Engagement, exhaustion, and work-life balance:
is employees well-being affected by
Flexible Work Designs?
A quantitative study taking occupational status and boundary management
preferences into account
Beatrice Vezzosi 11799730 Master’s thesis Graduate School of Communication Master’s programme Communication Science Supervisor: Claartje Ter Hoeven
Acknowledgements
I would like to thank my supervisor Claartje Ter Hoeven for her time, availability and her kindness in leading me during this final step towards my Master’s degree.
I would like to thank my boyfriend Luca who constantly supports and motivates me, believing in me even when I cannot believe in myself.
I would like to thank all the people I have met in Amsterdam, especially my classmates, for being such supportive friends and for making everything funnier and light-hearted; in particular, I would like to thank Renée, Elisa, Christine, Alexia, Sakura and Maria for being amazing team mates.
I would like to thank all my friends in Italy for keeping alive our bond that resists the distance. A special thanks goes to Greta and Michela, Elena, Stefano and my sister Irene.
Finally, the biggest thank you goes to my parents, Renza and Alessandro, without whom I would not be here, graduating at the University of Amsterdam in such inspiring and international environment which contributed to my professional and personal growth.
Abstract
Flexible work designs (FWDs) are defined as employment-scheduling approaches that allow employees to practice a certain degree of freedom in deciding when they work (i.e., schedule flexibility), where they work (i.e., location flexibility) and through which
communication technology. Since FWDs are increasingly adopted by organizations, several studies about the impact of these work strategies on employee well-being have been
conducted. On the one hand, FWDs positively relate to work engagement due to effective communication and reduction of time pressure, on the other they positively relate to exhaustion as well owing to increased interruptions and constant connectivity to work activities. Moreover, FWDs compromises work-life balance because employees can personally handle their time and tasks but simultaneously they could also experience work-family interference. On the basis of the study conducted by Ter Hoeven, Van Gemert, and Medved (2016a), the present research aims to confirm the relation between FWDs and well-being moderated by occupational status (i.e., supervisors, support staff and knowledge
workers) and work-home preferences (i.e., role integration and role segmentation). The results demonstrated that FWDs have a supportive impact on engagement and work-life balance; this last increases for supervisors. Furthermore, FWDs affect exhaustion which decreases for knowledge workers whereas increases for support staff, due to the nature of their job. Contrary to the expectations, segmentation-oriented employees are seen to increase their engagement and decrease their exhaustion when FWDs are adopted.
Keywords: flexible work designs, communication technologies use, occupational status,
Introduction
In a global competitive economy, flexible work designs (FWDs) are increasingly adopted by organizations as a result of economic changes and their current demands (Grant & Parker, 2009). FWDs, also referred to as new ways of working (NWW), are described as working modalities which allow employees to vary the approaches (i.e., how), timing (i.e., when) and location (i.e., where) of their work, while being supported by modern
communication technologies (Ten Brummelhuis, Bakker, Hetland, & Keulemans, 2012).
They meet needs of cost reduction (Baldry & Barnes, 2012), creation of competitive
advantage (Thompson, Payne, & Taylor, 2015), realisation of inspiring open-plan workspaces (Coenen & Kok, 2014) and other necessities that characterize nowadays’ service and
knowledge economy.
The adoption and implementation of FWDs in most companies, especially in big multinationals that benefit from them the most due to their considerable amounts of employees and external collaborators, are largely made possible by communication technology use (CTU). CTU changed radically after the advent of internet, then of social media and their devices, the smartphone above all. The main outcomes that modern
technologies have produced regard increased communication efficiency, control, speed and long-distance application (Ter Hoeven, van Zoonen, & Fonner, 2016b). However, this is having consequences on employees’ way of working and well-being, both positively and negatively. Actually, on the one hand FWDs enabled by CTU are positively associated with employee well-being because they enhance autonomy, effective communication and work-life balance, on the other they relate negatively with employee well-being because of increased interruptions, unpredictability (Ter Hoeven & van Zoonen, 2015), energy consumption and stress owing to the condition of being constantly connected to work (Ter Brummelhuis et al., 2012).
Ter Hoeven, Van Gemert, and Medved (2016a) conducted a qualitative case study about FWDs with interviews carried out among 23 employees working in an international construction company based in the Netherlands. The results showed that flexibility in the workplace is perceived as positive or negative depending on two factors: the employee’s occupational status, proving that different jobs and tasks have different needs, and the
employee’s boundary management preferences (Ashforth, Kreiner, & Fugate, 2000). This last concept refers to the employee’s personal preference in terms of roles integration (i.e., a preference for integrating and overlapping work and home roles) or segmentation (i.e., a preference for segmenting different roles and keeping them separated). In other words, it is expected that occupational status and work-home preferences of an employee are important and relevant conditions under which FWDs have a positive or negative effect on employee well-being (Ter Hoeven et al., 2016a).
The present study aims to specify and build on these qualitative findings by empirically testing this framework in a quantitative study, using a survey as research method. The
assumption is that FWDs are more supportive for certain occupational status (i.e., knowledge workers) than for others (i.e., supervisors and support staff), with a preference to integrate work-home roles and tasks, as opposed to those who prefer a clear separation and more predictable and stable routines. Indeed, it has been found that employees across different occupational groups experience different flexibility outcomes (Kossek & Lautsch, 2018). However, it has also been observed that flexible ways of working are not equally available for all occupational status, for example, workers with management positions access flexible work policies more easily and frequently than administrative jobs (Kossek & Lautsch, 2018). This research proposes a questionnaire thought to be distributed in a sample composed by all the occupational status identified in the previous interview study undertaken by Ter Hoeven et al. (2016a): knowledge workers, supervisors and support staff. The first have been divided in
flexible (i.e., integration-oriented) and stable workers (i.e., segmentation-oriented), according to the boundary management strategy by Ashforth et al. (2000).
