1. Introduction
The COVID-19 pandemic resulted in a massive shift in how people work, with millions of employees worldwide transitioning to work-from-home (WFH) arrangements (Wang et al. 2021; OECD 2021). While WFH offers several advantages, such as increased flexibility and reduced commute time (Gajendran and Harrison 2007; Nakrošienė et al. 2019), it also presents new challenges related to work–life balance (WLB), social isolation, stress, and mental health (Novianti and Roz 2020; Gálvez et al. 2020; Contreras et al. 2020).
In such circumstances, with workers and organizations trying to navigate a completely new and unpredictable situation, individual and organizational resources may play an essential role in a successful WFH experience. According to a recent systematic review on WLB and WFH during COVID-19 (Shirmohammadi et al. 2022), resources emerged as an important factor to understand the relation of WFH and WLB, but although the literature highlights the importance of several resources in the work and non-work domains, there is no reference to individual resilience (IR) and only one study approached social support (Wang et al. 2021), a concept similar to perceived organizational support (POS).
Given this gap in the literature and the increased demands and stressors associated with WFH during the pandemic (Novianti and Roz 2020; Gálvez et al. 2020; Contreras et al. 2020), it is crucial to understand the role of personal and organizational resources, such as IR and POS, in the WFH experience and, in turn, the relation of these with WLB. As such, this study aims to examine the relationship of POS and IR with WLB when mediated by WFH experience during COVID-19. In the context of the job demands–resources (JD–R) model (Bakker and Demerouti 2017; Xanthopoulou et al. 2007), IR and POS represent individual and organizational resources, respectively, and may be critical for managing and adapting to the challenges posed by the pandemic (Irawanto et al. 2021).
In order to achieve this objective, this study is based on a quantitative approach. Data from 27 European countries were extracted from the second round of the survey “Living, Working, and COVID-19” from Eurofound. Data were analyzed by performing a series of regressions using SEM-PLS to test the hypothesis of the theoretical model. Findings reveal that WFH experience negatively influences WLB. POS positively influences WFH and negatively influences WLB when mediated by WFH. IR negatively influences WFH and positively influences WLB when mediated by WFH.
Given the significance of this topic, examining the experiences and challenges of WFH during the pandemic can contribute to a better understanding of these dynamics to inform future health and organizational policies and practices.
Additionally, by studying the role of individual resilience in the context of remote work and work–life balance, this research can contribute to the identification of specific coping mechanisms that individuals can adopt to navigate the demands and stressors associated with remote work. This can inform resilience-building interventions and support programs.
Finally, researching the impact of organizational support on work–life balance can provide evidence-based insights to organizations on how to effectively design and implement supportive policies, such as flexible work arrangements (e.g., work from home), resources for managing work-related stress, and fostering a culture of work–life integration.
2. Theoretical Framework and Hypothesis Development
2.1. Job Demands–Resource Model
According to the JD–R model, stress results from the interaction between work demands and job resources (Bakker and Demerouti 2017). Job demands are the physical, psychological, and social demands of the job, including a heavy workload, tight deadlines, and little influence over how things are conducted. In contrast, job resources are employees’ social, psychological, and physical assistance at work, including possibilities for personal development, autonomy, and social support. Most research focused on organizational rather than personal resources (Britt et al. 2021). Nevertheless, Xanthopoulou et al. (2007) also refer to personal resources as a separate but complementary set of resources that employees may mobilize.
The JD–R model states that heightened levels of stress and burnout are related to limited job resources and high job demands. On the other hand, high job resources and low job demands might serve as stress reducers and promote favorable outcomes such as job satisfaction, engagement, and well-being (Bakker et al. 2005; Bakker and De Vries 2021; Bellmann and Hübler 2021).
The JD–R model’s capacity to describe the many outcomes resulting from the same job demands and resources is one of its significant contributions (Bakker and Demerouti 2017). For instance, if there are the right resources, some workers might be able to handle high demands well, while others might become stressed out even under mild demands. Individual characteristics, including coping mechanisms such as individual resilience, can be used to account for this variation.
2.2. Work from Home and Work–Life Balance
Working from home (WFH), also called remote work, teleworking, mobile work, or homeworking, consists of work performed by employees from home (Vega et al. 2015). Although this concept emerged in 1970 and progressed after the European Framework Agreement on Telework, signed in 2002, WFH inevitably grew with the confinements imposed by the pandemic and its maintenance, namely, in European countries (OECD 2021) whether total or partial, tending to be fervently discussed.
