its influence on the level of well-being in the workplace (Pujitha et al., 2023).
Work-life integration represents a paradigm shift in how work and personal life are interconnected. In the context of happiness at work (HAW), this approach explores how the holistic work experience can affect an individual’s level of happiness. Workers who have HAW in themselves will have higher job satisfaction and commitment to the organisation and lower stress levels (Salas-Vallina et al., 2020). Several studies have shown that achieving HAW requires strategies to maximise WLB (Yusoff et al., 2020). A work environment that supports WLI offers flexibility in work time and location, allowing individuals to fulfil their responsibilities without losing focus on work (Bataineh, 2019). Several studies have proven the role of WLB in shaping various outcomes in the form of work attitudes and behaviours, such as job satisfaction (Susanto et al., 2022), individual performance (Johari et al., 2018), more stable mental health (Haar et al., 2014) and reduced turnover intentions (Lestari & Margaretha, 2021). Thus, it is imperative to connect the concept of WLI and how it affects HAW with adequate literature. The concept of WLI is only recently developed, and many aspects still need to be researched. Therefore, the main objective of this study is to conduct an exploration that tests the role of WLI in influencing HAW by moderating work stress in blue-collar workers.
Boundary and border theory
Boundary and border theory was developed by Clark (2000). The theory states that individuals create and maintain spatial, temporal and cognitive boundaries between work and personal life, and they do so to limit the spillover of work-related elements into personal life and vice versa (Clark, 2000; Edwards & Rothbard, 2000). Individuals create boundaries between their personal and work lives that vary in flexibility from highly segmented to highly integrated (Clark, 2000; Desrochers & Sargent, 2004). Individuals may decide to limit their work activities to not interfere with their personal lives or to incorporate work tasks into their daily activities inseparably (Cobb et al., 2022). This theory attempts to explain the complex interactions across boundaries and their work and family lives to predict when conflict will occur and provide a framework for achieving balance (Clark, 2000).
Blue-collar workers
Blue-collar workers are defined as workers with relatively low positions within an organisation (Gilson et al., 2019). They are mainly involved in physical and manual work, using certain technical skills and in various locations such as factories, construction sites, transportation and other industrial sectors (Gilson et al., 2019). Examples of blue-collar worker jobs are technicians, construction workers, waiters, waitresses and manufacturing machine operators. According to Harumi et al. (2022), blue-collar workers have a very important role in various industries; they are the backbone of production in various goods and infrastructure industries.
Work-life integration on happiness at work
Happiness at work is described as a condition where a person enjoys a pleasant working relationship and career development, as well as the pleasure of feeling appreciated and treated well (Bataineh, 2019; Fisher, 2010; Martínez & Liu, 2024). Happiness at work is more than just feeling happy, having a positive affective experience, having a good feeling and enjoying work; also, a person feels that they have a meaningful WLB (Kaushal, 2020). Lyubomirsky et al. (2005) argue that a person’s happiness, including HAW, is influenced by three factors. These three factors can be happiness with life conditions (such as marriage and employment status), the principle of happiness that has been determined hereditarily and the extent to which a person is involved in activities that will increase happiness (such as gratitude, building relationships, physical exercise or spiritual activities) (Lyubomirsky et al., 2005). Lyubomirsky et al. (2005) further stated that one of these can affect a person’s level of happiness because he or she is involved in activities that ensure balance in his or her life, such as the implementation of WLI. The concept of WLI combines balance between work and personal life by integrating various aspects of life, creating a work atmosphere that supports success in all walks of life. According to Wepfer et al. (2018), there are two dimensions of WLI, namely ‘work-to-life integration’ and ‘life-to-work integration’. Work-to-life (WTL) integration can be defined as how a worker can manage the boundaries of their work and personal lives (Wepfer et al., 2018). Life-to-work integration can be defined as activities and personal responsibilities managed by and linked to work (Wepfer et al., 2018). Individuals with an integrated WLB are likelier to have satisfaction, happiness, maturity, health and well-being (Kumar et al., 2023). Workers who apply the WLI concept in their lives are likely to feel life satisfaction because they can carry out their work tasks along with their daily personal activities without any particular limitations; so, they will feel freer and increase their HAW (Samtharam & Baskaran, 2023). Several previous studies show that WLI has a positive role in improving an employee’s well-being in the health sector (Fein et al., 2017; Bataineh, 2019; Nishi et al., 2021):