Overall, the goal is to reconfirm previous findings through a quantitative research design and to explain that the consequences of FWDs on employee well-being are dependent on
occupational status (i.e., functional flexibility) and boundary management preferences (i.e., personal flexibility), answering the following research question:
To what extent do FWDs have a positive impact on employees well-being conditional upon occupational status and boundary management preferences?
The current study contributes to the literature in three ways. First, extending the body of knowledge that concerns FWDs particularly in relation to employee well-being, taking into account work-life balance as an additional dimension of well-being, together with the already tested variables engagement and exhaustion (such as in Ten Brummelhuis et al., 2012); second, including and analysing variables such as occupational status and boundary management preferences to better explain under which conditions FWDs are perceived positively or negatively by employees, as carried out by Ter Hoeven et al. (2016a), but providing a framework tested through a quantitative research; finally, giving insights that could help human resources departments understand how FWDs should be adapted depending on different roles, tasks and individual preferences, and which changes in management
practices should be taken on in order to increase employees’ well-being and satisfaction.
Theoretical background How FWDs affect well-being
Nowadays, an increasing number of companies has redesigned the modalities in which employees are allowed to work (Bakker, Rodríguez-Muñoz, & Derks, 2012). The main
reasons leading to this choice are cost reduction and need to facilitate multi-locational employees, mostly knowledge workers (Baldry & Barnes, 2012). This new approach, known as FWDs, has spatial and temporal flexibility as central features: employees can decide themselves when (schedule flexibility), where (telecommuting) and through which
communication medium (such as emails, smartphone, videoconferencing) they work (Ten Brummelhuis et al., 2012). Moreover, flexibility regards not only the individuals through practices that give them new ways and possibilities to balance their work and private sphere (Kossek & Lautsch, 2018), but also the organization and its overall performance through practices that enhance the ability to face market demands (Kossek & Lautsch, 2018) and to solve internal issues, such as labour turnover reduction and commitment increment (De Menezes & Kelliher, 2011). Despite this, according to the systematic review provided by De Menezes and Kelliher (2011), previous studies about the association of FWDs with
organizational and individual performance led to non-uniform findings.
These flexible work designs are enacted by modern CTU and their massive spread has followed the birth and availability of internet and the consequent social technologies, better known as social media (van Zoonen, Verhoeven, & Vliegenthart, 2017). Such technologies have been proved to have advantages (i.e., work engagement) and drawbacks (i.e.,
exhaustion) on employee well-being (Ter Hoeven et al., 2016b).
Employee well-being is characterized by positive feelings such as happiness, energy, motivation and a sense of success regarding one’s job and workplace (Ter Hoeven & van Zoonen, 2015). In this study, it has been investigated considering three dimensions:
engagement, exhaustion and work-life balance. It is assumed that high levels of engagement as well as low levels of exhaustion and a balance between work and private life lead to a state of general well-being for the employee. According to Schaufeli, Salanova, Gonzalez-Roma and Bakker (2002), engagement is a positive and fulfilling state of mind related to work,
characterized by high levels of energy and mental resilience, the willingness to invest efforts in one's work, and persistence when confronting difficulties. Exhaustion is defined as a whole of negative and overwhelming feelings such as pressure and stress that ultimately result in burnout (Maslach, Schaufeli, & Leiter, 2001). Employee well-being has been analysed also in relation to work-life balance (WLB), described as “the perceived sufficiency of the time available for work and social life” (Gröpel & Kuhl, 2009, p. 365). When off-work time is sufficiently available, well-being increases because people can satisfy their personal needs (Gröpel & Kuhl, 2009). Thus, the balance between work and personal life is another important and relevant aspect when examining employee well-being.
Several studies investigated which consequences flexible ways of working have on employee well-being, finding that the effects are opposed and even paradoxical (Mazmanian,
Orlikowski, & Yates, 2013; Ter Hoeven et al., 2016b). For example, Ter Hoeven et al. (2016b), explained that, whereas CTU leads to efficient communication and accessibility at any time and place, increasing engagement (Ten Brummelhuis et al., 2012), it also creates interruptions and unpredictability which might lead to stress and even exhaustion (Ten Brummelhuis et al., 2012) due to difficulties to disconnect from work as well (Ter Hoeven et al., 2016b).
FWDs have positive outcomes because employees have more control over their work processes, and communication between colleagues is facilitated (Gajendran & Harrison, 2007). Furthermore, communication results more coordinated and effective when employees use media technologies, and its quality is higher in virtual teams compared to face-to-face meetings (Warkentin, Sayeed, & Hightower, 1997). Additionally, employees can use time in a more efficient way (Kelliher & Anderson, 2008) saving it when they can work from home since they do not have to travel to the workplace, reducing pressure and stress (Peters & Van der Lippe, 2007).
All these advantages are positively correlated with employee well-being, in particular with work engagement (Ten Brummelhuis et al., 2012). In short, employees with high levels of engagement have great energy and enthusiasm which make them involved in their work achieving a positive state of mind (Ten Brummelhuis et al., 2012).