Reduction in human resource costs, greater worker productivity and performance, reduced turnover intention, and greater social sustainability are some advantages associated with organizations that utilize WFH (Contreras et al. 2020; Vega et al. 2015). On the workers’ side, WFH favors autonomy, makes time management more flexible, saves costs and time on commuting to the workplace, improves work planning and work capacity, and allows reconciliation with family life (Gajendran and Harrison 2007; Nakrošienė et al. 2019), in addition to reducing stressful situations (Baruch 2000; Ivasciuc et al. 2022).
Nevertheless, there are growing concerns about the impact of WFH on work–life balance (Kim et al. 2019). Several studies show that higher levels of WLB are experienced when workers who are in remote work are more satisfied with life and are more individualistic in social terms (Nakrošienė et al. 2019; Bellmann and Hübler 2021; Irawanto et al. 2021; Haar et al. 2014), with a positive effect of WFH on WLB (Fedáková and Ištoňová 2017). However, other studies report that WFH does not benefit WLB because, often, family and leisure hours are used to extend working time, which can increase workers’ stress if the hours are not flexible or if the workers do not have essential resources available for the performance of their work (Novianti and Roz 2020; Gálvez et al. 2020; Contreras et al. 2020). Thus, WFH can lead to an imbalance between work and personal life, since remote workers tend not to be able to divide their time between work and personal life, which may result in reduced performance and productivity.
This situation is aggravated if the worker lacks the resources to fulfill the dual roles people have to play in their professional and personal life (Irawanto et al. 2021). Facing a rapid and forced transition from face-to-face work to remote work, workers faced social disconnection. They could not create boundaries separating personal and working life, often due to the excessive workload, more intensive monitoring of employers, and sudden changes (work and personal) faced in a short time. In this context, WFH can negatively affect WLB (Irawanto et al. 2021; Adisa et al. 2022; Tejero et al. 2021; Palumbo 2020). In this way, we formulate the following hypothesis:
In emergency remote working, WFH negatively affects WLB.
2.3. Perceived Organizational Support and Individual Resilience
Perceived organizational support (POS) refers to employees’ perceptions of the extent to which the organization values their contributions and cares for their well-being (Eisenberger et al. 1986). In times of crisis, when conditions are chaotic and organizational resources are reduced, POS may be a critical asset (Chen and Eyoun 2021; Zheng 2020). As such, workers seek the organization to indicate strategic guidance and provide organizational support (Tu et al. 2021).
In the context of the COVID-19 pandemic, the imposed confinement forced WFH, and organizations found the adaptation to remote work complex (Daniels et al. 2022). In this way, the POS is an organizational resource that can be more difficult to implement when working remotely (Sygit-Kowalkowska et al. 2022; Charalampous et al. 2019) but is more valued by teleworkers than onsite employees (Kohont and Ignjatović 2022). When workers perceive that the organization supports them, remote work tends to feel less socially isolated, to be more engaging and productive, to create resources to feel less stress, and to be more satisfying (Bentley and Yoong 2000; Ali and Anwar 2021), as well as generating a more significant affective commitment to work and the organization (Irfan et al. 2021). In addition, when the worker feels organizational support at the work and personal level, there is greater harmony between work and personal life, reducing the ambiguity of roles that the worker plays in these two spheres (Irfan et al. 2021). Thus, POS can positively influence WLB (Sheikh 2023; Mishra and Bharti 2020; Lamprinou et al. 2021). In this context, the following hypotheses were formulated:
In emergency remote working, POS positively affects WFH.
In emergency remote working, the POS positively affects the WLB when mediated by WFH.
Resilience is the ability to bounce back from adversity and adapt to change, and it has been shown to play a crucial role in coping with stress and maintaining well-being (Van Kessel 2013). Individual resilience is a personal resource and can be interpreted as skills workers can mobilize during stress (Ford et al. 2011). Workers with higher levels of individual resilience tend to be more productive (Kwok et al. 2014), less stressed and more satisfied with life (Shatte et al. 2017), and have greater emotional control and be positive and mentally robust when facing adversity (Avey et al. 2011). In more complicated and adverse work environments, as was the context of emergency remote working during the pandemic, more resilient workers tend to be more committed to the work to be completed (Shatte et al. 2017; Avey et al. 2011; Robertson et al. 2015), also achieving a better balance between personal and work life, i.e., a greater WLB (Hopkins and Bardoel 2023; Marques and Berry 2021).
