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The International Journal of Human Resource
Management
ISSN: 0958-5192 (Print) 1466-4399 (Online) Journal homepage: https://www.tandfonline.com/loi/rijh20
The role of work–life balance and autonomy in
the relationship between commuting, employee
commitment and well-being
Onur Emre & Stan De Spiegeleare
To cite this article: Onur Emre & Stan De Spiegeleare (2019): The role of work–life balance and autonomy in the relationship between commuting, employee commitment and well-being, The International Journal of Human Resource Management, DOI: 10.1080/09585192.2019.1583270
To link to this article: https://doi.org/10.1080/09585192.2019.1583270
Published online: 21 Mar 2019.
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The role of work
–life balance and autonomy in the
relationship between commuting, employee
commitment and well-being
Onur Emreaand Stan De Spiegeleareb
a
Yalova University, Yalova, Turkey;bEuropean Trade Union Institute, Brussels, Belgium
ABSTRACT
Commuting can be tiring and stressful. An unavoidable part of life for many people, it is almost always associated with negative outcomes. This study examined the implica-tions of commuting time for the commitment and well-being of employees. This paper uses ‘conservation of resources’ theory and job demands–resources approaches to argue that employees with long commutes will be less committed and experience lower well-being. These effects are also expected to be mediated by the work–life balance of the employees and interact with the level of autonomy they perceive themselves to have. Data from the fifth European Working Conditions Survey indicate that there is a negative relationship between commuting time, commit-ment and well-being. Results also suggest that work–life balance mediates part of these relationships and, finally, that autonomy can act as a buffer against the effects of commuting time on both commitment and well-being.
ARTICLE HISTORY
Received 24 January 2018 Accepted 22 January 2019
KEYWORDS
commuting; autonomy; work–life balance; European Working Conditions Survey
Introduction
Only a few people work where they live. For most employees, a regular commute to and from work is an ordinary activity. While commuting is ubiquitous in our working lives and a frequent topic of conversation, it has received little attention in organizational studies, particularly as to how it might affect the well-being and motivation of employees.
Commuting has an impact on both the individual and society. Spending an hour in the car or train means losing an hour that could have been spent at home or on other activities, but it also means that valuable natural and individual resources are being used up. A car ride in rush hour contributes to traffic congestion and almost all commuting activities involve the usage of non-renewable natural resources (Stutzer & Frey,2008).
CONTACTOnur Emre [email protected] Yalova University, Yalova, Turkey ß 2019 Informa UK Limited, trading as Taylor & Francis Group
There are of course several advantages to commuting. It enables employees to combine desirable living conditions with a job of their lik-ing (Stutzer & Frey, 2007). More generally, physical mobility and trans-portation capabilities have dramatically increased human potential since the beginning of civilization (Novaco & Gonzalez, 2009). According to some researchers, the geographical separation of work and private life can also contribute to the mental well-being of employees due to the implied detachment from work (Misan & Rudnik, 2015; van Hooff,
2013). The main questions, therefore, are whether commuting affects the well-being of employees and whether this has work-related consequences. Majority of paid workers in urbanized regions have at least a short commute. It is surprising how little the existing research offer about commuting despite its prevalence in lives of people. On average, com-muting takes up about 38 min of a person’s day according to the How’s Life Survey (OECD, 2011) and also according to the fifth European Working Conditions Survey. Applying an annual calculation to this fig-ure, this means an employee spends several full, 24-h days on average to travel to work. The question of whether this time is well spent is there-fore an important one. However, academic literature on this topic is rela-tively thin on the ground, as the effects of commuting are not well known (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004; Krueger, Kahneman, Schkade, Schwarz, & Stone, 2009).
Despite the increased interest in how people travel to work in recent years, commuting has been studied mostly in transportation, environ-ment and urban studies domains (Cervero, 1996; Dill & Carr, 2003; Gordon, Richardson, & Jun, 1991). In addition, physiological risks asso-ciated with commuting activity has been a topic of concern for several health researchers (Chng, White, Abraham, & Skippon, 2016; Hamer & Chida, 2008; Martin, Goryakin, & Suhrcke, 2014; Van Ryswyk et al.,
2017) but most of the health research have been associated with well-being in non-organizational contexts (Dill, Strathman, Clifton, & Mohr,
2013; Guell & Ogilvie, 2015). Several important research have also emerged in the last decades in economics literature (Hamilton & R€oell,
1982; Rouwendal, 1999; Stutzer & Frey, 2008; Van Ommeren & Fosgerau, 2009; Vandyck & Rutherford, 2018). Finally, few research that does exist identify commuting as a stressor affecting employee well-being in organizational domain (e.g. Gottholmseder, Nowotny, Pruckner, & Theurl, 2009).