On the other hand, FWDs are also associated with negative effects on employee well-being because they foster continuous connection to work, even outside the actual working hours, intruding personal domains (Ten Brummelhuis et al., 2012). New technologies, especially their massive use and their continuous improvement, put employees in the condition of monitoring their devices persistently and providing immediate responses such that they cannot really disconnect from work, owing to a sense of obligation (Ter Hoeven et al., 2016b). FWDs give employees the possibility to work more autonomously and in control over their tasks, but at the same time they are constricted by their work and are seen to restrict their actual autonomy owing to the high demands of other team members as well as managers or clients (Mazmanian et al., 2013). Plus, the ease of connectivity leads to the expectation of being constantly online which in turn causes stress and exhaustion due to interruptions (Leonardi, Treem, & Jackson, 2010; Fonner & Roloff, 2012). Interruptions occur when an unscheduled interaction discontinues employees from their current activity (Ter Hoeven et al., 2016b), disrupting their workflow (Ten Brummelhuis et al., 2012). Putnam, Myers, and Gailliard (2014) discussed how communication technologies give birth to a situation of both autonomy and control: the more freedom employees have thanks to the adoption of FWDs in their workplace, the more intensively they feel constrained and controlled by their own work because of the lack of a clear separation between at-work and off-work time. Working flexibly could impact work-life balance negatively, caused by the fact that teleworking and work from home are always combined with the use of communication technologies such as smartphone, emails, and videoconferencing, which blur the work-family boundaries (Katz &
Aarhus, 2002). This means that work never really stops, interfering with the family domain (Ten Brummelhuis et al., 2012). Despite this, it is expected that FWDs are perceived
positively by employees since they can work from home and consequently have more time to spend out of the workplace and to dedicate to personal life (Ten Brummelhuis et al., 2012). Overall, the new job conditions created by FWDs impact employee physical and mental well-being both positively providing job resources (i.e., by giving more freedom and control over work, increasing engagement and providing a partial solution of the work-family
conflict) and negatively providing job demands (i.e., by reducing the ability to disengage from work, increasing exhaustion) (Mazmanian et al., 2013; Putnam et al., 2014; Ter Hoeven et al., 2016b). In sum, employee well-being has been taken into account in relation to FWDs by considering their advantages and drawbacks embodied in such relation (Ter Hoeven & van Zoonen, 2015). Based on the literature discussed, which demonstrated the impact that FWDs have on employee well-being, the first hypothesis has been formulated as following:
Hypothesis 1a: FWDs increase work engagement. Hypothesis 1b: FWDs increase exhaustion. Hypothesis 1c: FWDs increase work-life balance.
According to the study conducted by Ter Hoeven et al. (2016a), the effects of FWDs are perceived as more positive or negative depending on occupational status and employee boundary management preferences. The conceptual model (Figure 1) summarizes the expected relationship between FWDs and employee well-being, moderated by occupational status differences (supervisors, support staff, and knowledge workers) and role boundary preferences (integration or segmentation). These concepts are discussed below.
Figure 1. Conceptual model
Occupational status: knowledge workers, supervisors, and support staff
Advanced technologies inevitably led to changes regarding new paradigms for
manufacturing strategy, human resources practices, quality assurance and management (Dean Jr. & Snell, 1991). Overall, the adoption of new technologies encompasses a whole
organizational transformation and needs a new managerial mindset (Gunn, 1987; Hitt, 2000). Particularly, concerning human resources, Mortimer (1985) noted that new technologies demand the development of new skills and the ability to perform new tasks, and jobs and relationship structures will be derived from these. In the traditional factory, job design was characterized by division of labour, specialization, and standardization (Hirschhorn, 1984). Occupational status had also been described taking into account some salient notions such as task complexity, task variety, and task interdependence (Dean Jr. & Snell, 1991).
In today’s modern economy occupational status, which has been described in terms of upper, middle and lower levels jobs within a company, is less and less considered in relation to manual skills, but rather linked to skill requirements (higher, semi, and lower skills) and job rewards (higher, moderate, and lower wage) (Kossek & Lautsch, 2018). According to Kossek and Lautsch (2018), FWDs outcomes on employees are occupationally based.
Actually, different occupational status have different levels of what Ter Hoeven et al. (2016a) labelled as functional flexibility, defined as the extent to which a worker experiences
autonomy or dependency provided by one’s job description and practice. In short, an employment has high functional flexibility when its description and nature itself gives
opportunities for flexible ways of working and offers support for their execution (Ter Hoeven et al., 2016a).
Crossing high and low levels of functional flexibility, workers have been divided into three categories: knowledge workers, supervisors and support staff (Ter Hoeven, et al., 2016a). Knowledge workers are described as employees who experience independence in their job and whose main contribution to the organization is knowledge; they resort to theoretical and/or analytical knowledge in their work to develop, design, or sell products or services (Ter Hoeven et al., 2016a). Their key responsibilities do not include supervising other employees or managing a department (Ter Hoeven et al., 2016a). Because of the flexible nature of their employment, it is expected that they are positively affected by FWDs, as demonstrated by Ter Hoeven et al. (2016a). Knowledge positions have high levels of functional flexibility and can be described as characterized by high levels of task complexity because creation,
development, dissemination and use of knowledge aimed at providing competitive advantages or other benefits are involved (Harrison, Wheeler, & Whitehead, 2004). Further, this type of job implies high task variety since these employees have to cover different roles when necessary and be able to adapt. Last but not least, knowledge workers are also depicted by high levels of task interdependence since they usually work in collaboration with a team, often long distance. They generally do not have a stable routine and are frequently relocated and work out of the office, such as at clients’, traveling or at home (Ter Hoeven et al., 2016a). Supervisors are defined as workers whose formal job description states that they are primarily responsible for leading a department or unit and/or for supervising a certain amount
of employees (Ter Hoeven et al., 2016a). They need freedom of initiative and independence as knowledge workers do (high functional flexibility), but they also have supervisory and routine tasks (low functional flexibility) which tie them to their office in need of privacy since they treat confidential information too (Ter Hoeven et al., 2016a). Due to the ambivalent nature of their job, they are expected to perceive FWDs less positively than knowledge workers but more positively than support staff (Ter Hoeven et al., 2016a).
Actually, the term support staff stands for workers who are primarily responsible for
supporting knowledge workers through addressing operational matters and facilitating work processes, such as administrative and IT support (Ter Hoeven et al., 2016a). For this category of employees, FWDs are expected to be perceived as an interference because their
employment is characterized by low functional flexibility, fixed locations and routine tasks, that is why flextime and flexplace might cause inconveniences and tensions (Ter Hoeven et al., 2016a).
As a result, given the assumptions confirmed in the previous study conducted by Ter Hoeven et al. (2016a) the second hypothesis has been formulated as following:
Hypothesis 2a: FWDs have an impact on engagement and this relationship is moderated by occupational status. In particular, the relationship between FWDs and engagement is expected to be stronger for knowledge workers, compared to supervisors and support staff.