In an integrated approach, WLB can be achieved through individual, organizational, and family efforts (Marques and Berry 2021). The imbalance between work and personal life can be caused by individual characteristics and obstacles imposed by work and personal situations, whose solutions differ at the individual level. Thus, higher levels of individual resilience leads remote workers to maximize opportunities to promote WLB due to greater flexibility to carry out work tasks and greater autonomy and, at the same time, reduce its inhibitors, such as stress, exhaustion, and fatigue that moving to organizations can trigger (Marques and Berry 2021). The following hypotheses were formulated:
In emergency remote working, IR positively affects WFH.
In emergency remote working, IR positively affects WLB when mediated by WFH.
Based on the literature review, we propose the structural model shown in Figure 1.
3. Materials and Methods
3.1. Data and Sample
Data were taken from the “Living, Working and COVID-19” e-survey conducted by the European Foundation for the Improvement of Living and Working Conditions (Eurofound 2020). Eurofound’s innovative e-survey aims to assist policymakers in bringing about a fair recovery from the crisis by giving a glimpse of the pandemic’s effects on people’s life. The data include information on various subjects, such as employment status, working conditions, work–life balance, the extent of teleworking, job security, job satisfaction, and experiences with working from home. The total number of indicators collected was 393. The data in this study were taken from the second round (the project has five rounds), collected in July 2020, when most European economies and societies were gradually reopening from the first significant lockdown. According to information on methodological procedures provided by the European Foundation for the Improvement of Living and Working Conditions (Eurofound 2020), the questionnaire was collected using a non-probabilistic sampling method. Participants were recruited using snowball sampling methods and social media advertisements. As such, this method produced an unrepresentative sample. Only the second round was used because the relevant variables for the present study were only collected in this round.
The second wave sample consisted of 24,144 valid responses collected from 27 countries in Europe (Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden). Given the study’s objective, the sample was additionally filtered by the situation of the participants regarding employment, only retaining in the sample the participants employed by third parties and self-employed workers (with and without workers). Thus, the final sample was composed of 14,298 valid responses.
Four groups of variables from this questionnaire were used. All the items available for each each variable were used: (i) work-from-home with five items; (ii) work–life balance with five items; (iii) perceived organizational support with two items; and (iv) individual resilience with two items. In addition, sociodemographic variables such as age, gender, educational level, country, and employment status were collected. The work-from-home and individual resilience variables were measured on a five-point Likert scale: 1—strongly disagree and 5—strongly agree. The work–life balance and perceived organizational support variables were measured on a five-point Likert scale: 1—never and 5—always.
3.2. Data Analysis
First, a statistical analysis was performed on the variables and the items that measured the variables contained in the structural model using the SPSS (v.25) software. Then, multiple linear regressions were performed using the ordinary least squares (OLS) method to assess the impact of sociodemographic variables on the constructs of the structural model. Then, we used the partial least square (PLS) method of Smart PLS (V3.0) to test the relationships between variables that appeared in the structural model. The PLS method is a method that combines statistical analysis with regressions (Ringle et al. 2020). As assumptions, it has the non-normality of the data, having been confirmed that the items used in this study do not have a normal distribution from the kurtosis and skewness statistics. Thus, the data from this study were analyzed in five steps:
(i). Statistical analysis of variables and items that measure them;
(ii). Multiple linear regressions with the variables included in the structural model and the sociodemographic variables;
(iii). Analysis of the items that measure the constructs to ensure measurement validity and reliability and common method bias test;
(iv). Testing of the structural model and hypotheses via bootstrapping;
(v). Bootstrap analysis to measure the mediating effect of working from home.
4. Results
4.1. Statistics Description
The sample of this study is composed of 14,298 participants, of which 30.2% are men and 68.1% are women, with the remaining “another way” or did not respond. The average age is 50.6 years. There is an equal distribution of participants across countries, with the most expressive nationality being Irish (10.2%), followed by Portuguese (7.0%), Hungarian (6.9%), Romanian (5.4%), Spanish (5.1%), and German (4.8%). Regarding professional occupation, 85.9% are employed, 10.2% are self-employed without employees, and 3.9% are self-employed with employees. A total of 72% of participants have higher education. The details are shown in Table 1.