H1: Work-life integration has a positive impact on happiness at work
Work-life integration on happiness at work: Moderating work stress
In the world of work with complex challenges, workers are currently faced with a challenge in the balance of work and personal life (Dyrbye et al., 2020). The WLI concept emphasises the importance of a balanced integration between personal life and work, emphasising that both must influence and support each other. According to Dyrbye et al. (2020), individuals who have WLI in themselves will have lower work stress in their jobs. Work stress itself is a mechanism in individuals in the form of physical, emotional and mental responses felt by individuals when work demands exceed their abilities (Savitri & Widajati, 2023). High stress will affect workers, creating negative attitudes such as decreased work performance and productivity (Aruldoss et al., 2021). The concept of WLI recognises that the border between work and personal social life cannot always be separated (Yadav et al., 2022). This is because, to work in today’s organisations, the average lifetime is spent entirely in work. Segmenting the two affairs of the domain with clear boundaries without interference is ideal, but it is difficult to do (Badri et al., 2023). Suppose the two domains remain separate from each other; it is impossible to overcome the obligations of one domain within the physical and time boundaries of the other domain, so everyone must make flexible adjustments to integrate the two domains (Aditya et al., 2023). However, WLI tries to bridge this limitation by integrating work and personal life. For example, workers can be flexible in working hours to attend family events or arrange vacations to maintain mental health (Badri et al., 2023). Employees with WLI in their work lives will tend to have higher happiness in their workplace (Chauhan & Rai, 2024). However, WLI does not always improve well-being if a worker experiences work stress. This is because stress can hinder this integration by blurring the unclear domains of work and personal life (Dyrbye et al., 2019). According to research conducted by Pujitha et al. (2023), WLI significantly impacts employee well-being because it includes a combination of professional and personal responsibilities to improve overall life satisfaction and workers’ mental health. However, in reality, there are many challenges that workers must face in achieving happiness and well-being at work, one of which is being hindered by work stress (Mostafa, 2021):
H2: Work stress moderating impact of work-life integration on happiness at work
Research design
Research approach
This study uses an exploratory research approach with quantitative methods. This is because the proposed theoretical model is a relatively new model in the relationship between WLI, HAW and moderation of work stress. The quantitative exploratory approach is considered effective for testing new theoretical models because it is more flexible in finding new relationships between variables.
Participants
The population in this study were blue-collar workers in various industrial sectors in major cities in Indonesia. The research sample used a purposive sampling technique to determine the research object. The researcher determined several classifications, such as gender, occupation, length of service, education and status. The number of samples obtained in this study was 1885 samples (n = 1885). Simple descriptive analysis is used in this study to determine the demographics of respondents. Respondent demographics are important to see, as well as the distribution of respondents and the actual conditions. Respondent demographics can be seen in Table 1.
Measuring instruments
Happiness at work
Happiness at work in this study was measured using the HAW scale of five items developed by Vallina and Alegre (2021). Research respondents responded to HAW variable items using a 7-point Likert scale ranging from 1 = strongly disagree to 7 = strongly agree. The results of the consistency and reliability of HAW items have shown adequate consistency and reliability (α = 0.7–0.88).
Work stress
Work stress in this study was measured using the work stress scale, which consists of five items and was developed by Irawanto et al. (2021). Research respondents responded to the work stress variable items using a 7-point Likert scale ranging from 1 = strongly disagree to 7 = strongly agree. The results of the consistency and reliability of work stress items have shown adequate consistency and reliability (α = 0.7–0.88).