Despite these researches, we believe that there are many avenues for research as we are unable to fully address how commuting effect employ-ees and how employemploy-ees react to these stressors while they remain in the organization.
The reasons behind a commute being stressful are rather straightfor-ward. The time spent while commuting is valuable time which could otherwise be spent on other activities. This perceived loss of time might induce stress for the employee, but the commute itself might also cause stress because of frustration with road congestion, delays, overcrowded public transport options or dangers of driving. A commute obviously requires some physical effort, but also requires some cognitive effort in case there is a need to make extensive planning for more complex com-mutes with multiple vehicle changes. In addition, there is an affective component due to the potential unpleasant feelings that can be triggered by the conditions experienced during the commute.
Moreover, such daily hassles and chronic exposure to constant stres-sors are known to be more detrimental to well-being than one-off tragic events (DeLongis, Coyne, Dakof, Folkman, & Lazarus, 1982; Kanner, Coyne, Schaefer, & Lazarus, 1981).
In order to answer these questions, this article draws on the Conservation of Resources theory (COR) and the Job Demands-Resources (JD-R) approaches to develop a model that associates com-muting with employee commitment and well-being, two major outcomes that are detrimental to both the organization and the individual. According to the insights from these established approaches, we propose that the drawbacks of long and stressful commute depend on the non-work aspects of life and regulated by the discretionary power of the indi-vidual about organizing the work. We argue that employees with a healthy work–life balance can manage the negative effects of commuting better as they are inherently more resourceful when it comes to unwind and recover from work. Therefore, they can be more resistant to the urge to withdraw from work; they keep being committed to their organ-ization and easily maintain well-being if they are enjoying a health bal-ance between work and non-work. We also argue that employees enjoying higher levels of autonomy at work will experience the commute as less tiring, therefore used autonomy as a moderator to the relationship between commuting and organizational outcomes. People with higher levels of autonomy can organize their schedules and tasks around their own needs as compared to employees with low job autonomy. Since the job becomes less demanding and time is used rather efficiently, commut-ing becomes less of a threat on organizational commitment and well-being. The model is consequently tested using data from the European Working Conditions Survey. Finally, the results and their implications will be discussed, and avenues for further research are identified.
This article identifies several consequences of commuting and potential mechanisms and conditions that are at play. Second, the article uses a broad representative sample of employees in several countries to test the
hypothesis, contributing to the diversity of the literature. Third, by using two major theoretical approaches to model and explain the effects of commuting, this article contributes to the literature of commuting which generally has little theoretical explanations for their findings. Study also provides certain practical insights as it identifies some mediating and moderating variables in the relation between commuting and employee well-being and engagement which indicate that if workers can ensure a good work–life balance by other means, and if they have a certain amount of autonomy in their job, this can offset part of the negative effects of the commute.
Employee commitment
In terms of employee outcomes, this article focuses on two traditional concepts: employee commitment and subjective well-being. Organizational commitment is a well-established concept which was developed almost half a century ago. It simply refers to the attachment of the employee to the organization. According to the commonly accepted description, commitment is the strength of the individual’s identification with the organization (Mowday, Steers, & Porter, 1979). In the conceptualization of Meyer and Allen (1991) commitment contains three distinct components. First, there is ‘affective commitment’, which refers to the emotional attachment the employee feels for their organiza-tion, meaning that they identify with it personally. Second, ‘normative commitment’ refers to the feelings of obligation to remain in the organ-ization. Employees feel they owe the company their loyalty. Third, there is ‘continuance commitment’, which refers to the situation in which employees are committed to their organization because they fear the con-sequences and costs related to an exit from the organization. These com-ponents are thought to operate simultaneously, and employees can experience each of them separately, to the extent that the interplay between them leads to an individual staying in the organization.
Commitment is known to be affected by several demographic factors and group and leadership dynamics in the organization, as well as many other work and job-related characteristics such as tenure, job level and salary (Mathieu & Zajac, 1990). Increased levels of commitment are asso-ciated with increases in overall work-related outcomes, reduced absentee-ism, low turnover, increased extra-role behaviour and individual performance (Mathieu & Zajac, 1990; Randall,1990; Wasti,2003).
Well-being
The ‘subjective well-being concept refers to how people experience and evaluate their lives. Initially it was conceptualized as the ratio of positive
affect to negative (Eid & Jarsen, 2008). Later conceptualizations treated positive and negative affect as mutually exclusive (Diener & Emmons,
1984) but currently the view is that the frequency and intensity of posi-tive and negaposi-tive affect determine the overall subjecposi-tive well-being per-ceived by the individual (Diener, Larsen, Levine, & Emmons, 1985).