Table 2 contains the statistics (mean and standard deviation) of the variables that appear in the structural model and the indicators that measure them. The statistical description of these variables by country can be found in Appendix A.
Regarding the items that measure the WFH, in general, the participants agree with them. The items that generate more remarkable agreement among the participants are job satisfaction (M = 3.73) and adequacy of the equipment available at home for proper work performance (M = 3.69). The WLB items present an average below the scale’s mid-point (M = 2.46), meaning a perception of a good WLB. On average, the items that register a higher frequency are feeling too tired after work to carry out essential domestic tasks (M = 2.95) and preoccupied with work when not working (M = 2.76). Sample participants show average levels (M = 2.60) of IR but high POS (M = 3.46), especially from co-workers (M = 3.57).
4.2. Role of the Sociodemographic Condition of the Participants
Through multiple linear regressions, we tested the relationship between gender and age with the variables in the structural model (Table 3). The results reveal that men have higher levels of satisfaction with the experience of WFH. However, it is women who have higher levels of WLB and IR. Gender is not statistically significant in explaining POS. Concerning age, older workers tend to have higher levels of satisfaction with the experience of WFH, but younger workers have higher levels of WLB, IR, and POS.
4.3. Measures of Reliability and Validity
First, a confirmatory factor analysis (CFA) was performed to assess the reflexive nature of the structural model. In the reflective PLS model, the constructs are the common cause of the items that measure them, and the observed constructs have no causal effects on the corresponding constructs (Ringle et al. 2020). All items represent high confirmatory factor loads (>0.60), as shown in Table 4 and in Figure 2.
According to the reference values of Hair et al. (2019), the model has an excellent model fit GFI = 0.925 (reference value > 0.90); CFI = 0.967 (reference value > 0.90); IFI = 0.989 (reference value > 0.90); RMSEA = 0.067 (reference value < 0.08).
To assess the reliability of the sample, we used three measures proposed by Hair et al. (2019): Cronbach’s alpha (reference Ca > 0.70), composite reliability (reference CR > 0.70), and average variance extracted (reference AVE > 0.50). The discriminant validity of the constructs was assessed using the Fornell–Larcker criterion (Fornell and Larcker 1981). The results of these measurements are shown in Table 4. The results obtained for Ca, CR, and AVE are superior to the reference values, so the model is convergent and reliable. There is also discriminant validity according to the Fornell–Larcker criterion (in Table 4, in bold on the diagonal) since the square root of the AVE of each variable shown in bold on the diagonal is greater than the correlation of each latent variable (off the diagonal).
Given that the type of questions used to collect the variables’ indicators could suffer from some consistency of answers or social desirability trends that could bias the results, we performed a common method bias (CMB) through the Harman one-factor test. The structural model contains four constructs with an accumulated variance of 61.69%, with the most prominent factor explaining only the variation of 28.17%. As no single factor explains a variance more significant than 50%, it is unlikely that our data are affected by the CMB. Finally, we calculated the predictive relevance using Stone–Geisser (Q2). The predictive relevance to predict WFH and WLB is greater than zero (Q2 = 0.252 and Q2 = 0.173, respectively), so the model reveals predictive relevance.
4.4. Structural Model Testing
First, the direct relationships of the structural model were tested through a bootstrapping analysis. We then performed a second bootstrapping analysis to measure the mediating effect of WFH. The results of these two tests are shown in Figure 3.
The results reveal that WFH negatively influences WLB (b = −0.181), confirming H1. POS positively influences WFH (b = 0.107) and negatively influences WLB (b = −0.019) when mediated by WFH, confirming hypothesis H2 and rejecting H2a. IR negatively influences WFH (b = −0.177) and positively influences WLB (b = 0.032) when mediated by WFH, rejecting H3 and confirming H3a.