Hypothesis 2b: FWDs have an impact on exhaustion and this relationship is moderated by occupational status. In particular, the relationship between FWDs and exhaustion is expected to be weaker for knowledge workers, compared to supervisors and support staff.
Hypothesis 2c: FWDs have an impact on work-life balance and this relationship is moderated by occupational status. In particular, the relationship between FWDs and work-life balance is expected to be stronger for knowledge workers, compared to supervisors and support staff.
Boundary management preferences
The availability of technology and information has enabled companies to disengage task and time from location (Gajendran & Harrison, 2007), providing work-related flexibility which creates opportunities for work and home roles to overlap and possibly to generate conflict (Fonner & Stache, 2012). The boundary theory provided by Ashforth et al. (2000) explains how role boundaries and transitions are part of organizational life. Transitions are defined as the physical and psychological movement between different roles: how the disengagement from one role, called role exit, goes to the engagement to another role, called role entry (Ashforth et al., 2000). The roles played (i.e., work-home roles) are characterized by boundaries constructed around them which can be flexible or permeable and these boundaries define the extent to which a certain role depends on space and time (Ashforth et al., 2000). In the organizational context people make transitions between roles in different ways depending on their personal preferences: these transitions are affected by the flexibility and permeability of role boundaries (Ashforth et al., 2000). A role with flexible boundaries can easily be enacted at various times and in different locations and settings, whereas
inflexible boundaries belong to people who physically enact one role but psychologically are playing another one (Ashforth et al., 2000).
The ways to move from one role to another are distributed on a continuum: from complete segmentation to complete integration (Ashforth et al., 2000). Despite the fact that these two extremes are unlikely to occur, individuals have a personal preference for a segmentation or integration approach (Ashforth et al., 2000) which may be influenced by individual
differences, job structure (Kossek, Lautsch, & Eaton, 2005) and contextual factors such as institutionalised boundaries and the culture in which the subject is embed (Ashforth et al., 2000; Nippert-Eng, 1996). In addition, preferences for integrating or segmenting may also
depend on the perceived benefits and disadvantages of differentiating or blurring role differences (Fonner & Stache, 2012).
Segmentation is defined as the act of partitioning work and non-work activities, time and space, helping workers preserve their private lives (Rothbard, Phillips, & Dumas, 2005), limit interference between work and home (Rothbard & Edwards, 2003), and
compartmentalise role identities (Ashforth et al., 2000).
In the opposite direction, integration is the act of overlapping the work and non-work above mentioned features and it is denoted by roles that are weakly differentiated, not restricted to specific places and times since they have flexible boundaries (Ashforth et al., 2000). Given that, highly integrated roles tend to have similar identities and be embedded in similar contexts, and transitions between these roles tend to be frequent (Ashforth et al., 2000). Employees who show segmentation preferences (i.e., low personal flexibility) experience difficulties when they have to face situations in which they are required to be flexible due to unpredictable situations, tasks, or places, because they have an inclination for more stable routines (Ter Hoeven et al., 2016a). On the other hand, employees with integration
preferences (i.e., high personal flexibility) feel more comfortable when combining different roles and tasks (Ter Hoeven et al., 2016a). Both approaches have benefits and costs:
segmentation allows the creation and maintenance of clear boundaries between roles, for example keeping distance between the work role (i.e., a manager) and the private life one (i.e., a father), but crossing these boundaries when necessary results difficult and requires many efforts; integration, although it might create confusion and has a high level of interruptions, is characterized by favourable attitudes to flexibility and effortless roles transition (Ashforth et al., 2000). That is why high levels of personal flexibility are supposed to fit better with the concept of FWDs aforementioned and to have more positive effects on employees well-being.
On the basis of the boundary theory just described, knowledge workers have been divided into two sub-categories: flexible and stable (Ter Hoeven et al., 2016a). The first ones are described as employees who experience independence in their job combined with a personal tendency to integrate different roles and tasks, and this is the reason why they are predicted to be most positively affected by FWDs (Ter Hoeven et al, 2016a). The second ones are defined as workers who experience a high degree of flexibility due to their assigned tasks, alike flexible knowledge workers, but differently from those, they have a personal tendency to favour predictable work routines (Ter Hoeven, 2016a). They have a high level of functional flexibility but instead of integration, they prefer segmentation (Ashforth et al., 2000). Plus, they also experience a sort of moral failure as they are aware of the mismatch between their work preferences and the flexibility that their job and company demand from them (Ter Hoeven, et al., 2016a). Hence, the third and last hypothesis is formulated as following:
Hypothesis 3a: FWDs have an impact on engagement and this relationship is moderated by boundary management preferences. In particular, the relationship between FWDs and engagement is expected to be stronger for flexible knowledge workers, compared to stable knowledge workers, supervisors and support staff.
Hypothesis 3b: FWDs have an impact on exhaustion and this relationship is moderated by boundary management preferences. In particular, the relationship between FWDs and exhaustion is expected to be weaker for flexible knowledge workers, compared to stable knowledge workers, supervisors and support staff.
Hypothesis 3c: FWDs have an impact on work-life balance and this relationship is moderated by boundary management preferences. In particular, the relationship between FWDs and work-life balance is expected to be stronger for flexible knowledge workers, compared to stable knowledge workers, supervisors and support staff.
Method Design
The hypotheses have been tested employing a cross-sectional online survey. The anonymous link has been distributed via email and social media (WhatsApp, Facebook, LinkedIn) and the questionnaire had an estimated duration of 10 minutes. It has been organized as following: invitation and general information, informed consent section,
questions that aim to measure the variables involved and socio-demographic questions (such as gender, age and educational level). Last, participants could leave their email if interested in having the chance to be the winner of a 20 euros Amazon coupon, randomly selected among all respondents, used as incentive to encourage people to join the study.