Basically, well-being is an overall assessment about one’s life and depends on a multitude of factors, with a person’s working life being of particular importance (Dolan, Peasgood, & White, 2008). According to Judge and Klinger (2008), work is central to one’s identity and an overall
satisfaction at work is essential. Well-being at work is related to an extensive range of workplace behaviours and attitudes from attendance problems to incivility and abuse.
Organization researchers and organizations are becoming cognisant of the fact that workers’ well-being needs to be addressed seriously. It is not only detrimental in the short term in terms of counterproductivity and dissatisfaction, there are also long-term effects related to organiza-tions’ productivity and effectiveness. For a better workplace, modern organizations are striving to maintain a healthy working environment by promoting mental health as well as protecting the physical health of the employees, thus increasing overall well-being, particularly as it is per-ceived by the individual (International Labour Organization, 2018).
Conservation of resources approach
According to Hobfoll (1989), people strive to collect, protect, invest and minimize the loss of resources that are valuable to them, and, further-more, are motivated to gain new resources. Resources can be broadly described as anything that has value for the individual or anything that helps the individual in attaining his/her goals (Halbesleben, Neveu, Paustian-Underdahl, & Westman, 2014).
The ‘things’ that can have value or aid in individual goal attainment can be divided into four categories: objects, personal characteristics, con-ditions and energies. Of these categories, particular attention is paid to ‘energies’. Energies are valuable in gathering and accumulating other types of resources; therefore they can easily be converted to other ces and can create stress resistance. When there is a threat to the resour-ces or when there is an actual loss of resourresour-ces, the final reaction to the environment is stress (Hobfoll, 1989). This reaction is expected even when there is no prospective gain from investing in resources. If the recovery is not sufficient, or there are not enough available resources to cope with demand, the employee may resort to negative measures to conserve resources or try to minimize the potential loss of resources as a reaction to the environment.
The JD-R approach
The JD-R model is an extension of the job demand-control model (Karasek, 1979) and it states that all job characteristics can be classified as either job demands or job resources. According to Demerouti, Bakker, Nachreiner, and Schaufeli (2001), job demands are physical, social or organizational aspects of a job that are associated with certain physio-logical and psychophysio-logical costs after the exertion of physical or mental effort. People tend to manage demands through various coping mecha-nisms but there are consequences of failing to adequately meet those demands. On the other hand, people do have resources to meet the demands. Demerouti et al. (2001) define job resources as the aspects of the job that are functional in achieving work goals. Job resources are tools at the disposal of the employee to reduce the demands of the job or they are useful in coping with the consequences of those demands. Further, the availability of job resources stimulates personal growth. The JD-R model suggests that the demanding aspects of work lead to exces-sive mental or physical exertion. These efforts finally end up exhausting employees. Meanwhile, the demands deplete resources, making meeting ongoing demands impossible, which lead to further withdrawal.
One important aspect of the JD-R model is the ‘buffering hypothesis’. This states that certain job resources can limit, ‘buffer’ or even reverse the negative effects of job demands. Indeed, if high demands are combined with sufficient resources, they might not produce negative effects and they can even generate positive employee outcomes (Halbesleben,2010).
Hypotheses and model
Applied to our propositions, our study treats commuting as an important resource expenditure that drains job resources and leaves the individual short on reciprocating the job demands. The involuntary time spent dur-ing the commute is an unpaid work time that could otherwise be allo-cated for leisure, social or family activities (Hobfoll, 1989) and these in turn can be used to accumulate other resources. Energy expenditure dur-ing the commute is indeed energy that cannot be used elsewhere, with-out it bringing abwith-out positive consequences. If the spent energy is large (long commutes) and the employees have little recovery time, we would expect negative consequences to occur.
We propose that unpleasant and long commutes can be clearly seen as job demands and they are likely to produce negative feelings about work and that might lead to affective withdrawal from the job. Commuting constantly demands energy and time from employees and leaves the
individual short on resources to cope with the demands. By losing the ability to cope with the demands, employee will reduce the level of obli-gation towards the organization and thus their commitment will suffer. This line of thinking is in line with Bakker, van Veldhoven, and Xanthopoulou (2010) as they report that all job demands associate nega-tively and all job resources associate posinega-tively with commitment. In other words, employees enjoy being in the organization and they keep being committed to their organization as long as high demands of their work are compensated with resources. Similarly according to the study of Hakanen, Schaufeli, and Ahola (2008), commitment is contingent on excess resources that are not being consumed by regular demands.