5. Discussion
When confinement became mandatory in European countries between March and June 2020 due to the COVID-19 pandemic, companies were forced to implement remote work measures (Cuerdo-Vilches et al. 2021). Bearing in mind that it was a decision driven by an event, which until then had been unpredictable, the implementation of remote work took on an emergency nature (Oliveira et al. 2020). In this way, many companies did not have procedures designed and implemented for this type of work due to this combination of emergency and mandatory confinement. Many companies and workers experienced this work format for the first time. In this context, it became relevant to assess how the WFH experience conditioned the well-being of workers, how organizations and employees were equipped to deal with such situations, and how it affected the perception of WFH and workers’ WLB.
Framed by the JD–R model, this research aimed to examine the role of organizational and personal resources—POS and IR, respectively—in WLB in the context of emergency remote work. This study also examined the mediating role of WFH experience in that relationship.
The findings revealed that WFH is negatively related to WLB (b = −0.181). Remote working can contribute to stress reduction (Baruch 2000) by allowing to save costs, more flexible time management, and the conciliation of work and family life (Gajendran and Harrison 2007; Nakrošienė et al. 2019). However, WFH is a specific form of remote working (Sostero et al. 2020; ILO 2020) that blurs the line between professional and family life (Delanoeije et al. 2019). Additionally, WFH during the pandemic was an emergency solution, which means that companies and employees did not anticipate and prepare for this transition. An indicator of this ill-preparedness is reported in the study by Cuerdo-Vilches et al. (2021): in Spain, more than 25% of houses did not offer the proper conditions for WFH, such as the availability of exclusive spaces for teleworking, quality of digital resources, and the specific space features, among others. Finally, since confinement was a general measure for the population, employees could work at home while their family (spouses, children, and other relatives) were working or schooling at home.
Regarding the role of organizational and personal resources, the results show different behaviors for POS and IR. While POS is positively related to WFH (b = 0.107), IR is negatively related to WFH (b = −0.177). During the confinement, organizations and employees found the adaptation to remote work complex (Daniels et al. 2022), which made all the help and supported more valuable to employees (Charalampous et al. 2019). When workers perceived that their managers and co-workers were there to help and support them, the WFH experience became more engaging, less socially isolating, and less stressful (Kohont and Ignjatović 2022; Bentley and Yoong 2000).
On the opposite side of this, IR seems to be negatively related to WFH (b = −0.177). Facing a situation of emergency remote working, it would be expected that more resilient workers could cope better with remote working (Shatte et al. 2017; Robertson et al. 2015) and, thus, exhibit a better WFH experience. However, WFH experience measurement in this study includes the respondent’s satisfaction with the quality and amount of work completed (refer to Table 1). According to a study from Scheibe et al. (2022) with 1715 university employees during COVID-19 lockdown (in February 2021), resilience was negatively related to workload, meaning that more resilient workers tend to be less satisfied with the amount of work they perform or they are given.
Finally, this study measured the relationship of resources with WLB when mediated by the WFH experience. The findings support the hypothesis that IR positively relates to WLB when mediated by the WFH experience (b = 0.032). This result is aligned with the previous literature (Avey et al. 2011). More resilient workers deal better with stress and pressure by seizing the opportunities offered by challenging situations. In this case, WFH offered the opportunity to reduce costs and commute time while enjoying greater autonomy and flexibility in time management (Gajendran and Harrison 2007; Nakrošienė et al. 2019), potentially impacting WLB.
On the other hand, POS shows a negative relation with WLB when mediated by the WFH experience (b = −0.019). Once again, the emergency and involuntary remote working situation can contribute to explaining this result. Compulsory confinement forced companies and workers to transform their way of working overnight. The absence of a plan that accompanied this transition, including the reduced or no preparation of people to work, interact, and provide support from a distance, may have created the perception that organizational support was inadequate. The lack of preparation for an emergency remote work situation may also have led to longer and more irregular working hours, thus, conditioning the WLB of remote workers (Felstead and Henseke 2017; Vayre et al. 2022a).
6. Conclusions
The fulfillment of the objective of this study demonstrates that the quality and quantity of organizational and personal resources are relevant for WLB. In involuntary and emergency remote work, the need for these resources becomes even more evident for WLB, especially when considering WFH, a specific form of remote work.