Sample
A non-probability sample has been chosen, with a sample size of 184 employees. However, 50 participants have been excluded because their questionnaire resulted incomplete, thereby, the final sample is N=134. About the sampling type, the snowball one has been adopted: I contacted friends and acquaintances who work in companies as white collars asking them to fill the survey out and to forward it to colleagues, managers and other people they know having the required characteristics. In addition, I posted the survey on my
Facebook and LinkedIn profiles to recruit more participants, specifying the sample
characteristics I needed for the research. More in detail, to be part of the sample, participants had to be 18+ years old, both men and women, ideally working in big or middle-size
companies (minimum 30 employees). This choice is due to the fact that in companies of certain dimensions is more likely to find all four types of workers identified and that FWDs are adopted.
The sample was mainly composed by women (58.2%), the average age was 30 years old (SD=8.42) and the highest educational level completed by 46.3% of them was a Bachelor degree. Concerning the occupational status, 32 respondents defined their role in their
organization as supervisors (23.9%), 36 as support staff (26.9%), 46 as knowledge workers (34.3%) and 20 as none the above (14.9%). Lastly, 48 employees work in companies with more than 1000 workers (35.8%), followed by 33 employees working in companies with a number of employees from 30 to 100 (24.6%) and the average working hours per week was 36.6 hours (SD=10.01), with a minimum of 8 hours and a maximum of 54 hours.
Descriptive statistics also revealed which communication technologies are used by the sample and how often they are used for work weekly. Actually, analysing it is important because it is not possible to talk about FWDs in an organization without considering CTU (as discussed in the theoretical background, CTU is embedded in the concept of FWDs). Frequency of
technology use has been measured asking participants how often they have used, on a scale from 1 (“Never”) to 5 (“Very often”) the following communication technologies for work in the past week: emails, telephone, smartphone, instant messaging, videoconferencing
enterprise social media, social media, shared databases, internet and intranet. Results showed that the three most used technologies are internet (M=4.49, SD=1.15), emails (M=4.66,
SD=.96) and telephone (M=3.84, SD=1.49). Actually, respectively, 78.4%, 85.1% and 53.7%
of participants selected ‘Very often’ to indicate the frequency use of the aforementioned technologies for work.
Measures
FWDs. Since FWDs (the independent variable) refer to the flexibility that employees
have concerning when and where they work, and the communication technologies they use to work (Ten Brummelhuis et al., 2012), they have been measured using three sub-dimensions:
flexibility of time, flexibility of place and use of communication technologies (Ten
Brummelhuis, Halbesleben, & Prabhu, 2011). All the three dimensions include three items, such as ‘I can decide the time slots I work in’, ‘I can choose at which location I work’, and ‘I can decide when I send or reply to emails’. All items have been measured with a 5-point Likert scale from 1 (‘Strongly disagree’) to 5 (‘Strongly agree’). The reliability analysis showed a value of ⍺=.88.
Employee well-being: engagement, exhaustion, work-life balance. As previously
mentioned, well-being is considered as composed by three concepts: engagement has been measured with five items such as ‘While at work, I am bursting with energy’ (Schaufeli, Bakker, & Salanova, 2006); the reliability analysis showed a value of ⍺=.84. Exhaustion has been also measured with five items such as ‘I feel mentally drained by my work’ (Maslach et al., 2001); the reliability analysis showed a Cronbach’s alpha value of ⍺=.84. They have been measured on a 5-point Likert scale from 1 (‘Strongly disagree’) to 5 (‘Strongly agree’). Work-life balance has been measured on a 5-point Likert scale from 1 (‘Very unsatisfied’) to 5 (‘Very satisfied’) with five items such as ‘How satisfied are you with the way you divide time between work and personal life?’ (Valcour, 2007); this last concept shows the highest
Cronbach’s alpha, with a value of ⍺=.92.
Occupational status. In order to measure the occupational status, the first moderator, a
definition of supervisor, support staff and knowledge worker has been given in the
questionnaire, asking participants to select the one which fits the best with their actual role in their company. The definition for supervisor is “worker whose formal job description states that he/she is primarily responsible for either running a department or unit and/or for supervising multiple (at least 3) employees within their organization”; the definition for support staff is “a worker whose formal job description states they are primarily responsible
for supporting knowledge workers through addressing operational issues and facilitating primary work processes, such as administrative staff and IT support”; finally, the definition for knowledge worker is ‘an employee whose main contribution to the organization is knowledge; he/she uses theoretical and/or analytical knowledge in his/her work to develop, design, and/or sell (new) products and/or services, but is not responsible for supervising other employees or managing a department/unit’ (Ter Hoeven et al., 2016a). Participants are also allowed to select the option ‘None of the above’.
Boundary management preferences. In order to measure work-home preferences, the
second moderator, a scale composed by four items measured on a 5-point Likert scale from 1 (‘Strongly disagree’ i.e., integration, ) to 5 (‘Strongly agree’, i.e., segmentation) has been adopted (⍺=.83). An example item is ‘I don’t like to have to think about work while I am at home’ (Kreiner, 2006; Rothbard et al., 2005).
Control variables. Kreiner (2006) claimed that work-life balance is particularly
important for women and according to Fonner and Stache (2012) women seem to adopt segmenting strategies to a higher degree if compared to men; despite this, the studies of Ter Hoeven and van Zoonen (2015) and Ten Brummelhuis et al. (2012) found no different effect of FWDs between men and women. Further, they did not find any difference between people of different age who work different amounts of time per week. In order to confirm or reject what claimed previously, these three control variables have been taken into account. Gender has been measured as a dichotomous variable (1=male, 2=female), transformed in a dummy variable (1=male, 0=female); age as a continuous variable as well as working hours per week. A correlation table with all variables involved can be found in the Appendix (see: Table 7).