A similar pattern is to be expected for well-being. Most empirical stud-ies into the effects of commuting indicate a decline in quality of life. Besides eating up valuable resources, it is in conflict with all components of well-being. There are numerous health-related consequences of com-muting. It gives rise to several physical symptoms such as tiredness, pain or stiffness in joints, yet it is also known to increase stress, sickness absence, voluntary absenteeism and tardiness (Lyons & Chatterjee, 2008). It is also known to reduce job satisfaction, productivity, mood and mor-ale (Novaco, Stokols, & Milanesi, 1990). Regarding subjective well-being, the rare studies carried out on this topic found an overall negative rela-tionship (Stutzer & Frey, 2007), yet these studies do not consider pos-sible mediating and moderating effects (Dolan et al.,2008).
Accordingly, our first two hypotheses are as follows:
H1: Long commuting time reduces commitment. H2: Long commuting time reduces subjective well-being.
There are nevertheless some arguments to be made in favour of com-muting. As mentioned before, it enables employees to combine an opti-mal working environment with favourable living conditions. It also provides a temporal and geographical separation of work and private life. This can give the employee some time to disconnect from work and recover from it before arriving home. Such benefits of commuting might be associated with a better separation of work and private life and a potentially better work–life balance. These possible counter-arguments show that it is necessary to disentangle how the relations between com-muting and employee outcomes function. Through the identification of potential mediating and moderating variables, we might get a better insight into the significance of commuting and what can be done to improve the outcomes.
To identify the moderating variable in our study, we build on two pre-viously mentioned well-established theoretical frameworks. We argue
that long commutes are likely to affect the overall well-being and com-mitment of employees also through their negative effect on work–-life balance.
Work–life balance is an important concept that has been studied by many researchers for a long time and it is known to affect absenteeism rates, satisfaction levels and performance (Voydanoff, 2004). It refers to the idea that there should be a healthy proportion between the time and energy expenditure on work and non-work activities in life (H€ammig, Gutzwiller, & Bauer, 2009). The balance between work and family com-mitments is broadly defined as a balance between the different roles one takes and whether these roles are compatible with each other. While the concept of balance between roles is broader then only the time spent on work, family and other commitments, the time available for the different roles is always of central concern (Dex & Bond, 2005; Kalliath & Brough,2008).
Indeed, the losses of resources during the commute happen at the expense of resources which could be spent on private activities. A long commute will deplete energy levels both physically and mentally, increase stress levels and time urgency, cause several physical and mental response. In the event of an increase in work demands and lacking of work-related resources, resulting stress can only be compensated with resources that are accumulated in non-work aspects of life. Despite the need for reliance of resource accumulation outside of work, non-work domain is also under threat of losses from excessive hours spent for commuting. Therefore having fewer resources to offset work–life demands with non-work resources becomes harder, in turn, the com-mute will affect the overall subjective well-being of the employee even further.
The effects of a long commute on the perceived balance between work and non-work life can also influence the commitment the employee feels towards the job. Employees know that the time spent on a commute reduces their non-working time more than their working time. In that sense when individuals are faced with high demands of the job, non-work activities becomes important for detachment and recovery (Sonnentag & Natter, 2004). Recovery is an instant need to take a break from the demands of work and it becomes a priority among everything else when fatigue builds up (Sonnentag & Zijlstra, 2006). It is important for the individual to engage in activities that are not already used up during work, and detach from work as they step out of the workplace and if there is a delay between this transition after prolonged exposure to work demands, recovery is incomplete and the next phase of the life domain is not experienced in its best. If work demands are still taxing
individuals on the way to work, they also decrease the quality of their free time, thus impairing detachment (Demerouti, Bakker, Geurts, & Taris, 2009). Hours spent on the road might therefore worsen the per-ceived balance between work and leisure, provoke negative thoughts about the job and trigger the desire to find another job with a shorter commute.
Building on this premise, we identify the following four additional hypotheses, which are graphically depicted inFigure 1:
H3: Work–life balance is positively related to commitment. H4: Work–life balance is positively related to well-being.
H5: Work–life balance partly mediates the relationship between commuting and commitment.
H6: Work–life balance partly mediates the relationship between commuting and well-being.
As discussed, since the time dedicated to commuting is not transfer-able to any other activity, it can be counted among the demands of the job. Since not commuting is not an option in the short run for many people, this chronic job demand will eventually turn into a significant stressor unless it is counterbalanced by a resource.
A possible job resource which might buffer or limit the negative impact of commuting is job autonomy. Autonomy refers to the discre-tion and independence of the employee in deciding about how, where and/or when to perform the work tasks. So far, it is known that employ-ees with enough autonomy have lower levels of strain. Moreover, the JD-R approach identifies the interaction between job autonomy and work-load, as autonomy serves as a buffer against the negative effects of a high workload (Baillien, De Cuyper, & De Witte, 2011; Karasek, 1979). Autonomy also means that employees have a say in the scheduling of their work, leading to better integration and greater motivation (Morgeson, Delaney-Klinger, & Hemingway, 2005; Sims, Szilagyi, & Keller, 1976).