There are, nevertheless, some limitations. The tested model is not exhaustive in terms of organizational and personal resources. Although POS and IR represent organizational and personal resources, respectively, other resources could be relevant. Another limitation of this study is related to the variables provided by the e-survey “Living, Working and COVID-19” conducted by the European Foundation for the Improvement of Living and Working Conditions (Eurofound 2020). As it is a secondary database, the constructs were measured using the indicators available in this database. Different and additional indicators for the constructs can lead to different results. In addition, the sample is unbalanced in terms of gender (it has more women than men) and, in a balanced sample, the results may be different. It would be interesting in future lines of investigation to replicate the structural model of this study and compare the results for men and women, and even for generations (X, Y, and Z). Finally, despite the wide scope, this study is limited to European countries, and to a specific moment (July 2020), when European countries began to ease the restriction measures. The cross-sectional approach of this study limits the results to that specific moment, not allowing for the capture of the dynamic nature of the phenomenon. As such, future research should focus on comparisons of results not only in different moments of the pandemic, but also during and after the pandemic. Finally, there is a dearth of research examining the role of organizational policies, practices, and interventions in promoting work–life balance and mitigating the negative consequences of remote work (Pillai and Prasad 2023). Understanding the factors that contribute to successful work–life integration in a remote work setting and identifying effective strategies for managing work-related stress and maintaining personal well-being are areas that require further exploration.
This research allows us to make several contributions, both theoretical and practical. First, this research confirms the potential of the JD–R model not only to function as a theoretical lens to explain the dynamics between resources and demands in an extreme situation such as a pandemic, but also as a framework for an alternative way of working, such as WFH. By placing the WFH experience as a moderator between job resources (both organizational and individual) and workers’ well-being through WLB, this research makes another theoretical contribution by expanding and enriching the JD–R model. Additionally, the results of this research reveal the importance of combining individual and organizational resources in order to enhance WFH, and efficiently promote WLB.
Following the theoretical contributions, this research adds new insights to the management of the remote work experience, while keeping in mind employees’ well-being. One of the implications is related to preparing and training employees to deal with the effects of working remotely. This research draws attention to the need for organizations to develop remote work management training programs, focused on training resilience and supporting skills. On the one hand, this implication highlights the importance of training workers to cope with obstacles coming across when working remotely (especially from home), thus, improving their resilience; on the other hand, the need to focus on the crucial role of training leadership to develop more supportive behaviors and strategies.
Another practical implication is related to the development of policies and organizational practices capable of facilitating and promoting a supportive environment, while enhancing the work-from-home experience. This may include the development of specific teleworking support and assistance (Vayre et al. 2022b), and human resources policies that stimulate the sense of purpose and belonging (Nakrošienė et al. 2019; De Vries et al. 2018). Supportive environments create the conditions for greater social and organizational connectedness, which, in turn, contributes to well-being (Brown and Leite 2023).
The growing trend of remote work (OECD 2021), especially WFH, will put more pressure on workers’ health and well-being. As such, contributing to improving the experience of WFH, namely, by reinforcing personal and organizational resources, can be paramount to strengthening workers’ health and well-being.
Conceptualization, P.F.; methodology, S.G.; software, S.G.; validation, P.F.; formal analysis, S.G.; resources, P.F.; data curation, S.G.; writing—original draft preparation, P.F. and S.G.; writing—review and editing, P.F. and S.G.; visualization, S.G.; supervision, P.F.; project administration, P.F. All authors have read and agreed to the published version of the manuscript.
Ethical review and approval were waived for this study due to the use of secondary data, not collected directly from subjects.
According to the data source, informed consent was obtained from all subjects involved in the study.
The data presented in this study are available at
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 3. Results of structural model testing. Note: * p < 0.001; direct effects (→); indirect effects (---►).
Socio-demographic characterization of participants.
| Frequency | Percentage | |
| Gender | ||
| Male | 4322 | 30.20% |
| Female | 9740 | 68.15% |
| Other | 236 | 1.65% |
| Age | ||
| <25 years | 592 | 4.14% |
| 25–35 years | 1881 | 13.16% |
| 36–50 years | 4315 | 30.18% |
| 51–65 years | 6870 | 48.05% |
| >66 | 640 | 4.48% |
| Profession Occupation | ||
| Employed | 12,280 | 85.90% |
| Self-Employed without employees | 1461 | 10.20% |
| Self-Employed with employees | 557 | 3.90% |
| Education | ||
| Primary | 642 | 4.49% |
| Secondary | 3364 | 23.53% |
| Tertiary | 10,292 | 71.98% |
| Nationality | ||
| Irish | 1463 | 10.20% |
| Portuguese | 998 | 7.00% |
| Hungarian | 985 | 6.90% |
| Romanian | 768 | 5.40% |
| Spanish | 735 | 5.10% |
| Others (<5%) | 9349 | 65.39% |
Statistical description of variables.