Analysis
After data cleaning, descriptive statistics about the composition of the sample, and reliability analysis for each scale, all hypotheses have been tested through a multiple regression analysis, according to the steps provided by Dawson (2014) in order to test moderation. In detail, H1a, H1b and H1c have been tested with a multiple regression analysis with FWDs as independent variable, respectively engagement, exhaustion and work-life balance as dependent variable, and age, gender and working hours as control variables. H2a, H2b and H2c have been tested with a multiple regression analysis including FWDs as independent variable, respectively engagement, exhaustion and work-life balance as
dependent variable, and occupational status (computing dummy variables for each category, supervisors, support staff and knowledge workers) as moderator, with the purpose of
measuring the interaction effect between FWDs and occupational status (two-way
interaction). H3a, H3b and H3c have been tested with a multiple regression with FWDs as independent variable, respectively engagement, exhaustion and work-life balance as dependent variable and the boundary management preferences as moderator, in order to measure first the interaction effect between FWDs and work-life segmentation, second the interaction effect between FWDs and both the moderators taken together (three-way interaction).
Results
The regression model with engagement as dependent variable, FWDs as independent variable and age, gender, and working hours as control variables is significant F(4, 133) = 6.89, p < .001, with 18% of variance explained (R2 = .18). FWDs, b* = 0.39, t = 4.01, p < .001, 95% CI [0.18, 0.53] is a significant predictor of employee engagement. Per every unit increasing in FWDs, engagement increases by 0.39 units. Age, gender, and working hours are
non-significant predictors of engagement (see: Appendix, Table 1). The regression
assumptions (residuals normally distributed, collinearity VIF < 10, tolerance > .2) are also met. Consequently, H1a can be supported.
The regression model with exhaustion as dependent variable, FWDs as independent variable and age, gender, and working hours as control variables is not significant, F(4, 133) = 0.7, p = .595. FWDs, age, gender, and working hours do not have a significant effect on exhaustion (see: Appendix, Table 2). As a consequence, H1b cannot be accepted.
The regression model with work-life balance as dependent variable, FWDs as
independent variable and age, gender, and working hours as control variables is significant,
F(4, 133) = 4.27, p = .003, but only 12% of the variance is explained (R2 = .12). FWDs, b* =
0.29, t = 2.88, p = .005, 95% CI [0.09, 0.5] is a significant predictor of work-life balance. Per every unit increasing in FWDs, work-life balance increases by 0.29 units. Age, gender and working hours are non-significant predictors of work-life balance (see: Appendix, Table 3). Overall, H1c can be accepted.
The regression model with engagement as dependent variable, FWDs as independent variable and supervisor as moderator is significant F(3, 133) = 8.43, p <.001, with 16% of variance explained (R2 = .16). However, supervisor as well as the interaction between FWDs and supervisor are non-significant predictors of engagement (see: Appendix, Table 4).
The same regression model with support staff that takes supervisor’s place as moderator is significant F(3, 133) = 9.8, p <.001, with 18% of variance explained (R2 = .18). However,
support staff, as well as the interaction between FWDs and support staff are non-significant predictors of engagement (see Appendix, Table 4).
Substituting support staff with knowledge worker as moderator leads to a significant regression model F(3, 133) = 8.26, p <.001, with 16% of variance explained (R2 = .16). However, knowledge worker as well as the interaction between FWDs and the knowledge
worker are non-significant predictors of engagement (see Appendix, Table 4). Since there was no significant interaction (moderation effect), H2a cannot be accepted.
The regression model with exhaustion as dependent variable, FWDs as independent variable and supervisor as moderator is not significant F(3, 133) = 1.15, p =.330. FWDs, supervisor and the interaction between FWDs and the occupational status supervisor are non-significant predictors of employee exhaustion (see: Appendix, Table 5).
If support staff is used as moderator instead of supervisor, the regression model results significant, F(3, 133) = 3.22, p =.025, with 7% of variance explained (R2 = .07). FWDs and support staff are non-significant predictors of exhaustion (see: Appendix, Table 5), however, their interaction, b* = 0.23, t = 2.64, p = .009, 95% CI [0.06, 0.39] has a significant value. Consequently, exhaustion cannot be significantly predicted by FWDs and support staff separately, but when the two variables interact: per every unit which increases in the
interaction between FWDs and support staff as occupational status, exhaustion increases by 0.23 unit. In other words, employees working as support staff are seen to increases their work exhaustion due to flexible approaches adopted by their companies (see: Appendix, Graph 1). The regression model with exhaustion as dependent variable, FWDs as independent variable and knowledge worker as moderator is not significant F(3, 133) = 2.01, p =.116. FWDs and knowledge worker are non-significant predictors of employee exhaustion (see: Appendix, Table 5). However, the interaction between FWDs and the occupational status knowledge worker, b* = -0.17, t = -2, p = .048, 95% CI [-0.33, 0.00] is significant. That means that per every unit which increases in the interaction between FWDs and knowledge worker as occupational status, exhaustion decreases by 0.17 unit. In other words, employees working as knowledge workers are seen to decrease their work exhaustion due to flexible work designs adopted by their companies. Overall, H2b can be partially accepted because it
can be affirmed that FWDs have a more supportive impact on knowledge workers than on support staff (see: Appendix, Graph 2).
The regression model with work-life balance as dependent variable, FWDs as independent variable and supervisor as moderator is significant F(3, 133) = 9.06, p <.001, with 17% of variance explained (R2 = .17). FWDs, b* = 0.28, t = 3.29, p = .001, 95% CI [0.10, 0.41], supervisor, b* = -0.37, t = -4.23, p < .001, 95% CI [-0.5, -0.18], and the
interaction between FWDs and supervisor as occupational status b* = 0.21, t = 2.49, p = .014, 95% CI [0.04, 0.32] are all significant predictors of work-life balance: per every unit which increases in the interaction between FWDs and supervisor, work-life balance increases by 0.21 unit (see: Appendix, Table 6). In other words, employees working as supervisors are seen to increase their work-life balance due to flexible approaches adopted by their companies (see: Appendix, Graph 3).
Using support staff as moderator leads to a significant F(3, 133) = 3.13, p =.028, with 7% of variance explained (R2 = .07). However, support staff and the interaction between FWDs and the occupational status support staff are non-significant predictors of work-life balance (see: Appendix, Table 6).