The distance between home and work is not only a physical but also a psychological distance. The level of autonomy experienced by the
individual can influence how well they cope with the stress of transition-ing between roles and adapttransition-ing to the differtransition-ing demands of work and family life (Voydanoff, 2005). Greater autonomy enables the employee to help set boundaries between these domains, while also allowing them greater flexibility in responding to the work demands and any stress they may entail. Furthermore, allowing the employee to define their own work schedule is especially important to overcoming commuting-related problems. The opportunities associated with having more control over job demands are crucial to employees’ health and well-being (Bakker & Demerouti, 2007) as well as their commitment (Sadler-Smith, El-Kot, & Leat, 2003; Zacharatos, Barling, & Iverson, 2005). Thus, the role of autonomy as a variable that plays a buffering role between commuting time, commitment and well-being is hypothesized as follows (and also displayed in Figure 2):
H7: The relationship between commuting time and commitment is moderated by autonomy.
H8: The relationship between commuting time and well-being is moderated by autonomy.
Method
Responses to the fifth European Working Conditions Survey (EWCS 2010) provided the data for this study. EWCS 2010 was conducted in all 27 European Union Member States at that time and in Turkey, Croatia, the FYROM, Norway, Albania, Kosovo and Montenegro. The survey considered people to be employed if they had worked and had been paid for at least 1 h/week. Each country was divided into sections based on the level of urbanisation of the regions. While face-to-face visits were the standard approach, in rare cases telephone interviews were also used for facilitating first contact. A combination of computer-aided interviews and pen-and-paper based interviews was also implemented. Information about respondent selection, regional coverage, interview methods, coding and other details can be accessed in the EWCS Technical Report (2010). The total number of interviews in the fifth EWCS was 43,816.
In order to proceed further with the analysis, we included the people who reported being at work as an employee or employer at the time of the data collection. The remaining number of participants was 41,278. The mean age in the sample was 45.76 years; 51.5% of the participants were female and 45.5% were male. More than 65% of the participants had completed ‘secondary education’ and more than one third of the sample have reached to the ‘first stage of tertiary education’ according to the International Standard Classification of Education (ISCED) (Eurofound, 2012). The mean tenure of the participants within the same organization was about 10 years.
For the measurement of commuting time, the self-report-type question from the survey instrument was used. The question was ‘In total, how many minutes per day do you usually spend travelling from home to work and back?’ The mean commuting time was 38 min per day for our sample.
Organizational commitment was assessed in four of the survey items, with the response options ranging from 1 (strongly disagree) to 5 (strongly agree). A sample item was ‘I feel “at home” in this organiza-tion’ (a ¼ .72). Examples of studies using the same items for measuring commitment are Dhondt, Pot, and Kraan (2014), Sanders, Dorenbosch, and De Reuver (2008), Steijn and Leisink (2006).
Assessment of well-being was performed using the five items from the EWCS, which are identical to those used in the World Health Organization’s well-being index (WHO-5). A sample item was ‘I have felt fresh and rested’, with the response options ranging from 1 (at no time) to 6 (all of the time) (a ¼ .88).
For the assessment of work–life balance, the following survey item was used: ‘In general, do your working hours fit in with your family or social commitments outside work?’ The response option ranges from 1 (not at all well) to 4 (very well). This item of the EWCS has proven useful in several other papers such as Antai, Oke, Braithwaite, and Anthony (2015), and in Anttila, Oinas, Tammelin, and N€atti (2015).
Autonomy was assessed with three items from the questionnaire. A sample item is ‘Are you able to choose or change your methods of work’? The responses to these items were coded into 1 (No) and 2 (Yes) (a ¼ .79). The same items have been used for measuring autonomy in studies including Salas et al. (2015), and van den Bossche, Taris, Houtman, Smulders, and Kompier (2013).
We used age, gender, tenure, weekly work hours, education, job secur-ity, and country as control variables. Job security is measured using the item ‘I might lose my job in 6 months’, with responses ranging from 1 to 5.