Work-life integration
Work-life integration in this study was measured using the WLI scale consisting of 16 items developed by Wepfer et al. (2018). This study divides the WLI scale into two dimensions: WTL integration and life-to-work (LTW) integration. The research respondents responded to the WLI variable items using a 7-point Likert scale ranging from 1 = strongly disagree to 7 = strongly agree. The results of the consistency and reliability of WLI items on both dimensions of WLI and LTW integration have shown to have reliable consistency (α = 0.7–0.92).
Statistical analysis
As explained in the previous method section, the research data analysis used partial least squares (PLS) with the help of SmartPLS 03 software. The reason for using PLS in this study is that the model formed in this study has a fairly complex complexity, so to facilitate data analysis, PLS is used to complete data analysis simultaneously (Hair et al., 2014a). In data analysis with SmartPLS 03, data research was carried out through two main stages of analysis: external loading and internal loading (Hair et al., 2014b). This study adds a second-order method so that the dimensions contained in the latent variables can be seen comprehensively. Analysis of research data on several latent variables uses second-order constructs; this is where there are latent variables in the study that must be measured by several first-order constructs (Roni et al., 2015). The WLI variable as a second order is measured by its first-order construct, namely ‘WTL’ integration and ‘LTW’ integration. Before entering the further results stage, we explain that there are three variables in this study: the independent variable is WLI, the dependent variable is HAW and the moderating variable is work stress.
Ethical considerations
Ethical clearance to conduct this study was obtained from the Universitas Dian Nuswantoro, Ethical Review Committee Faculty of Economics and Business (036/B.23/UDN-03/11/2024).
Results
Outer loading
Outer loading in SmartPLS 03 can be explained as an outer test, which provides a validity test for each construct and latent variable used. Validity in the outer model itself is divided into several tests: convergent validity, discriminant validity and reliability testing.
Convergent validity
Convergent validity in the SmartPLS 03 analysis is a key determinant of whether the construct used in the latent variable can effectively measure the latent variable itself. This validity, as indicated by the loading factor value, must exceed 0.7. Hair et al. (2019) underscore the importance of this criterion in our research. In this study, all constructs used in the latent variables have demonstrated good discriminant validity. The analysis, conducted using second-order analysis, has divided the convergent validity analysis for the WLI variable into two dimensions, namely ‘WTL’ integration and ‘LTW’ integration. The convergent validity of this study is presented in Table 2.
Discriminant validity
Discriminant validity in the results of research data analysis is used to see whether the latent variables used in the study must be different from each latent variable used (Sarstedt et al., 2020). In this study, the discriminant validity used the Fornell Laker test. Discriminant validity can be achieved if the latent variable has a higher average variance extracted (AVE) value than other latent variables (Faizan et al., 2018). The results of data analysis show that all latent variables, WLI, work stress and HAW have met the good discriminant validity test. This can be seen from the AVE value produced by each variable, which is higher than the value of the other variables. Discriminant validity in this study can be seen in Table 3.
Reliability test of latent variables
The reliability testing in this study is a crucial step, as it determines the reliability of the construct used in the latent variable. The values of Cronbach’s alpha (CA), composite reliability (CR), and AVE are indicators of this reliability (Sarstedt et al., 2020). The CA and CR values in the analysis output must be > 0.7, while the AVE value must be > 0.5. The results of the reliability test of the HAW variable show a CA value of 0.838, which is > 0.7; a CR value of 0.885, which is > 0.7 and an AVE value of 0.607, which is > 0.5, so it can be concluded that the HAW variable has met the construct reliability criteria. The results of the reliability value of the work stress variable show a CA value of 0.836, which is > 0.7; a CR value of 0.884, which is > 0.7 and an AVE value of 0.604, which is > 0.5, so it can be concluded that the work stress variable has met the construct reliability test. The results of the reliability value of the WLI variable show a CA value of 0.911, which is > 0.7; a CR value of 0.929, which is > 0.7 and an AVE value of 0.653, which is > 0.5, so it can be concluded that the WLI variable has met the construct reliability test. The results of this analysis can be seen in Table 4.