Again, knowledge worker as moderation leads to a significant regression model F(3, 133) = 3.15, p =.027, with 7% of variance explained (R2 = .07). Notwithstanding, knowledge worker as well as the interaction between FWDs and the occupational status knowledge worker are non-significant predictors of work-life balance (see: Appendix, Table 6). In sum, H2c cannot be accepted.
The regression model with engagement as dependent variable, FWDs as independent variable and the boundary management preferences and occupational status (respectively supervisor, support staff and knowledge worker) as moderators is significant F(15, 133) = 2.96, p = .001, with 27% of variance explained (R2 = .27). However, the regression
assumptions are not met for all variables and interactions involved, and only the interaction between FWDs and work-home preferences b* = 1.23, t = 2.48, p =.014, 95% CI [0.05, 0.46] results to be significant: per every unit increasing in the interaction between FWDs and work-home preferences, engagement increases by 1.23 unit. In other words, employees who are segmentation-oriented are seen to increase their work engagement due to flexible approaches available in their companies (see: Appendix, Graph 4). Overall, H3a cannot be supported because the interaction between FWDs with boundary management preferences and occupational status is non-significant.
The same can be affirmed regarding the regression model with exhaustion as dependent variable, which is significant, F(15, 133) = 2.25, p = .008, with 22% of variance explained (R2
= .22). Not all the assumptions are met and only the interaction between FWDs and work-home preferences is significant, b* = -1.27, t = -2.48, p = .015, 95% CI [-0.51, -0.06]. Per every unit increasing in the interaction between FWDs and work-home preferences,
exhaustion decreases by 1.27 unit (see: Appendix, Graph 5). In sum, H3b cannot be supported because the interaction between FWDs with boundary management preferences and
occupational status is non-significant.
Last, the regression model with work-life balance as dependent variable, FWDs as independent variable and the boundary management preferences and occupational status (respectively supervisor, support staff and knowledge worker) as moderators since it is significant F(15, 133) = 2.81, p = .001, with 26% of variance explained (R2 = .26). However,
none of the interactions is significant, consequently H3c cannot be supported.
Discussion and conclusions
The goal of the present research was to confirm findings discussed in previous studies, to summarize, that FWDs have an impact on the three dimensions of well-being (engagement,
exhaustion and work-life balance) and that this relationship is moderated by occupational status and work-home preferences, with the assumptions that different occupational status perceive FWDs differently also depending on their preferences in terms of role integration or segmentation. Unfortunately, statistical analyses mostly showed non-significant values, therefore it is possible to confirm only few hypotheses and sometimes only partially. In line with Ten Brummelhuis et al., (2012), it has been found that FWDs are positively related to work engagement (H1a), but confirming that they are also positively associated to work exhaustion has not been possible due to non-significant results (H1b). In line with Ter Hoeven and van Zoonen (2015), it has been confirmed that FWDs also enhance work-life balance (H1c). Altogether, this study confirmed that FWDs have a positive effect on
employee well-being because they increase engagement as well as the balance between work and private life.
Concerning the relation of FWDs with engagement, exhaustion and work-life balance
moderated by occupational status, results were not able to confirm the hypotheses due to non- significant results. H2b has been partially supported because it has been found that FWDs have a more supportive impact on knowledge workers than on support staff, in line with Ter Hoeven et al. (2016a) who affirmed that the flexible nature of knowledge workers’ jobs (high functional flexibility) make them benefit from FWDs more than support staff that have more stable tasks and fixed locations (low functional flexibility). Actually, exhaustion increases for support staff while decreases for knowledge workers. A comparison with supervisors has not been possible, however it has been found that they increase their work-life balance when FWDs are adopted (H2c).
When taking the boundary management preferences as a moderator, contrary to Ter Hoeven et al. (2016a), it has been found that employees who are segmentation-oriented are seen to increase their work engagement (H3a) and to decrease exhaustion (H3b) in relation to the
adoption of FWDs. This goes to the opposite direction of the formulated hypotheses since it was expected that the role integration approach fits better with the concept of FWDs. This result is compliant with the idea that segmenting preferences diminish the blurring that occurs between work and home roles and consequently can facilitate coping with multiple role expectations (Hewlin, 2003), helping workers preserve their personal lives (Rothbard et al., 2005). These aspects can justify the findings of this study which showed that people who are segmentation-oriented benefit more from FWDs increasing their engagement and decreasing their exhaustion since they are able to differentiate their roles and to keep them from
interference. Moreover, according to the study of Liu, Kwan, Lee, and Hui (2013), work-home segmentation preferences attenuate the effect on work-family conflict.
Overall, the results were not able to fully answer the research question and to confirm the hypotheses, but in conclusion they confirmed that FWDs are positively associated with employee well-being (engagement and work-life balance); plus, they showed that
occupational status plays a relevant role embedded in this relationship: supervisors increases their work-life balance, knowledge workers decrease the level of exhaustion while support staff increases it when FWDs are adopted. This is in line with previous studies (Ter Hoeven et al., 2016a) whereas taking work-home preferences into analysis highlighted results opposite to the expectations: employees with segmentation preferences resulted to increase their engagement and decrease their exhaustion when FWDs are used as predictors.