Table 1. Means, standard deviations and correlations. Mean SD 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 Age 41.72 11.96 – 0.01 –0 –0.1 0.55 –0.1 0.09 –0 0.07 –0.1 0.08 –0 2 Gender –– – –0.1 0.08 –0.1 –0.2 0.01 –0 0.07 –0.1 0.01 –0 3 Country –– – –0.1 0.01 0.15 0 –0.1 –0.1 –0 –0 –0.1 4 Level of education –– – –0.1 –0 0.09 0.19 0.07 0.08 0.15 0.09 5 Tenure 10 9.91 – 0.05 0.2 0.03 0.04 –0 0.08 –0 6 Weekly work hours 39.1 13.25 –– 0 –0 –0.3 –0.1 0.01 0.01 7 Job Security 3.71 1.23 – 0.28 0.15 0.19 0.16 –0.1 8 Commitment 3.27 0.85 – 0.24 0.38 0.2 –0 9 Work-life Balance 3.06 0.78 – 0.23 0.12 –0.1 10 Well –being 4.25 1.07 – 0.07 –0.1 11 Autonomy 1.69 0.39 –– 0.1 12 Commuting ( minutes, daily) 38 34.47 – Note . p < .05, p < .01, p < .001.
Table 2. Mediation through work-life balance. Mediation estimates for Hypothesis 5 Path estimates for Hypothesis 5 Effect Path Estimate SE 95% CI Zp % Mediation Path Estimate SE 95% CI Zp Lower Upper Lower Upper Indirect a b 0.03 0.00 0.04 0.03 12.32 < .001 45.46 Commuting ! Work –life balance a 0.13 0.01 0.14 0.10 11.88 < .001 Direct C 0.06 0.01 0.06 0.02 3.82 < .001 54.54 Work –life balance ! Commitment b 0.23 0.01 0.22 0.25 42.24 < .001 Total cþ a b 0.09 0.01 0.11 0.07 7.98 < .001 100 Commuting ! Commitment c 0.06 0.01 0.06 0.02 5.47 < .001 Mediation Estimates for Hypothesis 6 Path Estimates for Hypothesis 6 Effect Path Estimate SE 95% CI Zp % Mediation Path Estimate SE 95% CI Zp Lower Upper Lower Upper Indirect a b 0.03 0.00 0.04 0.03 12.22 < .001 31 Commuting ! Work life balance a 0.12 0.01 0.14 0.10 11.88 < .001 Direct c 0.12 0.01 0.14 0.09 8.14 < .001 69 Work life balance ! Well-being b 0.29 0.01 0.27 0.30 39.34 < .001 Total cþ a b 0.15 0.01 0.18 0.12 10.43 < .001 100 Commuting ! Well-being c 0.12 0.01 0.14 0.09 8.14 < .001
Table 3. Buffering role of autonomy. Moderation estimates for Hypothesis 7 Simple slope estimates for Hypothesis 7 Estimate SE 95% CI Zp Estimate SE 95% CI Zp Lower Upper Lower Upper Commuting 0.08 0.01 0.10 0.05 6.97 < .001 Average 0.08 0.01 0.09 0.05 5.72 < .001 Autonomy 0.31 0.01 0.29 0.33 28.96 < .001 Low ( 1SD) 0.12 0.02 0.15 0.08 6.76 < .001 Commuting autonomy 0.12 0.03 0.06 0.17 4.20 < .001 High (þ 1SD) 0.01 0.01 0.04 0.02 0.78 0.438 Moderation estimates for Hypothesis 8 Simple slope estimates for Hypothesis 8 Estimate SE 95% CI Zp Estimate SE 95% CI Zp Lower Upper Lower Upper Commuting 0.15 0.01 0.18 0.12 9.79 < .001 Average 0.15 0.01 0.18 0.12 9.85 < .001 Autonomy 0.12 0.01 0.10 0.15 8.43 < .001 Low ( 1SD) 0.20 0.02 0.24 0.15 8.56 < .001 Commuting autonomy 0.10 0.04 0.02 0.17 2.61 < .001 High (þ 1SD) 0.08 0.02 0.11 0.04 4.39 < .001
The scales used in this study were further evaluated using a confirma-tory factor analysis. The final model showed a good fit (v2¼ 5339.85,
df¼ 78, CFI ¼ .97, TLI ¼ .95, SRMR ¼ .02, RMSEA ¼ .05). We tested sev-eral alternative measurement models to see whether our hypothesized measurement model fits the data better in other scenarios. When we allowed our commitment and work–life balance items load together on the same factor, the measurement model produced worse fit (v2¼ 25421.77, df ¼ 83, CFI ¼ .83, TLI ¼ .79, SRMR ¼ .10, RMSEA ¼ .10).
Further, in order to check the presence of common method variance in our model, we performed Harman single factor test. All items are allowed to load on one factor and they produced 26, 20% of the variance in the data and it showed poor fit v2¼ 80224.27, df ¼ 119, CFI ¼ .63, TLI¼ .57, SRMR ¼ .11, RMSEA ¼ .12); therefore, common method vari-ance was not considered to be a major problem in the data.