Inner model
The inner model in Smartpls 03 data analysis was used to see the influence between latent variables used in the model. This study uses inner model analysis to see hypothesis testing and model goodness of fit tests with R-Square (Hair et al., 2019).
R-squared
In SmartPLS 03, the R-squared value is used to see how good the model used in the study is. According to Hair et al. (2014b), R-squared provides a clear picture and indication of how good the model used in the study is in explaining data variability. A high R-squared value indicates that the model used by the researcher can explain most of the variability of the independent variable on the dependent variable. According to Sarstedt et al. (2020), R-squared values > 0.67 are considered stronger, > 0.33 are considered moderate and < 0.19 are considered weak. The results of the data analysis show that the R-squared value of the dependent variable HAW shows a strong indication of 0.650, so it can be justified that the model used in the study has been used well. The R-squared value can be seen in Table 5.
Hypothesis testing
Hypothesis testing in this study is used to see whether the proposed hypothesis is accepted or rejected. In this study, the proposed hypothesis test uses a significance level of 5% (0.05), so to see whether the hypothesis is accepted or rejected, it will be seen from the probability value, the value of which must be < 0.05 (Sarstedt et al., 2020). The results of the analysis that have been carried out on the SmartPLS 03 inner model show that all proposed hypotheses are accepted. Hypothesis testing can be seen in Figure 1 and Table 6.
Discussion
Work-life integration on happiness at work
In this study, WLI has a positive impact on HAW with a probability value of 0.00, which is smaller than the significance level set at < 0.05, and the positive impact is confirmed by a beta coefficient value (β = 0.364). Thus, we see that WLI allows workers to be more flexible in managing their responsibilities at work, increasing HAW in them. This is because hard workers who manage work well with their personal lives tend to experience more positive emotions, such as higher job satisfaction, commitment and engagement. This study highlights that at the blue-collar worker level, the ‘WTL’ dimension in the WLI variable shows a stronger impact than the ‘LTW’ dimension in influencing HAW. Blue-collar workers often face greater challenges in creating a boundary between work and personal life. In developing countries like Indonesia, blue-collar workers are often trapped in tiring routines, such as a protracted WLB. Several surveys state that workers in big cities in Indonesia spend 14 h working, starting from going to work full of traffic jams and going home with traffic jams and inadequate public transportation, which has an impact on the longer workers to go home (CNN, 2022). With a working life like this, WLI helps these workers to integrate their work and personal lives better. We see in this study that the integration between work and personal life is often marked by the need to bring experiences and feelings from the workplace into their daily lives. When they feel appreciated and recognised at work, it will create a positive feeling that can penetrate their personal life and create a domino effect that increases happiness positively and reduces work stress and personal life conflicts with work. According to Pujitha et al. (2023), positive WLI in a worker will contribute to a higher level of well-being; this mechanism can be explained by the fact that when workers can integrate work and personal life well, they tend to feel satisfied and mentally healthy. The results of this study are also in line with Ko’s (2024) study that integration and WLB can significantly affect workers’ well-being; this impact can be seen in better psychological acceptance by experiencing reduced work-family and family-work conflicts, thereby fostering a healthier work environment and increasing overall employee satisfaction and productivity. The results of this study are also in line with the results of Samtharam and Baskaran’s (2023), showing that WLI can increase organisational commitment and positive job satisfaction, thus leading to happiness in the workplace.