| Mean | Std. Deviation | |
|---|---|---|
| Work from home (WFH) * | 3.54 ** | 1.169 ** |
| WFH1. I am satisfied with the amount of work I managed to do | 3.51 | 1.125 |
| WFH2. I am satisfied with the quality of my work | 3.73 | 1.009 |
| WFH3. With the equipment I have at home I could do my work properly | 3.69 | 1.129 |
| WFH4. My employer provided all the equipment I need to work from home | 3.12 | 1.423 |
| WFH5. Overall, I am satisfied with my experience of working from home | 3.63 | 1.157 |
| Work–life balance (WLB) * | 2.46 ** | 1.030 ** |
| WLB1. Kept worrying about work when you were not working | 2.76 | 1.136 |
| WLB2. Felt too tired after work to do some of the household jobs that need to be done | 2.95 | 0.992 |
| WLB3. Found that your job prevented you from giving the time you wanted to your family | 2.63 | 1.121 |
| WLB4. Found it difficult to concentrate on your job because of your family responsibilities | 2.11 | 0.968 |
| WLB5. Found that your family responsibilities prevented you from giving the time you should to your job | 1.85 | 0.933 |
| Individual resilience (IR) * | 2.60 ** | 1.024 ** |
| IR1.I find it difficult to deal with important problems that come up in my life | 2.58 | 1.032 |
| IR2. When things go wrong in my life, it generally takes me a long time to get back to normal | 2.62 | 1.017 |
| Perceived organizational support (POS) * | 3.46 ** | 1.097 ** |
| POS1. Your colleagues or peers help and support you | 3.57 | 1.026 |
| POS2. Your manager helps and supports you | 3.35 | 1.168 |
Note: ** Mean value of items that measure latent variables.
Effect of gender and age on structural model constructs.
| Gender | Age | |||
|---|---|---|---|---|
| Beta | p-Value | Beta | p-Value | |
| WFH | −0.032 | <0.001 | 0.055 | <0.001 |
| WLB | 0.077 | <0.001 | −0.067 | <0.001 |
| IR | 0.026 | <0.001 | −0.070 | <0.001 |
| POS | 0.017 | >0.010 | −0.027 | <0.001 |
Evaluation of PLS reflexive model.
| Confirmatory Factor Loads | Cα | CR | AVE | WFH | WLB | IR | POS | |
|---|---|---|---|---|---|---|---|---|
| Work from home (WFH) | 0.766 | 0.849 | 0.542 | 0.736 | ||||
| WFH1. | 0.797 | |||||||
| WFH2. | 0.807 | |||||||
| WFH3. | 0.770 | |||||||
| WFH4. | 0.783 | |||||||
| WFH5. | 0.827 | |||||||
| Work–life balance (WLB) | 0.773 | 0.846 | 0.525 | −0.181 | 0.725 | |||
| WLB1. | 0.746 | |||||||
| WLB2. | 0.702 | |||||||
| WLB3. | 0.745 | |||||||
| WLB4. | 0.787 | |||||||
| WLB5. | 0.735 | |||||||
| Individual resilience (IR) | 0.757 | 0.891 | 0.804 | −0.192 | 0.230 | 0.897 | ||
| IR1. | 0.911 | |||||||
| IR2. | 0.882 | |||||||
| Perceived organizational support (POS) | 0.696 | 0.868 | 0.767 | 0.132 | −0.216 | −0.142 | 0.876 | |
| POS1. | 0.865 | |||||||
| POS2. | 0.886 |
Note: AVE square root in bold.