Results
The hypothesized direct or indirect relationships were tested using the ordinary least squares based path analytic procedures suggested by Hayes (2013) with Process macro in SPSS. Table 1 shows the correlations among the variables in the study as well as means and standard devia-tions. In all models, we controlled for country effects, age, gender, educa-tion, tenure, number of hours worked in a week and job security.
We performed linear regression analyses for hypotheses 1 to 4. The results (see Table 2) suggest that commuting time reduces employee commitment (b ¼ –.09, SE ¼ .01, t ¼ –7.87, p < .001) and well-being (b ¼ –.15, SE ¼ .01, t ¼ –10.66, p < .001), thus confirming H1 (R2¼ .12, F
(8, 34928)¼ 574.09, p < .001) and H2 (R2¼ .06, F (8, 34864) ¼ 266.41, p< .001). Similarly, the analyses confirmed our hypotheses about the positive effect of work–life balance on employee commitment (b ¼ .23, SE¼ .01, t ¼ 43.64, p < .001), and subjective well-being (b ¼ .29, SE¼ .001, t ¼ 41.74, p < .001). Therefore, H3 (R2¼ .16, F (8, 37684)¼ 867.62, p < .001) and H4 (R2¼ .10, F (8, 37620) ¼ 500.96, p< .001) are accepted.
The mediating role of work–life balance (H5 and H6) was tested using the bootstrapping method with bias-corrected confidence estimates (Preacher & Hayes,2004,2008). Results (Table 2) confirmed the (partial) mediating role of work–life balance in the relationship between commut-ing time and commitment (b ¼ –.03; CI ¼ –.04, –.03) as well as between commuting time and well-being (b ¼ –.03; CI ¼ –.04, –.03).
Moderation analyses were used to test the role of job autonomy (H7 and H8) as reported inTable 3. Both tests showed that autonomy indeed
moderates the relationship between commuting time and commitment (b ¼ .12; SE ¼ .03; t ¼ 4.20, p .001) as well as the relationship between commuting time and well-being (b ¼ .10; SE ¼ .04; t ¼ 2.61; p .001). A graphical presentation of the moderating effect is depicted in Figures 3
and 4.
Both patterns clearly show that employees with high job autonomy experience less negative effects of long commuting time in terms of well-being and commitment. These patterns are in line with our hypotheses and confirm that job autonomy might buffer the negative effects of the
Figure 4. Hypothesis 8.
long commutes. The buffering is more pronounced for commitment than it is for well-being. Indeed, employees enjoying high-levels of autonomy do not even seem to experience negative consequences of long commutes in terms of commitment. For well-being, autonomy might alleviate some of the negative effects of a long commute, but the com-mute remains to be negatively related to employee well-being.
In summary, all hypotheses were confirmed by the regression analysis, showing that commuting time negatively affects employees’ commitment to the firm, with a decreased work–life balance as a partial mediator. Further, autonomy works as a buffer to the negative effect of commuting on work commitment and subjective well-being.
Discussion
In this study, we inspected the relationship between commuting time on the one hand and employee commitment and subjective well-being on the other. As expected, we observed that longer commutes are related to lower commitment and lower perceived well-being. In line with conser-vation of resources theory, a longer commute means using more per-sonal resources and energy which can then no longer be used for other purposes. A longer commute means less time and energy for activities related to private life. The partial mediating role of work–life balance in the relationships between commuting and commitment and between commuting and well-being confirms this reasoning.
It could, hypothetically, be feasible to counter this decline in commit-ment and well-being levels by decreasing the commuting time by various means: for example, increasing the commute speed, moving work closer to home or vice versa. Obviously, these solutions are not under the direct control of many companies or employees. Therefore, employers are forced to search for alternative solutions to address and counter the negative side effects of the commute. We believe our study could poten-tially provide help due to its introduction of the partial mediating role of work–life balance. If companies can, by other means, improve their employees’ work–life balance, they might offset some of the negative consequences of a long commute. In addition to the eradication of some effects of an unpleasant commute, it would be reasonable to expect improvements in commitment and well-being due to increased levels of flexible work scheduling, increasing control over working time and increased workplace support. Future research on commuting could include such variables in the model to evaluate their effectiveness.