Theoretical and practical implications
This study gives a contribution to the scientific community because it partially supports previous findings about the impact of FWDs on employee well-being (Ter Hoeven et al., 2016b; Ten Brummelhuis et al., 2012) and that FWDs appear to be more supportive for
knowledge workers than for support staff (Ter Hoeven et al., 2016a). Consequently, FWDs are tailored and match with a way of working belonging to a specific occupational status (Ter Hoeven et al., 2016a). The present study also provided with findings which contradict what was previously found in the qualitative research of Ter Hoeven et al. (2016a) concerning the moderation role of work-home preferences: it seemed that people who have a tendency to separate different roles benefit more from FWDs than people who are integration-oriented, in line with the studies of Hewlin (2003), Rothbard et al., (2005) and Liu et al. (2013). Further investigation is required in order to reach more homogeneous findings. As demonstrated by Kreiner (2006) integrating or segmenting work and home roles is not inherently positive or negative, but rather it depends on the interaction itself between the individual and the
workplace. A positive association between FWDs and segmentation preferences has also been showed by Park and Jex (2011) who affirmed that maintaining inflexible boundaries around CTU for cross-role enactment can be beneficial in order to diminish work-family intrusion. Companies can benefit from this research in order to have a further confirmation of the fact that FWDs impact positively employee well-being when analysing the possibility to implement this approach; furthermore, they can get some insights regarding differences in the perception of FWDs as positive or negative between different occupational status and the role of work-home preferences. In particular, companies should consider the idea of making available flexible ways of working for knowledge workers, in order to decrease exhaustion, and supervisors, who seem to increase work-life balance due to FWDs. Most importantly, not only should they reflect on occupational status, but also take employees’ opinions into
account, in order to find out which their preferences are in terms of integration and
segmentation. This way, companies could understand which kind of employees would benefit from FWDs and which would not, for the purpose of maximising their well-being and their performance accordingly.
Limitations and suggestions for future research
The present study presents limitations especially in relation to the method chosen since a cross-sectional survey makes verifying causality problematic; plus, the snowball sampling type does not allow to have a strong external validity because it is not possible to extend the results to the population. Actually, causality is assumed but cannot be tested. Repeating the research with a longitudinal study and with a random sample could be a solution.
Moreover, a bigger sample with the same amount of respondents for each occupational status would conduct to more accurate results and limit the bias.
In addition, the definition provided for each occupational status could have been confusing for some respondents (keeping in mind that since the survey has been distributed mostly in the Netherlands and in Italy among people belonging to several countries, only few of them are English mother tongue which could also lead to misunderstandings) who could have had difficulties in selecting the best description for their actual job, also because of the complexity that characterize today’s jobs. The validation of the operationalization for occupational status would improve this aspect as well as a deeper analysis to get to more reliable conclusions about one’s occupational status.
Future researches should repeat the study focusing on the idea that a more structured and systematic research is needed: for example, distributing the questionnaire in a few companies characterized by the same kind of business and carrying a prior investigation in order to select only companies which actually adopt FWDs only among full-time employees. As discussed in the previous section, results concerning boundary management preferences were not conform to studies conducted previously, that is why this study should be repeated with the above mentioned improvements regarding methodology and especially with the support of a new validated scale to get to more accurate findings.
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Appendix Table 1.
Regression model to predict engagement (H1a)
_______________________________ Model 1 b* FWDs 0.39*** Age -0.06 Gender 0.14 Working hours -0.03 ________________________________ Note. N = 134. * p <.05. ** p <.01. *** p <.001. Table 2.
Regression model to predict exhaustion (H1b)
_______________________________ Model 1 b* FWDs -0.15 Age 0.03 Gender 0.03 Working hours 0.05 ________________________________ Note. N = 134. * p <.05. ** p <.01. *** p <.001. Table 3.
Regression model to predict work-life balance (H1c)
_______________________________ Model 1 b* FWDs 0.29* Age 0.06 Gender -0.13 Working hours -0.17 ________________________________ Note. N = 134. * p <.05. ** p <.01. *** p <.001.
Table 4.
Regression model to predict engagement, two-way interaction (H2a)
_________________________________________________________ Model 1 b* Model 2 b* Model 3 b*
FWDs 0.38*** Supervisor 0.01 FWDs*Supervisor FWDs Support staff FWDs*Support staff FWDs Knowledge worker FWDs*Know.worker 0.05 0.37*** 0.08 -0.16 0.4*** 0.02 0.01 __________________________________________________________ Note. N = 134. * p <.05. ** p <.01. *** p <.001. Table 5.
Regression model to predict exhaustion, two-way interaction (H2b)
_________________________________________________________ Model 1 b* Model 2 b* Model 3 b*
FWDs -0.14 Supervisor 0.11 FWDs*Supervisor FWDs Support staff FWDs*Support staff FWDs Knowledge worker FWDs*Know.worker -0.06 -0.1 -0.01 0.23** -0.15 0.01 -0.17* __________________________________________________________ Note. N = 134. * p <.05. ** p <.01. *** p <.001.
Table 6.
Regression model to predict work-life balance, two-way interaction (H2c)
_________________________________________________________ Model 1 b* Model 2 b* Model 3 b*
FWDs 0.28*** Supervisor -0.37*** FWDs*Supervisor FWDs Support staff FWDs*Support staff FWDs Knowledge worker FWDs*Know.worker 0.21* 0.2* -0.01 -0.14 0.21* 0.14 0.03 __________________________________________________________ Note. N = 134. * p <.05. ** p <.01. *** p <.001. Graph 1.
Graph 2.
Moderation effect FWDs x knowledge worker – exhaustion (H2b)
Graph 3.
Graph 4.
Moderation effect FWDs x work-home preferences – engagement (H3a)
Graph 5.
Table 7.
Means (M), standard deviations (SD) and correlations among the study variables (N=134).
Note: *Significant values at p < .05. **male=1, female=2
Variables M SD 1 2 3 4 5 6 7 8 9 10 1. FWDs 2.90 .90 2. Engagement 3.20 .82 .40* 3. Exhaustion 2.81 .89 -.12 -41* 4. Work-life balance 3.31 .92 .22* -35* -.48* 5. Knowledge worker .34 .48 .09 .05 -.01 .16 6. Supervisor .24 .43 .30* .14 0.5 -.21* -.40* 7.Support staff .27 .44 -.20* -.12 -.04 -.02 -.44* -.34* 8. Work-home pref. 3.95 .88 -.26* -.28* .31* -.04 -.11* -.02 .07 9. Gender** 1.58 .49 -.30* -.23* -.01 .12 .13 -.45* .21* .16 10. Age 30.10 8.42 .54* .20* -.02 -.01 -.15 .55* -.18* -.17* -.44* 11. Working hours 36.60 10.01 .32* .16 .17 -.20* .01 .32* -.10 -.14 -.36* .32*