Work-life integration on happiness at work with moderating work stress
The results indicate that work stress can increase the impact of WLI on HAW; this can be seen from the probability value of 0.02, less than the significance level of < 0.05. Furthermore, this study shows that work stress can weaken the effect of WLI on HAW; this can be seen from the beta coefficient value of (β = -0.034). This result can explain that WLI can be a key strategy to achieve HAW, but the positive effects of WLI are often weakened by work stress. Work stress can reduce the integration of work and personal life; this is possible because blue-collar workers are operational-level workers in an organisation, so their work demands are quite high. Thus, they are susceptible to work stress. When blue-collar workers try to align work and personal life to achieve better work happiness, work stress can be a significant barrier to achieving that goal. The results of this study are supported by the opinion of Dyrbye et al. (2020), who stated that WLI is often weakened by work stress accounts that lead to burnout because stress experienced by workers is usually because of work demands and personal life conflicts. This phenomenon indicates that work stress often arises; it can come from within or outside of work, and from within, the triggers of work stress include work demands, conflict in the workplace and role ambiguity.
Meanwhile, stress triggers from outside work are triggered by several factors, such as personal problems, unbalanced lifestyles, mental health and family conflicts (Fein et al., 2017). Work stress will impact the boundaries between work and personal life, causing conflict and increased stress levels. We also know that work stress also has a psychological impact on workers; when it occurs, a worker may feel unhappy in his or her work, which will interfere with the achievement of productivity and performance produced. Work stress can damage the effectiveness of WLI strategies, which aim to align both work and personal domains (Astuti et al., 2023). However, with the implementation of WLI, the number of work-likely collar workers may decrease. This is expressed by Hämmig (2014), who stated that workers with an integrated WLB have a greater chance of having satisfaction, happiness, maturity, health and well-being.
Practical implications
This study provides a deeper understanding for WLI integration among the blue-collar workers. Organisations can support their workers by offering worker-friendly programmes, such as flexible working hours, and this may increase HAW through WLI. Future research could use other perspectives or other indicators that support WLI and HAW.
Limitations
This study acknowledges several limitations. For example, this study was conducted in a developing country, where the characteristics of blue-collar workers are different from those in developed countries, so the results of this study may not be generalisable to developed countries with varying levels of welfare. Then, the research data collected from blue-collar workers in various industrial sectors are susceptible to social bias or subjectivity of respondents’ responses, especially when assessing different levels of HAW and stress.
Conclusions
Ultimately, this study showed that HAW is not impossible for blue-collar workers in Indonesia. Through the role of WLI, blue-collar workers can find a way to achieve HAW. Although work stress can weaken the positive impact of WLI on HAW, the positive impact of WLI on HAW is much greater. The impact of WLI on blue-collar workers is a significant transformation, where work life and personal life do not conflict but strengthen each other, improving a positive psychological effect that increases happiness, decreases work stress and reduces personal conflict.
This study explains the importance of achieving effective WLI in workers to achieve HAW, especially for blue-collar workers; we see that to achieve positive WLI, employer support to make policies that support worker welfare is important. For example, providing flexibility in working hours and creating programmes for workers’ mental health support will help workers reduce their work stress, which will help workers achieve better satisfaction, commitment and organisational attachment. Thus, in the future, implementing the WLI organisation will not only view workers as production machines but also as human beings with aspirations, dreams and hopes. We recommend future research to investigate the impact of WLI on organisational support and worker performance or at a higher level of workers in the organisation.
Acknowledgements
This article is partly based on the author, F.R.’s dissertation entitled, ‘Digital Skill Capability to improve worker performance and well-being in Indonesia’, towards the degree of Doctor in the Department of Doctoral Management, Faculty of Economics and Business, Dian Nuswantoro University, Indonesia, with supervisors Amron Amron and Sih Darmi Astuti.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
Authors’ contributions
F.R. contributed to this research by providing conceptualisation, conducting research methodology, investigation, software, writing the original draft and funding acquisition. A.A. contributed by managing project administration, validation and editing the manuscript. S.D.A. contributed by formal analysis and validation.
Funding information
The authors received no financial support for the research, authorship and/or publication of this article.
Data availability
The data that support the findings of this study are available from the corresponding author, F.R., upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder or agency or that of the publisher. The authors are responsible for this article’s results, findings and content.
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