Next, the focus was on identifying more job design-related moderators in the relationship between commuting time, commitment and well-being. In line with the job demands–resources model, autonomy was
hypothesized as a buffer in this negative relationship, and this relation-ship was confirmed by the results of our study. Next to providing further evidence for the validity of the buffering hypothesis of the job demands–resources model, it also shows that work organization has far-reaching consequences, beyond those of more ‘external’ elements such as the commuting time. Indeed, having the possibility of organizing work the way one chooses seems to decrease the negative effects of a long commute. One could hypothesize that for jobs with a great deal of autonomy; the commute cannot entirely be considered ‘time lost’ as in jobs with less autonomy. Employees with a large degree of control over the work can use their commuting time in a valuable way. They can plan the work, think creatively about difficult topics or even work during the commute. In this way, the energy spent on the commute will be spent more usefully and will have fewer negative consequences. An indi-vidual with a high level of autonomy has flexibility regarding what time they start work, can leave earlier to avoid rush hour, and can utilize commuting time better for such activities as reading and planning, thus mentally preparing for work. Employees enjoying job autonomy might organize their work as such that they have some recovery time from the commute when they arrive. They can schedule lighter jobs directly after the commute and harder jobs later. In line with the Conservation of Resources theory, job autonomy might provide employees with enough recovery and therefore lessen the negative effects of the commute. Furthermore, with better control over other activities the employee can build resistance to the stress caused by their commute. In line with the reasoning provided by stress theories, autonomy can provide energy to individuals, helping them to cope with regular demands. This buffering effect will likely be at play for both well-being and employee commit-ment. For commitment, the fact that the employee experiences less costs from the commute will make him more likely to stay in the organization. For well-being, there’s an additional argument that employees with job autonomy can even organize they private life better during work and the commute, eliminating the negative effect of long commutes on their well-being even further.
Limitations
This study has some potential limitations. First, we used self-reports from the EWCS for assessing commuting time. This might be subject to varying judgements as to what should be included or not in the overall commuting time. Commuting duration alone is not the sole determinant of a stressful commute. In further attempts to investigate the effects of
commuting, we highly encourage researchers to employ alternative meth-ods to operationalize commuting.
We used single items from the EWCS survey to measure work–life balance and job security. In their study, Gardner, Cummings, Dunham, and Pierce (1998) state that one good item can be better than many sin-gle items in terms of validity and reliability. Furthermore, Fugate, Prussia, and Kinicki (2012) state that despite criticism, the use of single items demonstrates more favourable psychometric properties than multi-item scales, while Bergkvist and Rossiter (2007) found no evidence of common method bias with the use of single items. Finally, according to Siemsen, Roth, and Oliveira (2010), common method variance under-mines interactions and they become more difficult to detect statistically. Although there are studies to warrant the use of single item measures (Debus, Probst, K€onig, & Kleinmann, 2012; Judge, Hurst, & Simon,
2009), we are wary about the interpretability of the results and advise caution to the reader.
The measurement of work–life balance in EWCS only refers to the availability of time between work, family life and other social commit-ments. This is a relatively narrow operationalization of the work–life bal-ance concept which refers to a general balbal-ance of roles. Further studies could employ detailed and broader operationalizations of this construct to add to the robustness of the findings.
Results from this study are highly relevant to the working population, especially considering the properties and quantity of the sample. However, the extent of commuting depends on the dynamics of trans-portation within the urban environment, infrastructure and certain geo-graphic features. Even though our model controls for country, the distances to work in various regions, the availability of commuting alter-natives, the degree of urbanization and the level of industrialization in a country are various considerations for further studies.
Conclusion
Time spent on commuting is time lost for other activities. Therefore, we observe that long commutes negatively affect the employee’s commit-ment to the organization and his/her subjective well-being. There are no easy solutions for decreasing the time spent commuting. Switching jobs, moving the company or moving residence are often not feasible.
This research has therefore used traditional theories to identify a medi-ator and a modermedi-ator in the relation between commuting, commitment and well-being. The findings show that the negative effects of commuting are partially mediated by work–life balance. This suggests that companies
could compensate for workers’ long commutes by providing other means to increase their work-life balance such as introducing flexi-time and telework and providing social support.
The observation that workplace autonomy acts as buffer in the nega-tive relationship between commuting, commitment and well-being clearly indicates that there are potential solutions to the stress caused by com-muting. Hypothetically, people will be less resistant to long commutes if they are given more control over their work organization.
Funding
The research leading to these results has received support under the European Commission’s 7th Framework Programme (FP7/2013-2017) under grant agreement no. 312691, Ingrid – Inclusive Growth Research Infrastructure Diffusion.
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Annex EWCS Items
Commuting Time
In total, how many minutes per day do you usually spend travelling from home to work and back?
Organizational commitment I am well paid for the work I do My job offers good prospects I feel‘at home’ in this organization The organization I work for motivates me Well-Being
I have felt cheerful and in good spirits I have felt calm and relaxed
I have felt active and vigorous I woke up feeling fresh and rested
My daily life has been filled with things that interest me Work-Life Balance
In general, does your working hours fit in with your family or social commitments out-side work?
Autonomy
Are you able to choose or change your order of tasks? Are you able to choose or change your methods of work? Are you able to choose or change your speed or rate of work? Job Security