Appendix A. Statistical Description of Variables by Country
| WLB | WFH | IR | POS | ||
| Austria | Mean | 3.75 | 2.32 | 3.56 | 2.32 |
| Std. deviation | 0.761 | 0.837 | 0.932 | 0.949 | |
| Belgium | Mean | 3.40 | 2.37 | 3.49 | 2.53 |
| Std. deviation | 0.750 | 0.824 | 0.905 | 0.939 | |
| Bulgaria | Mean | 3.42 | 2.47 | 3.29 | 2.32 |
| Std. deviation | 0.767 | 0.995 | 1.008 | 1.019 | |
| Croatia | Mean | 3.51 | 2.60 | 3.37 | 2.79 |
| Std. deviation | 0.741 | 0.951 | 0.830 | 0.996 | |
| Cyprus | Mean | 3.29 | 2.58 | 3.35 | 2.56 |
| Std. deviation | 0.777 | 0.950 | 1.001 | 1.073 | |
| Czechia | Mean | 3.76 | 2.24 | 3.48 | 2.62 |
| Std. deviation | 0.705 | 0.828 | 0.881 | 1.016 | |
| Denmark | Mean | 3.82 | 2.08 | 3.62 | 2.27 |
| Std. deviation | 0.690 | 0.784 | 0.880 | 0.930 | |
| Estonia | Mean | 3.61 | 2.38 | 3.35 | 2.32 |
| Std. deviation | 0.727 | 0.916 | 0.919 | 0.927 | |
| Finland | Mean | 3.56 | 2.18 | 3.31 | 2.63 |
| Std. deviation | 0.701 | 0.800 | 0.860 | 0.891 | |
| France | Mean | 3.60 | 2.43 | 3.50 | 2.61 |
| Std. deviation | 0.758 | 0.913 | 0.924 | 1.099 | |
| Germany | Mean | 3.78 | 2.47 | 3.44 | 2.52 |
| Std. deviation | 0.766 | 0.835 | 0.927 | 0.994 | |
| Greece | Mean | 3.30 | 2.68 | 3.21 | 2.51 |
| Std. deviation | 0.717 | 0.897 | 0.897 | 0.986 | |
| Hungary | Mean | 3.80 | 2.19 | 3.28 | 2.43 |
| Std. deviation | 0.747 | 0.791 | 0.981 | 1.056 | |
| Ireland | Mean | 3.50 | 2.48 | 3.55 | 2.60 |
| Std. deviation | 0.747 | 0.805 | 0.939 | 0.927 | |
| Italy | Mean | 3.31 | 2.58 | 3.42 | 2.69 |
| Std. deviation | 0.751 | 0.913 | 0.892 | 1.136 | |
| Latvia | Mean | 3.45 | 2.43 | 3.28 | 2.53 |
| Std. deviation | 0.753 | 0.957 | 0.974 | 0.994 | |
| Lithuania | Mean | 3.66 | 2.35 | 3.38 | 2.52 |
| Std. deviation | 0.757 | 0.906 | 0.816 | 1.016 | |
| Luxembourg | Mean | 3.39 | 2.45 | 3.42 | 2.51 |
| Std. deviation | 0.765 | 0.885 | 0.901 | 1.024 | |
| Malta | Mean | 3.34 | 2.36 | 3.43 | 2.69 |
| Std. deviation | 0.712 | 0.930 | 0.900 | 0.936 | |
| Netherlands | Mean | 3.78 | 2.44 | 3.69 | 2.54 |
| Std. deviation | 0.699 | 0.985 | 0.871 | 0.992 | |
| Poland | Mean | 3.49 | 2.66 | 3.33 | 2.68 |
| Std. deviation | 0.763 | 0.939 | 0.914 | 0.989 | |
| Portugal | Mean | 3.31 | 2.39 | 3.50 | 2.73 |
| Std. deviation | 0.738 | 0.858 | 0.812 | 1.031 | |
| Romania | Mean | 3.42 | 2.36 | 3.16 | 2.59 |
| Std. deviation | 0.733 | 0.865 | 0.919 | 1.017 | |
| Slovakia | Mean | 3.72 | 2.47 | 3.28 | 2.75 |
| Std. deviation | 0.667 | 0.930 | 0.886 | 1.074 | |
| Slovenia | Mean | 3.76 | 2.47 | 3.46 | 2.37 |
| Std. deviation | 0.672 | 0.932 | 0.923 | 0.899 | |
| Spain | Mean | 3.47 | 2.56 | 3.49 | 2.62 |
| Std. deviation | 0.763 | 0.953 | 0.896 | 0.989 | |
| Sweden | Mean | 3.77 | 2.27 | 3.45 | 2.34 |
| Std. deviation | 0.649 | 1.094 | 1.006 | 0.848 |
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